[{"data":1,"prerenderedAt":2222},["ShallowReactive",2],{"blog-category-hu-llmops":3},[4,77,124,181,250,312,369,439,493,547,605,659,705,812,902,1013,1083,1192,1251,1294,1377,1429,1482,1534,1613,1654,1702,1750,1798,1850,1905,1939,1987,2036,2080,2122],{"slug":5,"published":6,"minutes":7,"category":8,"tags":9,"tagPages":15,"keywords":17,"about":29,"sources":36,"cover":67,"og":68,"expertise":69,"locales":70,"lang":73,"title":74,"description":75,"coverAlt":76},"claude-plans-pricing-usage-limits","2026-10-21",11,"llmops",[10,11,12,13,14],"Claude","Pricing","Usage limits","Claude Code","LLM costs",[16],"claude-code",[18,19,20,21,22,23,24,25,26,27,28],"claude árak","claude pro vagy max","claude max csomag","claude team csomag ár","claude használati korlátok","claude 5 órás limit","claude heti limit","claude code használati korlátok","claude előfizetés vs api","claude api kredit max csomag","claude ingyenes csomag",[30,33],{"name":31,"url":32},"Claude 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mellett.",{"slug":125,"published":126,"minutes":80,"category":8,"tags":127,"tagPages":133,"keywords":134,"about":148,"sources":156,"cover":175,"og":176,"expertise":69,"locales":177,"lang":73,"title":178,"description":179,"coverAlt":180},"deepseek-v4-1-flash-1017","2026-10-17",[128,129,130,131,132],"DeepSeek","Open-weight models","KV cache","Mixture of experts","LLM cost",[],[135,136,137,138,139,140,141,142,143,144,145,146,147],"deepseek v4.1 flash 1017","deepseek v4.1 flash","causal encoder-decoder","compressed sparse attention 2","csa2","fp4 kv cache","kv cache tömörítés","swa bounded replay","engram feltételes memória","dspark spekulatív dekódolás","deepseek árak","deepswe benchmark","nyílt súlyú kódoló modell",[149,151,153],{"name":128,"url":150},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FDeepSeek",{"name":131,"url":152},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMixture_of_experts",{"name":154,"url":155},"Transformer (deep learning 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DeepSeek opens V4.1-Flash-1017 weights and $0.75 Search API","https:\u002F\u002Faiweekly.co\u002Falerts\u002Fdeepseek-opens-v41-flash-1017-weights-and-075-search-api",{"title":173,"url":174},"DeepSeek on X: DeepSeek-V4.1-Flash-1017 announcement","https:\u002F\u002Fx.com\u002Fdeepseek_ai\u002Fstatus\u002F2111375640717033777","\u002Fimages\u002Fblog\u002Fdeepseek-v4-1-flash-1017\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fdeepseek-v4-1-flash-1017\u002Fog.jpg",[71,72,73],"DeepSeek V4.1 Flash 1017: 890 bájt KV-cache tokenenként, és mit nyer ezzel egy ügynök","A DeepSeek V4.1 Flash 1017 elemzése: encoder-decoder felosztás, CSA2 cache-megosztás, FP4 KV-cache, benchmarkok és a valós költség ügynökfeladatonként.","DeepSeek V4.1 Flash 1017: apró hátizsákos robot indul hosszú útra egy hatalmas bőrönd mellől.",{"slug":182,"published":126,"minutes":183,"category":8,"tags":184,"tagPages":190,"keywords":191,"about":203,"sources":213,"cover":244,"og":245,"expertise":69,"locales":246,"lang":73,"title":247,"description":248,"coverAlt":249},"llm-rate-limits-retries-provider-fallback",10,[185,186,187,188,189],"Rate limits","Retries","Fallback","Reliability","LLM gateway",[],[192,193,194,195,196,197,198,199,200,201,202],"llm rate limit kezelés","429 too many requests llm api","exponenciális backoff jitterrel","anthropic 529 overloaded hiba","openai rate limit újrapróbálás","llm szolgáltató fallback","litellm fallback","retry-after fejléc","llm circuit breaker","többszolgáltatós llm átállás","claude api rate limitek",[204,207,210],{"name":205,"url":206},"Token bucket","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FToken_bucket",{"name":208,"url":209},"Exponential backoff","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FExponential_backoff",{"name":211,"url":212},"Circuit breaker design 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egy rétegben, és olyan fallback-láncok, amelyek nem rontják el a kimenetet.","LLM rate limitek élesben: homokórás furgon vár egy sorompónál, egy mellékút egy nyitott kapuhoz vezet.",{"slug":251,"published":126,"minutes":252,"category":8,"tags":253,"tagPages":259,"keywords":260,"about":271,"sources":281,"cover":306,"og":307,"expertise":69,"locales":308,"lang":73,"title":309,"description":310,"coverAlt":311},"prompt-versioning-management",8,[254,255,256,257,258],"Prompt management","Versioning","LLMOps","Evals","Langfuse",[],[261,262,263,264,265,266,267,268,269,270],"prompt verziókezelés","prompt menedzsment","prompt registry","verziókezelés promptokhoz","langfuse prompt menedzsment","mlflow prompt registry","promptok gitben","openai prompt objektum kivezetés","prompt visszaállítás éles rendszerben","prompt ci értékelés",[272,275,278],{"name":273,"url":274},"Prompt engineering","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPrompt_engineering",{"name":276,"url":277},"Version 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rejtő üveget húz elő a címkézett sorból.",{"slug":313,"published":126,"minutes":314,"category":8,"tags":315,"tagPages":320,"keywords":321,"about":332,"sources":342,"cover":363,"og":364,"expertise":69,"locales":365,"lang":73,"title":366,"description":367,"coverAlt":368},"llm-batch-api-offline-workloads",9,[316,317,256,318,319],"Batch API","Cost","Prompt caching","Data pipelines",[],[322,323,324,325,326,327,328,329,330,331],"llm kötegelt api","anthropic message batches api","openai batch api","gemini batch mód","batch inferencia llm költség","batch api 50 százalékos kedvezmény","openai flex processing","bedrock batch inferencia","llm offline feldolgozás","batch api és prompt caching",[333,336,339],{"name":334,"url":335},"Batch processing","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBatch_processing",{"name":337,"url":338},"JSON Lines","https:\u002F\u002Fjsonlines.org\u002F",{"name":340,"url":341},"Large language 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2412.08905)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.08905",{"title":413,"url":414},"Qwen: Qwen3 Technical Report (arXiv 2505.09388)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.09388",{"title":416,"url":417},"Apple: Apple Intelligence Foundation Language Models, Tech Report 2025 (arXiv 2507.13575)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2507.13575",{"title":419,"url":420},"Ong et al.: RouteLLM, Learning to Route LLMs with Preference Data (arXiv 2406.18665)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.18665",{"title":422,"url":423},"LMSYS: RouteLLM, an open-source framework for cost-effective LLM routing","https:\u002F\u002Flmsys.org\u002Fblog\u002F2024-07-01-routellm\u002F",{"title":425,"url":426},"Gomes et al.: On the Generalization vs Fidelity Paradox in Knowledge Distillation (arXiv 2505.15442)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.15442",{"title":428,"url":429},"llama.cpp: quantize tool documentation","https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\u002Fblob\u002Fmaster\u002Ftools\u002Fquantize\u002FREADME.md",{"title":431,"url":432},"European Commission: AI Act","https:\u002F\u002Fdigital-strategy.ec.europa.eu\u002Fen\u002Fpolicies\u002Fregulatory-framework-ai","\u002Fimages\u002Fblog\u002Fsmall-language-models-when-enough\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fsmall-language-models-when-enough\u002Fog.jpg",[71,72,73],"Kis nyelvi modellek: mikor elég egy 4B modell","Mikor veri a kis modell a frontier API-t: költség esetenként, latencia, adatvédelem, desztilláció, kvantálás, routing és eval-terv.","Kis nyelvi modellek: kéz zsebkést vesz fel a nagy szerszámláda helyett egy zsinór elvágásához.",{"slug":440,"published":126,"minutes":183,"category":8,"tags":441,"tagPages":444,"keywords":445,"about":457,"sources":463,"cover":487,"og":488,"expertise":69,"locales":489,"lang":73,"title":490,"description":491,"coverAlt":492},"reasoning-models-effort-thinking-budget",[442,443,256,317,257],"Reasoning models","Effort",[],[446,447,448,449,450,451,452,453,454,455,456],"reasoning effort paraméter","claude effort paraméter","claude adaptív gondolkodás","budget_tokens kivezetve","openai reasoning effort","gemini thinking_level","gondolkodási tokenek költsége","reasoning tokenek számlázása","claude feladatkeret","túlgondolkodás reasoning modellek","max_tokens thinking",[458,461,462],{"name":459,"url":460},"Reasoning language model","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FReasoning_language_model",{"name":340,"url":341},{"name":273,"url":274},[464,466,469,472,475,478,481,484],{"title":465,"url":111},"Claude Platform Docs: Effort",{"title":467,"url":468},"Claude Platform Docs: Extended thinking (manual budget_tokens mode)","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Fextended-thinking",{"title":470,"url":471},"Claude Platform Docs: Steering thinking (cost, caching, pricing)","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Fthinking-steering-and-cost",{"title":473,"url":474},"Claude Platform Docs: Task budgets","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Ftask-budgets",{"title":476,"url":477},"OpenAI API Docs: Reasoning models","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Freasoning",{"title":479,"url":480},"Google AI for Developers: Gemini thinking","https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Fthinking",{"title":482,"url":483},"Chen et al.: Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs (arXiv 2412.21187)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.21187",{"title":485,"url":486},"Gema et al.: Inverse Scaling in Test-Time Compute (arXiv 2507.14417)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2507.14417","\u002Fimages\u002Fblog\u002Freasoning-models-effort-thinking-budget\u002Fcover.webp","\u002Fimages\u002Fblog\u002Freasoning-models-effort-thinking-budget\u002Fog.jpg",[71,72,73],"Reasoning effort és gondolkodási keret: mennyit gondolkodjon egy modell?","Így működik az effort, az adaptív thinking és a thinking_level Claude, OpenAI és Gemini modelleknél, mennyibe kerül a thinking, és hogyan válassz szintet.","Reasoning effort és gondolkodási keret: robot gondolkodik egy gondolatbuborék alatt, mellette homokóra és háromállású tárcsa.",{"slug":494,"published":495,"minutes":314,"category":8,"tags":496,"tagPages":502,"keywords":503,"about":515,"sources":522,"cover":541,"og":542,"expertise":69,"locales":543,"lang":73,"title":544,"description":545,"coverAlt":546},"structured-outputs-json-schema-llm","2026-10-16",[497,498,499,500,501],"Structured outputs","JSON Schema","Constrained decoding","LLM APIs","Validation",[],[504,505,506,507,508,509,510,511,512,513,514],"strukturált kimenet llm","json schema llm kimenet","openai structured outputs strict mode","claude strukturált kimenet","gemini strukturált kimenet","korlátozott dekódolás","json mode vs strukturált kimenet","llm json kimenet validálása","zod strukturált kimenet","pydantic llm kimenet","vllm strukturált