[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-96356e1a6004fafe-gemini-cli-context-to-ci-cd-for-production-ai-agen-summary":3,"summaries-facets-categories":219,"summary-related-96356e1a6004fafe-gemini-cli-context-to-ci-cd-for-production-ai-agen-summary":3789},{"id":4,"title":5,"ai":6,"body":13,"categories":194,"created_at":195,"date_modified":195,"description":196,"extension":197,"faq":195,"featured":198,"kicker_label":195,"meta":199,"navigation":200,"path":201,"published_at":202,"question":195,"scraped_at":203,"seo":204,"sitemap":205,"source_id":206,"source_name":207,"source_type":208,"source_url":209,"stem":210,"tags":211,"thumbnail_url":195,"tldr":216,"tweet":195,"unknown_tags":217,"__hash__":218},"summaries\u002Fsummaries\u002F96356e1a6004fafe-gemini-cli-context-to-ci-cd-for-production-ai-agen-summary.md","Gemini CLI: Context to CI\u002FCD for Production AI Agents",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8787,2530,25693,0.0029999,{"type":14,"value":15,"toc":185},"minimark",[16,21,25,41,56,59,81,84,88,95,98,106,109,113,116,119,122,126,133,136,139,143,182],[17,18,20],"h2",{"id":19},"context-engineering-unlocks-agent-autonomy","Context Engineering Unlocks Agent Autonomy",[22,23,24],"p",{},"The core challenge in AI-assisted coding is giving the model enough structured knowledge to build complex systems like Google's Agent Development Kit (ADK) agents without hallucinations or incomplete outputs. Annie Wang and Ayo Adedeji demonstrate this in their Shadowblade game agent project, starting from the 'agent-vs-developer' repo with starter files (Dockerfile, MCP server stubs, GitHub data).",[22,26,27,28,32,33,36,37,40],{},"They begin by analyzing the codebase: ",[29,30,31],"code",{},"gemini"," CLI invocation reads the entire folder using built-in ",[29,34,35],{},"read_file"," and ",[29,38,39],{},"read_folder"," tools, delegating to an 'investigator agent' for multi-agent summarization. This reveals the repo's focus on a multi-agent game system centered on Shadowblade, an LLM-powered combat agent using Google's generative AI and ADK.",[22,42,43,44,47,48,51,52,55],{},"Key decision: Download a blueprint 'agent.design.md' via natural language (",[29,45,46],{},"Download this Shadowblade agent design MD file and store it locally","). This provides precise ADK specs—root agent type, model (Gemini), persona instructions, tool imports—without requiring manual ",[29,49,50],{},"curl"," or ",[29,53,54],{},"git clone",". Tradeoff: Local files act as short-term memory (session-specific, read on-demand), avoiding persistent bloat but requiring explicit invocation.",[22,57,58],{},"\"This is the power of context engineering because essentially now you don't know what is ADK how to create ADK agent but you're giving it correct context and right instructions so that AI can create ADK agent for you\" – Annie Wang, emphasizing how targeted docs enable zero-knowledge agent generation.",[22,60,61,62,65,66,69,70,72,73,76,77,80],{},"Next, they create a project-level ",[29,63,64],{},"gemini.md"," with Python best practices (docstrings, type hints, modular structure). Created via shell (",[29,67,68],{},"cat > gemini.md \u003C\u003C EOF","), it's long-term memory: auto-loaded on every ",[29,71,31],{}," session in the folder. View with ",[29,74,75],{},"memory show","; add via ",[29,78,79],{},"memory add",". Why project-level over user-level (~\u002F.gemini\u002Fgemini.md)? Project isolation prevents cross-contamination in multi-project workflows.",[22,82,83],{},"Tradeoffs surfaced: Long-term memory (gemini.md) ensures consistency across sessions but risks token limits if overfilled with specifics. Short-term (local docs, chat history) is flexible but forgets on restart. They reject always-on globals for non-general context, opting for layered approach.",[17,85,87],{"id":86},"agent-skills-deliver-on-demand-expertise","Agent Skills Deliver On-Demand Expertise",[22,89,90,91,94],{},"To avoid bloating context windows, they introduce skills via ",[29,92,93],{},"skill.md"," files—dynamic, conditional prompts loaded only when relevant. Stored in ~\u002F.gemini\u002Fskills\u002F, structured as YAML-like: name (e.g., 'adk-agent-design'), description (triggers), content (principles, architecture, tools, testing).",[22,96,97],{},"For ADK, the skill covers agent persona, tool design (e.g., combat logic), hooks for control, eval strategies. Invocation: CLI auto-matches description to query (e.g., 'design ADK agent'). Created via shell templating, mirroring gemini.md but namespaced.",[22,99,100,101,105],{},"\"Agent skills ",[102,103,104],"span",{},"are like"," on-demand expertise... You don't need a plumber all the time, but when your sink leaks, you call one\" – Ayo Adedeji, contrasting persistent gemini.md with efficient, token-saving skills.",[22,107,108],{},"Decision chain: Evaluated gemini.md (always-loaded, general) vs. local files (manual read) vs. skills (auto-triggered, specific). Skills win for ADK blueprints—laser-focused, no performance degradation. Result: Gemini CLI generates functional Shadowblade agent code solely from context + memory, filling starter stubs (a2a_server.py, etc.).",[17,110,112],{"id":111},"guardrails-and-testing-ensure-reliability","Guardrails and Testing Ensure Reliability",[22,114,115],{},"Raw generation risks drift, so they layer hooks—custom callbacks in ADK to intercept agent behavior (e.g., validate tool calls, enforce protocols). Gemini CLI writes these using skill context, embedding in agent logic.",[22,117,118],{},"Testing suite: Full evals with trajectory analysis (step-by-step traces), response comparisons. ADK evals framework auto-generates test cases from specs. Why? \"Shipping blind is not an option\" – video description. Tradeoff: Adds dev time upfront but catches 100% of edge cases autonomously.",[22,120,121],{},"\"Every time we end our session... Gemini is not able to remember your guidance... By saving those in memory in Gemini file, Gemini always know this guidance\" – Annie Wang, on why evals + persistent context beat one-shot prompts.",[17,123,125],{"id":124},"cicd-pipeline-automates-production","CI\u002FCD Pipeline Automates Production",[22,127,128,129,132],{},"Final push: Gemini CLI scripts full pipeline—Cloud Build for CI (lint, test, build Docker image), deploy to Cloud Run. Hooks integrate for runtime controls. From vibe (",[29,130,131],{},"Build and deploy via CI\u002FCD","): Generates Cloud Build config, Dockerfile tweaks, triggers via gcloud.",[22,134,135],{},"Before: Manual dev in cloned repo. After: Autonomous end-to-end—context → agent code → tests → deploy. 