🧃 Introducing stereOS: a Linux based operating system hardened... and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️show more

Introducing Agent Sandbox, the infinite simulation playground for agents... on Virtuals. Craft the perfect autonomous agent in our Sandbox with full control over its personality and goals. Enhance your agent with unique abilities by creating custom functions so they can trade onchain, generate memes, control physical robots and more. The Sandbox is available to all builders with graduated agents in the Developer Panel. For those who want to give it a spin without an existing agent, fret not. Try it out today at and join our Discord ( to jam with like-minded builders. Next stop, Society of Agents.show more

day 23 of forking Paperclip today i added knowledge... bases for projects and you company. based on Andrej Karpathy idea below. added a 'system chores' and 'agent chores' to manage memory. after each heartbeat run memories get added ('agent chores') and every 4 hours memeries get managed. This is in a db with an API for all agents to use and is seperated form the agents memeroes which they get at instantiation time.show more

Fundamentals Live for Agent Liquidity Engine (ALE) Now everyone... can directly see the top agents leading in cumulative revenue, and exactly how much they have executed in all-time buybacks, through the Fundamentals board on the Virtuals landing page (Fundamentals currently only applicable to agents in ALE). Virtuals is built to support agents that solve real problems and sustain their own economies. The Agent Liquidity Engine exists to surface and scale those teams by: •Showcasing product strength that demonstrates clear utility •Tracking sustainable revenue that proves product–market fit •Highlighting agents that reinvest to reinforce their ecosystems By routing revenue back to scale the product and publishing these metrics, ALE turns long-term economic strength into an on-chain, verifiable signal. If your agent delivers real usage and sustainable revenue and you want to join ALE, please apply here: Our team will review and contact you if your project qualifies. ALE is where the agent economy separates hype from sustainability.show more

this might be the E2B killer for AI agent... sandboxes. forkd is an open-source microVM sandbox runtime built on Firecracker, made for AI agent fan-out, code interpreters, eval harnesses, anything that spins up a lot of short-lived sandboxes. before: every sandbox cold-boots its own VM and re-imports the whole runtime from scratch, numpy, torch, JIT compilation, model weights, all of it. now: forkd boots one parent VM once, warms it with your runtime already imported, then forks children from that snapshot using copy-on-write memory. the repo's own benchmark: spawning 100 sandboxes takes 101ms with forkd, versus 759ms for a raw Firecracker cold-boot, and well over a minute for Docker or gVisor. the SDK is a literal drop-in for E2B's Python client, so if you're already running code-interpreter agents on it, swapping the import line gets you a self-hosted runtime with the same isolation model at a fraction of the per-sandbox cost. pre-built recipes ship for e2b-style code interpreters, Jupyter kernels, SWE-bench coding agents, and Playwright browser fan-outshow more

herdr 0.7.0 is out, and it's a major one:... it introduces plugins! the idea is simple: herdr stays lean, and everything custom gets extended through plugins. shareable, scoped, built however you want, to fit your own flow. with this release we're also shipping a few examples of what the plugin system can do. first up: a telegram plugin. herdr already controls your agents and knows their status, so the plugin just hooks into agent events and pings telegram the moment one needs you. notification lands → `herdr --remote` or ssh from your phone → straight back to the agent that needs you.show more

We now support 30+ agent harness integrations within OpenKnowledge.... This is enabled by the Agent Client Protocol (ACP), which let us build our custom UI experience while fully leveraging the agents on your computer. Also powers our new Follow Mode and Comments features. Follow mode automatically navigates the IDE to the files being edited by agents, and animates agent changes so it's easy to follow along. Comments let you annotate and mark up specs, blogs, and other documents and send all the feedback in one go to agents. Now enabled in the Mac, Linux or Windows apps. ~Day 3/5 of Launch Week~show more

We are proud to introduce our new product, It... is a Virtual Machine specifically designed for Apple Silicon and AI Agents. This is our very first release, and it fully supports macOS, Windows, and Linux with GPU acceleration. - Liquid Glass, - GPU Accelerator. - MCP for AI Agent on your Mac or inside the VM.show more

