[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-96a055d05be96c33-custom-telegram-ai-agent-replaces-openclaw-for-new-summary":3,"summaries-facets-categories":164,"summary-related-96a055d05be96c33-custom-telegram-ai-agent-replaces-openclaw-for-new-summary":3733},{"id":4,"title":5,"ai":6,"body":13,"categories":139,"created_at":141,"date_modified":141,"description":142,"extension":143,"faq":141,"featured":144,"kicker_label":141,"meta":145,"navigation":146,"path":147,"published_at":148,"question":141,"scraped_at":149,"seo":150,"sitemap":151,"source_id":152,"source_name":153,"source_type":154,"source_url":155,"stem":156,"tags":157,"thumbnail_url":141,"tldr":161,"tweet":141,"unknown_tags":162,"__hash__":163},"summaries\u002Fsummaries\u002F96a055d05be96c33-custom-telegram-ai-agent-replaces-openclaw-for-new-summary.md","Custom Telegram AI Agent Replaces OpenClaw for News Automation",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8772,2314,24327,0.0028889,{"type":14,"value":15,"toc":130},"minimark",[16,21,25,31,35,38,41,44,54,58,61,69,72,76,79,82,87,92,96],[17,18,20],"h2",{"id":19},"ditched-openclaw-for-full-control-the-custom-build-decision","Ditched OpenClaw for Full Control: The Custom Build Decision",[22,23,24],"p",{},"OpenClaw handled basic news scanning for the author's Telegram channel (@GenAI_Spotlight), pulling from GitHub, Reddit, major media, and X accounts every few hours. But it lacked depth—no seamless backend switching, account rotation, or persistent memory across sessions. The author stripped OpenClaw to just scanning duties and built CC Claw (Coding CLI Claw) from scratch using CLIs for Claude, Gemini, CodeX, and Cursor as backends. Why? OpenClaw's one-size-fits-all couldn't evolve or handle nuances like process spawning\u002Fclosing without losing context. Tradeoff: A month of nightly tuning (not hours as hyped videos claim), multiple refactors via spec → plan → review cycles, and ongoing bugs delaying release. Result: An agent that does everything OpenClaw did, plus gated permissions, verbose debugging, and Telegram-only interface for focus—no Discord\u002FWhatsApp distractions.",[26,27,28],"blockquote",{},[22,29,30],{},"'Oh, you can build an agent very easily, you know, like takes you few hours. I've seen some videos like that. No, if you really want to build a serious agent with memory and evolution... it kind of became a massive project.' (Author contrasts hype with reality; this sets expectations for serious builds requiring iteration.)",[17,32,34],{"id":33},"multi-backend-architecture-with-persistent-state","Multi-Backend Architecture with Persistent State",[22,36,37],{},"CC Claw spawns CLI processes per interaction, resuming with summarized context to mimic continuity. Switch backends (e.g., Gemini → Claude) via commands; it saves chats, offers 'don't save silly conversation' option, and uses a dedicated summarizer (Gemini Pro high by default). Account rotation for Gemini handles rate limits across personal API keys, auto-switching on timeouts—fixed OpenClaw's failure here. Permissions tiers: 'safe' (read-only tools), 'plan' (analyze\u002Fpropose), 'gated' (manual approve\u002Freject actions like file creation). Verbosity toggles show tool calls, model signatures (e.g., 'Gemini 1.5 Pro, high, key #2'), and actions. Shell access (\u002Fpwd, \u002Frestart) bypasses agent for direct backend control; \u002Fstatus reveals issues instantly, unlike OpenClaw's delays.",[22,39,40],{},"Skills system loads modular prompts (built-in, CC Claw-specific, universal manager downloads\u002Fscans for risks\u002Fprompt injection). MCPs (e.g., Perplexity for fact-checks, Google Search, NotebookLM) extend capabilities. Memory: Episodic with decay, browsable (\u002Fmemory), slash commands to pin facts. Whiteboard logs session state (drafts, images) across backend switches. Chrome jobs run nightly: news scanner, GitHub PR alerts, YouTube stats, reflection (analyzes interactions for evolution suggestions), heartbeat.",[22,42,43],{},"Tradeoffs: CLI spawning adds latency\u002Fresume complexity; non-dev author spent hours debugging stability. But it enables 'yolo' mode for power users vs. sandbox hype.",[26,45,46],{},[22,47,48,49,53],{},"'I don't believe in that approach ",[50,51,52],"span",{},"sandboxed agents",". And I know there are a