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finally liquid glass web is here and it's open source. real WebGL refraction, chromatic aberration, liquid motion not usual backdrop-filter css hack. playground at code at MIT so do whatever you want with it #LiquidGlass# #OpenSource# #ReactJS# #WebGL# #UIComponents# #FrontendDevelopment# #CSS# #JavaScript# #WebDev# #UI# #UX# #React# #ComponentLibrary# #OpenSourceSoftware# #Frontend#

Let's take a look at what kind of automation is possible with an AI team. This time on the OpenSource project Kuberhealthy. First, I got an idea to add Claude and Codex skills for Kuberhealthy. I picked up my phone and informally told one of the agents in the Kuberhealthy stack that I wanted this in a single sentence. The agent made this issue: (1/...)

The AI Business model trap: LLMs want cash flow to fund the race to AGI or the next model. Enter free consumer AI - they are losing a lot of money on the breadth of models to serve consumers for free! They are caught in the post training data trap, free consumer usage feeds post training needs, it can't be right to stop serving customers for free? But they need money for the compute: The monetization challenge is being pointed to Enterprises. Phase 1 - seemed easy, value capture in coding, the most bottom up motion in enterprise - with low customization per customer. Developers continue to train coding, tasks and eventually will train flawless skills. Phase 2 is where the challenge lies, showing true enterprise value. The promise of efficiency, accuracy, elimination of resources - that requires a different approach, build depth with harnesses, context, memory, solving for edge cases with deterministic guardrails! Build skill libraries - enter FDEs. Yes,FDEs will train the enterprise Waymos of the world. The risk - high token pricing for enterprises while consumers for free! Yes for consumer distribution businesses (aka Google, Meta, Apple, etc) it makes sense to hold on the distribution with free AI. If you want to win enterprise, you should be forward pricing tokens. The cheaper the tokens for enterprises it will allow for experimentation, workflow reimagination - instead CIOs are busy restricting AI use and working on making the use more efficient! Paradox: They still haven't fully understood and embraced the value of AI in the enterprise. If I were them: 1. Cut token pricing now, else send enterprises to secure opensource and end up with friction filled routing layers. 2. Show me how enterprises can use their context, training and data as their competitive advantage. 3. Build tools for rapid edge case learning and reducing false positives. @HarryStebbings @sama @DarioAmodei @demishassabis

🧵 Deli AutoResearch SKILL is now officially open source! 🎉 Alongside it, we’re dropping our 4th survey paper — this time on Self-play. Inspired by AlphaZero, we got a powerful insight: prior knowledge doesn’t always lift the ceiling. Models can discover more globally optimal solutions just by playing against themselves. The biggest change in this paper? For the first time, the AutoResearch Agent autonomously planned GPU experiments — and submitted actual RL runs on the DeepSeek 285B model. The entire RL pipeline — experiment design, code writing, running, debugging, and conclusion summarization — was 100% automated, with zero human intervention from me. This was incredibly difficult, but an incredibly important step. GRPO is the tool being called by the AutoResearch Agent here. We see this as the beginning of our Continual Learning research journey. 🚀 As always, this is my personal research project, unaffiliated with any organization. All views are my own. #AI# #ReinforcementLearning# #SelfPlay# #OpenSource# #AutoML# #ContinualLearning# #DeepSeek#

Introducing Mirage, a unified virtual filesystem for AI agents! 6 weeks. 1.1M+ lines of code. We rewrote bash from the ground up so cat, grep, head, and pipes work across heterogeneous services. S3, Google Drive, Slack, Gmail, GitHub, Linear, Notion, Postgres, MongoDB, SSH, and more, all mounted side-by-side as one filesystem. Bash that AI agents already know works on every format! cat, grep, head, and wc parse .parquet, .csv, .json, .h5, even .wav! One pipe can stitch S3, Drive, GitHub, Slack, and Linear together, same Unix semantics throughout. Workspaces are versioned too. Snapshot, clone, and roll back the whole thing with one API call. A two-layer cache turns repeated reads into local lookups, so agent loops stay fast and cheap. Drop a Workspace into FastAPI, Express, or a browser app. Wire it into OpenAI Agents SDK, Vercel AI SDK, LangChain, Mastra, or Pi. Run it alongside Claude Code and Codex. Site: GitHub: #AIAgents# #OpenSource# #AgenticAI# #Strukto# #Filesystem# #VFS#

I just deleted iTerm2, Warp, and Ghostty from my Mac. One terminal replaced all three. It's called Kaku. And it's the first terminal I've used in 10 years that didn't make me configure anything before it felt right. Here's what makes it different from everything else: Most terminals give you a blank box and a manual. Kaku opens looking like someone already spent 40 hours setting it up for you. Beautiful fonts, smart autocomplete, colour-coded commands, instant navigation that learns your most visited folders and jumps to them from a two-letter command. It also boots in half the time of every competitor I tested. The creator tried Alacritty, Kitty, Ghostty, Warp, and iTerm2. All of them had the same problem: you either got power without polish, or polish without power. So he spent months building the one that ships with both out of the box. 40% smaller than the terminal it's built on. Starts instantly. Never asks you to log in. Never asks for your credit card. Just download, drag to Applications, and open it. 4.1k stars. MIT License. 100% Opensource.

Introducing AIO Sandbox, All-in-One Sandbox Environment for AI Agents. Unchecked AI autonomy is a ticking time bomb; it’s time to pull the plug on full system unfettered access. We can no longer afford to give AI agents the 'keys to the kingdom' without oversight. The 'wild west' of AI agents running with total system control is officially over. AIO Sandbox is an open-source project designed to solve these problems. It is everything your agent needs, out of the box. No more juggling multiple services. AIO Sandbox ships a complete, pre-wired environment in a single Docker container. The AIO (All-in-One) Sandbox is a containerized environment designed for both human developers and AI agents. Its architecture is built around a "Batteries-Included" philosophy, providing a full Linux desktop-like environment inside a single Docker container. Unified Environment: One Docker container with shared filesystem. Files downloaded in the browser are instantly accessible in Terminal and VSCode. Out of the Box: Built‑in VNC browser, VS Code, Jupyter, file manager, and terminal—accessible directly via API/SDK. Agent-Ready: Pre-configured MCP Server with Browser, File, Terminal, Markdown, Ready-to-use for AI agents. Developer Friendly: Cloud-based VSCode with persistent terminals, intelligent port forwarding, and instant frontend/backend previews. Secure Execution: Isolated Python and Node.js sandboxes. Safe code execution without system risks. Production Ready: Enterprise-grade Docker deployment. Lightweight, scalable. Calling all AI agent developers! How are you securing your builds? Let’s try running your agent in AIO Sandbox and compare notes. AIO Sandbox is open-sourced under the Apache License 2.0. Contributions welcome. GitHub: Official website: #OpenSource# #AIAgent# #Docker#

(1/2) Glad to announce our OpenMAIC! 🎉 Open-sourcing MAIC (Multi-Agent Interactive Classroom) from Tsinghua University — LLM-driven multi-agent classroom for scalable & adaptive online education. 🏗️ Core Architecture: ✅ MAIC-Craft: Read (multimodal extraction) → Plan (course components + agent generation) ✅ Adaptive Engine: Cognitive student modeling + Token-level personalization (RAG + Bloom's/ZPD/UDL) ✅ Multi-Agent Classroom: 1 Student + N Agents (Teacher, Assistant, 4 Peer Archetypes) ✅ Manager Agent: Class state receptor for turn-taking orchestration 🔗 Give it a try 👉🏻 GitHub: #AI# #EdTech# #MultiAgent# #LLM# #Research# #OpenSource# #Tsinghua#