Atụmatụ kwa Afọ nke Wikimedia Foundation/2024-2025/Ngwa ahịa na Teknụzụ OKRs

This page is a translated version of the page Wikimedia Foundation Annual Plan/2025-2026/Product & Technology OKRs and the translation is 59% complete.

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Afọ na-abịa n'ihu

Ọ bụlagodi ka ụwa na-agbanwe, Wikimedia Foundation kwudosiri ike ma jide n'aka na anyị chọrọ ka ije ozi anyị – mee ma dobe ozi ndị bara uru sitere na ọrụ Wikimedia dị na ịntanetị n'efu – Ị bụ agbam mbọ nke ọtụtụ ọgbọ: anyị chọrọ ka ihe ọmụma n'efu gaa n'ihu na-adikwa nye ọtụtụ ọgbọ na-abịa n'ihu.

Ịntanetị na-eme mgbanwe ngwa ngwa. Ndị Ọgbọ ọhụrụ na-enweta ozi site na vidiyo mmekọrịta na mmụtara AI, ma, nakwa, I jiri ya tụnyere ndị ọgbọ gara aga, mmadụ ole na ole n'ime ha maara maka Wikipedia. Anyị na-ahụ mbelata ọnụọgụ ndị mmadụ na-abịa na saịtị anyị yana ọnụọgụ ndị mmadụ na-eme ndezi. Ka ọ dị ugbu a, ngalaba ndị ọzọ na gburugburu ịntanetị dabeere na ọdịnaya Wikimedia iji kwado AI na nzaghachi ọchụchọ ha. Ọnọdụ mgbanwe ndị a bụ isi ihe ịma aka, mana ha na-eme ka o doo anya ihe kpatara ihe ọmụma n'efu a pụrụ ịdabere na ya anyị na-ekepụta ọnụ ji dị oke mkpa. Ụwa chọrọ ihe ọmụma mmadụ nnyochara karịa ka emeerela na mbụ, ebe enwere ike ịtụkwasị obi, ma ọrụ Wikimedia na-aga n'ihu na-egosi na ha nwere ike inye nke a.

Iji merie ihe ịma aka ndị a n'afọ na-abịanụ, anyị ga-ewu ụzọ iji mmezuo ọdịnaya Wikimedia n'ụzọ ga-adịgide adịgide, anyị ga na-eweta ọdịnaya Wikimedia na oghere mmekọrịta n'ịntanetị ebe ndị ọgbọ ọhụrụ na-etinye oge ha. Anyị ga-emeziwanye saịtị anyị ka ndị na-agụ ọrụ anyị nwee agụụ ịlaghachi, tinye uchu nke ukwuu, ma nye aka n'ụzọ ndị bara uru nye ha. Anyị ga-elebanya na ikike anyị iji teknụzụ ọhụrụ mee nnwanle ngwa ngwa, ka mgbapụ ọsọ mmepe anyị wee nwee ike ịdaba na mgbanwe ọsọ ọsọ ụwa na-agbanwe.

Ebumnuche gbasara akụrụngwa bụ ka ngalaba na mmụtara onye ọrụ ga-esi kwado nsogbu ndị a na iru ọtụtụ ndị nsonye na mmegharị ahụ. Ọ bụghị ndepụta nke oru ngo, kama nke ahụ, usoro ntụziaka iji kwalite uto afọ ofufo, mee ka ndị ọrụ afọ ofufo nwee ike kepụta ọdịnaya ensaịklopedik a pụrụ ịdabere na ya, kwado ọrụ anyị, ma kwalite onyinye anyị iji mezie ịntanetị na-agbanwe agbanwe. Ị nwere ike ịgụkwu gbasara ihe nkwụsike anọ dị mkpa.

Kwalite Uto ndị ọrụ afọ ofufo

Ngalaba nke ndị ọrụ afọ ofufo bụ ebe nrụọrụ pụrụ iche na-akpata ihe ịga nke ọma nke ọrụ Wikimedia, ma anyị chọrọ ka ọ dị mma ma na-eto eto. Ma n'ime afọ gara aga, anyị ahụla mbeleta chiri anya nke ọnụ ọgụgụ ndị editọ ọhụrụ na ndị na-alọghachi na ọrụ ndị ahụ. Iji ghọta nke ọma na inye nzaghachi nke mkpa ndị ọrụ afọ ofufo anyị ugbu a, Foundation emegharịala ọkpụkpọ oku ihe achọrọ nke ndị ngalaba otu ga site na nnyocha ntinye n'abịa otu ugboro n'afọ gaa n'ịbụ usoro na-emeghe mgbe niile ebe mkpa onye ọrụ na echiche ọrụ nwere ike isokwunye n'ọrụ nke ọtụtụ otu na Foundation. Anyị chịkọtara ndetu ihe ndị achọrọ n'ime "Mpaghara Nlekwasị anya" wee jikọta atọ n'ime ebe a na-elekwasị anya n'okpuru isi mpụtaara na atụmatụ afọ. Anyị malitekwara onye usoro Kansụl Ndụmọdụ Ngwaahịa na Nkà na ụzụ iji gbakwunye ọtụtụ mkparịtaụka ndị otu Foundation na ndị ngalaba otu nwere na wiki nakwa na mpụga wiki n'ime afọ. Na mgbakwunye, anyị achọpụtala ohere iji weta ndị ọgbọ ọhụrụ n'ime ọrụ anyị, dị ka eziokwu ahụ bụ na ndị na-eto eto na-eji ịnụ ọkụ n'obi na-esonye na oghere mmekọrịta ndị ọzọ n'ịntanetị ebe ha nwere ụzọ dị mfe, bụ kwa nke e nwere ike iji mkpanaka mee ntunye n'isiokwu ha nwere na ya mmasị.

N'afọ na-abịa, anyị ga-akwalite uto afọ ofufo site n'ime ka ntinye aka dị mfe ma bụrụ nke na-adọta mmasị nke ndị ọgbọ ọhụrụ site na ịgbasa mobile-first, ụzọ ọhụrụ iji mee ndezi ("ọrụ ahaziri ahazi"), na ịgbakwunye ọrụ dị mma na-eme ka ndezi eji mkpanaka eme dị dị mfe maka ndị ntinyeaka ọhụrụ ("edit checks"). Iji tinyekwuo aka na ijide ndị ọrụ afọ ofufo nọ ugbu a, anyị ga-enye omume na ọrụ ndị akwadoro ma tinye ha na họbụ etiti na-eme ka ọ dị mfe ịhazi ọrụ wiki. Anyị ga-eji nlezianya jiri AI na-akwado ndị ọrụ afọ ofufo ike n'ọrụ ha, na-eme ntunye nke mmadụ mgbe niile ma ka ihe izi ezi bute ụzọ. Maka ma ndị ọrụ afọ ofufo ọhụrụ na ndị nwere mmụtaara, anyị ga-ewepụta ụzọ iji jikọọ ma rụkọọ ọrụ ọnụ na saịtị anyị - nke sitere na mgbasa ozi na-aga nke ọma na WikiProjects - na-enye ha ohere ịchọta ndị editọ ha na ha nwere otu mmasị na imeziwanye ọdịnaya metụtara ọdịmma ha (dabatara na Mpaghara a na-elekwasị anya n'ọchịchọ).

Nye ọdịnaya encyclopedik ekwesịrị ịtụkwasị obi

Ka ihe AI mepụtara na-abawanye na ịntanetị, ụwa chọrọ ọdịnaya encyclopedik a pụrụ ịtụkwasị obi karịa mgbe ọ bụla. Anyị chọrọ ịbawanye ikike nke ndị ọrụ afọ ofufo iji mepụta ọdịnaya ọhụrụ, hụ na ọdịnaya dị ugbu a ka bụ ihe a pụrụ ịtụkwasị obi, ma nye ọgbọ ọhụrụ nke ndị na-agụ akwụkwọ ọdịnaya a pụrụ ịdabere tinyere mkpa na mmasị ọhụrụ.

Iji nyere ndị ọrụ afọ ofufo aka ịmepụta ọdịnaya ọhụrụ, anyị ga-esoro ngwaọrụ nduzi dị nụ na usoro ọrụ (dị ka Ọdịnaya NgwáỌrụ Ntụgharị), ka ngalaba buru ibu na nke dị nta wee nwee ike ilebanya na ọdịnaya dị mkpa. Iji hụ na ọdịnaya ndị dị adị ka kwesịrị ntụkwasị obi, anyị ga-enyere ndị ọrụ afọ ofufo nwere mmụtaara aka ijikwa uto ọrụ ha site n'ịkwalite ngwa ọrụ ha na-eji chọta ọrụ ndị chọrọ nlebara anya ha - na-eme ka ọ dịrị ha mfe imelite ederede na izighachi ndezi na-adịghị enye aka (dabara na Mpaghara a na-elekwasị anya n'ọchịchọ).

Anyị ga-enyere ndị na-arụ ọrụ aka ichekwa ọdịnaya anyị site na igosipụta akara ọhụrụ (gafee adreesị IP) nke na-achọpụta ndị na-eme ntinye na-adịghị mma, na-enye ohere mgbochi ndị ọrụ n'ụzọ na-ebelata mmejọ mgbochi nke ndị editọ n'eme ezi ihe.

Iji nye ọdịnaya encyclopedik nye ndị ọgbọ ọhụrụ, anyị ga-ewu atụmatụ ndị ga-enyere ụdị ndị ọgụụ ọhụrụ aka ịghọta ederede dị mfe, nyere ha aka ịchọta ozi masịrị ha, ma nye ha ohere isonye nke ọma ka ha na-agụkwa ihe ọgụgụ. Mgbanwe ndị a bụ iji gbaa ndị ọhụrụ na-agụ Wikipedia ume ka ha bụrụ ndị na-agụ Wikipedia kwudosiri ike, nakwa ka ụfọdụ n'ime ha bụrụ ndị na-enye onyinye (dabara na Mpaghara a na-elekwasị anya n'ọchịchọ a).

Ịnye ọdịnaya ekwesịrị ịtụkwasị obi pụtakwara ịkwado ụdị “inye ihe ọmụma dị ka ọrụ”, ebe ịntanetị niile na-enweta ihe na ọdịnaya Wikimedia. N'ime udi ndị a, akụrụngwa anyị abụghị naanị na ọ ga abara ụmụ mmadụ na-abịa na weebụsaịtị anyị uru, kamakwa maka ọchụchọ na ụlọ ọrụ AI, nke na-enweta ọdịnaya anyị na-akpaghị aka dị ka ntunye ụlọ ọrụ na mmepụta site na ngwaahịa ha. Ụdị ụlọ ọrụ ndị a na-anọchi anya nanị otu n'ime ọtụtụ ojiji na-ebuwanye ibu na-ewata akwudosighi ike nke akụrụngwa anyị. N'afọ gara aga, mmụba dị ukwuu nke iri elu arịrịọ na-abịa site na ngwá ọrụ scraper na bots mere ka ọ dịkwuo ngwa ngwa maka anyị imezi usoro a. Anyị kwesịrị iwepụta ụzọ na-adigide adịgịde maka ndị mmepe na ndị na-ejigharị ọrụ ka ha nweta ọdịnaya ihe ọmụma ka mmadụ wee dị mkpa karịa bots.

Nye Ego nkwalite ọganịnihu nke 'n'efu'

Ngalaba Ngwaahịa & Teknụzụ na-arụ ọrụ dị mkpa n'ịhụ na Otu anyị ga-adigide. N'afọ na-abịa, anyị na ndị otu na-eme nnweta arịrịọ ego ga-ejikọ aka ọnụ ka ndị na-enye onyinye anyị wee nwee mmụtaara doro anya ma baa uru. Na saịtị anyị nakwa ngwa mkpanaka, anyị ga-enye ohere ndị ọgụụ igosipụta ekele ha maka Wikipedia site na inye onyinye, anyị ga-emepụta ụzọ ọhụrụ maka a ga-eji kelee ndị na-enye onyinye ka ha wee nwee ike ịga n'ihu n'inye onyinye ha kwa afọ.

Hazie ịntanetị na-agbanwe agbanwe

Iji weta ihe ọmụma n'efu nye onye ọ bụla nọ n'ụwa, anyị kwesịrị izute ha ebe ha nọ, tinyere mmụtaara ndị ga-enyere ha aka ịmụta. Ndị mmadụ dị afọ iri na asatọ ruo iri abụọ nwere mmata dị ala nke ojiji Wikipedia karịa ọgbọ ndị ha n'eso. Ha na-amụta ihe n'ụzọ dị ukwuu ma na-etinyekarị aka na ihe omume vidiyo dị mkpụmkpụ, ndị mmadụ atụkwasịrị obi n'ịntanetị, ahụmịhe egwuregwu mmekọrịta, yana, mbawanye, ngwa AI. N'afọ a, anyị ga-eme ka Wikipedia pụta ihe nye ndị na-ege ntị a ga na ebe ha na-etinye oge na ịntanetị, ka ha wee mara Wikipedia dị ka isi iyi nke ihe ọmụma a pụrụ ịtụkwasị obi ma bụrụ nke mmadụ kere. Anyị ga-akwalite mpụta ihe anyị ebe nyiwe video ama ama, na-agbasa ọdịnaya Wikipedia ma na-amụba ngalaba anyị na ebe ndị ahụgasị. Anyị ga-enyocha echiche maka iweta ihe ọmụma Wikipedia na egwuregwu na ebe mmekọrịta.

N'ime akụrụngwa, a na-ekewa nke a na Pọtụfoliyo ọrụ atọ (a na-akpọ "bọket"): Ahụmahụ Wiki, Mgbama na Ọrụ Data, na ndị na-ege ntị n'ọdịnihu. bọket ndị a dị ka afọ gara aga na afọ abụọ gara aga.

N'ịchịkọta ọnụ, anyị kwenyere na atụmatụ a na-abịa oge dị mkpa n'akụkọ ihe mere eme nke ịntanetị, ma na-eme ka anyị hụ na ihe ọmụma n'efu na-aga n'ihu bụrụ ihe ọgbọ niile chịkọbara ma na-enweta. Ebumnuche na Isi mpụtaara anyị na-egosi nhazi na ọdịnaya nke atụmatụ a n'ụzọ zuru ezu, anyị na-atụkwa anya ịnụ ajụjụ na echiche site na n'aka ọha mmadụ n'ozuzu.

Iwulite, imelite na ịkwado akụrụngwa maka ọrụ Wikimedia na ndị ọrụ afọ ofufo, gbanyere mkpọrọgwụ na ụkpụrụ anyị

"Foundation ga-eme ma debe ozi bara uru sitere na ọrụ ya dị na ịntanetị n'efu, na-adịgide adịgide."

Otu ngwaahịa na teknụzụ na-ewepụta ihe na-adịgide adịgide, ihe kacha mkpa kwa afọ iji wuo, melite na idobe akụrụngwa na-enyere ọrụ Wikimedia aka. Anyị na-etinye ego n'ịkwado ọrụ Wikimedia, ịmepụta ngwanrọ mepere emepe na sistemu mmewe, yana idokwa ma melite akụrụngwa maka ngwaahịa data yana ụdị AI.

Akụkụ pụrụ iche nke ọrụ anyị na-elekwasị anya na ntọala nke ịmepụta na ịkwado nnukwu weebụsaịtị ama ama. Anyị na-akwado ọrụ Wikimedia anyị na ebe data, na sava na ngwaike anyị na-azụta, wụnye ma na-ejikwa, jikọọ ndị ibe na ibe anyị na ịntanetị ndị ọzọ n'elu netwọk dị elu. Anyị na-enyocha ma na-agbakwunye ikike ebe ọ dị mkpa, ma mee ka akụrụngwa nweta ume mgbe ọ ga-aka nká. Dịka ọmụmaatụ, n'afọ a, anyị na-atụ anya ịgbasa ikike anyị na ime ka ngwaike anyị dị ọhụrụ na ebe data anyị dị na Ashburn, Virginia na Carrollton,Texas.

Anyị na-echepụta ma na-emepụta ngwanro na-enweghi mgbochi ojiji (ọkachasị MediaWiki). Anyị na-ejikwa ma na-eziga ọtụtụ ngwa enweghi mgbochi ojiji nke ndị ọzọ dị adị, ọba akwụkwọ na ebe ọrụ. A na-ebute ụzọ nhọrọ na mmezi nke bug ndị dị mkpa na ngwanrọ anyị. Idokwa sọftụwia enweghi mgbochi ojiji chọrọ ọrụ nnka site n'aka ndị nwere nka pụrụiche na ngwanrọ enweghi mgbochi ojiji , injinịa saịtị atụkwasịị obi (SRE), njikwa ngwaahịa, njikwa mmemme, imewe, na ndị ọzọ. Ndị ọrụ anyị na-arụ ọrụ iji hụ na sọftụwia na sistemu anyị dị ọhụrụ ma na-eme mgbanwe na gburugburu ebe na-agbanwe agbanwe. Nke a gụnyere imelite koodu anyị ka ọ gaa n'ihu na-erite uru na ndozi nchekwa yana iji ngwa ngwa ndị ọzọ na-arụ ọrụ nke ọma. Dịka ọmụmaatụ, edere MediaWiki na PHP, n'afọ gara aga, anyị si na PHP 7.4 gaa na 8.1, nke chọrọ mgbanwe na koodu na akụrụngwa ebe anyị na-akwado saịtị na ọrụ anyị. N'afọ a, anyị ga-agbado ụkwụ na mbọ ahụ wee gbagọ na 8.3, na-eji ihe ndị amụtara na ngwaọrụ ndị emepụtara na nkwalite 8.1. Mmelite a ga-eme ka sistemụ anyị na-arụ ọrụ ngwa ngwa maka ndị ọgụụ, dị mfe iji maka ndị ọrụ na ndị ọrụ afọ ofufo, yana nchekwa maka onye ọ bụla. Ọ ga-enye ekele oge mmepe n'ọdịnihu site na nchekwa, arụmọrụ yana nkwalite nkwado ndị na-abịa na mmelite asụsụ.

Iji hụ na ọrụ anyị na ọdịnaya anyị ga-adigide na ịntanetị, site taa ma na-adịgide adịgide, ndị otu anyị na-agba mbọ dị ukwuu iji hụ na e nwere ọdịdị nke saịtị na ọrụ anyị. Otu akụkụ nke ọrụ a na-elekwasị anya na mgbake ọdachi site na ọdachi ma ọ bụ ihe ọjọọ. Dịka ọmụmaatụ, anyị na-ahụ na anyị nwere nkwado ndabere nke data dị mkpa, ma nwee ike ịwetahachi ha. N'otu aka ahụ, ugboro abụọ n'afọ anyị na-anwale ikike anyị ịgbanye saịtị anyị n'etiti ebe data anyị n'ụzọ akpaaka, ma dozie nsogbu ọ bụla anyị chọtara. Akụkụ ọzọ nke ọrụ a na-elekwasị anya n'ịchọpụta na ime mgbanwe maka mgbanwe n'ewu ewu nke ụdị na iri elu nke ndị mbanye anyị na-enweta. Dịka ọmụmaatụ, site na uto a na-enwetụbeghị ụdị ya na automatic Scrapers, anyị na-ebute ọrụ ụzọ iji hụ na saịtị na ọrụ anyị ka dị maka nye ndị ọrụ mmadụ, na-agbaso usoro nhazi iji guzobe ụkpụrụ gburugburu iji akụrụngwa anyị eme ihe.

Ọ bụghị ọrụ niile ka a na-eme atụmatụ tupu oge eruo. Anyị na-anabatakwa ihe omume na ihe omume na-apụta na-atụghị anya ya, dị ka mwepu saịtị, akụkọ nchekwa ma ọ bụ ihe nchekwa, ma ọ bụ mwakpo mbibi buru ibu na ọrụ anyị. Anyị na-enyocha arụmọrụ anyị yana ihe mgbochi iji nweta ike iru n'ofe ụwa (gụnyere nsogbu njikọta ịntanetị, ma ọ bụ mgbochi nnyocha), wee nyochaa nsogbu ọ bụla anyị hụrụ. Ụfọdụ n'ime ihe omume ndị a na-atụghị anya ya ma ọ bụ usoro nsogbu nke nkuzi nduzi na-eme ka ndị ọrụ na-ebu ụzọ na-eme ọrụ nsonye obere oge nke na-achọ ibelata ma ọ bụ gbochie kpamkpam mmetụta ọjọọ ọzọ. Dịka ọmụmaatụ, ụdị mbọ ndị a dị oke mkpa iji mee ka ọrụ Wikimedia anyị nwee ike iguzogide mkpọmkpọ okporo ụzọ zuru ụwa ọnụ n'ihi nnukwu akụkọ (dịka ọmụmaatụ, ọnwụ ndị ama ama ama ama), site na nchikota nke njikarịcha arụmọrụ, nhazigharị ụkpụrụ ụlọ nke ebe ndị nwere nkụchi, na mmụba ikike. N'otu aka ahụ, mmelite na nso nso a maka iji ngwa anyị na sistemụ anyị jikwaa okporo ụzọ anyị na-enweta enyerela anyị aka imeghachi ngwa ngwa na nke ọma maka ọnọdụ mgbanwe. Ụdị ọrụ mgbanwe a bụ ihe dị mkpa na ikike anyị ịzaghachi ihe omume na-apụta, mgbe mgbe na obere oge, ma hụ na ọrụ anyị na ọdịnaya anyị ka dị.

Ebumnuche Ngwaahịa na Teknụzụ

Ebumnobi ndị ewepụtara ebe a ka dị n'udi enwere ike ịkpa aka ma ohere dị maka inye ntunye okwu na mkparịtaụka.

Ebumnobi ndị a na-anọchi anya ntụzịaka dị elu.
" Key Results" (KRs) na-anọchi anya ụzọ enwere ike iji nweta ọganihu nke ebumnobi ha.
Isi "Haịpotesis" maka KR ọ bụla na-anọchite anya ọrụ anyị na-arụ iji nweta ụsa atụrụanya. A ga-ebipụta ha n'akwụkwọ a yana n'ọrụ dị mkpa ma ọ bụ ibe wiki otu dị ka a na-enweta ntunye uche n'ime afọ.
bụ maka ọrụ Foundation họọrọ n'okpuru Ndepụta Ọchịchọ Ngalaba otu.

Mmụtara Wiki (WE)

Mmụtaara ndị ntinye aka (WE1)

Ebumnobi: Ntunye na-abawanye n'ihi na a na-enye ndị ọrụ afọ ofufo ohere dị elu ma ghọta mmetụta ha. Nwee mkparịtauka
- Ọnọdụ eji maka kwuo Okwu: Ebumnobi a ga-abụ ntọala maka iwepụta atụmatụ ndị ntinye aka ọhụrụ yana ihe mgbado ukwu atọ ya: 1) inye ndị ọrụ afọ ofufo otu ụzọ ha ga-esi hazie ọrụ ha na wiki, 2) inye obere ọrụ dị iche iche iji mepụta nkọwa doro anya ma nyere ndị ọrụ afọ ofufo aka imezu ikike ha, yana 3) ime ka ime ntinye aka kwekwuo nghọta. Na FY25/26, anyị na-eme atụmatụ iwepụta akụrụngwa bụ isi iji nyere ndị ọrụ afọ ofufo aka ịhazi ọrụ ha na wiki n'otu ụzọ , malite na ntinye aka gbadoro anya na ndị editọ nwere ahụmahụ na ndị nhazi. N'afọ ndị na-esote, anyị ga-agbakwunye ntinye aka n'ofe ọrụ niile na-enye aka ma tinye ọtụtụ oghere nsogbu. Na mgbakwunye, anyị ga-aga n'ihu ikwalite na Dezie Lelee na Ọrụ Haziri, na-ewulite ntọala maka otu esi eji AI n'ụzọ nwere ike, ma dịka ntuziaka n'oge usoro edezi yana dịka ụzọ isi tụọ ndị ọrụ afọ ofufo aka n'ohere dị egwu. Na n'ikpeazụ, anyị ga-eme ka mmetụta ndị ọrụ afọ ofufo pụta ìhè iji mepụta mmụtara bara uru nye ha.
  - Key Results WE1.1: Na-abawanye ogo nchịkọta nke ndị ndezi nwere nchịkọta ndezi ≤otu Narị ji ebipụta ndezi ndị ziri ezi na weebụ mkpanaka [i] site na pasentị anọ [ii], dịka ihe atulere na nwale ach ikwara (na njedebe nke Q2).
    - i. "Ndezi ndị bara uru" = ndezi na ihu mbụ nke Wikipedia ọ bụla a na-ezighachighị n'ime awa iri anọ na asatọ ebipụtara ya.
    - ii. T389403#10960480
    - Ndị ọrụ afọ ofufo ọhụrụ na-agbasi mbọ ike ịmalite idezi nke ọma. Karịsịa, ndị mmadụ na-eji ngwaọrụ mkpanaka ebe ohere ihuenyo dị obere ma ntinye uchu na-abụ nke n'ekesa.
    - Ụfọdụ na-enwe ike ọgwụgwụ maka nke a, ndidi, na nnwale na njehie achọrọ iji nye aka n'ụzọ dị mma. Ndị ọzọ enwetabeghị ohere siri ike ịnwale.
    - WE 1.1 ga-akọwa nsogbu ndị a site na:
      - Igosipụta ntụputa ndezi
      - Inye ohere ngosipụta n'ime nduzi ime ndezi
      - Na-ewulitewanye ọrụ ndezi ndị ọzọ a kapịrị ọnụ
    - Isi ihe dị mkpa n'ọrụ ndị a bụ mkpa ọ dị maka ụzọ ndị a pụrụ isi chọpụta otu esi eme ka mgbanwe ndị na-aga n'ihu na ọdịnaya dị ugbu a ka mma. Iji mee ka ikike a dịkwuo elu, anyị ga-anọgide na-anwale mmụta ngaw igwe iji mụta otu o si arụ ọrụ nke ọma nye ndị editọ, n'ọtụtụ ọrụ na ọkwa mmụtara.
    - Usoro akara KR akwadoro: Dabere na ntọala nhiwe ọ bụla, anyị ga-agbakọ oke nke ihe ndị anyị tinyere ma nyochaa site na nnwale ndị a na-achịkwa nke ruru ma/ma ọ bụ gafere ebumnuche ndezi anyị wepụtara na mbido afọ a. Lee phab:T379285#10782051 maka iche echiche.
      - Mata na: June 30, 2025, WE 1.1 nwèrè nnwale nchịkwa akwadebere
  - Isi Mpụtara WE1.2: Mbawanye na ọnụọgụ nke mmekorita na wiki site na 55% YoY site na njedebe nke Q3.
    - Ndị ntinye aka na-agbasikarị mbọ ike ịchọta ohere iji soro ibe ha rụkọ ọrụ, ọkachasị gburugburu isiokwu na ọrụ ndị masịrị ha. Nke a nwere ike ibutere ndị bịara ọhụrụ inwe mmetụta inọ naanị ha na Wiki, ọ pụkwara ibute ike ọgwụgwụ nye ndị editọ nwere mmutara. Na mgbakwunye, mmetụta nke mmemme imekọ ihe ọnụ anaghị edokarị anya, nke nwere ike ikpata obere mmadụ ịchọ isonye, hazie ma ọ bụ kwado njikọ aka rụọ ọrụ na wiki.
    - Anyị chọrọ ime ka uru ijikọ aka rụọ orụ doo anya site n'ime ihe ndị a:
    1. Mepụta ụzọ ọhụrụ iji kesaa mmetụta nke ọrụ mmekorita ọrụ ọnụ na wiki
    2. Malite ịnakọta data gara gburugburu gbasara mmetụta nke ọrụ mmekorita
    3. Depụta akụrụngwa dị mkpa iji soo ntunye nrụkọ ọrụ ọnụ, ka anyị wee nwee ike inye ụzọ ọhụrụ iji mata ma kwụghachị ụgwọ ntunye n'ọdịnihu
    - A ga-atụle njikọ aka n'ọrụ ọnụ site na mmemme ọhụrụ emepụtara site na Event Registration na CampaignEvents extension. Ebumnuche bụ na, na njedebe nke KR a, anyị ga-enwekwu ndị oji eme nke ngwa ndọtị yana ụzọ ọhụrụ nke isi nweta mmetụta njịkọ aka n'ọrụ. Nke a ga-etinye anyị n'ebe dị mma iji jikọọ akụrụngwa anyị dị ugbu a na ụzọ ndị ọzọ nke ịmata na ịnye ekele maka ọrụ na wiki (dị ka modul mmetụta, ekele, wdg).
    - Mpaghara nlekwasị anya na listi ọchịchọ: Ndepụta ọchịchọ Ngalaba otu/Ebe ndị a na-elekwasị anya/na-ejiko ndị ntinye aka
  - Ihe dị mkpa WE1.3: Ka ọ na-erule ngwụcha nke Q3, pasentị iri nke ndị ntinye aka bụ ndị e nyere ha ebe mbụ ha ga-ahụ maka ndị nhazi ọhụrụ gara lee ya izu abụọ n'usoro.
    - Anyị kwenyere na anyị nwere ike ịrụ ọrụ ka mma n'ịkọwapụta ohere onyinye nye ndị ọrụ afọ ofufo. Ruo ogologo oge, anyị kwenyere na ebe mbanye mbụ nwere ike inyere onye ndezi ọ bụla aka ịhazi ọrụ ha, chọta ohere ọhụrụ, ma ghọta mmetụta ha. Ebumnobi anyị na FY25/26 bụ iwepụta ohere ọhụrụ nye ndị ndezi nwere ahụmahụ iji mee ihe nhazi nke ha na-agaghara emeli ma ọ bụrụ na enyeghi ha ohere ya.
    - Anyị ga-ebu ụzọ nwalee echiche a site n'ịghọta otú ndị nchịkọta akụkọ nwere ahụmahụ ga-esi tinye aka na ibe mbụ, yiwere ihe ndị ọhụrụ nwere ike ịnweta.
    - Anyị ga-akọwapụta kpọmkwem nhazi ihe omume (a ga-ekpebi nkọwa) nye ndị ntinye aka bụ ndị ọhụrụ na ụdị nhazi ahụ, na mbunobi iji nyere aka belata ibu arọ nke ndị ndezi nwere ahụmahụ site na mbelata backlogs (n'okpuru KR ọhụrụ).
    - Ọ bụrụ na echiche mbụ anyị nwere ihe ịga nke ọma, anyị na-eme atụmatụ ime ka ibe a bụrụ modula iji gboo mkpa ndị ngalaba otu. Modulu ndị a nwere ike ịgụnye ihe ndị dị ka ime ka ọ dịrị ndị ndezi mfe ịghọta mmetụta ha.
    - Ihe omuma banyere usoro:
    - Anyị ga-enwe echiche nnyocha iji kọwaa ndị na-ege anyị ntị, nke ga-abụ akụkụ nke WE1.3.1.
    - "Ndị nhazi" ga-agbaso nkọwa nke malitere na Research:Develop a working definition for moderation activity and moderators, n'agbanyeghị na ọ ga-adị mkpa ka enwee ọrụ nsochi anya iji nye nkọwapụta ọnụọgụgụ.
    - A ga-akọwa izu nke abụọ ma e jiri ya tụnyere oge onye ọrụ ọ bụla ga-abịa na mbụ. N'ebe a, anyị ga-enyocha ndị nhazi ọhụrụ niile gara na ibe mbụ n'oge akọwapụtara wee mee nleta ọzọ ma ọ dịkarịa ala otu ugboro (ụbọchị asaa ruo ụbọchị iri anọ) mgbe e mesịrị.
    - Mpaghara ana elekwasị anya : Ndepụta ọchịchọ Ngalaba otu/Ebe a na-elekwasị anya/Ọrụ ndị ga-ebute ụzọ
  - Isi mpụtara WE1.4: Meziwanye pasentị nke ndị ọbịa pụrụ iche na ndepụta nlele na/ma ọ bụ mgbanwe ndị ọhụrụ nke ịpị iji lee ndezi.
    - Ebumnobi anyị bụ inyere ndị editọ aka na ndezi Otu Narị karịa ka ha chọta ma mepee ndezi ndị metụtara mmasị ha nke ọma. Anyị ga-enyocha Ebe Edobere Ọrụ Kachasị Mkpa, nye ihe achọrọ n'akụkụ a, ma rịọ nzaghachi ndị ọzọ site n'aka ndị ọrụ afọ ofufo gbasara otu esi eme ka elu ndị a ka mma. Anyị nwere ike ịtụ ihe ịga nke ọma site n'imeziwanye arụmọrụ nke ibe ọ bụla na "ịchọta ọrụ", nke a kọwara site na ihe ngosi nke ogo click-though.
  - Isi mpụtara WE1.5: Kọwaa ma tinye n'ọrụ ọrụ ihe atụ asaa dị mkpa [1] dị mkpa maka ịgbaso ọganihu iji nweta ebumnobi ndị e depụtara na atụmatụ ndị na-enye aka tupu ngwụcha nke Q4, site na ịmepụta dashboard na ntinye n'ọrụ ihe atụ ndị na-arụ ọrụ kwa ọnwa.
    [1] Ndị ndizi ewere, mmeghe dị mma, ndezi ndị ziri ezi, ndebanye aha akaụntụ ndị ọhụrụ ewere, ndị ndezi na-arụ ọrụ site na oge, ndị nchịkọta akụkọ na-arụ ọrụ site na ogo ahụmahụ.
    - Atụmatụ ahụmahụ nke ndị na-enye aka a na-atụ anya maka mgbalị afọ atọ ruo ise iji "mee ka ndị ọrụ afọ ofufo nwee ike ịba ụba" na "ịbawanye njigide na mkpali" nke ma ndị ọhụrụ na ndị nọ anọ site na isi ihe atọ nke ọrụ:
    1. Ịkwado otú ndị ọrụ afọ ofufo si enweta ntuziaka, jikwaa ọrụ ha na ọdịmma ha, hụ ihe na-eme na wikis, ma ghọta mmetụta ha
    2. Inye ọrụ ndị a haziri nke ọma iji mee ka o doo anya ma nyere ndị ọrụ afọ ofufo aka imezu ikike ha site na imeziwanye usoro ọrụ anyị na-eziga ha, gụnyere ntinye ego n'inye nduzi nhazi na ime ka ọrụ ndị a bụrụ nke a na-emegharị na-akpaghị aka, na-nlekwasị anya na ahụmahụ mkpanaka weebụ.
    3. Ime ntinye aka bara uru site n'igosi ndị ọrụ afọ ofufo mmetụta ha site n'itinye ego n'ụzọ maka njikọ mmadụ na gburugburu ebe dabere na nzaghachi dị mma
    - Ọrụ atụmatụ nha wee wepụta ọtụtụ netwọk nke nha maka ịgbaso echiche mgbanwe a. O kwubiri na ihe bụ isi nha nke ihe ịga nke ọma ("isi metric") kwesịrị ịbụ ọnụọgụ nke ndị editọ ejikwara, nke ejiri obere ihe ngosi dịka mkpali ihe owuwu na ebumnuche ndị na-enye aka iji weghachite ma gbasaa "mpụta n'isi" dị ka ndị editọ na-arụ ọrụ na ọdịnaya dị mma. Anyị kwesịrị ijide n'aka na anyị nwere usoro ndị a na-arụ ọrụ ma a na-ahụ ya na dashboard, ka anyị wee nwee ike ịchọpụta ọganihu anyị na-enweta na atụmatụ ahụ.
  - Key result WE1.6: By the end of Q3, watchlist users can more easily organize their work and act more effectively on taking patrolling or editing action, as measured by qualitative feedback.
    - Our goal is to help editors with 100+ edits to more efficiently find edits that relate to their interests. We will explore the Task Prioritization Focus Area, deliver wishes in this area, and solicit additional feedback from volunteers on how to improve these surfaces.
  - Key result WE1.7: Primary: Achieve a ≥ 10% relative increase in the edit completion rate of qualified newcomer and Junior Contributor mobile edit sessions, based on controlled A/B test(s).[i]
    [i] Qualified edit session = edit session in which a logged-in user with ≤100 cumulative edits spent at least ≥2 seconds in VE's "ready" state.
    Guardrail: No meaningful decreases in constructive edit rate among mobile edits published by newcomer and Junior Contributors, based on controlled A/B test(s).
    - 150,000–200,000 times/day, someone will tap "Edit" on mobile web, wait for the editing interface to load, look around for at least 2 seconds, and then leave without doing anything. No keystrokes, no taps on the toolbar…nothing.
    - WE 1.7 will meet these "curious clicks" with clear, compelling, and structured edit suggestions that cause the people making them to experience the satisfaction, joy, and meaning that can come with making Wikipedia, the resource they chose to visit, a bit better.
  - Key result WE1.8: By the end of Q4, target a 5 percent overall relative increase in the mobile web account creation completion rate, with success measured by at least three controlled experiments achieving a minimum 2 percent relative improvement each.
    - Account creation is the gateway to meaningful participation on Wikimedia projects, yet it remains an outdated experience in the newcomer journey. The current flows impose high cognitive load, present limited or confusing value propositions, and rely on legacy interface patterns that no longer reflect contemporary expectations. As a result, many potential contributors disengage before they have a chance to join the community.
    - This initiative aims to modernize account creation, reduce friction for good faith contributors, and introduce thoughtful experiments that encourage registration at the moments where motivation is naturally highest. The work lays essential groundwork for long term improvements in newcomer activation, retention, and editor diversity.
    - We estimate a 5 percent relative increase in account creation on Wikipedia on mobile would translate to several thousand additional new accounts per month, and several hundred more retained new editors per month, based on current registration volumes.
  - Key result WE1.9: By the end of Q4, deliver one tangible product recommendation each for goal setting, newcomer progression, and recognition to enable junior contributors to stay engaged and evolve.
    - Background: we know that retention of junior contributors is quite low despite recent gains (data). We believe that a fundamental challenge for overcoming this low junior contributor retention is developing our platforms to help editors stay highly motivated to contribute – i.e. not just reducing the barrier to contribute but also making the experience rewarding. This is not trivial though – e.g., editors enter the project with varying motivations; interventions such as gamification that may boost initial engagement might not help with long-term retention.
    - Why now: much of the focus of Contributors Product teams to date has been related to reducing technical barriers to activation/engagement but there are a suite of upcoming projects that are more retention-focused – e.g., Progression System and Recognition. The research under this KR aims to get ahead of this implementation work and provide clear recommendations to Product teams in this space about how they should design their platforms to better support the diverse motivations of editors and increase long-term retention. This links back to key questions related to newcomers and junior editors in the Contributor Strategy.
    - Proposed KR scoring methodology: for each identified project (goal setting; progression system; recognition), we will make at least one concrete recommendation with implications for design. Research is experimental work and the projects may evolve over the course of the KR but we will keep in close communication with the relevant product teams. In practice, these recommendations may include insights about what is most demotivating for newcomers (things to avoid) what is most satisfying (things to emphasize), common "tripping points" in journeys that need to be addressed, strategies for getting through these challenges, etc. The recommendation should help the PM identify clear next steps for design and/or engineering.
  - Key result WE1.10: By the end of Q4, implement a new guided article creation flow where the survival rate of articles created by junior editors on mobile is 5% greater on each deployed wiki than articles created by other means while the volume of surviving articles stays the same or higher, as measured by a controlled experiment.
    - The Article guidance initiative aims to develop new approaches that support editors in creating initial, well-structured contributions to Wikipedia that align with community policies and content quality. As an initial intervention, a new workflow for article creation was implemented last quarter, based on community-editable outlines that encapsulate the guidance for specific types of articles. In Q4, the goal is to run an A/B test experiment to measure the impact on a set of pilot wikis to evaluate the impact. Since the guidance is community-dependent, the experiment preparations will requires collaboration with the communities of the pilot wikis to adapt the system to their needs.

Ihe Ọmụma dị mkpa (WE2)

Ebumnobi: Mee ka ihe ọmụma dị mkpa karịa ma gosipụta nke ọma n'asụsụ nakwa na isiokwu. Nwee mkparịtauka
- okwu gbasara Ebumnuche : Ebumnuche a ga-akwalite uto ọdịnaya nke na-azaghachi mmasị nye ndị nnyere aka n'otu isiokwu na asụsụ, yana ọchịchọ onye na-agụ maka ihe ọmụma dị mkpa nke egosipụtara nke ọma. Ihe ọmụma dị mkpa bụ nchịkọta akụkọ na-enye obosara na omimi nke isiokwu achọrọ maka ọrụ asụsụ Wikipedia nwere ike iji me ihe. Ndị Otu kọwapụtara ya n'ịrụ aka ịbụ nke ama ama, ịdị mkpa, ndị agụmagụ akara aka, na njikọ dị n'etiti ederede.
- Anyị ga-ewere usoro mmekọrịta ọha na eze, na-emeziwanye arụmọrụ nke njirimara, ngwaọrụ na usoro mmekọrịta ọha. Anyị ga ewulite njirimara ngwaahịa nwere mmetụta dị elu dị ka ọrụ atụpụtara aro, ọchụchọ mgbasa ozi, na ntụgharị ọdịnaya nakwa kwalite ntinye na mmepe nke obere Wikipedia asụsụ. Anyị ga-akwado ndị nhazi Wikimedia ndị na ewebata ndị ọhụrụ, ndị na-azụ, ma na-akwado ndị nyere aka ka ha lekwasa anya ma rụọ ọrụ na ebumnuche ọdịnaya nkekọrịta site na nhazi nkwado dị ka WikiProjects na kampen. (Anyị na-eme atụmatụ na ọ dịkarịa ala ndị nhazi Narị atọ na-arụsi ọrụ ike na nkeji ọbụla.) Anyị ga-enwekwa ezi mmekọrịta kacha mkpa iji wepụ ihe mgbochi nye ihe ndị eji emepụta ihe. (Anyị nwere mmekọrịta ọrụ ugbu nye ihe karịrị otu Narị ọdụ data naanị ndebanye aha kachasị n'ụwa.)
- Iji hụ na ntinye aka anyị nwere mmetụta dị mma na ihe ọmụma dị mkpa, anyị ga-atụle mmụba nke ọdịnaya Ọtu họọrọ na ịdị mma nke ọdịnaya ahụ, na-eleba anya n'ihe ndị dị ka ọnụọgụ ntụgharị na ọnụọgụ nke nrụtụaka ebemsidee na ihe oyiyi.
  - Isi mpụtara WE5.1: Ka ọ na-erule njedebe nke Q4,Pasentị iri ise nke arịrịọ maka ọwa ịnweta mmemme nwere ike ị bụ nke e nyere onye nrụpụta ama ama ma ọ bụ ngwa.
    - KR a ga-egosipụta oghere ọdịnaya n'ime usoro ndezi, dị ka nchọta ihe oyiyi na Wikipedia, ntụgharị ọdịnaya, na nduzi nke edemede ọhụrụ. Anyị ga-emejuputa ma nwalee ntinye aka na mmekọrịta teknụzụ ọha iji kwado ọrụ mmepụta ọdịnaya maka ndị obere ngalaba asụsụ. A ga-atụle ihe ịga nke ọma n'ime echiche ọ bụla.
  - Isi Mpụtara WE2.2: Tupu ngwụcha nke Q4, wulite ikike nhiwe dị mkpa iji gosi na anyị nwere ike ịkwado ebumnobi ogologo nke Abstract Wikipedia n'ọtụtụ. Anyị ga-ama na anyị na-aga nke ọma ma ọ bụrụ na anyị egosi sistemụ ahụ mmepụta ọdịnaya encyclopedic bara ụba, nke nwere ọtụtụ asụsụ site na iji Wikidata na mmepụta asụsụ okike, nke ngaklaba otu Wikimedia na-ejikwa, ma na-anọgide na-arụ ọrụ nke ọma na mpụta dị ukwuu.
    - Ugbua anyị nwere ike iji Wikidata wepụta isi ọdịnaya ederede na Wikipedia, ihe ọzọ ga-eme bụ ịnọgide na-ewulite ikike nhiwe nke nwere ike ịkwado Abstract Wikipedia n'ọtụtụ. Nhiwe a a ga-achọ ịkwado ọdịnaya dí mma, nke nwere ọtụtụ asụsụ nke ngalaba otu nwere ike ijikwa ma nọgide na-arụ ọrụ nke ọma. Nke a bụ KR dị mkpa ebe anyị na-aga site na 0-1.
  - Isi mp WE2.3: Tupu ngwụcha nke Q4, tinye ụdị mbụ nke wiki ọhụrụ maka imepụta ederede Abstract na ngalaba otu mbụ.
    - KR a na-enye anyị ohrer iji nwalee ike nhiwe nke Abstract wiki n'afọ na-abịa. Wiki ọhụrụ ahụ, nke na-anọpụ iche, ga-enwe ọbá akwụkwọ nke edemedede abstract wuru na Wikifunctions, ma nye ikike nhiwe dị mkpa iji tinye isiokwu abstract na Wikipedia n'ọdịnihu.
  - 'Isi mpụtara WE2.4: mee ka WMF na WMDE kwekọrịta na nkọwa nke ihe ịga nke ọma maka mmezi na akụrụngwa teknụzụ na nkwado okwu ojiji dị mkpa nke Wikidata na njedebe nke Q2, gụnyere metrik na ebumnuche ruo 25-26 FY.
    - E guzobere ma tinye ndị otu WMF Wikidata Platform (WDP) n'ọrụ - otu Onye Nduzi Nrụpụta na otu Onye Nduzi Teknụzụ - n'ọnwa Ọgọst 2025. Dịka mgbakwunye ọhụrụ na mmemme nwere ọtụtụ afọ nke mmepe site n'aka ndị nwe teknụzụ na nrụpụta na WMF na WMDE, n'otu n'otu, ebumnuche a na-egosipụta ebumnuche anyị nke ịgbanwe ikike site na nhazi na okwu ojiji, ndabere na ya, na usoro ihe ịga nke ọma dị mkpa. KR a ga-ewulite ntọala nke nghọta nke oghere nsogbu, nke anyị ga-ewulite site na ya ruo afọ mmefu ego fọdụrụnụ (Mee 2026).
  - Key result WE2.5: By the end of Q4, our backend replacement has been stood up in parallel to Blazegraph and is capable of supporting the cutover of select users. We define “migration ready” for this KR as capable of supporting the pilot phase of our migration in Q1 of FY26-27.
    - Following the progress made towards defining the success criteria as a part of WE2.4, we are now shifting into execution mode. In the next two quarters, we will outline all of the variables associated with the Blazegraph cutover in a migration plan, determine which are critical for pilot launch, implement them in a new RDF database, and define the migration timeline for all requirements beyond our pilot personas. Our work between now and our target launch of a new WDQS backend (est. July 2026) will be guided by the requirements laid out in this plan.

Mmụtara ndị n'eji ihe eme ihe (WE1)

Ebumnobi: Ndị ọgụụ sitere na ọgbọ dị iche iche na-etinye aka, ma na-arụkọ ọrụ, na Wikipedia, na-ebụte mbawanye njide na ọrụ inye onyinye. Nwee mkparịtauka
- okwu gbasara Ebumnuche : Ebumnuche a ga-elekwasị anya na idowe ndị ọgụụ ọhụrụ site na ụdị ọdịnaya ọhụrụ, ndị na-ege ntị bụ isi site na ime ka mmụtaara ọgụgụ nke amaara sikwuo ike, na ịhụ na nkwado dị ogologo oge site na ime ka njikọ ndị ọgụụ na-adịwaga ọmịmị ma mee ka inye onyinye dị iche iche. Nke a ga-agụnyekwa ịga n'ihu n'ọrụ anyị ime ka ọdịnaya dị mfe ịchọpụta site na njirimara nnwale ọhụrụ dịka nchịkọta AI ma ọ bụ oghere ahaziri ahazi. Ọ ga-agụnyekwa ọrụ na-ejigide nakwa imeziwanye ogo nke mmụtaara ọgụgụ n'ime ohere nke ọgụgụ na n'ịchọgharị ihe ọgụgụ ahaziri site na ndepụta ihe ọgụgụ na nsonye ndị ọzọ na-adịghị eme ndezi. Maka ndị na-enye onyinye, ọrụ a ga-aga n'ihu na-elekwasị anya n'ịgbanwe ebe nnweta ego gasị si ebe.
  - Isi mpụtara WE3.1: Ka ọ na-erule njedebe nke Q2, gosipụta mmụba dị ịrịba ama na njigide nke onye na-agụ akwụkwọ na-abanyeghi, dị ka a tụrụ site na nnwale A/B nke otu njirimara kwa nhiwe
    - KR a ga-elekwasị anya n'ịga n'ihu na-etinye ego na ahụmahụ ndị na-emeziwanye ụzọ ọhụrụ nke nchọgharị na ịmụta ọdịnaya, mgbe niile site na iji teknụzụ ọhụrụ na usoro - na-egosi ọdịnaya dị ugbu a n'ụzọ ọhụrụ na-adọrọ mmasị. Na FY a, anyị ga-achọ ịga n'ihu na-anwale atụmatụ njirimara ọhụrụ ma na-elekwasị anya n'ịkwalite nnwale na-aga nke ọma n'ofe wikis na nhiwe. Ọrụ dị na KR ga-agafe na webụsaịtị mkpanaka na desktọpụ, yana ngwa iOS na appụ Android ma lekwasị anya na nchọpụta ọdịnaya (ntụgharị ihe ntinye na ndụmọdụ) na usoro mmụta enwere ike ịdabere na mgbanwe ya (nchịkọta enyemaka igwe, ntụgwagharị ọdịnaya).
    - Mpaghara nlekwasịrị anya: ahụmahụ ndị oji eme ihe ọhụrụ
  - Isi mpụtara WE3.2: Mbawanye ọnụọgụ nke onyinye site na egosipụtaghị ọkọlọtọ ma ọ bụ udi emailu site na pasentị ise nke ngafe afọ kwa afọ nhiwe site na ntinye aka ngwaahịa na-akwalite njikọ miri emi ma belata aramahụ nke ndị na-enye onyinye site na njedebe nke Q2
    - KR a ga-ahụ ka anyị na-aga n'ihu na-enyocha ebe ntinye ọhụrụ maka inye onyinye na ohere ndị ọzọ iji tọghata ndị ọgụụ ka ha bụrụ ndị na-enye onyinye ma jigide ha site na ime ka njikọ ha dị omimi na wiki, gụnyere ọdịnaya ahaziri iche. KR a ga-elekwasị anya na iwebata ihe ntinye ọhụrụ na ịkọwapụtagharị isi ihe ntinye dị na ngwa app na webụ, na njikọ aka ọrụ ya na ndị otu na-arịọta onyinye ego.
  - Isi Mpụtara WE3.3: Ka ọ na-erule njedebe nke Q2, gosipụta mmụba dị ịrịba ama na njigide nke ndị ọgụụ na-abanyeghi, dị ka a hara site na nwale A/B nke otu njirimara kwa ikpo okwu
    - KR a ga-elekwasị anya n'ịkwalite mmụtaara ọgụgụ na mmụta nke ndị Ọgụụ dị ugbu a na ndị nwere ahụmahụ, na ebumnuche nke ijigide ndị na-ege anyị ntị ugbu a na ime ka njikọ ha dịkwuo elu na saịtị ahụ ka ha nwee ike ịmụtakwu ihe, yana ịdị njikere ma mepeekwa ụzọ maka inye onyinye na ime ndezi. Ọrụ ebe a ga-elekwasị anya n'ịkwalite ahụmịhe ọgụgụ na webụ na ngwa appụ (mmeziwanye ịgụ ihe, nchọgharị na nchọta ka mma), yana mwulite na ịkọwapụtagharị na onyinye nhazi na nhazi onwe anyị (ndepụta ọgụgụ, ntụpụta ahaziri iche, onye ọrụ ,akụkọ ederede, wdg)
  - Key result WE3.4: Site na njedebe nke Q3, wepu ihe mgbochi niile achọpụtara maka ntinye ebe nchekwa obere (PoPs) nke na-emezu ụkpụrụ ọrụ na nchekwa anyị dị ugbu a dịka ntinye ebe nchekwa anyị dị ugbu a si dị
    - KR a ga-elekwasị anya n'igosipụta echiche na anyị nwere ike imeziwanye arụmọrụ weebụsaịtị wee belata egbumoge nke ndị ọgụụ anyị site n'ime ka akụrụngwa caching anyị dị mfe na imeziwanye usoro maka nzije caching saịtị site n'ibelata oge nzije ihe ndabere site n'ihe dị ka otu afọ na nkezi ruo otu ụzọ n'ụzọ anọ kachanụ. Ihe a ga-elekwasị anya ebe a bụ ime ihe ịdị mfe, izije PoC, mee nnyocha nchekwa wee mezue mkpebi ma a ga-aga n'ihu na-ezije cache ebe nrụ ọrụ. Mbelata egbomoge nwere ike ibute mmụba pụtara ìhè na nlele ihu akwụkwọ yana ebe nnweta ndị ọgụụ dị iche iche na mpaghara.
  - isi Mpụtara WE3.5: kwalite njirimara onye na-enye onyinye - hụ na ndị niile na-agụ akwụkwọ banyere nwere ike mmata site n'aka ọkwa onye nyere onyinye maka mmụtara ahaziri - site na njedebe nke Q4.
    - Anyị ga-emejuputa atumatu njirimara onye na-enye onyinye iji hụ na enwere ike mata ndị niile na-agụ akwụkwọ abanyela site na ọkwa onye na-enye onyinye, na-eme ka ahụmịhe ahaziri ahazi na itinye aka. A ga-ebute mbọ njirimara onye na-nye onyinye site na Q4 iji kwado nhazi ahaziri nke ọma na atụmatụ nkwalite n'ọdịnihu.
  - isi Mpụtara WE3.6: Meruo n'isi, bipụta, ma kwupụta atụmatụ maka onye na-agụ Wikipedia na mmụtara ndị ahịa n'ofe nhiwe site na njedebe nke Q4, na ebumnuche akọwapụtara na isi metrik, emepụtara na mmekorita ya na ndị isi nsɪonye na ngalaba otu, iji duzie ọrụ anyị site na 2030.
    - Ọrụ gbasara atụmatụ ndị ji ọrụ anyị eme ihe ga-aga n'ihu, na nlekwasị anya iwulite na inwe mkparịtaụka atụmatụ a n'ime anyị ya nakwa na ngalaba otu ma kọwapụta na guzobe metrik maka ndị oji eme ihe, ya na usoro ha.
  - Key result WE3.7: Increase the number of donations through non-banner or email methods by 10% YoY per platform through product interventions that foster deeper connections and reduce friction for donors by the end of Q4.
    - This KR will see us continue to explore new entry points for donation and other opportunities to convert readers into donors and retain them by deepening their connections to the wikis, including more personalized content. The KR will focus on introducing new entry points and iterating on existing entry points on apps and web, in collaboration with the fundraising team.
  - Key result WE3.8: By the end of Q4, scale at least one experiment per platform (web and apps) that displayed improvement to retention or an indicator metric for active readers in a test environment, monitoring a guardrail appropriate for the feature.
    - This KR will focus on scaling features that showed promise in improving engaged reader retention (or related indicator metric) across web and apps, based on experiment results from Q1/Q2. This includes scaling of the reading list on web (to drive account creation and internal referral rate), activity tab on iOS (for account creation and retention), and a potential longer production analysis of activity tab on Android (already released) to validate feature retention improvements.
  - Key result WE3.9: By the end of Q4, scale at least one experiment per platform (web and apps) that displayed improvement to retention or an indicator metric for logged-out casual readers in a test environment, monitoring a guardrail appropriate for the feature.
    - In this KR, we will scale successful experiments that have proven to provide a high value to readers, new and lapsed, who do not currently engage with wiki projects. We will scale improvements focused on logged-out reader experiences that support knowledge seeking- content discovery experiences, visual presentations and modalities for sharing (knowledge, content, topics of interest). This KR spans across mobile web and apps platforms (iOS and Android).
  - Key result WE3.10: By the end of Q4, perform at least one experiment per platform (web and apps) that shows a practically significant improvement in logged-out casual reader retention or another indicator metric over control (with casual reader retention defined as 21-day cumulative retention for web, and 14-day cumulative retention for apps).
    - We are continuing our investment in experiments that convey Wikipedia's value to readers, new and lapsed, who do not currently engage with wiki projects. We will look to test improvements to the logged-out reader experience focusing on content discovery (e.g., Minerva TOC, semantic search, Q&A), visual presentations (e.g., visually engaging link cards), and modalities for sharing (e.g., share action). This KR spans web (mobile and desktop, though with an emphasis on mobile due to the audience) and apps (iOS and Android).

Nchebe na Nchekwa (WE4)

Ebumnobi: Usoro ọrụ anyị ga a ka mma ịnọ na nchebe akaụntụ ndị edịtọ na ozi ndị zoro ezo d elu,ọ bụlagodi ka a na-enye ụzọ nkwalite nye ndị editọ na ndị nsonye nwere ikike dị elụ iji chebe ma zaghach ihe omume mmetọ ọ bụla. Nwee mkparịtauka
isi Mpụtara WE4.1: Nwe usoro mkpesa ihe omume na-arụ ọrụ na wiki anyị niile, nke ngalaba otu ha na-eji ma bụrụ nke ha nabatara, na njedebe nke Q2.
- Ịhụ maka nchekwa na ọdịmma onye ọrụ bụ ọrụ dị mkpa nke Otụ anyị. Ọtụtụ ikike nwere ụkpụrụ chọrọ ọgbakọ dị n'ịntanetị dị ka nke anyị iji nyochaa ma mee ihe megide mmaja, iyi egwu ịntanetị na ọdịnaya ndị ọzọ na-adịghị mma na nhiwe ha. Ịghara ilebara ihe ndị a anya nwere ike ime nhiwe ịdaba na ụgwọ iwu na mmachi iwu.
- Anyị chọrọ inye ndị ọrụ anyị ike ka ha nwee ike ime mkpesa banyere ihe iyi egwu nke mmerụ ahụ ozugbo site na usoro mkpesa a na-achọpụta ngwa ngwa iji hụ na anyị nwere ike ịmata maka ụdị ihe ahụ wee mee ihe ozugbo ebe ọ dị mkpa. Nke a bụ nzọụkwụ iji mee ka ndị ọrụ anyị nwee ahụ iru ala mgbe ha na-enye aka na nhiwe anyị. Anyị na-eme nke a site na itinye usoro mkpesa ihe mberede na wiki anyị.
Isi mpụtara WE4.2: Meziwanye idị mma na izi ezi nke ngwá ọrụ mgbochi mmetọ, site na imepụta mmelita abụọ na ngwụcha Q2
- Anyị na ndị ngalaba otu anyị kwesịrị ịchọpụta ma gbochie omume ọjọọ na nke rụrụ arụ na wiki. Anyị ga-eme nke a site n'ịbawanye ọnụọgụ na ogo nke akara ngosi dị na nhiwe, ijikọta akara ndị a n'ime ngwá ọrụ ka anyị ga-eme ka ọdị nye ndị ọrụ nwere ikike dị elu, ma chọpụta ebe anyị nwere ike ịme ihe mgbochi nke oji aka ya eme n'ọrụ a na-enyo enyo.
- Anyị hụkwara ohere iji melite nnweta Wikipedia nakwa ọrụ anyị ndị ọzọ n'otu oge. Dịka ọmụmaatụ, otu ọrụ bụ iji dochie wikis' CAPTCHA na-achịkwa onwe ya nke ọma, nke na-egbochi onye ọrụ ịbanye ruo mgbe ha mezuchara okwe mgbagwoju anya, na-ojiji ọrụ nyocha ihe egwu nke na-anaghị esiri onye ọrụ ike. Kama, ọ ga-eji nwayọ họpụta akaụntụ nwere ihe a nɑ-enyo anyị nwere ike iji gbanyụọ ịrụ ọrụ, ma mee ka ndị nhazi nwere nnukwu ohere hụ ọkwa a iji nyere aka n'ọrụ ha.
- N'ozuzu, ọrụ Wikimedia na-adabere karịa na mgbochi adreesị IP iji belata mmetọ site n'aka ndị omume ọjọọ. Nke a na-adịchaghị ike n'ịkwụsị mmetọ ma na-enwe mmetụta n'ezighi ezi nye ndị ọrụ na-eme ezi ihe nk IP na na nhazi IP na-emetụta. Na KR a, anyị bu n'obi imeziwanye ikike ndị anyị nwere ugbu a na ibuga ngwaọrụ ọhụrụ na-enyere aka igbochi ndị na-eme ndị omume ọjọọ n'ụzọ ziri ezi ma bụrụ nke dị irè, ma belata mmebi zuru ọnụ nke ihe mgbochi IP na IP yitewere.
- Iji chọpụta ịdị irè anyị, anyị ga-eleba anya na nzaghachi ịdị mma sitere n'aka ndị ọrụ afọ ofufo na-arụ ọrụ mgbochi mmetọ, yana ihe ngosi ọnụọgụ dị ka ogo nke mgochi IP na-ezitere, nnabata nke aha IP na mgbama ihe nchọgharị dabere na mbelata, ogo nke mmekọrịta mmadụ na ibe ya mgbe e nwere mgbochi onye ọrụ, na nnabata nke akara ọhụrụ na ngwá ọrụ mgbochi.
- Ọrụ na KR a gụnyere nkwalite sokpupetị na nchọpụta mmachibido na mbelata, na-egosi ozi gbasara iihe nwere ike bụrụ mmebi arụghị aka, na-eme ka nchọpụta bot sie ike, na-egosi ndị ọrụ afọ ofufo na-emegide mmetọ akara, ịrụ ọrụ dị ire na ebe ngwaọrụ ndị na-eme mkpochi ihe ike, na-emezi metrik metụtara mmetọ, nakwa itunye aro gbasara akaụntụ a na-enyo enyo maka nnyocha n'aka ndị CheckUsers.
Isi Mpụtara WE4.3: Belata ọnụọgụ nke nnukwu mwakpo nke chọrọ enyemaka SRE mmadụ site na pacenti iri ise(tụlee FY-over-FY), na ngwụcha Q4.
- Mgbanwe nke mmebi nke ịntanetị, gụnyere ịrị elu nke botnets buru ibu na mwakpo ugboro ugboro emeela ka usoro ọdịnala anyị nke ịmachi oke mmekpa ahụ gharazie isi ike. Mwakpo dị otú ahụ nwere ike ime ka saịtị anyị ghara ịdị site na iji arịrịọ jubaa akụrụngwa anyị, ma ọ bụ mebie ikike nke otu anyị ịlụso nnukwu mmebi ọgụ. Nke a na-ebutekwa nsogbu nye ndị editọ anyị nwere nnukwu ihe ùgwù yana Otu nka anyị.
- Anyị kwesịrị imelite ikike anyị n,akpaghị aka ịchọpụta , iguzogide, na ibelata ma ọ bụ kwụsị ụdị mwakpo ahụ.
- N'afọ a, anyị ga-elekwasị anya na nchọpụta akpaaka nke adreesị IP na netwọkụ ndị na-eme mwakpo megide anyị mgbe niile, ma belata oke ibu ndị ụlọ ọrụ na-emebi ihe na-ahapụ na sistemụ anyị.
isi mpụtara WE4.4: Nzije akaụntụ nwa obere oge ruo otu Narị pacentị nke ọrụ anyị, nke na mkpughe ozi nke njirimara nke ndị editọ anyị na-edenyeghị aha dị ihe na-erughị 0.1% nke ndị ọrụ, na njedebe nke Q2.
- Akaụntụ nwa oge bu ebumnobi imeziwanye nzuzo yana site na nchekwa nke ndị editọ anyị edebanyeghị aha site na ichebe ozi njirimara ha (adreesị IP) n'ihu ọha na ịmachi ịnweta naanị ndị chọrọ ya maka ebumnuche nche. Wezụga ịbụ nnukwu nkwalite maka nchekwa onye ọrụ, ọrụ a dịkwa mkpa iji kwado usoro iwu dị iche iche.
Isi mpụtara WE4.5: Nyochaa mmetụta nke generative AI na ebe ometụtara ntụkwasị obi na nchekwa, ma chọpụta ntinye aka ngwaahịa iji nwere ohere ma gbochie ihe ịma aka maka ọrụ Wikimedia, site na njedebe nke Q3
- Ojiji AI nakwa karịsịa generative AI na-abawanye na gburugburu ịntanetị. Enwere ntụkwasị obi na ohere nchekwa yana ihe egwu na-apụta na ọghọm AI ebe niile. Dịka ọmụmaatụ, ọdịnaya dị mfe ma dị ọnụ ala inweta, mana mbelata na-esikwu ike. N'otu aka ahụ, enwere ike iji obere agbambọ mee nnyocha mana abụghị eziokwu nke ihe si na AI na-esi ike ịchọpụta.
- Ọru a bu n’obi ikwalite nnwale mmetụta ikike mmadụ nke ML/AI, site n’ịtụle mmetụta AI na-ebe o metụtara ntụkwasị obi nakwa mpaghara nchekwa nke gburugburu ebe obibi Wikimedia. Nke a gụnyere:
  - Mkpakọrta ha na ndị ọrụ nwere ikike di elu.
  - Ịmata ihe atụ nke mmetọ generative AI nyeere aka na mbelata ihe ịma aka.
  - Ịmata ohere ML iji belata ibu n'ahụ ndị ọrụ nwere ikike dị elu.
  - Na-eme ọsọ nnwale iji ghọta ihe anyị kwesịrị ilekwasị anya na ya, iji mee mmetụta kachasị elu.
Isi mpụtara WE4.6: Jiri nlezianya mee ka otu Narị pacentị ikike nke na-enyere ndị ọrụ aka ịme nchekwa ma ọ bụ omume ihe nzuzo bụrụ nke akaụntụ ndị nweerela nkwenye mgbanye abụọ, site na njedebe nke Q4.
- Anyị kwesịrị ikwalitesi nchekwa nke akaụntụ onye ọrụ na wikis anyị, ọkachasị maka ndị ọrụ nwere ikike dị elu. Isi ihe nlekwasị anya bụ ịhụ na naanị ndị ọrụ nweerela usoro nchekwa mgbanye ihe abụọ (2FA) ga-eme ihe ọ bụla nwere mmetụta. Anyị ga-ewulite usoro a na-apụghị ịgbagha agbagha maka mmanye ihe ùgwù nke ga-agafe mkpa nnyocha na ntinye akwụkwọ ntuziaka nke 2FA, wee gbasaa ohere ndị a chọrọ ka 2FA rụọ ọrụ na nhiwe ha.
- Dịka akụkụ nke nke a, anyị ga na-emeziwanye usoro idi ire nke anyị na sistemu mwehachi ka anyị bụ (WMF) na ndị ọrụ anyị nwee ike ịkwado ọnọdụ siri ike karịa n'ebe 2FA nọ. Anyị ga na-agbasa n'ozuzu nnweta nke mgbanye nchekwa abụọ nnyocha idi ire na gburugburu nhiwe, nke mere na onye ọ bụla ọrụ nwere ike ime ka ọ dị ka ọ chọrọ nakwa iji hụ na ọ na-a gbanyere ya tupu e nye ikike nwere mmetụta. Anyị ga-elekwasịkwa anya anyị n'ibelata ibu ọrụ nke usoro mweghachite na nkwado akaụntụ anyị na-ebu, na-enyere aka ịhazi usoro mmegharị na mwehachite metụtara gburubguru mbanye akaụntụ. Anyị na-ezube imeziwanye ojiji nke mmejuputa 2FA anyị, na-enye ndị ọrụ ọtụtụ nhọrọ iji chekwaa akaụntụ ha yana izeere mkpọchi mberede.
Key result WE4.7: Publicly conclude our bot detection trial, by the end of Q4.
- This KR is a focused, one-quarter effort to evaluate the results of this trial, to decide inside WMF about whether to maintain and expand this system across our wikis, and to publicly publish the results of the trial and our path forward.
Key result WE4.8: Simplify the patrolling of temporary accounts, by making it quicker to identify and address abuse, by the end of Q4.
- The purpose of temporary accounts is to continue to safely support participation from good-faith unregistered editors. However, some anti-vandalism workflows became more complicated by the release of temporary accounts. To ensure temporary account vandalism can be sustainably managed, we will make it easier and quicker for patrollers to understand and respond to temporary-account activity, both good- and bad-faith.
  We will surface clusters of related temporary accounts to patrollers, while also exploring other interventions that could improve early identification and rapid response.
Key result WE4.9: Empower volunteer investigators to deter and block more inauthentic activity on wikis where they actively review flagged accounts, as measured by a 20% increase in the rate of mitigating actions on those accounts, by the end of Q4.
- For Q3 and Q4, recognizing the strategic potential that Suggested Investigations has demonstrated, we will invest in deploying new signals independent of bot detection, and will set aside time to prioritize a variety of efficiency and quality-of-life features that have been requested by SI users since its original MVP release.
Key result WE4.10: By the end of Q4, we'll increase hCaptcha bot detection coverage from 18% to 100% of account creations and from 18% to 90% of higher risk edits.
- In Q3, we concluded our hCaptcha trial, during which we enabled hCaptcha during account creation, and later expanded it to include new users on the desktop wikitext editor, on eight large Wikipedias, including English Wikipedia. Based on the results of that trial, we’re now expanding hCaptcha to more editing interfaces and more wikis. Our main priority will be expanding what we currently have to all wikis, but we’ll also focus on new avenues to (discussion tools, uploads), as well as categories of users whose actions will be protected by hCaptcha.
Key result WE4.11: By the end of Q4, complete an IRS trial on enwiki with a graduated deployment, reaching at least 50% of logged in users and resulting in 5 new reporting metrics.
- We are trialing an incident reporting system (IRS) on English Wikipedia that is designed to help less experienced community members more easily report potentially bad behavior to the community-managed place that can best deal with it. For more rare and severe cases, it also provides a form to directly report imminent threats of harm to the WMF Trust and Safety team.
- This trial will be primarily focused on calibrating the first use case: helping editors report potentially bad behavior, without overloading the system.
- During the trial, we will focus on monitoring the volume of new reports, checking that reports are routed correctly, and identifying any immediate issues. We will be coordinating closely with all community members to fix bugs if they arise, and to otherwise streamline the process. For example, we are exploring some ways to tighten the user experience and help people more directly submit their reports, which we may deploy and measure during the trial as well.
Key result WE4.12: By end of Q4, we will have defined a detection pipeline for classifiers that automatically detect English Wikipedia policy-prohibited content, and evaluated the impact of at least one classifier detecting at least one type of prohibited content, validated against community datasets, on its potential for reducing volunteer workload.
- We want to support volunteers and UWERs by reducing work needed to remove harmful content from the projects, to enable volunteers to handle more difficult cases.
- In this quarter, we want to gain experience in defining repeatable processes for building detection pipelines for different types of content, and take first steps to evaluate these pipelines safely on large projects (A/B tests, log-only mode deployments, etc). We will start with a focus on content that should very likely be suppressed, such as threats, or disclosure of personal information.
- We plan to expand on these initiatives in FY26-27 work as part of an overhaul of anti-abuse tooling that supports UWERs and volunteers in reducing the time to mitigation for bad-faith activity.

Ime ezi ojiji akụrụngwa eme ihe (WE5)

Ebumnobi: Ndị mmepe na ndị na-ejigharị ọrụ na-enweta ọdịnaya ọmụma n'ụzọ a chịkọtara, na-ahụ ma nkwado nke akụrụngwa anyị na ime ezi njigharị nke ọdịnaya anyị. Nwee mkparịtauka
- Nkọwa gbasara Ebumnobi: Ebumnuche a ga-elekwasị anya na ịmepụta ụzọ maka njigharị ọdịnaya eme ihe.
- Wikimedia na-akwado nchịkọta ihe ọmụma kacha ukwuu nke mmadụ chepụtara na webụ. Nke a emeela akụrụngwa ihe ọmụma anyị ka ọ bụrụ ebe bara uru nke n'abụghị naanị maka mmadụ, kamakwa maka ndị na-eji data akpaaka. Ọdịnaya anyị na-abanye nke ọma n'ime igwe ọchụchọ, nhiwe mgbasa ozi mmekọrịta, azụmahịa ikuku, nakwa kemgbe ịrị elu nke AI pụtara, a na-eji ya zụọ nnukwu ụdị mmụta igwe. Ndị oji eme ihe na-enweta data site na ibe mwepụta, na-eji API, na mbudata ọdịnaya - na-enweghị ngosipụta nye onye mebere ya. N'ime ụwa nke njuputa akwadoghị karịrị, anyị enweghị ike ikewa ọdiche otu onye ọrụ nye onye ọzọ, nke na-egbochi ike anyị iji mee ka anyị nwee ike na-arụ ọrụ nke ọma na iji akụrụngwa anyị eme ihe: Olee otú anyị ga-esi nọgide na-enyere ngalaba anyị aka, ma na-etinyekwa mgbochi nye gburugburu ojiji akpaka nke ọdịnaya ? Kedu otu anyị nwere ike isi mee ka ndị ọrụ banye na ọwa ndị masịrị ha, akwadoro? Kedu ntuziaka anyị kwesịrị iji kpalie iji ọdịnaya eme ihe? Kedu ka anyị ga-esi gaa na ahụmịhe ezi onye nrụpụta ọnụ, wee wuo ngwaahịa na-egbo mkpa nke ndị mmepe afọ ofufo, ndị ọrụ na ndị na-ejigharị ọrụ eme ihe? Ọ bụ ezie na ajụjụ ndị a abụghị ihe ọhụrụ, ịdị ngwa nke ịza ajụjụ ndị a amụbaala nke ukwuu: Kemgbe 2024 anyị na-ahụ mmụba dị egwu na ogo arịrịọ, yana ọtụtụ mmụba na-abịa site na ikpochapụ bots na-anakọta data maka arụmọrụ AI na-arụ ọrụ na ngwaahịa. Ibu dị na akụrụngwa anyị anaghị adigide ma na-etinye ohere mmadụ inweta ihe ọmụma n'ihe ịma aka: Anyị kwesịrị ime ihe ugbu a iji weghachi nguzosi ike ahụike otu a, ka anyị wee nwee ike ịkwado ọrụ Wikimedia nke ọma ma mee ka anyị nwee ọganihu na-agam n'ihu nke ozi anyị.
  - Isi mpụtara WE5.1: Ka ọ na-erule njedebe nke Q4, pasentị iri ise nke arịrịọ maka ọwa ịnweta mmemme nwere ike ịbụ nke a ga-enye onye nrụpụta ama ama ma ọ bụ ngwa ekele.
    - Ugbu a anyị nwere obere ụzọ iji chọpụta onye na-ahụ maka akpa aka abụghị mmadụ mere yana, n'adịghị ka na wiki, ụzọ nwere oke iji kpọtụrụ ndị ọrụ ma ọ bụ jikwa ohere ha. Anyị ahụwo mmụba dị ịrịba ama na olu nke akpa aka abụghị mmadụ mere nke mpụga, nke na-adịghị ewetere anyị ndigide ma na-etinye ohere mmadụ inweta ihe ọmụma n'ihe ize ndụ. Anyị bu n'obi ịbawanye pasentị nke akpa aka abụghị mmadụ mere nke ewepụtara na akaụntụ ama ama, site na-ịchọ nnyocha na ikike dabere na ọkwa nke ohere maka mwepu dị elu na iji API. Nke a ga-enyere anyị aka ịchọpụta ndị na-ejigharị ọdịnaya n'ọtụtụ, na-enyere anyị aka ichekwa akụrụngwa anyị na imeziwanye ọchịchị gburugburu iji ezi omume eme ihe, ma na-egbo mkpa ha nke ọma. Anyị ga-enyochakwa ka a ga-esi kwado obodo teknụzụ ka mma site na mmụtara onye nrụpụta na-ejikọ ọnụ nke na-echebe ohere mmasị maka ndị otu ma na-enye ọrụ ọhụrụ nye ndị mmepe.
  - Nsonaazụ isi WE5.2: Ka ọ na-erule njedebe nke Q4, pasentị nke njedebe webụ API Wikimedia ga-abụ nke akwadoro site na akụrụngwa nkịtị.
    - Anyị bu n'obi imeziwanye mmụtaara na nkwado nke ụzọ ndị nrụpụta anyị site n'inye ọtụtụ API webụ na-adigide, nke kwụsiri ike na nke enwere ike ịchọpụta nye ndị ọrụ mmepe Wikimedia niile. Anyị ga-eme ka API dị mfe site n'iwebata akụrụngwa zuru ọnụ maka isi ike API, na-enye anyị ohere ịnwe ụzọ na ọchịchị na-adigide adigide maka: nghọta mmadụ gbasara API na ndebanye, njirimara onye nrụpụta na njikwa nnweta, mmanye amụma API, ụzọ, nhazi, na njikwa njehie. Site n'ịkwalite onyinye API anyị, anyị ga-eme ka ọ dị ngwa, dị mfe, ma bụrụ ihe na-atọ ụtọ iwu ngwaọrụ, bots, ọrụ nnyocha, na atụmatụ ndị na-eje ozi Wikimedia. Usoro a na-akwado ọdịnihu ọtụtụ ọgbọ nke ozi ahụ site n'ibelata ụgwọ ọrụ ndozi API, mbawanye mmpụta ihe na njikwa ohere maka ịlụso ndị na-eme ihe ọjọọ ọgụ, na ịkwalite otu ndị ọrụ mmepe siri ike.
  - Isi Mpụtara WE5.3: Ka ọ na-erule na njedebe nke Q4, a ga-ebipụta ntuziaka njirimara ọhụrụ nke webụ, ngwa, ndị enyemaka olu, na LLM n'ofe saịtị Wikimedia, site na-ojiji ngosi mmegharị ugboro abụọ na-ebute nsonye enwere ike ịha, yana otu onye mmekọ enwere ike ijigharị eme ihe na mpụga nke na-agbaso njirimara kacha mma.
    - Iji nye ọdịnaya dị na Wikimedia nkowa kwesiri ekwesi, anyị ga-enye ntuziaka dị mma nke na-akwalite ojiji eme ihe n'uzo ziri ezi. Nke a na-agụnye ịmepụta ntuziaka dị mma maka nhiwe ndị bụ isi (appụ, weebụ, olu, mọltịmedịa ) ma na-egosi opekata mpe uru ihe atụ nke ọdịnaya Wikimedia abụọ. Ihe atụ nke mmepụta gụnyere ịgba ndị otu mgbasa ozi ume ka ha nye foto Wikimedia Commons njirimaras ojiji, igbe ọchịchọ iji gosipụta data wikimedia dị mkpa n'ụzọ dị ire, ma ọ bụ ndị enyemaka AI iji jikọta ihe ọmụma Wikipedia n'ụzọ doro anya na nke dị mma nke na-eme ka ntụkwasị obi ha dịkwuo elu. Iwusi usoro nkọwapụta ojiji ike ọ bụghị naanị maka ọha na eze ịma banyere nakwa njigide nke ntinye aka n'ọrụ Wikimedia, kamakwa na-enyere aka mee ka enwe ụzọ esi agwakọta ihe ọmụma, ma na-egbochi iji ya eme ihe ebe n'ezighi ezi.
  - Key result WE5.4: Belata ọnụọgụ nkwoko na-esite n'aka ndị scraper site na 20% mgbe a tụlere ya na usoro nke ogo arịrịọ, nakwa site na 30% na Bandwidth
    - Usoro nnweta ozi abụrụla ihe n'adigidela ebe a: Ihe nchọta ọchụchọ adabarawo na Wikipedia iji nye ndị ọrụ ha ihe ọtụtụ iri afọ; Agbanyeghị n'oge na-adịbeghị anya, enwere nnukwu mkpali ọzọ ịdokwa data anyị: ọ bụ nnukwu ihe omuma nke ihe omuma asụsụ dị iche iche ịnwere ike ịchọta na Intanetị ya na ngwaọrụ dị mkpa iji zụọ ụdị asụsụ buru ibu. Nke a bụ eziokwu ma maka ọdịnaya ọmụmụ anyị na ụlọ akwụkwọ mgbasa ozi anyị, Wikimedia commons, nke bara uru maka ụdị mmụta na-emepụ ihe onyonyo.
    - N'ihi ya, anyị na-ahụ ihe dị ka afọ gara aga, anyị hụrụ mmụbaa nkwokọ ndi n'enweta ozi, yana ndị injinịa na-eme nke ọma na-ejigide ma ọ bụ machibido ndi nkwoko anyị ugboro ugboro iji chebe akụrụngwa anyị. usoru nweta ozi abụrụla ihe ama ama nke ọma nke na bandwidth na-apụ apụ na-abawanye site na 2024. Ihe ọzọ, nyocha e mere n'oge na-adịbeghị anya gosiri na ọ dịkarịa ala 65% nke arịrịọ anyị kachasị ọnụ (ndị anyị na-enweghị ike ijere ozi site na sava cache anyị na ndị a na-ejere ozi site n'ebe nchekwa data dị mkpa) bụ bot na-eme ya.
    - Ihe onwunwe kọmputa anyị n'enwe mgbochi di elu tụnyere ọnụ ọgụgụ nkwoko anyị na-eme, yabụ anyị ga-ahọrọ onye anyị ji ihe ndị ahụ na-ejere ozi, anyị chọkwara ịkwado ojiji eme ihe mmadụ, ma bute iji ihe ọrụ anyị dị ụkọ na-akwado ọrụ Wikimedia na ndị na-enye aka.

Mụbaa Uzo metụtara mpụtaara ngwaahịa (WE6)

Ebumnuche Ndị ọrụ mmepe Wikimedia na-eme ngwa ngwa ma jikwara ntụkwasị obi nwedara n ngwa ahịa ha nye ndị ojieme. Nwee mkparịtauka
- Okwu gbasara Ebumnuche: 'Iji dị uchu na nnweta atụmatụ anọ, ndị ọrụ mmepe Wikimedia kwesiri ị na-etinye mgbalị nakwa oge ha na ihe omume dị elu nke na-ebute inyefe ngwaahịa ngwa ngwa dị ka atụrụ anya. ọrụ mgbagwoju anya, enweghị ngwa ọrụ zuru oke, yana usoro sistemụ na-enweghị atụ na-esokwa n'ụzọ ndị ahụ.
- Ọrụ a bụ nkwalite nke ike dị ire anyị họọro na atụmatụ afọ abụọ nke na-ebugharị MediaWiki dị ka ngwanrọ na-akwado mmepe ya na nzipu ya. Ọrụ maka afọ a ga - elekwasị anya na inye ebe ndị ọrụ mmepe a pụrụ ịtụkwasị obi karịa, na-eme ka ọrụ di mfe tupu mmepụta, na ibelata ihe ọghọm dị na akụrụngwa nakwa nhiwe.
  - Key result WE6.1: site na njedebe nke Q4, ọnụ ọgụgụ nke train-blocking bugs ga agafe ntule Wiki ka aga ebelata site na 10%
    - Na 2024, enwere ndị ọrụ mmepe otu narị na iri anọ na anọ laghachitere ọrụ n'ihi na ihe mberede na-egbochi nzije nke Mediawiki. N'ọtụtụ n'ime ihe ndị ahụ, ejidere bụgs ndị ahụ mgbe e zigachara ha na testwiki, nke pụtara na nsogbu ahụ ruru ọtụtụ ijeri ndị ọrụ ojieme. Anyị enweghị ike ijikwa ihe bụ na bugs ga-adị, mana ijide ha tupu oge aga kwesịrị ka ọ bụrụ ọrụ bụ obere ihe nye dike. Ọ ga-ewulitekwa ntụkwasị obi ndị ọrụ mmepe na mgbe ihe na-aga n'ezie n'oge imepụta ihe, a gaghị enwe ọkụ ọgbụgba.
    - Anyị ga-ejide bụgs ndị a n'oge site n'inye ndị ọrụ mmepe ebe ha chọrọ dị mma maka ọrụ ha iji ji ezi ntụkwasị obi wepụta ma nwalee koodu ha site n'oge mmepe na nke mwepụta. Anyị kwesịkwara ijide n'aka na mmelite ndị a anaghị abụ dị ka nke ga egbochi ha ime ọsọ ọsọ.
  - Key result WE6.2: Site na njedebe nke Q4, enwere ike ịme usoro anọ site na ndepụta nlele mmepụta ngwaahia na-enweghị enyemaka SRE
    - Inweta ọrụ ọhụrụ ma ọ bụ atụmatụ etinyere na nrụpụta ugbu a dabere na ndepụta nke usoro iri abụọ na anọ nke nzọụkwụ ọ bụla na-achọkarị nkwado sitere na SRE. Anyị hibere mmemme onye nnọchi anya SRE iji tinye aka na mbụ na usoro mmepe wee wulite ikike n'ime otu mmepe n'onwe ha, mana ọtụtụ n'ime ọrụ ndị a kwesịrị ịbụ nke ekwesiri imewu n'onwe kpamkpam. Ugbu a, nke a na-arụ ọrụ nke bụ akwụkwọ ntuziaka, mmegharị ugboro ugboro, nke akpaghị aka, na nke na-adabere na ọnụ ọgụgụ ndị otu ọrụ mmepụta. Nke a anaghị adigide adigide nye ndị otu SRE n'ikpeazụ.
    - N'oge gara aga, ewepuru ọtụtụ ọrụ a site n'aka otu ọrụ mmepe site n'ijikwa otu ọba akwụkwọ na-emekọrịta ihe na omume kachasị mma iji soro nhiwe anyị na-emekọrịta ihe. A gbahapụrụ ndị a mgbe anyị kwagara na akụrụngwa ndowe Kubernetes ọhụrụ anyị ma enweghị nnọchi anya chiri anya. Site n'iwepụta ụlọ ọba akwụkwọ ndị yiri ya, ndebanye, na ọzụzụ ndị na-emetụta otú anyị si ekepụta ma na-ezipụ ihe taa, anyị kwenyere na anyị nwere ike belata ọnụ ọgụgụ nke ntinye aka dị mkpa site na SRE tupu ibuga ọrụ ọhụrụ ma ọ bụ njirimara maka mmepụta.
  - Key result WE6.3: Site na njedebe nke Q4, 100% nke nlenle ibe Wikipedia bụ nke a na-enye site na Parsoid
    - Parsoid na-enye ikike ka mma maka evolushọnụ wikitext na ikwado n'ọdịnihu nke nhiwe. Idokwa parsers abụọ n'otu oge anaghị adigide ogologo oge, ebe ọ na-abawanye ụgwọ ọrụ nka na mbute mgbagwoju anya. Na mgbakwunye, ọganihu nke ụfọdụ ọrụ ọhụrụ dị ka Wikifunctions dabeere na Parsoid ịdị ebe niile.
    - Anyị na-agbasawanye gaa na obere ọrụ ma na afọ a anyị ga-adị njikere maka Wikipedia. Ije ozi ihu ibe Wikipedia niile a na-agụ site na Parsoid bụ ihe kacha mkpa na-esote. Na mgbakwunye na mwepụta ahụ n'onwe ya, ọrụ a gụnyekwara idozi nsogbu arụmọrụ yana ịkparịta ụka nke ọma banyere mmetụta nye ndị ọgụ na ndị editọ.
  - Key result WE6.4: na njedebe nke Q2, opekata mpe ihe ịma aka abụọ achọpụtara nke na-ama ikike anyị aka ịga n'ihu na-ezipu ma ọ bụ mweli elu nke wiki bụ nke emere ka o kwusịtụ ma ọ bụ belata na ọkwa gaa n'ọkwa dị mma
    - Site na atụmatụ ole na ole elekwasara anya, anyị ga-ebelata ma ọ bụ kwụsịtụ ọtụtụ iri elu, ịtụkwasị obi, ma ọ bụ ịma aka nchekwa nke anyị chọpụtara dị ka ihe nwere ike ịma uto na nkwudo nke Nhiwe anyị na ọrụ ọha anyị aka.
    - Dịka ọmụmaatụ, anyị ga-agbanwe ọdịdị nke isi nchekwa data nke Commons iji hụ na uto ya agaghị ejedebe site na ikike nke ngwaike sava dịnụ n'ime afọ ole na ole na-abịanụ. Anyị ga-emekwa ka PHP dị elu, asụsụ mmemme na-akwado MediaWiki na ọrụ ndị metụtara ya, gaa na ụdị ọgbara ọhụrụ. Ihe ize ndụ ndị ọzọ a chọpụtara ga-achọ ntinye usoro nchebe ndị ọzọ iji chebe ma mee ka akụrụngwa anyị sie ike.
  - Key result WE6.5: By the end of Q4, determine the feasibility of and next steps for scalable cross-wiki code collaboration and logged-in reader support.
    - One of the central features of wikis is collaborative content creation. In the context of the Wikimedia movement, the collaboration needs are very specific to the evolution of the projects and the challenges that arise at the scales that some of the larger projects operate in.
    - Code collaboration (cross-wiki or on-wiki) is a legitimate need and should be accommodated. This is less a single problem and more a problem space that includes several overlapping problems around code (templates & modules primarily) and solving problems in this space requires shared understanding around priorities that are most impactful.
    - With an experimentation mindset, we're exploring a leaner approach to test shared code libraries that could improve cross-wiki collaboration in a small and controlled environment. This means we will go through a phase of technical feasibility and scope exploration to approach the experiment roll-out in small iterations to collect insights that can help us decide how we want to tackle the aforementioned problem. Our intention is to demonstrate our learnings and progress in the Wikimedia Hackathon 2026.
    - Similarly, if we want to improve the experience of logged-in users, we need to know where the performance gains are, what product work will make demands, and what a sustainable approach will look like for this work next FY. Research in this area will set us up for starting platform work quickly next FY.
  - Key result WE6.6: By end of Q4, developers are able to get the results of MediaWiki Core CI in under 10 minutes.
    - Our current median CI cycle time baseline is over 24 minutes while a DevEx industry standard for high performing teams is 10 minutes or less.
    - To bridge this gap, we're planning to optimize the CI workflows, address main bottlenecks such us browser tests by optimizing how we run them, the underlying testing framework and its configuration.
    - By slashing median CI wait times from 24 to 10 minutes we ensure the rapid feedback loop needed to test and fix issues quickly, significantly accelerating iteration speed. Additionally, improving this metric speeds up merge times, shortening the time between a change being ready and being available for deployment, which directly contributes to the top-level OW5 metric of making it "easier and faster to build products".
  - Key result WE6.7: By end of Q1 of 2026-27 fiscal year, developers are able to test MediaWiki code in production within 1 day of it being merged.
    - Currently, on average developers can test their code on production ~4 days after merging. By shrinking this time, developers can test sooner, and by improving test environments they will have more confidence that their tests will result in fewer bugs reaching production.

Akara ngosi na Ọrụ Data

Metriks (SDS1)

Ebumnobi: Ndị na-eme mkpebi na-eji karịa ihe ntụkwasị obi na metrik na oge iji gba ama gbasara ngwaahịa na mkpebi atụmatụ. Nwee mkparịtauka
- Nkọwa Ebumnobi: Anyị na-eji metrik kọwapụta mkpebi nke Foundation gbasra ebe anyị ga-elekwasị anya n'gba mbọ anyị ka anyị wee nwee ike ijere Otu a ozi nke ọma. Agbanyeghị, ụfọdụ ụdọ ji ụzọ mnweta data anyị nwere ike igbaji, na-ebute igbu oge nke mmepụta. Mgbe nsogbu data pụtara, oge ịmata na oge mmezianyị na-adị oke elu dị oke elu. Na mgbakwunye, ọtụtụ n'ime dataset anyị anaghị eme ka ọ dị mfe inyocha ihe ndị na-emegasị ma bụrụ akụkụ ndị pụtara dị ka ihe dị mkpa maka ịkọwa data ahụ. Okwu ndị a na-ebelata ma gbochitu ikike anyị ịtụle metrik.
- Na FY25-26, anyị ga-elekwasị anya n'ụdị atụmatụ eji eme ihe kwa afọ maka idozi oghere ịdị mma data na usoro anyị ugbu a, ịmepụta akụrụngwa na usoro maka ileba anya na idozi nsogbu ịdị mma data, yana inye ngwaọrụ ndị na-enyere ndị na-eme mkpebi aka ịghọta ọnọdụ dị nụ.
- otu ụzọ ojiji bụ maka otu anyị si atụle nkwkọ mmadụ na bot. Mmụba nke nkwokọ akpaghị aka n'ime afọ ole na ole gara aga emewo ka ọ sie ike ịghọta ókè ụmụ mmadụ na-etinye na inye aka na ọrụ Wikimedia. Anyị bu n'obi imeziwanye ikike anyị iji nyochaa usoro nkwokọ mmadụ na bot, nke bụ ntinye dị oke mkpa maka atụmatụ na mkpebi ngwaahịa.
  - Key result SDS1.1: Ka ọ na-erule njedebe Q1, ndị nkọwa na-eji metrik nlele peeji nwere ike ịnweta usoro mmalite nke idi mma data na nha nchọpụta nkwokọ na-akpaghị aka nke usoro nchọpụta ihe ịma aka
    - Site na echiche ndị a akọwapụtara na KR a, anyị bu n'obi ịmata oghere dị na nchọpụta nkwokọ na-akpaghị aka ugbu a ma ghọta ebe ha na-adịghị ahazi nlele peeji nke ọma. Nghọta ndị a ga-eme ka mmelite dị na paipulain na-emepụta ma n'ahazi metrik nlele peeji. Na mgbakwunye, anyị ga-akọwapụta metrik idi mma data iji nyochaa ma tulee mmelite na izi ezi nke data.
    - KR a ga-atọ ntọala nye nsonye KR lekwasịrị anya na mmejuputa mmezi paipụlain dị mkpa akọwapụtara ebe a. Metirik dị mma data etinyere na ngalaba a ga-arụ ọrụ dị ka ihe nrịba ama iji chọpụta ịdị irè nke nkwalite ndị ahụ n'ọdịnihu.
  - Isi mpụtara SDS1.2 : Na njedebe nke Q1, Ọdịnaya nke data nchịkọta ihe mere eme nke mediawiki ga-adị site na mbupụ faịlụ nwere nkwa nnyefe kwa izu (SLOs). Data faịlụ ebupụrụ ga-enwe nha nhata ma e jiri ya tụnyere nke ochie XML Dumps 1 export pipeline.
    - Ebumnobi nke FY24/25 KR 1.4 bụ iwepu ndabere na mgbasa ozi mediawiki_wikitext_history na mediawiki_wikitext_history_current data sets maka atọ kacha mkpa dị n'okpuru pipeline na iji nye usoro data ọzọ nwere SLO nke emelitere kwa izu.
    - Ọ bụ ezie na FY24/25 KR 1.4 nyere aka belata okwu ndị a pụrụ ịdabere na ya maka pipụlaịnụ dabeere na ya, enwere pipụlaịnụ ndị fọdụrụ na isi mmalite ntinye aka na-enweghị ntụkwasị obi. Ekwesịrị ịwega ndị a yana isi iyi ntinye dabeere na faịlụ na data akụkọ ihe mere eme wikitext setịpụrụ onwe ya.
  - Isi mpụtara SDS1.3: Ka ọ na-erule ngwụcha nke Q2, nchọpụta bot na-etinye otu mgbaama ọzọ ma na-emepụta ama ọkwa akpaghị aka maka ihe ndị na-adịghị mma.
    - Na gburugburu Foundation, ndị otu na-eme mkpebi nrụpụta na nke ego ọrụ dabere na inwe ike ịchọpụta ọdịiche dị n'etiti mmadụ ndị na-agụ akwụkwọ na ndị na nkwonkwo na-akpaghị aka. ebe Nhiwe Data bụ ebe isi nchekwa maka akara nchọpụta bot na nnyocha n'ọtụtụ. Site na echiche anyị enyochala site na Q1/Q2, anyị na-eme atụmatụ ịmalite iwebata akara nchọpụta bot ọhụrụ iji mee ka nnyocha anyị nke nkwokọ akpaaka dịkwuo mma, ma malite ime ka usoro nke iwebata akara ọhụrụ dị irè ma dịkwa mma.
  - Isi mpụtara SDS1.4: Ka ọ na-erule ngwụcha nke Q2, ndị na-eme mkpebi nwere nghọta doro anya banyere ọnọdụ nghọta dị ugbu a nke usoro nhazi anyị nyere. Anyị ga-ama na anyị na-enwe ihe ịga nke ọma ma ọ bụrụ na anyị enye nzukọ bọọdụ ihe ngosi dị njikere maka nzukọ nke na-enyocha usoro ihe metrik anyị ma na gburugburu Wikimedia, yana n'ime usoro ịntanetị zụrụ ụwa ọnụ na ihe ịma aka dị n'ahịa.
    - A na-eji nghọta sitere na usoro nhazi anyị eme ọtụtụ mkpebi na foundation, tinyere mkpebi gbasara otu anyị si akwalite ngwaahịa anyị, otu anyị si ekenye akụrụngwa ngwaọrụ, na otu anyị si anakọta ego. N'otu oge ahụ, ọdịdị ịntanetị na-agbanwe agbanwe, ọkachasị nkwonkwo akpaaka na-emetụta usoro anyị. Ebumnuche bụ ka ndị ndu Foundation banye na nzukọ bọọdụ nke ọnwa Disemba ma nye nkọwa doro anya gbasara ihe iyi egwu na, na ohere dị n'ime, gburugburu Wikimedia bụ nke metrik ime na ihe ndị na-eme ugbua na mpụga na-akwado. Anyị nwere ike ịkọ akụkọ a site n'ịchịkọta nghọta, usoro, na isi data nke na-agwa anyị na inwe ntụkwasị obi gbasara:
      - Usoro dị iche iche n'ihe gbasara ịha nha ime nke ọgụgụ (nlele peeji)
      - Omume ndị n'emegasị ugbua na gburugburu ebe ọrụ anyị
      - Omume ndị n'emegasị ugbua sitere na data si mpụga na ike na mmeri nke ndị ama aka
      - Nghọta sitere na ọmụmụ ihe dị n'ime na nke mpụga na nchọcha a tụkwasịrị obi
  - Key result SDS1.5: By the end of FY25-26 Q4, analytics bot detection will incorporate 2 signals calibrated against 1 trusted classification.
    - In Q1/Q2, SDS1.3 addressed the big gap in incident detection time and analyzed additional signals for bot detection. We learned that:
      - Modern and feasible signals come with a number of caveats and uncertainties that need to be expressed in our metrics.
      - Evaluating the quality of our model, as well as doing robust analysis of signals to enable future iteration, will require labeled data, trusted by definition and preferably sourced from independent systems (formerly called “ground truths”).

We will need knowledge and calibration from third-parties that specialize in this domain to be able to quantify our current state and prioritize future improvements.

- - - In Q3/Q4, we will follow that with three main deliverables:

- - - - Extend our pageview metric to incorporate a numerical confidence score in addition to the existing labels, computed from at least 2 new signals, which will allow analysts to quantify and convey the nuances of those signals.
      - Curate trusted labels, preferably sourced from a third-party that specializes in this domain, and use it to evaluate our current performance and better understand the new signals.
      - Operationalize a client-side signal, which remains the most promising internally developed detection method.
  - Key result SDS1.6: Deliver a Movement Insights report on a regular basis, with consistent coverage across readership, contributors, and content thematic areas. We’ll know we’re successful when we’ve established the following by the end of Q4:
    - A consistent delivery cadence
    - Most valuable content for our stakeholders
    - Areas for future automation
    - Today, critical signals about the health of the Wikimedia Movement are fragmented across systems and teams. Readership trends, contributor health, brand perception, SEO/AEO, and competitor intelligence are monitored independently, without a consistent process or systems to aid in interpreting them together. We have existing monthly metric monitoring processes but they don’t address the scope and focus that is needed by executive decision-makers. The Movement Trends Brief is a concise, recurring intelligence brief that provides leadership and product teams with a shared understanding of how the Wikimedia movement is evolving week over week. Rather than describing everything that happened, this communication answers the following questions consistently:
      - What meaningfully changed in the past week/2 weeks?
      - Have we learned anything new about existing trends that we’re questioning?
      - Why does it matter now?
      - What requires attention or action?
    - The brief is designed to support situational awareness and provide a forum to present deeper analysis. It surfaces early signals, connects related trends across various data sources, and creates an entry point to inform decision-making.

Ebe nnwale (SDS2)

Ebumnobi: Ndị njikwa ngwaahịa nwere ike n'ụzọ dị mfe na ijiu ntụkwasị obi nyochaa mmetụta mgbanwe njirimara ngwaahịa na Wikipedia ngwa ngwa. Nwee mkparịtauka
- Okwu gbasara Ebumnobi: Iji mee ma nye nkwalite ọsịsọ nke mkpebi data gbasara mmepe njirimara ngwaahịa, ndị njikwa ngwaahịa kwesiri inwe ebe nnwale ebe ha nwere ike ịkọwapụta atụmatụ, họrọ ndị na-ege ntị site n'usoro nke ndị ọrụ,ma hụ nha mmetụta. Ịme ngwa ngwa site na mmalite ruo nnyocha dị oke mkpa, n'ihi na ime ka usoro ihe omume dị mkpụmkpụ maka mmụta ga-eme ka nwalee dịkwuo ngwa, na n'ikpeazụ, nchepụta ihe ọhụrụ. Achọpụtala ọrụ ndị eji aka eme na usoro emeere onye dị ka ihe mgbochi nye ime ọsọ ọsọ. Ọnọdụ dị mma bụ na ndị njikwa ngwaahịa nwere ike nweta site na mbido nnwale ruo na nchọpụta site na-iji obere aka ma ọ bụ enweghị enyemaka sitere n'aka ndị injinia na ndị nnyocha.
- Anyị na-elekwasị anya na Wikipedia na afọ mmefu ego na-esote n'ihi na ọ bụ ebe ahụ ka Isi mmụtaara nwere mmasị na nnwale (atụmatụ Otu a na-eme ka anyị na-etiwanye aka na Wikipedia), maka n'ihi na ọ na-enye anyị ohere ilekwasị anya na igosi Otu na ọrụ anyị na-etinye aka na ya nke ọma. Ndị otu ndị ọzọ ejirila usoro nnwale nhiwe ma nwee ike ịga n'ihu na-eme nke a, mana ojiji ahụ agaghị abụ ihe nlekwasi anya n'ebumnuche a.
  - Isi mpụtara SDS2.1: Site na njedebe nke Q2, mee ka mmezu nke opekata mpe usoro nnwale abụọ zuru oke site na iji nnwale nhiwe.
    - Dị ka ọgbakọ a na-esiwanye ike na-akwusi ike na data ngwaahịa, anyị ga-eme ka nnwale dị mfe nnweta nye ndị otu ngwaahịa niile, ọ bụghị naanị ndị nwere nka. Otu ngwaahịa chọrọ ụkpụrụ ha nwekọrọ na akụrụngwa nke na-enyere ha aka:
      - Nwalee echiche ngwa ngwa na gburugburu ndị ọrụ zuru ụwa ọnụ anyị
      - Jiri metrik a haziri ahazi tulee Mmetụta mgbanwe ngwaahịa
      - Kkọwapụta mpụtsra n'ụzọ doro anya n'etiti ndị otu anyị bụ isi.
    - Ihe mere anyị ji atụgharị nlekwasị anya anyị na ọnụ ọgụgụ "otu ndị akwadoro" gaa na nke "nnwale emecharala":
    1. Ndakọ ahaziri ahazi : Ọ bụ metrik ihe ịga nke ọma bụ isi nke nhiwe.
    2. Usoro mnweta ihe ọmụma data: Nnyocha onye ọrụ anyị (na-aga n'ihu) na-atụ aro ịdị njikere otu dị iche iche n'ofe nzukọ ahụ, ebe anyị maara na ndị otu Weebụ ekwenyela mmasị na nnwale abụọ akọwapụtara.
    3. Nkwalite akụrụngwa: Otu MVP anyị ga-achọ ntinye aka dị elu, ma mee ka ọ rụọ ọrụ nke ọma n'oge dị nso ilekwasị anya na ohere nnwale kama ilekwasị anya gburugburu ọtụtụ otu. Anyị na-eme atụmatụ ịga n'ihu kwupụta ntọhapụ izugbe ma achọghị itinye ego na ọzụzụ otu ọzọ, ma ọ bụrụ na anyị nwere ike izere ya.
    4. Nlekwasị anya n'ọdịniihu: Nzaghachi sitere na gburugburu ozuzu oke nnwale ga-eme ka nkwalite nhiwe anyị dịkwuo mma karịa mnweta mmụta site na nnabata ezughị ezu ma ọ bụ ezuchaghị ezu. Ka anyị na-aga n'ihu na mwepụta izugbe, ilekwasị anya na mmecha nnwale na-ezere itinye ego na ụzọ nwa oge ga-adị mkpa ka ewelitegharịa.
    - Anyị nwere nnyocha onye ọrụ n'aga n'ihu iji chịkọta mkpa na ihe ndị achọrọ nke otuː e mere nnyocha ajụjụ odide na ajụjụ ọnụ iji nye nkowa doro anya gbasara mkpa otu ngwaahịa na nkeji nke abụọ nke May 2025. Ozugbo emechara nnyocha e dechara, anyị ga-eme ka ihe kalenda nnwale e nwere ike iji hazie nlekwasị na ihe ndị anyị ga-ebu ụzọ mee KR.
  - Key result SDS2.2: Before the end of Q3, results for at least one web experiment can be analyzed and viewed in GrowthBook.
    - After a long and arduous process, we finally made a decision to integrate GrowthBook as a third-party experimentation solution that offers experiment flagging, automated analysis, and dashboarding, with support for guardrail metrics. Experiment Platform intends to replace the UI to define and deploy experiments (1) and the experiment analytics pipeline (5) with GrowthBook.
    - Because of the risks associated with this integration, the Experiment Platform Team believes that GrowthBook should be integrated as an experiment analytics pipeline first because it's non-disruptive and because it's the greatest source of risk.
    - The architectural decisions we made during FY24/25 SDS2 and GrowthBook’s modularity allow us to replace parts of the Experimentation Lab out of order, i.e. WMF staff can define and deploy experiments with xLab UI while analyzing them with GrowthBook. Further, we can run the current experiment analytics pipeline and GrowthBook’s in parallel, which allows for side-by-side comparisons as well as User Acceptance Testing in real-world scenarios.
  - Key result SDS2.3: By the end of Q4, all new Test Kitchen experiments are configured in GrowthBook.
    - After having enabled WMF staff to use GrowthBook for analyzing experiment results, the Experiment Platform team held a design sprint to assess options for experiment configuration. The decision was to use GrowthBook as the source of truth for experiment configuration but keep Test Kitchen UI (TKUI) as the source of truth for instrument configuration.
      In making GrowthBook the source of truth for experiment configuration, we aim to:
      - Reduce coordination when running an experiment
      - Enable more frequent and repeatable experimentation
      - Clearly delineate between instruments and experiments
      - Move toward a mental model centered on metrics rather than instruments
  - Key result SDS2.4: At least 14 out of 20 product teams have used Test Kitchen to inform a strategic decision for an OKR initiative, by the end of Q4.
    - The work done under SDS2.1 revealed a critical insight: building an experiment platform is not enough — Product & Tech teams face some barriers to adoption. Even though teams see value, they often lack time, infrastructure, instrumentation, or confidence to begin. In addition to this, they may encounter technical blockers that will need to be addressed by thoughtful partnership.
    - KR SDS2.4 shifts our focus from building to scaling adoption. By continuing to partner with teams as they onboard onto the platform, overcoming technical barriers and providing hands-on migration support, we aim to consolidate experimentation onto Test Kitchen as the unified platform for product teams, enabling fast, self-service testing cycles that reduce dependence on engineering and analytics support.
    - This KR was planned after we decided to split SDS2.2 in two pieces of work, which is why the numbering was affected. SDS2.3 is an upcoming KR that is sequential to GrowthBook for Experiment Analytics.

