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UmkhiqizoAI Understanding ukwaziswa

I-Amazon Bedrock ingeza ukusekelwa kwamamodeli esisindo esivulekile ku-AI yokugeleza kwe-ejenti yekhodi

I-AWS ihlanganise usekelo lwamamodeli esisindo esivulekile ku-Amazon Bedrock, okuvumela onjiniyela ukuthi basebenzise i-ejenti yomdabu ye-OpenCode ukuze benze imisebenzi yokubhala ikhodi ngokuphephile ngaphakathi kwama-akhawunti abo e-AWS.

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Source-provided image accompanying Amazon Bedrock adds support for open weight models in AI coding agent workflows
Idokhumenti yomthombo oyinhlokoUmthombo urekhodiwe
Umshicileli
aws.amazon.com
Isixhumanisi somthombo
aws.amazon.comhttps://aws.amazon.com/blogs/machine-learning/use-open-weight-models-as-your-ai-coding-agent-with-amazon-bedrock/
Uhlobo lomthombo
Idokhumenti eyisisekelo — isimemezelo esisemthethweni, iphepha, ukugcwalisa, noma ikhasi lomuntu wokuqala esilifunda ngokuqondile.
UmongoQonda lokhu ngemizuzwana engama-60

Qala lapha

Imigomo ebalulekile

Isisindo
Inani lezinombolo elifundiwe elikala amasignali adlula kunethiwekhi ye-neural.
I-API (I-Application Programming Interface)
Indlela ehlelekile yesistimu yesofthiwe eyodwa ukuthumela izicelo futhi yamukele izimpendulo ezivela kwenye isistimu.
Abaqaphi
Imithetho, amasheke, nezilawuli ezikhawulela ukuziphatha kwemodeli okungaphephile noma okungafuneki.
ZihloleImibuzo ye-AI Agents

Kwenzekeni

I-Amazon ibuyekeze insizakalo yayo ye-Bedrock ukuze isekele amamodeli esisindo avulekile kuma-ejenti wokubhala amakhodi we-AI, igqamisa ngokukhethekile ithuluzi le-OpenCode le-terminal-native. Lokhu kuhlanganiswa kuvumela onjiniyela ukuthi benze umzila wemisebenzi yokubhala amakhodi—njengokuhlela, ukukhiqiza ikhodi, nokulungisa iphutha—kumamodeli ahlukene ngokusekelwe ezidingweni ezithile njengokujula kokucabanga noma umphumela. Izakhiwo zigcina idatha ngaphakathi kwe-akhawunti yomsebenzisi ye-AWS, kusetshenziswa i-Bedrock Converse API ukuze ithole incazelo ngaphandle kokudinga ukuphathwa kwe-GPU yendawo noma ukubhaliswa kwesihlalo ngasinye.

I-Amazon Bedrock manje isekela amamodeli esisindo avulekile ama-ejenti wokubhala amakhodi we-AI, okuvumela abathuthukisi ukuthi basebenzise ithuluzi le-OpenCode CLI ukuze baphathe imisebenzi yokuthuthukisa isofthiwe. Uhlelo luklanyelwe ukugcina yonke ikhodi, ukwaziswa, kanye nezimpendulo ku-akhawunti yomsebenzisi AWS, ukuqinisekisa ukuhlala kwedatha nokuhambisana.

Izakhiwo zisekela ukuhamba komsebenzi kwamamodeli amaningi, lapho abasebenzisi banganikeza khona amamodeli athile ezindimeni ezahlukene. Isibonelo, imodeli enzima yokucabanga efana ne-Kimi K3 ingasetshenziselwa ukuhlela, kuyilapho imodeli yokuphumelela okuphezulu efana ne-NVIDIA Nemotron 3 Super 120B iphatha ukukhiqizwa kwekhodi. Lokhu kulungiselelwa kulawulwa ngefayela lendawo le-opencode.json.

Amanani asekelwe ekusetshenzisweni ngezigaba ze-Amazon Bedrock: Okubalulekile kwemisebenzi ezwelayo ukubambezeleka, Izinga lokunquma lapho kudingeka khona, kanye ne-Flex yokucubungula inqwaba ngenani eliphansi elingu-50%. Isevisi ayisebenzisi okokufaka kwekhasimende noma okuphumayo ukuze iqeqeshe amamodeli ayo ayisisekelo.

Ukuhlanganiswa kusekela ingqalasizinda yezokuphepha ye-AWS ekhona, ehlanganisa izinqubomgomo ze-IAM, AWS CloudTrail yokugawulwa kwemithi, kanye ne-Amazon Bedrock yokuhlunga okuqukethwe kanye nokuphinda kwenziwe kabusha kwe-PII.

