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.

0xdevalias

AI Agent Rule / Instruction / Context files / etc

Some notes on AI Agent Rule / Instruction / Context files / etc.

Table of Contents

• AGENTS.md
• AGENT.md (Legacy)
• DESIGN.md

Fanman03

UniFi AP Buyers Guide

(updated May 2025)

The APs in this list are ordered from highest to lowest performance. However, unless you have a very high number of devices you likely do NOT need to buy the most expensive, highest performance AP. You can also check Ebay for deals, especially on older equipment.

All APs in this list support both wired backhaul and mesh modes. However, wired connections are strongly recommended for better performance and reliability.

Number of spatial streams are listed in order of 2.4GHz, 5GHz, 6GHz.

Good Choices:

kvpb

Nemo64

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lucjross

resource "aws_alb_listener_rule" "http_80" {
  count = local.lb ? 1 : 0
  listener_arn = data.aws_alb_listener.http_80.arn
  action {
    type = "forward"
    target_group_arn = aws_alb_target_group.http_80[0].arn
  }
  condition {
    host_header {
      values = [local.host_name]

IanColdwater

Mute these words in your settings here: https://twitter.com/settings/muted_keywords

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jamiegs

Adaptive Container Scaling

Problems with AWS's recommended scaling method

• Based on a fixed percentage of memory and CPU reservation.
• Scaling on both cpu and memory metrics can cause the metrics to fight with each other where it'll scale out then back in and repeat.
• If you're scaling threshold is at 80% memory, current reservation utilization is at 79% and you deploy a container that requires 25% memory, It will fail to launch.
• ASGs are not container aware.

Solution

dayne

Install linux subsystem Ubuntu 18.04

See Microsoft's WSL install guide for windows 10 for details.

Work-in-progress - command line only install:

• Open Powershell as Administrator and run:
  Enable-WindowsOptionalFeature -Online -FeatureName Microsoft-Windows-Subsystem-Linux
• Open Windows App Store
  • Search for "Ubuntu" and install Ubuntu 18.04

(work in progress - command line install steps):

eugenestarchenko

Deploy updates to aws ecs services based on github commits. Posts deploy notifications to slack.

Assumptions:

• Task definitions map 1:1 with services, and they have the same name.
• Code is stored on github
• You want to deploy the latest commit in a branch
• Docker images are tagged with commit SHA
• Docker images are stored on AWS ECR