Ndị na-ege ntị n'ọdịnihu (FA)

Ndị na-ege ntị n'ọdịnihu (FA1)

Ebumnobi: Wikimedia Foundation nwere ntziaka gbasara ntinye ego n'usoro iji gbasoo nke na-enyere Otu anyị aka ijere ndị na-ege ntị ọhụrụ ozi na ịntanetị na-agbanwe agbanwe. Nwee mkparịtauka
- Okwu gbasara Ebumnuche: N'ihi mgbanwe na-aga n'ihu na teknụzụ yana omume onye ọrụ n'ịntanetị (dịka ọmụmaatụ, mmụba mmasị nke inweta ozi site na ngwa mmekọrịta, njupụta nke vidiyo nkuzi na ngori dị mkpirikpi, ịrị elu nke generative AI), nhiwe Wikimedia na-eche ihe ịma aka metụtara ịdọta mmasị na ijide ndị ọgụụ na ndị ntinye aka. Mgbanwe ndị a na-ewetakwa ohere iji jeere ndị na-ege ntị ọhụrụ ozi site na ịmepụta na ịnye ozi n'ụzọ ọhụrụ. Otú ọ dị, anyị dị ka otu nwere ebe anyị lekwasara anya iru enweghị nkọwa doro anya nke data gbasara uru na ire elu na ọdịda nke atụmatụ dị iche iche nwere ike ịchụso iji merie ihe ịma aka ma ọ bụ were ohere ọhụrụ. Dịka ọmụmaatụ, anyị kwesịrị...
  - Tinye aka na nnukwu atụmatụ ọhụrụ dị ka chatbots?
  - Weta amamihe na ụzọ Wikimedia na ntinye aka nye nyiwe ndị ọzọ ama ama?
  - O nwere ihe ọzọ?
- Iji hụ na Wikimedia ghọrọ ọrụ ọtụtụ ọgbọ, anyị ga-anwale echiche iji ghọta nke ọma ma tụọ arọ atụmatụ ndị dị mma - maka Wikimedia Foundation na Wikimedia movement- ịji chụsoo,dọta ma jigidesie ndị na-ege ntị n'ọdịnihu.
  - Key result FA1.1: N'ihi mpụtaara nnwale na ndụmọdụ ndị na-ege ntị n'ọdịnihu, na njedebe nke Q3 opekata mpe otu ebumnuche ma ọ bụ isi mpụtaara nke otu na-abụghị ndị ga-eme n'ọdịnihu dị na ihe ndepụta maka atụmatụ afọ nke afọ na-esote.
    - Kemgbe 2020, Wikimedia Foundation nọ na-enyocha usoro mgbanwe mpụga nke nwere ike imetụta ikike anyị ijere ọgbọ nke ndị na-ojieme ihe na ndị na-enye aka inye ihe ọmụma ozi n'ọdịnihu ma nọgide n'ịbụ nhiwe ihe ọmụma n'efu nye ọgbọ na-abịa n'ihu. Ndị na-ege ntị n'ọdịnihu, otu obere R&D, ga-:
      - Mee nnwale ngwa ngwa nakwa oge (na ebumnobi ime ọ dịkarịa ala nnwale atọ kwa afọ) iji chọpụta ụzọ isi lebara mgbanwe n'ewu ewu ndị a anya.
      - Dabere na nghọta sitere na nnwale, nye ndụmọdụ maka ntinye ego n'ọrụ na-abụghị nnwale nke WMF kwesịrị ịchụso - ya bụ ngwaahịa ma ọ bụ mmemme ọhụrụ nke ndị otu ma ọ bụ otu zuru ezu ga-ewere - n'oge nhazi atụmatụ kwa afọ anyị.
    - A ga-enweta isi mpụtaara a ma ọ bụrụ na opekata mpe otu ebumnobi ma ọ bụ isi mpụtaara nke otu nwere na mpụga nke ndị na-ege ntị n'ọdịniihu ma bụrụkwa nke nkwado ndị na-ege ntị n'ọdịnihu na-akwalite pụtara na ndetu atụmatụ afọ nke afọ mmefu ego na-esote.

Ihe onyonyo mmekọrịta ọhaneze (FA2)

Ebumnobi: Ndị na-eto eto (< dị afọ iri abụọ na ise)hụrụ n'anya, na amụta ihe, n'esoro na ma na-ekekọrịta ọdịnaya Wikipedia na nyiwe ebe ha chọrọ iwepụta oge n'ịntanetị. Nwee mkparịtauka
- Okwu gbasara Ebumnobi: Nnwale ndị na-ege ntị n'ọdịnihu na obere vidiyo nke afọ mmefu ego a egosila na anyị nwere ike iru ebe ndị na-ege ntị n'eto eto nọ n'ogo n'ogo na niwe ndị a, mana data n'egosi ka esi ahụta anyị na-egosi na ntinye ego anyị ugbu a ezughị iji gbochie mbelata nke mmata na mmekọrịta na Wikipedia n'etiti ndị na-ege ntị ọlọrọ ọhụrụ bụ Gen-Z.
- Iji hụ na anyị nwetara ma mee ka ọgbọ a sonye nke ọma, anyị kwenyere na ọ ga-adị anyị mkpa itinye aka na ụzọ dị iche iche, ma mee ka ntinye aka anyị dịkwuo elu na mpaghara ndị dị ka azụmahịa onye nwere ike imetụta mkpebi nke ndị na-eso ya a ga-akwụ ụgwọ, mkpọsa nchepụta, na-anabata mgbanwe a na-enwegasị mana abawanye ọkwa nke nnwale anyị na ọwa ndị a.
- Anyị na-atụ anya na ihe ịma aka ndị na-eche anyị ihu ga-achọ itinye ego buru ibu karịa iji nyere anyị aka inweta ha, ọkachasị n'ịgba mbọ nzikọrịta ozi na ịzụ ahịa iji meeka esonye, yana imekọ ihe ọnụ na ngalaba na-ekepụta ngwaahịa ọhụrụ na mpụtaara akwadoro na mbawanye ọnụnọ nke akara Wikipedia na ọdịnaya dị na nhiwe ndị a.
  - Key result FA2.1: Mepụta ọnụọgụgụ nlele 9,500,000 site na ọdịnaya vidiyo dị mkpụmkpụ gafee ọwa niile anyị nwe site na njedebe nke H1.
    - N'afọ a, anyị nwetara ihe ruru otu nde nke ilele ibe anyị n'ime ọnwa atọ ibido obere vidiyo na ọwa @Wikipedia na TikTok, Instagram na YouTube. Ka ọ na-erule mmalite nke afọ mmefu ego na-abịa, anyị na-atụ anya ka ọtụtụ ndị na-eso anyị na ọwa nke anyị nwere yana inwe ezi nghọta gbasara ọdịnaya ka mma/na nke ga akwalite nsonye nke anyị nwere ike itinye n'omume iji ruo ọbụla kọ́rịọ́ ndị nlele.
    - Site n'itinye ebumnobi dị oke mkpa na ọkara mbụ nke afọ, anyị na-atụ anya inweta mmetụta dị ịrịba ama karịa, ma nye ohere ịmepụta atụmatụ/ usoro ọhụrụ iji kwado ọrụ ahụ, ma nwee ike ịkwado maka ihe ndị ọzọ iji mezuo ebumnuche a.
  - Isi mpụtara FA2.2: Mụbaa ndị na-eso anyị na TikTok site na ọkwa etiti (ndị osuso 100k–250k) ruo na ọkwa Macro (ndị na-eso ụzọ 250k–1M) na njedebe nke FY25/26 (June 2026).
    - Anyị nọ n'ọkwa etiti nke TikTok (nke ndị osuso 100k–250k), ebumnuche anyị bụkwa iru ọkwa Macro (nke ndị osuso 250k–1M) na njedebe nke FY25/26 (June 2026). Ọkwa ndị a—Micro, Mid, na Macro—bụ ihe atụ ike n'ahịa maka nha na iru ndị na-ege ntị. Iji ruo ebe ahụ, anyị ga-emeziwanye atụmatụ ọdịnaya anyị iji dọta ndị na-eso ụzọ Gen Z nke ọma ma mee ka ọhụhụ anyị dịkwuo elu site na njikwa ngalaba otu. Arụmọrụ H1 ga-enye aka na mgbanwe atụmatụ na H2 iji mee ka uto dịkwuo ngwa ngwa ma ruo ebe a dị mkpa.
  - Key result FA2.3: Mwepụta ngwaahịa pụrụ apụ na mpụga maka ebumnuche ndị na-ege ntị n'ọdịnihu' ụzọ ọhụrụ nke mmụta/ojiji nke mgbasa ozi ma weta ya n'ahịa site na njịkọ aka nke akara ngwaahịa na ahịa mgbasa ozi .
    - Ndị na-ege ntị n'ọdịnihu na-arụkarị ọrụ na obere nnwale na obere/ahịa nkwalite ọmụma. N'afọ a, anyị ga-achọ idebe oge maka ngwaahịa ọhụrụ+ mkpọsa ahịa na-elekwasị anya na ndị na-eto eto na mpụga nhiwe .

Nkwado ngwaahịa na ọrụ injinia (PES)

Nkwado ngwaahịa na ọrụ injinia (PES1)

Okwu gbasara Ebumnobi: Ndị otu ngwaahịa na ọrụ injinia WMF dị irè karịa n'ihi usoro emelitere, na-akwalite mgbanwe dị mma na ọrụ anyị. Nwee mkparịtauka
- Okwu gbasara Ebumnobi: Ebumnuche a bụ ime ka ụzọ ọrụ Wikimedia Foundation dị ngwa ngwa,gosipụta amamihe, ma dị mma. Ọ bụ ihe niile gbasara ka anyị si arụ ọrụ. Nke a pụtara enweghi esemokwu na ihe mgbochi (enweghi ike imepụta ihe na iri mperi) na usoro, yana nnweta mmetụta ngwa ngwa. Ebumnuche nke a bụkwa maka ịmụta ụzọ ọrụ nke enwere ike ịnabata n'ofe ngalaba na Otu.
  - Key result PES1.1: Site na njedebe nke Q2, kọwapụta SLOs maka ọrụ mmepụta isii dabeere na Ederede nhọpụta ndị ekwesịrị ibu ụzọ lebara anya nke atụrụanya ịbawanye mmụta anyị banyere otu esi akọwapụta na iji SLO mee mkpebi ziri ezi gbasara ntụkwasị obi ihe ndị ekwesịrị ibu ụzọ lebara anya site n'aka otu ndị isi.
    - Service Level Objective (SLO) bụ nkwekọrịta n'etiti ndị otu na-emetụta na ọkwa ọrụ (ịtụkwasị obi/arụmọrụ) nke ndị otugasị na-arụkọ ọrụ iji zute (ma ọ bụghị karịa). Dịka ọmụmaatụ, ọ na-enyere aka ikpebi mgbe ekwesiri ịhọrọ ma ọ bụ ịhapụ ịhọrọ ọrụ ntụkwasị obi ma ọ bụ ọrụ arụmọrụ site n'aka ndị otu ọrụ mmepe, ma ọ bụ ihe kpatara nsogbu. Ndị otu kwesịrị ịkpachara anya ịchọpụta ihe kwesiri nlebara anya (ihe ekwesiri ilebere/nzaghachi ihe omume/nsogbu dị oke egwu) na ihe na-abụghị. Ebumnuche bụ iji belata ọgbachi n'ofe ọrụ site na ịmekọrịta ihe a na-atụ anya ọsọ na ịkọwa ihe ndị ana ekerita ahọrọ doro anya.
  - Isi mpụtara PES1.2: Ka ọ na-erule njedebe nke Q2, akara ngosi ngalaba otu (gụnyere ihe ndepụta achọrọ) emetụlatu WMF ka ọ buru ụzọ ma ọ dịkarịa ala usoro ọrụ nke ngwaahịa maka Q3 - Q4
    - Ebumnuche anyị bụ ịchọpụta na ime ememe mgbe otu họọrọ ọrụ n'ihi na ọ bụ arịrịọ ngalaba otu nwere ihe akaebe.
    - Echiche abụọ e mere atụmatụ na-elekwasị anya na listi ọchịchọ naanị. Emebere ha iji kwalite ntụkwasị obi, kwalite usoro, na ịbawanye nsonye n'etiti ndị ọrụ na ndị ọrụ afọ ofufo. Echiche ọzọ bụ nnwale emebere iji hụ ma enwere akara ngosi bara uru zuru oke sitere na ọgbọ mkparịtọa ụka, wdg, yana ọ bụrụ na AI nwere ike ịkwado mgbama anyị na-achịkọta ahụ ike.
  - Key result PES1.3: Nnwale ngalaba abụọ nke mmalite nke mbụ, nke ndị ahịa anyị na mpụga , ndị na-enye onyinye na ndị na-enye aka kwadoro, bụ nke Foundation webatara n'ime atụmatụ kwa afọ.
    - Ọrụ a bụ maka ịmepụta nnwale na usoro nnwale maka nnabata n'ofe nzukọ anyị.
    - Foundation na-akwalitesi ọnọdụ nnwale nrụkọ ọrụ ngalaba ike site n'itinye mmalite nnwale abụọ akwadoro n'ime atụmatụ ya kwa afọ. Ebumnuche a na-akwalite imeko ihe ọnụ gafee ngalaba ndị otu njirimara ngwaahịa & Teknụzụ, na-akwado nchepụta ọhụrụ na ngalaba ndị ọzọ na nzukọ (dị ka Nkwukọrịta na Ọganihu). Site n'ịweta echiche ọhụrụ a na-anwalebeghị yana usoro nhazi maka nnwale, otu dị iche iche na-akwalite nrụpụta ma tụọ mmetụta. A na-atụ ihe ịga nke ọma site na imecha nnwale ngalaba abụọ n'ejikọ kwa afọ, na-etinye ha na ọrụ OKR n'ọdịnihu, yana mmụba nke nnwebata nke nke omume nnwale. Ọmụmaatụ nke mpụtaara bụ ụdị ọhụrụ iji kwalite uto na nrụpụta nke ndị editọ ọhụrụ, na njirimara nnwale na-eme ka njikọ ndị ọgụụ na ndị na-enye onyinye na Wikipedia dị omimi. Otu ohere a kapịrị ọnụ nke achọpụtara bụ ijikọ nnyocha ihe dị nta iji mee mmemme ụbọchị mmalite Wikipedia nke afọ iri abụọ na ise 25th.
  - Key result PES1.4: By the end of Q4, we'll see a 10% adoption rate increase for Codex among P&T teams.
    - As a standardized UI library, Codex vastly reduces both the maintenance burden of creating custom UI components, as well as the time needed for implementing product UIs. Codex also provides a shared vocabulary for talking about design and implementation, which increases efficiency between Design & Engineering.
    - Codex will lose utility if adoption doesn’t increase and Codex isn’t widely used, and currently it is not being adopted or widely used in some places because the tooling isn't ready for some use cases. It may also need more prominent advertising/awareness. This work is a priority because teams must be able to adopt Codex organically, and currently not all can without blockers to adoption being addressed first.
    - We're anticipating that this will mean:
      - Discovery - What do different teams show us is blocking them? What are the use cases and blockers about which we are not yet aware?
      - Improvement - Example: We know that Codex PHP 1.0 will unblock server-side adoption.
      - Metrics deep dive - Example: What do the baseline metrics established in Q1 tell us about where organic adoption is not working (and are there lessons from OOUI adoption in years past)?
    - The KR will focus on tracking “official” usage (i.e. adoption that follows the documented guidance) separately from partial or piecemeal usage, e.g. when teams use only part of a component or just the CSS. The latter type of adoption incurs a higher maintenance cost than out-of-the-box component usage.
  - Key result PES1.5: 20% of service SLOs result in an impactful decision made by the end of Q4.
    - Service Level Objectives (SLOs) are a tool used to determine priorities based on data from service reliability. For instance, whether a down service needs immediate attention. SLOs work most effectively when ownership is clear, and everyone is using them. To make this happen we need to shift development culture toward adopting SLOs for every service. Building on the last few quarters of creating SLOs and testing them with service teams, we've identified an opportunity to clarify the value of SLOs, so that we can continue to spread this culture.
      - We have 19 SLOs currently.
      - We want to incentivize SLOs for services that would be most likely to leverage them. If we get ~3-4 (~20% 0f 19) "impactful decisions" from our current crop, great, but we anticipate we'll need to make more. The denominator will increase, but that further incentivizes targeting the right services.
      - Q4 is the end of the timebox because we want more than one cycle of data, and each quarter is a cycle. It also gives us space to shore up tooling, pilot SRE-alerting, etc.
    - Success looks like:
      - Impactful decision = “is this service currently reliable enough, or do we need to prioritize work to fix it” -- that is, it's as much a decision to find an error and say it's OK not to prioritize it, as it is to find an error and prioritize correcting it.
      - We want decisions to be diverse (e.g. Architectural vs Organizational vs Implementation; or affirmative vs deciding not to do something), because this is about spreading the value of SLOs and shifting culture. All the same type of decision or all from the same team doesn't accomplish that, even if it's good.
      - Even if we don't meet the KR target, we hypothesize that we'll have learned valuable information about why (e.g. we're targeting the wrong services).
      - Important: 80% of our SLOs not having an impactful decision is not a failure state for those, because most of the time services should not be failing.
  - Key result PES1.6: 20% of critical unowned services, according to a risk analysis framework, get owners committed by the end of Q4.
    - Clear code and service ownership is critical to ensuring the Wikimedia Foundation’s technical infrastructure remains reliable, scalable, and secure. Addressing current gaps in ownership will improve accountability, enhance cross-team collaboration, fast-track effective decision-making, and reduce risks to platform stability, security and sustainability. Additionally, it will provide greater clarity for Wikimedia affiliates and volunteers, helping them understand who is responsible for maintaining and supporting key services.
      - We estimate there are ~20 critical services without owners.
      - We think we can assign 4 owners over 4 months during Q3/4. The first 2 months or so will be setting ourselves up for success with prep work.
      - We plan to develop a risk analysis framework to determine criticality, as part of hypothesis work under this KR.
  - Key result PES1.7: By the end of Q4, 85% of wishes have a response within 10 working days, and a monthly update is posted on the work we committed to implement.
    - Having revamped Wishlist triage in the first half of the year, we want to consistently demonstrate that volunteers are getting responses to their Wishes plus a monthly update consisting of what’s coming, what’s pending or blocked, and what’s being scoped. We will judge the metric by measuring response time on a rolling average over a month, and aim to sustain that for at least a month.

Echiche a kà ga-anwale

Q1

Nkeji nke mbụ (Q1) nke atụmatụ afọ WMF ga-eru Julaị-Septemba.