Imininingwane yomthombo: aws.amazon.com ↗

Kungani kubalulekile

Lokhu kuthuthukiswa kubhekana nokukhathazeka okubalulekile kwebhizinisi mayelana nokuhlala kwedatha, izindleko, nokukhiya komthengisi ekuthuthukisweni okusizwa yi-AI. Ngokuvumela ukusetshenziswa kwamamodeli esisindo esivulekile ngesevisi ephethwe, i-AWS ivumela izinhlangano ukuthi zigcine ukuphepha okuqinile nokuhambisana namazinga (afana ne-HIPAA ne-SOC 2) kuyilapho zisebenzisa izinzuzo zokusebenza nokwenza ngendlela oyifisayo amamodeli omthombo ovulekile. Ikhono lokuhambisa imisebenzi kumamodeli ahlukene—njengokusebenzisa amamodeli anengqondo kakhulu ukuhlela namamodeli asheshayo okusetshenziswa—linikeza indlela engokoqobo yamathimba ukuze athuthukise izindleko nokusebenza ezindaweni zokukhiqiza.

Ukushintshela kumamodeli esisindo esivulekile kuvumela izinhlangano ukuthi zigweme imikhawulo yama-API wobunikazi, wezinkampani zangaphandle, njengokulayisensa kwesihlalo ngasinye kanye nokuntuleka kwemodeli yokuguquguquka.

Ngokusebenzisa ingqalasizinda ephethwe yi-Bedrock, izinkampani zingasebenzisa ama-ejenti wokufaka amakhodi we-AI ngaphandle kokunikeza noma ukugcina amaqoqo azo e-GPU.

Indlela yomzila wamamodeli amaningi yenza amaqembu akwazi ukulinganisa izindleko nokusebenza ngokuqhathanisa imodeli efanele nobunkimbinkimbi obuthile bomsebenzi, okungenzeka kunciphise izindleko eziphelele zobunikazi bokugeleza komsebenzi wobunjiniyela osizwa yi-AI.

Ukufakwa kwezilawuli zokuphepha zezinga lebhizinisi kuqinisekisa ukuthi ikhodi yobunikazi ebucayi ihlala ivikelekile, okuwumgoqo oyinhloko wokwamukelwa kwabasizi bekhodi be-AI emikhakheni elawulwayo.

Interactive Mechanism

I-Interactive Mechanism: Indlela Esebenza Ngayo Ngempela

Hlola ubuchwepheshe obuyisisekelo ngemuva kwalokhu kuthuthukiswa ngokuhlanganyela.

Thinking Budget (Test-Time Tokens):1,024 tokens
Complex Accuracy79%Math & Code Logic
Latency3.2sTime to first full output
Inference Cost$0.0092Per query estimated
Reasoning StyleStep VerificationInternal chain depth
Active Thinking Trace:
1Deconstruct user problem into formal constraints
2Propose candidate hypotheses & step-by-step calculation
3Self-correction: Backtrack and refute subtle edge cases
4Exhaustive consistency check & final output synthesis
Core takeaway: Test-time compute fundamentally changes AI economics. Instead of only scaling during pre-training, giving reasoning models more tokens at inference time allows them to systematically solve PhD-level STEM problems.
I-Interactive Concept Check+10 Points
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Ongakubuka ngokulandelayo

Umthelela wesikhathi eside waleli su lamamodeli amaningi omzila ezindlekweni zokuthuthukiswa kwe-AI yebhizinisi kanye nokwamukelwa kwamamodeli anesisindo esivulekile ezimbonini ezilawulwayo. Ukwengeza, ukuvela kwezinhlelo zama-ejenti azithuthukisayo, njenge-'SkillClaw' esekwe ocwaningweni okukhulunywe ngayo ikhasimende le-AWS Ethara.AI, iphakamisa ukushintshela kuma-ejenti ajwayela ukuziphatha kwawo ngokusekelwe emlandweni wokubulawa. Ukuqapha ukuthi lezi zendlalelo ze-orchestration zifinyelela kanjani emaqenjini amakhulu onjiniyela kuzobaluleka ekuhloleni ukuvuthwa kokugeleza kokusebenza kwekhodi ye-ejenti.

Ukusebenza kahle kwesakhiwo 'somzila wamamodeli amaningi' ezindaweni zokukhiqiza zomhlaba wangempela njengoba amaqembu edlula ukusetha konjiniyela oyedwa.

Ukuthuthukiswa kwezinhlaka ze-ejenti ezithuthukisayo eziguqukayo ngokusekelwe kumzila ophumelelayo wokubulala, njengoba kuboniswa abamukeli bokuqala njenge-Ethara.AI.

Ukunwetshwa okuqhubekayo kwekhathalogi yemodeli ye-Amazon ye-Bedrock nokuthi iqhathaniswa kanjani nezinye izindlela zobunikazi kumabhentshimakhi afana ne-Artificial Analysis Coding Index.

Imihlahlandlela ehlobene nemibuzo

Ama-AI AgentsAmamodeli e-AI AchaziweUkuziphatha kwe-AIHlola okwaziyo — zama imibuzo ye-AI yamahhalaBheka igama le-AI kuhlu lwethu lwamagama
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