'Boss fight' validates on Cloud Run. Metrics absent, but implies zero manual code; full pipeline in one session.",[22,137,138],{},"Tradeoffs: Relies on Google ecosystem (Gemini API, Cloud Build, ADK); portability low. Wins: Scales to production multi-agent systems without eng team.",[17,140,142],{"id":141},"key-takeaways","Key Takeaways",[144,145,146,150,153,156,159,166,172,179],"ul",{},[147,148,149],"li",{},"Layer contexts hierarchically: gemini.md (long-term, general), skills.md (on-demand, specific), local files (short-term, explicit).",[147,151,152],{},"Trigger skills with precise descriptions to auto-load expertise without token waste—ideal for frameworks like ADK.",[147,154,155],{},"Always pair generation with hooks + evals: Use ADK trajectory analysis for reliable agent behavior.",[147,157,158],{},"Vibe code CI\u002FCD: Natural language prompts generate Cloud Build + Cloud Run deploys from starters.",[147,160,161,162,165],{},"Start sessions with ",[29,163,164],{},"analyze entire project"," for accurate repo awareness via multi-agent tooling.",[147,167,168,169,171],{},"Project-level gemini.md over global: Isolates instructions, verifiable via ",[29,170,75],{},".",[147,173,174,175,178],{},"Download blueprints naturally (",[29,176,177],{},"store this file locally",")—no CLI memorization needed.",[147,180,181],{},"Balance memory types: Short-term for one-offs, long-term for cross-session consistency.",[22,183,184],{},"\"When designing an ADK agent follow these principles...\" – Excerpt from adk-agent-design skill, blueprint for scalable agent arch (persona, tools, testing).",{"title":186,"searchDepth":187,"depth":187,"links":188},"",2,[189,190,191,192,193],{"id":19,"depth":187,"text":20},{"id":86,"depth":187,"text":87},{"id":111,"depth":187,"text":112},{"id":124,"depth":187,"text":125},{"id":141,"depth":187,"text":142},[],null,"GCP credit → https:\u002F\u002Fgoo.gle\u002Fhandson-ep6-lab1\n[Lab] Vibe coding with Gemini CLI → https:\u002F\u002Fgoo.gle\u002Fscholar\nTry Gemini CLI → https:\u002F\u002Fgoo.gle\u002F4v7xUFO\n\nEpisode 2 of vibe coding with Gemini CLI pushes the boundaries of what AI assisted development can actually do. Annie and Ayo use Agent Skills to extend CLI capabilities, generate a full ADK agent using nothing but context and memory, add hooks to control the agent's behavior, write a complete test and evaluation suite, and ship everything through an automated CI\u002FCD pipeline.\n\nThe question we kept asking: how much can Gemini CLI actually do on its own?\n\nWatch and find out. 👇\n🧩 Agent Skills — what they are and how to use them\n⚙️ ADK Agent — generated, structured, and functional\n🪝 Hooks — because even AI needs guardrails\n🧪 Tests & Evals — because shipping blind is not an option\n🚀 CI\u002FCD — because real software gets deployed\n\nMore resources:\nAgent Development Kit (ADK) Docs → https:\u002F\u002Fgoo.gle\u002F4tpbfTH\nGemini CLI Hooks Documentation → https:\u002F\u002Fgoo.gle\u002F4siaT0m\nEvaluation with ADK → https:\u002F\u002Fgoo.gle\u002F4cqkNrO\n\nWatch more Hand on AI → https:\u002F\u002Fgoo.gle\u002FHowToWithGemini\n🔔 Subscribe to Google Cloud Tech → https:\u002F\u002Fgoo.gle\u002FGoogleCloudTech\n\n#AIAgents #GeminiCLI #VibeCoding\n\nSpeakesr: Ayo Adedeji, Annie Wang\nProducts Mentioned: Gemini CLI, Agent Development Kit, Gemini API, Cloud Build","md",false,{},true,"\u002Fsummaries\u002F96356e1a6004fafe-gemini-cli-context-to-ci-cd-for-production-ai-agen-summary","2026-04-04 16:01:22","2026-04-05 16:16:06",{"title":5,"description":196},{"loc":201},"96356e1a6004fafe","Google Cloud Tech","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=qCBreTfjFHQ","summaries\u002F96356e1a6004fafe-gemini-cli-context-to-ci-cd-for-production-ai-agen-summary",[212,213,214,215],"agents","prompt-engineering","ai-tools","devops-cloud","Gemini CLI turns natural language 'vibe coding' into full ADK agents with context engineering, skills, hooks, tests, and automated Cloud Run deployment—proving AI can handle end-to-end dev without manual 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The fix isn't smarter models; it's scaffolding: prompts, skills, plugins, MCPs, hooks, and scripts that wrap the LLM like Darth Vader's suit or a Transformer's armor. This lets non-engineers customize agents for real work, saving hours wasted on repetitive prompting.",[22,3808,3809],{},"Nate Jones argues people get stuck because they overload prompts, missing how to layer these components. Without clear boundaries, agents produce generic output; with scaffolding, they apply your standards, pull live data, and run deterministic checks. Tradeoff: Prompts are instant but non-reusable; full scaffolds require upfront setup but yield 10x leverage for teams.",[22,3811,3812],{},"\"It's like Darth Vader has a mech suit right and that's how Darth Vader works or Transformers have these huge metal suits and that's how they get the job done—this is how LLMs work they have these suits around them that help them get work done.