| Filter | Description | Example |
|---|---|---|
| allintext | Searches for occurrences of all the keywords given. | allintext:"keyword" |
| intext | Searches for the occurrences of keywords all at once or one at a time. | intext:"keyword" |
| inurl | Searches for a URL matching one of the keywords. | inurl:"keyword" |
| allinurl | Searches for a URL matching all the keywords in the query. | allinurl:"keyword" |
| intitle | Searches for occurrences of keywords in title all or one. | intitle:"keyword" |
Discover gists
| /* eslint-disable unicorn/no-null */ | |
| /* | |
| * Resetting window.location between tests is unfortunately a hard topic with JSDOM. | |
| * | |
| * https://gist.github.com/tkrotoff/52f4a29e919445d6e97f9a9e44ada449 | |
| * | |
| * FIXME JSDOM leaves the history in place after every test, so the history will be dirty. | |
| * Also its implementations for window.location and window.history are lacking. | |
| * - https://github.com/jsdom/jsdom/blob/22.1.0/lib/jsdom/living/window/Location-impl.js |
| Xperia 1 VI - XQ-EC72 HK | |
| firmware update notes | |
| This is for a HongKong device in the USA. | |
| https://xdaforums.com/t/tool-xperifirm-xperia-firmware-downloader-v5-8-1.2834142/ | |
| https://xdaforums.com/t/tool-newflasher-xperia-command-line-flasher.3619426/ | |
| https://xdaforums.com/t/tool-unsin-sin-v3-v4-v5-unpacker-v2-0.3128106/ | |
| ------------------------------------------- | |
| xperia boot modes: |
다음 레퍼런스를 바탕으로, 내 코딩 에이전트 설정을 토큰 효율 관점에서 점검해줘.
- https://code.claude.com/docs/en/settings.md
- https://code.claude.com/docs/en/env-vars.md
- https://developers.openai.com/codex/config-reference.md
- https://github.com/cnighswonger/claude-code-cache-fix
목표는 성능 저하를 크게 만들지 않으면서 토큰 낭비를 줄이는 거야.
- 직접 절약(자동 주입 문맥, 긴 툴 출력 제한 등)과 간접 절약(검색/IDE/앱 경로 차단 등)을 구분해서 봐줘
- 현재 버전 공식 문서나 현재 설치본에서 확인되지 않은 키는 절대 추천하지 마
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.
| #!/bin/bash | |
| ############################################### | |
| # CONFIGURATION | |
| ############################################### | |
| PORTAINER_URL="https://portainer-host:40005" | |
| PORTAINER_COMPOSE_DIR="/mnt/data/containers/portainer/compose" | |
| DOCKHAND_STACK_DIR="/mnt/data/containers/dockhand/stacks/Env" |
Before we look at some common commands, I just want to note a few keyboard commands that are very helpful:
Up Arrow: Will show your last commandDown Arrow: Will show your next commandTab: Will auto-complete your commandCtrl + L: Will clear the screen
| blueprint: | |
| name: Energy Disaggregation | |
| description: Approximate power usage of an appliance | |
| domain: automation | |
| input: | |
| power_helper: | |
| name: Input helper to store the approximate power usage | |
| description: in W | |
| selector: | |
| entity: |