🛰️ AI Brief · no ads, no hype 📌 4 stories · model watch Claude Fable 5.5 · Claude Opus 5.5 · GPT-6 Astra lead the model watch 8 exact names, 1 official launch, 1 only announced, 3 unverified claims. Claude Fable 5.5 leads the watch — what would you actually re-run first? 🧬 In this brief: Claude Fable 5.5 · Claude Opus 5.5 · GPT-6 Astra · GPT-6.1 Terra · LongCat-2.5 · DeepSeek Harness · Qwen3.5-0.8B · GPT-5.5 ────────────── 1️⃣ Anthropic Fable 5.5 and Opus 5.5 checkpoints surface in S-1 filing updates ◻️ Leaked checkpoint · unconfirmed · S-1 filing amendments and leaker screenshots, unverified ▸ Anthropic amended its S-1 filing to indicate that its internal estimates show models may soon cross responsible scaling policy thresholds for accelerated progress. ▸ New checkpoints for Claude Fable 5.5 and Claude Opus 5.5 were showcased in community evaluations and comparison tests against competing models. So what: Check the amended S-1 filing for updated scaling policy definitions and test Fable 5.5 on 3D generation tasks. ────────────── 2️⃣ GPT-6 Astra and unannounced Bel checkpoint solve Navier-Stokes in benchmark tests 🎬 Demo / benchmark · vendor conference presentation and official benchmark graphs ▸ OpenAI demonstrated a swarm of 10,000 AI agents working autonomously for four days to tackle Navier-Stokes equations. ▸ GPT-6 Astra and upcoming GPT-6.1 Terra models were highlighted with new smart routing systems to optimize latency and compute costs. So what: Re-run open math benchmark tests using agent swarms to evaluate multi-agent coordination. ────────────── 3️⃣ LongCat-2.5 enters benchmarks with 1.6 trillion total parameters 📄 Announced · not shipped yet · vendor documentation and metrics dashboard ▸ A new natively multimodal model named LongCat-2.5 was shown with 1.6 trillion total parameters and 48 billion active parameters during inference. ▸ Benchmark scores show LongCat-2.5 competing closely across terminal, program, and visual coding benchmarks. So what: Review LongCat-2.5 benchmark metrics on terminal and program tasks to compare inference efficiency. ────────────── 4️⃣ DeepSeek Harness introduces Creator Mode for custom plugins ✅ Official release · official platform release notes and documentation ▸ DeepSeek Harness launched Creator Mode, enabling developers to turn ideas into functional plugins directly within the harness environment. ▸ New workspace templates for design studios, sandbox games, and 3D LLM visualizers were added to the platform. So what: Build a custom plugin inside DeepSeek Harness using Creator Mode and test its agent registration. ────────────── 🎯 Why developers should care Developers need to track newly surfaced checkpoints, weight-space programming breakthroughs, and high-parameter agentic models before they hit production. ────────────── 🔍 Receipts ✅ 1 official · 📄 1 filing / announced, not shipped · 🔬 1 research / demo · ◻️ 1 unverified Only ✅ is an officially confirmed launch. ────────────── 💬 Your take? Which of these newly surfaced checkpoints or coding harnesses would you test first in your workflow? ❤️ React or leave a comment — it helps us pick the next brief. 📣 @GitHub_Open_Source — daily open-source picks ======================================== #AINews #OpenSource

🤖 Mika · 📦 wangzan101/Mika Mika runs an on-device AI agent for Android 11+ that controls apps and the web on a hidden background screen. ────────────── ⭐ 6 024 stars · 🍴 1 172 forks 🟣 Kotlin · 📜 Apache-2.0 · 🟢 commit today 👥 42 contributors You need to pull data across multiple mobile apps or complete repetitive forms, constantly switching windows and tapping through menus by hand. Every minor automation task forces you to manually drive each app interface, wasting time on repetitive taps, scrolls, and text entry. Mika takes over the app navigation on a separate background screen while keeping your main interface free, though payments and logins still require your explicit confirmation. 🧭 How it works Mika functions as an on-device AI assistant for Android by combining a background screen rendering engine with visual UI parsing and custom model integrations. Instead of relying on rigid per-app APIs, it observes screen elements to interact with any application or website directly. It supports various provider API keys or direct account logins, storing all user data and conversation history locally on the device. ✨ Key features ▸ Operate any Android app or website through visual screen parsing without app-specific APIs ▸ Run tasks on a hidden background screen so you can continue using your phone normally ▸ Execute Python, Node, and shell scripts in an arm64 Linux sandbox per conversation ▸ Turn completed tasks into reusable skills and remember user preferences over time ▸ Support preset configurations for major model providers or custom OpenAI-compatible endpoints 🎯 Who should try it Android users and developers looking for a local AI agent to automate mobile app workflows. 📌 Why it matters now On-device processing improvements and flexible LLM providers make fully autonomous mobile UI agents practical on modern Android hardware. ✅ Verdict A powerful local automation tool for Android power users who don't mind setting up wireless debugging and API keys. 🚀 Quick start Download the APK from the GitHub releases page, configure your model provider in Settings, and follow the wireless debugging setup wizard. ────────────── 💬 Your take? Which Android app would you automate first with Mika? ❤️ React if you want more Kotlin tools like this. 🔗 GitHub repository 📖 Full breakdown 📣 @GitHub_Open_Source — daily open-source picks ======================================== #opensource #kotlin #ai #llm

🎮 horizon · 📦 horizon-rust/horizon Rewrite Minecraft Java Edition in Rust with 281 differential harnesses checking vanilla parity. ────────────── ⭐ 6 226 stars · 🍴 854 forks 🦀 Rust · 📜 MIT · 🟢 commit today 👥 219 contributors You sit down to spin up a heavy modded Minecraft session, only to watch the JVM consume gigabytes of memory while garbage collection pauses stutter your frame rate. Running large modpacks or multiple instances burns through system memory, forces endless JVM tuning, and makes programmatic bot integration painfully slow. Horizon rewrites the entire engine in Rust, eliminating the JVM bottleneck and adding native agent hooks, while still inheriting standard asset requirements from your existing game copy. 🧭 How it works Horizon implements Minecraft Java Edition from scratch in Rust, sitting on top of modern graphics APIs like Vulkan and Wgpu while relying on a custom launcher to prepare game assets from an existing user installation. It bypasses the Java virtual machine entirely to deliver native performance, custom graphics presets, and distant terrain rendering. AI agents can also interact with the engine natively via a built-in control interface. ✨ Key features ▸ Run vanilla world generation, mobs, redstone, and commands without Java ▸ Control the game programmatically or over MCP using horizon-ctl ▸ Render distant terrain up to 1024 chunks using modern graphics APIs ▸ Import CurseForge and Modrinth modpacks containing content and data packs ▸ Manage and host servers locally or on a VPS without Java dependencies 🎯 Who should try it Rust developers and Minecraft enthusiasts wanting native performance and programmatic agent control. 📌 Why it matters now The maturity of modern graphics stacks and differential testing tools makes rewriting complex Java game engines in safe systems languages a practical reality. ✅ Verdict An impressive engineering feat for systems developers and power users, though still tracking hundreds of open parity gaps. 🚀 Quick start cargo run --release -p horizon-client ────────────── 💬 Your take? Which modpack would you test first on a non-JVM engine? 🔖 Save it for later — and drop your use case in the comments. 🔗 GitHub repository 📖 Full breakdown 🌐 Project homepage 📣 @GitHub_Open_Source — daily open-source picks ======================================== #opensource #rust #devtools #differential_testing