kimenet",[516,519,521],{"name":517,"url":518},"JSON","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FJSON",{"name":498,"url":520},"https:\u002F\u002Fjson-schema.org\u002F",{"name":340,"url":341},[523,526,529,532,535,538],{"title":524,"url":525},"OpenAI API docs: Structured model outputs","https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Fstructured-outputs",{"title":527,"url":528},"Claude API docs: Structured outputs","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Fstructured-outputs",{"title":530,"url":531},"Gemini API docs: Structured outputs","https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Fstructured-output",{"title":533,"url":534},"vLLM docs: Structured Outputs","https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Flatest\u002Ffeatures\u002Fstructured_outputs.html",{"title":536,"url":537},"Tam et al.: Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of LLMs (2024)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2408.02442",{"title":539,"url":540},"Geng et al.: JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models (2025)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2501.10868","\u002Fimages\u002Fblog\u002Fstructured-outputs-json-schema-llm\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fstructured-outputs-json-schema-llm\u002Fog.jpg",[71,72,73],"Strukturált kimenet JSON Schemával: mit fed le a garancia, és mit nem","Hogyan működik a strukturált kimenet és a korlátozott dekódolás az OpenAI, Claude, Gemini és vLLM esetén, mely sémaszabályok számítanak, és mit validálj.","Strukturált kimenet JSON Schemával: kiszúró tökéletes formát vág, mellette jó alakú, de repedt keksz.",{"slug":548,"published":495,"minutes":314,"category":8,"tags":549,"tagPages":555,"keywords":556,"about":569,"sources":577,"cover":599,"og":600,"expertise":69,"locales":601,"lang":73,"title":602,"description":603,"coverAlt":604},"evaluating-ai-agents-trajectories",[550,551,552,553,554],"Agent evals","Trajectories","Tool calling","pass^k","LLM-as-judge",[],[557,558,559,560,561,562,563,564,565,566,567,568],"ai ügynökök értékelése","ügynök trajektória értékelés","ügynök evals","eszközhívás pontossága","trajektória egyeztetési értékelő","pass^k metrika","tau-bench","llm bíróként ügynökökhöz","ügynök regressziós tesztelés","eredmény vs trajektória értékelés","agentevals","eszköz helyesség metrika",[570,573,576],{"name":571,"url":572},"Intelligent agent","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FIntelligent_agent",{"name":574,"url":575},"Software 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metrics","https:\u002F\u002Fdocs.ragas.io\u002Fen\u002Fstable\u002Fconcepts\u002Fmetrics\u002Favailable_metrics\u002Fagents\u002F",{"title":597,"url":598},"OpenAI API docs: Trace grading","https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Ftrace-grading","\u002Fimages\u002Fblog\u002Fevaluating-ai-agents-trajectories\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fevaluating-ai-agents-trajectories\u002Fog.jpg",[71,72,73],"MI-ügynökök értékelése: trajektóriák, eredmények és megbízhatóság pontozása","Hogyan értékeld az MI-ügynököket: eredmény- és trajektória-értékelők, egyeztetési módok, pass^k, izolált környezet és megbízható LLM-bírák.","MI-ügynökök értékelése: egy nagy ceruza ellenőrzi a robot lábnyomait a labirintuson át, a kijáratnál zászló.",{"slug":606,"published":495,"minutes":183,"category":8,"tags":607,"tagPages":613,"keywords":614,"about":626,"sources":633,"cover":653,"og":654,"expertise":69,"locales":655,"lang":73,"title":656,"description":657,"coverAlt":658},"llm-model-migration-regression-testing",[608,609,610,611,612],"Model migration","Regression testing","Deprecations","LLM evals","Canary releases",[],[615,616,617,618,619,620,621,622,623,624,625],"llm modellváltás","llm regressziós tesztelés","openai modell kivezetés","claude modell kivezetés","llm modell váltása törés nélkül","llm modell frissítési ellenőrzőlista","prompt regressziós tesztelés","shadow testing llm","modell kivezetési dátum","golden dataset llm","llm drift monitorozás",[627,629,630],{"name":609,"url":628},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FRegression_testing",{"name":340,"url":341},{"name":631,"url":632},"Feature toggle","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FFeature_toggle",[634,637,640,643,646,649,652],{"title":635,"url":636},"Claude API docs: Model deprecations","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fabout-claude\u002Fmodel-deprecations",{"title":638,"url":639},"OpenAI API docs: Deprecations","https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fdeprecations",{"title":641,"url":642},"Gemini API docs: Models and version name patterns","https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Fmodels",{"title":644,"url":645},"Claude API docs: Claude Opus 5.5 migration guide","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fmodels\u002Fopus-5-5\u002Fmigration-guide",{"title":647,"url":648},"Chen, Zaharia, Zou: How is ChatGPT's behavior changing over time? (2023)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.09009",{"title":650,"url":651},"Sclar et al.: Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design (2023)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11324",{"title":579,"url":580},"\u002Fimages\u002Fblog\u002Fllm-model-migration-regression-testing\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fllm-model-migration-regression-testing\u002Fog.jpg",[71,72,73],"LLM-modellmigráció regresszió nélkül: elavulás, evalok, árnyékforgalom","Forgatókönyv LLM-modellek cseréjéhez: elavulási határidők, szerződésjavítás, regressziós készlet, prompthangolás, árnyékforgalom és canary.","LLM-modellmigráció regresszió nélkül: költözőládák kézikocsin a régi házból az újba, közöttük mérőszalag.",{"slug":660,"published":495,"minutes":661,"category":8,"tags":662,"tagPages":667,"keywords":669,"about":680,"sources":688,"cover":699,"og":700,"expertise":69,"locales":701,"lang":73,"title":702,"description":703,"coverAlt":704},"semantic-caching-llm-responses",6,[663,132,664,665,666],"Semantic cache","Latency","Embeddings","Redis",[668],"embeddings",[670,671,672,673,674,675,676,677,678,679],"szemantikus gyorsítótárazás llm","szemantikus cache","llm válasz cache","gptcache","szemantikus cache hasonlósági küszöb","prompt caching vs szemantikus gyorsítótárazás","llm api költség csökkentése","llm válaszok cache-elése redisben","szemantikus cache hamis találat","llm cache érvénytelenítés",[681,684,687],{"name":682,"url":683},"Cache (computing)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCache_(computing)",{"name":685,"url":686},"Sentence 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hogyan kerüld a téves találatokat.","Szemantikus gyorsítótár LLM-appokhoz: kéz leadja a jegyet a ruhatárban, és visszakap egy kabátot.",{"slug":706,"published":707,"updated":708,"minutes":183,"category":8,"tags":709,"tagPages":714,"keywords":717,"about":726,"sources":736,"cover":806,"og":807,"expertise":69,"locales":808,"lang":73,"title":809,"description":810,"coverAlt":811},"self-hosted-llm-gdpr-cost","2026-10-09","2026-10-10",[710,711,712,713,129],"Self-hosting","GDPR","vLLM","GPU cost",[715,716],"self-hosting","gdpr",[718,719,720,721,722,723,724,725],"saját llm gdpr","llm saját üzemeltetés adatvédelem","llm gpu memória igény","vllm throughput benchmark","eu gpu felhő llm-ekhez","llm api vs saját üzemeltetés megtérülés","nyílt súlyú llm hardverköltség","adatfeldolgozási megállapodás llm",[727,730,731,734],{"name":728,"url":729},"General Data Protection Regulation","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGeneral_Data_Protection_Regulation",{"name":340,"url":341},{"name":732,"url":733},"Graphics processing unit","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGraphics_processing_unit",{"name":712,"url":735},"https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm",[737,740,743,746,749,752,755,758,761,764,767,770,773,776,779,782,785,788,791,794,797,800,803],{"title":738,"url":739},"openai\u002Fgpt-oss-120b model card (Hugging Face)","https:\u002F\u002Fhuggingface.co\u002Fopenai\u002Fgpt-oss-120b",{"title":741,"url":742},"mistralai\u002FMistral-Small-3.2-24B-Instruct-2506 model card (Hugging Face)","https:\u002F\u002Fhuggingface.co\u002Fmistralai\u002FMistral-Small-3.2-24B-Instruct-2506",{"title":744,"url":745},"meta-llama\u002FLlama-3.3-70B-Instruct model card (Hugging Face)","https:\u002F\u002Fhuggingface.co\u002Fmeta-llama\u002FLlama-3.3-70B-Instruct",{"title":747,"url":748},"mistralai\u002FMistral-Large-3-675B-Instruct-2512 model card (Hugging Face)","https:\u002F\u002Fhuggingface.co\u002Fmistralai\u002FMistral-Large-3-675B-Instruct-2512",{"title":750,"url":751},"vLLM blog: v0.6.0 performance update (September 2024)","https:\u002F\u002Fblog.vllm.ai\u002F2024\u002F09\u002F05\u002Fperf-update.html",{"title":753,"url":754},"vLLM documentation: engine arguments","https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Flatest\u002Fconfiguration\u002Fengine_args.html",{"title":756,"url":757},"vLLM documentation: vllm bench serve","https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Flatest\u002Fcli\u002Fbench\u002Fserve.html",{"title":759,"url":760},"SemiAnalysis InferenceX: Llama 3.3 70B on H100 vs H200","https:\u002F\u002Finferencex.semianalysis.com\u002Fcompare\u002Fllama-3-3-70b-h100-vs-h200",{"title":762,"url":763},"Scaleway GPU instances pricing","https:\u002F\u002Fwww.scaleway.com\u002Fen\u002Fpricing\u002Fgpu\u002F",{"title":765,"url":766},"Scaleway blog: a transparent update on Scaleway pricing (27 April 2026)","https:\u002F\u002Fwww.scaleway.com\u002Fen\u002Fblog\u002Fa-transparent-update-on-scaleway-pricing\u002F",{"title":768,"url":769},"Scaleway Generative APIs pricing","https:\u002F\u002Fwww.scaleway.com\u002Fen\u002Fpricing\u002Fmodel-as-a-service\u002F",{"title":771,"url":772},"OVHcloud public cloud price list","https:\u002F\u002Fwww.ovhcloud.com\u002Fen\u002Fpublic-cloud\u002Fprices\u002F",{"title":774,"url":775},"OVHcloud AI Endpoints","https:\u002F\u002Fwww.ovhcloud.com\u002Fen\u002Fpublic-cloud\u002Fai-endpoints\u002F",{"title":777,"url":778},"OVHcloud AI Endpoints model catalogue","https:\u002F\u002Fwww.ovhcloud.com\u002Fen\u002Fpublic-cloud\u002Fai-endpoints\u002Fcatalog\u002F",{"title":780,"url":781},"Hetzner dedicated GPU servers","https:\u002F\u002Fwww.hetzner.com\u002Fdedicated-rootserver\u002Fmatrix-gpu\u002F",{"title":783,"url":784},"NVIDIA newsroom: GeForce RTX 50 Series launch","https:\u002F\u002Fnvidianews.nvidia.com\u002Fnews\u002Fnvidia-blackwell-geforce-rtx-50-series-opens-new-world-of-ai-computer-graphics",{"title":786,"url":787},"NVIDIA GeForce RTX 5090","https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fgeforce\u002Fgraphics-cards\u002F50-series\u002Frtx-5090\u002F",{"title":789,"url":790},"NVIDIA GeForce software licence","https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdrivers\u002Fgeforce-license\u002F",{"title":792,"url":793},"GDPR Article 28: processor","https:\u002F\u002Fgdpr-info.eu\u002Fart-28-gdpr\u002F",{"title":795,"url":796},"GDPR Article 44: general principle for transfers","https:\u002F\u002Fgdpr-info.eu\u002Fart-44-gdpr\u002F",{"title":798,"url":799},"European Commission: EU-US data transfers (Data Privacy Framework)","https:\u002F\u002Fcommission.europa.eu\u002Flaw\u002Flaw-topic\u002Fdata-protection\u002Finternational-dimension-data-protection\u002Feu-us-data-transfers_en",{"title":801,"url":802},"EDPB Opinion 28\u002F2024 on AI models (adopted 17 December 2024)","https:\u002F\u002Fwww.edpb.europa.eu\u002Fsystem\u002Ffiles\u002F2024-12\u002Fedpb_opinion_202428_ai-models_en.pdf",{"title":804,"url":805},"Mistral AI pricing","https:\u002F\u002Fmistral.ai\u002Fpricing","\u002Fimages\u002Fblog\u002Fself-hosted-llm-gdpr-cost\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fself-hosted-llm-gdpr-cost\u002Fog.jpg",[71,72,73],"Saját LLM-üzemeltetés a GDPR miatt: mikor kell és mibe kerül","Saját LLM a GDPR miatt: mikor kell, mennyi GPU-memória kell a nyílt modellekhez, az EU-s árak 2026 októberében és a break-even millió tokenenként.","Saját LLM-üzemeltetés a GDPR miatt: szerverszekrény egy zárt házban, malacpersely és érmék mellett.",{"slug":813,"published":814,"minutes":252,"category":8,"tags":815,"tagPages":821,"keywords":823,"about":831,"sources":842,"cover":896,"og":897,"expertise":69,"locales":898,"lang":73,"title":899,"description":900,"coverAlt":901},"deepeval","2026-10-06",[816,817,818,819,820],"LLM evaluation","pytest","LLM-as-a-judge","Red teaming","Open source",[822],"open-source",[813,824,825,826,827,828,829,830],"deepeval vagy ragas","llm értékelő keretrendszer","pytest llm kimenetekhez","g-eval metric","llm bíró költsége","deepeval árak","llm red teaming nyílt forráskódú",[832,835,838,841],{"name":833,"url":834},"DeepEval","https:\u002F\u002Fdeepeval.com",{"name":836,"url":837},"Confident AI","https:\u002F\u002Fwww.confident-ai.com",{"name":839,"url":840},"Pytest","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPytest",{"name":340,"url":341},[843,846,849,852,855,858,861,863,866,869,872,875,878,881,884,887,890,893],{"title":844,"url":845},"DeepEval documentation: getting started","https:\u002F\u002Fdeepeval.com\u002Fdocs\u002Fgetting-started",{"title":847,"url":848},"GitHub: confident-ai\u002Fdeepeval, the README and licence","https:\u002F\u002Fgithub.com\u002Fconfident-ai\u002Fdeepeval",{"title":850,"url":851},"PyPI: deepeval, the latest release and Python requirement","https:\u002F\u002Fpypi.org\u002Fproject\u002Fdeepeval\u002F",{"title":853,"url":854},"DeepEval documentation: metrics introduction","https:\u002F\u002Fdeepeval.com\u002Fdocs\u002Fmetrics-introduction",{"title":856,"url":857},"DeepEval documentation: G-Eval","https:\u002F\u002Fdeepeval.com\u002Fdocs\u002Fmetrics-llm-evals",{"title":859,"url":860},"DeepEval documentation: Faithfulness","https:\u002F\u002Fdeepeval.com\u002Fdocs\u002Fmetrics-faithfulness",{"title":862,"url":592},"DeepEval documentation: Tool Correctness",{"title":864,"url":865},"DeepEval documentation: generate goldens from documents","https:\u002F\u002Fdeepeval.com\u002Fdocs\u002Fsynthesizer-generate-from-docs",{"title":867,"url":868},"DeepEval documentation: unit testing in CI\u002FCD","https:\u002F\u002Fdeepeval.com\u002Fdocs\u002Fevaluation-unit-testing-in-ci-cd",{"title":870,"url":871},"DeepEval FAQ","https:\u002F\u002Fdeepeval.com\u002Fdocs\u002Ffaq",{"title":873,"url":874},"DeepEval documentation: data privacy","https:\u002F\u002Fdeepeval.com\u002Fdocs\u002Fdata-privacy",{"title":876,"url":877},"GitHub: confident-ai\u002Fdeepteam, the red-teaming framework","https:\u002F\u002Fgithub.com\u002Fconfident-ai\u002Fdeepteam",{"title":879,"url":880},"Confident AI pricing","https:\u002F\u002Fwww.confident-ai.com\u002Fpricing",{"title":882,"url":883},"Confident AI documentation: data residency","https:\u002F\u002Fwww.confident-ai.com\u002Fdocs\u002Fsettings\u002Fdata-residency",{"title":885,"url":886},"Confident AI subprocessor list","https:\u002F\u002Fwww.confident-ai.com\u002Fsubprocessors-list",{"title":888,"url":889},"GitHub: explodinggradients\u002Fragas, the README","https:\u002F\u002Fgithub.com\u002Fexplodinggradients\u002Fragas",{"title":891,"url":892},"GitHub: promptfoo\u002Fpromptfoo, the README","https:\u002F\u002Fgithub.com\u002Fpromptfoo\u002Fpromptfoo",{"title":894,"url":895},"Braintrust pricing","https:\u002F\u002Fwww.braintrust.dev\u002Fpricing","\u002Fimages\u002Fblog\u002Fdeepeval\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fdeepeval\u002Fog.jpg",[71,72,73],"DeepEval bemutató: pytest az LLM-kimenetekhez, és a bíró számlája","A DeepEval pytest-szerű tesztekként futtat LLM-ellenőrzéseket. A könyvtár ingyenes, Apache-2.0, a valódi költség a bíróhívásokban és az adatáramlásban van.","DeepEval bemutató: bírói kalapács egy kémcsőtartó és egy nyitott pénztárca mellett.",{"slug":903,"published":814,"minutes":252,"category":8,"tags":904,"tagPages":910,"keywords":912,"about":920,"sources":931,"cover":1007,"og":1008,"expertise":69,"locales":1009,"lang":73,"title":1010,"description":1011,"coverAlt":1012},"dspy",[905,906,907,908,909],"Prompt optimisation","LLM programs","MIPROv2","GEPA","Python",[911],"python",[903,913,914,915,916,917,918,919],"dspy útmutató","dspy optimalizáló","miprov2","gepa prompt optimalizálás","dspy vagy promptmérnökség","automatikus prompt optimalizálás","dspy signature-ök",[921,924,925,928],{"name":922,"url":923},"DSPy","https:\u002F\u002Fdspy.ai",{"name":273,"url":274},{"name":926,"url":927},"Bayesian optimization","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBayesian_optimization",{"name":929,"url":930},"Fine-tuning (deep learning)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FFine-tuning_(deep_learning)",[932,935,938,941,944,947,950,953,956,959,962,965,968,971,974,977,980,983,986,989,992,995,998,1001,1004],{"title":933,"url":934},"DSPy home and cost example","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002F",{"title":936,"url":937},"DSPy installation","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fgetting-started\u002Finstallation\u002F",{"title":939,"url":940},"DSPy: program, don't prompt","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fgetting-started\u002Fprogram-dont-prompt\u002F",{"title":942,"url":943},"DSPy signatures","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fdiving-deeper\u002Fsignatures-in-depth\u002F",{"title":945,"url":946},"DSPy metrics","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fgetting-started\u002Fmetrics\u002F",{"title":948,"url":949},"DSPy Example API","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fapi\u002Fprimitives\u002FExample\u002F",{"title":951,"url":952},"DSPy Predict API","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fapi\u002Fmodules\u002FPredict\u002F",{"title":954,"url":955},"DSPy ChainOfThought API","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fapi\u002Fmodules\u002FChainOfThought\u002F",{"title":957,"url":958},"DSPy ReAct API","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fapi\u002Fmodules\u002FReAct\u002F",{"title":960,"url":961},"DSPy optimizers index","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fapi\u002Foptimizers\u002F",{"title":963,"url":964},"DSPy MIPROv2 API","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fapi\u002Foptimizers\u002FMIPROv2\u002F",{"title":966,"url":967},"MIPROv2 source code","https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy\u002Fblob\u002Fmain\u002Fdspy\u002Fteleprompt\u002Fmipro_optimizer_v2.py",{"title":969,"url":970},"DSPy BootstrapFewShot API","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fapi\u002Foptimizers\u002FBootstrapFewShot\u002F",{"title":972,"url":973},"DSPy BootstrapFinetune API","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fapi\u002Foptimizers\u002FBootstrapFinetune\u002F",{"title":975,"url":976},"DSPy GEPA guide","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fgetting-started\u002Fgepa-optimization\u002F",{"title":978,"url":979},"GEPA source code","https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy\u002Fblob\u002Fmain\u002Fdspy\u002Fteleprompt\u002Fgepa\u002Fgepa.py",{"title":981,"url":982},"DSPy saving programs","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Ftutorials\u002Fsaving\u002F",{"title":984,"url":985},"DSPy caching","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Ftutorials\u002Fcache\u002F",{"title":987,"url":988},"DSPy LM API","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Fapi\u002Fmodels\u002FLM\u002F",{"title":990,"url":991},"DSPy FAQ","https:\u002F\u002Fdspy.ai\u002Fcurrent\u002Ffaqs\u002F",{"title":993,"url":994},"DSPy GitHub repository","https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy",{"title":996,"url":997},"DSPy 3.4.0 release","https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy\u002Freleases\u002Ftag\u002F3.4.0",{"title":999,"url":1000},"DSPy paper, arXiv","https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.03714",{"title":1002,"url":1003},"MIPRO paper, arXiv","https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.11695",{"title":1005,"url":1006},"GEPA paper, arXiv","https:\u002F\u002Farxiv.org\u002Fabs\u002F2507.19457","\u002Fimages\u002Fblog\u002Fdspy\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fdspy\u002Fog.jpg",[71,72,73],"DSPy-kritika: promptok metrikához igazítva, nem kézzel","A DSPy aláírásokból, metrikából és példákból állítja elő a promptokat. Mibe kerül az optimalizálás modellhívásokban, és mikor nyer a kézzel írt prompt?","DSPy-kritika: egy robot egy gyűrött lapot kisimító gépet hajt, fölötte céltábla.",{"slug":1014,"published":1015,"updated":708,"minutes":252,"category":8,"tags":1016,"tagPages":1022,"keywords":1023,"about":1031,"sources":1039,"cover":1077,"og":1078,"expertise":69,"locales":1079,"lang":73,"title":1080,"description":1081,"coverAlt":1082},"llama-cpp","2026-10-05",[1017,1018,1019,1020,1021],"llama.cpp","GGUF","Quantisation","Local inference","llama-server",[],[1017,1024,1025,1026,1027,1028,1029,1030],"llama.cpp teszt","gguf kvantálási szintek","llama-server openai kompatibilis","llama.cpp vagy ollama","llm helyi futtatás gdpr","llama.cpp cuda metal vulkan","q4_k_m vs q8_0",[1032,1034,1035,1038],{"name":1017,"url":1033},"https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp",{"name":340,"url":341},{"name":1036,"url":1037},"Quantization (signal processing)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantization_(signal_processing)",{"name":728,"url":729},[1040,1042,1045,1048,1051,1053,1056,1059,1062,1065,1068,1070,1073,1074],{"title":1041,"url":1033},"llama.cpp repository: goals, backends, licence",{"title":1043,"url":1044},"llama.cpp releases: builds b11538 to b11541","https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\u002Freleases",{"title":1046,"url":1047},"llama.cpp build documentation: CMake flags","https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\u002Fblob\u002Fmaster\u002Fdocs\u002Fbuild.md",{"title":1049,"url":1050},"llama.cpp