This guide runs CLIProxyAPI in an Amp orb around the clock and publishes it at a stable public HTTPS URL through Tailscale Funnel. An Amp Custom URL model-routing connection then sends Claude requests there, and CLIProxyAPI serves them with your Claude Pro or Max subscription (OAuth login) instead of Amp credits.

The host scripts, templates, and an agent skill that walks you through every step live in ben-vargas/amp-plugins/skills/amp-cli-proxy-api. The easiest path is to install the skill and ask Amp to set it up:

amp skill add --global ben-vargas/amp-plugins/skills/amp-cli-proxy-api

Picking the right architecture = Picking the right battles + Managing trade-offs

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

I've been running Andrej Karpathy's LLM Wiki pattern for several months — reading sources, compiling them into a compounding, git-diffable wiki instead of re-deriving everything from scratch every session — and I'm a genuine convert. But two things kept nagging at me: query salience (finding the right page reliably, not just something plausible) and token economy (not re-reading half the wiki to answer one question).

This is a compiled list of falsehoods programmers tend to believe about working with time.

Don't re-invent a date time library yourself. If you think you understand everything about time, you're probably doing it wrong.

• There are always 24 hours in a day.
• February is always 28 days long.
• Any 24-hour period will always begin and end in the same day (or week, or month).

Contains AI, ML and Full Stack projects with links and also unique ideas without GitHub references. You can pick from here, implement them in your own way, and then add your repository link here later.

So first of all, the reason behind this gist. I thought about something we all have in common as developers, the constant struggle to find project ideas. So for that, I made this gist. It contains all my ideas for some cool, slightly crazy projects.

[!IMPORTANT]

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.

Table of Contents

• General Usage
• Shortcuts
• Commands
• Scripting tmux
• Configuring tmux