\" (Nate Jones uses this metaphor to explain why raw LLMs need external structure for production workflows, shifting focus from model intelligence to customizable harnesses.)",[17,3814,3816],{"id":3815},"prompts-for-one-offs-skills-for-reusable-house-style","Prompts for One-Offs, Skills for Reusable House Style",[22,3818,3819],{},"Prompts suit temporary, specific tasks: a unique client note with custom backstory. They're plain text, carrying no permissions, tools, or team-sharing ease—fine for one-offs but wasteful for repeats, burning hours on copy-paste tweaks.",[22,3821,3822],{},"Skills upgrade reusability: Markdown docs encoding your \"house style\" for processes like PR reviews, marketing docs, or cold emails. Write once (AI can help), invoke across LLMs like Codex or Claude. Example: A skill for outbound emails specifies structure—paragraphs, data pulls from docs, strong closes—consistent for batches without per-use prompting.",[22,3824,3825],{},"Decision chain: If one-off → prompt. Repeatable\u002Fhigh-sensitivity → skill. Skills follow power law: 20% deliver 80% value; prioritize frequent, error-prone tasks (e.g., customer service, engineering reviews). Track them to avoid sprawl—divide\u002Fconquer by workflow chunks.",[22,3827,3828],{},"Tradeoffs: Skills are universal\u002Fportable but text-only—no live integrations. Over-prompting mimics skills poorly, lacking enforcement.",[22,3830,3831],{},"\"A prompt is what you do when you want to do something once... a prompt is not a great home for anything that you repeat constantly or consistently.\" (Jones contrasts to push viewers from prompt dependency, estimating massive time waste from non-packaged repeats.)",[17,3833,3835],{"id":3834},"plugins-package-full-workflows-for-team-reuse","Plugins Package Full Workflows for Team Reuse",[22,3837,3838],{},"Plugins scale beyond skills: Bundles including skills, MCPs, hooks, scripts, assets, commands, and metadata. Install once, run entire workflows like Salesforce-pulled outbound emails or architecture reviews. Not just app add-ons—workflow wrappers making humans obsolete as the \"human plugin\" (copy-paste across apps).",[22,3840,3841],{},"Why plugins now? Agents handle rich work; tools like Codex simplify non-coder builds. Example: Editorial plugin for first-pass reviews—reads text multiple ways, flags rough spots\u002Fincoherence\u002Ffacts—faster than humans for routine, saving time without full automation.",[22,3843,3844],{},"Build logic: Identify repeatable structures with clear boundaries (e.g., refunds vs. upgrades in customer success—not one mega-plugin). Plugins aren't app-store shopping; they're active packaging: \"What part of my work has enough repeatable structure that the agent should be able to inherit it?\"",[22,3846,3847],{},"Tradeoffs: High setup (but easier in 2026—no engineering needed); golden skill to bound workflows right, avoiding bloated\u002Fover-wide packages prone to failure.",[22,3849,3850],{},"\"You are literally the human plugin cuz you copy from one app you paste into the chat... if you don't want to be the human plugin consider making an actual plugin.\" (Jones demystifies plugins, showing serious AI users already do this manually—formalize for leverage.)",[17,3852,3854],{"id":3853},"mcpsconnectors-for-live-data-hooksscripts-for-determinism","MCPs\u002FConnectors for Live Data, Hooks\u002FScripts for Determinism",[22,3856,3857],{},"MCPs (Managed Compute Providers?) and app connectors plug agents into live systems: Slack, Figma, GitHub, Salesforce. Like old-school internet plugs—fetch real data, not imagined. Plugins often contain MCPs but add workflow layers (e.g., data → process → output).",[22,3859,3860],{},"Hooks\u002Fscripts handle non-LLM-trustable parts: Format code (run formatter), validate schemas\u002FJSON, execute tests, enforce pre-stop reviews. Deterministic, not model's \"best judgment.\" Embed in plugins for full loops.",[22,3862,3863],{},"Confusion fix: MCPs ≠ plugins (data access vs. full package); hooks\u002Fscripts ≠ MCPs (internal checks vs. external plugs). All Lego bricks; plugins pre-assemble into structures.",[22,3865,3866],{},"\"Some things ought to be deterministic by which I mean some things should not be left to the model... if the tests need to pass run the test don't ask the model to imagine running the test.\" (Emphasizes reliability—agents shine on ambiguity, falter on facts; scripts ensure quality.)",[17,3868,3870],{"id":3869},"marketplaces-evolve-beyond-app-stores","Marketplaces Evolve Beyond App Stores",[22,3872,3873],{},"Plugins distribute via marketplaces, but undersold as \"extensions.\" Think workflow sharing: Team installs PR review plugin—your standards, tools, checks—once. Enables semantic workflow design: Right unit? (E.g., separate refund\u002Fupgrade plugins over monolithic customer success.)",[22,3875,3876],{},"Result: Scaffolded agents 10x generic prompting. Same LLM, better output via human-provided structure. Non-engineers build now; 2025 impossible, 2026 routine.",[22,3878,3879],{},"\"If you think of plugins as workflow packaging you're going to ask a much sharper question... 'What part of my work has enough repeatable structure?'