Mmụtara Wiki (WE) nke Echiche a ka ga-anwale [ Isi mpụtara WE ] Ńkàtá
Aha mkpirisi echiche a ka ga-anwale	Ederede Q1	Details & Discussion
WE1.1.1	Ọ bụrụ na anyị na-akpali ndị ọrụ afọ ofufo ọhụrụ na-amado ederede site na saịtị dịpụrụ adịpụ iji gosi ma ha dere ọdịnaya ha na-achọ ịgbakwunye, mgbe ahụ, anyị ga-ahụ mbelata ≥10% na pasent nke ọdịnaya ndezi ọhụrụ ndị ọrụ afọ ofufo ọhụrụ nke eweghachiri na WP: COPYVIO (na amụma ndị metụtara ya).
WE1.1.2	Ọ bụrụ na anyị wepụta ụdị beta nke mbụ nke “Mmelite ụda” nke ndezi arọpụtara mgbe ahụ anyị nwere ike mụta ma usoro ọhụrụ nke ndezi aropụtara bụ ụzọ bara uru iji mụbaa ndezi maka ndị nkwado ọhụrụ na-nwezuga ibu ọrụ nke ndị njegharị/ndị nnyocha.
WE1.1.3	Ọ bụrụ na anyị weta usoro nzubere nrọpụta ọhụrụ ezubere iche maka ndị ntinyeaka nọ ogo dị elu n'ime vishụal editọ (mobile + desktọpụ) dị ka a ngwa Beta nwere ≥ atọ ndezi nropụta ọhụrụ n'ime ya, anyị ga-achọpụta ihe a - ma e nwere mgbanwe ekwesiri ime tupu e jiri onye ọrụ afo ofufo ọhụrụ mee ntule site na a na nnwale ejikwara.
WE1.1.4	Ọ bụrụ na anyị emee nnyochaa edensidee na en.wiki site na nnwale a na-achịkwa, anyị ga-ahụ mmụba ≥10% na ezi ndezi ndị ọrụ afọ ofufo ọhụrụ bipụtara ma mara ma enwere nkwado zuru oke n'etiti ndị na-ahụ maka nchekwa na ndị nhazi iji mee ka ngwa ahụ dịkwuo ukwuu.
WE1.1.5	Ọ bụrụ na anyị anwalee usoro agamnihu site na udi ihe eji ndị bịara ọhụrụ mema, mgbe ahụ anyị nwere ike ịchọpụta ụdị ihe dị ịrịba ama, nduzi, na nnabata a na-ahụta dị ka ihe na-akpali mụọ, ma jiri nghọta ndị a mechaa nhazi maka nwale mmalite wiki n'ọdịnihu.
WE1.1.6	Ọ bụrụ na anyị na-enyocha ihe mgbochi teknụzụ kachasị elu, mmekọrịta ọha na eze na omume na ndị na-enyere aka na-edezi webụ mkpanaka site na nnyocha onye ọrụ na nnyocha data, anyị ga-ewepụta ma ọ dịkarịa ala nghọta atọ nwere ike imechi oghere isi ihe ọmụma ma kwalitesie ike anyị iji ntụkwasị obi buru ụzọ na nhọrọ ntinye ego na ngwaahia ọkara nke abụọ nke FY25/26 ma gafee.
WE1.2.1	Ọ bụrụ na anyị mepụta ihe akaebe nke echiche maka igosi data enyemaka na wiki, anyị nwere ike ịnakọta nzaghachi site na ọ dịkarịa ala ndị na-enye aka iri atọ na pasentị asaa nke ndị zaghachirinụ na-akọwap na ngwa ahụ bara uru nakwa o nwere ike inye aka mee ka enwee uto ejikọtara aka nwee.
WE1.3.1	Ọ bụrụ na anyị ejiri mkpa ndị achọpụtara site na nnyocha na mmema gara aga wee kọwapụta ihe ngosi ọrụ mbụ nke top X kacha nweemmetụta, anyị nwere ike iji ha gbanwee ebe ọrụ maka ime ihe n'ụzọ kwesịrị ekwesị.
WE1.3.2	Ọ bụrụ na anyị megharịa ihu ebe ọrụ onye bịara ọhụrụ ka ọ bụrụ nanị inye modul , mgbe ahụ anyị nwere ike gosipụta ike iji ihu ebe ọrụ ebe obibi maka ndị nhazi.
WE1.4.1	Ọ bụrụ na anyị emee ọtụtụ ndozi akpọpụtara na T396489, anyị ga-ebelata ajụjụ mgbanwe ndị na-adịbeghị anya site na pasent X na wiki buru ibu. Mgbe ahụ ngwaọrụ nhazi ga-enwe ike ibunye modul homepage na wiki ndị ahụ na-enweghị nchegbu arụmọrụ db pụrụ iche.	T400696
WE2.1.1	Ọ bụrụ na anyị na-akpọ ndị na-asụ asụsụ amaala nke obere wiki site na ọkọlọtọ CentralNotice na Wikipedia nwere na mpaghara ha ka ha nye aka na SuggestedEdits na atụmatụ Uto ndị ọzọ, anyị nwere ike ịchọpụta ma usoro a na-adọta ndị na-asụ amaala ọhụrụ ma ha na ejiri ngwaọrụ ndezi ndị a emeziwanye ọdịnaya dị mkpa.
WE2.1.2	Ọ bụrụ na anyị mere ma wepụta atumaro ntụgharị asụsụ ahaziri maka ndị ndezi ọhụrụ, anyị ga-enwe ike ịnwale ma usoro a ọ na-arụpụta mpụtara ntụgharị ka mma ma e jiri ya tụnyere ụsoro anyị dị ugbu a. Nke a na-eleba anya n'ihe ịma aka ndị ama ama ndị ndezi ọhụrụ chere ihu, ndị nwere ike inwe ọtụtụ nhichapụ ederede dị elu. Site n'ịtụ aka n'ịtụgharị asụsụ ọdịnaya enwere ike ijikwa, ebumnuche bụ iweta ihe n'adịchaghị mfe ma nduzi mmalite gbasra usoro ntụgharị asụsụ. Ezi edemede na ngalaba ndị dị ka mgbagwoju anya nwere oke n'usoro nhazi na ogo ogologo.
WE2.1.3	Ọ bụrụ na anyị na-amụta banyere mmụtara onye editọ mgbe ọ na-ede ederede na ngalaba ederede(gụnyere mkpali, isi ihe mgbu na mmeghachi omume ha gbasra echiche ọhụrụ banyere otú ka mma na-akwado ha), mgbe ahụ anyị ga-egosipụta mkpa na àgwà onye ọrụ nke na-enye nghọta nkọwa ngwa ngwaahịa, mmewe, na ọrụ ndị injịnịa na-mmeziwanye mmụtara odide ederede.
WE2.1.4	Ọ bụrụ na anyị enyocha, site na nkuzi mmemme ma ọ bụ ajụjụ ọnụ, ka Wikipedia atọ nwere ọkara si elebanye anya na oghere ọmụma na ịdị mkpa, anyị ga-ekpughe nkọwa ọrụ ma ọ bụ mmepụta atụmatụ maka “ihe ọmụma dị mkpa” dịkwa mkpa nye ngalaba otu ọ bụla.
WE2.2.1	Ọ bụrụ na anyị agbaso mbipụta Parsoid ma jikọta Wikifunction na ọtụtụ Wiktionary na ụfọdụ Wikipedia dị ala, anyị ga-enweta nnwnale anyị chọrọ iji nwee ntụkwasị obi gbasaa na wiki gaa buru ibu.
WE2.2.2	Ọ bụrụ na anyị enyere Wikifunctions ka owepụta tebụl HTML, udi omume na nkokọ, anyị ga-egosipụta site na ọrụ nke na-egosipụta tebụl njikọ ike ya maka ịmepụta nchịkọta ihe ọmụma ọhụrụ na Wiktionary gafee ntụgharị dị mfe.
WE2.2.3	Ọ bụrụ na anyị gbakwunye nkwado maka ụlọ ọrụ Wikidata na oku ọrụ agbakwunyere, anyị ga-eme ka ọrụ ọhụrụ narị abụọ nwee ike iwepụta ahịrịokwu zuru oke site na iji ụlọ ọrụ Wikidata, na-ime ka ọrụ function dị mfe ojiji na ọrụ Wikimedia.
WE2.2.4	Ọ bụrụ na anyị wulite atụmatụ ụkpụrụ maka ebe Ọdịnaya Abstract ga-anọ yana otu yana Wikipedia ga-esi na-emekọrịta ihe na , anyị ga-adịkwu njikere itinye n'ọrụ ebe nhiwe Abstract Wikipedia iji bulie ntinye nke ọdịnaya encyclopedik dị elu.
WE2.2.5	Ọ bụrụ na anyị a kọwapụta ma na-akpakọrịta n'ofe otu ngwaahịa & Teknụzụ na mkpa ngwaahịa achọrọ maka ọdịnaya Abstract, anyị ga-enwe ike idu ọrụ Wikimedia metụtar iji wepụta ozi pụtara ìhè agbakwunyere na Ọdịnaya Abstract, nke dị oke mkpa maka ị nweta ihe ịga nke ọma na gburugburu wiki.
WE2.2.6	Ọ bụrụ na anyị eme ka usoro arịrịọ ime anyị pụta ìhè na ma dị nkenke, anyị nwere ike ịbawanye nkwụsi ike nke sistemu ahụ, si otú a na-akwado mwegharị ka ukwuu.
WE2.2.7	Ọ bụrụ na anyị na-enye mmepụta mbụriri mperi site na iji oku Wikidata na Wikifunctions iji wepụta obere ihe n'asụsụ amaala, anyị ga-egosi ịdị njikere maka ọrụ ahụ, ma anyị ga-adịkwa njikere maka iji ya zụọ AI ka ụmụ mmadụ ghara iche echiche nke ukwuu banyere ọrụ.
WE2.2.8	Ọ bụrụ na anyị ewepụta nzibanye nkwupụta Wikidata na ihe ndi na-esochi, ọ ga-ekwe omume ịmepụta eziokwu dị iche iche (eziokwu chọrọ karịa isiokwu/ amụma/uru iji gosipụta), nke gụnyere atụmatụ pasenti nke ọdịnaya encyclopedic dị na Wikidata.
WE2.2.9	Ọ bụrụ na anyị na-enye ihe ndị ezoro ezo enweghi ike ịkpa aka nke ụlọ ọrụ Wikidata e nwetara, anyị ga-ebelata site nke oge arụmọrụ ọrụ Wikidata dabeere na ọdịnaya na ọ dịkarịa ala pasentị iri ise, na-ebelata oge na nkụda mmụọ onye ọrụ.
WE2.2.10	Ọ bụrụ na anyị enye nchịkọta lexeme sense Wikidata n'ime UI Wikifunction , mgbe ahụ ndị na-enye aka ga-enwe ike ịchọpụta ma họrọ lexemes dị mkpa n'ahapụghị na-ebe ọrụ/Wikifunction - na-ebelata mgbanwe gburugburu ma na-eme ka ọ na-adị ngwa ngwa ma mee nke ọmana mmepụta ọrụ metụtara asụsụ.
WE2.2.11	Ọ bụrụ na anyị leba anya na nchọpụta ojiji sitere n'aka ndị ngalaba otu a bụDagbani na njikọta Wikifunctions na Wikipedia, anyị ga-ahụ na ndị editọ na-enwe obere nsogbu maọbụ enweghi kama nsogbu ojiji mgbe ha na-etinye ọrụ n'ime ederede n'oge nnwale.
WE3.1.1	Ọ bụrụ na anyị eme nnwale A/B nye ụdị nchọgharị ka mma nke Tabbed, na Appụ IOS , anyị ga-ahụ mmụba pasentị ise na ojiji ọtụtụ ụbọchị n'etiti ndị oji eme Tabs.
WE3.1.3	Ọ bụrụ na anyị enye ụzọ ọhụrụ nye ndị ọrụ iji nyochaa ihe oyiyi ma ọ bụ ọdịnaya vidiyo dị mkpa n'ime ibe ederede, anyị ga-ahụ pasentị atọ ma ọ dịkarịa ala nke ịpịpị dị mma nke ọnụego ndị ọrụ a na-enye atụmatụ a.
WE3.1.4	Ọ bụrụ na anyị egosi ndị ọgụụ ọtụtụ echiche gbasara ịpịgharị netwọk ihe ọmụma na wiki, anyị ga-enweta ndepụta nke echiche ndị e bu ụzọ họrọ ka nke ka dị mkpa maka mmepe ka a ga n'ihu.
WE3.1.5	Ọ bụrụ na anyị enye ndị ọgụụ weebụ nhọrọ iji lelee ụdị ntụgharị nke kọmpụta mere nke ọdịnaya Wikipedia na-adịghị n'asụsụ ha, anyị ga-amata ma e nwere mmụba ọrụ ịgụ ihe, nke a na-atụle dị ka mmụba pasentị atọ na mmekọrịta ibe, na-adọta ndị na-agụ akwụkwọ na asụsụ wiki mpaghara na mmụba atụrụ anya imezi ọrụ mpaghara. A ga-enye nke a dị ka ọnọdụ nwale A/B e jikwara n'ihe na-erughị ọnwa isi, nke nkwenye tupu emebe na Wikipedia iri na atọ dị, ma na-eji ọrụ ntụgharị kọmpụta nke dị adị nye ndị editọ Wikipedia.
WE3.2.1	Ọ bụrụ na anyị ejikọ aka ịnakọta ego, anyị ga-emepụta ihe ngosi ndị ọzọ jikọtara ọnụ na-adọrọ mmasị, na nke emebere ka ngafe nke onwe onye na nlebanya afọ nke iOS dị ka a hara ya site na nnwale onye ọrụ. A ga-eji echiche a ka-ga anwale na Q2 sochie ya iji nyochaa ma afọ akwalitere a ka ga-eleba anya o nwetara onyinye ji pasentị ise karịa afọ 2024 na nlebanya.
WE3.2.2	Ọ bụrụ na anyị akpalie ndị ji ngwa appụ Android na ahịa ndị na-abụghị mgbasa ozi ka ha guzobe ihe ncheta nhọrọ, nke ahaziri ahazi (tinyere ego na ugboro ole) maka onyinye dabere na ojiji ihe eme ihe ha na Wikipedia, anyị ga-ahụ mmụba pasentị ise na onyinye ndepụ ngwa ihe enwegasịrị n'ahịa ndị ahụ.
WE3.2.3	Ọ bụrụ na anyị na-eme nnwale A/B nke ndị ọrụ n'abanyeghi iji gosipụta ụdị mnweta dị iche iche nke ntinye onyinye maka igwe mkpanaka na desktọpụ, anyị ga-ahụ ọnụọgụ pasentị abụọ nke onyinye dị elu site na ụzọ mmeta, ma e jiri ya tụnyere njikwa.
WE3.3.1	Ọ bụrụ na anyị gbakwunye ihe ahaziri ahazi nke dị ala na nke dị ọkara nke ndị ojiji iOS rịọrọ na 2024 ruo af nlebanya 2025, anyị ga-ahụ mmụba pasentị atọ nke inwe afọ ojuju ma e jiri ya tụnyere afọ gara aga, dịka atụpụtara site na nnwale ojiji ma ọ bụ nnwale beta.
WE3.3.2	Ọ bụrụ na anyị gbasaa taabụ ndezi Android ka ọ bụrụ ebe mmemme ahaziri ahazi nke gụnyere nghọta n'ime ọgụgụ na nsonye abụghị nke ime ndezi, anyị ga-ahụ mmụba pasentị ise na ntinye aka ọtụtụ ụbọchị na taabụ ma e jiri ya tụnyere ụdị mbụ.
WE3.3.3	Ọ bụrụ na anyị ewebata opekata mpe otu avatar na-agbachighi agbachi na ngwa Android nke ndị nwere akaụntụ—enwetara site na omume ndị ọgụụ nwere uru dị ka ichekwa ọnụ ọgụgụ akụkọ - anyị ga-abawanye ntinye aka ugboro ugboro na omume ndị ọrụ abanyela na 10% karịa ọtụtụ ụbọchị.
WE3.3.4	Ọ bụrụ na anyị enye ndị na-agụ akwụkwọ banyere abanye ikike ịchekwa ederede na ndepụta ọgụgụ nke onwe, anyị na-atụ anya na nsonye na saịtị ahụ ga abawanye, dị ka ahara na mbawanye pasentị ise nke nkwokọ ndị mbata maka ndị na-agụ akwụkwọ na-eji ngwa ahụ, yana mmụba dị ịrịba ama maka ndị ọrụ niile.
WE3.3.5	Ọ bụrụ na anyị emee ọmụmụ ihe onye ọrụ nke na-enye ndị na-agụ weebụ ohere ịnakọta/chepụta ọdịnaya site na Wikipedia, mgbe ahụ, ọ dịkarịa ala pasenti iri nke ndị sonyere ga-echekwa ụdị ọdịnaya abụọ ma ọ bụ karịa dị iche iche (dịka, ederedeedemede, mgbasa ozi) na nchịkọta.
WE3.4.1	Ọ bụrụ na anyị elekwasị anya na Nzije POP/CDN ọ ga-enye anyị ohere iweta ma PoP zuru ezu na Obere PoP (phydi) dị ka achọrọ, n'aka prototaịp maka ntinye obere PoP prototype n'ọdịnihu.
WE3.6.1	Ọ bụrụ na anyị emeenwale A/A na ogo njide maka ndị ọrụ abanyeghi abanye, anyị ga-ewepụta usoro maka ogo njide anyị nwere ike iji maka oge onune n'ọdịnịhụ.
WE3.6.2	Ọ bụrụ na anyị mepụta ma bipụta nkọwa nke onye ọgụụ banyere banye, anyị ga-enwe ike iji nkọwa a gburugburu otu niile na echiche ndị metụtara WE 3.3 KR.
WE3.6.3	Ọ bụrụ na anyị akpọkọọ ndị ngalaba otu banyere mkpa ndị na-agbanwe agbanwe nke ndị na-agụ akwụkwọ nwere, na banyere mgbanwe ọdịdị nke ihe ọmụma na ịntanetị, anyị nwere ike ịmepụta otu ihe na-elekwasị anya n'otú a ga-esi ejere ndị na-agụ akwụkwọ ozi ma na-arụkọ ọrụ ọnụ ma na otu esi anwale echiche anyị dị iche iche (gụnyere ndị dị gburugburu multimedia, ọchụchọ na nchọpụta, na igwe mmụta).
WE3.6.4	Ọ bụrụ na anyị nyochaa mkpali dị iche iche, omume, na mkpa dị n'azụ mgbe, ihe kpatara na otu ndị na-agụ akwụkwọ si eji Wikipedia na usoro ihe ọmụma ndị ọzọ, anyị ga-enwe nchoputa anyị nwere ike iji mee ka anyị mara ma kwalite atụmatụ ndị ahịa anyị.
WE4.1.1	Ọ bụrụ na anyị na-egosipụta ntakịrị ihe na-erugharị na-abụghị ihe mberede, ma hapụ imechi ebe nzaghachi ugboro ugboro ka anyị na-akwalite ya nye ndị ọrụ nwere ikike karịa, mgbe ahụ, otu ndị a ga-akwado nzije karịrị.	Project page
WE4.2.1	Ọ bụrụ na anyị na-egosiputa ọkwa ihe egwu hCaptcha metụtara ya na nkepụta akaụntụ nye ndị ọrụ atụkwasịrị obi, anyị ga-ebelata oge achọrọ iji chọpụta ndị na-eme ihe ọjọọ, ma mụbaa ọnụọgụ nchọpụta nke akaụntụ ndị na-eme ihe ọjọọ ekepụtara na nhiwe a. Anyị nwere ike ịha ihe ịga nke ọma nke echiche a site n'ịlele ọnụ ọgụgụ nke mgbochi enwere na akaụntụ, nhazi ịma aka nke ọkwa hCaptcha na mgbochi nke akaụntụ n'ozuzu, yana nzaghachi ịdị mma site n;aka ndị ọrụ.
WE4.2.2	Ọ bụrụ na anyị wepụta nnyocha atụpụtara ka ndị CheckUsers soro ha, anyị ga-ahụ mbelata n'oge achọrọ iji chọpụta akaụntụ ndị n'eme ihe ọjọọ yana mmụba nke ọnụ ọgụgụ akaụntụ ndị na-eme ihe ọjọọ ga-abụ nke achọpụtara. Anyị ga-amata na anyị na-eme nke ọma mgbe anyị na-ahụ ojiji nke ngwa ntuziaka “nnyocha atụpụtara”, mmụba nke mbelata etinyere na akaụntụ ndị achọpụtara site na nnyocha atụpụtara, yana site na nzaghachi nnyocha idị mma.
WE4.2.3	Ọ bụrụ na anyị nyochara data sitere na nnwale imepụta akaụntụ hCaptcha, anyị ga-aghọta oghere mmepụta akaụntụ, ịdị irè nke okwe mgbagwoju anya na akara hCaptcha, ma nwee data dị mkpa iji mee ka mwepụta n'ihu hCaptcha na ihe nrụpụta akaụntụ.
WE4.2.4	Ọ bụrụ na anyị ezije UserInfoCard na gburugburu wiki niile, anyị ga-enye ndị na-arụ ọrụ na ndị nhazi aka ịchọpụta nke ọma ma ibelata akaụntụ ndị na-eme ihe na-adịghị mma.	Project page
WE4.2.5	Ọ bụrụ na anyị emee nnyocha, soro ndị ngalaba otu na-akpakọrịta, ma nnyochaa ụzọ ọrụ nka na ụzụ, anyị ga-enwe ike ịkọwapụta usoro mgbochi ahaziri ahazi nke enwere ike iji arụ ọrụ na wiki WMF niile.
WE4.2.6	Ọ bụrụ na anyị zụlite ikike maka nzije nchịkọta dabeere na OpenSearch na nyiwe Data, mgbe ahụ, a ga-enye ndị otu njikwa ngwaahịa nkwado ịmepụta usoro na-achịkọta ike a, nke ga-enwe nnwere onwe, nkwụsi ike, na inọọrọ onwe site na usoro ndị ọzọ dabeere na ọchụchọ. Ihe mbụ na onye isi nke sistemụ a ga-abụ ọrụ Ipoid.
WE4.2.7	Ọ bụrụ na anyị ezije njikọta hCaptcha Enterprise na ọtụtụ Wikipedia mmepụta dị ka nnwale mbụ, anyị ga-enwe ike ịnakọta data na arụmọrụ na uru nke hCaptcha Enterprise na mgbochi mmegbu, nchọpụta bot, ojiji na nnweta.
WE4.3.1	Ọ bụrụ na anyị ejikọta nkwado maka kuki ọhụrụ Edge Uniques n'ime requestctl, ntuziaka anyi ga-arụ ọrụ SREs iluso mmetọ , anyị ga-enwe ike ịgbachite site na DDoS na njigharị ọdịnaya ọzọ.
WE4.4.1	Ọ bụrụ na anyị nwere ike ime mgbanwe dabere na nzaghachi mbụ ma zije Akaụntụ nwa oge na ọrụ niile anyị ga-enwe ike ichedo nkpughe ozi nkeonwe (adreesị IP) nke ndị ọrụ na-edeghị aha na ọrụ anyị niile ka ọ dị ihe na-erughị 0.1% nke ndị ọrụ niile (edenyere aha)ha.	Project page
WE4.4.2	Ọ bụrụ na anyị na ndị nsonye dị mkpa (gụnyerengalaba otu wiki na ndị na-arụ ọrụ zuru ụwa ọnụ) kparịtara nke ọma ma mee nke a n'oge, anyị ga-enwe ike izije na wiki niile fọdụrụnụ, belata ọrụ achọpụtara na nkeji ikpeazụ, ma zeere nlachi azụ nke nzije.
WE4.4.3	Ọ bụrụ na anyị na-eme ka ọ dịịrị ndị na-ahụ maka nchekwa mfe inyochaa omume na hụ ọrụ nke akaụntụ obere oge, dabere na adreesị IP ha, mgbe ahụ anyị ga-eme ka e nwee ihe ịga nke ọma nke mmalite akaụntụ nwa oge na wiki niile.
WE4.4.4	Ọ bụrụ na anyị ekwe ka ohere nkpughe IP bụrụ ihe akagburu na nkwerịta nke iwu ohere mkpughe IP, ma gosipụta ngwa an'ọtụtụ ebe, mgbe ahụ anyị ga-eme ka enwee ihe ịga nke ọma nke akaụntụ nwa obere oge ịbụ nke amalitere na wiki niile.
WE4.5.1	Ọ bụrụ na anyị emee nnyocha ịdị mma iji chọpụta ọmụmatụ mmetọ sitere n'aka ndị na-eme ihe ezighi ezi nke AI generative na-enyere aka site na ihe (dị ka spam, iyi egwu, ndị mmetọ ogologo oge, ndezi akwụrụ ụgwọ n'ekupụtaghị, ma ọ bụ mgbasa ozi nduhie), anyị ga-enwe ike nyochaa ihe ize ndụ dị na ụdị Ngalaba otu anyị ma mepụta echiche iji belata ụdị mmetọ dị iche iche nke generative AI na-enyere aka.
WE4.6.1	Ọ bụrụ na anyị megharịa usoro akaụntụ mmekọrịta n'ime Zendesk maka mmegharị paswọọdụ, nke a ga-ebelata ibu dị na T&S wee nye ha ohere ijikwa arịrịọ mmegharị 2FA na-abata ọzọ.
WE4.6.2	Ọ bụrụ na anyị akwado ma gbaa ndị ọrụ ume ka ha debanye aha na ọtụtụ ihe nnyocha mbanye, ndị ọrụ nwere 2FA agaghị enwe ike igbochi onwe ha ịbanye akaụntụ ha.
WE4.6.3	Ọ bụrụ na anyị ekwe ka ndị ọrụ niile nwere adreesị ozi emailu ekwenyesiri ike ka ha nwee ike gbanye 2FA maka akaụntụ ha, mana agbasaghị ọrụ mgbanwe a nye ndị ọrụ, usoro nkwado mwechi anyị ga-anọgide na ọkwa nkwudosike.
WE5.1.1	Ọ bụrụ na anyị na-eji ihe ndowe ime dị iche iche maka nnọkọ izi ezi na nke amaghị aha, anyị ga-enwe ike ichekwa Sessionstore site na mwakpo DDoS na oji eme dị elu, site n'ịghara ibufe ya na nnọkọ amaghị aha nke emepụtara iji nye mgbochi CSRF na ibe nnyocha.	T398814
WE5.1.2	Ọ bụrụ na anyị agbanwee kuki nnọkọ MediaWiki ka ọ bụrụ usoro ahaziri ahazi na mbinye aka nke kryptografik, anyị ga-enwe ike iji ọnụnọ nnọkọ dị ka ihe nchebe megide ndị ojiji, site n'ịkwado nkwenye ntụkwasị obi nke nnọkọ na nsọtụ n'ụzọ na-arụ ọrụ nke ọma na nke ukwuu.	T398815
WE5.1.3	Ọ bụrụ na anyị emepụta ihe ngwọta na-ebelata ogo maka usoro API site na iji mpaghara mmepe gburugbure dabeere na Kubernetes, anyị ga-enwe ike ikpebi nhọrọ kacha mma iji jiri nwalee nkwokọ mmepụta, site n'iji atụnyere arụmọrụ na ọrụ nke ọ dịkarịa ala atọ dị iche iche na-egbochi ọrụ.	T398913
WE5.2.1	Ọ bụrụ na anyị hazigharịa REST API Sandbox UI iji gboo mkpa nnweta ozi nke ndị nrụpụta nke ọma, anyị ga-emeziwanye ndebanye doro anya, dịka akwadoro site na nnwale ojiji.
WE5.2.2	Ọ bụrụ na anyị ezije API niile n'okpuru rest.php site n'usoro etiti, anyị ga-emeghe ụzọ isi njikwa API nke etiti ma nwee ike ịmalite ịha API REST na usoro ojiji iji nweta nghọta nke ga-enye ntuziaka mkpebi na omume n'ọdịnihu.
WE5.2.3	Ọ bụrụ na anyị mejuputa dashbọọdụ nlekota na ihe mkpọturu maka MediaWiki REST API, mgbe ahụ, anyị nwere ike igosipụta ụzọ na-adigide, bara uru na nke nwere ike imeziwanye mpụta ihe na omume sistemu anyị na okwu ndị na-apụta ọsọ ọsọ, karịsịa n'oge mgbanwe dị egwu.
WE5.3.1	Ọ bụrụ na anyị gbasaa ma mezie ụkpụrụ ntụzịaka UX ka anyị na-emelite ntuziaka ọ bụla dị adị, anyị ga-ewepụta usoro ntụzịaka ka mma nke dị njikere ịnwale ma nụchaa nke ọma ka a kwadebe ya maka ojiji ọhaneze zuru ọnụ.
WE5.3.2	Ọ bụrụ na anyị emepụta ihe ngosi nke na-egosipụta uru dị n'ịnye Wikipedia ndị na-eji ọdịnaya na ndị oji eme ha, anyị nwere ike ịkwado WME4.1 & WME4.2 site n'inyere ma ọ dịkarịa ala otu onye mmekọ ọzọ iji kwenye ịpụta n'ime nrụaka onwunwe ma ọ bụ ngosi site na njedebe nke Q1.
WE5.4.1	Ọ bụrụ na anyị hụ na ọtụtụ n'ime arịrịọ weebụ nwetara kuki pụrụ iche, ọ ga-adị mfe ịchọpụta bots na arịrịọ adịgboroja.
WE5.4.2	Ọ bụrụ na anyị wulite ụzọ nwere ike ịchọpụta ndị ahịa amaara, anyị nwere ike ịhapụ ihe mwezuga na oke maka bot sitere na ebe amaarala, wee gaa n'ihu n'usoro mmanye iwu anyị.
WE5.4.3	Ọ bụrụ na anyị ahazigharịa nzacha nke arịrịọ ederede na CDN n'akụkụ usoro inye ohere/ịgọnarị ụzọ, anyị nwere ike ịmanye mmachi ogo izugbe maka bots ma mezie nzacha nkwokọ.
WE5.4.4	Ọ bụrụ na anyị ewepụta atụmatụ nha , anyị ga-eme ka nnyocha nke atụmatụ ọtụtụ afọ maka 'iji akụrụngwa eme ihe nke ọma' ma kọwapụta ntuziaka iji duzie mmepe metrik na ikd nkọwa ihe emeerela.
WE6.1.1	Ọ bụrụ na anyị weghachi onyonyo kwa ụbọchị arụrụ na nzije sava ma gbakwunye mmelite onyonyo kpalitere site na nhọrọ mbugharị ọrụ, anyị ga-ekpughe ihe mgbochi wee guzobe ntọala maka oge achọrọ iji rụọ ọrụ nrụnye na-aga n'ihu.
WE6.1.3	Ọ bụrụ na anyị gbakwunye wikifarms na gburugburu nnwale ijikọ nke ọma, nke a ga-eme ka ndị otu mmepe wulite ụlọ megide mmepụta nke chọrọ otutu wiki iji nwalee patches ha na iche na-enye ha ntụkwasị obi tupu mmepụta ihe ma mee ka mgbapụ dị ole na ole.
WE6.2.1	Ọ bụrụ na anyị enyochaa ma bipụta Ndepụta Njikere Mmepụta anyị nke na-akọwa n'ụzọ doro anya ihe achọrọ maka ọrụ a ga-ewere dị ka ọ dị njikere nrụpụta, yana ọrụ ndị inwere ike ime n'onwe ya, anyị ga-edozi atụmanya n'etiti SREs na ndị otu mmepe, na-eme ka arụmọrụ anyị zuo oke ma digide.
WE6.2.2	Ọ bụrụ na anyị kwuwapụta imepụta ọba akwụkwọ Golang na nodejs na mwepụta ọtụtụ ọrụ siri ike maka ndị mmepe, ha ga-enye nzaghachi ma gosipụta nmmasị.
WE6.2.3	Ọ bụrụ na anyị mepụta ndepụta nlele/akwụkwọ ọrụ, ndị ọrụ mmepe nwere ike ịkwadebe nke ọma tupu oge eruo maka nyocha nleba anya nke data.
WE6.3.1	Ọ bụrụ na anyị ewepụta opekata mpe pasenti iri asaa Wikipedia dị ala na Q1, na mwezuga wiki ndị nwere nkwado asụsụ dị iche iche, anyị ga-abawanye ntụkwasị obi anyị maka mwepu n'ikpeazụ ruo wiki iri kacha elu nke ga-enwe mmetụta ka ukwuu na nlele peeji nke sitere na Parsoid.
WE6.4.1	Ọ bụrụ na anyị ezije tebulu njikọ nke Commons na nchịkọta ya ya, anyị ga-abawanye ohere na uto nchekwa data maka Commons ka ga na-adigide.	T398709
WE6.4.2	Ọ bụrụ na anyị (SRE) nyere ndị otu injinia MediaWiki aka - site n'idepụta, ịkwadebe akụrụngwa ndị dị mkpa (dịka, nchikọba PHP, onyonyo akpa), na inye ntụzịaka na nnyocha - ha ga-enwe ike iji obi ike malite mmepụta PHP 8.3 kwalite site na mmalite nke Q2.	T360995
WE6.4.3	Ọ bụrụ na anyị chọrọ ihe nke abụọ (igodo nchekwa ngwa) maka mbanye SSH site n'aka ndị ọrụ nwere ikikere sistemu dị elu, anyị ga-ebelata ihe ịmaaka mmebi nchekwa nke laptọọpụ eruchaghị ogo ga-ebute.
WE6.4.4	Ọ bụrụ na anyị ejikọta ngalaba anyị site n'igosi echiche ibe niile na saịtị MediaWiki site na ngalaba kanonikal, mgbe ahụ anyị ga-ebelata mgbagwoju anya na nhiwe yana ihe egwu Search Engine Optimization (SEO) site n'iwepụ redirect mobile-subdomain. A na-atụle mmecha site n'ịbawanye nlele ibe mkpanaka na ngalaba kanonikal site na otu narị pasentị ruo 0%.	T214998
WE6.4.5	If the MediaWiki Engineering Team actively monitor and fix issues in MediaWiki related to the PHP 8.3 upgrade, the SRE team will be unblocked to proceed with the PHP 8.3 upgrade by the start of Q2.	T360995

Ngosipụta & Ọrụ Data (SDS) Echiche a ga-anwale [ Isi mpụtara SDS ] Ńkàtá
Aha mkpirisi echiche a ka ga-anwale	Ederede Q1	Nkọwa & Mkparịtaụka
SDS1.1.1	Ọ bụrụ na anyị enyochaa ịdị irè nke nchọpụta ụzọ ndozi nkwokọokporo na-akpaghị aka na pageviews dataset , anyị ga-enwe ike ịmepụta metriks data dị mma iji kọwaa arụmọrụ ha ma chọpụta mkpa ọ dị maka itinyekwu ego na uzọ ndozi ndị a.
SDS1.2.2	Ọ bụrụ na anyị na-akwaga usoro mkpofu XML site na akụrụngwa 'Dumps 1' dị ugbu a gaa na pipeline data nke na-eji ọdịnaya MediaWiki, anyị ga-enwe ike ikwe nkwa SLOs wee gbanyụọ 'Dumps 1'. dabere na XML.
SDS1.2.3	Ọ bụrụ na anyị na-eme njem nlegharị anya wee nyochaa SLOs maka nchịkọta ọrụ ọdịnaya MediaWiki na Platform /Event Gate, anyị nwere ike ịkwado ndị ahịa, metrik, na ndị nwere mmasị na-adabere na ya, ma chọpụta mmezi ndị e nwere ike ịchọ maka SLO, nke ga-enyere anyị aka ịkọwa ọdịiche ọ bụla na nkwa nnyefe anyị kwa izu.
SDS2.1.1	ọ bụrụ na anyị na ndị otu na-eme nnwale jikọta aka ọnụ, anyị ga-amụta otú e si eme ka usoro ahụ bụrụ nke dịịrị onwe ya onwe ya n'ọdịnihu na ihe ịma aka nchepụta ma ọ bụ nke nkà na ụzụ ha nwere ike inweta.
SDS2.1.2	Ọ bụrụ na anyị nwere ike mejuputa ncọpụta na mmezi nsogbu ka mma maka mbanye ihe omume, mgbe ahụ ndị otu ngwaahịa ga-ama na nnwale ha na-anakọta data mmemme dịka a tụrụ anya ya, na nbawanye ntụkwasị obi nke ndị nwe nnwale.
SDS2.1.3	Ọ bụrụ na anyị kwalite ndekọ na nleba anya maka nnwale A/B nke sistemu (xLab) nke ọgbọ nnwale, yana maka akụkụ MediaWiki metụtara ya, anyị ga-enwe ike guzobe ntọala ntọala maka arụmọrụ sistemụ wee zaghachi ọdịda metụtara nnwale.
SDS2.1.4	Ọ bụrụ na anyị kọwapụta nnwale akụkọ na mpụtara na gburugburu nzukọ otu ugboro n'ọnwa (site na nzukọ Product Ops, nzukọ ndị otu mmewe, na ihe ngosi otu), mgbe ahụ anyị ga-emepụta nnabata oganik nke ọgbọ nnwale.
SDS2.1.5	Ọ bụrụ na anyị agwa ndị ọrụ na ngwa ha, ọ bụrụ na emebere ya na xLab, nwere ọtụtụ njirimara na-agbanwe ụdị ihe egwu, anyị ga-egbochi ndị ọrụ na ịnakọta data karịrị akarị ma nwekwuo nghọta n'ihe nchikota njirimara chọrọ nnyocha nzuzo.

Echiche a ga-anwale nke ndị na-ege ntị n'ọdịnihu (FA). [ Isi mpụtara FA ] Ńkàtá
Aha mkpirisi echiche a ka ga-anwale	Ederede Q1	Nkọwa & Mkparịtaụka
FA1.1.1	Ọ bụrụ na anyị 1) na-enye ụzọ maka ndị na-anakọta mgbasa ozi na nhiwe ndị ọzọ (dị ka Letterboxd, Goodreads, na RateMyMusic) iji jiri ihe ọmụma Wikipedia pụrụiche kwalite mkpokọta ha, ma ọ bụ 2) na-enye ndị na-anakọta mgbasa ozi ndị a mgbasa ozi na-adọrọ mmasị, mgbe ahụ anyị ga-enwe ike ịbawanye nhie Wikipedia na-eru.
FA2.1.1	Ọ bụrụ na anyị na-ewulite ikike anyị iji mepụta ọdịnaya vidiyo dị mkpirikpi (site n'ịbawanye otu anyị na inyocha na ịchọpụta ohere iji bawanye arụmọrụ dị ugbu a na usoro mmepụta anyị ugbu a) na Q1, anyị ga-enwe ike iji mmụta site na ọdịnaya emepụtara na FY2024-5 wee nweta ọdịnaya YoY dị elu nke emepụtara na Q2 FY2025-6.

Haịpotesis Nkwado ngwaahịa na injinia (PES) [ Isi mpụtara PES ] Ńkàtá
Aha mkpirisi echiche a ka ga-anwale	ederede Q1	Nkọwa & Mkparịtaụka
PES1.1.1	Ọ bụrụ na anyị na-akwado xLab, Charts, na ToneCheck n'ịkọwa metrik maka SLI (Service Level indicators) na Prometheus, ma malite Service Level Objective (SLOs) ndị ahụ na Pyrra, anyị ga-amata oke na nsogbu nke akụrụngwa anyị n'ọnọdụ ọtụtụ usoro. Ma dokwuo anya mmegharị dị mkpa maka templati SLO, nke ga-enyere anyị aka ịkwado SLO nke 6 zubere nke ọma maka KR.
PES1.1.2	Ọ bụrụ na anyị amalite mkpọturu abụọ nke dashbọọdụ SLO , anyị ga-amata otú ọ ga-esi dị ike iji mejuputa ngwá ọrụ kwesịrị ekwesị nke na ndị nwe ọrụ ghọtara ntinye uchu ha nke ọma, ya na ma anyị kwesịrị ịlachị na ngwá ọrụ dị iche iche nke na-enye naanị otu nlebanye n nke SLO. Otu dashbọọdụ ga-abụ maka nkọwapụta ihe emerela na otu n'ime omume anọ (ebe edobere n'ezie Service Level Agreement maka mmefu ego na-edezighi nke ọma) yana obere nke n'agbanwe (nke a na-akpọ "rolling") ga-abụ maka ịrụ ọrụ kwa ụbọchị na mkpọtụrụ.
PES1.1.3	Ọ bụrụ na anyị na-akwado otu Wikipedia Abstract n'ịdepụta SLO (Service Level Objectives) maka ọrụ Wikifunctions, anyị ga-amụta otu esi akọwapụta ndepụta ebumnuche SLO (tinyere ya na metrik ihe ngosi ọkwa ọrụ ha) maka njiri dị mgbagwoju anya a na-agbakwunye ugbu a na usoro ọrụ onye ọrụ dị oke mkpa: na-ede ederede Wiki. Anyị ga-amụtakwa ka anyị ga-esi were leba anya na nchepụta atụmatụ mmejo mmefu ego ndị yitewere ma mee ka a mara ha site na iji dashbọọdụ na ihe nleba anya nke SRE wetara.
PES1.1.4	Ọ bụrụ na anyị akwado otu Data Platform na nnyocha na nkwugharị nke Service Level Objectives (SLO) maka nkọwapụta nke ọdịnaya oru MediaWiki, anyị ga-amụta ka esi eji SLO iji kwado onwunwe ọrụ mgbe a na-ahazi nchikota nke baachị na usoro ngosi ọrụ bụ nke achịkọbara iji melite dataset, na-idebe ya ka ọ digide ma dịkwa nye ndị oji eme ihe..
PES1.2.1	Ọ bụrụ na anyị mepụta nkwalite atọ ezubere iche na ndepụta ihe achọrọ, mgbe ahụ anyị ga-agba pacentị iri atọ ume pụrụ iche na ndetu ihe ndị achọrọ.
PES1.2.2	Ọ bụrụ na anyị na eme nwale nke ihe ndị atụrụ anya ma kenye onye na-elekọta (dịka ndị na-ahụ maka njikwa ngwaahịa) n'ime awa iri asaa na abụọ (gụnyere ịweghachi, ịkọwapụta, na-igosi ọrụ ndị a na-adịghị edozi, wdg), site na-nrụtụakagasị ọhụrụ na megide tebụl na-elekọta ma na-ekenye "udi ndị mmezi ahọpụtara" na otu kachasị mkpa ngwaahịa ma ọ bụ onye ọ bụla, ndị na-azụ ahịa ga-eme ka ndị na-azụ ahịa nwee ike. ụbọchị iri ma ọ bụ ọ gaghị erucha.
PES1.2.3	Ọ bụrụ na anyị na-arụ ọrụ iji chọpụta akara ngosi ngalaba otu ga n'ozuzu, anyị ga-etinyekwu olu ndị ọrụ afọ ofufo na mbọ anyị na-ebute ụzọ.
PES1.2.4	Ọ bụrụ na anyị amalite ohere ugboro anọ nke ndepụta ihe ndị achọrọ na usoro nlebara nke ndepụta ngalaba otu nke gbakwunyere otu atọ na Q1 , anyị ga-etinye ndị njikwa ngwaahịa ka ha tinye ihe ndị ngalaba otu deturu n'ime usoro nhazi nke nkeji na nke kwa afọ.
PES1.3.1	Ọ bụrụ na, na njedebe Q1, anyị ahazie oge nhazi ọrụ atọ anyị na ngalaba Nkwukọrịta ozi na ndị otu ngwaahịa iji nwe nkwekọrịta ozi, mkpa nchepụta ihe, na usoro mgbasa ozi maka atụmatụ WP25, mgbe ahụ anyị ga-emecha ihe nchịkọta obere nkọwa gbasara nchepụta maka nnyocha mgbasa ozi atọ niile (25YiR, Easter Eggs, WikiRun).
PES1.3.2	Ọ bụrụ na anyị hiwe kọmitii nduzi na ndị nnọchi anya sitere na ndị dịzaịn na ngwa enjinịarịn , Anyị ga-enwe ike ịkọwapụta metrik ndabere gbasara ntinye aka na Codex: mmata, ojiji, ogo onyinye, na ọnụọgụ. Nghọta sitere na ntule megide metrik na ga-enyere anyị aka ikpebi ụzọ maka ịmekọrịta uto na iche iche nke ntọala ntinye aka Codex.

Q2

Nkeji nke abụọ (Q2) nke atụmatụ afọ WMF ga-eru Octoba-Desemba.

Mmụtara Wiki (WE) nke Echiche a ka ga-anwale [ Isi mpụtara WE ] Ńkàtá
Aha mkpirisi echiche a ka ga-anwale	Ihe odide Q2	Nkọwa & Mkparịtaụka
WE1.1.1	Ọ bụrụ na anyị akọwa usoro ihe ngosi ndị na-akọwapụtabeghi n'usoro ≥izu abụọ ka mmalite nke nwale Paste Check A/B gasịrị, anyị ga-enwe ike ịchọpụta kedu - ma ọ bụ- akụkụ nke usoro mmetụta mmụtaara ọbụla kwesịrị ka ọ gbanwe ma ọ bụ ka enyochaa ha tupu anyị enwee ntụkwasị na-enyocha mmetụta gbasara ọdinihu.
WE1.1.4	Ọ bụrụ na anyị emee nnyochaa edensibịa na en.wiki site na nnwale ejikwara, anyị ga-ahụ mmụba ≥pasantị anọ nke ezi ndezi ndị ọrụ afọ ofufo ọhụrụ bipụtara ma mara ma enwere nkwado zuru oke n'etiti ndị na-ahụ maka nchekwa na ndị nhazi iji mee ka ngwa ahụ dịwanye ukwuu.
WE1.1.7	Ọ bụrụ na anyị usoro ihe ngosi ndị-akọwapụtaghị ≥ izu abụọ ka nnyocha Tone Check A/B malitere, anyị ga-enwe ike ịchọpụta kedu - ma - akụkụ mmetụta mmụtaara kwesịrị ka ọ gbanwe ma ọ bụ ka enyochaa tupu anyị enwee ntụkwasị na-enyocha mmetụta ọdịnịhụ.
WE1.1.8	Ọ bụrụ na anyị ewebata usoro Tone Check n'ederede ndị e bipụtara, anyị ga-amata ma anyị e nwere ike ịchọpụta nsogbu olu ≥puku iri (nke ọ bụla nwere ohere nke 0.8 ma ọ bụ karịa) nke dị mkpa iji chịkọba (ihe dị mma ≥ 70%) ọkpụtọrọọkpụ aro iji nyere aka iduzi ndị editọ n'imeziwanye olu eji dee ederede.
WE1.1.10	Ọ bụrụ na anyị agbaa ndị ọrụ afọ ofufo mmadụ iri nwere mmụtaara na en.wiki na fr.wiki ajụjụ ọnụ bụ ndị na-ede AbuseFilters (na ngwa ndị ọzọ/skript/Templeti/ọkwa ndezi) iji mee ka usoro ọrụ nlekota/nhazi rụọ ọrụ nke ọma, anyị ga-achọpụta ụkpụrụ/mkpa ≥atọ atọ ga-enyere aka ịgbazi echiche bara uru nke nlebananya ndezi nke ngalaba otu dere.
WE1.1.11	Ọ bụrụ na anyị kesaa nnyocha ajụjụ nye ≥narị ndị ọhụrụ ise nwere ihe ịga nke ọma[i] ma nweta data dị elu nke na-anọchite anya ndị ọhụrụ nwere ihe ịga nke ọma, anyị ga-enwe ike ịchọpụta nghọta ≥anọ a ga-eme iji mee ka mpaghara nke mmụtaara mmalite a ga-eme ka ọ dịkwuo mma.
WE1.1.12	Ọ bụrụ na anyị enye ndị ọrụ afọ ofufo ≥atọ ohere inyocha ndezi eji mee ọmụmaatụ ≥iri atọ ọ bụla, maka nke ọ bụla n'ime asụsụ ọhụrụ iri anyị na-achọ ịha ogo nlebanya olu bụ Tone Check, anyị ga-amata otú ndị ọrụ afọ afọ ofufo si kwenye na amụma usoro akọwapụtara ma nwee ike ikpebi ụdị wiki ọhụrụ ha ga-elebanya iwebata Tone Check na ya.
WE1.1.13	N'iburu n'uche na anyị gbakwunyere "Ịtinye njikọ" ruo pasenti otu narị nke ndị ọrụ afọ ofufo ọhụrụ na Wikipedia Bekee, mgbe ahụ, ịrụ ọrụ na njigide ehiri asaa n'anya mee ga-akawanye mma, nke ga-eme ka ndezi ehiri aka n'anya dee nke ndị ọrụ afọ ofufo ọhụrụ jiri ≥pasentị anọ.
WE1.2.3	Ọ bụrụ na anyị ewepụ ihe atụmanya na onye na-ahazi ihe omume kwesiri inwe ikikere onye nhazi iji jirinwuo ebe ndebanye aha ihe omume na obere wiki na Wiki na-ebuchaghị ibu, mgbe ahụ anyị ga-ahụ ma ọ dịkarịa ala ihe omume X ndị ọzọ na njedebe nke afọ mmefu ego nke a ka nwegbeghi nkwudo/mgbakọ atụrụanya.
WE1.2.4	Ọ bụrụ na anyị ekwugharịa gbasara MVP ime ntunye njikọta aka na ọ dịkarịa ala mmelite abụọ, Nke a bụ na a ga-emepụta njikọ aka ndị ọzọ site na ebe Ndebanye aha Ihe Omume.
WE1.2.5	Ọ bụrụ na anyị debe otu usoro mwebata nke ebe Ndebanye aha Ihe Omume na Wikimedia Commons na mbido Q2, anyị ga-enwe ike iji ndị nhazi nwalee ya ma ọ dịkarịa ala n'otu nnukwu mkpọsa ma mee ka ndị na-hazi mpaghara ise jiri ngwa arụ ahụ rụọ ọrụ.
WE1.3.3	Ọ bụrụ na anyị amalite nnwale iji wepụta dashboard moderator na ndị editọ ọhụrụ, pasentị iri nke ndị na-enye aka na-eme nke izu abụọ n'usoro.
WE1.4.1	Ọ bụrụ na anyị emee mmezi akọwapụtara na T396489, anyị ga-ebelata kweri nke mgbanwe ndị adịbeghi anya emere site na opekata mpe pasentị iri atọ na nnukwu wiki, nke ga-eme ka Community Tech nwee ike izinye Watchlist Labels n'ebunyeghị database mgbanwe na-adibeghi anya oke ibu.
WE1.4.3	Ọ bụrụ na anyị ejiri mgbanwe ndị e mere n'oge na-adịbeghị anya na ndepụta ihe nlele mee ihe, anyị nwere ike ịkọwa isi mmalite nke ugboro ole ndị mmadụ pịrị aka na ibe.
WE1.5.1	Ọ bụrụ na anyị etinye dashboard iji nyochaa usoro nhazi asaa nke ndị na-enye aka ma hazie ngụkọ nke opekata mpe otu usoro nhazi site na iji dbt, mgbe ahụ anyị nwere ike ime ka ndị otu nrụpụta ngwa ndị na-enye aka nwee nghọta metrik nke ha ha ma mepụta ụkpụrụ maka ịchekwa metrik mgbakọ logịc.
WE1.5.2	Ọ bụrụ na anyị ekpebie na Q2 ihe ndị a ga-eme iji dozie nsogbu nhazi na nkọwa nke onye bụ onye nlebanya, ndị otu Movement Insights nwere ike ịmepụta usoro nhazi nke ndị nlebanya kwa ọnwa na Q3/Q4.
WE2.1.3	Ọ bụrụ na anyị amata banyere mmụtara onye editọ mgbe ọ na-ede ederede nakwa ngalaba ederede( nke gụnyekwara mkpali, ihe mgbu na omume ha gbasara echiche ọhụrụ banyere otú ka mma ikwado ha), mgbe ahụ anyị ga-egosipụta mkpa na àgwà onye ọrụ nke na-enye nghọta nkọwa na atụmatụ iji gwa ndị nrụpụta ngwa , dịzaịnụ, na ọrụ ndị injịnịa na-mmeziwanye mmụtara odide ederede.
WE2.2.12	Ọ bụrụ na anyị ewere Wikifunctions gaa na wiki ndị Parsoi nwere ike ịrụ ọrụ, anyị ga-enwe ike ịga n'ihu na nwale ma sistemụ ahụ ọ ka na-arụ ọrụ ma na-arụ ọrụ mgbasawanye dị ukwuu.
WE2.2.13	Ọ bụrụ na anyị ejikọta ọrụ tebụl njikọta na ngalaba otu Wikshọnarị, anyị ga-enweta nzaghachi bara uru gbasara ojiji ọrụ na nghọta gbasara ndị ọrụ anyị nke anyị nwere ike itinye na mwepụta n'ọdịnihu.
WE2.2.14	Ọ bụrụ na anyị eleba anya n'ime ọrụ Databox nke ngalaba otu site n'iji Wikidata mere infoboxes ma nyochaa ma Wikifunctions nwere ike inyere aka, anyị ga-enwe ike ịchọpụta nnwale mbụ maka Wikifunctions na infoboxes.
WE2.2.15	Ọ bụrụ na anyị emee ka ndị ngalaba otu mara maka ikike ịmepụta na ịtụgharị ozi mgbaziri na Wikifunctions, anyị ga-ahụ mmụba na ọnụọgụ ozi mgbaziri bara uru.
WE2.2.16	Ọ bụrụ na anyị egosi ọrụ nkọwa okwu dị nye ngalaba otu, anyị ga-ahụ mmụba pasentị iri ise na ọrụ asụsụ.
WE2.2.17	Ọ bụrụ na anyị enye ngwa pụrụ iche maka ilele nkọwapụta Wikidata na Wikifunctions, ndị ọrụ ga-enwe ike ịghọta data e si na Wikidata nweta ma ghara inwe mmetụta nke ịda mba.
WE2.2.18	Ọ bụrụ na anyị enwee ike igbochi 10x memory consumption spikes, onye na-ahazi ihe ga-enwe ngwa ijikwa ihe Wikidata nke ọma, na-akwado uru nke Wikifunctions dị ka ebe ọrụ Abstract Wikipedia.
WE2.2.19	Ọ bụrụ na anyị enyere ndị ọrụ aka ịkesarịta njikọ n'ebe achọrọ ya n'ọrụ kpọmkwem - tinyere ntinye ha - ndị na-enye aka ga-enwe ike ịmepụtaghachi, inyochaa, ma kparịta gbasara omume ọrụ ngwa ngwa, nke ga-eme ka nchọpụta nsogbu dị ngwa, kwalite usoro nnwale, ma kwado njịkọaka na ndozi nsogbu n'ofe ngalaba Wikifunctions.
WE2.3.1	Ọ bụrụ na anyị emechaa mkpebi maka ikepụta wiki ọhụrụ ma anyị na ngalaba otu ekpebie aha ọ ga-aza, anyị na ndị nsonye anyị ga-enwe ike ịkpakọrịta mmepụta wiki ọhụrụ ma kwadebe maka nhazi nke mgbanwe aha nrụpụta e nwere ike inwe.
WE2.3.2	Ọ bụrụ na anyị akọwapụta MVP maka udidi mmalite Abstract wiki nke gụnyere mmụtaara kachasị nta maka ịnwale ikike ebe ọrụ anyị na NLG, ma nye anyị ohere ime mmepụta ugboro ugboro, anyị ga-enwe ike ịhazi ma wepụta udịdị mmalite enwere a ga na-agbanwe ozugbo na Q3.
WE2.3.3	Ọ bụrụ na anyị amalite ịgwa ndị ngalaba otu okwu ma chọpụta atụmatụ ndị nwere ike ime maka mmụtaara onye ọrụ nke Abstract wiki, anyị ga-enwe ike ime ka ọrụ na-aga n'ihu na Q3.
WE2.4.1	Ọ bụrụ na anyị anakọta ihe ndị a na-enwetakarị na Wikidata na WDQS site n'aka ndị otu WMDE na WMF, anyị ga-enwe ike ịkọwapụta nrụpụta ndị achọrọ maka nkwalite akụrụngwa.
WE2.4.2	Ọ bụrụ na anyị ejiri ebumnuche ọkwa ọrụ dị adị bụ service level objectives(SLOs) mepụta nkọwapụta ọrụ achịkọtara nke ihe ngosi arụmọrụ ndị dị mkpa bụ key performance levels (KPIs) maka Wikidata na WDQS, anyị ga-enwe ike ịkọwapụta ma soro usoro ihe ịga nke ọma maka nkwalite nke akụrụngwa teknụzụ na nkwado ihe gbasara ojiji Wikidata dị oke mkpa.
WE2.4.3	Ọ bụrụ na anyị enwee ike inyocha ma tụlee ebe ndị ọzọ nke Blazegraph site na iji usoro mmepụta ihe n'ime nkeji anọ ahụ, anyị ga-enwe ike ime mkpebi mbugharị dabere na data ma kọwaa usoro nhazi doro anya yana oge mmepụta na akụrụngwa ndị achọrọ.
WE3.1.1	Ọ bụrụ na anyị ejiri A/B nwalee ụdị nchọgharị ka mma nke Tabbed, anyị ga-ahụ mmụba pasentị ise na ojiji ọtụtụ ụbọchị n'etiti ndị oji Tab eme ihe.
WE3.1.3	Ọ bụrụ na anyị enye ụzọ ọhụrụ ndị ọrụ ga-eji elele ọdịnaya onyonyo dị mkpa n'ime ibe ederede, ma jiri ya mee nwalee site na iji obere ndị na-agụ akwụkwọ bụ ndị edebanye aha ha site na nwale A/B n'ofe otutụ wiki, anyị ga-ahụ opekata mpe irielu ogo ọpịpị pasentị atọ n'etiti ndị ọrụ enyere atụmatụ a.
WE3.1.4	Ọ bụrụ na anyị egosi ọtụtụ ndị ọgụụ echiche maka ịgafe netwọk ihe ọmụma na ọtụtụ wiki, anyị ga-eji ndepụta nke echiche ndị a ga-agbakwasa ụkwụ na ha maka nkwalite ga.
WE3.1.5	Ọ bụrụ na anyị enye ndị na-agụ akwụkwọ weebụ ohere nhọrọ ịhụ ụdị ọdịnaya Wikipedia nke igwe sụgharịrị nke na-adịghị n'asụsụ ha, anyị ga-amata ma ọrụ ịgụ ihe ọ mụbaala, ma tulee ya dị ka mmụba pasentị atọ na mmekọrịta peeji, na-adọta ndị na-agụ akwụkwọ gaa na wiki asụsụ ngalaba otu yana mmụba e nwere ike iweta na ọrụ ndezi ihe si mpaghara. A ga-enye nke a dị ka nhazi nnwale A/B aachịkwara maka ihe na-agaghị agafe ọnwa isii, na Wikipedia iri na atọ na nkwenye mbụ, site na iji ọrụ ntụgharị igwe dị mfe mnweta nke dịlarị maka ndị editọ Wikipedia.
WE3.1.6	Ọ bụrụ na anyị emepụta udidi mmalite maka nchọta nkọwa okwu nakwa na ajụjụ na azịza n'ederede, nke e nyere dị ka ngosipụta ihu nke na-egosi ọdịiche dị n'etiti usoro dị ugbu a na ụzọ nchọpụta ọhụrụ, mgbe ahụ ndị otu ndị ọgụụ ga-enwe ike inyocha nke ọma otu usoro ọ bụla si arụ ọrụ n'ogo ojiji ndị ọrụ dị iche iche na oghere enwere ma ọ bụ ohere maka mmegharị ndị ọzọ.
WE3.1.7	Ọ bụrụ na anyị elebanya na nnyocha dị ugbu a gbasara otu ndị ọgụ si eji ngwaọrụ ọchụchọ ihe na mkpagharị na Wikipedia, nakwa otu ha si eji nchọta mpụga achọ ihe ọmụma na Wikipedia, anyị ga-enwe ike inye ndị otu ọgụụ ntuziaka di ire na nchọpụta ≥3 a ga-eme nke ga-enyere ha aka ịchọpụta MVP iji dozie oghere dị na atụmanya na mkpa ndị na-agụ akwụkwọ.
WE3.1.8	Ọ bụrụ na anyị ejiri ndị si esi sonye nyochaa prototypes ọchụchọ nkọwa okwu abụọ (ọchụchọ asụsụ okike, ajụjụ na azịza), anyị nwere ike ịmata ma ndị oji ihe eme ihe a hụrụ uru na ngwaọrụ ọchụchọ akwalitere, ma nye ndị otu ndị na-agụ akwụkwọ ntuziaka maka otu esi aga n'ihu na ọchụchọ na nchọpụta MVP.
WE3.1.9	Ọ bụrụ na anyị egosi echiche mmezi dị elu maka nchọpụta ọdịnaya site na nchọta nkọwa okwu nye ndị na-agụ akwụkwọ Wikipedia dị iche iche iri ruo iri abụọ n'ọmụmụ ihe gbasara ogo ịdị mma, anyị ga-ahụ mmetụta dị mma maka atụmatụ ahụ ma nweta ntụkwasị obi dị mkpa iji gaa n'ihu na nchọta na nchọpụta MVP nke dabere na obere ihe ederede mmadụ dere na ajụjụ ọchụchọ.
WE3.1.10	Ọ bụrụ na anyị egosi ndị na-agụ akwụkwọ na-anaghị abịa kwadaa iri udidui a na-eme ozugbo nke mpụtara nchọgharị onyonyo ọhụrụ n'ọmụmụ ihe onye ọrụ na-achịkwaghị nke ọma, anyị ga-achọpụta ma ọ dịkarịa ala otu mmezi UX maka mmegharị nke atụmatụ ahụ n'ọdịnihu.
WE3.1.11	Ọ bụrụ na anyị eme ka njikọ nke mkpụrụokwu dị na ngwaọrụ achọm ihe belata, anyị ga-akwado ajụjụ asụsụ okike nke ọma, ma mee ka Nrụpụta nwee ike inyocha ikike a, ma tinye ya n'otu ha si emepụta mmezi, tinye ihe ndị dị mkpa n'ọrụ ma nyefee ọrụ na ebe achọmihe nkọwa okwu.
WE3.1.14	If we launch an A/B test of a version of the mobile site which introduces navigation that opens all sections by default, we will see early indicators that signal towards an increase in session length (will report on full A/B test results in Q3)	T409163
WE3.2.5	Ọ bụrụ na anyị ewebata nnyocha atụmatụ Afọ na Android nke na-egosipụta mmetụta onye ọrụ ma tinyere ozi ndị na-enye onyinye agbakwunyere, anyị ga-enweta uzo onyinye ọhụrụ - anyị ga-ahụkwa mmụba pasentị ise na ndepụta ga nke ngwa app ma e jiri ya tụnyere nke 2024.
WE3.2.6	Ọ bụrụ na anyị emee ihe ngosi ndị na-enye onyinye na nlebanya nke afọ iOS, nke jikọtara ọnụ, ma bụrụ nke ahaziri ahazi, anyị ga-ahụ mmụba nke pasentị ise na onyinye ma e jiri ya tụnyere nke afọ 2024.
WE3.3.3	Ọ bụrụ na anyị ewebata opekata mpe otu avatar enweghi ike igbachị na ngwa Android maka ndị nwere akaụntụ—nke enwetara site na ihe omume ndị na-agụ akwụkwọ bara uru dịka ịchekwa ederede ụfọdụ — anyị ga-eme ka enwee mkpakọrịta ugboro ugboro nke mmewere yiwere nke ndi ojieme banyere site na pasentị iri n'ime ọtụtụ ụbọchị.
WE3.3.4	Ọ bụrụ na anyị enye ndị na-agụ akwụkwọ debanyere aha ojieme ha ohere ideba ederede na ndepụta ọgụgụ nkeonwe, anyị na-atụ anya na itinye aka na saịtị ahụ ga-abawanye, dịka a tụrụ ya site na mmụba pasentị ise na nkwokọ sitere na nrụaka nke ebe ọrụ maka ndị na-agụ akwụkwọ na-eji atụmatụ ahụ, yana mmụba dị ukwuu nye ndị ọrụ niile.
WE3.3.6	Ọ bụrụ na anyị emee ka data nchịkọta ederede dị site na ọrụ nke na-emezu ihe achọrọ maka nhazi na nnweta, tinyere ihe ndị ọzọ dị mkpa maka nchekwa data, mgbe ahụ anyị ga-eguzobe ntọala teknụzụ dị mkpa iji kwado mmụtaara ndị na-agụ akwụkwọ na-abịa ahaziri iche nke dabere na data a.
WE3.3.7	Ọ bụrụ na anyị ejiri ikike nhazi nke data mee ihe iji chịkọta usoro nchịkọta ọrụ ndị ndezi ahaziri ahazi ma metụta data ma nyefee data agbakọtara site na ọrụ kwesịrị ekwesị n'iji SLO akọwapụtara, anyị nwere ike ime ka mgbanwe n'ọdịnihu nke Afọ mmegharị Nlebanya WE3.3.1 na Ọrụ Taabụ WE3.3.2 ka mma.
WE3.3.9	Ọ bụrụ na anyị ewepụta nlebanya Afọ na Android na nwale A/B nke na-enye ndị ọrụ na-esonyekarị ohere iji chekwaa ndepụta ọgụgụ ahaziri ahazi, anyị ga-ahụ mmụba otu pasentị na ogo njigide ngwa app n'ozuzu n'etiti ndị na-agụ akwụkwọ enyere ohere a ma e jiri ya tụnyere ndị na-anaghị esonye.
WE3.3.10	Ọ bụrụ na anyị nwalee A/B nke chọrọ akaụntụ iji lee nghọta ọgụgụ nkeonwe nke Afọ nlebanya, anyị ga-ahụ mmụba otu pasentị na ogo njigide zuru oke nke ndị ọrụ achọrọ ka ha nwee akaụntụ, ma e jiri ya tụnyere ndị na-enweghị.
WE3.3.11	Ọ bụrụ na anyị anwale ụdị "Ọrụ" tab ka mma na iOS nke na-egosipụta ịgụ ihe, idezi ihe na omume ndị ọzọ na-esonye, anyị ga-ahụ mmụba pasentị ise nke mbanye ọtụtụ ụbọchị site n'aka ndị na-agụ akwụkwọ banyere na taabụ ahụ ma e jiri ya tụnyere ụdị prototype ahụ.
WE3.4.1	Ọ bụrụ na anyị rụọ ọrụ iji mee ka e nwee njikọ n'etiti ebe a na-etinye ihe (PoP/CDN), ọ ga-enye anyị ohere ịkpọlite ma PoP zuru oke na obere pop ( fịsịkal na kloud) dịka ọ dị mkpa, na-enye ohere maka ntinye obere PoP n'ọdịnihu.
WE3.5.1	Ọ bụrụ na Nrụpụta nakwa Teknụzụ na nnakọta Ego ga-ejikọ aka nyochaa ma detuo usoro teknụzụ maka ịchọpụta ndị na-enye onyinye n'ime nhiwe anyị, anyị ga-enwe ike ịkwado ihe igbo nsogbu dị mkpirikpi na nke ogologo oge nke ga-eme ka nzochi, ohere ịdị ire, na mmetụta daba. Nnwekọrịta nghọta a ga-enyere aka ịhazi ime mkpebi, mee ka nnabata ndị na-enye onyinye na-aga n'ihu n'ofe nhiwe, yana nnwale ezubere iche na atụmatụ ndị metụtara nnakọta ego n'ọdịnihu.
WE3.6.3	Ọ bụrụ na anyị eme ka ndị ngalaba otu mata gbasara mkpa ndị na-agụ akwụkwọ na-agbanweharị, nakwa gbasara mgbanwe ihe ọmụma na ịntanetị, anyị nwere ike itinye uche n'otu a ga-esi nyere ndị na-agụ akwụkwọ aka ma rụkọ ọrụ ọnụ otu otu anyị ga-esi anwale echiche dị iche iche anyị (tinyere ndị gbasara mọltịmedịa, achọmchọ na nchọpụta, na mmụta a na-enweta n'igwe).
WE3.6.4	Ọ bụrụ na anyị enyocha ihe ndị na-akpali mụọ, omume, na mkpa dị iche iche bụ ihe kpatara na nakwa otu ndị ọgụụ si eji Wikipedia na nhiwe ihe ọmụma ndị ọzọ, anyị ga-enwe ike ịtụpụta aro ebe ndị kacha mkpa na atụmatụ pụrụ iche maka atụmatụ ndị ndị oji ihe eme ihe.
WE3.6.5	Ọ bụrụ na Nrụpụta na Teknụzụ nakwa nnakọta Ego ejikọ aka na nkekọrịta atụmatụ iji mee ka ohere onyinye dị iche iche dị na nhiwe na ebe nledo anya ma na-ahụ maka ndị na-agụ akwụkwọ na-enye onyinye, anyị ga-edobe ebumnuche na usoro doro anya, nke dabara adaba nke gbakwasịrị ụkwụ n'atụmatụ ndị ji ọrụ anyị eme ihe na nke nnakọta ego enyemaka.
WE3.6.6	Ọ bụrụ na anyị emepụta atụmatụ ịha nha dịkọtara ọnụ, anyị ga-eme ka inyocha atụmatụ nke ọtụtụ afọ nke ndị oji ọrụ anyị eme ihe ma kọwaa ntuziaka iji jikwaba ọnụọgụ mmepụta na ike nkọwapụta ihe nd i emegasịrị.
WE4.1.1	Ọ bụrụ na anyị emepụta usoro mmepụta na-anaghị akpata nsogbu mberede, ma na-enye ohere usoro nzaghachi ugboro ugboro ka anyị na-emepụta ya site na ndị ọrụ nwere ikike extended rights, mgbe ahụ otu ndị a ga-akwado mgbasa nke usoro a.
WE4.1.3	Ọ bụrụ na anyị emelite Wikipedia asaa (French, German, asụsụ Spain, Hungary, Italy, Poland, na Portugal) ka ọ na-erule na ngwụcha ọnwa Ọktoba, anyị ga-emecha nkebi nke mbụ nke mbipụta Legal Footer ọhụrụ dịka iwu DSA siri dị.
WE4.1.4	Ọ bụrụ na anyị etinye MVP Sistemụ nkọwa Ihe Mere na wiki iri na ise ma ọ dịkarịa ala, na-elekwasị anya na ngalaba otu ndị buru ibu, anyị ga-ahụ na a na-eji ya dịka ngalaba otu ahụ zubere, anyị ga-egosikwa ụdị ọrụ maka ịkọwa ihe mere eme na-abụghị nke mberede.
WE4.1.5	Ọ bụrụ na anyị emepụta ihe osise maka ịkọwapụta ihe omume nke mmetọ na wikis na-enweghị usoro nchịkwa mmetọ, nke a ga-enye ohere nnabata usoro nkọwapụta ihe mere eme na wiki ndị dị otú ahụ ma mee ka ndị ọrụ na wiki ahụ nwee ụzọ nkwado doro anya na nke bara uru.
WE4.2.3	Ọ bụrụ na anyị eme nnyochaa data site na nnwale imepụta akaụntụ hCaptcha, anyị ga-aghọta usoro imepụta akaụntụ, arụmọrụ nke mgbagwoju anya na akara hCaptcha, ma nwee data dị mkpa iji nye zie ozi gbasara mgbasawanye nke hCaptcha na nkepụta akaụntụ.
WE4.2.5	Ọ bụrụ na anyị emee nnyocha, kpọtụrụ ndị ngalaba otu anyị ga, ma nyochaa ndozi nsogbu teknụzụ, anyị ga-enwe ike ịkọwa otu ihe kpatara usdoro nhazi nke enwere ike iji na wiki WMF niile.
WE4.2.6	Ọ bụrụ na anyị emepụta ikike maka izinye OpenSearch dabere na nhiwe Data, a ga-enye ndị otu injinia njirimara ngwaahịa ike ịmepụta sistemụ ndị jikọtara ikike a, yana nnukwu nnwere onwe, iguzogide, na ikewapụ onwe ha site na sistemụ ndị ọzọ dabere na ọchụchọ. Onye mbụ na onye isi maka sistemụ a ga-abụ ọrụ IPoid.
WE4.2.7	Ọ bụrụ na anyị ezibanye njikọ hCaptcha Enterprise na ọtụtụ Wikipedia nrụpụta dị ka obere nnwale, anyị ga-enwe ike ịchịkọta data gbasara arụmọrụ na uru nke hCaptcha Enterprise na mgbochi mmegbu, nchọpụta bot, ojiji na nnweta.
WE4.2.8	Ọ bụrụ na anyị emee ka mmepụta ihe nnọchiteanya hCaptcha dị nkwadebe site n'ime ka ọ dị mma ma na-eleba ya anya ya, anyị ga na-enye ọrụ kwụsiri ike ma dịkwa mma maka mmepụta Wikipedia na Q1.
WE4.2.9	Ọ bụrụ na anyị ewebata hCaptcha SDK n'ime ngwa app mkpanaka , nyochaa mmụtaara onye oji ngwa app eme ihe ma nyochaa mgbanye ihe ịmaaka hCaptcha dịka akụkụ nke API imepụta akaụntụ, anyị ga-enwe nghọta zuru oke iji nyekwuo nkówa gbasara mwepụta ọzọ nke hCaptcha maka API mmepe akaụntụ.
WE4.2.11	Ọ bụrụ na agbanye hCaptcha maka ịchọpụta bot n'ọnọdụ ndezi ihe ịma aka, anyị ga-ahụ na hCaptcha nwere ike ibelata mmetọ ji aka ya eme.
WE4.2.16	Ọ bụrụ na anyị kpọturu ndị otu WMF dị mkpa, anyị ga-enwe ike ịmepụta atụmatụ nkwekọrịta iji jikwaa ohere ndị ọrụ na-enweghị isi na data na-abụghị nke ọha, ma kwado ntinye iwu ngwanrọ nchebe na-abụghị nke ọha.
WE4.2.17	Ọ bụrụ na anyị enyochaa ihe atụ a na-ahụ anya n'ezie ma gbaa CheckUsers ajụjụ ọnụ iji chọpụta ma ọ dịkarịa ala ihe mgbaàmà abụọ nke omume mmetọ sitere na ihe nlereanya akụkọ ihe mere eme nke onye ndezi, ndị otu Nchedo Nrụpụta na Ịdị n'otu bụ Product Safety and Integrity team ga-enwe ike itinye ihe mgbaàmà ndị a na atụmatụ Nnyocha Ndị a atụrụ arọ maka ya na ọkwa dị elu nke ntụkwasị obi na ihe mgbaàmà ndị ahụ ga-enwe uru.
WE4.3.2	Ọ bụrụ na anyị etinye akara JA4H, nke na-achịkọta omume ndị ahịa HTTP, anyị ga-enwe ike ịchọpụta ma dokọọ nkwokọ bot nke ọma.
WE4.4.1	Ọ bụrụ na anyị enwee ike imeziwanye ihe dabere na nzaghachi obere nzipu emere ma tinye Akaụntụ Nwa Oge na ọrụ niile, anyị ga-enwe ike ichekwa mkpughe nke ozi njirimara nkeonwe (adreesị IP) nke ndị ọrụ na-edeghị aha na ọrụ anyị niile ka ọ dịrị ihe na-erughị 0.1% nke ndị ọrụ niile (debara aha).
WE4.4.2	Ọ bụrụ na anyị eziruo ndị isi nsonye gasị pụtara ihe ozi doro anya ma bụrụ nke emere n'oge (nke gụnyere ngalaba otu wiki na ndị ọrụ zuru ụwa ọnụ), anyị ga-enwe ike izinye ya na wiki niile fọdụrụ, belata ibu ọrụ a chọpụtara n'oge ikpeazụ, ma zeere nlaghachị azụ ọrụ ahụ ezipurụ.
WE4.4.5	Ọ bụrụ na anyị belata esemokwu nye ndị na-eme njikwa iji mata ndị mmetọ na-eji akaụntụ nwa oge ha emebi ihe, anyị ga-enwe ike igbochi mbawanye nke mmebi ihe site n'ịtụle enweghi mmụba ọ bụla n'ogo nzighachị na wiki niile nwere akaụntụ nwa oge.
WE4.4.6	Ọ bụrụ na anyị akwúsí ngwa LiquidThreads, anyị ga-emepe akaụntụ nwa Oge etinyere na ọrụ niile na-eji ngwa a ugbu a.
WE4.6.1	Ọ bụrụ na anyị megharịa usoro akaụntụ mmekọrịta n'ime Zendesk maka mmegharị paswọọdụ, nke a ga-ebelata ibu dị na T&S wee nye ha ohere ijikwa arịrịọ mmegharị 2FA na-abata ọzọ.
WE4.6.3	Ọ bụrụ na anyị ekwe ka ndị ọrụ niile nwere adreesị ozi-e akwadoro nwee ike ịgbanye 2FA na akaụntụ ha, mana ha akọwapụtagh mgbanwe a ozugbo nye ndị ọrụ, ibu ọrụ anyị nke nkwado mweghachi ga-anọgide na-adị n'ogo dị mma.
WE4.6.4	Ọ bụrụ na anyị gaa n'ihu na-elebanya ma ọ bụ na-agbanwe mmụtara onye ọrụ anyị nke usoro 2FA anyị, ma gbakwunye nkwado maka paswọọdụ, mgbe ahụ ọtụtụ ndị ọrụ ga-edebanye aha ọtụtụ ihe nkwenye ma chebe ha nke ọma megide ihe ịma aka ekwenye mbanye.
WE4.6.5	Ọ bụrụ na anyị emepụta ma wulite usoro n'ozuzu maka ịkọwapụta ihe ndị otu mpaghara ma ọ bụ otu ndị zuru ụwa ọnụ ga-emezu, anyị ga-eji usoro a mee ka ndị otu n'ahụ maka nlebanya- na- ip akaụntụ nwa oge mezuo ihe iwu dị ugbu a chọrọ.
WE4.6.6	Ọ bụrụ na anyị mee nchọcha etu ndị ọrụ nwere ikike bụ extended Rights (UWER) si dabere na Ihe odide Onye Ọrụ, anyị ga-enwe ike ịtụ atụmatụ, nke ndị ngalaba UWER nwere ike ịkwado, iji mee otu ntinye aka teknụzụ dị mkpa ma ọ bụ karịa nke ga-echebe usoro Ihe odide Onye ọrụ.
WE4.6.7	Ọ bụrụ na anyị enyocha mmụtara onye ọrụ na mgbanwe teknụzụ achọrọ maka ngwa mkpanaka iji hazie mmụtara mbanye ekwentị na nhiwe weebụ, site na inyocha usoro ndị ọzọ dị ka OAuth, anyị nwere ike ikpebi ohere nke njikọta, na mbunuche inye ndị ọrụ nchekwa ziri ezi na nke na-adịgide.
WE4.6.8	Ọ bụrụ na anyị enyocha mmetụta nke ụdị Zendesk na MediaWiki anyị wuru na Q1, mgbe ahụ anyị nwere ike ịtụ aro maka ntinye aka teknụzụ maka ihe ndị ga-eme n'ọdịnihu nke ga-eme ka usoro mweghachị akaụntụ ndị ọzọ rụọ ọrụ nke ọma.
WE5.1.2b	Ọ bụrụ na anyị ewebata ọtụtụ ụzọ maka njirimara na nkwenye ndị nrụpụta na ngalaba API, anyị ga-enwe ike ịnye oke ogo kwesịrị ekwesị maka arịrịọ ọ bụla, site n'ịchọpụta arịrịọ ndị sitere n'aka ndị otu ndị ọrụ dị iche iche nke ọma.
WE5.1.3b	Ọ bụrụ na anyị emee mmegharị maka mmachi ogo na opekata mpe ụzọ atọ nke ngwa njịkọ REST, nke a ga-enye anyị ohere ịchọpụta ohere nke mmachi ogo n'ihe gbasara ojiji akụrụngwa eme ihe na ịkọwapụta oke mbụ nke enwere ike itinye n'ọrụ site n'iji obere mmetụta onye ọrụ.
WE5.1.4b	Ọ bụrụ na anyị akwado usoro nkewa ndị ọrụ API akwadoro site na iji data buru ibu na nyocha eji aka mee nke otu ndị achọpụtara, anyị ga-enwe ike ichịkọta otu ndị ọrụ, mezie usoro eji eme mgbakọ na mwepụ, ma ghọta id ire ha nke ọma.
WE5.1.5	Ọ bụrụ na anyị na ndị otu nhiwe MediaWiki rụkọọ ọrụ na nchọputa nkwokọ na mbelata ogo, anyị ga-enwe ike itinye mbelata ogo maka mmegharị nnwale na mmepụta, site n'ịkwado ndị otu nhiwe na imepụta ma tinye ikike a.
WE5.2.1b	Ọ bụrụ na anyị na ndị nwere ike iji REST API Explorer ọhụrụ ahụ mee ihe, nke a ga-enyere anyị aka ịchọpụta nghọta dị mkpa gbasara ojiji nke na-egosi ma mmewe ọhụrụ ahụ ọ dị mfe iji ma ọ babakwara na ụdị uche nke ndị mmepụta.
WE5.2.2b	Ọ bụrụ na anyị agafee API Action site na ngwa njikọ API etiti, anyị nwere ike ịmalite ịtụle nkwokọ na usoro ojiji mgbe niile iji nweta nghọta ga-enyere anyị aka ime mkpebi na ihe ndị a ga-eme n'ọdịnihu.
WE5.2.4	Ọ bụrụ na anyị etinye ụkpụrụ usoro ndetu maka API abụọ, anyị ga-enwe ike imeziwanye ntuziaka ọdịnaya, ghọta ihe ndị nwe API chọrọ iji nabata ntuziaka ndị ahụ, ma tulee mbọ achọrọ iji tinye ntuziaka ndị ahụ n'ọrụ n'ime akwụkwọ ndetu Wikimedia API ndị ọzọ.
WE5.2.5	Ọ bụrụ na anyị anwale ịkọwapụta na itinye iwu OpenAPI spec linting na MediaWiki REST APIs, anyị ga-egosi ụzọ isi mee ka ntuziaka ụdị API dị mma iji melite ịdị mma na nguzosi ike nke API ndị e bipụtara na Wikimedia na ngalaba otu anyị.
WE5.3.1	Ọ bụrụ na anyị agbasaa ma mee ka ntuziaka njirimara UX dị mfe mgbe a na-emelite ntuziaka ọ bụla dị adị, anyị ga-eguzobe usoro ntuziaka ka mma ga adị njikere ịnwale n'ime ma gbanwee ya ugboro ugboro iji kwadebe maka ojiji ọha na eze e be ọ dị ukwuu.
WE5.3.1b	Ọ bụrụ na anyị ebipụta ma megharịa ka ntuziaka na ngosipụta UX dị na mbụ pụta ìhè, anyị ga-eguzobe usoro dị mkpa ga adị njikere ịnwale n'ime ma mee ka ọ dị mma ugboro ugboro iji kwadebe maka ojiji ọha na eze dị ukwuu.
WE5.3.2	Ọ bụrụ na anyị emepụta ngosipụta echiche nke na-egosi uru dị n'ịkọwa Wikipedia nye ndị na-eji ọdịnaya ndị ọzọ eme ihe na ndị na-eji ha eme ihe, anyị nwere ike ịkwado WME4.1 & WME4.2 site n'inyere ma ọ dịkarịa ala otu onye mmekọ ọzọ aka ime ihe ọzọ ka o kweta ịpụta na ọmụmụ ihe gbasara njirimara ma ọ bụ ngosipụta tupu ngwụcha nke Q1.
WE5.4.2b	Ọ bụrụ na anyị ewulite ụzọ dị mfe iji chọpụta ndị ahịa a maara, anyị nwere ike ikwe ka e wepụ oke mbelata ogo izugbe maka bot ndị enyochara sitere na mmalite, ma gaa n'ihu na itinye iwu anyị n'ọrụ nke ọma.
WE5.4.5	Ọ bụrụ na anyị amalite itinye oke mbelata ogo nke ahaziri maka ụdị dị iche iche nke ndị ahịa anyị, anyị ga-ebelata ibu ọrụ nke ndọghachị azụ n'ime akụrụngwa anyị.
WE5.4.6	Ọ bụrụ na tupu ngwụcha nke Q2, anyị ekenyela n'otu N spiders kachasị elu dị ka bots a ma ama, anyị nwere ike igbochi ọnụọgụ akụrụngwa ha na-eji.
WE5.4.7	Ọ bụrụ na anyị enwee mkpebi gbasara usoro nhazi tọmbnelụ akụrụngwa mịdịa anyị akwadoro, anyị ga-enweta ndị dị oke ọnụ ma na-ahụ mbelata ogo nnweta onyonyo dị iche iche, anyị ga-ebelata ibu ọrụ n'akụrụngwa ọrụ mgbasa ozi.
WE6.1.2	Ọ bụrụ na anyị gbakwunye wikifarms na ebe ọdịdị nnwale ejikọbeghi nke ọma, nke a ga-eme ka ndị otu mmepụta mee mwulite megide nrụpụtapụta nke chọrọ otutu wiki iji nwalee patches ha iche iche na-enye ha ntụkwasị obi tupu arụpụta ihe ma mee ka enwee nsogbugbu nrụpụta dị ole na ole.
WE6.2.1	Ọ bụrụ na anyị enyochaa ma bipụta Ndepụta Njikere Mmepụta anyị nke na-akọwa nke ọma ihe ndị dị mkpa maka ọrụ a ga-ewere dị ka nke dị njikere maka mmepụta, yana ọrụ ndị a ga-arụ ọrụ onwe onye, anyị ga-ahazi atụmanya dị n'etiti SREs na ndị otu nrụpụta, ma na-eme ka arụmọrụ ọrụ anyị na nhazi ya ka mma.
WE6.2.2	Ọ bụrụ na anyị ekwupụta ịmepụta Golang na ọbá akwụkwọ nodejs na-akọwapụta ọtụtụ ọrụ siri ike nye ndị nrụpụta, ha ga-azaghachi ma nye nzaghachi na mmasị.
WE6.2.4	Ọ bụrụ na anyị etinye akwụkwọ, ma kwado nnyocha nhazi Data ndobe nke ọma, anyị nwere ike ịchọpụta ụzọ ndị e si arụpụta ihe.
WE6.3.2	Ọ bụrụ na anyị emepụta usoro metrik ọhụrụ, melite akụrụngwa nchekwa nke Parsoid, ma tinye wikipedia abụọ "kachasị elu" n'ime ha, anyị ga-emepụta usoro arụmọrụ maka usoro ntụkwasị obi, nke ga-enyere aka ịkwado njikere anyị maka izinye wiki ndị ọzọ buru ibu ma gosipụta ikike anyị nwere ijikwa nkwokọ dị elu n'ọtụtụ.
WE6.3.3	Ọ bụrụ na anyị emee mmezi nkwado Asụsụ dị oke mkpa ma tinye Parsoid nke ọma na wiki Asụsụ atọ dị iche iche na Q2, anyị ga-achọpụta ma dozie nsogbu teknụzụ dị mkpa ịtụkwasị obi iji gbasaa na wiki Asụsụ dí iche iche niile fọdụrụ.
WE6.4.6	Ọ bụrụ na SRE enye aka nye ndị otu injinia MediaWiki - site na njikwa ikike na njikwa nkwonkwo, nkwadebe na nnyocha nke mgbanwe nhazi, yana mmekorita iji nyochaa ma dozie nsogbu - anyị ga-ejikọ mejupụta mmepúta PHP 8.3 na Q2 ma dekọọ usoro ntuaka iji belata ịdabere na ụzọ dị mkpa na SRE maka mmelite n'ọdịnihu.	T360995
WE6.4.7	Ọ bụrụ na anyị ebuga ma ọ dịkarịa ala pasentị iri itoolu nke ndị ọrụ niile nwere ikike ojiji zuru ụwa ọnụ iji igodo SSH nke nwere ngwaike maka ịnweta sava nrụpụta Wikimedia, anyị ga-ebelata ihe ịma aka mmebi nchekwa laptọọpụ na-enwechaghị ogo dị elu ga-akpata.
WE6.4.8	Ọ bụrụ na Ndị otu Injinịa MediaWiki na-aganihu na nnyocha ma na-edozi nsogbu dị na MediaWiki metụtara nkwalite PHP, nke a ga-enyere ndị otu SRE aka imecha nrụpụta PHP 8.3 emelitara ka ọ na-erule Nọvemba 2025.	T360995

