tetsuo (@tetsuoai)

grok plugin marketplace add 7etsuo/write-legible-c grok plugin install write-legible-c --trust Grok Build with Grok 4.5 is producing the most beautiful C I have ever seen come out of a model. Every function under 25 lines with one job. Nesting never past depth 2. Zero naked literals. Every failure greps to a single producing line and the happy path runs straight down the left margin. Code you can read at 2am with the pager going off. It took a standard to get there. I shipped it as a Grok Build plugin. write-legible-c gates Grok with strict C11 machine legibility every time it touches a .c or .h file. Every function is an orchestrator, a leaf, or an adapter, never a mix. A TRY macro that makes the forgotten check bug impossible to hide. An 18 item checklist the model runs before it shows you code. Clean code is a performance feature too: rewrites that cut model perceived complexity lift LLM task performance by up to 21 percent. Full 18 section standard inside.

This is insanely cool. Set this up in Grok Build! Unity shipped a free terminal Unity CLI for coding agents, CI, and custom tooling. MCP stays supported, the CLI includes MCP Mode for existing MCP agents, and Unity says both the CLI and MCP are free with no concurrency limits on the MCP server. Dan got Grok 4.5 via Grok Build to install the CLI, build a 3D scene, and export a video. "Same session shipped a playable macOS Island Explorer with Poly Haven CC0 trees, rocks, and cliffs, Crest FFT ocean with shoreline foam, irregular hills, grounded props, FPS walk, and a half-size performance pass when the scene got heavy." -- Daniel Farinax

Clone any voice. Keep it local. Voice Clone Lab turns 5–15 minutes of audio into a voice model on your own GPU, cleanup, transcription, fine-tuning, and generation included. Open source: github/tetsuo-ai/voice_clone_lab

I used Grok 4.5 in Grok Build to create a game engine for agentic coding, then built an ARPG on it, with all game assets generated via Grok Imagine in Grok Build. AI agents can build worlds, combat, loot, progression, maps, tests, and playable games. ⚒️🎮

The Long-Horizon Terminal-Bench paper landed around May and concluded that the results showed headroom for improvement. The best of the 15 models they tested finished seven of the 46 tasks, and the mean across all models was about two. That ceiling is what fifth place looks like on the current board. Grok 4.5 is now at 13, and Fable 5 is at 12. A single task costs around 9.9M tokens, 231 episodes, and 85 minutes of wall clock time. That means agents are holding a plan across all of it and finishing, and that capability nearly doubled in two months. SpaceXAI is on top, and they marketed the 4.2x output token efficiency, which undersells it. Two dollars in, six out, per million. On a benchmark where one task burns ten million tokens, the bill is dominated by input replay, and they say Grok 4.5 solves tasks in under half the number of steps, so there is less accumulated context to resend on every call. The efficiency compounds on the input side, which is the side that costs money. Fable 5 is one task behind. Their own launch chart has them losing DeepSWE 1.1 to Fable by 17 points, and Grok 4.20 sits on this same board at 0.080 with zero completions, so whatever happened in 4.5 is not a family trait. My read is that the 4.5 jump came out of training alongside Cursor, which is a stream of real agentic edit trajectories nobody else has at that volume, and nothing in the counterevidence argues against it compounding into the next checkpoint.

Grok 4.5 in Grok Build created an FPS game in under an hour. The prompt was simple. I told it to write a game design document and pull free assets from the web. Then I had it create a TODO.md with implementation phases and run a loop to build out each phase. SpaceXAI and Cursor cooked here. This model is insane.

Grok 4.5 goes public tomorrow. Here’s everything that’s known. The model runs on V9, xAI’s new 1.5 trillion parameter foundation, roughly three times the size of the v8-small architecture behind Grok 4.3. It’s the largest model the company has shipped, trained on Blackwell GPUs at the Colossus cluster in Memphis. Coding is the clear focus. Cursor’s developer data was added in supplemental training, and reinforcement learning has been improving the model daily alongside the Grok Build harness. The next foundation model already in training will have Cursor data included from the start of pre-training. Elon calls it Opus-level, and says it’s faster, more token-efficient, and cheaper. It’s been in private beta at SpaceX and Tesla since June 28, with early internal evals reportedly at or above that mark. Public benchmarks land once the model does. This is the first flagship under SpaceXAI since the merger, and the plan is a from-scratch foundation model shipping every month through the end of 2026. Tomorrow we get to use it!

The hard part of training agents is the infrastructure: long rollouts, slow environments, tool calls, API delays, and keeping training compute busy while all of that happens. Berkeley CS294-196

X just launched hosted MCP servers so AI tools can connect directly to the platform. Connect Grok Build, Cursor, Claude, VS Code, or any MCP client to two official servers: • X MCP (httpx://api.x.com/mcp) search posts, manage bookmarks, fetch trends/news, and draft/publish Articles with your account permissions. • Docs MCP (httpx://docs.x.com/mcp) instantly search and read X API docs inside your workflow. Quick Grok setup: X Developer Portal: httpx://developer.x.com/ Before you begin. Create an X app in the X Developer Portal with OAuth 2.0 enabled. Register the redirect URI https://localhost:8080/callback on the app. Copy your CLIENT_ID and CLIENT_SECRET. Replace YOUR_X_APP_CLIENT_ID and YOUR_X_APP_CLIENT_SECRET in the attached image with these. Run the commands from: [Image attached at bottom] Then verify: grok mcp doctor xapi grok mcp list One-time browser login on first run. Tokens cached and auto-refreshed locally after. Big unlock for agentic systems that need live X data.

Benjamin Bycroft’s 3D interactive tool visualizes an LLM’s layers, weights and matrix operations. This is the best educational tool to understand how an LLM works i've seen. httpx://bbycroft.net/llm