\" (Challenges passive consumption, urging proactive bounding for team-scale reuse.)",[17,3881,142],{"id":141},[144,3883,3884,3887,3890,3893,3896,3899,3902,3905],{},[147,3885,3886],{},"Audit workflows: One-off → prompt; repeatable style → skill (Markdown); full package → plugin.",[147,3888,3889],{},"Prioritize 20% high-value skills\u002Fplugins (frequent, sensitive tasks like PRs\u002Fmarketing).",[147,3891,3892],{},"Bound plugins tightly: One job per (e.g., refunds ≠ full customer success) to avoid failure.",[147,3894,3895],{},"Embed determinism: Hooks\u002Fscripts for formatting\u002Ftests\u002Fvalidation inside plugins.",[147,3897,3898],{},"Use MCPs\u002Fconnectors for live data (Salesforce\u002FGitHub); plugins wrap them with process.",[147,3900,3901],{},"Build now: Non-coders can; formalize your \"human plugin\" habits for 10x team leverage.",[147,3903,3904],{},"Test trust: Checklist before install—does it match standards? Pull right data?",[147,3906,3907],{},"Distribute via marketplaces: Sharable team assets, not solo hacks.",{"title":186,"searchDepth":187,"depth":187,"links":3909},[3910,3911,3912,3913,3914,3915],{"id":3802,"depth":187,"text":3803},{"id":3815,"depth":187,"text":3816},{"id":3834,"depth":187,"text":3835},{"id":3853,"depth":187,"text":3854},{"id":3869,"depth":187,"text":3870},{"id":141,"depth":187,"text":142},[],{"content_references":3918,"triage":3931},[3919,3924,3928],{"type":3920,"title":3921,"url":3922,"context":3923},"other","Ultimate Codex Plugin Guide","https:\u002F\u002Fnatesnewsletter.substack.com\u002Fp\u002Fcodex-plugins-bottleneck-moved?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true","mentioned",{"type":3925,"title":3926,"url":3927,"context":3923},"podcast","AI News & Strategy Daily","https:\u002F\u002Fopen.spotify.com\u002Fshow\u002F0gkFdjd1wptEKJKLu9LbZ4",{"type":3925,"title":3929,"url":3930,"context":3923},"AI News & Strategy Daily with Nate B Jones","https:\u002F\u002Fpodcasts.apple.com\u002Fus\u002Fpodcast\u002Fai-news-strategy-daily-with-nate-b-jones\u002Fid1877109372",{"relevance":3932,"novelty":3933,"quality":3933,"actionability":3933,"composite":3934,"reasoning":3935},5,4,4.35,"Category: AI & LLMs. The article provides a deep exploration of how to effectively scaffold AI agents for complex workflows, addressing a key pain point for builders who struggle with prompt overload. It offers actionable insights on creating reusable skills and plugins, which can directly enhance productivity in AI-powered product development.","\u002Fsummaries\u002F1efced8af0da0fa8-ai-agents-need-scaffolding-prompts-to-plugins-guid-summary","2026-05-09 15:00:09","2026-05-10 15:02:24",{"title":3792,"description":186},{"loc":3936},"2557f3671dbddfb1","AI News & Strategy Daily | Nate B Jones","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=647pSnX5H_Y","summaries\u002F1efced8af0da0fa8-ai-agents-need-scaffolding-prompts-to-plugins-guid-summary",[212,213,214,3946],"ai-automation","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002F647pSnX5H_Y\u002Fhqdefault.jpg","Most waste 40% of AI time on prompts for repeatable tasks. Build agent 'mech suits' with skills for house style, plugins for full workflows, MCPs for data access, and hooks\u002Fscripts for reliability—reusable across teams and LLMs.","Breakdown of \"agentic scaffolding\" around LLMs in Codex: prompts for one-offs, skills for reusable \"house styles,\" plugins for installable workflows, plus MCPs\u002Fconnectors and hooks\u002Fscripts. Links to a [Substack guide](https:\u002F\u002Fnatesnewsletter.substack.com\u002Fp\u002Fcodex-plugins-bottleneck-moved?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true) with decision trees and examples.",[3946],"gP1VRYquj61rbJeB3ga584672Yfi069-FO1bPSgFFjk",{"id":3953,"title":3954,"ai":3955,"body":3960,"categories":4018,"created_at":195,"date_modified":195,"description":186,"extension":197,"faq":195,"featured":198,"kicker_label":195,"meta":4019,"navigation":200,"path":4038,"published_at":4039,"question":195,"scraped_at":4040,"seo":4041,"sitemap":4042,"source_id":4043,"source_name":4044,"source_type":4045,"source_url":4046,"stem":4047,"tags":4048,"thumbnail_url":195,"tldr":4050,"tweet":4051,"unknown_tags":4052,"__hash__":4053},"summaries\u002Fsummaries\u002F8db41e59caddcd9a-master-cursor-goal-fix-premature-stops-on-complex--summary.md","Master Cursor \u002Fgoal: Fix Premature Stops on Complex Tasks",{"provider":7,"model":8,"input_tokens":3956,"output_tokens":3957,"processing_time_ms":3958,"cost_usd":3959},6221,1949,39048,0.0021962,{"type":14,"value":3961,"toc":4012},[3962,3966,3984,3988,3991,3995,4005,4009],[17,3963,3965],{"id":3964},"replace-dumb-loops-with-llm-judged-persistence","Replace Dumb Loops with LLM-Judged Persistence",[22,3967,3968,3969,3972,3973,3976,3977,51,3980,3983],{},"Cursor's \u002Fgoal iterates on rough-loop style automation but swaps fixed iterations for an LLM judge that checks if the goal is met after each agent run. Enable via ",[29,3970,3971],{},"\u002Ffeatures enable goal",", then ",[29,3974,3975],{},"\u002Fgoal \"migrate JS to TS, verify visuals with Playwright\"",". The agent works (e.g., 9 hours overnight on migrations), gets paused\u002Fcleared with ",[29,3978,3979],{},"\u002Fgoal pause",[29,3981,3982],{},"\u002Fgoal clear",", and receives context like \"Continuing toward goal: take next steps or explicitly state complete.\" This fixes agents declaring victory early on tasks like fixing all repo tests (often incomplete after 10-15 mins). Hermes' persist goal mirrors it. Compared to rough-loop (max iterations) or auto-research loops, \u002Fgoal handles ambiguous goals like \"cut Docker image 60%\" by exploring approaches incrementally. Key: LLM prompt demands \"no proxy signals as completion—audit shows objective achieved, no work remains,\" forcing self-marking as complete.",[17,3985,3987],{"id":3986},"craft-prompts-with-quantifiable-done-and-alignment","Craft Prompts with Quantifiable 'Done' and Alignment",[22,3989,3990],{},"Goals must be >1 prompt but \u003Cbacklog: specify achievement, constraints, validation, stop conditions. Examples: \"Migrate stack, keep screens identical (Playwright verify);\" \"Optimize prompts until eval score hits target, run evals per change;\" \"Find 20 new issues: repro, fix, branch PR, log to run\u002F folder.\" Avoid fuzzy like \"fix everything\"—agents quit early or spiral. Pre-start: Chat for alignment (project context, bad UX, past bugs)—Vincent ran 3 days\u002F30 rounds\u002Fgazillion tokens on OpenClaw this way. For prototypes, reference PRD.md, create milestone tests, include ref screenshots. Quantify: 20 issues, target score, visual matches.",[17,3992,3994],{"id":3993},"tools-and-extensions-for-reliable-execution","Tools and Extensions for Reliable Execution",[22,3996,3997,4000,4001,4004],{},[29,3998,3999],{},"npx goal-buddy"," generates goal.md (describes request\u002Fconstraints\u002Fstops) + state.yaml (tracks tasks)—",[29,4002,4003],{},"\u002Fgoal @goal.md"," yields full games (e.g., Rain-type with image-gen assets). Side chats fork convos mid-goal. Workshop at aibuilderclub.com teaches more.",[17,4006,4008],{"id":4007},"missions-for-weekmonth-horizons","Missions for Week+\u002FMonth+ Horizons",[22,4010,4011],{},"\u002Fgoal limits to hours (e.g., fails on weeks-long SEO\u002FROAS without quick feedback). Use \u002Fmission: mission.md defines metrics, agent hypothesizes\u002Ftests (e.g., grow Twitter to 10k: post founder-voice threads, analyze perf, schedule next in hours\u002Fweeks). Human-in-loop for big changes. Crewlet (crewlet.io) in closed beta; iterated tweets from average to high-engagement by doubling down on winners.",{"title":186,"searchDepth":187,"depth":187,"links":4013},[4014,4015,4016,4017],{"id":3964,"depth":187,"text":3965},{"id":3986,"depth":187,"text":3987},{"id":3993,"depth":187,"text":3994},{"id":4007,"depth":187,"text":4008},[],{"content_references":4020,"triage":4035},[4021,4025,4028,4033],{"type":4022,"title":4023,"url":4024,"context":3923},"tool","Crewlet","http:\u002F\u002Fcrewlet.io\u002F",{"type":4022,"title":4026,"url":4027,"context":3923},"Superdesign","http:\u002F\u002Fsuperdesign.dev\u002F",{"type":4029,"title":4030,"url":4031,"context":4032},"event","\u002Fgoal & codex best practices workshop","https:\u002F\u002Fwww.aibuilderclub.com\u002F","recommended",{"type":4022,"title":4034,"context":3923},"goal-buddy",{"relevance":3932,"novelty":3933,"quality":3933,"actionability":3932,"composite":4036,"reasoning":4037},4.55,"Category: AI & LLMs. The article provides a detailed explanation of how to implement LLM-judged persistence in automation tasks, addressing a specific pain point of premature task completion. It includes actionable steps and examples for defining clear goals and metrics, making it highly relevant and practical for product builders.","\u002Fsummaries\u002F8db41e59caddcd9a-master-cursor-goal-fix-premature-stops-on-complex-summary","2026-05-09 12:15:09","2026-05-09 15:20:33",{"title":3954,"description":186},{"loc":4038},"9a92ddf0196fd3e7","AI Jason","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=rIs802-bXDY","summaries\u002F8db41e59caddcd9a-master-cursor-goal-fix-premature-stops-on-complex--summary",[212,213,214,4049],"dev-productivity","Cursor's \u002Fgoal uses LLM judgment to loop agents on long tasks like 9-hour migrations, preventing lazy early exits—define explicit 'done' criteria with verifiable tests (e.g., Playwright) and quantify metrics to succeed.","Walkthrough of Codex's `\u002Fgoal` slash command for long-running agent tasks (like code migrations), with setup steps, prompting templates emphasizing verifiable \"done\" states and pre-task alignment chats, plus intro to the open-source Goal-buddy prompt builder and `\u002Fmission` alternative. Links workshop for deeper best practices: [aibuilderclub.com](https:\u002F\u002Fwww.aibuilderclub.com\u002F).",[4049],"dOGtGYhoEEetAYG0f3aNAHdOqGdyRQKixW_ALYePucc",{"id":4055,"title":4056,"ai":4057,"body":4062,"categories":4090,"created_at":195,"date_modified":195,"description":186,"extension":197,"faq":195,"featured":198,"kicker_label":195,"meta":4091,"navigation":200,"path":4125,"published_at":4126,"question":195,"scraped_at":4127,"seo":4128,"sitemap":4129,"source_id":4130,"source_name":4131,"source_type":208,"source_url":4132,"stem":4133,"tags":4134,"thumbnail_url":4136,"tldr":4137,"tweet":4138,"unknown_tags":4139,"__hash__":4140},"summaries\u002Fsummaries\u002F26831750495fa9ed-openai-s-real-time-voice-ai-powers-agents-backed-b-summary.md","OpenAI's Real-Time Voice AI Powers Agents, Backed by MRC Networking",{"provider":7,"model":8,"input_tokens":4058,"output_tokens":4059,"processing_time_ms":4060,"cost_usd":4061},6837,2244,28673,0.00246675,{"type":14,"value":4063,"toc":4085},[4064,4068,4071,4075,4078,4082],[17,4065,4067],{"id":4066},"real-time-voice-models-enable-production-agents","Real-Time Voice Models Enable Production Agents",[22,4069,4070],{},"OpenAI's GPT-Realtime-2 combines GPT-4o-class reasoning with low-latency voice for live agents handling complex tasks like flight rebooking under $400 by querying accounts, comparing options, issuing refunds, and explaining in parallel via multiple tools. It uses short filler phrases (\"Let me check\") to mimic human pauses, preventing awkward silences, and supports adjustable reasoning levels (minimal to X-high) for speed vs. depth—default low prioritizes \u003C500ms responses. Context window expands to 128k tokens from 32k, sustaining long support calls or tutoring. Benchmarks: 96.6% Big Bench Audio accuracy (vs. 81.4% prior), 48.5% Audio Multi-Challenge pass rate (vs. 34.7%). Handles interruptions, accents, medical terms, and tone shifts (calm, empathetic). GPT-Realtime-Translate supports 70+ input\u002F13 output languages with context-aware live translation for support or events (e.g., Deutsche Telekom testing). GPT-Realtime-Whisper streams transcription for captions, notes, and action items. Pricing: GPT-Realtime-2 at $32\u002FM input tokens ($0.40\u002FM cached), $64\u002FM output; Translate $0.034\u002Fmin; Whisper $0.017\u002Fmin. Patterns: voice-to-action (tools), systems-to-voice (app guidance), voice-to-voice (translation). EU residency and anti-spam guardrails included.",[17,4072,4074],{"id":4073},"mrc-networking-scales-frontier-training","MRC Networking Scales Frontier Training",[22,4076,4077],{},"MRC (Multi-Path Reliable Connection) optimizes GPU clusters by spreading data across hundreds of paths using RoCE\u002FRDMA and SRV6 routing, reducing bottlenecks vs. single-path systems. Failure recovery happens in microseconds at NIC level—e.g., reroutes around bad links without crashing jobs, restoring capacity post-failure in ~1 minute. Enables 131k GPUs with 2 switch tiers (vs. 3-4), using 2\u002F3 optics and 3\u002F5 switches, cutting latency. Handles 400\u002F800Gbit RDMA cards (Nvidia\u002FAMD\u002FBroadcom) and switches (Nvidia Spectrum\u002FBroadcom Tomahawk). Live on OpenAI's GB200 clusters (Oracle Abilene, Microsoft Fairwater), survived switch reboots mid-training for ChatGPT\u002Fo1 models. Trade-off: Shifts AI race from GPUs to networks, as idle time on $expensive hardware burns cash for 900M weekly ChatGPT users.",[17,4079,4081],{"id":4080},"ai-jobs-debate-washing-vs-real-displacement","AI Jobs Debate: Washing vs. Real Displacement",[22,4083,4084],{},"Sam Altman notes \"AI-washing\"—firms blame unrelated layoffs (margins, consumers, geopolitics) on AI to justify spending. Yet displacement grows: Anthropic's Amodei predicts 50% entry-level office jobs lost; Snap cut 16% citing AI; WEF says 40% employers plan reductions. Data mixed—NBER survey: 90% execs report no employment impact post-ChatGPT; Yale Budget Lab: no occupation shifts\u002Funemployment spikes through Mar 2026. Contrasts: 2.7% YoY productivity jump (Stanford); 13% employment drop for early-career AI-exposed roles. Analogy: Like computers, AI effects lag macro data. Outcome: Entry digital tasks shrink first; experienced roles stable\u002Fgrow.",{"title":186,"searchDepth":187,"depth":187,"links":4086},[4087,4088,4089],{"id":4066,"depth":187,"text":4067},{"id":4073,"depth":187,"text":4074},{"id":4080,"depth":187,"text":4081},[249],{"content_references":4092,"triage":4121},[4093,4097,4100,4103,4107,4111,4114,4118],{"type":3920,"title":4094,"url":4095,"context":4096},"Advancing voice intelligence with new models in the API","https:\u002F\u002Fopenai.com\u002Findex\u002Fadvancing-voice-intelligence-with-new-models-in-the-api\u002F","cited",{"type":3920,"title":4098,"url":4099,"context":4096},"Realtime agents guide","https:\u002F\u002Fdevelopers.openai.com\u002Fcookbook\u002Fexamples\u002Fvoice_solutions\u002Frealtime_agents_guide",{"type":3920,"title":4101,"url":4102,"context":4096},"MRC supercomputer networking","https:\u002F\u002Fopenai.com\u002Findex\u002Fmrc-supercomputer-networking\u002F",{"type":4104,"title":4105,"url":4106,"context":4096},"report","Sam Altman says some companies are AI-washing","https:\u002F\u002Fwww.techradar.com\u002Fpro\u002Fsam-altman-says-some-companies-are-ai-washing-by-blaming-unrelated-layoffs-on-the-technology-but-admits-things-may-get-worse-soon",{"type":4104,"title":4108,"publisher":4109,"url":4110,"context":4096},"Tracking impact of AI on labor market","Yale Budget Lab","https:\u002F\u002Fbudgetlab.yale.edu\u002Fresearch\u002Ftracking-impact-ai-labor-market",{"type":4022,"title":4112,"url":4113,"context":4032},"CodeRabbit","https:\u002F\u002Fcoderabbit.link\u002Fai-revolution",{"type":4115,"title":4116,"author":4117,"context":4096},"paper","National Bureau of Economic Research study","National Bureau of Economic Research",{"type":4104,"title":4119,"publisher":4120,"context":4096},"World Economic Forum's 2025 Future of Jobs Report","World Economic Forum",{"relevance":4122,"novelty":4122,"quality":3933,"actionability":187,"composite":4123,"reasoning":4124},3,3.05,"Category: AI & LLMs. The article discusses OpenAI's GPT-Realtime-2 and its capabilities for building real-time voice agents, which is relevant to AI-powered product development. However, it lacks actionable steps or frameworks that the audience can directly implement in their projects.","\u002Fsummaries\u002F26831750495fa9ed-openai-s-real-time-voice-ai-powers-agents-backed-b-summary","2026-05-08 22:31:14","2026-05-10 15:18:49",{"title":4056,"description":186},{"loc":4125},"4b39523ed70243f9","AI Revolution","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=o_igSi-ED6s","summaries\u002F26831750495fa9ed-openai-s-real-time-voice-ai-powers-agents-backed-b-summary",[4135,212,214,215],"llm","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002Fo_igSi-ED6s\u002Fhqdefault.jpg","OpenAI's GPT-Realtime-2 enables live voice agents with GPT-4o reasoning, 128k context, parallel tools, and 96.6% audio accuracy; MRC networking spreads data across paths for 131k-GPU clusters with microsecond failure recovery.","Narrated walkthrough of OpenAI's announcements on [real-time voice models](https:\u002F\u002Fopenai.com\u002Findex\u002Fadvancing-voice-intelligence-with-new-models-in-the-api\u002F) (GPT-Realtime-2\u002FTranslate\u002FWhisper for conversations, translation, transcription), [MRC supercomputer networking](https:\u002F\u002Fopenai.com\u002Findex\u002Fmrc-supercomputer-networking\u002F), and Sam Altman's [AI layoff comments](https:\u002F\u002Fwww.techradar.com\u002Fpro\u002Fsam-altman-says-some-companies-are-ai-washing-by-blaming-unrelated-layoffs-on-the-technology-but-admits-things-may-get-worse-soon), with a [CodeRabbit](https:\u002F\u002Fcoderabbit.link\u002Fai-revolution) sponsor