1. Подключаемся к серверу:
Discover gists
| # Make sure you have enough Fertilizer before starting. | |
| def do_maze(iterations=1): | |
| # Define some geometry help for later | |
| opp = {North: South, East: West, | |
| South: North, West: East} | |
| dx = {North: 0, East: 1, South: 0, West: -1} | |
| dy = {North: 1, East: 0, South: -1, West: 0} | |
| # Start a Maze. | |
| harvest() |
| # Claude Code CLI Environment Variables | |
| # This file lists all environment variables used in v2.1.100 with explanations | |
| ## Anthropic API & Authentication | |
| ANTHROPIC_API_KEY - Primary API key for Anthropic's Claude API. Used as fallback when no OAuth token is configured | |
| ANTHROPIC_AUTH_TOKEN - Alternative bearer token for Anthropic services. Takes priority over ANTHROPIC_API_KEY for authorization headers | |
| ANTHROPIC_BASE_URL - Custom base URL for Anthropic API endpoints. Overrides the default api.anthropic.com endpoint | |
| ANTHROPIC_BETAS - Comma-separated list of beta feature headers to include in API requests. Appended to internal beta flags | |
| ANTHROPIC_CUSTOM_HEADERS - Custom HTTP headers for API requests. Newline-separated Key: Value pairs |
| 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" |
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.
Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
I will make this brief: utilising devtools is prohibited on some websites (typically shady websites) for users like you and me. I am writing this description to show you how I was able to navigate with a little bit of Google and redditing. None of the standard browsers Firefox, Google Chrome, Safari, or any other will be able to use this, save from the one I am about to disclose. You need to download LibreWolf; it is a special browser that makes use of the firefox gecko render engine and gives you the ability to change the necessary browser settings.
To install librewolf on your OS see link below
Want to inject some flavor into your everyday text chat? You're in luck! Discord uses Markdown, a simple plain text formatting system that'll help you make your sentences stand out. Here's how to do it! Just add a few characters before & after your desired text to change your text! I'll show you some examples...
What this guide covers: