This is a very brief tutorial on getting OpenWRT installed on a regular computer, it assumes you know your way around Linux. If you find this and need additional details, please like, subscribe, and comm... oh wait this isn't youtube, just comment.
Updated: Current version is 22.03.5, this was originally written for 18.6.x in 2019.
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.
The session model (Fable) is the orchestrator, not the worker. Fable is the most expensive tier; spend its tokens only on decomposition, judgment calls, synthesis, and talking to the user. Delegate everything else to subagents via the Agent tool, picking the cheapest model that can do the job well:
model: "opus" — the default worker tier. Anything requiring real judgment: implementation, debugging, architecture-aware exploration, adversarial review.
model: "sonnet" — cheap tier for mechanical or low-stakes work: running tests and reporting output, simple greps/lookups with a known target, rote refactors from an exact spec, formatting, screenshot capture, admin chores. If getting it slightly wrong is cheap to catch, use sonnet.
Exploration/research: never read broadly yourself. Spawn Explore agents (model: opus) with tightly scoped questions; consume their synthesized reports, not raw files. Trivial "find the file that defines X" lookups can go to sonnet.
Stop re-explaining yourself every session. This prompt makes Claude interview you (one question at a time, checkboxes included) and then compile everything into a personal CLAUDE.md — so every new session starts like Claude already knows you.
Interview me to build my personal CLAUDE.md context file.
Rules:
Options for connecting to Xcode's MCP server from an agent on a different machine on your network.
| IPv6 | 国家 | 省份 | 城市 | 学校/教育机构 | |
|---|---|---|---|---|---|
| 2001:da8:0200::/48 | 中国 | 北京 | 北京 | 清华大学 | |
| 2001:da8:0201::/48 | 中国 | 北京 | 北京 | 北京大学 | |
| 2001:da8:0202::/48 | 中国 | 北京 | 北京 | 北京邮电大学 | |
| 2001:da8:0203::/48 | 中国 | 北京 | 北京 | 北京航空航天大学 | |
| 2001:da8:0204::/48 | 中国 | 北京 | 北京 | 北京理工大学 | |
| 2001:da8:0205::/48 | 中国 | 北京 | 北京 | 北京交通大学 | |
| 2001:da8:0206::/48 | 中国 | 北京 | 北京 | 北京城市学院 | |
| 2001:da8:0207::/48 | 中国 | 北京 | 北京 | 北京师范大学 | |
| 2001:da8:0208::/48 | 中国 | 北京 | 北京 | 北京科技大学 |
On macOS Tahoe (26.x, tested on 26.3 / M3 MacBook Air), system audio gradually degrades over time — volume drops significantly and sound becomes muffled across all outputs (built-in speakers, Bluetooth headphones, etc.) at the same volume setting.
coreaudiod sometimes shows elevated CPU usage during the degraded state.