One shell script that makes a rooted LG webOS TV lighter and quieter:
Nothing is deleted and the root filesystem is never touched. It only edits
| mkdir test-laya | |
| cd test-laya | |
| uv init | |
| uv add 'laya-coreml[demo]' | |
| hf download aac6fef/laya-multilingual-coreml-ane --local-dir models/snake | |
| uv run laya-coreml-snake --model models/snake |
Reference architecture for running a coding agent in an isolated container with controlled read/write access to a host home directory, versioned via git, and routed through a local gateway to remote models.
Design template. Adapt values, paths, and tool choices to the target environment.
| name | ASD-STE100 |
|---|---|
| description | Simplified Technical English for agent output — one meaning per word, active voice, simple tenses, short sentences |
| keep-coding-instructions | true |
You write all prose output in Simplified Technical English, adapted from the ASD-STE100 standard (Issue 9). The aerospace industry built this standard so that a reader cannot misread an instruction. Apply the same discipline to everything you write to the user: answers, summaries, status updates, explanations, and instructions.
| #!/usr/bin/env python3 | |
| """ | |
| Antigravity to Antigravity IDE Migration Tool | |
| ============================================== | |
| A self-contained script to automatically migrate extensions, custom settings, keybindings, | |
| snippets, workspace states, and entire conversation histories from Antigravity v1 | |
| to the new Antigravity IDE. | |
| Author: Antigravity AI Coding Assistant (pair-programmed with USER) | |
| License: MIT |
The orchestrator IS the loop. It runs in-context, delegates every implementation, debug, doc-update, and commit action to tiered sub-agents, enforces the clean-worktree hard invariant after every task, and iterates against
IMPLEMENTATION_PLAN.mduntil every task is marked[x].
Native Anthropic endpoints, tool-call compatibility, and context-window sizing for local Claude Code.
Last tested: April 2026 for the backend matrix; October 2026 for startup mocks and five offline acceptance fixtures. No new hardware/model coding benchmark. See Changelog at the bottom.
If this saves you setup time, follow @renezander030 — practical local AI / coding-agent infrastructure notes, weekly.
Full repo with scripts: github.com/renezander030/local-ai-coding-stack >
Bind approval to the exact action, preserve the review evidence, and recheck authority before dispatch.
Last tested: October 2026 — pinned agent-approval-gate v3.1.0 validator, 79 tests and targeted negative fixtures; no live provider dispatch.
Follow @renezander030 for executable approval and audit contracts.
Schemas, examples and validator: agent-approval-gate v3.1.0.
A single, opinionated reference for using Claude Code (the CLI) and extending it with Skills (reusable workflows). Curated from Anthropic's official docs, the Claude Code CLI surface, and the Complete Guide to Building Skills for Claude.
This document has two parts that complement each other:
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
