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867 postsLocal Motion Brings Local LLM Agents to Cursor and VS Code
Local Motion auto-picks a compatible local coding model on Mac, manages memory, and links to Cursor via tunnel or runs fully local inside VS Code.
Code or Diffusion: A Field Guide to Programmatic Image Generation
Generating diagrams and charts as code instead of diffusion cuts costs and produces testable, diffable, version-controlled image assets.
Making Documentation AI-Agent Ready with llms.txt
RevoGrid's team explains how llms.txt, llms-full.txt and Skill bundles work together to help AI coding agents find accurate documentation.
CommitBrief — AI code reviews, right in your terminal
A provider-agnostic, local-first CLI that reviews your staged changes, a historic range, or a whole GitHub pull request. Zero telemetry, no server. Free and open source.
commitbrief.comEdge Gateway Architecture: Diskless Design and the Rust DashMap Trap
A deep dive into Cloudflare-style edge gateway architecture: data/control plane separation, in-memory TLS, WASM sandboxing, and the Rust DashMap deadlock pitfall.
aeovim: Orchestrating Claude Code agents like Neovim
aeovim is a Rust TUI that applies Neovim's modal, split-based workflow to LLM coding agents, letting developers orchestrate multiple Claude Code sessions from the keyboard.
Open-Source QuadRF Sees WiFi Through Walls, Tracks Drones
QuadRF, an open-source phased-array radio built on Raspberry Pi 5 and FPGA, can see WiFi traffic through walls and detect drones in flight.
Token Governor: The Smart Gateway That Cuts LLM Costs
Token Governor re-engineers LLM prompts to spend the fewest tokens needed, acting as a governing gateway that works with Anthropic, OpenAI, Gemini and any compatible provider.
Unified Memory: Why Mini PCs Run 70B Models a Big GPU Can't
Unified-memory mini PCs like AMD's Strix Halo can load 70B-parameter models that a $2,000 RTX 5090 cannot fit. Here's why capacity and bandwidth pull in opposite directions.
Scarf Moves From Haskell to Python in the AI Coding Era
Scarf explains why faster LLM-driven code generation made Haskell's long compile times and ecosystem friction unsustainable, prompting a move to Python.
Multimodal Models Fail at Sampling, Not Understanding
Multimodal models aren't bottlenecked by capability but by sampling defaults—frame rate, chunking, cropping—that silently limit what they perceive.