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What stood out to me is that context is becoming the real interface. Not prompts in a blank box. Context. You add: → a screenshot → a document → a note → an email thread And the system responds based on what is actually in front of you. That is much closer to how executives and operators really work.
→ View original post on X — @ronald_vanloon, 2026-04-07 08:30 UTC
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This connects to something I've been thinking about a lot around Agent Harness. I wrote about the anatomy of an agent harness yesterday and every component I covered (tool execution, memory, context management, orchestration) is basically an attack surface:
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Just stumbled upon the absolute BEST repo for Claude Code 🤯 If you're building with AI agents, this is pure gold. It is a continuously updated hub of best practices: → Clear subagent architectures and skills → The only MCP servers you actually need (DeepWiki, Context7, Playwright) → Proven workflows from AI legends (Karpathy, Cherny) → Secret slash commands not found in the official docs! You can literally set up a tailored AI team built for your exact workflow by tonight. Insane value. Best part? It's FREE and open-source! I've included the link to the repo in the 🧵 ↓
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“SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization” Most agent systems use skills like cheat sheets. It retrieves them at runtime, pastes them into the prompt, and hopes the model follows them. This paper suggests why not train the model with those skills, then slowly remove them until it can do the job from memory? So the agent starts training with skill guidance, but over time the helpful skills are taken away. And instead of depending on instructions forever, it learns to absorb them into its own parameters. This turns skills from something the model reads into something the model actually knows, and the result is a more efficient agent with much less context overhead, but still better performance. Empirically, SKILL0 beats strong RL baselines on ALFWorld and Search-QA while using under 0.5k tokens per step.
→ View original post on X — @askalphaxiv, 2026-04-07 07:37 UTC
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so you're telling me a dev actually coded a virtual whip to micro-manage Claude and make him work faster?pic.twitter.com/d62KBwCxKj
— Charly Wargnier (@DataChaz) 7 avril 2026
so you're telling me a dev actually coded a virtual whip to micro-manage Claude and make him work faster?
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I don't do that. I have Claude/Codex/Gemini do it for me.
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A Practical Guide to Reinforcement Learning from Human Feedback: Foundations, Aligning Large Language models, and the Evolution of Preference-Based methods! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #Books #100DaysofCode geni.us/Practical-Guide-RL
→ View original post on X — @gp_pulipaka, 2026-04-07 06:26 UTC
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We at The Zero-Human Company have been testing MemPalace by the amazing @bensig and Milla Jovovich and are absolutely blown away!
— Brian Roemmele (@BrianRoemmele) 7 avril 2026
It is a freaking masterpiece and we have deployed it to 79 employees at the company. Each worker will be testing and expanding on MemPalace.
I will… https://t.co/Y53xJb0ucG pic.twitter.com/Tp6tQSZPNO
We at The Zero-Human Company have been testing MemPalace by the amazing @bensig and Milla Jovovich and are absolutely blown away! It is a freaking masterpiece and we have deployed it to 79 employees at the company. Each worker will be testing and expanding on MemPalace. I will have a lot to say about how we are using it and how you should to. Ben Sigman (@bensig) My friend Milla Jovovich and I spent months creating an AI memory system with Claude. It just posted a perfect score on the standard benchmark – beating every product in the space, free or paid. It's called MemPalace, and it works nothing like anything else out there. Instead of sending your data to a background agent in the cloud, it mines your conversations locally and organizes them into a palace – a structured architecture with wings, halls, and rooms that mirrors how human memory actually works. Here is what that gets you: → Your AI knows who you are before you type a single word – family, projects, preferences, loaded in ~120 tokens → Palace architecture organizes memories by domain and type – not a flat list of facts, a navigable structure → Semantic search across months of conversations finds the answer in position 1 or 2 → AAAK compression fits your entire life context into 120 tokens – 30x lossless compression any LLM reads natively → Contradiction detection catches wrong names, wrong pronouns, wrong ages before you ever see them The benchmarks: 100% recall on LongMemEval — first perfect score ever recorded. 500/500 questions. Every question type at 100%. 92.9% on ConvoMem — more than 2x Mem0's score. 100% on LoCoMo — every multi-hop reasoning category, including temporal inference which stumps most systems. No API key. No cloud. No subscription. One dependency. Runs on your machine. Your memories never leave. MIT License. 100% Open Source. github.com/milla-jovovich/me… Community note: The claimed 100% LongMemEval score uses targeted fixes for the 3 failing questions and LLM reranking (held-out score: 98.4%). The 100% LoCoMo score uses top-k=50 exceeding session count with reranking (honest top-10 no rerank: 88.9%). github.com/milla-jovovich… — https://nitter.net/bensig/status/2041236952998171118#m
→ View original post on X — @ceobillionaire, 2026-04-07 06:08 UTC
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The developers getting good output have essentially encoded their engineering standards into the context before writing a single prompt. The ones getting garbage skipped that entirely.
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I built this completely with AI and don't know how to code: https://
alignednews.com/ai Generalists rule indeed. (It watches the whole AI community here on X and shows you the best posts about AI).