[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-172b79615a38a463-build-agent-evals-traces-to-experiments-summary":3,"summaries-facets-categories":341,"summary-related-172b79615a38a463-build-agent-evals-traces-to-experiments-summary":3976},{"id":4,"title":5,"ai":6,"body":13,"categories":303,"created_at":305,"date_modified":305,"description":80,"extension":306,"faq":305,"featured":307,"kicker_label":305,"meta":308,"navigation":321,"path":322,"published_at":323,"question":305,"scraped_at":324,"seo":325,"sitemap":326,"source_id":327,"source_name":328,"source_type":329,"source_url":330,"stem":331,"tags":332,"thumbnail_url":336,"tldr":337,"tweet":338,"unknown_tags":339,"__hash__":340},"summaries\u002Fsummaries\u002F172b79615a38a463-build-agent-evals-traces-to-experiments-summary.md","Build Agent Evals: Traces to Experiments",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8665,2929,36004,0.00317485,{"type":14,"value":15,"toc":296},"minimark",[16,21,25,28,35,39,47,50,56,61,65,68,74,121,128,142,148,183,186,192,197,201,208,228,231,237,243,249,255,260,264,292],[17,18,20],"h2",{"id":19},"replace-vibes-testing-with-systematic-evals-to-catch-regressions","Replace Vibes Testing with Systematic Evals to Catch Regressions",[22,23,24],"p",{},"Agents fail silently on untested inputs like adversarial queries, edge cases, or simplistic user phrasing because traditional unit tests break on non-deterministic outputs—same prompt yields varying but potentially correct text. Human review doesn't scale, misses regressions in CI, and can't validate model switches or prompt tweaks without retesting everything. Evals solve this by treating traces (nested JSON logs of LLM\u002Ftool calls with inputs, outputs, metadata like tokens\u002Ftiming) as test data. Run evals in CI to ensure prompt fixes don't hallucinate features or alter tone adversely. Real teams like Decrypt, Bolt, and Anthropic iterated from vibes to evals for production agents.",[22,26,27],{},"Agents amplify issues via cascading failures: wrong tool choice, bad parameters, misparsed tool output, or multi-agent routing errors compound into disasters like confusing Tesla (car) with Nikola Tesla in reports. Evals must handle non-prescriptive paths—agents evolve with model upgrades, finding clever shortcuts that break rigid tests. Distinguish capability evals (hard tasks to benchmark improvement) from regression evals (ensure baselines hold). Eval outputs include score, label, and LLM explanations revealing patterns like systematic prompt flaws vs. one-offs.",[22,29,30,34],{},[31,32,33],"strong",{},"Quote:"," \"The usual fix unit test doesn't work here... because the same prompt will produce different text on every single run, but those outputs might all be correct.\"",[17,36,38],{"id":37},"trace-first-then-diagnose-failures-before-writing-evals","Trace First, Then Diagnose Failures Before Writing Evals",[22,40,41,42,46],{},"Start every pipeline with instrumentation: use Phoenix (Arize's open-source observability) to capture spans (LLM\u002Ftool steps) without local install via Phoenix Cloud (free account, API key). Install ",[43,44,45],"code",{},"pip install arize-phoenix[crewai] claude-agent-sdk"," (assumes Claude API key; adaptable to OpenAI\u002FGemini). Run pre-built financial analysis agent (Claude-powered, fetches Yahoo Finance data, generates reports) on 13 test queries, auto-tracing to Phoenix UI.",[22,48,49],{},"Inspect traces in Phoenix before evals: filter by spans, view inputs\u002Foutputs, identify failure modes manually. Categorize root causes—e.g., model unaware of current year fails forward-looking data; tool param errors; hallucinated facts. Use LLM to auto-categorize eval explanations at scale (LLMs all the way down). This data-driven step skips most tutorials' mistake: writing evals blind, measuring wrong metrics. Example: correctness eval scores 0\u002F13 (can't verify future data), but faithfulness (sticks to sources) scores 13\u002F13—proves eval choice > tuning.",[22,51,52,55],{},[31,53,54],{},"Key principle:"," Read traces to define rubrics—what's \"good\"? For financial agent: accurate tool use, source fidelity, complete reports without extras. Avoid prescriptive evals (e.g., exact tool sequence) that fail smarter agents. Humans build golden datasets for novel failures; code\u002FLLM evals handle volume.",[22,57,58,60],{},[31,59,33],{}," \"We're going to do something that most of the tutorials skip. We're going to actually look at the data. We're going to read our traces, categorize what went wrong, and figure out what to measure before we write a single eval.\"",[17,62,64],{"id":63},"layer-code-built-in-and-custom-llm-evals-for-comprehensive-coverage","Layer Code, Built-in, and Custom LLM Evals for Comprehensive Coverage",[22,66,67],{},"Build evals post-tracing, complementary: code for deterministic checks (fast\u002Fcheap), LLM-as-judge for semantics (flexible but costly\u002Fnondeterministic).",[22,69,70,73],{},[31,71,72],{},"Code evals (Python functions):"," Validate JSON output, token limits (\u003C500), required fields, forbidden phrases, keyword presence. Example:",[75,76,81],"pre",{"className":77,"code":78,"language":79,"meta":80,"style":80},"language-python shiki shiki-themes github-light github-dark","def json_eval(output: str) -> dict:\n    try:\n        json.loads(output)\n        return {\"score\": 1.0, \"label\": \"valid\", \"reason\": \"Parses as JSON\"}\n    except:\n        return {\"score\": 0.0, \"label\": \"invalid\", \"reason\": \"JSON parse error\"}\n","python","",[43,82,83,91,97,103,109,115],{"__ignoreMap":80},[84,85,88],"span",{"class":86,"line":87},"line",1,[84,89,90],{},"def json_eval(output: str) -> dict:\n",[84,92,94],{"class":86,"line":93},2,[84,95,96],{},"    try:\n",[84,98,100],{"class":86,"line":99},3,[84,101,102],{},"        json.loads(output)\n",[84,104,106],{"class":86,"line":105},4,[84,107,108],{},"        return {\"score\": 1.0, \"label\": \"valid\", \"reason\": \"Parses as JSON\"}\n",[84,110,112],{"class":86,"line":111},5,[84,113,114],{},"    except:\n",[84,116,118],{"class":86,"line":117},6,[84,119,120],{},"        return {\"score\": 0.0, \"label\": \"invalid\", \"reason\": \"JSON parse error\"}\n",[22,122,123,124,127],{},"Run via Phoenix: ",[43,125,126],{},"evaluate(pnx.Eval(name=\"json\") .with_code(json_eval), dataset)","—milliseconds, reproducible.",[22,129,130,133,134,137,138,141],{},[31,131,132],{},"Built-in LLM evals (Arize Phoenix):"," ",[43,135,136],{},"pnx.qa_correctness",", ",[43,139,140],{},"pnx.answer_relevancy","—prompt powerful LLM (e.g., Claude-3.5-Sonnet) vs. agent output\u002Freference. Scores 0-1 with explanations.",[22,143,144,147],{},[31,145,146],{},"Custom LLM rubric evals:"," Define rules in prompt, add few-shot examples from traces. For faithfulness:",[75,149,151],{"className":77,"code":150,"language":79,"meta":80,"style":80},"faithfulness_eval = pnx.LLMEval(\n    name=\"faithfulness\",\n    prompt_template=\"\"\"Judge if {output} is faithful to {sources}...\"\"\",\n    examples=[{\"input\": ..., \"output\": ..., \"reference\": ..., \"score\": 1.0, \"explanation\": ...}],\n    model=\"claude-3-5-sonnet-20240620\"\n)\n",[43,152,153,158,163,168,173,178],{"__ignoreMap":80},[84,154,155],{"class":86,"line":87},[84,156,157],{},"faithfulness_eval = pnx.LLMEval(\n",[84,159,160],{"class":86,"line":93},[84,161,162],{},"    name=\"faithfulness\",\n",[84,164,165],{"class":86,"line":99},[84,166,167],{},"    prompt_template=\"\"\"Judge if {output} is faithful to {sources}...\"\"\",\n",[84,169,170],{"class":86,"line":105},[84,171,172],{},"    examples=[{\"input\": ..., \"output\": ..., \"reference\": ..., \"score\": 1.0, \"explanation\": ...}],\n",[84,174,175],{"class":86,"line":111},[84,176,177],{},"    model=\"claude-3-5-sonnet-20240620\"\n",[84,179,180],{"class":86,"line":117},[84,181,182],{},")\n",[22,184,185],{},"Meta-evaluate judges: run golden dataset through your eval, score agreement (e.g., 90%+ reliable). Use stronger model for judging.",[22,187,188,191],{},[31,189,190],{},"When to use:"," Code for format\u002Flength; LLM for accuracy, faithfulness, tone. Agents need end-to-end: tool selection, params, output parsing.",[22,193,194,196],{},[31,195,33],{}," \"Choosing the right eval matters more than tuning it. A correctness eval scored 0 out of 13 on the same agent that a faithfulness eval scored 13 out of 13.\"",[17,198,200],{"id":199},"datasets-and-experiments-prove-iterations-work","Datasets and Experiments: Prove Iterations Work",[22,202,203,204,207],{},"Create datasets from traces: ",[43,205,206],{},"dataset = pnx.Dataset.from_pandas(traces_df)"," or golden sets (human-labeled). Run experiments: baseline vs. prompt variants.",[75,209,211],{"className":77,"code":210,"language":79,"meta":80,"style":80},"exp = pnx.Experiment(name=\"prompt-v2\", trace_dataset=dataset)\nexp.log_evals([json_eval, faithfulness_eval], variant=\"v2_prompt\")\nexp.compare()  # Tables\u002Fcharts: scores, spans, explanations\n",[43,212,213,218,223],{"__ignoreMap":80},[84,214,215],{"class":86,"line":87},[84,216,217],{},"exp = pnx.Experiment(name=\"prompt-v2\", trace_dataset=dataset)\n",[84,219,220],{"class":86,"line":93},[84,221,222],{},"exp.log_evals([json_eval, faithfulness_eval], variant=\"v2_prompt\")\n",[84,224,225],{"class":86,"line":99},[84,226,227],{},"exp.compare()  # Tables\u002Fcharts: scores, spans, explanations\n",[22,229,230],{},"Visualize regressions, filter low-scorers, iterate prompts. Scale to thousands: patterns emerge (e.g., budget query misses costs).",[22,232,233,236],{},[31,234,235],{},"Advanced frameworks:"," Impact hierarchy (prioritize high-failure evals); data flywheel (evals → insights → prompts → better traces); pairwise (A\u002FB outputs); reliability scoring (judge variance).",[22,238,239,242],{},[31,240,241],{},"Common pitfalls:"," Overly brittle code evals; unaligned LLM judges (meta-eval fixes); ignoring cascades\u002Fmulti-agents; prescriptive tests.",[22,244,245,248],{},[31,246,247],{},"Quality criteria:"," 100% regression suite; explanations actionable; CI-runnable; humans validate outliers.",[22,250,251,254],{},[31,252,253],{},"Practice:"," Fork speaker's notebook (GitHub\u002Fseldo), trace your agent, build 3 evals, experiment on 50+ traces. Prerequisites: Python, LLM API, basic agents (2+ years dev exp).",[22,256,257,259],{},[31,258,33],{}," \"Without evals you can't change your system prompt to fix a tone issue because the tone might get better but suddenly the bot might be hallucinating product features.\"",[17,261,263],{"id":262},"key-takeaways","Key Takeaways",[265,266,267,271,274,277,280,283,286,289],"ul",{},[268,269,270],"li",{},"Instrument with Phoenix traces before any evals—inspect spans to pinpoint failures like wrong tools or date ignorance.",[268,272,273],{},"Layer evals: code for deterministic (JSON, length), LLM for semantic (faithfulness > correctness for sourced tasks).",[268,275,276],{},"Meta-evaluate LLM judges on golden data to ensure reliability >90%.",[268,278,279],{},"Use capability evals for new skills, convert to regressions; run experiments to validate changes, not eyeballing.",[268,281,282],{},"Categorize failures from explanations to fix systematic prompt issues at scale.",[268,284,285],{},"Avoid prescriptive tests—agents optimize paths; focus non-brittle metrics.",[268,287,288],{},"Humans for golden sets\u002Foutliers only; evals scale CI for model\u002Fprompt upgrades.",[268,290,291],{},"Start simple: 13-query financial agent → full pipeline in notebook.",[293,294,295],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":80,"searchDepth":93,"depth":93,"links":297},[298,299,300,301,302],{"id":19,"depth":93,"text":20},{"id":37,"depth":93,"text":38},{"id":63,"depth":93,"text":64},{"id":199,"depth":93,"text":200},{"id":262,"depth":93,"text":263},[304],"AI & LLMs",null,"md",false,{"content_references":309,"triage":318},[310,315],{"type":311,"title":312,"url":313,"context":314},"tool","Arize Phoenix","https:\u002F\u002Fphoenix.arize.com\u002F","recommended",{"type":311,"title":316,"author":317,"context":314},"Claude","Anthropic",{"relevance":111,"novelty":105,"quality":105,"actionability":105,"composite":319,"reasoning":320},4.35,"Category: AI & LLMs. The article provides a detailed framework for implementing systematic evaluations of AI agents, addressing a key pain point for developers who struggle with traditional testing methods. It offers actionable steps, such as using Phoenix for instrumentation, which can be directly applied to improve testing processes.",true,"\u002Fsummaries\u002F172b79615a38a463-build-agent-evals-traces-to-experiments-summary","2026-05-14 18:00:06","2026-05-14 23:00:14",{"title":5,"description":80},{"loc":322},"172b79615a38a463","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Xfl50508LZM","summaries\u002F172b79615a38a463-build-agent-evals-traces-to-experiments-summary",[333,334,335],"agents","llm","ai-tools","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FXfl50508LZM\u002Fhqdefault.jpg","Replace vibes-based testing with a full eval pipeline: trace agent runs with Phoenix, categorize failures from data, build code\u002FLLM evals, run experiments to validate prompt changes on a financial agent.","Hands-on workshop by [Laurie Voss](https:\u002F\u002Fx.com\u002Fseldo) where you code along building an eval pipeline for a financial analysis agent: Phoenix tracing, failure 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These deep neural networks, with billions of parameters (weights), map word relationships from vast datasets of books, articles, and transcripts. When prompted, they predict the most likely next tokens. Neural networks form their backbone: multi-layered structures mimicking brain neurons, enabling deep learning to auto-discover data features without manual engineering. Deep learning needs millions+ data points and extended training, driving high costs but yielding complex correlations beyond simple ML like decision trees.",[22,3995,3996],{},"AGI remains vague: Sam Altman calls it a 'median human co-worker'; OpenAI's charter defines it as autonomous systems outperforming humans in most economically valuable work; DeepMind sees it as matching humans on cognitive tasks. Even experts disagree, so prioritize narrow capabilities over chasing AGI hype when building.",[17,3998,4000],{"id":3999},"training-optimization-and-deployment-trade-offs","Training, Optimization, and Deployment Trade-offs",[22,4002,4003],{},"Distillation transfers knowledge from a large 'teacher' model to a smaller 'student' by recording outputs and retraining—creating efficient versions like GPT-4 Turbo. It risks ToS violations if distilling competitors' APIs. Fine-tuning adapts pre-trained LLMs with domain-specific data for targeted tasks, letting startups specialize general models.",[22,4005,4006],{},"Inference runs trained models to generate predictions; it demands optimized hardware (GPUs, TPUs) as large models crawl on laptops. Memory cache like KV caching speeds this in transformers by reusing computations, slashing power and latency for repeated queries. Compute denotes the GPUs\u002FCPUs fueling training\u002Finference—the AI economy's bottleneck.",[22,4008,4009],{},"Hallucinations occur when LLMs fabricate facts from training gaps, risking misinformation (e.g., bad medical advice). Mitigate with domain-specific fine-tuning to close knowledge holes.",[17,4011,4013],{"id":4012},"generation-techniques-and-reasoning-boosts","Generation Techniques and Reasoning Boosts",[22,4015,4016],{},"Diffusion models generate art\u002Fmusic\u002Ftext by learning to reverse 'noise destruction' of data, enabling realistic outputs from randomness. GANs pit generator vs. discriminator networks to refine fakes like deepfakes, best for narrow tasks like images\u002Fvideos.",[22,4018,4019],{},"Chain-of-thought prompting breaks problems into steps (e.g., legs\u002Fheads riddle: 20 chickens, 20 cows), improving LLM accuracy on logic\u002Fcoding via reasoning models optimized with reinforcement learning. This trades speed for reliability.",[17,4021,4023],{"id":4022},"agents-unlock-autonomous-workflows","Agents Unlock Autonomous Workflows",[22,4025,4026],{},"AI agents chain LLMs with tools for multi-step tasks like booking or expense filing, using API endpoints as 'buttons' to control services autonomously. Coding agents extend this to dev workflows: writing, testing, debugging, and fixing code across repos—like tireless interns needing review.",[22,4028,4029],{},"Infrastructure lags, but agents amplify automation; pair with RAG (not detailed here) to ground outputs and curb hallucinations.",{"title":80,"searchDepth":93,"depth":93,"links":4031},[4032,4033,4034,4035],{"id":3989,"depth":93,"text":3990},{"id":3999,"depth":93,"text":4000},{"id":4012,"depth":93,"text":4013},{"id":4022,"depth":93,"text":4023},[304],{"content_references":4038,"triage":4058},[4039,4044,4047,4052,4055],{"type":4040,"title":4041,"url":4042,"context":4043},"other","OpenAI Charter","https:\u002F\u002Fopenai.com\u002Fcharter\u002F","cited",{"type":4040,"title":4045,"url":4046,"context":4043},"Sam Altman Artificial Intelligence OpenAI Profile","https:\u002F\u002Fnymag.com\u002Fintelligencer\u002Farticle\u002Fsam-altman-artificial-intelligence-openai-profile.html",{"type":4048,"title":4049,"publisher":4050,"url":4051,"context":4043},"report","A Primer on Compute","Carnegie Endowment","https:\u002F\u002Fcarnegieendowment.org\u002Fposts\u002F2024\u002F04\u002Fa-primer-on-compute",{"type":4040,"title":4053,"url":4054,"context":4043},"A Brief History of Diffusion, the Tech at the Heart of Modern Image-Generating AI","https:\u002F\u002Ftechcrunch.com\u002F2022\u002F12\u002F22\u002Fa-brief-history-of-diffusion-the-tech-at-the-heart-of-modern-image-generating-ai\u002F",{"type":4040,"title":4056,"url":4057,"context":4043},"KV