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Introducing the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. AI is only as smart as the context we give it. As we build more advanced, agentic AI systems, they need accurate metadata and context to be useful. But in most organizations, that context is locked inside fragmented data catalogs, isolated wikis, scattered code comments, or the minds of senior engineers. Every time a new AI agent is built, teams are forced to solve the exact same context-assembly problem from scratch. To solve this, we've announced OKF, a vendor-neutral, open specification that formalizes the "LLM-wiki pattern" into a portable, interoperable format. It provides a standardized way to represent the enterprise knowledge that modern AI systems rely on. — Just markdown: readable in any editor, renderable on GitHub, indexable by any search tool — Just files: shippable as a tarball, hostable in any git repo, mountable on any filesystem — Just YAML frontmatter: for the small set of structured fields that need to be queryable: type, title, description, resource, tags, and timestamp We’ve also shipped reference implementations to help you hit the ground running, including an enrichment agent for BigQuery, a static HTML visualizer, and live sample bundles on @github → ➕ Knowledge Catalog can now natively ingest OKF! Stop reinventing data models and building bespoke integrations for every new AI tool. Here's more about how OKF works →

GLM-5.2 (Max) by @Zai_org ranks #10# on the new Agent Arena leaderboard, closely matching Claude-Opus-4.8 (non-thinking) and is the #1# open model by a wide margin! In Agent Arena, we measure models on millions of real-world, long-horizon agentic tasks from a global community of users. Models can access web search, filesystem, and terminal tools to complete complex workflows. The leaderboard measures model performance on outcomes relative to the average model using a causal tracing methodology. Compared to 5.1, GLM-5.2 (Max) climbs from #13# to #10#. Its clearest gains are confirmed task success, and user praise vs. complaint. Bash capabilities and tool hallucination remain stable. There is a tradeoff in steerability compared to the previous model (-6.0% vs. +1.2%). GLM-5.2 remains the same price as GLM-5.1, $1.4/$4.4 per input/output MTokens. 1M context window. Huge congrats @Zai_org for the incredible release! See thread for details on how GLM-5.2 (Max) performs across 5 different signals.

Linux File System Architecture - Beginner Friendly Explained Linux File System Architecture Chapter 1 — The Root Directory. Everything in Linux begins with this single directory. Chapter 2 — Why the Linux Hierarchy Matters Chapter 3 — /bin and /sbin directories - /bin directory contains essential command-line programs /sbin contains administrative commands used mostly by the root user. Chapter 4 — /etc Configuration Philosophy - stores system configuration files Chapter 5 — /home and /root directories. /home directory stores user data /root - home directory for the root administrator account Chapter 6 — Everything Is a File Chapter 7 — /dev directory. hardware devices appear as files. Chapter 8 — /proc directory. a virtual filesystem generated dynamically by the Linux kernel Chapter 9 — /var dir. Runtime Data including log files Chapter 10 — /tmp dir. Temporary Files Chapter 11 — /usr dir . Including User Applications Chapter 12 — /lib Shared Libraries Chapter 13 — /boot and the Startup Process Chapter 14 — /mnt dir and Mount Philosophy Chapter 15 — /sys and Modern Kernel Interfaces Final Chapter — Why Linux Won

How powerful is Gemini 3.5 Flash? In a recent research preview, we pushed it to the extreme, powering 93 subagents across 15,314 model calls that wrote a custom kernel, filesystem, and drivers from scratch. 12 hours later, it booted Doom. 🖥️ This is just a glimpse of the model's reasoning depth and code generation capabilities. Full breakdown below 👇

Mole, a macOS cleaner that can free up tens of GBs in one go. The CLI is also updated and will always be free and open source. But if you prefer a polished GUI with more fun stuff coming, give a try. Mole V1.39.0 Fox 🦊 What's new: 1. Orphan detection now respects matching .app bundles and Homebrew casks 2. UTM / Lima cache cleanup support 3. --dry-run and real clean fully aligned on protected services 4. simctl probe timeout fix for cold boot 5. Gradle DSL caches preserved for build integrity 6. mo purge hardened against hangs on slow filesystems 7. mo touchid PAM writes use atomic install for safer permissions 8. Uninstall bundle ID matching switched to boundary-aware, no more accidental hits

Security in OpenClaw is getting sharper 🦞 🔒 fs-safe for root-bounded filesystem 🌐 Proxyline for policy-driven network egress 📦 ClawHub trust evidence 🛡️ smarter command approvals Powerful agents need guardrails you can actually audit.

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#

seeing a pattern shift in how I use agents for long-horizon tasks in 2026 vs. how i do deep research in 2025. 2025's DR: search → read → reason → repeat until done. Everything hits the web on every loop. but 2026 long-horizon tasks have two distinct phases. - Phase 1: Web IO for research & planning. Search, read, reason. but the goal isn't to produce an answer directly. It's to materialize web knowledge into local files (.md/.json/.csv). - Phase 2: Agent "mounts" the files and starts the loop. The agent reads, runs, writes against those local files only. No more web calls for grounding. Why I cut web grounding IO in Phase 2? • Determinism: local files are immutable snapshots. Web content shifts, 404s, hits paywalls • Speed: filesystem reads are ms, web fetches are seconds. Agent loops need tight iteration • Consistency: cross-checking requires operating on the same knowledge base, not fetching different versions each time • Cost: web IO burns tokens parsing HTML noise. Local files are already clean

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#