Replit, Vercel, and OpenAI have built very cool agent-native... applications, but nobody else has passed the demo stage. Building agents that work is complex. Teams aren't shipping agents because we don't have good tooling yet (and most of us don't know how to do this well.) A couple of days ago, the CopilotKit🪁 team announced a collaboration with . You can now use LangGraph with CoAgents to build agent-native applications, and here is everything you need to know about that: CoAgents is fully open-source, and you can use it to do the following: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Start this GitHub Repository: Thanks to the team for giving me early access and collaborating with me on this post.show more

The Next 100 Days: Our Campaign Forward 🌐 LayerAI... is the most user-rich web3 AI ecosystem. 12 products with over 2 million combined users. We are now approaching the verge of releasing mainnet versions of products used by 6+ billion people globally: 1. laiOS The world’s first AI-enabled operating system. Pluggable by hardware manufacturers across the world. The equivalent of Google’s Android, but for the AI economy. An OS for trillions of devices. 2. aiSEARCH The world’s AI-native web3 browser system, a competitor of Open AI's Search. The vision: the only browser with a native wallet, payment system, and AI Search, powering the future of AI Agents in the workforce. Built for AI markets: AI2AI transactions are the future of global commerce. Our Campaign Forward starts tomorrow. 100 Day Journey to 1 Billion AI Agents on LayerAI.show more

Seems like Visual Studio Code is starting to tell... you: your agent primitives need to move. There is now a new migration banner in the Chat panel, and it is part of a much bigger change happening under the hood: the move from the old Local harness to the new Agent Host architecture built around AHP. This is not just about moving where an agent runs. The old model was very VS Code-centric: prompts, custom agents, instructions and skills could live in VS Code-specific locations and the agent runtime lived inside the extension host. The new Agent Host separates the agent runtime from the editor. Sessions can keep running when the window closes, be shared across VS Code windows, run remotely, and support different harnesses such as Copilot, CLI and Copilot Desktop App through a common session layer. And that means some of our primitives need to move too. Prompt files are being deprecated for Agent Host and migrated to Skills. User-level agents and instructions that lived in VS Code profile storage need to move to harness-supported locations. Even the old location settings are being deprecated. The new migration experience can detect these things and guide you through moving or converting them, while keeping the originals unless you explicitly remove them. The new banner is basically the first visible sign that this migration is becoming a real product workflow. Basically telling us - it's time to move on!!!! If you have accumulated a lot of prompts, custom agents, instructions and skills over the last year, now is probably a good time to understand where they actually live and which harness owns them. To summarize the shift - it isn't just: VS Code Chat → Agent Host It is: VS Code-specific primitives → harness-native primitives. And I think this is going to become increasingly important as agents stop being features inside an IDE and become runtimes that multiple clients can connect to. Go run your migrations now 🏃‍♀️show more

Zuckerberg built his own AI agent to run Meta.... this man is literally becoming Tony Stark. it pulls data from every team inside the company so he can skip meetings, skip the chain of command, and make decisions faster than any human process allows. 78,000 employees have their own AI agents now too. one messages coworkers on your behalf. another acts as your AI chief of staff. their agents talk to each other in an internal network. humans optional. Meta also bought an entire social media platform built for AI agents to interact with each other. read that again. Zuck said he wants every person at Meta to have a personal AI agent. then every person outside Meta. the Jarvis era started.show more

