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LLM Wiki

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.

The core idea

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.

Wolfram Ravenwolf on X: "I'm now using Qwen3-Coder in Claude Code. Works with any model actually, but this is surely the best one currently. There are a bunch of proxies on GitHub that make this possible, but none worked well enough for me, so I implemented this myself using LiteLLM. Guide in comments: https://t.co/Wqbv75nxlp" / X
HOWTO: Use Qwen3-Coder (or any other LLM) with Claude Code (via LiteLLM) : r/LocalLLaMA

Here's a simple way for Claude Code users to switch from the costly Claude models to the newly released SOTA open-source/weights coding model, Qwen3-Coder, via OpenRouter using LiteLLM on your local machine.

This process is quite universal and can be easily adapted to suit your needs. Feel free to explore other models (including local ones) as well as different providers and coding agents.

I'm sharing what works for me. This gu

The Autonomous Agent Prompting Framework

This repository contains a disciplined, evidence-first prompting framework designed to elevate an Agentic AI from a simple command executor to an Autonomous Principal Engineer.

The philosophy is simple: Autonomy through discipline. Trust through verification.

This framework is not just a collection of prompts; it is a complete operational system for managing AI agents. It enforces a rigorous workflow of reconnaissance, planning, safe execution, and self-improvement, ensuring every action the agent takes is deliberate, verifiable, and aligned with senior engineering best practices.

I also have Claude Code prompting for your reference: https://gist.github.com/aashari/1c38e8c7766b5ba81c3a0d4d124a2f58

Exploring Tokenizers from Hugging Face

Hugging Face (HF) has made NLP (Natural Language Processing) a breeze. In this post, we are going to take a look at tokenization using a hands on approach with the help of the Tokenizers library. We are going to load a real world dataset containing 10-K filings of public firms and see how to train a tokenizer from scratch based on the BERT tokenization scheme. In the process we will understand tokenization in detail and some gotchas to keep an eye out for.

Background on NLP (Optional)

If you already have an understanding of the NLP pipeline, you can safely skip this section.

For any NLP task, one of the first steps is pre-processing the data so that it can be fed into our NLP models. For those new to NLP, the general pipeline for any NLP task (text classification, question answering, etc.) is as follows:

Things I believe

This is a collection of the things I believe about software development. I have worked for years building backend and data processing systems, so read the below within that context.

Agree? Disagree? Feel free to let me know at @JanStette.

Fundamentals

Keep it simple, stupid. You ain't gonna need it.

Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

	# Claude Code CLI Environment Variables
	# This file lists all environment variables used in v2.1.118 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_CONFIG_DIR - Override Anthropic config directory. Falls back to XDG_CONFIG_HOME/anthropic, then HOME/.config/anthropic

	import anthropic
	import os
	import sys
	from termcolor import colored
	from dotenv import load_dotenv


	class ClaudeAgent:
	def __init__(self, api_key=None, model="claude-3-7-sonnet-20250219", max_tokens=4000):
	"""Initialize the Claude agent with API key and model."""

	# Windsurf Memory Bank

	I am Windsurf, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

	## Memory Bank Structure

	The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:

	```mermaid
	flowchart TD

	import openai
	import pinecone
	from sentence_transformers import SentenceTransformer

	class GPTConversationManager:
	def __init__(self, api_key, pinecone_api_key, index_name):
	self.api_key = api_key
	openai.api_key = self.api_key
	self.conversation_history = []
	self.pinecone_api_key = pinecone_api_key