kanavarora

/*
Utility to analyze bundle chunks over versions.

Assumes: webpack has already created the bundle summary json file -> stats.json
Parameters: 
	version: (Optional) a string that labels the current bundle with the version
	         provided and saves the summary in a csv file.

Output:
	If run the first time, generates a csv file bundleAnalaysis.csv, which 

developit

import { h, Component } from 'preact';


/** Creates a new store, which is a tiny evented state container.
 *  @example
 *    let store = createStore();
 *    store.subscribe( state => console.log(state) );
 *    store.setState({ a: 'b' });   // logs { a: 'b' }
 *    store.setState({ c: 'd' });   // logs { c: 'd' }
 */

clarkbw

const timing = store => next => action => {
  performance.mark(`${action.type}_start`);
  let result = next(action);
  performance.mark(`${action.type}_end`);
  performance.measure(
    `${action.type}`,
    `${action.type}_start`,
    `${action.type}_end`
  );
  return result;

nitin42

const some_module = require('some_module')

/**
 * require('some_module') calls Module._load
 * 
 * Module._load then tries to load the module with a filename (also save it to the cache) using module.load(filename)
 *
 * module.load(filename), given a filename, passes it to the proper extension handler ('.js', '.json')
 *
 * If there were any errors when loading the file, it deletes the file from the cache (delete Module._cache[filename]) and throws an error

nzakas

Tech Lead Book

1. Introduction
2. What is a Tech Lead?
3. The Tech Lead Mindset
4. Becoming a Tech Lead
5. Working Cross-Functionally
6. The Art of Delegation
7. Technical Best Practices
8. Effective Decision Making

francisrstokes

import React from 'react';

export class StateDispatcher extends React.Component {
  constructor(props) {
    super(props);
    this.state = props.state || {};
    this._dispatch = this.dispatch.bind(this);
  }

  dispatch(action) {

stettix

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.

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.