Signals & Data Services (SDS) Hypotheses [ Isi mpụtara SDS ] Ńkàtá
Aha mkpirisi echiche a ka ga-anwale	Ihe odide Q2	Nkọwa & Mkparịtaụka
SDS1.2.1	Ọ bụrụ na anyị ebuga usoro XML Dumps site na akụrụngwa 'Dumps 1' dị ugbu a gaa na pipeline data nke na-eji ọdịnaya MediaWiki eme ihe, anyị ga-enwe ike ikwe nkwa SLOs ma gbanyụọ mbupụ XML dabere na 'Dumps 1'.
SDS1.2.2	Ọ bụrụ na anyị emee nnyocha ma lelee SLO maka nkọwapụta ihe ndị mere nụ nke ọdịnaya MediaWiki na Event Platform/Event Gate, anyị nwere ike ịkwado ndị ahịa, metrik, na ndị nwere mmasị na ya, ma chọpụta mmezi ndị nwere ike ịdị mkpa maka SLO, nke ga-enyere anyị aka ịkọwa oghere ọ bụla dị na nkwa mwepụta anyị kwa izu.
SDS1.3.1	Ọ bụrụ na anyị ewebata akara ndị ahịa ma nyochaa ha ma e jiri ya tụnyere ndekọ webrequest nke sava, anyị ga-ahụ usoro bot ndị ọzọ enwere ike ịkọwa.
SDS1.3.2	Ọ bụrụ na anyị ewere bot dị ugbu tunyere nkesa mmadụ dị ka ntọala ma mepụta mkpọturu akpaghị aka maka mgbanwe na nkesa ahụ, anyị ga-ebelata oge nchọpụta nke usoro ọzọ a na-atụghị anya ya nke nkwonkwo aka emeghi site na izu ruo nkeji.
SDS1.3.3	Ọ bụrụ na anyị ejiri igwe akpa aka mejupụta oghere maka webrequest ma jiri ya na ndekọ Mee, anyị ga-ebelata oge ndozi maka ihe ndị ga-eme n'ọdịnihu site na ọnwa ruo ụbọchị ma dozie "mmụba na nlele peeji nke Mee" nke ihe ndị mere.
SDS1.3.4	Ọ bụrụ na anyị emepụta usoro nyocha ime nke a na-arụ ọrụ mgbe niile maka mmepụta nhazi bot, anyị ga-ewulite ntụkwasị obi na ndozi nsogbu anyị ma tụọ anya mgbanwe na nkwonkwo nke a na-anaghị achọpụta na akpaghị aka.
SDS1.3.5	Ọ bụrụ na anyị nyochaa akara ndị ahịa ma tinye ya na emem mkpebi anyị, anyị ga-achọpụta usoro bot ndị ọzọ na nhiwe Data.
SDS1.3.6	Ọ bụrụ na anyị ebubata, nyochaa ma tinye aha IP Spur.us n'ime egbom nsogbu anyị, anyị ga-achọpụta ụkpụrụ bot ndị ọzọ na nhiwe Data.
SDS1.3.7	Ọ bụrụ na anyị ebubata, nyochaa ma tinye n'ime egbom nsogbu anyị otu ihe ịgba ama site n'isi, anyị ga-achọpụta usoro bot ndị ọzọ na nhiwe Data.
SDS1.4.1	Ọ bụrụ na anyị ekwenyegharịa nnyocha dị ugbu a nke usoro dị na gburugburu ebe ọdịdị Wikimedia - echiche peeji, nha ndị na-enye aka na ndị na-agụ akwụkwọ, nkwoko, wdg - anyị ga-enwe ike iji ịtụkwasị obi ịkwado isi okwu ndị dị mkpa maka nghọta Otu anyị kachasị mkpa.
SDS1.4.2	Ọ bụrụ na anyị ekwenyegharịa nnyocha dị ugbu a nke usoro dị na gburugburu ebe ọdịdị Wikimedia - echiche peeji, nha ndị na-enye aka na ndị na-agụ akwụkwọ, nkwọkọ, wdg - anyị ga-enwe ike iji ntụkwasị obi kwado isi okwu ndị dị mkpa maka nghọta otu anyị kachasị mkpa.
SDS2.1.1	ọ bụrụ na anyị na ndị otu na-eme nnwale jikọta aka ọnụ, anyị ga-amata otú e si eme ka usoro ahụ bụrụ nke nsonye onwe onye n'ọdịnihu nakwa ihe ịma aka nchepụta ma ọ bụ nke nkà na ụzụ ha nwere ike inweta.
SDS2.1.2	Ọ bụrụ na anyị nwere ike ime nwepụ ndehie ka mma maka mbanye n'ihe omume, ndị otu nrụpụta ga-ama na nnwale ha na-anakọta data ihe omume dịka a tụrụ anya ya, na-eme ka obi sie ndị nwe nnwale ike.
SDS2.1.3	Ọ bụrụ na anyị akwalite ndekọ na nlebanya maka sistemụ nnwale A/B (xLab) mejupụtara nke nhiswe nnwale ahụ, yana maka akụkụ MediaWiki ndị metụtara ya, anyị ga-enwe ike ịtọ ntọala maka arụmọrụ sistemụ ahụ ma zaghachi ọdịda ndị metụtara nnwale ahụ.
SDS2.1.4	Ọ bụrụ na anyị kọwapụta akụkọ nnwale nakwa mpụtara na gburugburu nzukọ otu ugboro n'ọnwa (site na nzukọ Product Ops, nzukọ ndị otu mmewe, na ihe ngosi otu), mgbe ahụ anyị ga-emepụta nnabata oganik nke ebe nnwale.
SDS2.1.5	Ọ bụrụ na anyị agwa ndị ọrụ na ngwaọrụ ha, ọ bụrụ na e mepụtara ya na xLab, nwere otu njirimara nke na-agbanwe ụdị ihe egwu ahụ, anyị ga-egbochi ndị na-eji ngwaọrụ anakọta data ịnata data gabiga ókè ma mee ka o doo anya gbasara njikọta nke njirimara chọrọ nnyocha nzochi.
SDS2.1.6	Ọ bụrụ na Ndị otu nkwado uto na-arụ ọrụ na iji ngwaọrụ abụọ (otu nwere ule A/B iji nweta nghọta gbasara ikike ịwụ bọket, na nke ọzọ nwere ngwaọrụ ogologo oge iji mụta maka nkwado maka usoro ihe atụ KPI) na Experiment Lab, mgbe ahụ anyị nwere ike inyocha ma ọ na-emezu ihe achọrọ iji dochie nhazi nnwale anyị ahaziri na GrowthExperiments.

Future Audience (FA) Hypotheses [ Isi mpụtara FA ] Ńkàtá
Aha mkpirisi echiche a ka ga-anwale	Ihe odide Q2	Nkọwa & Mkparịtaụka
FA1.1.4	[malite ịga n'ihu site na FY gara aga] Ọ bụrụ na anyị ewulite mmụtara Wikipedia ọhụrụ na Roblox, anyị ga-amata ma ọ bụrụ na nke a nwere ike ịbụ ụzọ dị irè iji webata akara anyị nye ndị na-eto eto (Gen Alpha).
FA1.1.2	Ọ bụrụ na anyị ewube ebe etiti maka mmụtara Wikipedia ọhụrụ na [1], mgbe ahụ anyị ga-enwe ike ịbawanye ndị na-ege ntị karịrị mmadụ iri ise na-abụghị ndị Wikipedia nwere mmasị ga-enye anyị nzaghachi, nke ga-enyere anyị aka ịmụta ihe gara nke ọma na ihe na-agaghị nke ọma.
FA2.2.1	Ọ bụrụ na anyị etinye ego na njikwa ngalaba otu n'ebe nhiwe vidiyo dị mkpirikpi, mgbe ahụ na njedebe nke Q2 (Disemba 2025) anyị ga-ahụ mbawanye pasentị iri atọ nke QoQ na pasentị nke ndị nkiri ọhụrụ na na TikTok - na n'ofe nhiwe SFV niile, anyị ga-enweta mkpokọta ntinye aka puku iri ise (mmasị na nzaghachi okwu) na nkwupụta aha ndị fọdụrụ na ọdịnaya mpụga, nke ga-enyere anyị aka ịbawanye mpụta ihe ma kwalite njikọ aka ndị na-ege anyị ntị anyị bụ ndị anyị enwebeghị ike ịweta ka ọ dị ugbu a.
FA2.2.2	Ọ bụrụ na anyị emepụta ma wepu nkwekọrịta na atụmatụ ime nke Mmemme Mmekọrịta Ndị Okike Wikipedia na ihe ndị ọzọ dị na mpụga a na-ekesarịta (gụnyere nkọwa nke ụkpụrụ anyị nye ndị nkepụta, ụkpụrụ mmekọrịta, usoro nkwekọrịta, na otu ọdịnaya nke onye okike ga-esi pụta n'ofe ọwa ndị nwe ihe na ndị okike), anyị ga-enwe ike ịmepụta atụmatụ onye nkepụta dị mma nke ga-enyere anyị aka n'iji ọdịnaya ihe ọmụma anyị nweta ndị na-ege ntị ọhụrụ na mgbasa ozi mmekọrịta.

Product and Engineering Support (PES) Hypotheses [ Isi mpụtara PES ] Ńkàtá
Aha mkpirisi echiche a ka ga-anwale	Ederede Q2	Nkọwa & Mkparịta ụka
PES1.1.5	Ọ bụrụ na anyị etinye ebumnuche ọkwa ọrụ Service level objectives (SLOs) maka Akụkọ Ihe mere eme Ọdịnaya MediaWiki na Wikifunctions gaa na Sloth/Pyrra, anyị ga-etinye SLO abụọ ọzọ na usoro nrụpụta.
PES1.1.6	Ọ bụrụ na anyị ejiri data enwere ike ileghachị anya sitere na SLO ndị dị we malite Sloth, anyị ga-aghọta ma Pyrra ma ọ bụ Sloth (ma ọ bụ ihe ọzọ) bụ ngwaọrụ kwesịrị ekwesị maka usoro fixed-window iji dozie ihe emetaghị na mgbakọ nke mmefu ego. Anyị ga-amata otu esi akwado ndị nwe ọrụ site n'iji ụzọ nnyere aka onwe onye na metrik SLO ma jiri ha mee mkpebi.
PES1.2.4	Ọ bụrụ na anyị ehiwe usoro nnyocha nke ọchịchọ nakwa usoro nlebanya nke ndi ngalaba otu site n'iji ndị otu atọ na Q1, anyị ga-etinye ndị njikwa nrụpụta iji webata nlebanya nke ndị ngalaba otu n'ime usoro atụmatụ ha kwa ọnwa atọ nakwa kwa afọ.
PES1.2.5	Ọ bụrụ na anyị etinye ikike nzacha na nhazi ọchịchọ na ebe ntinye Ndepụta Ihe ndị achọrọ, na mmezi ndị na-enye ohere nhazi eji mkpọturu na ntuli aka, nkwalite atọ ahụ ga-eme ka enwe mmụbawanye ndị nsonye pụrụ iche na Ndepụta Ihe achọrọ site na opekata mpe pasentị iri atọ.
PES1.3.3	Ọ bụrụ na anyị emepụta ma ọ dịkarịa ala ihe ise dị mma na nhiwe nke mpụta ihe ya dabere na mmekọrịta onye ọrụ, anyị ga-akọwa ihe mkpali ibe pọtal na ngwaọrụ Ụdị Ụbọchị Ọmụmụ. Nnwale ntinye n'ọrụ ga-agwa anyị ihe ndị na-eme ka enwee njikọ dị mma na akara anyị. Echiche a nwere oge nke na ọ ga-akwụsị na WikiCon North Amerika na njedebe nke ọnwa Ọktoba.
PES1.3.4	Ọ bụrụ na anyị wepụta weebụsaịtị nyere ezi ohere nsonye nke na-enyocha akụkọ ihe mere eme Wikipedia, nke ugbu a, na nke ọdịnihu, site na nkịkọzaka ndị otu ngalaba nkwụkọrta ọha, nke ebumnuche ya bụ inye ndị na-ege ntị ohere nsonye n'ịntanetị bụ ndị nọ n'etiti afọ iri na asatọ ruọmiri atọ na anọ na mpaghara ndị a na-atụanya nlebaanya, ọ ga-eme ka enwee njikọ dị ukwuu na Wikipedia site na usoro mmekọrịta nkesa ihe na ihe omume ndị ọzọ dị n'ịntanetị. Nke a ga-enye aka ịkwalite KR ndị ngalaba nkwụkọrịta site na mmbawanye mpụta ihe akara site na pasentị iri, nke a ga-na-agwa anyị ma isoro ụzọ mbanwe ọdịnaya ọ na-eme ka inwe mmasị metụtara akara dịkwuo mma.

Q3

The third quarter (Q3) of the WMF annual plan covers January-March 2026.

Wiki Experiences (WE) Hypotheses [ WE Key Results ] Ńkàtá
Hypothesis shortname	Q3 text	Details & Discussion
WE1.1.3	If the Editing Team makes an initial set of edit suggestions available within VE via a URL parameter and invites ≥10 newcomers and patrollers to offer feedback about it, we will learn what improvements would need to be made before running a controlled experiment to evaluate the intervention's impact.
WE1.1.4	If we deploy Reference Check at en.wiki through a controlled experiment, we will see a ≥4% increase in the constructive edits new(er) volunteers publish and learn whether there is sufficient support among patrollers and moderators to enable the feature more widely.
WE1.1.12	If we enable ≥3 volunteers to evaluate ≥30 sample edits each, for each of the 10 new languages we are seeking to scale Tone Check to, we will learn how often volunteers agree with model predictions and be able to decide which new wikis to approach about deploying Tone Check to.
WE1.1.14	If we prompt new(er) volunteers to consider the tone they are writing in when an AI model detects them using non-neutral language, then we will see a ≥4% decrease in the percentage of new content edits new(er) volunteers publish that are reverted on the grounds of WP:NPOV (and related policies).
WE1.1.17	If we develop a task generation engine for the Revise Tone structured task, integrate our recent learnings about which content to include or filter out, and provide pipelines that automatically refresh the task list, we'll enable a qualitative evaluation of the tasks generated and an A/B experiment that tests whether this type of task helps newcomer editors to make more constructive edits.
WE1.1.18	If we analyze a pre-predetermined set of leading indicators ~2 weeks after the start of the Revise Tone Structured Task A/B test, we will be able to identify what – if any – facets of the end-to-end experience need to be adjusted or investigated before we can be confident evaluating the impact of the feature.
WE1.1.19	If we enable people on mobile web to edit any article section, regardless of which edit icon they first tap, then the newcomer mobile edit abandonment rate will decrease by #% because they will be able to more more easily locate the content they tapped edit seeking to change.
WE1.1.20	If the Growth team scales the “Add a Link” task to at least 10 additional Wikipedias, then we can complete leading indicator analysis to confirm that the task is performing as expected and identify any wikis that may require further review.
WE1.1.21	If we deploy the new Add-a-Link model version to both newly onboarded wikis and wikis currently using Add-a-Link, then the Growth team will be able to roll out Add-a-Link as a structured task to wikis where it did not previously exist, and wikis that already had the task will receive an updated model with fresher training data and improved offline performance.
WE1.1.22	If we improve the initial “Welcome to Wikipedia” verification email, the percentage of new accounts that verify their email address will increase. This would allow Growth to re-engage these accounts through follow up emails and ensure they receive talk page notifications.
WE1.1.23	If we prompt readily available GenAI models to generate and rank a set of edit suggestions for a diversified sample of 150 English Wikipedia articles, then we will learn what types of editing tasks these generic models can produce at scale and gain a rough, anecdotal understanding of the usefulness of these suggestions. This early signal will help us assess whether some task types could plausibly be generated at scale with generic models (with or without fine-tuning), or whether they would require more specialized approaches - ultimately helping us validate whether pursuing this "single model many suggestions" direction is worthwhile.
WE1.1.24	If we prompt new(er) volunteers pasting text from an external site to confirm whether they wrote the content they are attempting to add, then we will see a ≥4% increase in the percentage of new content edits new(er) volunteers publish that are reverted on the grounds of WP:COPYVIO (and related policies).
WE1.2.6	If we develop a goal-setting feature via Event Registration, then more collaborations will be created via Event Registration.
WE1.3.3	If we launch an experiment to surface a moderator dashboard to newer editors, 10% of contributors who visit it do so two weeks in a row.
WE1.3.4	If we deploy the Revert Risk filter to 150+ additional Wikipedias that currently lack damaging/goodfaith models, then we will see an increase in moderator patrol counts for contributors who use the personal moderator dashboard compared to those who don't get access to the dashboard.
WE1.5.1	If we implement a dashboard to explore 7 contributor metrics and standardize the calculation of at least one metric using dbt then we can enable contributor product teams to self-serve metric insights and develop a standard for storing metric calculation logic.
WE1.5.3	If Data Engineering productionizes a data product of edit types by the end of Q3, then the 6 moderator actions that are "Complicated" to measure will become "Simple", allowing Movement Insights to define a monthly active moderator metric as a next step.
WE1.5.4	If we produce a prototype dashboard with active moderator metrics that are currently available, then we will be able to produce a full dashboard by end of Q4.
WE1.6.1	If we introduce custom watchlist labels, we expect the labels to be used by 5% of users with more than 100 pages on their watchlist in the first month.
WE2.2.13	If we socialize the availability of the conjugation table function with the Wiktionary community, we will gather early signals about editor understanding and usability that can guide future improvements.
WE2.2.20	If we roll Wikifunctions out to wikis that have Parsoid enabled, we will be able to continue testing whether the system remains performant and usable in increasingly broad rollouts.
WE2.2.21	If we allow a limited set of reentrant functions in the evaluator, it will be possible to increase reliance on evaluated implementations, thereby leveraging the most performant part of the backend.
WE2.2.22	If we write an explicit interpreter for the composition language, the orchestrator will be more performant, and further performance-enhancing features will be easier to implement.
WE2.2.23	If we enable contributors to reuse whole composition blocks across functions, we will reduce repetitive work and significantly speed up the creation of new implementations, especially for similar linguistic functions.
WE2.2.24	If we define a clear documentation structure and entry points, function creators will more easily find relevant guidance and experience less friction when creating functions.
WE2.2.25	If we systematically identify the biggest friction points in the function creation experience, we can surface a small set of high-impact improvements that make creating and iterating on functions more efficient.
WE2.2.26	If we introduce a lightweight, local reference solution (via pop-ups) for Abstract Wikipedia, we can establish an initial citation mechanism to inform whether more complex solutions are necessary.
WE2.3.4	If we build and release an initial version of the Abstract Wikipedia platform, we can demonstrate the technical feasibility of the ecosystem working across multiple languages.
WE2.3.5	If we engage with a small number of under-resourced Wikipedia language communities with a concrete example of abstract content, we can validate whether content created outside their local wiki is acceptable and identify conditions needed for adoption.
WE2.3.6	If we design how Abstract Wikipedia content is surfaced and presented within local Wikipedias, and how local communities make integration decisions, we can socialize the proposal, reach agreement, and confidently plan Q4 development work.
WE2.5.1	If we deploy the Blazegraph candidate replacement in eqiad and augment existing evaluation work with production WDQS traffic-replay analysis, then we will confirm that the new backend performs comparably, supports real-world SPARQL queries, and is ready for limited live-traffic exposure once the surrounding ecosystem (UI, monitoring, onboarding, and real-time index update pipeline) is prepared.
WE2.5.2	If we define access guidelines for the Wikidata platform, we will better be able to control the load put on WDQS servers at the time of our backend migration.
WE2.5.3	If we define a cohort of user personas to test our new backend system, we will be prepared for the pilot and subsequent phases of the Blazegraph cutover.
WE2.5.4	If we produce a migration plan in a single document, we will be able to align upon and drive the execution of all aspects of the migration.
WE2.5.5	If we optimize the Wikidata Revert Risk model for low-latency inference and deploy it in a stable, scalable manner on LiftWing, then the Wikimedia Enterprise team will be able to integrate revert risk signals into their product pipeline, enabling customers to receive timely, high-quality model outputs.
WE3.1.8	If we evaluate 2 semantic search prototypes (natural language search, Q&A) with external participants, we can learn whether users see value in improved search tools, and provide the Readers teams with a recommendation on how to move forward with a search and discovery MVP.
WE3.1.12	If we collect a benchmark dataset consisting of a set of representative queries and annotations of relevant search results, we will be able to perform offline (i.e. without user studies) search evaluation of the quality of search results of different search models.
WE3.1.15	If we test hybrid search MVPs that blend keyword, natural-language, and meaning-based queries, in the Android app, we will rapidly identify the approach and design patterns that increases total search engagement by 10% without a negative impact on satisfaction, latency, or retention
WE3.1.16	If we define requirements for a Q&A model, we will produce model outputs to share with the community for feedback that we can incorporate into a production experiment.
WE3.1.17	If Search provides a production-ready (stable, performant) vector search infrastructure which supports semantic query processing — including a MediaWiki endpoint, then ML and Research will be able to generate embeddings and integrate their models with the system, enabling the MVP’s embedding-powered retrieval.
WE3.1.18	If we deploy a Qwen3 embeddings inference service that is compatible with our existing OpenSearch stack, then Mobile Apps can experiment with generating article-chunk and query embeddings through Qwen3, which should outperform the embeddings produced by OpenSearch’s built-in models.
WE3.3.6	If we make article topic inference data available via a service that meets agreed-upon scalability and availability requirements, plus any necessary data backfills, then we will have established the technical foundation necessary to support upcoming personalized reader experiences that depend on this data.
WE3.4.1	If we work towards a hybrid point of presence (PoP/CDN) deployment, it will allow us to bring up both full PoPs and mini PoPs (physical and cloud) as required, laying the foundation for a prototype mini PoP deployment in the future.
WE3.5.2	If we offer donors a badge acknowledging their status, at least 70% of donors who opt-in will report positive sentiment on a linked survey.
WE3.6.5	If Product & Technology and Fundraising collaborate on a shared strategy to diversify on-platform donation opportunities and steward and recognize readers who donate, we will set clear, aligned goals and metrics tied to our consumer and fundraising strategies.
WE3.6.6	If we develop a unified measurement strategy, we will enable evaluation of the consumers’ multi-year strategy and define a roadmap to guide metric development and reporting capabilities.
WE3.6.7	If we run a secondary A/A test on retention rate for logged-out users, we will begin to establish seasonal trends for retention rate that we can use for future quarters
WE3.6.8	If we systematically compare the information seeking needs and behaviors of 1) new and infrequent English Wikipedia readers and non-readers with those of 2) current English Wikipedia readers by simultaneously surveying both populations, we will be able to identify key insights about the demographic characteristics, online information needs, and online platforms used by these audiences, identifying potential opportunities for growing our readership as well as potential threats to our existing readership.
WE3.8.1	If we add additional modules to the activity tab and scale it to all users, we’ll see a 5% increase in overall iOS app account creation compared to before release
WE3.9.1	If we update the Wikipedia iOS app’s visual foundations, component defaults, and navigation behaviours to align with Apple’s Liquid Glass design language—while clearly defining design and behaviour for high-priority fallbacks across OS versions—then downstream engineering implementation will be clearer, lower-risk, and the app will feel more platform-native when Apple forces user switchover
WE3.10.1	If we user test a design prototype of the Explore Feed built with lightweight personalization, clear freshness cues, and repeatable Wikipedia-native patterns (e.g., topical rabbit holes, time-bound highlights, and interactive elements), casual reader participants will report understanding of the proposed feed’s purpose, reach an “aha” moment faster, and show a stronger intent for daily use. The visual design we recommend moving forward with will be described as digestible and aligned with the Wikimedia movement.
WE4.1.3	If we update 6 Wikipedias (English, French, German, Spanish, Italian, Polish) by the end of Q2, we will complete phase 1 of the new Legal Footer roll-out in response to DSA requirements.
WE4.2.5	If we conduct research, consult with communities, and investigate technical solutions, we will be able to define a set of structured block reasons that can be used across all WMF wikis.
WE4.6.8	If we monitor the impact of the Zendesk and MediaWiki forms we built in Q1, then we can propose technical interventions for future quarters that would better automate the rest of the account recovery process.
WE5.1.3c	If we roll out rate limiting to all APIs routed through the gateway, we will be able to reduce the number of anonymous API requests that reach our backend infrastructure, by limiting requests based on user classes that give higher limits to authenticated and community users.
WE5.1.5	If we improve the sustainability and reliability of our OAuth 2.0 implementation, we will be able to convince more developers to use OAuth and support the migration of the Wikipedia mobile apps, by increasing confidence that it can be relied upon for high profile applications.
WE5.1.5b	If we improve the developer experience for creating and managing OAuth 2.0 clients, we will be able to increase the number of applications that use OAuth 2.0, by deprecating OAuth 1.0 for new applications and promoting OAuth 2.0 as the preferred option for API authentication.
WE5.2.2c	If we reroute all APIs currently going through the API Gateway through the common gateway, we will be able to fully deprecate the historic API Gateway and ensure consistency for all community facing Wikimedia HTTP/web APIs
WE5.2.6	If we introduce access controls to the sitemap API to only allow trusted bots, we can improve strategic alignment and reduce the risk of abusive scraping.
WE5.2.8	If we create dedicated API modules for all API extensions and self-contained functionality within MediaWiki Core, we will enable enforcement for higher quality documentation with independent management and versioning.
WE5.2.9	If we have better separation of concerns for API registration and spec definition processes, we will simplify the complexity of API management and create a better developer experience for API authors.
WE5.2.10	If we conduct a listening tour specifically focused on v2 API consolidation and feature gaps, we will be able to move more quickly with a v2 implementation.
WE5.2.11	If we experiment and establish processes for standardizing documentation currently in the API Portal, we can consolidate sources of information and improve documentation consistency.
WE5.3.1b	If we publish and iterate on the draft UX guidelines and demos, we will establish a core framework ready to be internally tested and iteratively refined to be prepared for broader public use.
WE5.3.2b	If we establish a partner pilot program to experiment with Wikimedia attribution, we can show mutually beneficial impacts of attribution by reusers to drive engagement and contributions to Wikimedia.
WE5.4.6	If by the end of Q2 we've classified the top N spiders as known bots, we can constrain the amount of resources they're using.
WE5.4.8	If we start enforcing rate limits tailored to different classes of individual clients for media files, we will reduce the burden of crawling on our infrastructure.
WE5.4.9	If we add ip-reputation data on residential proxies to our bot detection, it will improve our ability to block scrapers
WE5.4.10	If we stop allowing generation of non-standard thumbnail sizes, it will reduce the strain on our backend media serving infrastructure
WE5.4.11	If we identify two high-confidence signals from the December 2025 scraping/DDoS incidents and deliver them to SRE, then SRE will be able to develop more effective automated mitigation rules for future similar incidents.
WE5.4.12	If we are able to attest where and from whom a request for an image is originated, we can make more informed decisions about rate-limiting them
WE6.1.2	If we add wikifarms to a pre-merge testing environment this will enable development teams building against production who require multiple wikis to test their patches in isolation giving them greater pre-production confidence and result in fewer defect escapes.
WE6.1.3	If we resolve the top 5 flaky Selenium tests over the next 90 days, we will increase developer confidence in automated browser testing as a valuable part of the pre-deployment workflow.
WE6.2.4	If we apply, and actively support the Data Persistence design review, we may identify paved pathways to production
WE6.2.5	By collaborating with the SLO group and gathering feedback from teams currently working with them, will help us not only identify gaps and improvements for the Production Readiness checklist, but also adapt it in such a way to directly facilitate SLO adoption and onboarding
WE6.2.6	By piloting the production readiness checklist on the hCaptcha proxy service against launch and high-importance items, we will identify untracked technical debt and create a visible work backlog, which will demonstrate the framework's value, while shaping a repeatable process for consistent adoption across services.
WE6.2.7	By collaboratively refining the production readiness checklist with SRE input and contributions, we will ensure it addresses real operational needs, build shared ownership, and create a practical tool that teams see value in adopting.
WE6.2.8	By analysing feedback from our collaboration with the SLO group, hCaptcha proxy pilot, and SRE collaboration, we will identify which checklist items and process steps require clearer guidance or supporting resources, and determine the next steps for streamlining the framework to make adoption achievable before the December break.
WE6.2.9	If we adopt node.js service-utils, we will be able to release, in a tested way, either of: service-mesh routing or OpenTelemetry publishing.
WE6.3.2	If we develop new metrics, improve Parsoid's cache infrastructure, and deploy on two "top-ten" wikipedias, we will develop performance criteria for the confidence framework, which will help validate our readiness for deployment to other large wikis and demonstrate our ability to handle high-traffic volumes at scale.
WE6.3.3	If we implement critical Language Variant support improvements and successfully deploy Parsoid to at least 3 Language Variant wikis in Q2, we will identify and resolve the key technical challenges necessary to confidently roll out to all remaining Language Variant wikis.
WE6.3.4	If we QA, identify, triage, and fix bugs within the reading experience across platforms for Parsoid Read Views, we will maintain the quality of the reading experience and unblock further scaling across wikis
WE6.3.5	If we target a Time To First Byte (TTFB) metric of <= 110% of legacy for parser cache hits and <= 150% of legacy for parser cache misses, we can deploy to all Wikipedias except for Chinese and English by the end of Q3 with no performance complaints. This will help validate our readiness for deployment on English Wikipedia, preparing us to handle high-traffic volumes at scale by understanding our cache capacity.
WE6.4.4	If we unify our domains by serving all page views on MediaWiki sites through a canonical domain, then we will reduce platform complexity and SEO risks by eliminating the mobile-subdomain redirect. Completion is measured by decreasing redirects for mobile visits on canonical domains from 100% to 0%.
WE6.4.7	If we migrate at least 90% of all users with global root access to use a hardware-backed SSH key for accessing Wikimedia production servers, we will reduce the risk that a compromised laptop will cause a severe security breach.
WE6.4.8	If the MediaWiki Engineering Team actively monitors and fixes issues in MediaWiki related to the PHP upgrade, this will enable the SRE team to complete the production PHP 8.3 upgrade by November 2025.

Signals & Data Services (SDS) Hypotheses [ SDS Key Results ] Ńkàtá
Hypothesis shortname	Q3 text	Details & Discussion
SDS1.5.1	If we create a regular, operationalized internal audit process for bot classification outputs, we will build trust in our solutions and anticipate changes in traffic patterns that are not automatically detected.
SDS1.5.2	If we deploy a Test Kitchen instrument with 100% sampling and a request identifier, we'll be able to capture all requests regardless of whether or not they sent client-side events and analyze them for bot behavior.
SDS1.5.3	If we analyze the basic client-side signal and incorporate it into our heuristics, we will detect additional bot patterns in the Data Platform.
SDS2.2.4	If Product Safety and Integrity reviews GrowthBook and FerretDB, we will be able to identify, then mitigate and/or address any material risks before production rollout.
SDS2.2.5	If we update Test Kitchen JS and PHP SDKs with methods to log experiment exposure, we will not need to treat all events as exposure events, which will improve performance of experiment assignment queries in GrowthBook and yield more accurate experiment results.
SDS2.4.2	If we safely expand traffic enrolment through monitored stress testing, we will increase statistical power for Reader team experiments, shortening experiment timelines and improving decision confidence.

Future Audience (FA) Hypotheses [ FA Key Results ] Ńkàtá
Hypothesis shortname	Q3 text	Details & Discussion
FA1.1.1	If we build a central hub for new Wikipedia experiences on itch.io, then we’ll be able to grow an audience of >50 interested non-Wikipedians giving us feedback, which will help us learn what works and what doesn’t in games.
FA1.1.2	If we create and test 3 different app-based content discovery features as short experiments, we can share recommendations with the Apps team about how to effectively engage with and retain a new generation of apps users
FA1.1.3	If we create 4 TikTok filters and publish them on our TikTok account, we will be able to learn how well we can incentivize creation of Wikipedia content off-platform.
FA1.1.6	If we create 3 different potential previews for Wikipedia links shared on Discord, we can align internally on our measurement and execution strategy and collaborate with Discord's ProdEng team.
FA2.2.1	If we invest in community management across short-video platforms, then by the end of Q2 (December 2025) we will see a 30% QoQ increase in the percentage of views from New Viewers on TikTok — and across all SFV platforms, we’ll earn 50,000 total engagements (likes and reply comments) on brand comments left on external content, which will help us increase visibility and drive engagement with audiences we’re not currently reaching.
FA2.2.2	If we develop and get sign-off on a Wikipedia Creator Partnerships Program internal strategy and external shareables (inclusive of an outline of our value to creators, partnership criteria, contracting process, and how creator content will show up across owned and creator channels), we will be able to establish a robust creator strategy that will help us reach new audiences across social media with our knowledge content.

Product and Engineering Support (PES) Hypotheses [ PES Key Results ] Ńkàtá
Hypothesis shortname	Q3 text	Details & Discussion
PES1.3.5	If we build community configuration for Easter Eggs, and leverage Reader Experience full time for code review, we’ll be able to launch on Feb 15 and track impact of the project.
PES1.3.6	If we create bespoke social media posts for 25 Years of Wikipedia (25YoW), we can achieve similar success with social impressions as Comms’ existing templates, and prove we can support Comms’ by increasing their capacity. Benchmarking will be off of Truth Collective posts about the same project.
PES1.4.1	If we meet with 4-5 teams that are not primarily using Codex (including, but not limited to, teams primarily using OOUI), we will be able to document blockers to those teams adopting Codex for current and future projects.
PES1.4.2	If we make Codex PHP easier to use and stable enough to do a 1.0 release, then teams will adopt Codex PHP for server-generated UIs. This will increase Codex PHP usage from 3 projects to 5.
PES1.5.1	If we upgrade Sloth from a prototype to a replacement for Pyrra, by onboarding existing services, we can converge on a unified set of SLO dashboards, refine our alerts, and streamline the SLO onboarding experience.
PES1.5.2	If we continue to onboard SLI metrics for Wikifunctions and MediaWiki Content History, and check metrics for WikiData Query Service and EditCheck, we will debug issues with both dashboard reporting and service-owner engagement on loud-but-unaddressed SLO reports.

Q4

The fourth quarter (Q4) of the WMF annual plan covers April-June 2026.

Wiki Experiences (WE) Hypotheses [ WE Key Results ] Ńkàtá
Hypothesis shortname	Q4 text	Details & Discussion
WE1.3.3	If we launch an experiment to surface a moderator dashboard to newer editors, 10% of contributors who visit it do so two weeks in a row.
WE1.5.1a	If we add new metrics to the Contributors dashboard using DBT we will enable contributor product teams to get more actionable insights into editing trends
WE1.5.5	If we automate updates of DBT models with Airflow on a fixed schedule, we will allow Movement Insights to build analyses and reports on those models using up-to-date information.
WE1.5.6	If we document workflows and establish per-team default output locations and access controls for dbt models, we will allow dbt usage to scale across team members in Movement Insights and other teams.
WE1.5.7	If we extend the MAM dashboard as specified, then we will have a more complete picture of moderator pipeline health that can inform decisions in the Contributor strategy.
WE1.6.1	If we introduce custom watchlist labels, we expect the labels to be used by 5% of users with more than 100 pages on their watchlist in the first month.
WE1.7.2	By testing a design prototype of the in-app webview editing flow with newcomers on the mobile apps, we will identify the highest-risk friction points that could cause abandonment before publishing - specifically around context shift, editor comprehension, and edit attribution - so that we can prioritize what must be resolved before this approach can successfully onboard new editors at scale.
WE1.7.4	If we prompt readily available GenAI models to generate and rank a set of edit suggestions for a diversified sample of 150 English Wikipedia articles, then we will learn what types of editing tasks these generic models can produce at scale and gain a rough, anecdotal understanding of the usefulness of these suggestions. This early signal will help us assess whether some task types could plausibly be generated at scale with generic models (with or without fine-tuning), or whether they would require more specialized approaches - ultimately helping us validate whether pursuing this "single model many suggestions" direction is worthwhile.
WE1.7.5	If we replace the unstructured Copyedit task with the Revise Tone structured task in a controlled experiment, then the task completion rate for Revise Tone edits made by junior editors will be higher than the completion rate for edits made through the Copyedit task.
WE1.7.6	If we test 2 structured workflows via design prototypes that align contribution opportunities with what readers arrive motivated to learn, and frame contributions as ways to act on curiosity or share knowledge they already possess, then concept testing will help us identify approaches that inspire readers to imagine themselves as contributors.
WE1.7.7	If we present an exit survey to users with ≤100 cumulative edits who abandon mobile editing sessions after spending ≥2 seconds in either the mobile visual or source editor, we will discover patterns in the reasons behind this behavior and be able to decide what interventions we will prioritize to increase the mobile web edit completion rate.
WE1.7.8	If we present junior contributors who enter the mobile VisualEditor with immediately actionable edit suggestions, then the proportion of edit sessions that result in someone publishing a constructive edit will increase by ≥10%.
WE1.7.9	If we publish a proposal on-wiki to enable VisualEditor by default for newcomers on mobile web at en.wiki, we will define the steps needed to make this happen.
WE1.8.2	If we improve the logged out edit warning, the account creation rate among newcomers on mobile exposed to the warning will increase by at least 2% relative to the control group.
WE1.8.3	If we improve the account creation form, the registration completion rate among mobile users who land on the form will increase by at least 2% relative to the control group.
WE1.8.4	If we add a user account button to the header on mobile web, then the number of new accounts created on mobile will increase by at least 2% relative to the control group.
WE1.8.5	If we surface Reading Lists to logged-out readers on mobile web, the registration rate will increase by at least 2% relative to the control group.
WE1.9.2	If we introduce key questions about factors that impact editor motivations [i] into the 2026 Community Insights survey, by the end of Q4 we will be able to provide ≥5 research insights which can inform our Contributors strategy implementation, with a focus on editor progression and recognition. [i] Defined through the lens of competence (ability to use tools and resources), autonomy (ability to navigate the platform and make informed decisions), and relatedness (ability to join the community, feel supported and valued).
WE1.9.3	If we co-design guidance for mobile-first onboarding with at least two affiliates, by the end of June we will be able to identify which gaps are perceived by organizers as most detrimental to the retention of mobile-first newcomers.
WE1.10.3	If we work with a few selected communities to customize the Article Guidance workflow for their wikis, editors will get guidance that’s tailored to each community’s content and policies.
WE1.10.5	If we complete all path-to-production requirements for the Article Guidance A/B test (including security review, legal consultation, instrumentation, community outline review and translation, and experiment configuration) we will launch the experiment for it to run to completion before the end of Q4 and start generating statistically meaningful data on the impact of Article Guidance on the 30-day survival rate of articles created by junior editors.
WE2.2.13	If we socialize the availability of the conjugation table function with the Wiktionary community, we will gather early signals about editor understanding and usability that can guide future improvements.
WE2.3.7	If we identify and address small but frequent friction points in contribution workflows together with early contributors, we can support and sustain the engaged core community that is building the foundational building blocks for Abstract Wikipedia.
WE2.3.8	If we implement the capabilities for article-level opt-in integration of Abstract Wikipedia content into local Wikipedias, we can demonstrate how the model works in practice and be ready to engage pilot communities in Q1.
WE2.3.9	If we implement basic instrumentation for Abstract Wikipedia (in addition to existing MediaWiki instrumentation), we will better understand how users interact with Abstract Wikipedia and identify areas for improvement.
WE2.3.10	If we support displaying images from Commons in Abstract articles via Wikifunctions, we enable communities to incorporate visual elements commonly used in Wikipedia articles.
WE2.3.13	If we build an analytical capability to map content availability, production, and consumption against a flexible topic taxonomy, then we'll have greater visibility to content trends that we can derive insights from.
WE2.3.16	If the Rust evaluator is brought to feature parity with the Node evaluator and the Node evaluator phased out, we will eliminate a huge maintenance burden and free up engineering resources.
WE2.5.4	If we produce a migration plan in a single document, we will be able to align upon and drive the execution of all aspects of the migration.
WE2.5.6	If we develop the capability to automate output comparison between WDQS v1 and v2 for selected sets of queries, we will be able to improve confidence in the correctness of the new service.
WE2.5.7	If we assemble a plan for messaging about our migration, we will experience a smoother transition to the new endpoints by aligning with the Wikidata community on when to expect changes and how to prepare for them.
WE2.5.8	If we start to build the decoupled architecture described in our design doc, we should be able incrementally deliver value throughout the quarter and stand by a service that is able to support the pilot cohort by FY27 Q1.
WE3.1.15	If we test hybrid search MVPs that blend keyword, natural-language, and meaning-based queries, in the Android app, we will rapidly identify the approach and design patterns that increases total search engagement by 10% without a negative impact on satisfaction, latency, or retention.
WE3.1.16	If we define requirements for a Q&A model, we will produce model outputs to share with the community for feedback that we can incorporate into a production experiment.
WE3.1.17	If Search provides a production-ready (stable, performant) vector search infrastructure which supports semantic query processing — including a MediaWiki endpoint, then ML and Research will be able to generate embeddings and integrate their models with the system, enabling the MVP’s embedding-powered retrieval.
WE3.3.6	If we make article topic inference data available via a service that meets agreed-upon scalability and availability requirements, plus any necessary data backfills, then we will have established the technical foundation necessary to support upcoming personalized reader experiences that depend on this data.
WE3.4.1	If we work towards a hybrid point of presence (PoP/CDN) deployment, it will allow us to bring up both full PoPs and mini PoPs (physical and cloud) as required, laying the foundation for a prototype mini PoP deployment in the future.
WE3.5.3	If we implement consent-based donor segment storage in MediaWiki and establish a reliable CiviCRM → MW sync, Product and Fundraising will be able to successfully identify and differentiate donor segments on-platform across web and apps by the end of the quarter.
WE3.5.5	If we offer logged-out donors on mobile web a persistent Thank You badge by default that triggers a brief delightful interaction upon tap (C), then we will see a 1% improvement in 21-day cumulative retention rate compared to having a badge with a simple popover message (B), and users who do not get a badge at all post-donation (A). We will also track whether a larger percentage of donors in group (C) tap on the badge again in at least one future return session compared to those in (B), to inform whether playful interactions create a re-engagement loop.
WE3.6.9	If we coordinate across identified stakeholders, we will gather the necessary information on development needs, dependencies, and timeline to create a decision brief on the consumer strategy measurement plan that gets signed off by all relevant stakeholders.
WE3.6.10	If we run an A/A test on retention rate for logged-in readers, we will establish a baseline for retention rate we can use for future quarters.
WE3.6.11	If we analyze existing data to see what user factors or actions correlate with retention, we can document our understanding of what product interventions/levers might increase retention.
WE3.6.12	If we experiment with measuring whether increasing the amount of translated Wikipedia content leads to a direct increase in pageviews, we will be able to make a recommendation on future strategic investment into translations.
WE3.7.2	If we roll out the new “heart” donate button design to all projects with a header donate link on desktop, based on the positive results of the previous clickthrough rate experiment, the total number of donations coming from that entry point will not decrease.
WE3.8.2	If we provide reading lists on web as an opt-in beta feature, as well as opt-in all new user accounts, at least 50% of users will rate the feature as useful when surveyed.
WE3.8.3	If we add a temporary 25th Birthday reading challenge widget on the apps that motivates users to meet a reading goal, we'll see a 5% higher conversion rate from casual (2-week return) to active (2-day returned) for users who joined the challenge in the first 14 days, compared to our baseline of 16.8%.
WE3.9.3	If we widely roll out the image browsing carousel and detail view on mobile web, then we will maintain CTR > 5% on the carousel.
WE3.9.4	If we test the addition of the “Which came first” daily-play trivia game to the iOS App, we’ll see 15% of engaged logged-out readers return to play the game on multiple days.
WE3.10.3	If we show readers several concepts for traversing the knowledge network on the wikis, we will come away with a prioritized list of concepts for further development.
WE3.10.5	If we give readers an enhanced sharing option then [33%] of readers who see the dialog will complete the enhanced share action without harming logged-out reader retention.
WE3.10.6	If we redesign the mobile apps Explore Feed, we'll see a 10% practically significant increase in Explore Feed engagement over multiple days per unique logged-out reader within 14 days of release.
WE3.10.7	If we identify the most frequent and important unmet needs for casual users (as rated by them) as well as which early-stage concept ideas are most desirable and useful, we will be able to prioritize which concepts to put into A/B testing in FY26-27 to give us the best shot at increasing retention.
WE3.10.8	If we test adding Page Previews on Minerva, we will see practically significant improvement to logged-out reader retention.
WE4.6.13	If we encourage users with an unconfirmed email address to confirm their email address, then 10% of active users who receive this notification will attempt to confirm their email address.
WE4.6.16	If we build support for enforcing group membership restrictions on global groups, we will be able to use this to enforce a 2FA requirement for global groups.
WE4.6.17	If we announce and enforce 2FA requirements for an additional set of groups, the number of users who have sensitive rights but don't have 2FA will drop to 0.
WE4.6.18	If we build support for enforcing 2FA on private wikis, we will be able to use this to secure user accounts who have access to private information.
WE4.6.19	If we require reauthentication for sensitive actions and implement other hardening measures, we will be less vulnerable to an attacker exploiting user scripts.
WE4.6.20	If we proactively scan user scripts for malicious code, we will be less vulnerable to an attacker exploiting user scripts.
WE4.6.21	If we reduce the number of staff accounts with advanced rights and how long they have those rights for, attacks targeting staff accounts are less likely to cause damage.
WE4.8.1	If we introduce a mechanism that detects and surfaces related temporary accounts, including a connected-accounts panel on Special:Contributions, then patrollers will identify abusive temporary-account activity faster and more accurately.
WE4.8.3	If we investigate recent complaints about patrolling taking longer than before, we would be able to determine whether or not they are correlated with the introduction of Temporary Accounts.
WE4.10.1	If we roll out hCaptcha to more wikis (Special:CreateAccount, wikitext editor on desktop) in stages, we will increase bot detection coverage across all projects.
WE4.10.2	If we deploy the hCaptcha bot detection in the visual editing, discussion tools, file upload wizard, and mobile editing pathways on pilot wikis, we will see an increase of 35% in the percentage of actions on Wikimedia Foundation wikis that were verified by hCaptcha.
WE4.10.3	If we deploy an hCaptcha risk score collection for blocked edit notices, we will better understand collateral damage impacts of IP range blocks.
WE4.11.1	If we roll out the Incident Reporting System (IRS) on enwiki through a staged deployment, starting small and scaling to at least 50% of logged-in users, then we will have enough data from our newly instrumented metrics to determine if IRS should be adopted on enwiki.
WE4.12.1	If we optimize and compare the performance of at least 2 content policy models with a community-vetted dataset of oversighted (WP:Oversight) content from English Wikipedia, we will be able to recommend a model or combination of models for automatic oversighting or flagging of content that very likely should be oversighted.
WE4.12.2	If we host the gpt-oss-safeguard-20b, CoPE-A-9B, and CoPE-b-12b models on LiftWing and optimize each model's performance to meet the PSI team's initial evaluation requirements, then the PSI team will be able to test the three models and compare their behavior.
WE5.1.3e	If we create a data product for analysis of API requests, we'll simplify analysis and segmentation of API requests, and allow Product Analytics to prototype the API Analytics Dashboard by further segmenting and aggregating this new data.
WE5.1.3f	If we reduce API rate limits for requests that only provide a User-Agent, we will increase the number of authenticated and known clients, by encouraging UA-only users to move to a more responsible pathway.
WE5.2.2c	If we reroute all APIs currently going through the API Gateway through the common gateway, we will be able to fully deprecate the historic API Gateway and ensure consistency for all community facing Wikimedia HTTP/web APIs
WE5.2.5b	If we finalize the linter architecture and a core set of linting rules, we can improve the quality of OpenAPI descriptions and start introducing programmatic guarantees of their compliance into CI workflows in API development processes.
WE5.2.8b	If we onboard at least 3 more API modules demonstrating a variety of use cases (at least 1 stand-alone service API, 1 feature team owned extension API, 1 internal module), we will be able to refine usability and set the foundation for the future of the API Platform.
WE5.2.11b	If we complete API Portal documentation migration, we will be able to fully deprecate the API Portal system, which will simplify API documentation discovery, increase documentation consistency, and reduce the number of API support systems.
WE5.2.12	If we iterate on the design and discovery work required for building the unified front-door for developers early, we will be able to effectively expedite engineering work in FY26-27.
WE5.2.13	If we begin enforcing existing user-agent policies on the dumps website, we can better understand dumps users and use cases while ensuring more consistent access expectations across developer pathways.
WE5.3.2b	If we establish a partner pilot program to experiment with Wikimedia attribution, we can show mutually beneficial impacts of attribution by reusers to drive engagement and contributions to Wikimedia.
WE5.3.3	If we provide content reusers with canonical, low-friction retrieval paths, we will lower the effort required to adopt trust signals from Wikimedia’s Attribution Framework, avoid the standardization of suboptimal retrieval paths, and unlock our ability to reliably measure attribution adoption.
WE5.3.3b	If we build a data pipeline to compute unique contributor counts and develop a method to serve it to MediaWiki, we will be able to surface contributor counts as a signal to Attribution API users.
WE5.3.3c	If we design and implement an API and event stream offering a naive, pageviews-based "relative trending" metric for pages, we will allow the Attribution API to use it as a Trust & Engagement metric and enable prototyping for the mobile apps.
WE5.3.4	If we define “qualified traffic” (specific outcomes of interest) and distinguish referrers by platform and surface, then we will observe systematic differences in downstream engagement and contribution outcomes that are relevant for product and partnership decisions.
WE5.3.4b	If we establish a consistent set of traffic health indicators, then we can reliably detect and explain meaningful shifts in Wikimedia’s incoming traffic over time and by referrer beyond raw pageview counts.
WE5.3.4c	If we analyze real-world visibility shocks as natural experiments, then we will see measurable changes in content quality and maintenance outcomes, supporting the relationship between visibility and content health.
WE5.4.2c	If we can identify the official Wikimedia mobile apps on iOS and Android in the CDN using their application-layer fingerprints, we can add them as a known-client in requestctl, allowing us to set exceptions and custom rate-limits on them to ensure a smooth user experience.
WE5.4.8b	If we rate limit requests for multimedia files based on a tally of response sizes, we will increase fairness in allocating the available bandwidth to users.
WE5.4.10	If we stop allowing generation of non-standard thumbnail sizes, it will reduce the strain on our backend media serving infrastructure.
WE5.4.12	If we create signals to distinguish on-site media traffic from external reuse, SRE can prioritize on-site traffic when under load by rate-limiting expensive media requests from external sources.
WE5.4.12b	If we can get the image provenance information from MediaWiki, we can use that in conjunction with other identifiers such as referrers and the assigned X-Is-Browser score to relax or vary the rate-limits for on-wiki users.
WE5.4.13	If we establish regular processing and reporting of WE5 objective indicator metrics (request rate, bandwidth, and User-Agent compliance), it will help us track progress across the KRs and assess the effectiveness of blocking high-volume scraping and enforcing the User-Agent policy.
WE5.4.14	If we make our WAF software less tied to our CDN infrastructure, it will be able to serve more use-cases, internal or not.
WE5.4.15	If we can identify and categorize known large-scale NATs containing wiki users, we can use this information to fine-tune ratelimits and other defenses more-strictly on a per-IP level to blunt some of the impact of scraping.
WE5.4.16	If we create a threat model for selected operators of automated traffic, including their intentions, level of investment and techniques employed, we will be able to advance the SRE teams’ ability to identify scrapers and other actors.
WE5.4.17	If we can do near-real time analysis on webrequest traffic to detect one kind of persistent scraping campaign, it will allow us to export rules from the data lake that we can push out to the edge automatically to block/throttle that scraping.
WE6.3.3	If we implement critical Language Variant support improvements and successfully deploy Parsoid to at least 3 Language Variant wikis in Q2, we will identify and resolve the key technical challenges necessary to confidently roll out to all remaining Language Variant wikis.
WE6.5.1	If we investigate and audit the performance concerns and catalog the content-caching fragility for logged-in users, informed by the Readers' strategy for readership retention, we can plan ST6.4 confidently with the right prioritisation framework by observing which cataloged concerns deliver the most value.
WE6.5.2	If we experiment with a platform for cross-wiki code collaboration, testing on at least 4 Indian communities, we will learn how to empower community developers to reuse modules across wikis, and receive positive feedback of these findings at the Hackathon the first week of May. This will be scoped to Lua Modules only, and integrated with WMF's GitLab infrastructure.
WE6.6.1	If we reduce the runtime of the Browser Test jobs to under 10 minutes, we will remove them as the bottleneck and reduce the feedback time of the MW Core Main test build.
WE6.6.2	If we reduce the runtime of the Unit Test jobs to under 10 minutes, we will remove them as the bottleneck and reduce the feedback time of the MW Core Main test build.

Signals & Data Services (SDS) Hypotheses [ SDS Key Results ] Ńkàtá
Hypothesis shortname	Q4 text	Details & Discussion
SDS1.5.1	If we create a regular, operationalized internal audit process for bot classification outputs, we will build trust in our solutions and anticipate changes in traffic patterns that are not automatically detected.
SDS1.5.2	If we deploy a Test Kitchen instrument with 100% sampling and a request identifier, we'll be able to capture all requests regardless of whether or not they sent client-side events and analyze them for bot behavior.
SDS1.5.3	If we analyze the basic client-side signal and incorporate it into our heuristics, we will detect additional bot patterns in the Data Platform.
SDS1.5.4	If we move our heuristics to a private repository, we will protect the exact logic and data sources we use from motivated attackers.
SDS1.5.5	If we add a numerical score to our pageview metric based on 2 of the signals we’ve evaluated, we will provide a more complete and nuanced view of our traffic.
SDS1.5.6	If we use hCaptcha scores collected as events on account creation and edits as trusted labels, we’ll be able to correlate them to signals and our current bot detection heuristics to evaluate both.
SDS2.2.4	If Product Safety and Integrity reviews GrowthBook and FerretDB, we will be able to identify, then mitigate and/or address any material risks before production rollout.
SDS2.2.5	If we update Test Kitchen JS and PHP SDKs with methods to log experiment exposure, we will not need to treat all events as exposure events, which will improve performance of experiment assignment queries in GrowthBook and yield more accurate experiment results.
SDS2.2.8	If we conduct end-to-end user acceptance testing (UAT) with analyzing Reader Growth’s upcoming A/B/C test exclusively in GrowthBook, then we will learn which aspects of the experience meet production-readiness standards and which need improvement and supporting content before wider rollout.
SDS2.3.1	If we validate and adapt GrowthBook's API to our own, we can use GrowthBook as the canonical experiment control plane, and if we regularly poll GrowthBook's API, we can deliver experiments configured in GrowthBook quickly and easily.
SDS2.3.2	If we implement custom validation in GrowthBook, we’ll know people can’t accidentally misconfigure experiments in ways that violate the constraints of the platform.
SDS2.3.3	If we confirm role-based needs for GrowthBook users (humans, automation scripts from WMF, affiliates with established trust, and prospectively, vetted NDA end users), ensure automatic de-provisioning of stale users with regular report back to DPE, and update documentation to describe the role-based approach and user expectations regarding permissions and system interaction, user onboarding will be more predictable, and we'll have some additional guards to avoid exceeding our paid licensed seating.
SDS2.4.2	If we safely expand traffic enrolment through monitored stress testing, we will increase statistical power for Reader team experiments, shortening experiment timelines and improving decision confidence.
SDS2.4.3	If we introduce support for non-cache splitting experiments, then teams will be able to test JS-only features beyond the current traffic caps (10% all wikis, 0.1% enwiki), removing one of the primary barriers preventing teams from adopting Test Kitchen.

Product and Engineering Support (PES) Hypotheses [ PES Key Results ] Ńkàtá
Hypothesis shortname	Q4 text	Details & Discussion
PES1.4.2	If we make Codex PHP easier to use and stable enough to do a 1.0 release, then teams will adopt Codex PHP for server-generated UIs. This will increase Codex PHP usage from 3 projects to 5.
PES1.5.3	If we enable SLO-based alerting, and make it possible to generate reports quarterly and automatically, service owners will be able to respond to service reliability data without (always) needing SRE to initiate.
PES1.5.4	If we set expectations with SRE Ambassadors about roles and responsibilities for SLOs and production readiness in FY26-27, onboard TPgMs and Objective owners to those expectations, and define how the SLO working group will operate, we will achieve buy-in and scalability for “business as usual” SLO and production readiness work starting in Q1.
PES1.6.2	If we onboard directors to the Service Catalog, they will populate it with "obvious" candidates, and we will identify and find owners for at least 2 more critical unowned services.

Future Audience (FA) Hypotheses [ FA Key Results ] Ńkàtá
Hypothesis shortname	Q4 text	Details & Discussion
FA1.1.2	If we create and test 3 different app-based content discovery features as short experiments, we can share recommendations with the Apps team about how to effectively engage with and retain a new generation of apps users
FA1.1.6	If we create 3 different potential previews for Wikipedia links shared on Discord, we can align internally on our measurement and execution strategy and collaborate with Discord's ProdEng team.
FA2.2.1	If we invest in community management across short-video platforms, then by the end of Q2 (December 2025) we will see a 30% QoQ increase in the percentage of views from New Viewers on TikTok — and across all SFV platforms, we’ll earn 50,000 total engagements (likes and reply comments) on brand comments left on external content, which will help us increase visibility and drive engagement with audiences we’re not currently reaching.
FA2.2.2	If we develop and get sign-off on a Wikipedia Creator Partnerships Program internal strategy and external shareables (inclusive of an outline of our value to creators, partnership criteria, contracting process, and how creator content will show up across owned and creator channels), we will be able to establish a robust creator strategy that will help us reach new audiences across social media with our knowledge content.

Ịrụkọ ọrụ ọnụ

Mmelite nke Jenụwarị 2025.

Atụmatụ Kwa Afọ bụ nkọwa nke Wikimedia Foundation banyere ihe anyị na-atụ anya inweta n'afọ na-abịa. Anyị na-arụsi ọrụ ike iji mee ka atụmatụ ahụ bụrụ nke aga esonye, na-agụsi agụụ ike na nke a ga-enweta. Kwa afọ, anyị na-arịọ ndị na-enye aka ka ha kesaa echiche ha, olileanya na arịrịọ ụfọdụ ka anyị na-emepụta atụmatụ ahụ. Ụfọdụ n'ime ụzọ anyị si achọ ntinye ahụ bụ site na mkparịtaụka otu ngwaahịa na ngalaba otu, Ndepụta Ọchịchọ ngalaba otu, mkparịtaụka ngalaba otu dịka usoro mkparịtaụka Commons, na nnọkọ nakwa site na ibe wiki dịka nke a.

Maka atụmatụ afọ anyị na-esote, site na ọnwa Julaị 2025 ruo Juun 2026, anyị na-eche maka otu anyị nwere ike isi jee ozi ọhụụ ọtụtụ ọgbọ, n'ihi mgbanwe ngwa ngwa n'ụwa nakwa na ịntanetị yana otu nke ahụ si emetụta ozi ihe ọmụma efu anyị.

Dị ka m kwuru n'afọ gara aga, anyị kwesịrị ilekwasị anya n'ihe na-eme ka anyị dị iche: ikike anyị ịnye ọdịnaya a pụrụ ịdabere na ya ọbụna dị ka mgbasa ozi na ozi na-ezighị ezi na-agbasa na ịntanetị na ikpo okwu na-asọmpi maka nlebara anya nke ọgbọ ọhụrụ. Nke a na-agụnye ịhụ na anyị nwetara nlegara anya anyị iji chịkọta ma nyefee nchikota nke ihe ọmụma mmadụ niile n'ụwa site n'ịgbasa mgbasa ozi nke ozi na-efu, nke ezighi ezi,ịkpa ókè na mkparị nwere ike ịkpata. Ọdịnaya anyị kwesịrị ije ozi ma nọgide na-adị mkpa na ịntanetị na-agbanwe agbanwe nke ọgụgụ isi na ahụmịhe bara ụba na-akwalite. N'ikpeazụ, anyị kwesịrị ịchọta ụzọ anyị ga-esi na-akwado nkwado nke Njem anyị site n'ịmepụta atụmatụ nkekọrịta maka ngwaahịa anyị na ịnakọta ego ka anyị wee nwee ike ịkwado ọrụ a ogologo oge.

Iji mee nhọrọ na mgbanwe banyere ebe anyị ga-elekwasị anya na mgbalị anyị n'afọ na-abịa, anyị chịkọtara ajụjụ na echiche banyere otu esi ekenye ihe onwunwe anyị iji nweta mmetụta kachasị.

Ọ bụrụ na ị nwere mmasị kpọmkwem n'ime atụmatụ ma ọ bụ ọrụ ndị ngalaba ngwaahịa na teknụzụ ga-ewu na-adabere na ihe ndị e debere ebe a, a ga-enwe oge na ọnwa Maachị ikwu banyere ebumnuche kpọmkwem na isi ihe ga-esi na ya pụta. (Nke a bụ ebumnobi na isi mpụtara maka atụmatụ afọ ugbu a, maka nrụtụaka.)

Ọ bụrụ na ịchọrọ iche echiche banyere ihe ịma aka na ohere dị na gburugburu teknụzụ anyị na ntụziaka anyị kwesịrị itinye na atụmatụ afọ ọzọ, biko sonye anyị tụlee ajụjụ ndị dị n'okpuru.

Anyị na-aga n'ihu na-enweta ozi gbasara ajụjụ ndị a n'ọtụtụ ụzọ -- site na mkparịtaụka ngalaba otu, data anyị na-anakọta, ajụjụ ọnụ nnyocha anyị na-eme, na ndị ọzọ. Nke a abụghị nke mbụ anyị na-ajụ ma na-ama banyere ọtụtụ n'ime ihe ndị a-ma anyị na-arụ ọrụ na gburugburu ọtụtụ n'ime ha!Anyị chọrọ ịjụ ha ọzọ ugbu a ma nọgide na-amata ihe banyere ya, ebe ọ bụ na ha bụ isi n'uche anyị n'oge a nke atụmatụ anyị.

Ajụjụ:

Metrik na data
- Kedu ụzọ ụfọdụ data na metrik nwere ike isi kwado ọrụ gị dị ka ndị ndezi? Ị nwere ike iche maka data gbasara ndezi, ịgụ akwụkwọ, ma ọ bụ nhazi nke ga-enyere gị aka ịhọrọ otu isi eji oge gị eme, ma ọ bụ mgbe ihe chọrọ nlebara anya? Nke a nwere ike ịbụ data gbasara ọrụ nke gị ma ọ bụ ọrụ ndị ọzọ.
Ime ndezi
- Kedu mgbe ime ndezi kacha enye gị ezigbo uru ma na-atọ ụtọ? Kedu mgbe ọ na-enwe nkụda mmụọ kacha sie ike?
- Anyị chọrọ ka ndị ntinye aka nweta nzaghachi na nkwado maka ọrụ ha, yabụ ka ọ hapụ idị ka ọ nweghị onye na-ahụ mbọ ha na-etinye na wiki. Kedu ụdị nzaghachi na nnabata na-akpali gị? Kedu ihe na-enyere gị aka ịnọgide na ime ndezi?
- N'ihi na wiki buru ibu, ọ nwere ike isiri ndị editọ ike ikpebi ọrụ wiki kacha dị ha mkpa itinye oge ha n'ụbọchị ọ bụla. Kedu ozi ma ọ bụ ngwaọrụ nwere ike inyere gị aka ịhọrọ otu esi etinye oge gị? Ọ ga-aba uru ịnweta ebe dị na etiti, nke a haziri ahazi nke na-enye gị ohere ịchọta ohere ọhụrụ, jikwaa ọrụ gị, ma ghọta mmetụta gị?
- Anyị chọrọ imeziwanye ahụmịhe nke imekọ ihe ọnụ na wiki, yabụ na ọ dịịrị ndị na-enye aka mfe ịchọta ibe ha ma rụkọọ ọrụ ọnụ, ma ọ bụ site na draịv backlog, edit-a-thon, WikiProjects, ma ọ bụ ọbụna ndị editọ abụọ na-arụkọ ọrụ ọnụ. Kedu otu ị chere na anyị nwere ike inyere ọtụtụ ndị enyemaka aka ịchọta ibe ha, jikọọ na ịrụkọ ọrụ ọnụ?
Ogụgụ/Mmụta
- Wiki na-emehe ngwa ngwa ma ọ bụ jiri nwayọ dabere na ebe ndị mmadụ bi n'ụwa. Enwere akụkụ ọ bụla nke ụwa ebe ị chere na arụmọrụ dị mma kacha mkpa?
- Kedu otu anyị nwere ike isi nyere ọgbọ ọhụrụ nke ndị na-agụ akwụkwọ aka inwe mmasị na ọdịnaya Wikipedia ma nọgidesie ike? Anyị atụlewo echiche gbasara ọdịnaya mmekọrịta yana vidiyo n'oge gara aga, na n'ime afọ ugbu a etinyewo uche na chaatị na nnwale na ụzọ ọhụrụ iji gosipụta ọdịnaya Wikipedia dị ugbu a. Kedu otu anyị nwere ike isi gaa n'ihu na egwu a iji ọdịnaya anyị dị n'ụzọ ọhụrụ pụrụ iche na Wikimedia?
Ndị nhazi
- Kedu ihe nwere ike ịgbanwe gbasara Wikipedia ka ọtụtụ ndị mmadụ nwee ike itinye aka na ọrụ afọ ofufo dị elu, dị ka ndị njegharị ma ọ bụ ndị nchịkwa?
- Kedu ozi ma ọ bụ ọnọdụ gbasara ndezigharị ma ọ bụ ndị ọrụ nwere ike inyere gị aka ịme njegharị ọrụ ma ọ bụ ịdị mfe na ngwa ngwa nke mkpebi ndị njịkwa ma ọ bụ ?
ihe owuwu Mpụga
- Kedu mgbanwe kacha mkpa ị na-ahụ n'ụwa na mpụga Wikimedia? Ndị a nwere ike ịbụ ihe gbasara usoro teknụzụ, agụmakwụkwọ, ma ọ bụ ka ndị mmadụ si amụta ihe.
- Wezụga Njem Wikimedia, kedu otu ịntanetị ndị ọzọ ị nọ na ha ? Gịnị ka anyị nwere ike iwere na ngwaọrụ na usoro na nhiwe ngalaba otu ndị ọzọ?
- Olee otu ị si eji ngwá ọrụ AI n'ime na n'èzí ọrụ Wikimedia gị? Gịnị ka ị na-ahụ AI bara uru iji mee?
Commons
- Kedụ mkpebi anyị nwere ike ime na ngalaba Commons iji mepụta ọrụ na-adịgide adịgide nke na-akwado ịmepụta ihe ọmụma ensaịklopedik?
Wikidata
- Olee otú ị chọrọ isi hụ ka Wikidata si agbanwe n'ọdịnihu? Olee otú ọ ga-esi bakarịsịa uru maka ịmepụta ọdịnaya encyclopedik a pụrụ ịtụkwasị obi?

Ńkàtá

–– Selena Deckelmann