karpathy

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.

hamelsmu

PRD: VibeTUI — Terminal UI for Managing Coding Agent Sessions

Introduction

VibeTUI is a terminal-based user interface (TUI) for managing multiple coding agent sessions running in tmux. It provides a unified dashboard to monitor, create, and switch between sessions running Amp, Claude Code, OpenCode, and Codex. The tool is designed for developers who run multiple AI coding agents in parallel and need visibility into what each agent is doing.

VibeTUI runs on a server accessed via SSH, displaying a two-pane interface: a collapsible sidebar showing all sessions with their status, and a main pane displaying the active agent's terminal output.

Goals

t3dotgg

// Types for the result object with discriminated union
type Success<T> = {
  data: T;
  error: null;
};

type Failure<E> = {
  data: null;
  error: E;
};

disler

Minimal Prompt Chainables

Sequential prompt chaining in one method with context and output back-referencing.

Files

• main.py - start here - full example using MinimalChainable from chain.py to build a sequential prompt chain
• chain.py - contains zero library minimal prompt chain class
• chain_test.py - tests for chain.py, you can ignore this
• requirements.py - python requirements

Setup

disler

This is not working complete code.

This is strictly a v0.2, scrapy, proof of concept version of a personal AI Assistant working end to end in just ~726 LOC.

This is the second iteration showcasing the two-way prompt aka multi-step human in the loop. The initial, v0, assistant version is here.

It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.

To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.

disler

This is not working complete code.

This is strictly a v0, scrapy, proof of concept for the first version of a personal AI Assistant working end to end in just ~322 LOC.

It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.

To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.

Stay focused, keep building.

jaens

/*
   Copyright 2024, Jaen - https://github.com/jaens

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software

disler

What is an AI Coding Assistant?

If the coding assistant can't run ITERATIVE CRUD on ALL of your code, it's not a True AI Coding Assistant (TACA)

Standards for TRUE AI Coding Assistants

1. Must work on existing codebases
2. Must have a file context mechanism
3. Must be iteratively controllable

ninely

"""This is an example of how to use async langchain with fastapi and return a streaming response.
The latest version of Langchain has improved its compatibility with asynchronous FastAPI,
making it easier to implement streaming functionality in your applications.
"""
import asyncio
import os
from typing import AsyncIterable, Awaitable

import uvicorn
from dotenv import load_dotenv

aaronrogers

import { BaseLLM } from 'langchain/llms'
import { CallbackManager } from 'langchain/callbacks'
import { LLMResult } from 'langchain/schema'
import { encoding_for_model, TiktokenModel } from '@dqbd/tiktoken'

export class AzureLLM extends BaseLLM {
  name = 'AzureLLM'
  batchSize = 20
  temperature: number
  concurrency?: number