[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-8af92acf69a5cde0-claude-masterclass-10-levels-to-ai-os-business-summary":3,"summaries-facets-categories":258,"summary-related-8af92acf69a5cde0-claude-masterclass-10-levels-to-ai-os-business-summary":3828},{"id":4,"title":5,"ai":6,"body":13,"categories":212,"created_at":213,"date_modified":213,"description":204,"extension":214,"faq":213,"featured":215,"kicker_label":213,"meta":216,"navigation":239,"path":240,"published_at":241,"question":213,"scraped_at":242,"seo":243,"sitemap":244,"source_id":245,"source_name":246,"source_type":247,"source_url":248,"stem":249,"tags":250,"thumbnail_url":213,"tldr":255,"tweet":213,"unknown_tags":256,"__hash__":257},"summaries\u002Fsummaries\u002F8af92acf69a5cde0-claude-masterclass-10-levels-to-ai-os-business-summary.md","Claude Masterclass: 10 Levels to AI OS & Business",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8824,2550,28208,0.0030173,{"type":14,"value":15,"toc":203},"minimark",[16,21,25,28,39,43,62,73,83,91,95,98,105,112,122,125,129,132,142,152,155,165,169],[17,18,20],"h2",{"id":19},"master-claude-fundamentals-models-setup-and-projects","Master Claude Fundamentals: Models, Setup, and Projects",[22,23,24],"p",{},"Start by treating Claude's models as specialized brains: Sonnet for daily tasks (fast, efficient), Opus for deep reasoning\u002Fcoding (slower, costlier), Haiku for quick bulk ops. Default to Sonnet, escalate to Opus for shallow responses, drop to Haiku for speed. Enable 'extended thinking' toggle for step-by-step reasoning on complex builds, but pace usage to avoid rate limits on Pro ($100\u002Fmo) or Max ($200\u002Fmo) plans—subscriptions beat API for heavy personal use.",[22,26,27],{},"Download the Claude Desktop App (Mac\u002FWindows, latest OS required) over browser for core features like Co-Work, Code, Artifacts. In settings: Enable Memory (remembers preferences across chats), Artifacts (side-panel outputs for docs\u002Fdecks\u002Fdiagrams). Create Projects via sidebar: Set system prompts (prepended to every message) for role\u002Fcontext, e.g., 'Marketing manager at B2B SaaS: Lead with numbers, bullets, no fluff, match brand.' Upload files (CSVs, PDFs) for analysis. Voice input via tools like Whisper Flow accelerates prompting—hold key to dictate anywhere.",[22,29,30,34,35,38],{},[31,32,33],"strong",{},"Common mistake",": Repeating instructions per chat—system prompts + memory eliminate this. ",[31,36,37],{},"Quality check",": Outputs should visualize data (pies, bars) and match brand (e.g., query site for guidelines). Practice: Build P&L project—upload receipts\u002Frevenue, prompt pie charts for spend\u002Fsources, channel ROI visuals.",[17,40,42],{"id":41},"gcps-framework-scale-prompts-into-production-systems","GCPS Framework: Scale Prompts into Production Systems",[22,44,45,46,49,50,53,54,57,58,61],{},"GCPS (Gather, Contextualize, Prompt, Scale) turns ad-hoc chats into workflows. ",[31,47,48],{},"Gather",": Collect data\u002Ffiles into Projects. ",[31,51,52],{},"Contextualize",": System prompts + memory set role\u002Fvoice. ",[31,55,56],{},"Prompt",": Use voice\u002Fdictation for clarity; request Artifacts for interactive visuals (sliders, dashboards). ",[31,59,60],{},"Scale",": Connectors wire Claude to tools (e.g., Google Drive, email); schedule via Automations.",[22,63,64,65,68,69,72],{},"Level 1 extends chat to graphics (charts\u002Fdiagrams), presentations (brand-matched decks export to Google Slides), interactive tools (budget sliders projecting leads\u002FCPA). ",[31,66,67],{},"Before",": Manual Excel pie charts. ",[31,70,71],{},"After",": Claude synthesizes multi-source data into shareable Artifact—publish via top-right button.",[22,74,75,78,79,82],{},[31,76,77],{},"Pitfall",": Stuck on one model mid-thread—switch Projects for flexibility. ",[31,80,81],{},"Pro tip",": Sonnet handles 90%; Opus for coding-heavy. Exercise: Recreate Alex's P&L—query 'revenue by source pie, expenses bar, ROI heatmap' then 'build CEO deck + interactive reallocator.'",[22,84,85,86,90],{},"\"Most people are using only about 10% of what ",[87,88,89],"span",{},"Claude"," can actually do... We'll go from typing your first prompt to having your full team of AI agents.\"",[17,92,94],{"id":93},"automations-agents-and-code-from-scripts-to-ai-workforce","Automations, Agents, and Code: From Scripts to AI Workforce",[22,96,97],{},"Level 3: Automations tab for scheduled workflows (e.g., daily reports). Web scraping via FireCrawl: Prompt cleans tables from sites. Level 4: Claude Code for dept-scale scripts—'go from \"I use Claude\" to \"my dept runs on Claude\".'",[22,99,100,101,104],{},"Level 5: Agentic workflows install 'skills' (reusable prompts). Build Carousel Maker: Agent sequences image gen → copy → export. Level 6: Trading bot with Alpaca API—paper-trade stocks via Claude reasoning. ",[31,102,103],{},"Steps",": Define agent role, tools (APIs), loop (observe-act).",[22,106,107,108,111],{},"Level 7: Deploy via Terminal (npx claude), Desktop\u002FMobile apps, Canvas (visual workspace: drag nodes for flows), Channels (Telegram\u002FDiscord\u002FiMessage triggers). Build Second Brain: Vault + Obsidian sync for portable knowledge base. ",[31,109,110],{},"Portable Claude Computer",": Bundle Projects\u002FArtifacts into shareable OS.",[22,113,114,117,118,121],{},[31,115,116],{},"Trade-offs",": Automations save hours but need error-handling prompts. ",[31,119,120],{},"Quality",": Agents should self-correct via loops. Avoid shiny objects—use Priority Matrix: Score tasks by time saved x frustration x ease.",[22,123,124],{},"\"These are the exact systems that I use to automate my business, eliminate all the busy work, and grow my income using Claude without having to hire a large team.\"",[17,126,128],{"id":127},"ai-os-and-monetization-prds-research-side-hustles","AI OS and Monetization: PRDs, Research, Side Hustles",[22,130,131],{},"Level 8: Second Brain → Side Hustle. Website Cloner skill: One-command site duplication. Karpathy Autoresearch: Loop (scrape → summarize → deep-dive). Level 9: AI OS via PRD-driven agents—prompt 'build PRD for X, engineer with agents.' Level 10: Claude Tutor (Clicky)—teaches any software via interactive sessions.",[22,133,134,137,138,141],{},[31,135,136],{},"Engineering flow",": PRD → agent swarm (researcher\u002Fcoder\u002Ftester). ",[31,139,140],{},"Stay ahead",": Bonus tracks trends, builds sellable co-workers (Skool communities package tutorials).",[22,143,144,145,147,148,151],{},"Monetize: Package automations (e.g., client acquisition systems) into products—websites, payments (Stripe), marketing. ",[31,146,103],{},": Clone site → customize → deploy → sell via funnels. ",[31,149,150],{},"Matrix for automation",": Prioritize high-impact\u002Flow-effort (e.g., lead gen over admin).",[22,153,154],{},"\"My default rule is use sonnet, escalate to opus when sonnet answers feel a little shallow, and drop to haiku if you want to do quick stuff or something in bulk.\"",[22,156,157,160,161,164],{},[31,158,159],{},"Before\u002Fafter",": Overloaded marketer → AI-handled P&L\u002Fdecks + side hustle selling scrapers\u002Fbots. ",[31,162,163],{},"Prerequisites",": Beginner-friendly; assumes no prior Claude use. Fits early in AI workflow—post-setup, pre-custom apps.",[17,166,168],{"id":167},"key-takeaways","Key Takeaways",[170,171,172,176,179,182,185,188,191,194,197,200],"ul",{},[173,174,175],"li",{},"Download Desktop App, enable Memory\u002FArtifacts, use Projects with system prompts to contextualize every interaction.",[173,177,178],{},"Follow GCPS: Gather data, Contextualize via instructions, Prompt for Artifacts, Scale with Connectors\u002FAutomations.",[173,180,181],{},"Build agents with skills\u002Floops: Role + tools + self-correction for workflows like carousels or trading bots.",[173,183,184],{},"Deploy everywhere (Canvas, Channels, Terminal) for portable AI OS controllable from phone.",[173,186,187],{},"Monetize via Priority Matrix: Automate high-frustration tasks first, package as products (e.g., cloners, tutors).",[173,189,190],{},"Voice prompting + Sonnet default accelerates 10x; pace Pro\u002FMax usage to unlock unlimited power.",[173,192,193],{},"Avoid: Shiny objects, one-model threads—switch Projects, use Extended Thinking sparingly.",[173,195,196],{},"Practice: Build P&L Artifact, then agentic carousel; clone a site for your hustle.",[173,198,199],{},"Quality: Outputs visualize, match brand, project outcomes (e.g., sliders for what-ifs).",[173,201,202],{},"Scale to business: Turn Second Brain into sellable systems via PRDs and autoresearch loops.",{"title":204,"searchDepth":205,"depth":205,"links":206},"",2,[207,208,209,210,211],{"id":19,"depth":205,"text":20},{"id":41,"depth":205,"text":42},{"id":93,"depth":205,"text":94},{"id":127,"depth":205,"text":128},{"id":167,"depth":205,"text":168},[],null,"md",false,{"content_references":217,"triage":234},[218,222,224,227,229,231],{"type":219,"title":220,"context":221},"tool","Claude Desktop App","recommended",{"type":219,"title":223,"context":221},"Whisper Flow",{"type":219,"title":225,"context":226},"FireCrawl","mentioned",{"type":219,"title":228,"context":226},"Alpaca",{"type":219,"title":230,"context":226},"Obsidian",{"type":219,"title":232,"url":233,"context":221},"Skool","https:\u002F\u002Fwww.skool.com\u002Fclaude",{"relevance":235,"novelty":236,"quality":236,"actionability":235,"composite":237,"reasoning":238},5,4,4.55,"Category: AI & LLMs. The article provides a comprehensive guide on transforming Claude into a full AI operating system, addressing practical applications for AI integration in product development. It includes actionable frameworks like GCPS for scaling prompts into production systems, which directly aligns with the audience's need for practical, implementable strategies.",true,"\u002Fsummaries\u002F8af92acf69a5cde0-claude-masterclass-10-levels-to-ai-os-business-summary","2026-04-21 12:00:39","2026-04-21 15:23:33",{"title":5,"description":204},{"loc":240},"979e32989505c43f","Samin Yasar","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=KTEe5705RHw","summaries\u002F8af92acf69a5cde0-claude-masterclass-10-levels-to-ai-os-business-summary",[251,252,253,254],"llm","agents","prompt-engineering","ai-automation","Progress through 10 levels to transform Claude from a chat tool into a full AI operating system with agents automating ops, building products, and generating side income—saving 10-20 hours 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AI agents break this: they adapt to users, rewrite harnesses like OpenClaw (where Vincent Koc is a core contributor), and exhibit behavioral drift over time. Handcrafted datasets miss 20% edge cases that break products, test suites stale quickly, and production traces reveal issues benchmarks ignore. Result: hyperfocus on static benchmarks at conferences, yet systems ship with unmeasured chaos. Trade-off: offline evals ensure compliance (e.g., no illegal financial advice) but skip real-world stretching, leaving gaps until failures hit.",[22,3847,3848],{},"Chaos engineering—randomly breaking systems to find limits—applies here but lacks in AI. Software now malleables too, shipping at lightning speed; benchmarks can't keep up without adapting.",[17,3850,3852],{"id":3851},"shift-from-prompt-to-intent-engineering-compounds-eval-challenges","Shift from Prompt to Intent Engineering Compounds Eval Challenges",[22,3854,3855],{},"AI evolved: prompt engineering (random word-bashing for outputs, like accidental painkillers from liver meds) died by 2023. Context engineering added RAG, tools, search—enabling modular testing of agent parts (e.g., sales MCP tools). Now, 2025's intent engineering: cheap, fast tokens fuel self-optimizing agents understanding user intent via harnesses like OpenClaw, Claude, or CodeEx. Models solve human-hard ARC-AGI 2\u002F3 puzzles via pattern recognition.",[22,3857,3858],{},"Problem: personalized experiences vary by user, making evals harder. Agents seem \"insecure\" without insight into layers. Need: measure ambiguity, personality rubrics (like art grading), not just 1+1=2.",[17,3860,3862],{"id":3861},"build-living-evals-as-self-optimizing-agents","Build Living Evals as Self-Optimizing Agents",[22,3864,3865],{},"Define end-state intent (e.g., user reward signal), let agents curate suites from traces: 80% traces repeat, but customer shifts trigger changes—agents detect, alert owners, update tests. Run online, always-on optimization; integrate telemetry (errors, costs) for self-correction—heal issues without prediction.",[22,3867,3868],{},"Applies broadly: auto-optimization like Python reward loops tunes anything (e.g., BBQ mixes). Evals become code\u002Fliving agents, not datasets: 80% static intent-defined, 20% adaptive for weird queries. At Comet, they're implementing; mindset: treat evals agentically as problem\u002Fdata shift.",{"title":204,"searchDepth":205,"depth":205,"links":3870},[3871,3872,3873],{"id":3841,"depth":205,"text":3842},{"id":3851,"depth":205,"text":3852},{"id":3861,"depth":205,"text":3862},[],{"content_references":3876,"triage":3885},[3877,3879,3882],{"type":219,"title":3878,"context":226},"OpenClaw",{"type":3880,"title":3881,"context":226},"dataset","ARC-AGI 2",{"type":3883,"title":3884,"context":226},"other","Adaptive testing for LLM evals paper",{"relevance":235,"novelty":236,"quality":236,"actionability":236,"composite":3886,"reasoning":3887},4.35,"Category: AI & LLMs. The article discusses the need for adaptive evaluation methods for AI agents, addressing a specific pain point about traditional static benchmarks failing to measure dynamic AI behavior. It provides actionable insights on building self-optimizing evaluation suites that can adapt to user intent, which is directly applicable to product builders working with AI.","\u002Fsummaries\u002Fb24309283167b83a-malleable-evals-adaptive-testing-for-changing-ai-a-summary","2026-05-12 16:00:06","2026-05-13 12:00:22",{"title":3831,"description":204},{"loc":3888},"b24309283167b83a","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4VhbYlfC7Gs","summaries\u002Fb24309283167b83a-malleable-evals-adaptive-testing-for-changing-ai-a-summary",[252,251,253,254],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002F4VhbYlfC7Gs\u002Fhqdefault.jpg","Static benchmarks fail self-adapting agents; use production traces for agent-curated, always-on eval suites that self-optimize toward user intent.","[Vincent Koc](https:\u002F\u002Fx.com\u002Fvincent_koc)'s conference talk on why static benchmarks fail for adaptive AI agents like OpenClaw, pushing a shift to \"malleable evals\" where agents self-generate test suites from production traces to handle behavioral drift and edge cases.",[254],"1xd66DttG0MMlsYN5aUG3JKuQffY2t3Ws867t1cXQiI",{"id":3905,"title":3906,"ai":3907,"body":3912,"categories":4425,"created_at":213,"date_modified":213,"description":204,"extension":214,"faq":213,"featured":215,"kicker_label":213,"meta":4426,"navigation":239,"path":4437,"published_at":4438,"question":213,"scraped_at":4439,"seo":4440,"sitemap":4441,"source_id":4442,"source_name":3894,"source_type":3895,"source_url":4443,"stem":4444,"tags":4445,"thumbnail_url":4446,"tldr":4447,"tweet":4448,"unknown_tags":4449,"__hash__":4450},"summaries\u002Fsummaries\u002F05800069e15ecf07-agentic-search-powers-80-of-llm-context-engineerin-summary.md","Agentic Search Powers 80% of LLM Context Engineering",{"provider":7,"model":8,"input_tokens":3908,"output_tokens":3909,"processing_time_ms":3910,"cost_usd":3911},8105,2819,34902,0.00273965,{"type":14,"value":3913,"toc":4417},[3914,3918,3930,3945,3952,3956,3959,3979,3988,3991,4053,4056,4059,4063,4066,4095,4098,4101,4104,4108,4115,4131,4342,4345,4348,4352,4370,4376,4379,4381,4413],[17,3915,3917],{"id":3916},"context-engineering-demystified-agentic-search-at-the-core","Context Engineering Demystified: Agentic Search at the Core",[22,3919,3920,3921,3925,3926,3929],{},"Context engineering selects what enters an LLM's context window from diverse sources like local files, databases, web, working memory, agent skills, and long-term memory. Leonie argues it's 80% agentic search—the mechanisms deciding ",[3922,3923,3924],"em",{},"what"," and ",[3922,3927,3928],{},"how"," to retrieve—over model choice. Early RAG used fixed vector search on user queries, retrieving irrelevant chunks or missing multi-hop needs. Agentic RAG introduces tools letting agents decide: retrieve? Rewrite query? Multi-round? This evolves retrieval from rigid pipelines to dynamic decisions.",[22,3931,3932,3933,3937,3938,3937,3941,3944],{},"Key principle: No single tool suffices. Native tools handle sources (e.g., file search for codebases, SQL\u002FESQL for DBs, web scrapers), but shell tools (LangChain's shell, Anthropic's bash, OpenAI's exec) add versatility via CLI commands like ",[3934,3935,3936],"code",{},"ls",", ",[3934,3939,3940],{},"grep",[3934,3942,3943],{},"curl",". Combine them: Vector for semantics, keyword for exact matches, general-purpose for complex filters. Trade-off: Shell is flexible but risky (security, errors); specialized tools are reliable but narrow.",[22,3946,3947,3948,3951],{},"\"Context engineering is about 80% agentic search because it's this little box right here ",[87,3949,3950],{},"arrow from sources to window",".\"",[17,3953,3955],{"id":3954},"building-reliable-search-tools-descriptions-and-parameters","Building Reliable Search Tools: Descriptions and Parameters",[22,3957,3958],{},"Effective tools start with precise descriptions. Poor ones: One-sentence generics (\"Search the database\"). Good ones specify:",[170,3960,3961,3967,3973],{},[173,3962,3963,3966],{},[31,3964,3965],{},"Core purpose",": What it does.",[173,3968,3969,3972],{},[31,3970,3971],{},"Triggers",": When to use (e.g., \"For conference sessions on AI constraints\"), avoid (e.g., \"Not for web data\").",[173,3974,3975,3978],{},[31,3976,3977],{},"Relationships",": Sequence (e.g., \"Load ESQL skill first\").",[22,3980,3981,3982,3984,3985,3951],{},"Reinforce in system prompts: \"You are a search agent... decide if retrieval needed. Use ",[87,3983,219],{}," for ",[87,3986,3987],{},"condition",[22,3989,3990],{},"Parameter complexity scales failure risk:",[3992,3993,3994,4010],"table",{},[3995,3996,3997],"thead",{},[3998,3999,4000,4004,4007],"tr",{},[4001,4002,4003],"th",{},"Complexity",[4001,4005,4006],{},"Example",[4001,4008,4009],{},"Agent Challenge",[4011,4012,4013,4027,4040],"tbody",{},[3998,4014,4015,4019,4024],{},[4016,4017,4018],"td",{},"Low",[4016,4020,4021],{},[3934,4022,4023],{},"get_customer(id: str)",[4016,4025,4026],{},"Easy ID generation.",[3998,4028,4029,4032,4037],{},[4016,4030,4031],{},"Medium",[4016,4033,4034],{},[3934,4035,4036],{},"semantic_search(query: str, k: int=3, filters: dict)",[4016,4038,4039],{},"Balancing params.",[3998,4041,4042,4045,4050],{},[4016,4043,4044],{},"High",[4016,4046,4047],{},[3934,4048,4049],{},"execute_esql(query: str)",[4016,4051,4052],{},"Full query syntax.",[22,4054,4055],{},"Always add try-except for self-correction: Return errors to agent (e.g., invalid wildcard) instead of crashing. Test tools standalone before agent integration.",[22,4057,4058],{},"\"Tool description is the most important aspect... add trigger conditions, relationships.\"",[17,4060,4062],{"id":4061},"diagnosing-and-fixing-agent-failure-modes","Diagnosing and Fixing Agent Failure Modes",[22,4064,4065],{},"Agents fail in predictable ways—address systematically:",[4067,4068,4069,4075,4081],"ol",{},[173,4070,4071,4074],{},[31,4072,4073],{},"No tool called",": Relies on parametric knowledge. Fix: Prompt \"Always retrieve for factual queries.\"",[173,4076,4077,4080],{},[31,4078,4079],{},"Wrong tool",": Picks web over DB. Fix: Detailed descriptions + system prompt prioritization.",[173,4082,4083,4086,4087,4090,4091,4094],{},[31,4084,4085],{},"Wrong parameters",": E.g., SQL ",[3934,4088,4089],{},"%"," wildcard vs. ",[3934,4092,4093],{},"*"," in ESQL. Fix: Skills for docs.",[22,4096,4097],{},"Quality criteria: Tool returns relevant, non-zero results (zero may signal rewrite). Evaluate: Does output cite retrieved context? Multi-turn coherence?",[22,4099,4100],{},"Common mistake: Over-relying on semantics—fails keywords (\"GPA\" matches \"Gemma\" via tokens). Solution: Hybrid stacks.",[22,4102,4103],{},"\"The most challenging aspect... was getting the agent to not call the web search tool but the database search tool.\"",[17,4105,4107],{"id":4106},"step-by-step-semantic-to-general-purpose-retrieval-with-skills","Step-by-Step: Semantic to General-Purpose Retrieval with Skills",[22,4109,4110,4111,4114],{},"Assumes: Python\u002FLangChain basics, local ElasticSearch cluster, chunked\u002Findexed data (e.g., conference sessions: ",[3934,4112,4113],{},"text"," embedded, metadata filterable).",[22,4116,4117,4119,4120,3937,4123,4126,4127,4130],{},[31,4118,163],{},": Mid-level (built basic agents); install ",[3934,4121,4122],{},"langchain",[3934,4124,4125],{},"elasticsearch",", embedding model (e.g., ",[3934,4128,4129],{},"gte-large-en-v1.5",").",[4067,4132,4133,4210,4278],{},[173,4134,4135,4138,4139,4199,4202,4203,4205,4206,4209],{},[31,4136,4137],{},"Vanilla Semantic Tool"," (Brittle baseline):",[4140,4141,4145],"pre",{"className":4142,"code":4143,"language":4144,"meta":204,"style":204},"language-python shiki shiki-themes github-light github-dark","from langchain_community.vectorstores import ElasticVectorSearch\nfrom langchain.tools import tool\nembeddings = HuggingFaceEmbeddings(model_name=\"thenlper\u002Fgte-large\")\nvectorstore = ElasticVectorSearch(elasticsearch_url, index_name, embeddings)\n@tool\ndef semantic_search(query: str) -> str:\n    \"\"\"Search conference sessions semantically.\"\"\"\n    docs = vectorstore.similarity_search(query, k=3)\n    return \"\\n\".join([doc.page_content for doc in docs])\n","python",[3934,4146,4147,4154,4159,4165,4170,4175,4181,4187,4193],{"__ignoreMap":204},[87,4148,4151],{"class":4149,"line":4150},"line",1,[87,4152,4153],{},"from langchain_community.vectorstores import ElasticVectorSearch\n",[87,4155,4156],{"class":4149,"line":205},[87,4157,4158],{},"from langchain.tools import tool\n",[87,4160,4162],{"class":4149,"line":4161},3,[87,4163,4164],{},"embeddings = HuggingFaceEmbeddings(model_name=\"thenlper\u002Fgte-large\")\n",[87,4166,4167],{"class":4149,"line":236},[87,4168,4169],{},"vectorstore = ElasticVectorSearch(elasticsearch_url, index_name, embeddings)\n",[87,4171,4172],{"class":4149,"line":235},[87,4173,4174],{},"@tool\n",[87,4176,4178],{"class":4149,"line":4177},6,[87,4179,4180],{},"def semantic_search(query: str) -> str:\n",[87,4182,4184],{"class":4149,"line":4183},7,[87,4185,4186],{},"    \"\"\"Search conference sessions semantically.\"\"\"\n",[87,4188,4190],{"class":4149,"line":4189},8,[87,4191,4192],{},"    docs = vectorstore.similarity_search(query, k=3)\n",[87,4194,4196],{"class":4149,"line":4195},9,[87,4197,4198],{},"    return \"\\n\".join([doc.page_content for doc in docs])\n",[4200,4201],"br",{},"Works: \"Regulatory constraints\" → relevant talks.\nFails: \"GPA\" → Gemma models (semantic drift).",[4200,4204],{},"Agent: ",[3934,4207,4208],{},"create_react_agent(llm, [semantic_search], system_prompt)",".",[173,4211,4212,4215,4216,4271,4273,4274,4277],{},[31,4213,4214],{},"General-Purpose ESQL Tool"," (More flexible, error-prone):\nSwitch to GPT-4o-mini (nano too weak for query gen).",[4140,4217,4219],{"className":4142,"code":4218,"language":4144,"meta":204,"style":204},"from elasticsearch import Elasticsearch\nclient = Elasticsearch(\"http:\u002F\u002Flocalhost:9200\")\n@tool\ndef execute_esql_query(esql_query: str) -> str:\n    \"\"\"Execute ESQL against conference index. Use ESQL skill first.\"\"\"\n    try:\n        response = client.esql.query(esql={\"query\": esql_query})\n        return json.dumps(response)\n    except Exception as e:\n        return f\"Error: {str(e)}\"\n",[3934,4220,4221,4226,4231,4235,4240,4245,4250,4255,4260,4265],{"__ignoreMap":204},[87,4222,4223],{"class":4149,"line":4150},[87,4224,4225],{},"from elasticsearch import Elasticsearch\n",[87,4227,4228],{"class":4149,"line":205},[87,4229,4230],{},"client = Elasticsearch(\"http:\u002F\u002Flocalhost:9200\")\n",[87,4232,4233],{"class":4149,"line":4161},[87,4234,4174],{},[87,4236,4237],{"class":4149,"line":236},[87,4238,4239],{},"def execute_esql_query(esql_query: str) -> str:\n",[87,4241,4242],{"class":4149,"line":235},[87,4243,4244],{},"    \"\"\"Execute ESQL against conference index. Use ESQL skill first.\"\"\"\n",[87,4246,4247],{"class":4149,"line":4177},[87,4248,4249],{},"    try:\n",[87,4251,4252],{"class":4149,"line":4183},[87,4253,4254],{},"        response = client.esql.query(esql={\"query\": esql_query})\n",[87,4256,4257],{"class":4149,"line":4189},[87,4258,4259],{},"        return json.dumps(response)\n",[87,4261,4262],{"class":4149,"line":4195},[87,4263,4264],{},"    except Exception as e:\n",[87,4266,4268],{"class":4149,"line":4267},10,[87,4269,4270],{},"        return f\"Error: {str(e)}\"\n",[4200,4272],{},"Agent generates: ",[3934,4275,4276],{},"from conference_sessions where text like '%GPA%'"," → Error (wrong wildcard). Self-corrects next turn.",[173,4279,4280,4283,4284],{},[31,4281,4282],{},"Agent Skills for Progressive Disclosure",":",[170,4285,4286,4328,4335],{},[173,4287,4288,4289,4292],{},"Markdown file: ",[3934,4290,4291],{},"skills\u002Fesql.md",[4140,4293,4297],{"className":4294,"code":4295,"language":4296,"meta":204,"style":204},"language-markdown shiki shiki-themes github-light github-dark","---\nname: ESQL Skill\ndescription: Generate ESQL queries.\n---\nStructure: from index | where condition | limit k\nStrings: double quotes. Wildcards: * not %.\n","markdown",[3934,4298,4299,4304,4309,4314,4318,4323],{"__ignoreMap":204},[87,4300,4301],{"class":4149,"line":4150},[87,4302,4303],{},"---\n",[87,4305,4306],{"class":4149,"line":205},[87,4307,4308],{},"name: ESQL Skill\n",[87,4310,4311],{"class":4149,"line":4161},[87,4312,4313],{},"description: Generate ESQL queries.\n",[87,4315,4316],{"class":4149,"line":236},[87,4317,4303],{},[87,4319,4320],{"class":4149,"line":235},[87,4321,4322],{},"Structure: from index | where condition | limit k\n",[87,4324,4325],{"class":4149,"line":4177},[87,4326,4327],{},"Strings: double quotes. Wildcards: * not %.\n",[173,4329,4330,4331,4334],{},"Tools: ",[3934,4332,4333],{},"create_openai_functions_agent"," + skill loader middleware.",[173,4336,4337,4338,4341],{},"Updated prompt\u002Ftool: \"Load ESQL skill before execute_esql_query.\"\nResult: Agent loads skill → ",[3934,4339,4340],{},"from conference_sessions where text like '*GPA*' | limit 3"," → Exact match (Samuel's talk).",[22,4343,4344],{},"Fits in workflow: Post-indexing, pre-agent loop. Practice: Index your data, break semantic tool, iterate fixes.",[22,4346,4347],{},"\"Doing good search is incredibly difficult... curate your own stack.\"",[17,4349,4351],{"id":4350},"shell-tools-filesystem-and-beyond","Shell Tools: Filesystem and Beyond",[22,4353,4354,4355,3937,4357,3937,4359,4362,4363,4366,4367,4209],{},"Shell unlocks local files: ",[3934,4356,3936],{},[3934,4358,3940],{},[3934,4360,4361],{},"cat",". LangChain ",[3934,4364,4365],{},"@tool"," wraps ",[3934,4368,4369],{},"subprocess",[22,4371,4372,4373,4375],{},"Limitations: No built-in semantics; security (sandbox). Extend: Custom CLIs (e.g., DB CLI, ",[3934,4374,3943],{}," for web).",[22,4377,4378],{},"Teaser: File search beats naive recursion for code agents; combine with skills for CLI docs.",[17,4380,168],{"id":167},[170,4382,4383,4386,4389,4392,4395,4398,4401,4404,4407,4410],{},[173,4384,4385],{},"Prioritize agentic search: 80% of context engineering success.",[173,4387,4388],{},"Craft tool descriptions with purpose, triggers, relationships; reinforce in prompts.",[173,4390,4391],{},"Add try-except everywhere—let agents self-correct from errors.",[173,4393,4394],{},"Use skills for complex params (e.g., query langs); progressive disclosure scales docs.",[173,4396,4397],{},"Hybrid tools: Semantic for concepts, keyword\u002FESQL for exact, shell for files\u002Fweb.",[173,4399,4400],{},"Test standalone: Break with keywords\u002Ffilters before agent.",[173,4402,4403],{},"Model matters: Nano for simple, mini+ for query gen.",[173,4405,4406],{},"Zero results? Rewrite, don't answer.",[173,4408,4409],{},"Sequence tools: Skills → General query → Shell fallback.",[173,4411,4412],{},"Build stacks, not silos: Match source to tool.",[4414,4415,4416],"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":204,"searchDepth":205,"depth":205,"links":4418},[4419,4420,4421,4422,4423,4424],{"id":3916,"depth":205,"text":3917},{"id":3954,"depth":205,"text":3955},{"id":4061,"depth":205,"text":4062},{"id":4106,"depth":205,"text":4107},{"id":4350,"depth":205,"text":4351},{"id":167,"depth":205,"text":168},[267],{"content_references":4427,"triage":4435},[4428,4430,4433],{"type":219,"title":4429,"context":226},"LangChain",{"type":219,"title":4431,"author":4432,"context":226},"ElasticSearch","Elastic",{"type":3883,"title":4434,"author":4432,"context":226},"ESQL",{"relevance":235,"novelty":236,"quality":236,"actionability":236,"composite":3886,"reasoning":4436},"Category: AI & LLMs. The article provides a deep dive into context engineering and agentic search, which are crucial for building AI-powered products. It offers practical insights on tool descriptions and retrieval strategies that can be directly applied by developers and product builders.","\u002Fsummaries\u002F05800069e15ecf07-agentic-search-powers-80-of-llm-context-engineerin-summary","2026-05-08 13:00:06","2026-05-10 15:05:55",{"title":3906,"description":204},{"loc":4437},"de920282ce217f10","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ynJyIKwjonM","summaries\u002F05800069e15ecf07-agentic-search-powers-80-of-llm-context-engineerin-summary",[252,251,253,254],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FynJyIKwjonM\u002Fhqdefault.jpg","Context engineering relies on agentic search tools to pull relevant data from files, DBs, web, and memory. Master tool descriptions, skills, and shell tools to avoid brittle retrieval—demoed with ElasticSearch and LangChain.","Practical workshop by Elastic's Leonie Monigatti on building reliable agentic search stacks for LLM context engineering—demos semantic search, database queries (e.g., ESQL\u002FSQL), shell tools, and hybrids, plus tool description tips and failure mode fixes with live code.",[254],"petQDU6OhxttglviH_WlFwK-aisF7C8n9iqKN9uDLhw",{"id":4452,"title":4453,"ai":4454,"body":4459,"categories":4496,"created_at":213,"date_modified":213,"description":204,"extension":214,"faq":213,"featured":215,"kicker_label":213,"meta":4497,"navigation":239,"path":4514,"published_at":4515,"question":213,"scraped_at":4516,"seo":4517,"sitemap":4518,"source_id":4519,"source_name":4520,"source_type":247,"source_url":4521,"stem":4522,"tags":4523,"thumbnail_url":213,"tldr":4524,"tweet":213,"unknown_tags":4525,"__hash__":4526},"summaries\u002Fsummaries\u002Fc770cf1fe76f0f1e-3-steps-to-custom-claude-code-agentic-os-summary.md","3 Steps to Custom Claude Code Agentic OS",{"provider":7,"model":8,"input_tokens":4455,"output_tokens":4456,"processing_time_ms":4457,"cost_usd":4458},7992,1784,32847,0.00246775,{"type":14,"value":4460,"toc":4491},[4461,4465,4468,4471,4475,4478,4481,4485,4488],[17,4462,4464],{"id":4463},"codify-workflows-into-repeatable-skills-and-automations","Codify Workflows into Repeatable Skills and Automations",[22,4466,4467],{},"Break daily personal and business activities into domains (e.g., memory, productivity, research, content, community), then subdivide domains into discrete tasks (e.g., YouTube search, deep research across Twitter\u002FGitHub\u002Fweb\u002FYouTube\u002FObsidian, morning reports, competitor tracking). Convert tasks into consistent skills using Claude Code's skill creator—simple ones like YouTube reports replace manual searches; complex ones like deep research consolidate multi-source data with past Obsidian entries.",[22,4469,4470],{},"Turn suitable skills into automations: local for on-device tasks, remote for API-driven ones (Claude Code decides type). Use a single prompt in Claude Code terminal (microphone-enabled stream-of-consciousness recommended) to iterate: describe day-to-day tasks\u002Fdomains, let it propose skills\u002Fautomations per domain. This creates a trackable backbone—execute skills identically every time, eliminating random prompting. Value scales to teams\u002Fclients: hand off system for consistent results without deep Claude expertise.",[17,4472,4474],{"id":4473},"implement-obsidian-memory-layer-for-persistence","Implement Obsidian Memory Layer for Persistence",[22,4476,4477],{},"Designate an Obsidian vault as the OS home (Claude Code runs from here). Use Karpathy-inspired structure: \u002Fraw (dumping\u002Fstaging for chats\u002Fresearch), \u002Fwiki (codified articles from raw, e.g., RAG system reports), \u002Foutputs (final artifacts like slide decks). Customize further: subfolders per domain (research, AI agency, sales) for intuitive data flow.",[22,4479,4480],{},"Create claude.md in vault root—appended to every prompt—to define OS purpose, behaviors, and exact folder structure (e.g., archive, content, ops, personal, projects, raw, wiki). This enables efficient navigation, lower token costs, and adherence to flows. Obsidian's Markdown suffices as lightweight RAG—no vector DB needed for most; Claude Code handles retrieval fine. Track\u002Foptimize outputs here since all skills\u002Fautomations populate it.",[17,4482,4484],{"id":4483},"deploy-observability-dashboard-for-visibility-and-accessibility","Deploy Observability Dashboard for Visibility and Accessibility",[22,4486,4487],{},"Build a web dashboard exposing key skills\u002Fautomations as clickable buttons (e.g., \"Deep Research\" auto-populates prompt, runs headless Claude Code instance via --headless flag, outputs to Obsidian with source links). Use Claude Code prompt to generate: conversation identifies skills for buttons, custom observability metrics (5-hour\u002Fweekly usage, daily routines count, vault changes, forecasts).",[22,4489,4490],{},"Overcomes terminal limits—visualize what terminal can't (e.g., usage trends). Ideal for non-technical teams\u002Fclients: anyone clicks buttons for Claude power without terminal\u002FVS Code. Fully customizable per user\u002Fclient needs. Combine with architecture\u002Fmemory for end-to-end OS: optimize via tracking, scale via sharing.",{"title":204,"searchDepth":205,"depth":205,"links":4492},[4493,4494,4495],{"id":4463,"depth":205,"text":4464},{"id":4473,"depth":205,"text":4474},{"id":4483,"depth":205,"text":4484},[270],{"content_references":4498,"triage":4512},[4499,4502,4505,4506,4510],{"type":3883,"title":4500,"url":4501,"context":221},"Master Claude Code","https:\u002F\u002Fwww.skool.com\u002Fchase-ai",{"type":3883,"title":4503,"url":4504,"context":221},"Chase AI Community","https:\u002F\u002Fwww.skool.com\u002Fchase-ai-community",{"type":219,"title":230,"context":226},{"type":3883,"title":4507,"author":4508,"context":4509},"Karpathy Obsidian RAG setup","Andrej Karpathy","cited",{"type":219,"title":4511,"context":226},"Claude Code",{"relevance":235,"novelty":236,"quality":236,"actionability":235,"composite":237,"reasoning":4513},"Category: AI Automation. The article provides a detailed framework for codifying workflows into automations using Claude Code, which directly addresses the audience's need for practical applications in AI integration. It offers specific steps for implementation, such as creating a structured Obsidian vault and utilizing a dashboard for observability, making it highly actionable.","\u002Fsummaries\u002Fc770cf1fe76f0f1e-3-steps-to-custom-claude-code-agentic-os-summary","2026-05-05 03:37:16","2026-05-05 16:07:17",{"title":4453,"description":204},{"loc":4514},"a91bfd724607582d","Chase AI","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Bgxsx8slDEA","summaries\u002Fc770cf1fe76f0f1e-3-steps-to-custom-claude-code-agentic-os-summary",[252,253,251,254],"Codify workflows into domains, tasks, skills, and automations; add Obsidian memory layer; build observability dashboard to track, optimize, and share with teams\u002Fclients ahead of 99% of users.",[254],"hqDGxnjTNUTthpKqY-trk_chizcVFbgro_HLw835dWQ",{"id":4528,"title":4529,"ai":4530,"body":4535,"categories":4563,"created_at":213,"date_modified":213,"description":204,"extension":214,"faq":213,"featured":215,"kicker_label":213,"meta":4564,"navigation":239,"path":4583,"published_at":4584,"question":213,"scraped_at":4585,"seo":4586,"sitemap":4587,"source_id":4588,"source_name":4589,"source_type":247,"source_url":4590,"stem":4591,"tags":4592,"thumbnail_url":213,"tldr":4593,"tweet":213,"unknown_tags":4594,"__hash__":4595},"summaries\u002Fsummaries\u002F37647e6f3737af38-fix-prompt-fragility-by-decomposing-agents-into-mi-summary.md","Fix Prompt Fragility by Decomposing Agents into Microservices",{"provider":7,"model":8,"input_tokens":4531,"output_tokens":4532,"processing_time_ms":4533,"cost_usd":4534},6924,1734,18216,0.00174495,{"type":14,"value":4536,"toc":4558},[4537,4541,4544,4548,4551,4555],[17,4538,4540],{"id":4539},"monolithic-prompts-cause-nonlinear-failures-from-tiny-changes","Monolithic Prompts Cause Nonlinear Failures from Tiny Changes",[22,4542,4543],{},"Single LLMs in production agents handle 5-6 tasks simultaneously—routing intent, reasoning over data, tool calling, schema validation, next-turn decisions, and history management—all in one context window. Adding one instruction shifts attention across everything, causing prompt fragility: semantically equivalent rewrites destabilize outputs, with accuracy dropping up to 54% unpredictably. A Palo Alto Networks Unit 42 study fuzzing LLMs found 97-99% of meaning-preserving prompt variants evaded content filters, and one model bypassed its safety policy 75\u002F100 times. Multi-agent studies confirm single agents suffer attention dilution, task interference, and error propagation; an essay-grading benchmark improved 26.6 and 10.8 percentage points by splitting into content, structure, and language specialists. Context bloat worsens this—reasoning degrades nonlinearly beyond 100k tokens per Anthropic research; one case cut from 140k to 6k tokens, boosting accuracy from 70% to 90%+ while slashing latency to single digits. Monoliths turn prompts into junk drawers, making every change a regression risk.",[17,4545,4547],{"id":4546},"decompose-into-sub-agents-nano-models-and-context-quarantine","Decompose into Sub-Agents, Nano Models, and Context Quarantine",[22,4549,4550],{},"Cognitive decomposition fragments tasks: use small language models (SLMs) or nano models for non-frontier work like routing, classification, validation, and formatting, reserving frontier models for core reasoning. NVIDIA's position paper argues SLMs suffice for agentic tasks, run 10-50x cheaper with lower latency and predictable behavior; examples include NVIDIA Nemotron 3 Nano (1M-token context), Microsoft Phi-4 (multimodal reasoning), and Anthropic Haiku 4.5 ($1\u002FM input tokens). Multi-model routing (70% cheap models, 10% frontier) with caching cuts spend 60-80%. Key wedges: (1) Nano-classifier for routing removes full option menus from main prompts, enabling network-gapped UI to isolate PII from compliance boundaries—vital for regulated sectors per 2026 guidance from CDC, UK CMA, Singapore IMDA, EU AI Act. (2) Post-hoc nano-model or function for schema\u002FJSON validation eliminates malformed outputs. (3) Dedicated agent for follow-up queries from UI clicks, using element metadata, screen state, and history. Context quarantine isolates sub-agents, preventing cross-contamination; e.g., per-company sub-agents in enterprise workflows avoid conflating data.",[17,4552,4554],{"id":4553},"production-wins-shrunk-prompts-costs-and-regressions","Production Wins: Shrunk Prompts, Costs, and Regressions",[22,4556,4557],{},"Decomposition yields 50-80% smaller main prompts, 60-80% lower per-query costs, and sharp regression drops by minimizing fragility surfaces. Customer-support agents route via nano-classifier (refunds, billing, etc.) to sub-agents, isolating new instructions. Coding assistants use intent classifiers for language-specific prompts, easing new support. RAG splits retrieval ranking, citation validation (nano), and generation (frontier). Generative UI filters element catalogs\u002Fexamples\u002Finstructions per-query and offloads click handling to small agents, avoiding regressions. Promptfoo-like tools test but don't prevent; architecture does. Labs signal the shift: Anthropic deprecated 1M-token betas, capped APIs at 300k tokens, calling infinite context an anti-pattern. Frontier models for frontier problems; SLMs for the rest.",{"title":204,"searchDepth":205,"depth":205,"links":4559},[4560,4561,4562],{"id":4539,"depth":205,"text":4540},{"id":4546,"depth":205,"text":4547},{"id":4553,"depth":205,"text":4554},[267],{"content_references":4565,"triage":4581},[4566,4571,4576,4579],{"type":4567,"title":4568,"author":4569,"publisher":4570,"context":4509},"report","Palo Alto Networks Unit 42 study","Palo Alto Networks Unit 42","Palo Alto Networks",{"type":4572,"title":4573,"author":4574,"publisher":4575,"context":4509},"paper","Small Language Models Are the Future of Agentic AI","NVIDIA Research","NVIDIA",{"type":3883,"title":4577,"author":4578,"publisher":4578,"context":4509},"Effective Context Engineering for AI Agents","Anthropic",{"type":219,"title":4580,"context":226},"Promptfoo",{"relevance":235,"novelty":236,"quality":236,"actionability":236,"composite":3886,"reasoning":4582},"Category: AI & LLMs. The article provides a deep dive into the concept of prompt fragility and offers a practical solution by decomposing agents into microservices, which directly addresses the pain point of building reliable AI features. It includes specific examples and data points that enhance its applicability.","\u002Fsummaries\u002F37647e6f3737af38-fix-prompt-fragility-by-decomposing-agents-into-mi-summary","2026-05-04 14:48:23","2026-05-04 16:13:14",{"title":4529,"description":204},{"loc":4583},"37647e6f3737af38","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fadded-one-line-to-your-prompt-and-everything-broke-youre-hitting-prompt-fragility-b2dcc4ff570e?source=rss----5517fd7b58a6---4","summaries\u002F37647e6f3737af38-fix-prompt-fragility-by-decomposing-agents-into-mi-summary",[251,252,253,254],"Monolithic LLM prompts fail unpredictably from tiny changes because one model juggles routing, reasoning, validation, and more—decompose into sub-agents and nano models to shrink context 50-80%, cut costs 60-80%, and eliminate cascades.",[254],"wsWkF4OdnDT_z4Q2Oz1pwkJuUnE8AHWaaRU9uGkIIwM"]