server README: endpoints, slots and grammars","https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\u002Fblob\u002Fmaster\u002Ftools\u002Fserver\u002FREADME.md",{"title":1052,"url":429},"llama.cpp quantisation README: bits, size and speed",{"title":1054,"url":1055},"GGUF specification in the ggml repository","https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fggml\u002Fblob\u002Fmaster\u002Fdocs\u002Fgguf.md",{"title":1057,"url":1058},"Performance of llama.cpp on Apple Silicon M-series","https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\u002Fdiscussions\u002F4167",{"title":1060,"url":1061},"Performance of llama.cpp on Nvidia CUDA","https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\u002Fdiscussions\u002F15013",{"title":1063,"url":1064},"Ollama README: supported backends and REST API","https:\u002F\u002Fgithub.com\u002Follama\u002Follama",{"title":1066,"url":1067},"LM Studio documentation: app overview","https:\u002F\u002Flmstudio.ai\u002Fdocs\u002Fapp",{"title":1069,"url":735},"vLLM README: features, hardware and licence",{"title":1071,"url":1072},"vLLM documentation: GGUF support","https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Flatest\u002Ffeatures\u002Fquantization\u002Fgguf.html",{"title":792,"url":793},{"title":1075,"url":1076},"GDPR Article 32: security of processing","https:\u002F\u002Fgdpr-info.eu\u002Fart-32-gdpr\u002F","\u002Fimages\u002Fblog\u002Fllama-cpp\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fllama-cpp\u002Fog.jpg",[71,72,73],"llama.cpp-teszt: a helyi motor az Ollama és az LM Studio alatt","A llama.cpp tiszta C\u002FC++ motor nyílt modellekhez, Metal, CUDA, Vulkan vagy CPU alatt. A GGUF-kvantálásokról, a llama-serverről és a korlátokról írok.","llama.cpp-teszt: fogaskerekes láma áll egy asztalon egy számítógép mellett.",{"slug":1084,"published":1015,"minutes":1085,"category":8,"tags":1086,"tagPages":1091,"keywords":1096,"about":1104,"sources":1116,"cover":1186,"og":1187,"expertise":69,"locales":1188,"lang":73,"title":1189,"description":1190,"coverAlt":1191},"opik",7,[1087,1088,1089,1090,710],"LLM observability","Tracing","Evaluation","OpenTelemetry",[1092,1093,1094,1095,715],"llm-observability","tracing","evaluation","opentelemetry",[1084,1097,1098,1099,1100,1101,1102,1103],"opik teszt","opik vagy langfuse","opik árak","saját szerveres llm nyomkövetés","opik opentelemetry","llm mint bíró metrikák","opik guardrailek",[1105,1108,1110,1113],{"name":1106,"url":1107},"Comet ML","https:\u002F\u002Fwww.comet.com",{"name":1090,"url":1109},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FOpenTelemetry",{"name":1111,"url":1112},"Observability (software)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FObservability_(software)",{"name":1114,"url":1115},"Apache License","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FApache_License",[1117,1120,1123,1126,1129,1132,1135,1138,1141,1144,1147,1150,1153,1156,1159,1162,1165,1168,1171,1174,1177,1180,1183],{"title":1118,"url":1119},"Opik repository README on GitHub","https:\u002F\u002Fgithub.com\u002Fcomet-ml\u002Fopik",{"title":1121,"url":1122},"Opik LICENSE file: Apache License 2.0","https:\u002F\u002Fgithub.com\u002Fcomet-ml\u002Fopik\u002Fblob\u002Fmain\u002FLICENSE",{"title":1124,"url":1125},"Opik on PyPI: package metadata","https:\u002F\u002Fpypi.org\u002Fpypi\u002Fopik\u002Fjson",{"title":1127,"url":1128},"Opik product page","https:\u002F\u002Fwww.comet.com\u002Fsite\u002Fproducts\u002Fopik\u002F",{"title":1130,"url":1131},"Opik pricing","https:\u002F\u002Fwww.comet.com\u002Fsite\u002Fpricing\u002F",{"title":1133,"url":1134},"Opik docs: run locally with Docker Compose","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fself-host\u002Flocal_deployment",{"title":1136,"url":1137},"Opik docs: Kubernetes deployment with Helm","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fself-host\u002Fkubernetes",{"title":1139,"url":1140},"Opik docs: platform architecture","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fself-host\u002Farchitecture",{"title":1142,"url":1143},"Opik docs: tracing getting started","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Ftracing\u002Fgetting-started",{"title":1145,"url":1146},"Opik docs: OpenTelemetry integration","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fintegrations\u002Fopentelemetry",{"title":1148,"url":1149},"Opik docs: evaluation metrics overview","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fevaluation\u002Fmetrics\u002Foverview",{"title":1151,"url":1152},"Opik docs: online evaluation rules","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fproduction\u002Fonline-evaluation\u002Frules",{"title":1154,"url":1155},"Opik docs: Prompt Library overview","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fdevelopment\u002Fprompt-library\u002Foverview",{"title":1157,"url":1158},"Opik docs: optimisation algorithms","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fdevelopment\u002Foptimization-runs\u002Falgorithms\u002Foverview",{"title":1160,"url":1161},"Opik docs: guardrails overview","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fguardrails\u002Foverview",{"title":1163,"url":1164},"Opik docs: guardrails server","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fguardrails\u002Fserver",{"title":1166,"url":1167},"Opik docs: SDK anonymizers","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fproduction\u002Fgateway-guardrails\u002Fanonymizers",{"title":1169,"url":1170},"Opik docs: data anonymization","https:\u002F\u002Fwww.comet.com\u002Fdocs\u002Fopik\u002Fadministration\u002Fdata_anonymization",{"title":1172,"url":1173},"Comet ML privacy policy","https:\u002F\u002Fwww.comet.com\u002Fsite\u002Fprivacy-policy\u002F",{"title":1175,"url":1176},"Comet Trust Center","https:\u002F\u002Ftrust.comet.com\u002F",{"title":1178,"url":1179},"Langfuse repository README (licence)","https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse",{"title":1181,"url":1182},"Arize Phoenix repository README (licence)","https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix",{"title":1184,"url":1185},"LangChain pricing (LangSmith plans)","https:\u002F\u002Fwww.langchain.com\u002Fpricing","\u002Fimages\u002Fblog\u002Fopik\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fopik\u002Fog.jpg",[71,72,73],"Opik teszt: nyílt forrású nyomkövetés és értékelés, amerikai felhővel","Nyomok, judge-metrikák, promptverziók és optimalizáló egy Apache 2.0 platformon. Saját szerveren ingyen, a Pro felhő havi 19 dollár, az USA-ban hosztolt.","Opik teszt: távcső egy jegyzetfüzet-kupacon, a háttérben felhő egy zászló fölött.",{"slug":1193,"published":1194,"minutes":7,"category":8,"tags":1195,"tagPages":1201,"keywords":1202,"about":1213,"sources":1223,"cover":1245,"og":1246,"expertise":69,"locales":1247,"lang":73,"title":1248,"description":1249,"coverAlt":1250},"local-text-to-speech-pipeline","2026-10-01",[1196,1197,1198,1199,1200],"Text-to-speech","Audio","Kokoro","FFmpeg","Vue",[],[1203,1204,1205,1206,1207,1208,1209,1210,1211,1212],"helyi szövegfelolvasás","szövegfelolvasó pipeline","kokoro tts","piper tts","bark tts összehasonlítás","blogcikkek felolvasása","wav m4a ffmpeg","aac 192 kbit\u002Fs faststart","vue hanglejátszó","onnx tts mac",[1214,1217,1220],{"name":1215,"url":1216},"Speech synthesis","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FSpeech_synthesis",{"name":1218,"url":1219},"Advanced Audio Coding","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAdvanced_Audio_Coding",{"name":1221,"url":1222},"ESpeak","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FESpeak",[1224,1227,1230,1233,1236,1239,1242],{"title":1225,"url":1226},"Kokoro onnx: runtime bindings","https:\u002F\u002Fgithub.com\u002Fthewh1teagle\u002Fkokoro-onnx",{"title":1228,"url":1229},"Kokoro-82M model card","https:\u002F\u002Fhuggingface.co\u002Fhexgrad\u002FKokoro-82M",{"title":1231,"url":1232},"Piper text-to-speech","https:\u002F\u002Fgithub.com\u002Frhasspy\u002Fpiper",{"title":1234,"url":1235},"Bark by Suno","https:\u002F\u002Fgithub.com\u002Fsuno-ai\u002Fbark",{"title":1237,"url":1238},"FFmpeg AAC encoder documentation","https:\u002F\u002Fffmpeg.org\u002Fffmpeg-codecs.html#aac",{"title":1240,"url":1241},"torch.load weights_only documentation","https:\u002F\u002Fpytorch.org\u002Fdocs\u002Fstable\u002Fgenerated\u002Ftorch.load.html",{"title":1243,"url":1244},"ESpeak NG phonemizer","https:\u002F\u002Fgithub.com\u002Fespeak-ng\u002Fespeak-ng","\u002Fimages\u002Fblog\u002Flocal-text-to-speech-pipeline\u002Fcover.webp","\u002Fimages\u002Fblog\u002Flocal-text-to-speech-pipeline\u002Fog.jpg",[71,72,73],"Helyi felolvasás nagyban: 96 cikk narrálása nyílt modellekkel","Három nyílt TTS-modell egy laptopon: 96 cikkből 1 512 perc felolvasás, az SQLite-soroktól a 192 kbit\u002Fs-os AAC-ig és az oldal mellett maradó lejátszóig.","Helyi felolvasás nagyban: könyvkupac egy kis számítógép mellett, kampókon fejhallgatók sora.",{"slug":1252,"published":1253,"minutes":7,"category":8,"tags":1254,"tagPages":1257,"keywords":1258,"about":1266,"sources":1270,"cover":1288,"og":1289,"expertise":69,"locales":1290,"lang":73,"title":1291,"description":1292,"coverAlt":1293},"ollama","2026-09-29",[1020,1255,1017,1018,1256],"Open models","Model serving",[],[1252,1259,1260,1261,1262,1263,1264,1265],"ollama vagy lm studio","ollama vagy vllm","helyi llm futtatókörnyezet","gguf modell szerver","ollama saját szerveren","ollama api","ollama teszt",[1267],{"name":1268,"url":1269},"Ollama (software)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FOllama",[1271,1274,1276,1279,1282,1285],{"title":1272,"url":1273},"Ollama API documentation","https:\u002F\u002Fdocs.ollama.com\u002Fapi",{"title":1275,"url":1064},"Ollama on GitHub, with the MIT LICENSE file",{"title":1277,"url":1278},"Ollama terms of service, last updated May 2026","https:\u002F\u002Follama.com\u002Fterms",{"title":1280,"url":1281},"Ollama pricing, cloud plans and per-token model rates","https:\u002F\u002Follama.com\u002Fpricing",{"title":1283,"url":1284},"Hardware support: Nvidia, AMD, Metal and Vulkan","https:\u002F\u002Fdocs.ollama.com\u002Fgpu",{"title":1286,"url":1287},"OpenAI compatibility, including what is not supported","https:\u002F\u002Fdocs.ollama.com\u002Fapi\u002Fopenai-compatibility","\u002Fimages\u002Fblog\u002Follama\u002Fcover.webp","\u002Fimages\u002Fblog\u002Follama\u002Fog.jpg",[71,72,73],"Ollama teszt: a barátságos út a nyílt modellekhez","Az Ollama egyetlen HTTP API-n szolgál ki nyílt modelleket saját hardveren. Ami jól megy, hol elmarad az átvitel, és mit nem fed le az MIT-licenc.","Ollama teszt: egy láma laptoppal kényelmesen ül egy kanapén.",{"slug":1295,"published":1296,"minutes":7,"category":8,"tags":1297,"tagPages":1302,"keywords":1304,"about":1312,"sources":1319,"cover":1371,"og":1372,"expertise":69,"locales":1373,"lang":73,"title":1374,"description":1375,"coverAlt":1376},"portkey","2026-09-28",[189,1298,1299,1300,1301],"Guardrails","Routing","Observability","Cost