lot of risks with letting your agent go yolo. You only live once.' (Author rejects locked-down agents; prioritizes utility with controls like gating.)",[17,55,57],{"id":56},"newsroom-workflow-from-scan-to-multi-platform-distribution","Newsroom Workflow: From Scan to Multi-Platform Distribution",[22,59,60],{},"Chrome job feeds proposals to Telegram 'forum topics' (newsroom chat). Author pastes URL (e.g., 'Agentic will redefine work'); agent invokes newsroom skill: dedupe vs. channel history, load voice\u002Fcontext (bold headline, spaced lines, 'why it matters'), generate image prompt, draft in HTML\u002FMarkdown for Telegram. Posts to staging channel for review; uses whiteboard for artifacts (draft path, image link). Inline edits fix issues (e.g., wordy → punchy, Flash model's formatting fails). Fact-check via Perplexity MCP: 'All core claims verified.' Author approves → agent posts to main channel (@GenAI_Spotlight), adds emojis, cleans whiteboard.",[22,62,63,64,68],{},"Then pushes to Buffer (API\u002FMCP integrated): Adapts for LinkedIn\u002FX (strips hyperlinks, appends channel promo: '",[65,66,67],"strong",{},"See more @GenAI_Spotlight","'), queues\u002Fpublishes\u002Fdrafts per predefined slots. Python scripts enforce formats—no agent forgetfulness. Full cycle: 5-10 mins hands-on (review\u002Fedit\u002Fapprove), automates scanning\u002Fcuration\u002Fposting\u002Fdistribution.",[22,70,71],{},"Before: Manual OpenClaw tweaks. After: End-to-end, multi-model resilient (switch on limits), human-in-loop for quality. No metrics shared, but daily posts shown (e.g., recent Agentic story live on Telegram\u002FX\u002FLinkedIn with matching image).",[17,73,75],{"id":74},"agent-evolution-and-optimization-controlled-improvement","Agent Evolution and Optimization: Controlled Improvement",[22,77,78],{},"Nightly reflection scans interactions, flags issues (e.g., 'u re actions to review'), suggests identity\u002Fskills\u002Fcontext edits. Manual apply\u002Freject\u002Freview\u002Fdiscuss—rejects auto-evolution to stay in control. Optimize audits: Souls.md (agent identity), identity.md, user.md for contradictions\u002Fmisplacements; skills for token bloat\u002Fineffectiveness. Universal skills manager imports MCPs securely.",[22,80,81],{},"Tradeoffs: Time-intensive (author skips auto for directionality), but yields tailored agent. Still: Context resume inaccuracies, Flash inconsistencies, gated timeouts.",[26,83,84],{},[22,85,86],{},"'I don't believe in automatic evolution because what if the direction the AI is taking is not what I am interested in. So I like to be in control still not fully autonomous but that's my route.' (Author on evolution; emphasizes human oversight for alignment.)",[26,88,89],{},[22,90,91],{},"'Everything that you see here, all these news, they're essentially being curated by the agent... We continuously scan multiple sources... and then I get a report with top stories that are aligned to my taste.' (Describes newsroom output; shows personalization payoff.)",[17,93,95],{"id":94},"key-takeaways","Key Takeaways",[97,98,99,103,106,109,112,115,118,121,124,127],"ul",{},[100,101,102],"li",{},"Start agent builds with spec\u002Fplan\u002Freview cycles; expect weeks for production stability, not hours.",[100,104,105],{},"Use CLI backends + summarization for cheap multi-model switching with memory.",[100,107,108],{},"Build whiteboards for state persistence across sessions\u002Ftools.",[100,110,111],{},"Gate high-risk actions but enable 'yolo' for real utility—balance beats sandbox hype.",[100,113,114],{},"Automate distribution via APIs (Buffer) with platform-specific scripts.",[100,116,117],{},"Run nightly reflections for evolution, but manual review to control direction.",[100,119,120],{},"Telegram-only UI simplifies; add \u002Fstatus\u002Fshell for ops visibility.",[100,122,123],{},"Skills + MCPs + context files modularize workflows without token overload.",[100,125,126],{},"Fact-check every draft (Perplexity MCP) before human approve.",[100,128,129],{},"Test rigorously: Author iterated refactors after bugs in usage rotation, context.",{"title":131,"searchDepth":132,"depth":132,"links":133},"",2,[134,135,136,137,138],{"id":19,"depth":132,"text":20},{"id":33,"depth":132,"text":34},{"id":56,"depth":132,"text":57},{"id":74,"depth":132,"text":75},{"id":94,"depth":132,"text":95},[140],"AI Automation",null,"I built a custom AI agent from scratch to run my newsroom workflow directly from Telegram.