- CSS Sprites - http://css-tricks.com/css-sprites/
- Put CSS at the top of your page
- Reduce number of HTTP requests
- Use a CDN
- Expires/cache-control header
- Specify character set UTF-8 meta tag
- Minify HTML, CSS, and JS (Grunt!) - https://github.com/gruntjs/grunt-contrib-uglify, https://github.com/gruntjs/grunt-contrib-cssmin, and https://github.com/gruntjs/grunt-contrib-htmlmin
- Concatenate CSS and JS (Grunt!) - https://github.com/gruntjs/grunt-contrib-concat
- Enable GZip compression - http://betterexplained.com/articles/how-to-optimize-your-site-with-gzip-compression/
- Use efficient CSS selectors (mainly, don't use too many) - http://csswizardry.com/2011/09/writing-efficient-css-selectors/
| def isolation_level(level): | |
| """Return a Flask view decorator to set SQLAlchemy isolation level | |
| Usage:: | |
| @main.route("/thingy/<id>", methods=["POST"]) | |
| @isolation_level("SERIALIZABLE") | |
| def update_a_thing(id): | |
| ... | |
| """ | |
| def decorator(view): |
| curl -sL https://deb.nodesource.com/setup_0.10 | bash - | |
| apt-get install -y nodejs=0.10.40-* |
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.