control",[1303],"guardrails",[1305,1306,1307,1308,1309,1310,1311],"portkey ai gateway","portkey vagy litellm","llm gateway teszt","llm gateway késleltetési többlet","llm guardrails gateway","portkey saját szerveren","portkey árak",[1313,1316],{"name":1314,"url":1315},"Portkey","https:\u002F\u002Fportkey.ai",{"name":1317,"url":1318},"API gateway","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAPI_gateway",[1320,1323,1326,1329,1332,1335,1338,1341,1344,1347,1350,1353,1356,1359,1362,1365,1368],{"title":1321,"url":1322},"Portkey docs: AI Gateway","https:\u002F\u002Fportkey.ai\u002Fdocs\u002Fproduct\u002Fai-gateway",{"title":1324,"url":1325},"Portkey docs: Getting started with the AI Gateway","https:\u002F\u002Fdocs.portkey.ai\u002Fdocs\u002Fguides\u002Fgetting-started\u002Fgetting-started-with-ai-gateway",{"title":1327,"url":1328},"Portkey docs: Gateway config object","https:\u002F\u002Fportkey.ai\u002Fdocs\u002Fapi-reference\u002Fconfig-object",{"title":1330,"url":1331},"Portkey docs: Guardrails","https:\u002F\u002Fportkey.ai\u002Fdocs\u002Fproduct\u002Fguardrails",{"title":1333,"url":1334},"Portkey docs: Guardrail endpoints and capabilities","https:\u002F\u002Fportkey.ai\u002Fdocs\u002Fproduct\u002Fguardrails\u002Fcapabilities",{"title":1336,"url":1337},"Portkey docs: Cache, simple and semantic","https:\u002F\u002Fportkey.ai\u002Fdocs\u002Fproduct\u002Fai-gateway\u002Fcache-simple-and-semantic",{"title":1339,"url":1340},"Portkey docs: Load balancing","https:\u002F\u002Fportkey.ai\u002Fdocs\u002Fproduct\u002Fai-gateway\u002Fload-balancing",{"title":1342,"url":1343},"Portkey docs: Enterprise hybrid deployment architecture","https:\u002F\u002Fportkey.ai\u002Fdocs\u002Fself-hosting\u002Fhybrid-deployments\u002Farchitecture",{"title":1345,"url":1346},"Portkey pricing","https:\u002F\u002Fportkey.ai\u002Fpricing",{"title":1348,"url":1349},"Portkey gateway on GitHub, MIT licensed","https:\u002F\u002Fgithub.com\u002FPortkey-AI\u002Fgateway",{"title":1351,"url":1352},"Portkey's own benchmark: gateway versus direct Bedrock","https:\u002F\u002Fgithub.com\u002FPortkey-AI\u002Fbenchmark-test",{"title":1354,"url":1355},"Portkey status page","https:\u002F\u002Fstatus.portkey.ai\u002F",{"title":1357,"url":1358},"Palo Alto Networks completes acquisition of Portkey, May 2026","https:\u002F\u002Fwww.paloaltonetworks.com\u002Fcompany\u002Fpress\u002F2026\u002Fpalo-alto-networks-completes-acquisition-of-portkey-to-secure-ai-agents",{"title":1360,"url":1361},"Palo Alto Networks: Prisma AIRS AI Gateway","https:\u002F\u002Fwww.paloaltonetworks.com\u002Fai-security\u002Fai-gateway",{"title":1363,"url":1364},"Cloudflare AI Gateway pricing","https:\u002F\u002Fdevelopers.cloudflare.com\u002Fai-gateway\u002Freference\u002Fpricing\u002F",{"title":1366,"url":1367},"LiteLLM pricing","https:\u002F\u002Fwww.litellm.ai\u002Fpricing",{"title":1369,"url":1370},"OpenRouter pricing","https:\u002F\u002Fopenrouter.ai\u002Fpricing","\u002Fimages\u002Fblog\u002Fportkey\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fportkey\u002Fog.jpg",[71,72,73],"Portkey: éles LLM-átjáró, routing, guardrailok és költség szerint","A Portkey OpenAI-kompatibilis végpont mögé szorítja a retry-t, fallbacket, cache-t, guardrailöket és költségkövetést. Mi működik, mibe kerül a késleltetés?","Portkey: világítótornyos kikötőkapu különböző csatornákba tereli a csónakokat.",{"slug":1378,"published":1379,"updated":1296,"minutes":252,"category":8,"tags":1380,"tagPages":1384,"keywords":1385,"about":1397,"sources":1405,"cover":1423,"og":1424,"expertise":69,"locales":1425,"lang":73,"title":1426,"description":1427,"coverAlt":1428},"artificial-analysis-leaderboard-claude-opus-5-5","2026-09-23",[1381,1382,1383,132],"Claude Opus 5.5","Artificial Analysis","LLM benchmarks",[],[1386,1387,1388,1389,1390,1391,1392,1393,1394,1395,1396],"claude opus 5.5 teszteredmények","claude opus 5.5 árak","claude opus 5.5","artificial analysis intelligence index","ai modell ranglista","opus 5.5 teszt","opus 5.5 vs gpt-6 astra összehasonlítás","llm költség feladatonként","reasoning effort beállítás","opus 5.5 ár","legjobb llm 2026 szeptember",[1398,1401,1402],{"name":1399,"url":1400},"Claude (language model)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FClaude_(language_model)",{"name":340,"url":341},{"name":1403,"url":1404},"Benchmark (computing)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBenchmark_(computing)",[1406,1409,1412,1415,1418,1421],{"title":1407,"url":1408},"Artificial Analysis: Claude Opus 5.5 takes the top spot on the Artificial Analysis Intelligence Index (22 September 2026)","https:\u002F\u002Fartificialanalysis.ai\u002Farticles\u002Fclaude-opus-5-5",{"title":1410,"url":1411},"Artificial Analysis: Claude Opus 5.5 (max) model page","https:\u002F\u002Fartificialanalysis.ai\u002Fmodels\u002Fclaude-opus-5-5",{"title":1413,"url":1414},"Artificial Analysis: Claude Opus 5.5 (medium) model page","https:\u002F\u002Fartificialanalysis.ai\u002Fmodels\u002Fclaude-opus-5-5-medium",{"title":1416,"url":1417},"Artificial Analysis: Claude Opus 5 (max) model page","https:\u002F\u002Fartificialanalysis.ai\u002Fmodels\u002Fclaude-opus-5",{"title":1419,"url":1420},"OfficeChai: Claude Opus 5.5 creates a 5-point lead over GPT-6 Astra","https:\u002F\u002Fofficechai.com\u002Fai\u002Fclaude-opus-5-5-creates-5-point-lead-over-gpt-6-astra-jumps-to-top-spot-on-artificial-analysis-intelligence-index\u002F",{"title":1422,"url":117},"Claude API docs: Models overview, context windows and prices (as of September 2026)","\u002Fimages\u002Fblog\u002Fartificial-analysis-leaderboard-claude-opus-5-5\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fartificial-analysis-leaderboard-claude-opus-5-5\u002Fog.jpg",[71,72,73],"Claude Opus 5.5 az Artificial Analysis élén, de az igazi hír a medium effort","Claude Opus 5.5 az 1. a 211 modell közül az Artificial Analysisnél, 58 ponttal. Medium efforttal hozza az Opus 5 szintjét, 5,86 $ helyett 1,34 $-ért.","Claude Opus 5.5 az Artificial Analysis élén: kupa a dobogó legfelső fokán, mellette középállású mutató.",{"slug":1430,"published":1431,"minutes":183,"category":8,"tags":1432,"tagPages":1438,"keywords":1439,"about":1449,"sources":1458,"cover":1476,"og":1477,"expertise":69,"locales":1478,"lang":73,"title":1479,"description":1480,"coverAlt":1481},"jev-typed-decisions-llm-routing","2026-09-17",[1433,1434,1435,1436,1437],"LLM routing","Classification","Calibration","OpenRouter","Human in the loop",[],[1440,1441,1442,1443,1444,1445,1446,1447,1448],"llm útvonalválasztás","llm osztályozás","kalibrált konfidencia llm","típusos llm kimenet","jev model","openrouter döntések api","ai triázs kódreview megállapításokhoz","hogyan adjuk át az alacsony bizonyosságú llm válaszokat embernek","llm osztályozó vs strukturált kimenet",[1450,1453,1456],{"name":1451,"url":1452},"Calibration (statistics)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCalibration_(statistics)",{"name":1454,"url":1455},"Statistical classification","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FStatistical_classification",{"name":1436,"url":1457},"https:\u002F\u002Fopenrouter.ai\u002F",[1459,1462,1465,1468,1471,1473],{"title":1460,"url":1461},"OpenRouter: Jev documentation","https:\u002F\u002Fopenrouter.ai\u002Fdocs\u002Fguides\u002Fcommunity\u002Fjev",{"title":1463,"url":1464},"OpenRouter: What is Jev?","https:\u002F\u002Fopenrouter.ai\u002Fblog\u002Finsights\u002Fwhat-is-jev\u002F",{"title":1466,"url":1467},"OpenRouter: How to use Jev","https:\u002F\u002Fopenrouter.ai\u002Fblog\u002Ftutorials\u002Fhow-to-use-jev\u002F",{"title":1469,"url":1470},"Guo et al.: On Calibration of Modern Neural Networks","https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.04599",{"title":1472,"url":528},"Claude API: Structured outputs",{"title":1474,"url":1475},"AI SDK: Generating structured data","https:\u002F\u002Fai-sdk.dev\u002Fdocs\u002Fai-sdk-core\u002Fgenerating-structured-data","\u002Fimages\u002Fblog\u002Fjev-typed-decisions-llm-routing\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fjev-typed-decisions-llm-routing\u002Fog.jpg",[71,72,73],"Tipizált döntések LLM routingra és triázsra: kalibrált konfidencia Jevvel","Tipizált döntések az LLM routingban: Choice, Score és igen-valószínűség válaszok kalibrált konfidenciával, küszöbökkel és átadással embernek, Jevvel.","Tipizált LLM-döntések Jevvel: egy kéz borítékot tesz három rekesz egyikébe, mellette mutatós műszer.",{"slug":1483,"published":1484,"updated":1485,"minutes":183,"category":8,"tags":1486,"tagPages":1487,"keywords":1488,"about":1496,"sources":1504,"cover":1528,"og":1529,"expertise":69,"locales":1530,"lang":73,"title":1531,"description":1532,"coverAlt":1533},"langfuse","2026-08-13","2026-10-07",[1087,1088,1090,710,1089],[1092,1093,1095,715,1094],[1483,1489,1490,1491,1492,1493,1494,261,1495],"langfuse vagy langsmith","llm nyomkövetési eszköz","saját szerveres llm megfigyelhetőség","langfuse árak","opentelemetry llm trace-ek","llm költségkövetés","langfuse teszt",[1497,1499,1500,1503],{"name":258,"url":1498},"https:\u002F\u002Flangfuse.com",{"name":1090,"url":1109},{"name":1501,"url":1502},"ClickHouse","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FClickHouse",{"name":1111,"url":1112},[1505,1508,1511,1514,1517,1520,1523,1526],{"title":1506,"url":1507},"Langfuse documentation: observability and application tracing","https:\u002F\u002Flangfuse.com\u002Fdocs\u002Fobservability\u002Foverview",{"title":1509,"url":1510},"Langfuse documentation: get started with tracing","https:\u002F\u002Flangfuse.com\u002Fdocs\u002Fobservability\u002Fget-started",{"title":1512,"url":1513},"Langfuse pricing: cloud plans, billable units and worked examples","https:\u002F\u002Flangfuse.com\u002Fpricing",{"title":1515,"url":1516},"Langfuse pricing: self-hosted plans and the feature comparison","https:\u002F\u002Flangfuse.com\u002Fpricing-self-host",{"title":1518,"url":1519},"Self-host Langfuse: deployment options, containers and storage services","https:\u002F\u002Flangfuse.com\u002Fself-hosting",{"title":1521,"url":1522},"Langfuse changelog: v4 is live (17 August 2026)","https:\u002F\u002Flangfuse.com\u002Fchangelog\u002F2026-08-17-langfuse-v4",{"title":1524,"url":1525},"Langfuse blog: Langfuse joins ClickHouse (16 January 2026)","https:\u002F\u002Flangfuse.com\u002Fblog\u002Fjoining-clickhouse",{"title":1527,"url":1179},"GitHub: langfuse\u002Flangfuse, the platform repository","\u002Fimages\u002Fblog\u002Flangfuse\u002Fcover.webp","\u002Fimages\u002Fblog\u002Flangfuse\u002Fog.jpg",[71,72,73],"Langfuse bemutatás: nyomkövetés, promptok és értékelések saját szerveren","A Langfuse MIT-licencű platform LLM-nyomokhoz, promptverziókhoz és kísérletekhez. Mibe kerül a saját telepítés, mennyi a usage-alapú ár, és hol veszít?","Langfuse bemutató: lábnyomok vezetnek egy ház kertjében egy nagyítós jegyzetfüzethez.",{"slug":1535,"published":1536,"updated":1194,"minutes":1537,"category":8,"tags":1538,"tagPages":1540,"keywords":1542,"about":1553,"sources":1557,"cover":1607,"og":1608,"expertise":69,"locales":1609,"lang":73,"title":1610,"description":1611,"coverAlt":1612},"agent-observability-opentelemetry","2026-08-06",12,[1090,1087,1539,1088,257],"AI agents",[1095,1092,1541,1093],"ai-agents",[1543,1544,1545,1546,1547,1548,1549,1550,1551,1552],"llm ügynök megfigyelhetőség opentelemetry","opentelemetry genai semantic conventions","hogyan kövessük nyomon az llm ügynököket","gen_ai semantic conventions attribútumok","llm token használat és költség mérőszámok","llm nyomkövetés mintavétel","személyes adatok llm nyomkövetésben","langfuse vs arize phoenix vs datadog összehasonlítás","ai ügynök nyomkövetés és értékelés","opentelemetry llm nyomkövetés",[1554,1555,1556],{"name":1090,"url":1109},{"name":1300,"url":1112},{"name":571,"url":572},[1558,1561,1564,1567,1570,1573,1576,1579,1582,1585,1588,1591,1594,1596,1598,1601,1604],{"title":1559,"url":1560},"OpenTelemetry: GenAI semantic conventions repository (open-telemetry\u002Fsemantic-conventions-genai)","https:\u002F\u002Fgithub.com\u002Fopen-telemetry\u002Fsemantic-conventions-genai",{"title":1562,"url":1563},"GenAI conventions: overview (status Development)","https:\u002F\u002Fgithub.com\u002Fopen-telemetry\u002Fsemantic-conventions-genai\u002Fblob\u002Fmain\u002Fdocs\u002Fgen-ai\u002FREADME.md",{"title":1565,"url":1566},"GenAI conventions: model spans, execute_tool and content capture","https:\u002F\u002Fgithub.com\u002Fopen-telemetry\u002Fsemantic-conventions-genai\u002Fblob\u002Fmain\u002Fdocs\u002Fgen-ai\u002Fgen-ai-spans.md",{"title":1568,"url":1569},"GenAI conventions: agent spans","https:\u002F\u002Fgithub.com\u002Fopen-telemetry\u002Fsemantic-conventions-genai\u002Fblob\u002Fmain\u002Fdocs\u002Fgen-ai\u002Fgen-ai-agent-spans.md",{"title":1571,"url":1572},"GenAI conventions: metrics","https:\u002F\u002Fgithub.com\u002Fopen-telemetry\u002Fsemantic-conventions-genai\u002Fblob\u002Fmain\u002Fdocs\u002Fgen-ai\u002Fgen-ai-metrics.md",{"title":1574,"url":1575},"GenAI conventions: inference token metrics","https:\u002F\u002Fgithub.com\u002Fopen-telemetry\u002Fsemantic-conventions-genai\u002Fblob\u002Fmain\u002Fdocs\u002Fgen-ai\u002Fgen-ai-token-metrics.md",{"title":1577,"url":1578},"GenAI conventions: events (gen_ai.evaluation.result)","https:\u002F\u002Fgithub.com\u002Fopen-telemetry\u002Fsemantic-conventions-genai\u002Fblob\u002Fmain\u002Fdocs\u002Fgen-ai\u002Fgen-ai-events.md",{"title":1580,"url":1581},"GenAI conventions: Model Context Protocol","https:\u002F\u002Fgithub.com\u002Fopen-telemetry\u002Fsemantic-conventions-genai\u002Fblob\u002Fmain\u002Fdocs\u002Fgen-ai\u002Fmcp.md",{"title":1583,"url":1584},"OpenTelemetry docs: GenAI conventions moved notice","https:\u002F\u002Fopentelemetry.io\u002Fdocs\u002Fspecs\u002Fsemconv\u002Fgen-ai\u002F",{"title":1586,"url":1587},"OpenTelemetry docs: Sampling","https:\u002F\u002Fopentelemetry.io\u002Fdocs\u002Fconcepts\u002Fsampling\u002F",{"title":1589,"url":1590},"John Hodge: The state of the OpenTelemetry GenAI semantic conventions (July 2026)","https:\u002F\u002Fjohn-hodge.com\u002Fblog\u002Fopentelemetry-genai-semantic-conventions\u002F",{"title":1592,"url":1593},"Langfuse docs: OpenTelemetry integration","https:\u002F\u002Flangfuse.com\u002Fdocs\u002Fopentelemetry\u002Fget-started",{"title":1595,"url":1179},"Langfuse repository and licence",{"title":1597,"url":1182},"Arize Phoenix repository",{"title":1599,"url":1600},"Arize OpenInference repository","https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fopeninference",{"title":1602,"url":1603},"Datadog docs: OpenTelemetry instrumentation for LLM Observability","https:\u002F\u002Fdocs.datadoghq.com\u002Fllm_observability\u002Finstrumentation\u002Fotel_instrumentation\u002F",{"title":1605,"url":1606},"Honeycomb docs: Send data with OpenTelemetry","https:\u002F\u002Fdocs.honeycomb.io\u002Fsend-data\u002Fopentelemetry\u002F","\u002Fimages\u002Fblog\u002Fagent-observability-opentelemetry\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fagent-observability-opentelemetry\u002Fog.jpg",[71,72,73],"LLM-ügynökök megfigyelhetősége OpenTelemetryvel: trace-ek, tokenek, PII és eval","Így trace-eld az LLM-ügynököket OpenTelemetryvel: GenAI semantic conventions és státuszuk, span-fa, token-metrikák, sampling, PII, eval és eszközök.","OpenTelemetry LLM-ügynökökhöz: egy sztetoszkóp fekszik egy kis roboton, mellette nyugodt hullámvonalat mutató monitor.",{"slug":1614,"published":1615,"updated":1616,"minutes":183,"category":8,"tags":1617,"tagPages":1621,"keywords":1622,"about":1630,"sources":1633,"cover":1648,"og":1649,"expertise":69,"locales":1650,"lang":73,"title":1651,"description":1652,"coverAlt":1653},"openrouter","2026-07-23","2026-08-01",[189,85,1618,1619,1620],"Fallbacks","OpenAI-compatible","Pay per token",[],[1614,1623,1624,1625,1626,1627,1628,1629],"openrouter vagy litellm","llm átjárók összehasonlítása","openrouter árak","openai kompatibilis api gateway","llm fallback útvonalválasztás","többmodelles api gateway","byok llm routing",[1631],{"name":1436,"url":1632},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FOpenRouter",[1634,1637,1638,1640,1643,1646],{"title":1635,"url":1636},"OpenRouter documentation: quickstart","https:\u002F\u002Fopenrouter.ai\u002Fdocs\u002Fquickstart",{"title":1369,"url":1370},{"title":1639,"url":240},"OpenRouter documentation: model fallbacks",{"title":1641,"url":1642},"OpenRouter documentation: provider routing","https:\u002F\u002Fopenrouter.ai\u002Fdocs\u002Fguides\u002Frouting\u002Fprovider-selection",{"title":1644,"url":1645},"OpenRouter documentation index","https:\u002F\u002Fopenrouter.ai\u002Fdocs\u002Fllms.txt",{"title":1647,"url":1632},"Wikipedia: OpenRouter","\u002Fimages\u002Fblog\u002Fopenrouter\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fopenrouter\u002Fog.jpg",[71,72,73],"OpenRouter: egy API-kulcs a katalógus minden modellje előtt","OpenRouter egyetlen OpenAI-kompatibilis végpont mögé tereli a 80+ szolgáltató 500+ modelljét, fallbackkel és listaáron. Mennyibe kerül, és hol törik el.","OpenRouter: egy kulcs a karikán, amelyből vonalak futnak különböző ajtók felé.",{"slug":1655,"published":1656,"minutes":183,"category":8,"tags":1657,"tagPages":1660,"keywords":1661,"about":1668,"sources":1674,"cover":1696,"og":1697,"expertise":69,"locales":1698,"lang":73,"title":1699,"description":1700,"coverAlt":1701},"braintrust","2026-07-07",[1658,1300,818,1659,1088],"Evaluations","CI gates",[1093],[1655,1662,1663,1664,1665,1666,1667],"braintrust árak","braintrust értékelés","autoevals könyvtár","braintrust vs langfuse összehasonlítás","llm értékelő platform","eval vezérelt fejlesztés",[1669,1672,1673],{"name":1670,"url":1671},"Braintrust","https:\u002F\u002Fwww.braintrust.dev",{"name":279,"url":280},{"name":1111,"url":1112},[1675,1677,1680,1683,1686,1689,1692,1694],{"title":1676,"url":895},"Braintrust pricing: plans, credits and usage rates",{"title":1678,"url":1679},"Braintrust documentation: plans and limits","https:\u002F\u002Fwww.braintrust.dev\u002Fdocs\u002Fplans-and-limits",{"title":1681,"url":1682},"Braintrust documentation: evaluation quickstart","https:\u002F\u002Fwww.braintrust.dev\u002Fdocs\u002Fevaluation-quickstart",{"title":1684,"url":1685},"Braintrust documentation: get started","https:\u002F\u002Fwww.braintrust.dev\u002Fdocs",{"title":1687,"url":1688},"GitHub: Braintrust organisation repositories","https:\u002F\u002Fgithub.com\u002Forgs\u002Fbraintrustdata\u002Frepositories",{"title":1690,"url":1691},"Arize Phoenix documentation: self-hosting","https:\u002F\u002Farize.com\u002Fdocs\u002Fphoenix\u002Fself-hosting",{"title":1693,"url":1513},"Langfuse pricing: cloud plans and billable units",{"title":1695,"url":1185},"LangChain pricing: LangSmith plans","\u002Fimages\u002Fblog\u002Fbraintrust\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fbraintrust\u002Fog.jpg",[71,72,73],"Braintrust teszt: eval-first observability kemény mérőórával","A Braintrust termelési trace-ekből épít datasetet és kapuzott kísérleteket. Mit ad a Starter és a Pro, és hol győz a Phoenix, a Langfuse vagy a LangSmith?","Braintrust teszt: három, különböző szintig töltött kémcső áll egy nagy kerek mérőóra mellett.",{"slug":1703,"published":1656,"updated":1485,"minutes":183,"category":8,"tags":1704,"tagPages":1707,"keywords":1708,"about":1716,"sources":1720,"cover":1744,"og":1745,"expertise":69,"locales":1746,"lang":73,"title":1747,"description":1748,"coverAlt":1749},"promptfoo",[816,819,1705,1706],"Prompt testing","CI\u002FCD",[],[1703,1709,1710,1711,1712,1713,1714,1715],"promptfoo vagy langsmith","llm evals keretrendszer","promptfoo red teaming","promptfooconfig.yaml példa","llm evals ci-ban","promptfoo árak","nyílt forráskódú llm tesztelés",[1717],{"name":1718,"url":1719},"Promptfoo","https:\u002F\u002Fwww.promptfoo.dev",[1721,1724,1727,1730,1733,1736,1739,1741],{"title":1722,"url":1723},"Promptfoo documentation: intro","https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fintro\u002F",{"title":1725,"url":1726},"Promptfoo pricing: plan comparison","https:\u002F\u002Fwww.promptfoo.dev\u002Fpricing",{"title":1728,"url":1729},"Promptfoo documentation: getting started","https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fgetting-started\u002F",{"title":1731,"url":1732},"Promptfoo documentation: command line usage (exit codes)","https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fusage\u002Fcommand-line\u002F",{"title":1734,"url":1735},"Promptfoo documentation: red team plugins","https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fred-team\u002Fplugins\u002F",{"title":1737,"url":1738},"Promptfoo documentation: assertions and metrics","https:\u002F\u002Fwww.promptfoo.dev\u002Fdocs\u002Fconfiguration\u002Fexpected-outputs\u002F",{"title":1740,"url":892},"Promptfoo repository on GitHub (MIT licence)",{"title":1742,"url":1743},"Promptfoo blog: Promptfoo is joining OpenAI (9 March 2026)","https:\u002F\u002Fwww.promptfoo.dev\u002Fblog\u002Fpromptfoo-joining-openai\u002F","\u002Fimages\u002Fblog\u002Fpromptfoo\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fpromptfoo\u002Fog.jpg",[71,72,73],"Promptfoo: LLM-evalok és red teaming egyetlen YAML-fájlból","Promptfoo 2026: MIT-licencű evalok és red teaming, 2026 márciusától az OpenAI-hoz tartozik. Mi működik jól, hol törik meg a YAML, és mennyibe kerül?","Promptfoo: csíptetős tábla egy céltáblasor előtt, egy nyíl mellé repül.",{"slug":1751,"published":1752,"updated":1194,"minutes":183,"category":8,"tags":1753,"tagPages":1755,"keywords":1756,"about":1764,"sources":1769,"cover":1792,"og":1793,"expertise":69,"locales":1794,"lang":73,"title":1795,"description":1796,"coverAlt":1797},"arize-phoenix","2026-07-03",[1087,1088,1090,1754,1089],"Self-hosted",[1092,1093,1095,1094],[1757,1758,1759,1760,1761,1762,1763],"arize phoenix","phoenix arize saját szerveren","arize phoenix docker telepítés","llm nyomkövetés opentelemetry","phoenix