\n\nIn this video, I show how CC-Claw replaced OpenClaw in my stack and how it works behind the scenes. It can switch between Gemini, Claude, and Codex backends, scan sources like GitHub, Reddit, and X, draft Telegram news posts, fact-check with Perplexity MCP and Google, make inline edits, and push content to LinkedIn and X.\n\nI also walk through its memory system, self-improvement workflow, skill optimization, backend switching, sub-agent support, and the controls I built to keep it useful without losing oversight.\n\nCC-Claw is still a work in progress, but this is the first real look at how I’m using it today.\n\nRESOURCES:\nJoin the Gen AI Spotlight AI News Channel on Telegram:\nhttps:\u002F\u002Ft.me\u002Fgenaispot\u002F\n\nFollow GenAI Spotlight on TikTok:\nhttps:\u002F\u002Fwww.tiktok.com\u002F@genai.spotlight\n\nFollow GenAI Spotlight on X:\nhttps:\u002F\u002Fx.com\u002FGenAISpotlight\n\nUniversal Skills Manager Repo:\nhttps:\u002F\u002Fgithub.com\u002Fjacob-bd\u002Funiversal-skills-manager\n\nPerplexity MCP & CLI Repo:\nhttps:\u002F\u002Fgithub.com\u002Fjacob-bd\u002Fperplexity-web-mcp\n\n#AIAgent #OpenClaw #TelegramBot #ClaudeCode #Codex #Gemini","md",false,{},true,"\u002Fsummaries\u002F96a055d05be96c33-custom-telegram-ai-agent-replaces-openclaw-for-new-summary","2026-04-03 16:17:47","2026-04-03 21:20:06",{"title":5,"description":142},{"loc":147},"96a055d05be96c33","Gen AI Spotlight","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=-wQPhXfLM7M","summaries\u002F96a055d05be96c33-custom-telegram-ai-agent-replaces-openclaw-for-new-summary",[158,159,160],"agents","content-pipelines","ai-automation","Built CC Claw, a multi-CLI AI agent controlled via Telegram, with memory, evolution, and skills that automates news curation from scan to multi-platform posts—taking a month of iteration for stability over OpenClaw's 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Feed transcripts to Claude Opus (version 4.7) to identify 3 high-engagement moments—scoring for virality from 1-hour content like an 89-minute Diary of a CEO episode. Apply YOLO for face detection and light Active Speaker Detection (ASD) to track who speaks, enabling dynamic reframing from 16:9 to vertical shorts that follow the active face. Finish with Remotion code-based edits: auto-captions, zooms, flashes, meme sound effects, and music overlays boost retention without manual tweaks. Total runtime: 5-15 minutes per batch, yielding ready MP4s that rival hand-edited clips.",[22,3752,3753],{},"This setup improves every 6 months as models advance—creator's channel hit 20k subs and 8.9M views on prior automated shorts, proving production viability.",[17,3755,3757],{"id":3756},"tool-stack-enables-hands-off-processing","Tool Stack Enables Hands-Off Processing",[22,3759,3760],{},"Core models handle decisions autonomously: Claude reads full transcripts, skims for 'strong moments,' and outputs clip timestamps; YOLO excels at multi-face detection in podcasts; ASD ensures seamless speaker switches in interviews. Remotion shines for programmatic effects, avoiding brittle no-code tools. For deployment, self-host n8n on Hostinger VPS (KVM 2: 2 vCPUs, 8GB RAM, 100GB disk, 8TB bandwidth; 10% off with ALLABOUTAI code atop 70% discount) via one-click install—triggers workflows like schedule-to-Gmail in seconds. Upload via Surf Agent: browser automation picks title (e.g., \"A doctor just exposed what's happening to male fertility\"), sets private visibility, and publishes without APIs, runnable on Mac Mini.",[22,3762,3763],{},"Trade-offs: Works best on face-heavy content; struggles with prop-focused segments (e.g., vials demo) or low-face reactions, but still produces usable clips. No manual intervention post-URL input.",[17,3765,3767],{"id":3766},"results-across-video-types-validate-scalability","Results Across Video Types Validate Scalability",[22,3769,3770],{},"Processed Diary of a CEO (fertility trajectory clip with clear visuals, effects); Charlie Moist Penguin reaction (precise head tracking in rants about Twitch mods); multi-speaker interviews (smooth YOLO\u002FASD switches on trivia questions); traditional talks ($10 coffee compounding to $150 in 40 years at 7% returns); gambling stream reactions (context switches on addiction admissions). All output engaging 1-minute shorts with pro styling—e.g., fertility clip captures drama despite minor framing hiccups. Creator plans mass posting to test views, iterating on Whisper+YOLO+ASD+Remotion combo. Run locally or scale via n8n\u002FHostinger for channels needing clip volume without editors.",{"title":131,"searchDepth":132,"depth":132,"links":3772},[3773,3774,3775],{"id":3746,"depth":132,"text":3747},{"id":3756,"depth":132,"text":3757},{"id":3766,"depth":132,"text":3767},[140],{"content_references":3778,"triage":3802},[3779,3784,3787,3790,3792,3796,3799],{"type":3780,"title":3781,"url":3782,"context":3783},"tool","Surfagent","https:\u002F\u002Fsurfagent-site.vercel.app\u002F","recommended",{"type":3780,"title":3785,"url":3786,"context":3783},"n8n on Hostinger VPS","https:\u002F\u002Fwww.hostinger.com\u002Fallaboutai10",{"type":3780,"title":3788,"context":3789},"Remotion","mentioned",{"type":3780,"title":3791,"context":3789},"YOLO",{"type":3793,"title":3794,"url":3795,"context":3789},"other","The AI Video Course","https:\u002F\u002Fwww.theaivideocourse.com\u002F",{"type":3793,"title":3797,"url":3798,"context":3789},"SkillsMD Store","https:\u002F\u002Fwww.skillsmd.store",{"type":3793,"title":3800,"url":3801,"context":3789},"AllAboutAI GitHub","https:\u002F\u002Fgithub.com\u002FAllAboutAI-YT\u002F",{"relevance":3803,"novelty":3804,"quality":3804,"actionability":3803,"composite":3805,"reasoning":3806},5,4,4.55,"Category: AI Automation. The article provides a detailed, actionable pipeline for automating the creation of viral video shorts from long-form content, addressing the audience's need for practical AI applications in content production. It outlines specific tools and techniques, such as using Whisper for transcription and Claude Opus for selecting moments, making it highly actionable.","\u002Fsummaries\u002Fff7cfe560c98254d-ai-pipeline-clips-videos-to-viral-shorts-in-10-min-summary","2026-04-30 13:01:47","2026-05-03 16:44:14",{"title":3736,"description":131},{"loc":3807},"e5f480b831a916bc","All About AI","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xfSeXZG3VmQ","summaries\u002Fff7cfe560c98254d-ai-pipeline-clips-videos-to-viral-shorts-in-10-min-summary",[3818,158,159,160],"llm","Use Whisper for transcription, Claude Opus to select viral moments, YOLO for face tracking, and Remotion for edits to automate long-form video to shorts pipeline, processing 89-min podcasts into styled clips with uploads via Surf Agent in 5-15 minutes.",[160],"RKtWJDeQzUxR0qtlla2wPDc3bBSq8Aha304udYIzvN4",{"id":3823,"title":3824,"ai":3825,"body":3830,"categories":3934,"created_at":141,"date_modified":141,"description":131,"extension":143,"faq":141,"featured":144,"kicker_label":141,"meta":3935,"navigation":146,"path":3953,"published_at":3954,"question":141,"scraped_at":3955,"seo":3956,"sitemap":3957,"source_id":3958,"source_name":3959,"source_type":3814,"source_url":3960,"stem":3961,"tags":3962,"thumbnail_url":141,"tldr":3964,"tweet":141,"unknown_tags":3965,"__hash__":3966},"summaries\u002Fsummaries\u002F5b5f02b81808c6ca-ai-greenhouse-agent-tends-ideas-from-seed-to-ripe--summary.md","AI Greenhouse Agent Tends Ideas from Seed to Ripe Content",{"provider":7,"model":8,"input_tokens":3826,"output_tokens":3827,"processing_time_ms":3828,"cost_usd":3829},8405,2032,10275,0.00219015,{"type":14,"value":3831,"toc":3928},[3832,3836,3852,3859,3863,3866,3869,3905,3908,3912,3915,3918,3922],[17,3833,3835],{"id":3834},"idea-growth-requires-greenhouse-conditions-not-static-storage","Idea Growth Requires Greenhouse Conditions, Not Static Storage",[22,3837,3838,3839,3843,3844,3847,3848,3851],{},"Notes apps store seeds in jars, leaving them unchanged and lifeless. A greenhouse creates growth conditions: ideas evolve through six states—seed (isolated thought), signal (attached evidence), seedling (planted raw), growing (attracting connections), ripening (near writable), wilting (needing decision), and composting (retired but retrievable). This shifts ideation from reactive capture to patient tending, where files physically move between folders like ",[3840,3841,3842],"code",{},"seeds\u002F",", ",[3840,3845,3846],{},"ready\u002F",", and ",[3840,3849,3850],{},"compost\u002F",". Unlike Karpathy's LLM knowledge bases for archiving consumed info, the greenhouse grows your creative output by enforcing patience—ideas ripen after 14+ days, collecting diverse signals to reveal angles invisible at planting.",[22,3853,3854,3855,3858],{},"Physical file movement and a central ",[3840,3856,3857],{},"garden-state.md"," index (tracking stats, themes, ripeness, convergences, orphans, user patterns) make the system scalable: agent reads index first, avoiding all files every time. Result: open 40 half-thoughts without overwhelm; get dashboard of vital clusters and warnings instead.",[17,3860,3862],{"id":3861},"modular-rules-and-skills-enforce-gardener-behavior","Modular Rules and Skills Enforce Gardener Behavior",[22,3864,3865],{},"11 rule files divide into identity (user detection, config, patient voice), mechanics (file specs, cross-referencing via index, 5 ripeness criteria at 3\u002F5 threshold), edges (no writing\u002Fresearch\u002Fpushing\u002Fdeleting; composting at 14-day wilt\u002F10-day orphan; pattern adaptation like shorter questions if ignored), and session integrity (sandbox to garden folder, end updates). This beats monolithic prompts: consistent, non-pushy agent adapts—e.g., prioritizes cross-referencing in planting bursts.",[22,3867,3868],{},"5 skills power 4 commands plus onboarding:",[97,3870,3871,3881,3887,3893,3899],{},[100,3872,3873,3876,3877,3880],{},[3840,3874,3875],{},"first-time-setup.md",": Creates ",[3840,3878,3879],{},"garden\u002F"," (inbox\u002Fseeds\u002Fready\u002Fcompost\u002Fgarden-state.md), syncs Notion\u002FObsidian.",[100,3882,3883,3886],{},[3840,3884,3885],{},"greenhouse.md",": Dashboard stats\u002Fthemes\u002Fripeness\u002Fconvergences\u002Forphans.",[100,3888,3889,3892],{},[3840,3890,3891],{},"plant.md",": Single entry classifies new seed vs. signal; asks germination upfront (\"What made you notice? Do you agree\u002Fresist?\") for personal stake\u002Ftension.",[100,3894,3895,3898],{},[3840,3896,3897],{},"ripen.md",": Checks 2+ criteria seeds, auto-moves at threshold, flags gaps (e.g., add tension).",[100,3900,3901,3904],{},[3840,3902,3903],{},"compost.md",": Reviews candidates with context, cross-checks retired ideas.",[22,3906,3907],{},"Commands: \"Show me the greenhouse\", \"Plant this\", \"Ripen\", \"Compost\". Single plant command (killed separate signal) offloads sorting to agent; immediate germination (no queue) prevents backlog.",[17,3909,3911],{"id":3910},"ripeness-threshold-and-germination-unlock-writable-angles","Ripeness Threshold and Germination Unlock Writable Angles",[22,3913,3914],{},"Harvest when 3\u002F5 criteria hit: 1) Signal diversity (2+ sources vs. echo chamber); 2) Cluster size (2+ seed links); 3) Tension (unresolved question\u002Fcontrarian); 4) Personal stake (your engagement); 5) Age (14+ days). Forces slow thinking: shower thought sits, gathers client convo\u002Fpiece signals over 18 days, angle emerges. Germination questions embed stake\u002Ftension at plant—skippable but boosts ripeness. Agent flags unclear tension for input.",[22,3916,3917],{},"Use cases: Newsletter writers connect weekly plants into angles; consultants spot client patterns after 4 weeks; pros surface 14 expert angles. Killed abstractions (separate clusters\u002Fstaging) simplify: filesystem mirrors metaphor, easing ops.",[17,3919,3921],{"id":3920},"build-and-limits-hands-off-tending-your-harvest-call","Build and Limits: Hands-Off Tending, Your Harvest Call",[22,3923,3924,3925,3927],{},"Paste article into Claude\u002FCodex for step-by-step build (directory, rules, skills); premium gets pre-built RobotsOS download, onboard in \u003C5 min via \"Help me onboard\". Agent learns patterns (plant frequency, theme dominance, response speed) without pushing—stays passive till summoned. Can't dictate next write (your judgment); reads external sources only on input, no auto-hunting (pair with WATSON for research). Trade-off: fewer decisions, better-informed via clean ",[3840,3926,3846],{}," table.",{"title":131,"searchDepth":132,"depth":132,"links":3929},[3930,3931,3932,3933],{"id":3834,"depth":132,"text":3835},{"id":3861,"depth":132,"text":3862},{"id":3910,"depth":132,"text":3911},{"id":3920,"depth":132,"text":3921},[140],{"content_references":3936,"triage":3951},[3937,3942,3945,3948],{"type":3793,"title":3938,"author":3939,"url":3940,"context":3941},"LLM knowledge bases","Karpathy","https:\u002F\u002Fx.com\u002Fkarpathy\u002Fstatus\u002F2039805659525644595","cited",{"type":3780,"title":3943,"url":3944,"context":3789},"WATSON","https:\u002F\u002Frobotsatemyhomework.substack.com\u002Fp\u002Fai-content-research-agent",{"type":3780,"title":3946,"url":3947,"context":3789},"RobotsOS","https:\u002F\u002Frobotsatemyhomework.com\u002Frobots",{"type":3793,"title":3949,"url":3950,"context":3789},"What are skills","https:\u002F\u002Frobotsatemyhomework.com\u002Flearn\u002Fwhat-are-skills",{"relevance":3803,"novelty":3804,"quality":3804,"actionability":3803,"composite":3805,"reasoning":3952},"Category: AI Automation. The article presents a detailed framework for building an AI agent that manages idea development through a structured process, addressing the audience's need for practical applications in AI-powered product development. It offers specific steps and modular rules that can be directly implemented, making it highly actionable.","\u002Fsummaries\u002F5b5f02b81808c6ca-ai-greenhouse-agent-tends-ideas-from-seed-to-ripe-summary","2026-04-08 12:50:10","2026-04-16 03:09:34",{"title":3824,"description":131},{"loc":3953},"5b5f02b81808c6ca","__oneoff__","https:\u002F\u002Frobotsatemyhomework.substack.com\u002Fp\u002Fi-built-an-ai-greenhouse-where-scattered","summaries\u002F5b5f02b81808c6ca-ai-greenhouse-agent-tends-ideas-from-seed-to-ripe--summary",[158,159,3963,160],"prompt-engineering","Build a file-based AI agent that tracks ideas through 6 growth states, cross-references connections, flags ripeness via 3\u002F5 criteria, and composts wilting ones after 14 days inactivity or 10 days without links.",[160],"IMEvs-beZT2gZgrLOZp30oJd1xvH1heE5NIh4dBVmW0",{"id":3968,"title":3969,"ai":3970,"body":3975,"categories":4050,"created_at":141,"date_modified":141,"description":4051,"extension":143,"faq":141,"featured":144,"kicker_label":141,"meta":4052,"navigation":146,"path":4053,"published_at":4054,"question":141,"scraped_at":4055,"seo":4056,"sitemap":4057,"source_id":4058,"source_name":4059,"source_type":154,"source_url":4060,"stem":4061,"tags":4062,"thumbnail_url":141,"tldr":4063,"tweet":141,"unknown_tags":4064,"__hash__":4065},"summaries\u002Fsummaries\u002Fb63d74ae51ee29da-persist-ai-agent-memory-with-adk-sessions-profiles-summary.md","Persist AI Agent Memory with ADK Sessions & Profiles",{"provider":7,"model":8,"input_tokens":3971,"output_tokens":3972,"processing_time_ms":3973,"cost_usd":3974},4693,1273,19961,0.00155245,{"type":14,"value":3976,"toc":4045},[3977,3981,3984,3988,3991,4005,4008,4035,4038,4042],[17,3978,3980],{"id":3979},"durable-chats-via-pluggable-session-services","Durable Chats via Pluggable Session Services",[22,3982,3983],{},"ADK agents lose in-memory session data on app restarts, wiping conversation history and state. Fix this by swapping InMemorySessionService (fast but ephemeral) for DatabaseSessionService, which persists user messages, agent replies, and state changes to a database file or Postgres. Agent logic stays unchanged—use the helper get_or_create_session: it fetches existing sessions by ID to resume or creates new ones. Same session ID post-restart loads full history and latest state, enabling seamless continuation. ADK also supports managed Vertex AI Session Service for cloud persistence, covered in future episodes on memory banks.",[17,3985,3987],{"id":3986},"long-term-personalization-with-user-profile-tools","Long-Term Personalization with User Profile Tools",[22,3989,3990],{},"Persistent sessions resume ongoing chats but fail for new sessions (different IDs). Store key user facts in a simple database table: one row per user ID and preference key like dietary, favorite_thing, or transport_mode—keep it structured and small for easy curation. Equip the agent with two tools tied to user ID context:",[97,3992,3993,3999],{},[100,3994,3995,3998],{},[65,3996,3997],{},"recall_user_preference",": Reads all saved preferences for the user.",[100,4000,4001,4004],{},[65,4002,4003],{},"save_user_preference",": Inserts or updates a key-value pair, returning success confirmation.",[22,4006,4007],{},"Embed instructions in agent prompts:",[4009,4010,4011,4017,4023,4029],"ol",{},[100,4012,4013,4016],{},[65,4014,4015],{},"Recall first",": Call recall at conversation start.",[100,4018,4019,4022],{},[65,4020,4021],{},"Personalize and plan",": Use retrieved data to tailor responses.",[100,4024,4025,4028],{},[65,4026,4027],{},"Present and learn",": After planning, ask about new facts to save.",[100,4030,4031,4034],{},[65,4032,4033],{},"Save last",": Call save before ending if new info provided.",[22,4036,4037],{},"This setup personalizes even brand-new chats weeks later without relying on transcripts.",[17,4039,4041],{"id":4040},"demo-validates-cross-session-recall","Demo Validates Cross-Session Recall",[22,4043,4044],{},"In practice, first-time user asks for trip planning: agent recalls nothing, proposes plan, asks for preferences. User specifies \"vegetarian\"; agent saves it successfully. App restart clears RAM but loads session from disk and profile from table. Existing chat resumes mid-flow. New chat (fresh ID) triggers recall, finds dietary=vegetarian, and instantly personalizes the trip plan. Source code via provided links (e.g., goo.gle\u002Fagentmemorylab) shows full integration. Next steps extend to memory banks archiving full conversations with semantic search across text, images, audio, video.",{"title":131,"searchDepth":132,"depth":132,"links":4046},[4047,4048,4049],{"id":3979,"depth":132,"text":3980},{"id":3986,"depth":132,"text":3987},{"id":4040,"depth":132,"text":4041},[],"Lab → https:\u002F\u002Fgoo.gle\u002Fagentmemorylab\nADK doc → https:\u002F\u002Fgoo.gle\u002F4vhpjR1 \nADK session → https:\u002F\u002Fgoo.gle\u002F4vfEBpd \n\nWant your AI agent to remember conversations across sessions and restarts? In this tutorial, you'll learn how to add persistent memory to your agent using Google's Agent Development Kit (ADK), including database-backed sessions and a user profile store.\n\nChapters:\n0:00 - Intro\n1:14 - How to make the chat durable with DatabaseSessionService\n3:02 - How to build a user profile store with recall and save tools\n4:43 - Demo \n6:14 - Recap\n\nWatch more AI agent crash course → https:\u002F\u002Fgoo.gle\u002FAIforBeginners\n🔔 Subscribe to Google Cloud Tech → https:\u002F\u002Fgoo.gle\u002FGoogleCloudTech\n\n#GoogleCloud #AIAgents #ADK\n\nSpeakers: Annie Wang\nProducts Mentioned: Agent Development Kit, Gemini",{},"\u002Fsummaries\u002Fb63d74ae51ee29da-persist-ai-agent-memory-with-adk-sessions-profiles-summary","2026-04-08 15:56:42","2026-04-10 03:09:44",{"title":3969,"description":4051},{"loc":4053},"b63d74ae51ee29da","Google Cloud Tech","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HDqzJJhZsxw","summaries\u002Fb63d74ae51ee29da-persist-ai-agent-memory-with-adk-sessions-profiles-summary",[158,160],"Replace ADK's InMemorySessionService with DatabaseSessionService to save chats across restarts; add recall\u002Fsave tools for user preferences in new sessions.",[160],"_GoSQPRcJ-cneT-w7cu0h_8Z01M4Fz9UXRZU9lxKsTo",{"id":4067,"title":4068,"ai":4069,"body":4074,"categories":4111,"created_at":141,"date_modified":141,"description":4112,"extension":143,"faq":141,"featured":144,"kicker_label":141,"meta":4113,"navigation":146,"path":4114,"published_at":4115,"question":141,"scraped_at":4116,"seo":4117,"sitemap":4118,"source_id":4119,"source_name":4120,"source_type":154,"source_url":4121,"stem":4122,"tags":4123,"thumbnail_url":141,"tldr":4124,"tweet":141,"unknown_tags":4125,"__hash__":4126},"summaries\u002Fsummaries\u002Fcdfe77182714c38a-autoagent-optimizes-harnesses-like-karpathy-s-auto-summary.md","AutoAgent Optimizes Harnesses Like Karpathy's Auto-Research",{"provider":7,"model":8,"input_tokens":4070,"output_tokens":4071,"processing_time_ms":4072,"cost_usd":4073},4800,1331,10221,0.00133465,{"type":14,"value":4075,"toc":4106},[4076,4080,4083,4086,4090,4093,4096,4100,4103],[17,4077,4079],{"id":4078},"core-self-improvement-loop-edit-eval-iterate-overnight","Core Self-Improvement Loop: Edit, Eval, Iterate Overnight",[22,4081,4082],{},"Karpathy's auto-research uses a simple setup with one GPU and 5-minute training runs: fix data prep\u002Ftokenizer (prep.py), let an agent edit training code (train.py) for model, loop, hyperparameters, then evaluate per human instructions in program.md. If metrics improve, commit changes; else revert. Humans \"program in natural language\" via program.md, agent handles code. Run overnight for real gains without manual coding.",[22,4084,4085],{},"AutoAgent applies identical loop to agent harnesses instead of ML training: meta-agent edits task agent's prompts, tools, orchestration (agent.py), runs evals on benchmarks via adapters, commits improvements based on results and reasoning traces. Starts with minimal bash tool; discovers domain-specific logic autonomously.",[17,4087,4089],{"id":4088},"architecture-enables-parallel-domain-agnostic-optimization","Architecture Enables Parallel, Domain-Agnostic Optimization",[22,4091,4092],{},"Split into meta-agent (orchestrates iterations, spins thousands of parallel sandboxes) and task agent (executes domain tasks). Connects to any benchmark (e.g., SpreadsheetBench, TerminalBench) for verification. Same files as auto-research: program.md for human guidance on goals\u002Favoidances, agent.py as editable target.",[22,4094,4095],{},"Simplicity mirrors Karpathy: no complex infra needed. Meta-agent reads traces\u002Fresults post-sandbox runs, decides keeps\u002Freverts, builds specialized tooling\u002Fverification\u002Forchestration nobody coded manually.",[17,4097,4099],{"id":4098},"benchmark-gains-and-harness-engineering-trade-offs","Benchmark Gains and Harness Engineering Trade-offs",[22,4101,4102],{},"On SpreadsheetBench\u002FTerminalBench, iterations show harness improving: better prompts\u002Ftools yield higher scores, compounding overnight. Enables cheaper, specialized agents per domain\u002Fworkflow vs. monolithic harnesses.",[22,4104,4105],{},"Harness optimization critical because domains need tailored prompts\u002Ftools (e.g., spreadsheets vs. terminals), requiring domain+model expertise. Companies gain from stack-specific harnesses running smaller models. Future: domain experts write program.md, meta-agents auto-engineer harnesses—like AI now writes code—define success, return in 24h with optimized setup.",{"title":131,"searchDepth":132,"depth":132,"links":4107},[4108,4109,4110],{"id":4078,"depth":132,"text":4079},{"id":4088,"depth":132,"text":4089},{"id":4098,"depth":132,"text":4099},[173],"Auto Agent: Self-Improving AI Harnesses Inspired by Karpathy’s Auto-Research Loop\n\nThe video explains self-improving agents and highlights Kevin Guo’s Auto Agent project as an extension of Andrej Karpathy’s auto-research idea. Auto-research lets an AI agent iteratively edit training code (e.g., train.py) under a small LLM training setup, run short trainings, evaluate results, and keep or discard changes based on improvement, guided by human-written instructions in program.md. Auto Agent applies the same loop to a different target: optimizing the agent harness itself (prompts, tools, orchestration) rather than ML training code. It uses a meta-agent and a task agent, connects to benchmarks via an adapter, and runs many parallel sandboxes to evaluate iterations using results and reasoning traces. Examples include SpreadsheetBench and TerminalBench, illustrating harness improvements and the broader implications for domain-specific workflows and cheaper, specialized agent setups.\n\nLinks;\nhttps:\u002F\u002Fx.com\u002Fkarpathy\u002Fstatus\u002F2030371219518931079\nhttps:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fautoresearch\nhttps:\u002F\u002Fx.com\u002Fkevingu\u002Fstatus\u002F2039843234760073341\nhttps:\u002F\u002Fgithub.com\u002Fkevinrgu\u002Fautoagent\u002Fblob\u002Fmain\u002Fprogram.md\n\n00:00 Self Improving Agents\n00:33 Auto Research Recap\n01:25 Why Simplicity Worked\n02:22 Auto Agent Architecture\n03:20 Benchmarks And Results\n03:52 Why Harness Optimization Matters\n04:36 Future Of Meta Agents\n05:01 Wrap Up",{},"\u002Fsummaries\u002Fcdfe77182714c38a-autoagent-optimizes-harnesses-like-karpathy-s-auto-summary","2026-04-04 20:07:15","2026-04-05 16:14:38",{"title":4068,"description":4112},{"loc":4114},"cdfe77182714c38a","Developers Digest","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=RoaPvj9Ovug","summaries\u002Fcdfe77182714c38a-autoagent-optimizes-harnesses-like-karpathy-s-auto-summary",[158,160],"Extend Karpathy's auto-research loop—edit code, run 5-min evals, keep improvements—to agent harnesses (prompts\u002Ftools) via meta-agents, yielding domain-specific agents overnight on benchmarks like SpreadsheetBench.",[160],"1x_fyw1usy6zFyOx11771dm61WSLeiQQ-1FNGvFFXfg"]