Caching","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fnot-lain\u002Fkv-caching",{"relevance":111,"novelty":99,"quality":105,"actionability":105,"composite":4059,"reasoning":4060},4.15,"Category: AI & LLMs. The article provides a glossary of key AI terms that are essential for integrating LLMs effectively, addressing the audience's need for practical applications in AI product development. It includes actionable insights on techniques like distillation and fine-tuning, which are directly applicable to building AI-powered products.","\u002Fsummaries\u002F1e8b4fa0c073eae3-ai-glossary-master-terms-for-building-with-llms-summary","2026-05-09 21:45:00","2026-05-10 15:26:48",{"title":3979,"description":80},{"loc":4061},"1e8b4fa0c073eae3","TechCrunch AI","article","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F05\u002F09\u002Fartificial-intelligence-definition-glossary-hallucinations-guide-to-common-ai-terms\u002F","summaries\u002F1e8b4fa0c073eae3-ai-glossary-master-terms-for-building-with-llms-summary",[334,333,335],"Decode 20+ key AI terms like AGI, chain-of-thought, distillation, and agents to integrate LLMs effectively, avoid pitfalls like hallucinations, and optimize for production.",[],"-mfvo92I8dJbP3YpZi0ZbXkyGrxjv2tAKNEhGABP5f4",{"id":4076,"title":4077,"ai":4078,"body":4083,"categories":4194,"created_at":305,"date_modified":305,"description":80,"extension":306,"faq":305,"featured":307,"kicker_label":305,"meta":4195,"navigation":321,"path":4209,"published_at":4210,"question":305,"scraped_at":4211,"seo":4212,"sitemap":4213,"source_id":4214,"source_name":328,"source_type":329,"source_url":4215,"stem":4216,"tags":4217,"thumbnail_url":4218,"tldr":4219,"tweet":4220,"unknown_tags":4221,"__hash__":4222},"summaries\u002Fsummaries\u002F5d4ca8619bb494bc-wrap-existing-chat-agents-in-voice-with-elevenlabs-summary.md","Wrap Existing Chat Agents in Voice with ElevenLabs Engine",{"provider":7,"model":8,"input_tokens":4079,"output_tokens":4080,"processing_time_ms":4081,"cost_usd":4082},5066,1685,17123,0.00183305,{"type":14,"value":4084,"toc":4189},[4085,4089,4092,4095,4099,4102,4170,4173,4176,4180,4183,4186],[17,4086,4088],{"id":4087},"voice-beats-chat-for-speed-accessibility-and-channels","Voice Beats Chat for Speed, Accessibility, and Channels",[22,4090,4091],{},"Voice upgrades chat agents by enabling faster interactions, better accessibility for keyboard\u002Fdyslexia users, and omni-channel use cases like Zoom calls (e.g., PostHog agent correcting stats) or phone support lines. Chat agents became the 2025 default UI—seen in viral examples from Linear, PostHog, Atio, and even gov.uk—but feel outdated. Adding voice unlocks natural, declarative AI without replacing tool calling, RAG, or LLM orchestration.",[22,4093,4094],{},"Trade-off: Pure TTS\u002FSTT falls short; you need a full voice layer for turn-taking (semantic pauses, emotion detection) to avoid interruptions or awkward silences.",[17,4096,4098],{"id":4097},"voice-engine-wraps-any-existing-agent-seamlessly","Voice Engine Wraps Any Existing Agent Seamlessly",[22,4100,4101],{},"ElevenLabs' new Voice Engine (preview in weeks) bundles Scribe (top STT accuracy), V3 TTS, 1000+ voices\u002Flanguages, and advanced turn-taking into a primitive that proxies to your agent. No rebuild: Attach it to your tuned chat agent (with evals, transcripts) in server SDK like this:",[75,4103,4107],{"className":4104,"code":4105,"language":4106,"meta":80,"style":80},"language-javascript shiki shiki-themes github-light github-dark","\u002F\u002F Server SDK example\nconst client = new ElevenLabsClient();\nconst voiceEngine = client.voiceEngine();\nvoiceEngine.attach(existingChatAgent);  \u002F\u002F Proxies sessions\n","javascript",[43,4108,4109,4115,4139,4156],{"__ignoreMap":80},[84,4110,4111],{"class":86,"line":87},[84,4112,4114],{"class":4113},"sJ8bj","\u002F\u002F Server SDK example\n",[84,4116,4117,4121,4125,4128,4131,4135],{"class":86,"line":93},[84,4118,4120],{"class":4119},"szBVR","const",[84,4122,4124],{"class":4123},"sj4cs"," client",[84,4126,4127],{"class":4119}," =",[84,4129,4130],{"class":4119}," new",[84,4132,4134],{"class":4133},"sScJk"," ElevenLabsClient",[84,4136,4138],{"class":4137},"sVt8B","();\n",[84,4140,4141,4143,4146,4148,4151,4154],{"class":86,"line":99},[84,4142,4120],{"class":4119},[84,4144,4145],{"class":4123}," voiceEngine",[84,4147,4127],{"class":4119},[84,4149,4150],{"class":4137}," client.",[84,4152,4153],{"class":4133},"voiceEngine",[84,4155,4138],{"class":4137},[84,4157,4158,4161,4164,4167],{"class":86,"line":105},[84,4159,4160],{"class":4137},"voiceEngine.",[84,4162,4163],{"class":4133},"attach",[84,4165,4166],{"class":4137},"(existingChatAgent);  ",[84,4168,4169],{"class":4113},"\u002F\u002F Proxies sessions\n",[22,4171,4172],{},"Your agent handles logic unchanged. Client SDK adds a widget in 3 lines, enabling telephony\u002FCES out-of-box. Shadcn\u002FVercel-style UI components let coding agents convert agents via one prompt: Analyzes codebase, wraps, deploys locally.",[22,4174,4175],{},"Demo outcome: Generic chat support agent (\"Hello, how are you?\") gains voice instantly, running background loops.",[17,4177,4179],{"id":4178},"tool-calling-and-agent-preservation-work-unchanged","Tool Calling and Agent Preservation Work Unchanged",[22,4181,4182],{},"Tool calling routes through your backend agent—no wrapper changes needed. Client-side tools (e.g., DOM manipulation) and server-side proxying supported. For new builds, use ElevenLabs' full agents platform; for existing, wrapper suffices.",[22,4184,4185],{},"Prediction: Chat agents add voice or die as SaaS goes AI-first. Design partners sought for early access.",[293,4187,4188],{},"html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":80,"searchDepth":93,"depth":93,"links":4190},[4191,4192,4193],{"id":4087,"depth":93,"text":4088},{"id":4097,"depth":93,"text":4098},{"id":4178,"depth":93,"text":4179},[304],{"content_references":4196,"triage":4207},[4197,4200,4203,4205],{"type":311,"title":4198,"author":4199,"context":314},"ElevenLabs Voice Engine","ElevenLabs",{"type":311,"title":4201,"author":4199,"context":4202},"Scribe","mentioned",{"type":311,"title":4204,"author":4199,"context":4202},"V3 TTS",{"type":4040,"title":4206,"context":4202},"Shadcn UI components",{"relevance":111,"novelty":105,"quality":105,"actionability":105,"composite":319,"reasoning":4208},"Category: AI & LLMs. The article discusses a practical application of adding voice capabilities to existing chat agents using ElevenLabs' Voice Engine, which directly addresses the needs of developers looking to enhance AI-powered products. It provides a clear SDK example that developers can implement, making it actionable.","\u002Fsummaries\u002F5d4ca8619bb494bc-wrap-existing-chat-agents-in-voice-with-elevenlabs-summary","2026-05-09 13:00:06","2026-05-10 15:04:51",{"title":4077,"description":80},{"loc":4209},"3d64e405c71dc3d4","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DCZZ3AJKzuc","summaries\u002F5d4ca8619bb494bc-wrap-existing-chat-agents-in-voice-with-elevenlabs-summary",[333,335,334],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FDCZZ3AJKzuc\u002Fhqdefault.jpg","ElevenLabs' Voice Engine adds voice to any built chat agent via a simple SDK wrapper, handling STT (Scribe), TTS (V3), emotion-aware turn-taking, and interruptions without rebuilding your RAG, tools, or evals.","ElevenLabs engineer [Luke Harries](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fluke-harries) pitches their upcoming Voice Engine SDKs, which wrap existing chat agents with speech-to-text, text-to-speech, turn-taking, and tool-calling support in a few lines of code—includes a live demo converting a local chat demo to voice via one prompt.",[],"vs4K4sKgZwdtS46bVdNr9mJVOcgXRCdQurnB0UYpqEA",{"id":4224,"title":4225,"ai":4226,"body":4231,"categories":4265,"created_at":305,"date_modified":305,"description":80,"extension":306,"faq":305,"featured":307,"kicker_label":305,"meta":4266,"navigation":321,"path":4277,"published_at":4278,"question":305,"scraped_at":4279,"seo":4280,"sitemap":4281,"source_id":4282,"source_name":4283,"source_type":4068,"source_url":4284,"stem":4285,"tags":4286,"thumbnail_url":305,"tldr":4287,"tweet":305,"unknown_tags":4288,"__hash__":4289},"summaries\u002Fsummaries\u002F0e3d61900698dc2e-claude-dreaming-boosts-agents-5-4x-on-repeat-tasks-summary.md","Claude Dreaming Boosts Agents 5.4x on Repeat Tasks",{"provider":7,"model":8,"input_tokens":4227,"output_tokens":4228,"processing_time_ms":4229,"cost_usd":4230},3985,1534,21641,0.0015415,{"type":14,"value":4232,"toc":4260},[4233,4237,4240,4243,4247,4250,4253,4257],[17,4234,4236],{"id":4235},"dreaming-curates-memories-to-eliminate-agent-drift","Dreaming Curates Memories to Eliminate Agent Drift",[22,4238,4239],{},"Anthropic's research-preview 'dreaming' runs as a scheduled background process that replays an agent's past sessions overnight. It extracts patterns from memory stores, prunes contradictions, and builds a refined memory bank. This single change—not model upgrades or prompt tweaks—yielded Harvey AI a 6x lift in task completion rates, per Anthropic's customer slide. The process targets repeat tasks where agents degrade over sessions due to inconsistent memories, turning raw session history into a reliable knowledge base that prevents error accumulation.",[22,4241,4242],{},"For production agents handling ongoing work like coding or legal research, dreaming addresses a core failure mode: without curation, memories bloat with conflicts, causing 80-90% failure rates on iterative tasks. Activate it via Claude's closed beta, point it at your session history, and let it run 4 hours to ship an optimized store.",[17,4244,4246],{"id":4245},"replicating-gains-54x-completion-on-18-go-billing-tasks","Replicating Gains: 5.4x Completion on 18 Go Billing Tasks",[22,4248,4249],{},"Test the same 18 coding tasks—building a Go billing service—against pre- and post-dream agents using identical Claude Opus 4.7 instances, system prompts, and evaluation rubric. Pre-dream agent struggled with repetition and low completion; post-dream version achieved 5.4x higher completion rates by leveraging curated memories to avoid past mistakes.",[22,4251,4252],{},"Token spend dropped 3.1x as the agent stopped redundant loops, directly tying memory quality to cost control. Run your own benchmark: log 18+ sessions on a real project, enable dreaming for 4 hours, then re-eval. This isolates dreaming's impact, proving it outperforms yesterday's agent without engineering lifts.",[17,4254,4256],{"id":4255},"trade-offs-and-path-to-production","Trade-offs and Path to Production",[22,4258,4259],{},"Dreaming shines on repeat, memory-intensive tasks but requires session history buildup—start with 10+ runs for meaningful pruning. It's beta-only now, so join Anthropic's waitlist for access. Expect iteration: early versions focus on contradiction removal, future ones may add pattern synthesis. For indie builders, pair with structured evals to quantify gains before scaling agents.",{"title":80,"searchDepth":93,"depth":93,"links":4261},[4262,4263,4264],{"id":4235,"depth":93,"text":4236},{"id":4245,"depth":93,"text":4246},{"id":4255,"depth":93,"text":4256},[304],{"content_references":4267,"triage":4275},[4268,4271,4273],{"type":4269,"title":4270,"author":317,"context":4202},"event","Code with Claude",{"type":4040,"title":4272,"author":317,"context":4043},"Anthropic product blog on dreaming",{"type":311,"title":4274,"context":4202},"Harvey",{"relevance":111,"novelty":105,"quality":105,"actionability":105,"composite":319,"reasoning":4276},"Category: AI & LLMs. The article discusses a specific AI feature ('dreaming') that significantly enhances agent performance on repeat tasks, addressing a key pain point for developers working with AI agents. It provides actionable insights on how to implement this feature in production, including specific metrics and a testing framework.","\u002Fsummaries\u002F0e3d61900698dc2e-claude-dreaming-boosts-agents-5-4x-on-repeat-tasks-summary","2026-05-09 12:37:24","2026-05-09 15:36:51",{"title":4225,"description":80},{"loc":4277},"0e3d61900698dc2e","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Fi-let-claude-dream-for-4-hours-todays-agent-just-killed-yesterday-s-by-5-4-on-18-repeat-tasks-0d09741fe6f1?source=rss----98111c9905da---4","summaries\u002F0e3d61900698dc2e-claude-dreaming-boosts-agents-5-4x-on-repeat-tasks-summary",[333,334,335],"Anthropic's 'dreaming' feature curates agent memories from past sessions, delivering 5.4x higher task completion and 3.1x token efficiency on 18 identical Go coding tasks using the same Claude Opus model and prompts.",[],"WrSg3-aTdB1T6gEiR71Sr0V_tPxJsZLmIHFkgLLgQJ8",{"id":4291,"title":4292,"ai":4293,"body":4298,"categories":4329,"created_at":305,"date_modified":305,"description":80,"extension":306,"faq":305,"featured":307,"kicker_label":305,"meta":4330,"navigation":321,"path":4348,"published_at":4349,"question":305,"scraped_at":4350,"seo":4351,"sitemap":4352,"source_id":4353,"source_name":328,"source_type":4068,"source_url":4354,"stem":4355,"tags":4356,"thumbnail_url":305,"tldr":4357,"tweet":305,"unknown_tags":4358,"__hash__":4359},"summaries\u002Fsummaries\u002Fa42b36082856d18a-run-gemma-4-agents-on-device-with-litert-stack-summary.md","Run Gemma 4 Agents On-Device with LiteRT Stack",{"provider":7,"model":8,"input_tokens":4294,"output_tokens":4295,"processing_time_ms":4296,"cost_usd":4297},8545,1937,33524,0.00217065,{"type":14,"value":4299,"toc":4324},[4300,4304,4307,4310,4314,4317,4321],[17,4301,4303],{"id":4302},"gemma-4-edge-models-enable-agentic-on-device-ai","Gemma 