import requests, os from dotenv import load_dotenv
load_dotenv()
resp = requests.post( "https://api.github.com/gists", headers={"Authorization": f"Bearer {os.getenv('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')}"}, json={ "description": "My gist",
import requests, os from dotenv import load_dotenv
load_dotenv()
resp = requests.post( "https://api.github.com/gists", headers={"Authorization": f"Bearer {os.getenv('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')}"}, json={ "description": "My gist",
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.
Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.
Warning
I see many people proposing solutions for other platforms (such as Windows or macOS). I cannot verify the authenticity of those methods, and you should not copy, paste, and run code from random people on the internet without first understanding what it does. Ideally, review every line carefully and, even then, run it with caution in a sandboxed environment.
I personally experienced slowdowns and issues while using the ja-netfilter agent.
I decided to investigate how JetBrains checks license validity, because despite explicitly configuring JetBrains to work offline, it still attempts to validate licenses.
Here are my findings: