[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-802cc6a93b1ed7a1-data-prep-pipeline-for-lora-qlora-llm-fine-tuning-summary":3,"summaries-facets-categories":155,"summary-related-802cc6a93b1ed7a1-data-prep-pipeline-for-lora-qlora-llm-fine-tuning-summary":3725},{"id":4,"title":5,"ai":6,"body":13,"categories":120,"created_at":121,"date_modified":121,"description":114,"extension":122,"faq":121,"featured":123,"kicker_label":121,"meta":124,"navigation":136,"path":137,"published_at":138,"question":121,"scraped_at":139,"seo":140,"sitemap":141,"source_id":142,"source_name":143,"source_type":144,"source_url":145,"stem":146,"tags":147,"thumbnail_url":121,"tldr":152,"tweet":121,"unknown_tags":153,"__hash__":154},"summaries\u002Fsummaries\u002F802cc6a93b1ed7a1-data-prep-pipeline-for-lora-qlora-llm-fine-tuning-summary.md","Data Prep Pipeline for LoRA\u002FQLoRA LLM Fine-Tuning",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",6373,1553,9668,0.00202845,{"type":14,"value":15,"toc":113},"minimark",[16,21,25,28,32,35,38,63,74,78,81,99,106],[17,18,20],"h2",{"id":19},"loraqlora-makes-fine-tuning-viable-on-consumer-hardware","LoRA\u002FQLoRA Makes Fine-Tuning Viable on Consumer Hardware",[22,23,24],"p",{},"Fine-tuning outperforms prompt engineering for production AI agents by embedding workflows directly into the model, ensuring consistency without repeated context injection. LoRA adds low-rank adapter layers to a frozen base model, capturing task-specific patterns without updating all parameters. QLoRA extends this with 4-bit quantization, slashing memory needs: a 1B-parameter model requires \u003C1GB VRAM, 7B needs ~5GB, and even 70B fits on a single high-end GPU at ~46GB—trainable on an RTX 4090 instead of enterprise clusters costing hundreds of thousands.",[22,26,27],{},"Use 500-1,000 high-quality examples for effective results; fewer can work if curated well, as quality trumps quantity. Skip full fine-tuning for smaller 20-30B models on consumer hardware, or rent GPUs hourly for larger ones.",[17,29,31],{"id":30},"structured-jsonl-format-unlocks-reliable-agent-behavior","Structured JSONL Format Unlocks Reliable Agent Behavior",[22,33,34],{},"Raw data like security logs or IT tickets must convert to JSONL (one JSON object per line) with a consistent instruction\u002Finput\u002Fresponse schema. This format teaches the model precise outputs, unlike unstructured prompts that yield inconsistent results.",[22,36,37],{},"Example transformation for log analysis:",[39,40,41,50,53,56],"ul",{},[42,43,44,45,49],"li",{},"Parse raw log: ",[46,47,48],"code",{},"2023-10-01 12:00:00 user123 login failed"," into timestamp, user, event.",[42,51,52],{},"Instruction: \"Analyze the following authentication logs and classify the security risk. Provide classification, severity, action, and reason in JSON format.\"",[42,54,55],{},"Input: Parsed log components.",[42,57,58,59,62],{},"Response: ",[46,60,61],{},"{\"classification\": \"credential stuffing\", \"severity\": \"high\", \"action\": \"block IP\", \"reason\": \"multiple failures\"}",".",[22,64,65,66,69,70,73],{},"For agent personas (e.g., TacoBot), pair customer queries like \"Do you have combo deals?\" with JSON responses: ",[46,67,68],{},"{\"response\": \"Yes, combo #1: two tacos, chips, drink for $8.99.\", \"category\": \"Deals\"}",". Classification datasets (e.g., IT tickets like \"VPN disconnects every 5 minutes\") use uniform instructions across varied inputs, outputting ",[46,71,72],{},"{\"category\": \"Network\", \"priority\": \"Medium\", \"team\": \"IT support\", \"reason\": \"VPN connectivity issue\"}",". Consistent JSON enables downstream parsing for workflows.",[17,75,77],{"id":76},"validate-data-quality-and-test-llm-alignment-pre-training","Validate Data Quality and Test LLM Alignment Pre-Training",[22,79,80],{},"Data prep comprises 80% of fine-tuning success—garbage in, garbage out. Automate checks in Python:",[39,82,83,90,96],{},[42,84,85,86,89],{},"Required fields present and non-empty (e.g., ",[46,87,88],{},"if field not in example or not example[field]:",").",[42,91,92,93,89],{},"Responses parse as JSON (",[46,94,95],{},"json.loads(response)",[42,97,98],{},"Minimum 50 examples; flag duplicates.",[22,100,101,102,105],{},"Capstone: Test dataset against a base LLM. Construct prompts as ",[46,103,104],{},"instruction + input"," and compare generated vs. expected JSON responses for alignment score. High similarity means the model already groks the patterns, so fine-tuning reinforces efficiently without fighting base behaviors.",[22,107,108,109,112],{},"Lab workflow (25-35 min): Setup verifies env (OpenAI API, packages); compare unstructured vs. structured prompts; transform logs; build persona\u002Fclassification data; validate; infer. Output files like ",[46,110,111],{},"log_training_data.jsonl"," ready for LoRA\u002FQLoRA training.",{"title":114,"searchDepth":115,"depth":115,"links":116},"",2,[117,118,119],{"id":19,"depth":115,"text":20},{"id":30,"depth":115,"text":31},{"id":76,"depth":115,"text":77},[],null,"md",false,{"content_references":125,"triage":131},[126],{"type":127,"title":128,"url":129,"context":130},"tool","Customize LLMs & Agents for FREE","https:\u002F\u002Fkode.wiki\u002F3QcX45W","recommended",{"relevance":132,"novelty":133,"quality":133,"actionability":132,"composite":134,"reasoning":135},5,4,4.55,"Category: AI & LLMs. The article provides a detailed guide on fine-tuning LLMs using LoRA\u002FQLoRA, which directly addresses the audience's need for practical applications in AI product development. It includes specific examples of data preparation and transformation, making it immediately actionable for developers looking to implement these techniques.",true,"\u002Fsummaries\u002F802cc6a93b1ed7a1-data-prep-pipeline-for-lora-qlora-llm-fine-tuning-summary","2026-04-15 13:45:28","2026-04-19 03:41:52",{"title":5,"description":114},{"loc":137},"802cc6a93b1ed7a1","KodeKloud","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=qIHaSPQYciM","summaries\u002F802cc6a93b1ed7a1-data-prep-pipeline-for-lora-qlora-llm-fine-tuning-summary",[148,149,150,151],"llm","prompt-engineering","machine-learning","ai-automation","Fine-tune LLMs with LoRA\u002FQLoRA on consumer GPUs using 500-1,000 JSONL examples in instruction\u002Finput\u002Fresponse format; data prep is 80% of success—transform logs, validate quality, test LLM alignment 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Consolidate into Markdown files with a fixed pattern: concept header, short explanation, personal analogy, and open questions section. Add metadata like topic and difficulty at the top. This creates predictable input the AI can parse consistently, reducing manual re-entry and making notes scannable even without AI. The key shift: treat notes as a structured collection, not isolated fragments—AI reliability starts with usable input, not model tweaks.",[22,3744,3745],{},"Shift from chat interfaces to API scripts for automation: load notes programmatically, send with queries, handle API keys securely, and monitor token-based billing to avoid surprise costs. Sending all notes every time works briefly but fails as volume grows due to context window limits—models drop unseen content without warning, causing inconsistent responses.",[22,3747,3748],{},"Tokens (sub-word chunks) accumulate fast in technical notes; a few pages hit thousands, exceeding limits like 128k for many models. Solution: work within constraints by sending only relevant info, turning AI from unpredictable chat into a buildable component.",[17,3750,3752],{"id":3751},"use-retrieval-to-fit-context-windows-and-boost-consistency","Use Retrieval to Fit Context Windows and Boost Consistency",[22,3754,3755],{},"Dumping all notes overloads the context window, so search notes first for query-relevant sections, extract those chunks, and feed only them to the model. This retrieval-augmented generation (RAG) grounds responses in your exact wording and analogies, making outputs mirror your thinking without dilution. RAG doesn't make the model smarter—it anchors it to your notes, preventing drift from pretrained knowledge.",[22,3757,3758],{},"Impact: answers stay consistent across queries, details from notes surface reliably, and token usage drops, cutting costs and fitting larger note collections. Flip from 'send everything' to 'retrieve precisely what's needed'—this scales as notes grow from dozens to hundreds.",[17,3760,3762],{"id":3761},"block-hallucinations-with-explicit-boundaries","Block Hallucinations with Explicit Boundaries",[22,3764,3765],{},"Even with retrieval, models blend notes with internal knowledge, inventing plausible details like unmentioned formulas (e.g., in backpropagation explanations). Hallucination isn't random error—it's the model helpfully filling gaps to complete responses, blending sources seamlessly so you accept fakes as fact.",[22,3767,3768],{},"Fix: prefix every prompt with a strict rule: \"Answer using only the provided notes. If info is missing, state clearly 'This isn't covered in your notes' instead of guessing.\" This enforces boundaries, yielding honest responses that flag knowledge gaps—turning limitations into study signals (e.g., 'focus here next').",[22,3770,3771],{},"Result: responses stick to your intuition-focused notes (no surprise math), build trust through transparency, and clarify what you truly understand versus assumed. Without this, AI blurs personal knowledge lines; with it, it becomes a precise learning mirror.",[17,3773,3775],{"id":3774},"tune-temperature-for-task-specific-outputs","Tune Temperature for Task-Specific Outputs",[22,3777,3778],{},"One system serves multiple needs: deterministic explanations (repeatable, grounded) versus creative practice questions (varied for testing). Use the temperature parameter—low (e.g., 0.0-0.2) for stable, confident outputs sticking to notes; high (e.g., 0.7+) for diverse phrasings and idea combinations.",[22,3780,3781],{},"No setup changes needed—just swap values per task. This reveals LLMs as constraint-driven systems: context limits spawn tokens\u002Fretrieval, gap-filling causes hallucinations (fixed by instructions), and output style tunes via params. Hands-on fixes demystify behavior, shifting AI from 'magic' to predictable tool for building study pipelines.",{"title":114,"searchDepth":115,"depth":115,"links":3783},[3784,3785,3786,3787],{"id":3738,"depth":115,"text":3739},{"id":3751,"depth":115,"text":3752},{"id":3761,"depth":115,"text":3762},{"id":3774,"depth":115,"text":3775},[],{"content_references":3790,"triage":3791},[],{"relevance":132,"novelty":133,"quality":133,"actionability":132,"composite":134,"reasoning":3792},"Category: AI & LLMs. The article provides a detailed approach to improving AI note-taking through structured input and retrieval-augmented generation (RAG), addressing practical pain points for developers integrating AI into their workflows. It offers actionable steps like structuring notes in Markdown and using API scripts, making it highly relevant and immediately applicable.","\u002Fsummaries\u002Fd009bef297bc0ca2-fix-ai-note-forgetting-unlock-llm-mechanics-via-ra-summary","2026-05-03 12:01:01","2026-05-03 17:00:57",{"title":3728,"description":114},{"loc":3793},"d009bef297bc0ca2","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Fai-kept-forgetting-my-notes-fixing-that-taught-me-how-it-actually-works-e08ff209403d?source=rss----98111c9905da---4","summaries\u002Fd009bef297bc0ca2-fix-ai-note-forgetting-unlock-llm-mechanics-via-ra-summary",[148,149,151],"Structure notes in consistent Markdown, retrieve relevant chunks to fit context windows (measured in tokens), instruct model to use only provided notes to avoid hallucinations, and tune temperature for consistent explanations or varied practice questions.",[151],"x-mKhfsn2ohViSgH6T16jUthOW247MMMlLvWLr-GKe4",{"id":3807,"title":3808,"ai":3809,"body":3814,"categories":3848,"created_at":121,"date_modified":121,"description":114,"extension":122,"faq":121,"featured":123,"kicker_label":121,"meta":3849,"navigation":136,"path":3863,"published_at":3864,"question":121,"scraped_at":3865,"seo":3866,"sitemap":3867,"source_id":3868,"source_name":3869,"source_type":144,"source_url":3870,"stem":3871,"tags":3872,"thumbnail_url":121,"tldr":3873,"tweet":121,"unknown_tags":3874,"__hash__":3875},"summaries\u002Fsummaries\u002F8ac2fb30e2408980-h2e-locks-llms-into-expert-only-responses-via-sema-summary.md","H2E Locks LLMs into Expert-Only Responses via Semantic Gates",{"provider":7,"model":8,"input_tokens":3810,"output_tokens":3811,"processing_time_ms":3812,"cost_usd":3813},4673,1635,12411,0.0017296,{"type":14,"value":3815,"toc":3843},[3816,3820,3823,3826,3830,3833,3836,3840],[17,3817,3819],{"id":3818},"three-layer-gating-prevents-semantic-drift-in-expert-ai","Three-Layer Gating Prevents Semantic Drift in Expert AI",[22,3821,3822],{},"Implement H2E by embedding user queries and expert knowledge as vectors, then compute SROI (cosine similarity) in the Intent Governance Zone (IGZ). Block responses below thresholds like 0.9583 with a HARD-STOP to enforce alignment to 'Expert DNA'—a gold-standard intent vector from domain pros. Only passing queries reach the Cognitive Reasoning Layer, using greedy decoding (temperature=0.0) for repeatable, non-hallucinated outputs. This Normalized Expert Zone (NEZ) + IGZ + reasoning stack transforms probabilistic LLMs into deterministic systems, ideal for safety-critical ops like Orion ECLSS diagnostics where generic answers risk failure.",[22,3824,3825],{},"Trade-off: High thresholds (0.9583) silence even relevant queries without perfect vector match, prioritizing certainty over flexibility; low ones (0.5) mimic standard assistants but invite drift.",[17,3827,3829],{"id":3828},"fit-70b-model-on-24gb-l4-gpu-with-quantization-and-offloading","Fit 70B Model on 24GB L4 GPU with Quantization and Offloading",[22,3831,3832],{},"Load DeepSeek-R1-Distill-Llama-70B in Q4_K_M GGUF format (42.5 GB compressed) to slash VRAM needs while preserving reasoning. Enable Flash Attention 2 for faster processing, offload 28 layers to GPU (n_gpu_layers=28), and handle the rest on CPU to dodge OOM errors. Result: Sovereign, on-premise inference without cloud leaks, maintaining industrial data privacy.",[22,3834,3835],{},"This setup proves massive models viable on edge hardware—L4's 24GB runs what once needed clusters— but demands tuning layers per workload to balance speed and accuracy.",[17,3837,3839],{"id":3838},"thresholds-deliver-sovereign-agency-with-auditable-certainty","Thresholds Deliver Sovereign Agency with Auditable Certainty",[22,3841,3842],{},"At SROI=0.5, AI details spacecraft oxygen protocols freely; at 0.9583, it rejects near-misses, embodying 'Expert Manifold' confinement. Outputs stay auditable via fixed decoding, ending unsupervised AI in pro environments. H2E shifts from prompt hacks to geometric governance: queries must geometrically align to expert reality or halt, yielding certainty over creativity for high-stakes like aerospace where one hallucination compromises safety.",{"title":114,"searchDepth":115,"depth":115,"links":3844},[3845,3846,3847],{"id":3818,"depth":115,"text":3819},{"id":3828,"depth":115,"text":3829},{"id":3838,"depth":115,"text":3839},[164],{"content_references":3850,"triage":3859},[3851,3855],{"type":127,"title":3852,"url":3853,"context":3854},"H2E_DS.ipynb","https:\u002F\u002Fgithub.com\u002Ffrank-morales2020\u002FMLxDL\u002Fblob\u002Fmain\u002FH2E_DS.ipynb","mentioned",{"type":3856,"title":3857,"url":3858,"context":3854},"other","The Wall Before the Word: H2E Geometric Governance and the Future of AI Government","https:\u002F\u002Fmedium.com\u002F@frankmorales_91352\u002Fthe-wall-before-the-word-h2e-geometric-governance-and-the-future-of-ai-government-89ff82c7598a",{"relevance":133,"novelty":133,"quality":133,"actionability":3860,"composite":3861,"reasoning":3862},3,3.8,"Category: AI & LLMs. The article discusses the H2E framework for ensuring deterministic outputs from LLMs in high-stakes environments, addressing a specific pain point of needing reliable AI responses. It provides a novel approach to prompt engineering and governance, though it lacks detailed step-by-step guidance for implementation.","\u002Fsummaries\u002F8ac2fb30e2408980-h2e-locks-llms-into-expert-only-responses-via-sema-summary","2026-04-13 09:44:23","2026-04-13 17:53:12",{"title":3808,"description":114},{"loc":3863},"8ac2fb30e2408980","AI Simplified in Plain English","https:\u002F\u002Fmedium.com\u002Fai-simplified-in-plain-english\u002Fthe-architecture-of-certainty-deterministic-governance-in-high-stakes-ai-with-deepseek-9d15e61cc38c?source=rss----f37ab7d4e76b---4","summaries\u002F8ac2fb30e2408980-h2e-locks-llms-into-expert-only-responses-via-sema-summary",[148,149,151],"H2E framework uses cosine similarity (SROI) thresholds like 0.9583 to gate queries against 'Expert DNA' vectors, ensuring deterministic AI outputs only for high-stakes industrial tasks with DeepSeek 70B on NVIDIA L4.",[151],"NF2_5_xq0uI9RAVnZKiamA6oV3F-WLp2Hq8aiFUOsgk",{"id":3877,"title":3878,"ai":3879,"body":3884,"categories":3912,"created_at":121,"date_modified":121,"description":3913,"extension":122,"faq":121,"featured":123,"kicker_label":121,"meta":3914,"navigation":136,"path":3915,"published_at":3916,"question":121,"scraped_at":3917,"seo":3918,"sitemap":3919,"source_id":3920,"source_name":3921,"source_type":3922,"source_url":3923,"stem":3924,"tags":3925,"thumbnail_url":121,"tldr":3926,"tweet":121,"unknown_tags":3927,"__hash__":3928},"summaries\u002Fsummaries\u002Fc17309e56b899c59-10x-claude-with-agents-memory-context-and-skills-m-summary.md","10x Claude with Agents, Memory, Context, and Skills MD Files",{"provider":7,"model":8,"input_tokens":3880,"output_tokens":3881,"processing_time_ms":3882,"cost_usd":3883},3344,1411,14122,0.0013518,{"type":14,"value":3885,"toc":3907},[3886,3890,3893,3897,3900,3904],[17,3887,3889],{"id":3888},"personalize-claudes-behavior-from-the-start","Personalize Claude's Behavior from the Start",[22,3891,3892],{},"Start by creating agents.md as your AI's onboarding document. Include your business details, preferred voice, and work style—Claude references this file before every interaction, ensuring outputs align with your needs consistently. Pair it with memory.md to log and update user preferences dynamically, like instructing 'stop signing emails with cheers.' This continuous learning prevents repetitive fixes and builds a tailored assistant that adapts over time, maximizing the value of a Claude subscription beyond basic queries.",[17,3894,3896],{"id":3895},"load-deep-context-without-overloading-prompts","Load Deep Context Without Overloading Prompts",[22,3898,3899],{},"Use a context folder for heavier, nuanced information that standalone prompts can't handle effectively. Claude pulls from here as needed, combining it with preferences from memory.md. This setup avoids context bloat in individual chats while providing rich background, enabling more accurate and relevant responses for complex tasks.",[17,3901,3903],{"id":3902},"turn-processes-into-one-shot-workflows","Turn Processes into One-Shot Workflows",[22,3905,3906],{},"In the skills folder, demonstrate a process once—Claude packages it into a reusable workflow. This compresses multi-step, time-intensive work, like 4-hour tasks, into a single instruction. The result: scalable automation where you define expertise upfront, then invoke it repeatedly without reteaching, effectively 10x-ing productivity on repetitive engineering or product workflows.",{"title":114,"searchDepth":115,"depth":115,"links":3908},[3909,3910,3911],{"id":3888,"depth":115,"text":3889},{"id":3895,"depth":115,"text":3896},{"id":3902,"depth":115,"text":3903},[164],"I sit down with Remy Gaskell to break down how anyone can build AI agents to run entire departments of their business. Remy walks through the core concepts: agent loops, context files, memory, MCP tool connections, and skills. We put everything together by building a fully functional executive assistant live on screen. This is a beginner-friendly crash course that covers Claude Code, Codex, Cowork, Antigravity, Manus, and OpenClaw, showing that once you understand how to \"drive,\" you can jump into any agent platform. By the end, listeners know exactly how to set up markdown-based context files, connect their everyday tools, and create reusable skills that compound over weeks and months.\n\nKey Points\n\n* Agent platforms (Claude Code, Codex, Cowork, Antigravity, Manus, OpenClaw) are all running the same observe-think-act loop under the hood — learning one means you can use any of them.\n* The shift from chat to agents requires moving from prompt engineering to context engineering: load the agent with rich context so simple prompts produce excellent results.\n* A memory md file creates a self-improving loop where the agent learns preferences across sessions and makes fewer errors over time.\n* MCP (Model Context Protocol), built by Anthropic, acts as a universal translator between your agent and every tool it needs — Gmail, Calendar, Stripe, Notion, and more.\n* Skills are reusable SOPs packaged as markdown files; once you explain a process once, you can invoke it repeatedly, and they compound as you add three to five per week.\n* Scheduled tasks turn skills into automated workflows — morning briefs, car searches, ad library analyses — that run on a cron without any manual trigger.\n\nNumbered Section Summaries\n\n1. The Agent Loop in Action\n\nRemy kicks off with a live demo, sending the same prompt — \"build a minimalist portfolio site for Greg Isenberg\" — to Claude Code, Codex, and Antigravity simultaneously. All three platforms run the same observe-think-act loop: research the subject, write the code, spin up a preview, and verify the result with a screenshot. The demo makes it tangible that every agent harness is just a different car with the same engine.\n\n2. Onboarding Your Agent Like a Real Employee\n\nRemy shows that without context, an agent asked to \"write me a cold email\" has no idea who you are or what you sell. The fix is an agents.md (or Claude.md) file — a persistent context document loaded at the start of every session. You fill it with your role, business details, tools, and working preferences, and the result is that a two-word prompt produces a fully informed output.\n\n3. Memory That Compounds\n\nChat models store memory invisibly in the cloud; agents require you to build it intentionally. Remy adds a memory.md file and a simple instruction in the context file: \"When I correct you or you learn something new, update memory.md.\" Preferences like tone, email sign-offs, and design choices persist across sessions, and errors decrease over time.\n\n\nThe #1 tool to find startup ideas\u002Ftrends - https:\u002F\u002Fwww.ideabrowser.com\u002F\n\nLCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https:\u002F\u002Flatecheckout.agency\u002F\n\nThe Vibe Marketer - Resources for people into vibe marketing\u002Fmarketing with AI: https:\u002F\u002Fwww.thevibemarketer.com\u002F\n\nFIND ME ON SOCIAL\nX\u002FTwitter: https:\u002F\u002Ftwitter.com\u002Fgregisenberg\nInstagram: https:\u002F\u002Finstagram.com\u002Fgregisenberg\u002F\nLinkedIn: https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fgisenberg\u002F\n\nFIND REMY ON SOCIAL\nX:https:\u002F\u002Fx.com\u002Fremy_gaskell\nYoutube: https:\u002F\u002Fwww.youtube.com\u002F@aiwithremy\nInstagram: https:\u002F\u002Fwww.instagram.com\u002Faiwithremy\u002F",{},"\u002Fsummaries\u002Fc17309e56b899c59-10x-claude-with-agents-memory-context-and-skills-m-summary","2026-03-31 16:50:31","2026-04-03 21:16:06",{"title":3878,"description":3913},{"loc":3915},"c17309e56b899c59","Greg Isenberg","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ovLAIhbk3ek","summaries\u002Fc17309e56b899c59-10x-claude-with-agents-memory-context-and-skills-m-summary",[148,149,151],"Create four .md files—agents.md for business onboarding, memory.md for evolving preferences, context folder for nuanced info, and skills folder for reusable workflows—to turn 4-hour tasks into single-prompt executions.",[151],"u2cnAlNGnLm0YWszObIoUJdI3XkmB6GifoApTGwBXbo",{"id":3930,"title":3931,"ai":3932,"body":3937,"categories":3974,"created_at":121,"date_modified":121,"description":114,"extension":122,"faq":121,"featured":123,"kicker_label":121,"meta":3975,"navigation":136,"path":3987,"published_at":3988,"question":121,"scraped_at":3989,"seo":3990,"sitemap":3991,"source_id":3992,"source_name":3993,"source_type":3922,"source_url":3994,"stem":3995,"tags":3996,"thumbnail_url":3998,"tldr":3999,"tweet":4000,"unknown_tags":4001,"__hash__":4002},"summaries\u002Fsummaries\u002Fb24309283167b83a-malleable-evals-adaptive-testing-for-changing-ai-a-summary.md","Malleable Evals: Adaptive Testing for Changing AI Agents",{"provider":7,"model":8,"input_tokens":3933,"output_tokens":3934,"processing_time_ms":3935,"cost_usd":3936},6788,1435,19735,0.0020526,{"type":14,"value":3938,"toc":3969},[3939,3943,3946,3949,3953,3956,3959,3963,3966],[17,3940,3942],{"id":3941},"static-benchmarks-fail-malleable-ai-systems","Static Benchmarks Fail Malleable AI Systems",[22,3944,3945],{},"Traditional software evals rely on unit tests, regression suites, CI\u002FCD, and chaos engineering to measure static code. 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.",[22,3947,3948],{},"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.",[17,3950,3952],{"id":3951},"shift-from-prompt-to-intent-engineering-compounds-eval-challenges","Shift from Prompt to Intent Engineering Compounds Eval Challenges",[22,3954,3955],{},"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.",[22,3957,3958],{},"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.",[17,3960,3962],{"id":3961},"build-living-evals-as-self-optimizing-agents","Build Living Evals as Self-Optimizing Agents",[22,3964,3965],{},"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.",[22,3967,3968],{},"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":114,"searchDepth":115,"depth":115,"links":3970},[3971,3972,3973],{"id":3941,"depth":115,"text":3942},{"id":3951,"depth":115,"text":3952},{"id":3961,"depth":115,"text":3962},[],{"content_references":3976,"triage":3984},[3977,3979,3982],{"type":127,"title":3978,"context":3854},"OpenClaw",{"type":3980,"title":3981,"context":3854},"dataset","ARC-AGI 2",{"type":3856,"title":3983,"context":3854},"Adaptive testing for LLM evals paper",{"relevance":132,"novelty":133,"quality":133,"actionability":133,"composite":3985,"reasoning":3986},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":3931,"description":114},{"loc":3987},"b24309283167b83a","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4VhbYlfC7Gs","summaries\u002Fb24309283167b83a-malleable-evals-adaptive-testing-for-changing-ai-a-summary",[3997,148,149,151],"agents","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.",[151],"1xd66DttG0MMlsYN5aUG3JKuQffY2t3Ws867t1cXQiI"]