vagy langfuse összehasonlítás","openinference instrumentation","llm megfigyelhetőségi eszközök",[1765,1767,1768],{"name":1766,"url":1182},"Arize Phoenix",{"name":1090,"url":1109},{"name":1111,"url":1112},[1770,1773,1775,1778,1781,1784,1787,1790,1791],{"title":1771,"url":1772},"Arize Phoenix: open-source AI observability and evaluation","https:\u002F\u002Fphoenix.arize.com",{"title":1774,"url":1691},"Phoenix documentation: self-hosting",{"title":1776,"url":1777},"Phoenix documentation: licence, Elastic License 2.0","https:\u002F\u002Farize.com\u002Fdocs\u002Fphoenix\u002Fself-hosting\u002Flicense",{"title":1779,"url":1780},"Phoenix documentation: architecture, storage and scaling","https:\u002F\u002Farize.com\u002Fdocs\u002Fphoenix\u002Fself-hosting\u002Farchitecture",{"title":1782,"url":1783},"Phoenix documentation: setup tracing","https:\u002F\u002Farize.com\u002Fdocs\u002Fphoenix\u002Ftracing\u002Fhow-to-tracing\u002Fsetup-tracing",{"title":1785,"url":1786},"Phoenix documentation: OpenTelemetry SDK setup","https:\u002F\u002Farize.com\u002Fdocs\u002Fphoenix\u002Ftracing\u002Fhow-to-tracing\u002Fsetup-tracing\u002Fsetup-using-phoenix-otel",{"title":1788,"url":1789},"Arize pricing: AX Free, AX Pro and AX Enterprise","https:\u002F\u002Farize.com\u002Fpricing\u002F",{"title":1693,"url":1513},{"title":1695,"url":1185},"\u002Fimages\u002Fblog\u002Farize-phoenix\u002Fcover.webp","\u002Fimages\u002Fblog\u002Farize-phoenix\u002Fog.jpg",[71,72,73],"Arize Phoenix teszt: LLM-tracing és értékelés saját szerveren","Az Arize Phoenix ELv2 licencű trace- és értékelőszerver, saját adatbázison. Mibe kerül, és hol jobb nála a Langfuse, a Braintrust vagy a LangSmith?","Arize Phoenix teszt: egy főnixmadár száll fel egy kis ház fölé, a tetőn nagyító fekszik.",{"slug":1799,"published":1752,"minutes":183,"category":8,"tags":1800,"tagPages":1804,"keywords":1805,"about":1812,"sources":1816,"cover":1844,"og":1845,"expertise":69,"locales":1846,"lang":73,"title":1847,"description":1848,"coverAlt":1849},"helicone",[1087,1090,1801,1802,1803],"Cost tracking","Proxy","Gateway",[1092,1095],[1799,1806,1807,1808,1809,1810,1491,1811],"helicone llm megfigyelhetőség","helicone vagy langfuse","llm költségkövetés eszköz","nyílt forráskódú llm megfigyelhetőség","llm átjáró proxy","helicone gyorsítótár",[1813,1814],{"name":1090,"url":1109},{"name":1815,"url":1115},"Apache License 2.0",[1817,1820,1823,1826,1829,1832,1835,1838,1841],{"title":1818,"url":1819},"Helicone quickstart","https:\u002F\u002Fdocs.helicone.ai\u002Fgetting-started\u002Fquick-start",{"title":1821,"url":1822},"Helicone AI Gateway overview","https:\u002F\u002Fdocs.helicone.ai\u002Fgateway\u002Foverview",{"title":1824,"url":1825},"Helicone: latency impact and benchmark","https:\u002F\u002Fdocs.helicone.ai\u002Freferences\u002Flatency-affect",{"title":1827,"url":1828},"Helicone: proxy versus async integration","https:\u002F\u002Fdocs.helicone.ai\u002Freferences\u002Fproxy-vs-async",{"title":1830,"url":1831},"Helicone: LLM caching","https:\u002F\u002Fdocs.helicone.ai\u002Ffeatures\u002Fadvanced-usage\u002Fcaching",{"title":1833,"url":1834},"Helicone: self-hosting with Docker","https:\u002F\u002Fdocs.helicone.ai\u002Fgetting-started\u002Fself-deploy-docker",{"title":1836,"url":1837},"Helicone: how we calculate cost","https:\u002F\u002Fdocs.helicone.ai\u002Ffaq\u002Fhow-we-calculate-cost",{"title":1839,"url":1840},"Helicone pricing","https:\u002F\u002Fwww.helicone.ai\u002Fpricing",{"title":1842,"url":1843},"Helicone repository on GitHub","https:\u002F\u002Fgithub.com\u002FHelicone\u002Fhelicone","\u002Fimages\u002Fblog\u002Fhelicone\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fhelicone\u002Fog.jpg",[71,72,73],"Helicone: observability, ami a kérés útjában ül","A Helicone Apache-2.0 licencű LLM gateway és megfigyelési platform. Mit ad a proxy-architektúra, mibe kerül, és hogyan néz ki a saját üzemeltetés?","Helicone observability: kerekes boríték halad át egy kamerás fizetőkapun.",{"slug":1851,"published":1852,"minutes":7,"category":8,"tags":1853,"tagPages":1859,"keywords":1860,"about":1868,"sources":1871,"cover":1899,"og":1900,"expertise":69,"locales":1901,"lang":73,"title":1902,"description":1903,"coverAlt":1904},"vllm","2026-06-30",[1854,1855,1856,1857,1858],"Self-hosted inference","OpenAI API","PagedAttention","GPU serving","LLM runtime",[],[1851,1861,1862,1863,1864,1865,1866,1867],"vllm teszt","vllm vagy sglang","vllm vagy tensorrt-llm","saját szerveres llm szerver","openai kompatibilis api szerver","llm inferencia áteresztőképesség","pagedattention",[1869],{"name":712,"url":1870},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FvLLM",[1872,1875,1878,1881,1884,1887,1890,1893,1896],{"title":1873,"url":1874},"vLLM documentation","https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Flatest\u002F",{"title":1876,"url":1877},"Optimization and tuning for the V1 engine","https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Fstable\u002Fconfiguration\u002Foptimization.html",{"title":1879,"url":1880},"Architecture overview and the V1 process layout","https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Fstable\u002Fdesign\u002Farch_overview.html",{"title":1882,"url":1883},"Metrics design","https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Fstable\u002Fdesign\u002Fmetrics.html",{"title":1885,"url":1886},"Automatic prefix caching and its documented limits","https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Fstable\u002Ffeatures\u002Fautomatic_prefix_caching.html",{"title":1888,"url":1889},"Online serving and the HTTP API surface","https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Fstable\u002Fserving\u002Fonline_serving.html",{"title":1891,"url":1892},"Inside vLLM: anatomy of a high-throughput inference system","https:\u002F\u002Fvllm.ai\u002Fblog\u002F2025-09-05-anatomy-of-vllm",{"title":1894,"url":1895},"vLLM: easy, fast and cheap LLM serving with PagedAttention","https:\u002F\u002Fblog.vllm.ai\u002F2023\u002F06\u002F20\u002Fvllm.html",{"title":1897,"url":1898},"Release history","https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm\u002Freleases","\u002Fimages\u002Fblog\u002Fvllm\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fvllm\u002Fog.jpg",[71,72,73],"vLLM áttekintve: inferencia saját hardveren","A vLLM egy Hugging Face checkpointból OpenAI-kompatibilis szervert épít: mit ad a PagedAttention és a folyamatos batching, és mit kerül az üzemeltetés.","vLLM áttekintés: egy nagy kemence egyszerre sok tepsi kenyeret süt, mellette sütőlapát.",{"slug":1906,"published":1907,"updated":1908,"minutes":314,"category":8,"tags":1909,"tagPages":1910,"keywords":1911,"about":1920,"sources":1925,"cover":1933,"og":1934,"expertise":69,"locales":1935,"lang":73,"title":1936,"description":1937,"coverAlt":1938},"llm-cost-latency-prompt-caching-routing","2026-06-01","2026-09-22",[318,85,132,664],[],[1912,1913,1914,1915,1916,1917,1918,1919],"prompt gyorsítótárazás","llm költségoptimalizálás","llm késleltetés","modell routing","batch api llm","llm költség kérésenként","olcsóbb llm modell","llm gyorsítótár találati arány",[1921,1922],{"name":340,"url":341},{"name":1923,"url":1924},"Latency (engineering)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLatency_(engineering)",[1926,1929,1932],{"title":1927,"url":1928},"Claude API docs: Prompt caching","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fdocs\u002Fbuild-with-claude\u002Fprompt-caching",{"title":1930,"url":1931},"OpenAI API docs: Prompt caching","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Fprompt-caching",{"title":1422,"url":117},"\u002Fimages\u002Fblog\u002Fllm-cost-latency-prompt-caching-routing\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fllm-cost-latency-prompt-caching-routing\u002Fog.jpg",[71,72,73],"Prompt caching és modell-routing: LLM költség és késleltetés csökkentése","A prompt caching, a kisebb modellre routing és a batch API-k csökkentik az éles LLM költségét és késleltetését. Így használd mindegyiket.","Prompt caching és modell-routing: váltó osztja a sínt kis és nagy állomás felé, mellette óra és érme.",{"slug":1940,"published":1941,"updated":1942,"minutes":183,"category":8,"tags":1943,"tagPages":1945,"keywords":1946,"about":1954,"sources":1960,"cover":1981,"og":1982,"expertise":69,"locales":1983,"lang":73,"title":1984,"description":1985,"coverAlt":1986},"langsmith","2026-05-25","2026-09-14",[1088,611,1090,1944,186],"Datasets",[1093,1095],[1940,1947,1948,1949,1950,1552,1951,1952,1953],"langsmith vagy langfuse","llm observability eszközök","llm nyomkövetés költsége","langsmith díjszabás","llm értékelő adathalmazok","phoenix vagy langsmith","langsmith teszt",[1955,1958,1959],{"name":1956,"url":1957},"LangSmith","https:\u002F\u002Fwww.langchain.com\u002Flangsmith",{"name":1090,"url":1109},{"name":1501,"url":1502},[1961,1963,1966,1969,1972,1975,1978],{"title":1962,"url":1185},"LangSmith pricing",{"title":1964,"url":1965},"LangSmith documentation: administration overview","https:\u002F\u002Fdocs.langchain.com\u002Flangsmith\u002Fadministration-overview",{"title":1967,"url":1968},"LangSmith documentation: custom instrumentation","https:\u002F\u002Fdocs.langchain.com\u002Flangsmith\u002Fannotate-code",{"title":1970,"url":1971},"LangSmith documentation: trace with OpenTelemetry","https:\u002F\u002Fdocs.langchain.com\u002Flangsmith\u002Ftrace-with-opentelemetry",{"title":1973,"url":1974},"LangSmith documentation: evaluation concepts","https:\u002F\u002Fdocs.langchain.com\u002Flangsmith\u002Fevaluation-concepts",{"title":1976,"url":1977},"LangSmith documentation: self-hosted deployment","https:\u002F\u002Fdocs.langchain.com\u002Flangsmith\u002Fself-hosted",{"title":1979,"url":1980},"LangChain: introducing end-to-end OpenTelemetry support in LangSmith","https:\u002F\u002Fwww.langchain.com\u002Fblog\u002Fend-to-end-opentelemetry-langsmith","\u002Fimages\u002Fblog\u002Flangsmith\u002Fcover.webp","\u002Fimages\u002Fblog\u002Flangsmith\u002Fog.jpg",[71,72,73],"LangSmith: egy observability termékből lett agentplatform","LangSmith-teszt: számlázás trace-enként, 14 és 180 napos megőrzés, OpenTelemetry-ingest, offline és online értékelés, hol csúszik az agent runtime felé.","LangSmith: kalapácsos üllő áll egy távcső és egy növekvő növény mellett.",{"slug":1988,"published":1989,"updated":1990,"minutes":183,"category":8,"tags":1991,"tagPages":1993,"keywords":1995,"about":2004,"sources":2011,"cover":2030,"og":2031,"expertise":69,"locales":2032,"lang":73,"title":2033,"description":2034,"coverAlt":2035},"litellm","2026-05-20","2026-07-09",[189,1619,1301,1299,1992],"MCP",[1994],"mcp",[1988,1996,1997,1998,1999,2000,2001,2002,2003],"litellm vagy portkey","llm gateway összehasonlítás","openai kompatibilis proxy","litellm proxy konfiguráció yaml","llm költségkövetés csapatonként","saját szerveres llm gateway","llm költségkeret betartatása","litellm teszt",[2005,2008],{"name":2006,"url":2007},"LiteLLM","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLiteLLM",{"name":2009,"url":2010},"Model