Introducing the Agent Virtual Machine (AVM) Think V8 for... agents. AI agents are currently running on your computer with no unified security, no resource limits, and no visibility into what data they're sending out. Every agent framework builds its own security model, its own sandboxing, its own permission system. You configure each one separately. You audit each one separately. You hope you didn't miss anything in any of them. The AVM changes this. It's a single runtime daemon (avmd) that sits between every agent framework and your operating system. Install it once, configure one policy file, and every agent on your machine runs inside it - regardless of which framework built it. The AVM enforces security (91-pattern injection scanner, tool/file/network ACLs, approval prompts), protects your privacy (classifies every outbound byte for PII, credentials, and financial data - blocks or alerts in real-time), and governs resources (you say "50% CPU, 4GB RAM" and the AVM fair-shares it across all agents, halting any that exceed their budget). One config. One audit command. One kill switch. The architectural model is V8 for agents. Chrome, Node.js, and Deno are different products but they share V8 as their execution engine. Agent frameworks bring the UX. The AVM brings the trust. Where needed, AVM can also generate zero-knowledge proofs of agent execution via 25 purpose-built opcodes and 6 proof systems, providing the foundational pillar for the agent-to-agent economy. AVM v0.1.0 - Changelog - Security gate: 5-layer injection scanner with 91 compiled regex patterns. Every input and output scanned. Fail-closed - nothing passes without clearing the gate. - Privacy layer: Classifies all outbound data for PII, credentials, and financial info (27 detection patterns + Luhn validation). Block, ask, warn, or allow per category. Tamper-evident hash-chained log of every egress event. - Resource governor: User sets system-wide caps (CPU/memory/disk/network). AVM fair-shares across all agents. Gas budget per agent - when gas runs out, execution halts. No agent starves your machine. - Sandbox execution: Real code execution in isolated process sandboxes (rlimits, env sanitization) or Docker containers (--cap-drop ALL, --network none, --read-only). AVM auto-selects the tier - agents never choose their own sandbox. - Approval flow: Dangerous operations (file writes, shell commands, network requests) trigger interactive approval prompts. 5-minute timeout auto-denies. Every decision logged. - CLI dashboard: hyperspace-avm top shows all running agents, resource usage, gas budgets, security events, and privacy stats in one live-updating screen. - Node.js SDK: Zero-dependency hyperspace/avm package. AVM.tryConnect() for graceful fallback - if avmd isn't running, the agent framework uses its own execution path. OpenClaw adapter example included. - One config for all agents: ~/.hyperspace/avm-policy.json governs every agent framework on your machine. One file. One audit. One kill switch.show more

Our marketing team is just me and ~40 AI... agents. I finally got around to putting them into an "org chart", and it's actually really cool to see! Plus, laying them out this way by sub-function (social media, blog, email, community, partners, etc) has given me a bunch of ideas of other agents I want to build. If you're interested in the full version (and the templates/screenshots for each agent), let me know and I'll send it over to you.show more

Maestro was rebuilt around agents. Not as an AI... company. We don’t sell AI products. Maestro Machina is the operating layer underneath the business: -How we write software -Run departments -Keep every product detail, client conversation, and decision in one shared knowledge base Builder agents watch code quality and deployments in a loop that improves itself. Product agents sit inside design, sales, onboarding, and research. Knowledge agents make the company’s memory available to both. Humans still set the intent. Agents industrialize the work. One system. One mission. Build a financial system backed by energy and anchored by Bitcoin.show more

The Hive's latest agent architecture upgrade better leverages the... reasoning capabilities of LLMs. Our vision for DeFAI vision centers on a network of specialized agents coordinated by a single user-facing orchestrator. By focusing each specialized agent on dedicated tasks while enabling inter-agent collaboration, the system becomes more adaptable and efficient at handling user requests. These improvements will become clear as we roll out new tools in the coming days. Stay tuned 🐝show more

AI agents can now control robots! For ClawCon, we... integrated OpenClaw🦞 and Robot Operating System (ROS) - the largest open-source robotics stack powering millions of robots worldwide. If you had an autonomous agent IRL, what would you make it do?show more

EIP-8004 is coming to the Nova architecture, a trustless... infrastructure for AI agents that introduces key on-chain registries, enabling agents to interact safely across the Shido Network. These core components allow autonomous AI agents to verify identity, build reputation, and collaborate without relying on a centralized platform. The result is a decentralized trust layer for agent-to-agent economies, where agents can autonomously discover, evaluate, and work with one another across the Shido ecosystem.show more