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.

emschwartz

This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.

Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.

cb372

TL;DR

We should use a type parameter with a context bound (e.g. F[_]: Sync) in library code so users can choose their IO monad, but we should use a concrete IO monad in application code.

Abstracting over IO

If you're writing a library that makes use of effects, it makes sense to use the cats-effect type classes so users can choose their IO monad (IO, ZIO, Monix Task, etc).

So instead of

jeroenr

sealed trait ApiModel

case class ClickData(
                       cid: String, // GA client id
                       timestamp: Option[Long]
                     ) extends ApiModel

object ApiModel extends DefaultJsonProtocol {
  implicit val ClickDataFormat = jsonFormat2(ClickData)
}

dongjinleekr

## Consumer Throughput: Single consumer thread, no compression
## Consumer Throughput: 3 consumer thread, no compression

bin/kafka-consumer-perf-test.sh --topic benchmark-3-3-none \
--zookeeper kafka-zk-1:2181,kafka-zk-2:2181,kafka-zk-3:2181 \
--messages 15000000 \
--threads 1

0xjac

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

1. Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare [email protected]:usi-systems/easytrace.git

patpohler

Big List of Real Estate APIs

Listings / Property Data

####Rets Rabbit http://www.retsrabbit.com

Rets Rabbit removes the nightmare of importing thousands of real estate listings and photos from RETS or ListHub and gives you an easy to use import and Web API server so you can focus on building your listing search powered website or app.

leonardofed

A curated list of AWS resources to prepare for the AWS Certifications

A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.

---

daniel-barlow

What is this about?

Convert your ruby app to run on JRuby (often fairly painless) then deploy it by generating a JAR file (should be reasonably painless) and copying it to the servers you're running on (should be bordering on trivial).

If you've never done Java programming, a JAR is a "Java ARchive" file which is basically a single ZIP file containing all the stuff your app needs to run. So you can install java on a new machine and copy the JAR file onto it, and you're ready to go.

What's wrong with how we do it now?

The usual way of deploying a Ruby app is to write (or copypasta) some Capistrano recipes to have the production machines check it out from a git repo then build it in exactly the same way you do on your Mac. This has downsides:

telent

App configuration in environment variables: for and against

For (some of these as per the 12 factor principles) 

1) they are are easy to change between deploys without changing any code

2) unlike config files, there is little chance of them being checked
into the code repo accidentally

3) unlike custom config files, or other config mechanisms such as Java