Context Protocol","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FModel_Context_Protocol",[2012,2015,2018,2020,2023,2026,2029],{"title":2013,"url":2014},"LiteLLM documentation: getting started","https:\u002F\u002Fdocs.litellm.ai\u002Fdocs\u002F",{"title":2016,"url":2017},"LiteLLM documentation: virtual keys","https:\u002F\u002Fdocs.litellm.ai\u002Fdocs\u002Fproxy\u002Fvirtual_keys",{"title":2019,"url":234},"LiteLLM documentation: router and load balancing",{"title":2021,"url":2022},"LiteLLM documentation: benchmarks","https:\u002F\u002Fdocs.litellm.ai\u002Fdocs\u002Fbenchmarks",{"title":2024,"url":2025},"LiteLLM documentation: supported endpoints","https:\u002F\u002Fdocs.litellm.ai\u002Fdocs\u002Fsupported_endpoints",{"title":2027,"url":2028},"LiteLLM documentation: Enterprise","https:\u002F\u002Fdocs.litellm.ai\u002Fdocs\u002Fenterprise",{"title":1366,"url":1367},"\u002Fimages\u002Fblog\u002Flitellm\u002Fcover.webp","\u002Fimages\u002Fblog\u002Flitellm\u002Fog.jpg",[71,72,73],"LiteLLM: az OpenAI-kompatibilis gateway, amit a legtöbb platformcsapat futtat","A LiteLLM a legtöbb platformcsapat előtt futó, MIT-licencelt OpenAI-kompatibilis gateway. Ami jól megy, mi kerül és hol törik el.","LiteLLM: egy univerzális úti adapter több különböző csatlakozót fog össze egy kábelbe.",{"slug":2037,"published":1989,"minutes":183,"category":8,"tags":2038,"tagPages":2043,"keywords":2044,"about":2053,"sources":2056,"cover":2074,"og":2075,"expertise":69,"locales":2076,"lang":73,"title":2077,"description":2078,"coverAlt":2079},"ragas",[2039,554,2040,2041,2042],"RAG evaluation","Test sets","CI quality","Ragas",[],[2037,2045,2046,2047,2048,2049,2050,2051,2052],"ragas útmutató","ragas vagy deepeval","llm értékelési metrikák","faithfulness context precision","rag értékelő keretrendszer","llm bíró ragas","ragas árak","ragas teszt",[2054],{"name":2042,"url":2055},"https:\u002F\u002Fwww.ragas.io\u002F",[2057,2060,2063,2066,2069,2072],{"title":2058,"url":2059},"Ragas documentation: introduction","https:\u002F\u002Fdocs.ragas.io\u002Fen\u002Fstable\u002F",{"title":2061,"url":2062},"Ragas documentation: quick start","https:\u002F\u002Fdocs.ragas.io\u002Fen\u002Fstable\u002Fgetstarted\u002Fquickstart\u002F",{"title":2064,"url":2065},"Ragas documentation: list of available metrics","https:\u002F\u002Fdocs.ragas.io\u002Fen\u002Fstable\u002Fconcepts\u002Fmetrics\u002Favailable_metrics\u002F",{"title":2067,"url":2068},"Ragas on GitHub","https:\u002F\u002Fgithub.com\u002Fvibrantlabsai\u002Fragas",{"title":2070,"url":2071},"Ragas on PyPI","https:\u002F\u002Fpypi.org\u002Fproject\u002Fragas\u002F",{"title":2073,"url":2055},"Ragas website","\u002Fimages\u002Fblog\u002Fragas\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fragas\u002Fog.jpg",[71,72,73],"Ragas-ismertető: az alapértelmezett RAG-evalualációs keretrendszer","A Ragas faithfulness, context precision és recall metrikákkal méri a RAG- és ügynök-pipeline-okat. Tesztadat, kísérletek, Apache-2.0, a bíró külön fizetős.","Ragas-ismertető: pipálható bizonyítvány csillaggal, mellette könyvek és ceruza.",{"slug":2081,"published":2082,"updated":1431,"minutes":252,"category":8,"tags":2083,"tagPages":2086,"keywords":2087,"about":2096,"sources":2099,"cover":2116,"og":2117,"expertise":69,"locales":2118,"lang":73,"title":2119,"description":2120,"coverAlt":2121},"llm-evals-for-product-features","2026-05-15",[611,554,2084,2085],"Error analysis","CI",[],[2088,2089,2090,2091,2092,2093,2094,2095],"llm evals","ai evals","llm mint bíró","eval-driven fejlesztés","pass^k vs pass@k","ügynök értékelő keretrendszer","regressziós eval csomag","hibaelemzés llm",[2097,2098],{"name":340,"url":341},{"name":574,"url":575},[2100,2102,2105,2108,2111,2113],{"title":2101,"url":580},"Anthropic: Demystifying evals for AI agents (2026)",{"title":2103,"url":2104},"Hamel Husain: LLM evals FAQ (updated September 2026)","https:\u002F\u002Fhamel.dev\u002Fblog\u002Fposts\u002Fevals-faq\u002F",{"title":2106,"url":2107},"Shankar et al., Who Validates the Validators? (2024)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.12272",{"title":2109,"url":2110},"Zheng et al., Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena (2023)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05685",{"title":2112,"url":583},"Yao et al., tau-bench: A Benchmark for Tool-Agent-User Interaction (2024)",{"title":2114,"url":2115},"Anthropic: Quantifying infrastructure noise in agentic coding evals (2026)","https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Finfrastructure-noise","\u002Fimages\u002Fblog\u002Fllm-evals-for-product-features\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fllm-evals-for-product-features\u002Fog.jpg",[71,72,73],"LLM evals termékfunkciókhoz: a valódi trace-ektől a CI kapuig","Az LLM evals teszthalmazzá alakítja a benyomást: hibaelemzés valódi trace-eken, grader választás, validált LLM-as-judge, pass^k és CI kapu.","LLM evalok termékfunkciókhoz: dobozok haladnak át egy ellenőrző kapun a szalagon, egy ember nagyítóval olvas.",{"slug":2123,"published":2124,"updated":2125,"minutes":1537,"category":8,"tags":2126,"tagPages":2130,"keywords":2132,"about":2143,"sources":2149,"cover":2216,"og":2217,"expertise":69,"locales":2218,"lang":73,"title":2219,"description":2220,"coverAlt":2221},"fine-tuning-vs-rag-vs-prompting","2026-05-12","2026-10-02",[2127,2128,2129,373],"Fine-tuning","RAG","Prompting",[2131],"rag",[2133,2134,2135,2136,2137,2138,2139,2140,2141,2142],"finomhangolás vs rag","prompting vs rag vs finomhangolás","mikor finomhangoljunk llm-et","rag vagy finomhangolás","sft vs dpo vs rft","megerősítéses finomhangolás","llm desztilláció","openai finomhangolás leállítás","finomhangolás döntési fa","finomhangolás tudásért",[2144,2145,2148],{"name":929,"url":930},{"name":2146,"url":2147},"Retrieval-augmented generation","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FRetrieval-augmented_generation",{"name":393,"url":394},[2150,2153,2156,2159,2162,2165,2168,2171,2174,2177,2180,2183,2186,2189,2192,2195,2198,2201,2204,2207,2210,2213],{"title":2151,"url":2152},"OpenAI community: OpenAI self-serve fine-tuning availability (wind-down announcement)","https:\u002F\u002Fcommunity.openai.com\u002Ft\u002Fopenai-s-self-serve-fine-tuning-availability\u002F1380481",{"title":2154,"url":2155},"Tessl: OpenAI is shutting down self-serve fine-tuning","https:\u002F\u002Ftessl.io\u002Fblog\u002Fopenai-shutting-fine-tuning-signals-for-enterprise-ai",{"title":2157,"url":2158},"OpenAI API docs: Model optimization","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Fmodel-optimization",{"title":2160,"url":2161},"OpenAI API docs: Supervised fine-tuning","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Fsupervised-fine-tuning",{"title":2163,"url":2164},"OpenAI API docs: Direct preference optimization","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Fdirect-preference-optimization",{"title":2166,"url":2167},"OpenAI API docs: Reinforcement fine-tuning","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Freinforcement-fine-tuning",{"title":2169,"url":2170},"OpenAI API docs: Fine-tuning best practices","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Ffine-tuning-best-practices",{"title":2172,"url":2173},"OpenAI Cookbook: Choosing between SFT, DPO and RFT","https:\u002F\u002Fdevelopers.openai.com\u002Fcookbook\u002Fexamples\u002Ffine_tuning_direct_preference_optimization_guide",{"title":2175,"url":2176},"Google Cloud: Introduction to tuning (Gemini)","https:\u002F\u002Fdocs.cloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Fmodels\u002Ftune-models",{"title":2178,"url":2179},"Google Cloud: About supervised fine-tuning for Gemini models","https:\u002F\u002Fdocs.cloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Fmodels\u002Fgemini-supervised-tuning",{"title":2181,"url":2182},"Google Cloud: About preference tuning for Gemini models","https:\u002F\u002Fdocs.cloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Fmodels\u002Fgemini-preference-tuning",{"title":2184,"url":2185},"Google Cloud: Reward functions for reinforcement learning fine-tuning","https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform\u002Fmodels\u002Ftuning\u002Freinforcement-tuning\u002Freinforcement-tuning-job\u002Freward-functions",{"title":2187,"url":2188},"Google Cloud: Supervised and distillation fine-tuning for open models","https:\u002F\u002Fdocs.cloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Fmodels\u002Fopen-model-tuning",{"title":2190,"url":2191},"AWS: Fine-tuning for Claude 3 Haiku in Amazon Bedrock is now generally available","https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Faws\u002Ffine-tuning-for-anthropics-claude-3-haiku-model-in-amazon-bedrock-is-now-generally-available\u002F",{"title":2193,"url":2194},"AWS: Amazon Bedrock now supports reinforcement fine-tuning","https:\u002F\u002Faws.amazon.com\u002Fabout-aws\u002Fwhats-new\u002F2025\u002F12\u002Fbedrock-reinforcement-fine-tuning-66-base-models",{"title":2196,"url":2197},"AWS: Amazon Bedrock Model Distillation (preview announcement)","https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Faws\u002Fbuild-faster-more-cost-efficient-highly-accurate-models-with-amazon-bedrock-model-distillation-preview\u002F",{"title":2199,"url":2200},"AWS: Bedrock reinforcement fine-tuning adds open-weight models","https:\u002F\u002Faws.amazon.com\u002Fabout-aws\u002Fwhats-new\u002F2026\u002F02\u002Famazon-bedrock-reinforcement-fine-tuning-openai",{"title":2202,"url":2203},"Microsoft Foundry blog: What is new in Foundry fine-tuning, April 2026","https:\u002F\u002Fdevblogs.microsoft.com\u002Ffoundry\u002Fwhats-new-in-foundry-finetune-april-2026\u002F",{"title":2205,"url":2206},"Microsoft: Announcing new fine-tuning models and techniques in Azure AI Foundry","https:\u002F\u002Fazure.microsoft.com\u002Fen-us\u002Fblog\u002Fannouncing-new-fine-tuning-models-and-techniques-in-azure-ai-foundry\u002F",{"title":2208,"url":2209},"Ovadia et al.: Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs","https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05934",{"title":2211,"url":2212},"Gekhman et al.: Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?","https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.05904",{"title":2214,"url":2215},"Hu et al.: LoRA: Low-Rank Adaptation of Large Language Models","https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.09685","\u002Fimages\u002Fblog\u002Ffine-tuning-vs-rag-vs-prompting\u002Fcover.webp","\u002Fimages\u002Fblog\u002Ffine-tuning-vs-rag-vs-prompting\u002Fog.jpg",[71,72,73],"Prompting vs RAG vs fine-tuning vs distillation: döntési útmutató 2026-ra","Fine-tuning, RAG vagy prompting: mit változtat (tudás, viselkedés, formátum), mennyibe kerül, ki kínál 2026-ban SFT-t, DPO-t és RFT-t, és egy döntési fa.","Prompting vs RAG vs fine-tuning: térképes robot négy ajtó előtt, szövegbuborék, könyvek, csavarkulcs és tölcsér jelekkel.",1793451238935]