4 Edge Models Enable Agentic On-Device AI",[22,4305,4306],{},"Gemma 4 E2B (1-2GB RAM) suits voice interfaces and summarization; E4B handles heavier tasks on laptops\u002FIoT. Both support built-in function\u002Ftool calling for local API interactions, native structured JSON output (no prompt hacks needed), and chain-of-thought \"thinking mode\" to expose reasoning steps. Download Apache 2.0-licensed quantized models from Hugging Face for immediate use. These shift from chatbots to autonomous agents, pairing with images (e.g., generate music from breakfast photo vibe) or voice (e.g., analyze sleep journal trends over 7 days). Build privacy-focused skills like Wikipedia querying or mood tracking entirely on-device, reducing cloud token costs via hybrid edge-cloud routing.",[22,4308,4309],{},"Gallery app playground demos these: fork its open-source GitHub repo, create skills in-app (e.g., animal sound classification switching CPU\u002FGPU), and share via community repo. QR codes provide skill-building guides.",[17,4311,4313],{"id":4312},"litert-stack-simplifies-cross-platform-deployment","LiteRT Stack Simplifies Cross-Platform Deployment",[22,4315,4316],{},"LiteRT (evolved from TensorFlow Lite) runs 100K+ apps with billions of users\u002Fdaily inferences. Convert PyTorch\u002FJAX\u002FTensorFlow models to unified .tflite format for deployment on Android, iOS, macOS, Linux, Windows, web, and IoT (e.g., Raspberry Pi robot wiggling antennas on \"move your antenna\" prompt). Use LiteRT Torch for conversions, model explorer for graph quantization\u002Fmix-precision tweaks, and AI Edge Portal for cloud benchmarking across device fleets (e.g., 5-year-old phones). Supports CPU\u002FGPU universally; NPU integrations (Qualcomm\u002FMediaTek) yield 3-10x perf\u002Fenergy gains for ASR\u002FTTS\u002FAR\u002FVR. CLI tool with Python bindings eases testing; ahead-of-time compilation optimizes reliability.",[17,4318,4320],{"id":4319},"benchmarks-prove-edge-speed-and-coverage","Benchmarks Prove Edge Speed and Coverage",[22,4322,4323],{},"Tested Gemma 4 across platforms: up to 13x NPU boost, 56 tokens\u002Fsec on iOS, 35x faster than Llama.cpp on mobile, at-par on desktop, 3x on IoT. Quantized models include per-platform perf details on Hugging Face. Real-world: local face recognition (like phone unlock) saves cloud costs for security cams; stream frames via Raspberry Pi, trigger only on detection. Hybrid setups route complex tasks (e.g., multi-node classifiers to higher agents) to cloud while keeping inference local.",{"title":80,"searchDepth":93,"depth":93,"links":4325},[4326,4327,4328],{"id":4302,"depth":93,"text":4303},{"id":4312,"depth":93,"text":4313},{"id":4319,"depth":93,"text":4320},[304],{"content_references":4331,"triage":4346},[4332,4334,4336,4338,4340,4343],{"type":311,"title":4333,"context":314},"LiteRT",{"type":311,"title":4335,"context":314},"Gallery App",{"type":4040,"title":4337,"context":314},"Gemma 4 Edge Models",{"type":4040,"title":4339,"context":4202},"AI Edge Portal",{"type":4040,"title":4341,"url":4342,"context":4202},"Weiyi Wang LinkedIn","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fweiyiwang1993",{"type":4040,"title":4344,"url":4345,"context":4202},"Chintan Parikh LinkedIn","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fchintansparikh",{"relevance":111,"novelty":105,"quality":105,"actionability":105,"composite":319,"reasoning":4347},"Category: AI & LLMs. The article provides in-depth insights into deploying on-device AI agents using Gemma 4 and LiteRT, addressing practical applications for developers looking to integrate AI into their products. It includes specific examples of model capabilities and deployment strategies, making it actionable for the target audience.","\u002Fsummaries\u002Fa42b36082856d18a-run-gemma-4-agents-on-device-with-litert-stack-summary","2026-05-05 15:00:06","2026-05-05 16:04:23",{"title":4292,"description":80},{"loc":4348},"62be44ea32649dc7","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Lm8BLHkxiAo","summaries\u002Fa42b36082856d18a-run-gemma-4-agents-on-device-with-litert-stack-summary",[334,333,335],"Gemma 4's 2B\u002F4B edge models enable on-device agents with tool calling, JSON output, and reasoning via LiteRT, delivering low latency, privacy, and cross-platform support on Android\u002FiOS\u002Fdesktop\u002FIoT.",[],"a17BfMLYra1RPFtqHZHGiET5R6NJM1E9IeMfG2B04bQ"]