Google Open Sources Gemma 4 For Private Local AI Workloads - Open Source For You
Google Open Sources Gemma 4 For Private Local AI Workloads Open Source For You
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ARCUS-H: Full Evaluation Results — 979,200 Episodes, 51 RL Policies
We completed a large behavioral stability evaluation of trained RL policies of : 979,200 evaluation episodes across 51 policy configurations , 12 environments, 8 algorithms, and 8 structured stress schedules. Here are three findings that matter for deployment. Finding 1: Reward explains 3.7% of behavioral stability variance. The primary correlation between ARCUS-H stability scores and normalized reward is r = +0.240 [0.111, 0.354], p = 1.1×10⁻⁴ (n = 255 policy-level observations, 2,550 seed-level). R² = 0.057. 94.3% of the variance in how a policy behaves under sensor noise, actuator failure, or reward corruption is not captured by its return in clean conditions. 87% of policies rank differently under ARCUS-H vs reward rankings, with a mean rank shift of 74.4 positions. Finding 2 : SAC’s e
I Renamed All 43 Tools in My MCP Server. Here's Why I Did It Now.
Charlotte has 111 stars. That's not a lot. But it's enough that a breaking change will annoy real people. I shipped one anyway. The naming problem When I started building Charlotte in February, I named every tool with a colon separator: charlotte:navigate , charlotte:observe , charlotte:click . It looked clean. It felt namespaced. Every tool call in every session used it. The problem: the MCP spec restricts tool names to [A-Za-z0-9_.-] . The colon character isn't in that set. It never was. I either didn't check or didn't care at the time. The MCP SDK was lenient about it until v1.26.0, which started emitting validation warnings on every tool registration. I had two options. Fix it now with 111 stars and a handful of active users. Or fix it later with more stars, more users, more documentat
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Claude Code subagent patterns: how to break big tasks into bounded scopes
Claude Code Subagent Patterns: How to Break Big Tasks into Bounded Scopes If you've ever given Claude Code a massive task — "refactor the entire auth system" — and watched it spiral into confusion after 20 minutes, you've hit the core problem: unbounded scope kills context . The solution is subagent patterns: structured ways to decompose work into bounded, parallelizable units. Why Big Tasks Fail in Claude Code Claude Code has a finite context window. When you give it a large task: It reads lots of files → context fills up It loses track of what it read first It starts making contradictory changes You hit the context limit mid-task The session crashes and you lose progress The fix isn't a bigger context window — it's smaller tasks. The Subagent Pattern Instead of one Claude session doing e
I Started Building a Roguelike RPG — Powered by On-Device AI #2
Running On-Device LLM in Unity Android — Everything That Broke (and How I Fixed It) In my last post, I mentioned I was building a roguelike RPG powered by an on-device LLM. This time I'll cover exactly how I did it, what broke, and what the numbers look like. The short version: I got Phi-4-mini running in Unity on a real Android device in one day. It generated valid JSON. It took 8 minutes and 43 seconds. 0. Why This Tech Stack Before the details, here's why I made each choice. Why Phi-4-mini (3.8B)? Microsoft officially distributes it in ONNX format — no conversion work needed. The INT4 quantized version fits in 4.9GB, which is manageable on a 12GB RAM device. At 3.8B parameters, it's roughly the minimum size that can reliably produce structured JSON output. Smaller models tend to fall ap
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