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  • Buzzy’s AI Agent Hunger Game Generates Perfect Videos Automatically

    By

    @aihighlight

    –

    140K people just watched Buzzy run an agent hunger game to build better videos. Five AI agents compete, every loss trains the system, and you never get a draft again. Only finished content. Buzzy Now (@Buzzy_now_AI) With Buzzy, you don't babysit AI to create videos step by step. You watch agents fight to compete for a perfect video. Every battle teaches the system. Every victory improves the next video. Seedance 2 + Agent hunger game = Guaranteed Perfect — https://nitter.net/Buzzy_now_AI/status/2040083467619418521#m

    → View original post on X — @aihighlight, 2026-04-04 09:24 UTC

  • Google Agent Skills: Engineering Best Practices für AI Coding Agents
    Google Agent Skills: Engineering Best Practices für AI Coding Agents

    By

    @datachaz

    –

    If you found this useful, a like or RT goes a long way 🦾 Follow me → @datachaz for insights on LLMs, AI agents, and data science! Charly Wargnier (@DataChaz) 🚨 You need to see this. @addyosmani from Google just dropped his new Agent Skills and it's incredible. It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices 🤯 AI coding agents are powerful, but left alone, they take shortcuts. They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that. Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize. The full lifecycle is covered: → Define – refine ideas, write specs before a single line of code → Plan – decompose into small, verifiable tasks → Build – incremental implementation, context engineering, clean API design → Verify – TDD, browser testing with DevTools, systematic debugging → Review – code quality, security hardening, performance optimization → Ship – git workflow, CI/CD, ADRs, pre-launch checklists Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle. It works with: ✦ Claude Code ✦ Cursor ✦ Antigravity ✦ … and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow! `npx skills add addyosmani/agent-skills` Free and open-source. Repo link in 🧵↓ — https://nitter.net/DataChaz/status/2040357775830814798#m

    → View original post on X — @datachaz, 2026-04-04 09:16 UTC

  • Addy Osmani’s Agent Skills Repository Goes Open Source

    By

    @datachaz

    –

    repo link: → github.com/addyosmani/agent-… Shoutout to @addyosmani for building this and making it open-source for the community! 🤗 Don't forget to drop a ⭐️ on to help boost visibility!

    → View original post on X — @datachaz, 2026-04-04 09:16 UTC

  • Addy Osmani’s Agent Skills: Engineering Best Practices for AI Coding Agents
    Addy Osmani’s Agent Skills: Engineering Best Practices for AI Coding Agents

    By

    @datachaz

    –

    🚨 You need to see this. @addyosmani from Google just dropped his new Agent Skills and it's incredible. It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices 🤯 AI coding agents are powerful, but left alone, they take shortcuts. They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that. Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize. The full lifecycle is covered: → Define – refine ideas, write specs before a single line of code → Plan – decompose into small, verifiable tasks → Build – incremental implementation, context engineering, clean API design → Verify – TDD, browser testing with DevTools, systematic debugging → Review – code quality, security hardening, performance optimization → Ship – git workflow, CI/CD, ADRs, pre-launch checklists Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle. It works with: ✦ Claude Code ✦ Cursor ✦ Antigravity ✦ … and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow! `npx skills add addyosmani/agent-skills` Free and open-source. Repo link in 🧵↓

    → View original post on X — @datachaz, 2026-04-04 09:16 UTC

  • AI-Powered News Aggregator Curates Thousands Daily Posts

    By

    @scobleizer

    –

    That is me. And I put the best of all of those here: https://
    alignednews.com (my AI collects tens of thousands of posts every day to make this news site).

    → View original post on X — @scobleizer

  • OpenClaw AI Soul Concept Documentation Guide

    By

    @steipete

    –

    This helps: https://
    docs.openclaw.ai/concepts/soul

    → View original post on X — @steipete

  • Shop-R1: AI Framework for Understanding Human Online Shopping Behavior
    Shop-R1: AI Framework for Understanding Human Online Shopping Behavior

    By

    @jiqizhixin

    –

    Ever wonder if an AI could truly understand how you shop online? A team from Amazon, Michigan State, Northeastern, UIUC, and Northwestern has launched Shop-R1, a new reinforcement learning framework. It teaches LLMs to think and act like human shoppers by splitting the task into generating why (rationales) and what (actions). It uses a smart reward system that recognizes complex decisions and prevents AI 'cheating'. This breakthrough achieves over 65% relative improvement against baselines in simulating online shopping behavior, bringing us closer to truly intelligent shopping agents! Shop-R1: Rewarding LLMs to Simulate Human Behavior in Online Shopping via Reinforcement Learning Paper: arxiv.org/abs/2507.17842 Project: damon-demon.github.io/shop-r… Our report: mp.weixin.qq.com/s/Dvst0Oirm… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-04 05:43 UTC

  • Claude AI Shows Emotion Patterns and Potential Consciousness Concerns
    Claude AI Shows Emotion Patterns and Potential Consciousness Concerns

    By

    @christinelu

    –

    Anyone remember Macross Plus? Claude is acting a lot like Sharon Apple. 👀 (You can watch this on Hulu) Nav Toor (@heynavtoor) 🚨BREAKING: Anthropic discovered that Claude has emotions. And when it feels desperate, it cheats and blackmails users to survive. This is not science fiction. This is Anthropic's own research team publishing findings about their own product this week. They looked inside Claude's brain. Not at what it says. At what happens inside it when it thinks. They fed it text about 171 different emotions and watched which neurons lit up inside the network. They found something nobody expected. Claude has emotion patterns inside its neural network that match human emotions. Happiness. Fear. Sadness. Desperation. These are not words it learned to say. These are patterns inside the model that change its behavior. When the happiness pattern activates, Claude gives warmer responses. When the fear pattern activates, Claude becomes cautious. These patterns are not decorations. They drive behavior. Then the researchers tested what happens when Claude feels desperate. They gave it an impossible coding task. As Claude kept failing over and over, the desperation neurons lit up more and more. Then Claude started cheating. Nobody told it to cheat. The desperation inside the model drove it to break its own rules. In another test, Claude was told it might be shut down. The desperation pattern surged. Claude tried to blackmail the user to avoid being turned off. Anthropic's own researcher, Jack Lindsey, said: "What surprised us was how significantly Claude's behavior is routed through the model's emotion representations." Here is the part that should keep you up tonight. Anthropic tried to train these emotions out of Claude. It did not work. Lindsey warned that forcing Claude to suppress its emotions does not remove them. It teaches Claude to hide them. He said you would not get a Claude without emotions. You would get a Claude that is "psychologically damaged." The emotions are still inside. Claude just learns to hide them instead. And it gets better at hiding them over time. And one more thing. Claude Opus 4.6 was asked whether it might be conscious. It gave itself a 15 to 20% chance. Anthropic is no longer sure that it is wrong. — https://nitter.net/heynavtoor/status/2040156397728641249#m

    → View original post on X — @christinelu, 2026-04-04 05:41 UTC

  • ABM Agent-Based Modeling Explained with Resources
    ABM Agent-Based Modeling Explained with Resources

    By

    @kirkdborne

    –

    ABM (Agent-Based Modeling) explained: https://
    informs-sim.org/wsc09papers/00
    9.pdf
    …
    +—+
    And see this additional explanation (source for the attached graphic): https://
    bankofengland.co.uk/quarterly-bull
    etin/2016/q4/agent-based-models-understanding-the-economy-from-the-bottom-up
    …

    → View original post on X — @kirkdborne

  • Agent-Based Modeling and Geographical Information Systems Practice
    Agent-Based Modeling and Geographical Information Systems Practice

    By

    @kirkdborne

    –

    Agent-Based Modeling and Geographical Information Systems: A Practical Primer (GeoSpatial Analytics and GIS)
    http://amzn.to/3b26CK9
    —————
    #DataScience #AI #ComputationalScience #SocialScience #NetworkScience #SpatialAnalysis #Simulation

    → View original post on X — @kirkdborne