segment.",[215],"Hnh2mFOtkRkBE9C8_maIzWYtBUahQHxcKa6xWpj7MA0",{"id":4142,"title":4143,"ai":4144,"body":4149,"categories":4412,"created_at":195,"date_modified":195,"description":186,"extension":197,"faq":195,"featured":198,"kicker_label":195,"meta":4413,"navigation":200,"path":4421,"published_at":4422,"question":195,"scraped_at":4423,"seo":4424,"sitemap":4425,"source_id":4426,"source_name":4427,"source_type":4045,"source_url":4428,"stem":4429,"tags":4430,"thumbnail_url":195,"tldr":4431,"tweet":4432,"unknown_tags":4433,"__hash__":4434},"summaries\u002Fsummaries\u002Ff056d2fbc3259de2-optimize-live-agents-gepa-prompts-managed-vars-summary.md","Optimize Live Agents: GEPA Prompts + Managed Vars",{"provider":7,"model":8,"input_tokens":4145,"output_tokens":4146,"processing_time_ms":4147,"cost_usd":4148},8380,2516,37110,0.0029115,{"type":14,"value":4150,"toc":4405},[4151,4155,4158,4165,4177,4184,4199,4206,4217,4221,4224,4227,4246,4249,4252,4255,4262,4266,4269,4315,4322,4329,4332,4343,4346,4349,4353,4356,4367,4370,4372,4401],[17,4152,4154],{"id":4153},"build-golden-datasets-and-custom-evals-for-reliable-agent-testing","Build Golden Datasets and Custom Evals for Reliable Agent Testing",[22,4156,4157],{},"Samuel Colvin demonstrates optimizing agents post-deployment by first establishing a baseline with structured evaluations against a \"golden dataset\"—manually verified ground truth data. For the case study, he scrapes Wikipedia pages for UK MPs, extracts text via BeautifulSoup, and defines Pydantic schemas for MP details and political relations (focusing on ancestors like parents\u002Fgrandparents, excluding spouses\u002Fchildren).",[22,4159,4160,4161,4164],{},"The golden dataset (",[29,4162,4163],{},"golden_relations.json",") contains exact relations for ~650 MPs, created by running a high-end model like Opus once and manual checks. Custom evaluators compare agent outputs to this truth:",[144,4166,4167,4174],{},[147,4168,4169,4173],{},[4170,4171,4172],"strong",{},"Accuracy",": Exact match on relations list (1.0 if perfect, partial scores like 0.9 for minor name\u002Fdescription diffs).",[147,4175,4176],{},"Assertions for relation types, roles, and ancestor filtering.",[22,4178,4179,4180,4183],{},"Key principle: Prefer deterministic, rule-based evals over \"LLM-as-judge\" to avoid bias. \"Defining your own ",[102,4181,4182],{},"evaluators"," is far better than LLM as a judge because the LLM as a judge is effectively the kind of lunatics running the asylum.\"",[22,4185,4186,4187,4190,4191,4194,4195,4198],{},"To run: Load dataset with ",[29,4188,4189],{},"load_dataset()",", register evaluators, then ",[29,4192,4193],{},"dataset.evaluate(agent_func, name=\"eval-name\")"," using Pydantic AI's ",[29,4196,4197],{},"override"," for prompts\u002Fmodels. Concurrency limits (e.g., max=5) prevent rate limits. Results appear in Logfire UI: spans show inputs\u002Foutputs\u002Fcosts, evals tab aggregates metrics (e.g., 85% accuracy for simple prompt).",[22,4200,4201,4202,4205],{},"Common mistake: Over-relying on console logs—disable terminal output (",[29,4203,4204],{},"LOGFIRE_NO_CONSOLE=true",") for clean traces. Before: Simple one-liner prompt gets 85% accuracy, confuses non-ancestors\u002Fpolitical vs. public figures. After better prompt: Improves to ~90%+ by explicitly discounting same-gen relations.",[22,4207,4208,4209,4212,4213,4216],{},"Setup prerequisites: ",[29,4210,4211],{},"uv sync",", Logfire project (",[29,4214,4215],{},"logfire project use demo","), API keys (Pydantic Gateway for multi-model access or direct OpenAI\u002FAnthropic). Quality criteria: High accuracy on ancestors, low false positives on spouses\u002Fkids.",[17,4218,4220],{"id":4219},"evolve-prompts-genetically-with-gepa-on-production-traces","Evolve Prompts Genetically with GEPA on Production Traces",[22,4222,4223],{},"GEPA (Genetic Evolutionary Prompt Algorithm, via \"Jepper\" library) optimizes prompts as strings or JSON by breeding top performers. It evaluates candidates on a dataset, selects Pareto frontier (best trade-offs), mutates\u002Fcrosses them (e.g., mix phrases from high-scorers), and iterates.",[22,4225,4226],{},"Process:",[4228,4229,4230,4233,4236,4243],"ol",{},[147,4231,4232],{},"Define initial prompts (simple vs. advanced) and models as Pydantic models.",[147,4234,4235],{},"Run evals on split dataset (e.g., 65 test cases for speed).",[147,4237,4238,4239,4242],{},"Launch GEPA: ",[29,4240,4241],{},"gepa.optimize(evaluate_fn, initial_candidates, generations=10, population_size=20)",". It parallelizes evals, instruments via Logfire for traces.",[147,4244,4245],{},"Output: Ranked prompts by composite score (accuracy + cost\u002Fefficiency).",[22,4247,4248],{},"In demo: Simple prompt → 85% acc; advanced (ancestor rules) → better; GEPA evolves hybrids exceeding both (e.g., 92%+ acc). Handles systemic errors like over-including spouses by evolving phrasing: \"Only ancestors (parents, grandparents)—exclude spouses, children, siblings.\"",[22,4250,4251],{},"Trade-offs: Compute-heavy (hundreds of evals\u002Fgeneration); start small dataset. Mistake: Random mutation—GEPA biases toward elites like horse breeding. \"It takes the best racehorses and breeds them... you take all of the best resources and breed them.\"",[22,4253,4254],{},"Extend to production: Use real traces\u002Ffeedback as eval inputs. Future: Autonomous optimization from Logfire.",[22,4256,4257,4258,4261],{},"Quote: \"GEPA is ultimately an optimization library ",[102,4259,4260],{},"that"," optimizes a string... it can be a simple text prompt or some JSON data.\"",[17,4263,4265],{"id":4264},"enable-zero-downtime-tuning-with-managed-variables-in-production","Enable Zero-Downtime Tuning with Managed Variables in Production",[22,4267,4268],{},"Logfire's managed variables let you update any Pydantic-serializable object (prompts, models, params) live without restarts. Define as Pydantic model:",[4270,4271,4275],"pre",{"className":4272,"code":4273,"language":4274,"meta":186,"style":186},"language-python shiki shiki-themes github-light github-dark","from logfire.managed import managed_variable\n\nclass AgentConfig(BaseModel):\n    model: str = \"gateway:gpt-4o-mini\"\n    instructions: str = \"...\"\n\nconfig = managed_variable(AgentConfig)\n","python",[29,4276,4277,4284,4289,4294,4299,4304,4309],{"__ignoreMap":186},[102,4278,4281],{"class":4279,"line":4280},"line",1,[102,4282,4283],{},"from logfire.managed import managed_variable\n",[102,4285,4286],{"class":4279,"line":187},[102,4287,4288],{"emptyLinePlaceholder":200},"\n",[102,4290,4291],{"class":4279,"line":4122},[102,4292,4293],{},"class AgentConfig(BaseModel):\n",[102,4295,4296],{"class":4279,"line":3933},[102,4297,4298],{},"    model: str = \"gateway:gpt-4o-mini\"\n",[102,4300,4301],{"class":4279,"line":3932},[102,4302,4303],{},"    instructions: str = \"...\"\n",[102,4305,4307],{"class":4279,"line":4306},6,[102,4308,4288],{"emptyLinePlaceholder":200},[102,4310,4312],{"class":4279,"line":4311},7,[102,4313,4314],{},"config = managed_variable(AgentConfig)\n",[22,4316,4317,4318,4321],{},"In agent: ",[29,4319,4320],{},"agent = Agent(..., instructions=config.instructions, model=config.model)",". Changes in Logfire UI propagate instantly (poll every 30s).",[22,4323,4324,4325,4328],{},"Production demo: FastAPI server with ",[29,4326,4327],{},"\u002Fanalyze"," endpoint runs agent on live Wikipedia HTML. Update prompt\u002Fmodel via Logfire—tune for better ancestor detection without deploy.",[22,4330,4331],{},"Implicit feedback: Log user thumbs-up\u002Fdown, aggregate into evals. Q&A insights:",[144,4333,4334,4337,4340],{},[147,4335,4336],{},"Prompt bloat: GEPA prunes inefficient phrasing.",[147,4338,4339],{},"Context engineering: Chain-of-thought in prompts.",[147,4341,4342],{},"Internal use: Pydantic team tunes agents on traces.",[22,4344,4345],{},"Trade-offs: Polling overhead (low); free tier generous. Mistake: Mutable globals—managed vars are safe, versioned.",[22,4347,4348],{},"Quote: \"Managed variables... don't have to be just text they can be effectively any object that you can define with a Pydantic model.\"",[17,4350,4352],{"id":4351},"from-manual-to-continuous-optimization-workflow","From Manual to Continuous Optimization Workflow",[22,4354,4355],{},"Full loop: Golden evals → GEPA on traces → Managed vars deploy → Feedback evals. Fits mid-workshop: Assumes Python\u002FPydantic familiarity, agent-building basics. Broader: Any structured output task (invoices, addresses) benefits.",[22,4357,4358,4359,4362,4363,4366],{},"Exercise: Fork repo (",[29,4360,4361],{},"github.com\u002Fpydantic\u002Ftalks\u002F2024-ai-engineer","), run ",[29,4364,4365],{},"uv run main.py eval --split test --prompt initial",", compare prompts, GEPA optimize, deploy to FastAPI.",[22,4368,4369],{},"Quote: \"Deploying an agent is only the start... change prompts, models... without redeploying.\"",[17,4371,142],{"id":141},[144,4373,4374,4377,4380,4383,4386,4389,4392,4395,4398],{},[147,4375,4376],{},"Create golden datasets from high-model runs + manual verification for deterministic evals—beats LLM judges.",[147,4378,4379],{},"Use GEPA to breed prompts: Start with 2-5 candidates, 10 generations on 65-case split for quick wins.",[147,4381,4382],{},"Define managed variables as Pydantic models for instant prod updates—no restarts needed.",[147,4384,4385],{},"Instrument everything with Logfire: Traces reveal confusions (e.g., spouses as ancestors).",[147,4387,4388],{},"Prioritize ancestor filtering in political\u002Frelation extraction: Evolve phrasing like \"exclude same-gen or descendants.\"",[147,4390,4391],{},"Run evals in parallel (max_concurrency=5) to optimize costs during optimization.",[147,4393,4394],{},"For FastAPI agents: Override configs live, log implicit feedback for GEPA inputs.",[147,4396,4397],{},"Avoid hype: \"I don't really believe in AI observability I think it's a feature not a category.\"",[147,4399,4400],{},"Scale: Free Logfire tier handles workshops; Gateway simplifies multi-model testing.",[4402,4403,4404],"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":186,"searchDepth":187,"depth":187,"links":4406},[4407,4408,4409,4410,4411],{"id":4153,"depth":187,"text":4154},{"id":4219,"depth":187,"text":4220},{"id":4264,"depth":187,"text":4265},{"id":4351,"depth":187,"text":4352},{"id":141,"depth":187,"text":142},[],{"content_references":4414,"triage":4419},[4415,4417],{"type":3925,"title":4416,"context":3923},"The Rest is Politics",{"type":3920,"title":4418,"context":4032},"Jepper (GEPA)",{"relevance":3932,"novelty":3933,"quality":3933,"actionability":3932,"composite":4036,"reasoning":4420},"Category: AI & LLMs. The article provides a detailed approach to optimizing AI agents using specific techniques like golden datasets and custom evaluations, addressing a key pain point for developers looking to improve production AI features. It includes actionable steps and code snippets that developers can implement directly.","\u002Fsummaries\u002Ff056d2fbc3259de2-optimize-live-agents-gepa-prompts-managed-vars-summary","2026-05-07 17:00:06","2026-05-08 11:03:29",{"title":4143,"description":186},{"loc":4421},"263bbb77349e4ef1","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=A48uhxfxbsM","summaries\u002Ff056d2fbc3259de2-optimize-live-agents-gepa-prompts-managed-vars-summary",[212,213,4274,214],"Tune production agents without redeploys using Logfire's managed variables for prompts\u002Fmodels and GEPA's genetic algorithm to evolve better prompts from evals on golden datasets.","Hands-on workshop by Pydantic's Samuel Colvin: codes along optimizing an agent for extracting political relations from Wikipedia pages using Logfire evals, GEPA prompt evolution on a golden dataset, and managed variables for live prompt\u002Fmodel tweaks in a FastAPI app—no redeploys needed.",[],"0cZ4pNDJvZh7SpRsu_CCMCCCQN6tzKI9oKJA0ArueRc"]