veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

joelonsql

🗺🐘 1000+ PostgreSQL EXTENSIONs

This is a list of URLs to PostgreSQL EXTENSION repos, listed in alphabetical order of parent repo, with active forks listed under each parent.

⭐️ >= 10 stars
⭐️⭐️ >= 100 stars
⭐️⭐️⭐️ >= 1000 stars
Numbers of stars might not be up-to-date.

amodm

On predictability of seemingly simple code

This post is in response to the WeekendDevPuzzle of 2022-02-05, which has submissions from people who chose to share their perspectives on the puzzle. I'll be linking this post to this Twitter thread, to avoid polluting the original one.

Motivation for today's topic

We all have a mental model of how something works. For today's puzzle, the relevant mental model is: the same series of instructions should result in the same time of execution. The astute would be quick to add: in non-noisy environments only. And they'd be correct.

But then where's the flaw in this model? Let's start.

Dissecting the puzzle

The puzzle seems simple enough: a program reads in an array of integers, and counts how many of them are less than a threshold (another parameter to the program). We've been informed that there are no noisy-neighbour issues, and we're measu

thomaspoignant

GOCMD=go
GOTEST=$(GOCMD) test
GOVET=$(GOCMD) vet
BINARY_NAME=example
VERSION?=0.0.0
SERVICE_PORT?=3000
DOCKER_REGISTRY?= #if set it should finished by /
EXPORT_RESULT?=false # for CI please set EXPORT_RESULT to true

GREEN  := $(shell tput -Txterm setaf 2)

lattner

Swift Concurrency Manifesto

Author: Chris Lattner

Chinese Translation by Jason Yu

Contents

• Introduction
• Overall Vision

JakeWharton

import java.util.Arrays;

class NonCapturing {
  public static void main(String... args) {
    run(new Runnable() {
      @Override public void run() {
        System.out.println("Hey!");
      }
    });
  }

daniellevass

<?xml version="1.0" encoding="utf-8"?>
<resources>

<!-- google's material design colours from 
http://www.google.com/design/spec/style/color.html#color-ui-color-palette -->

    <!--reds-->
    <color name="md_red_50">#FFEBEE</color>
    <color name="md_red_100">#FFCDD2</color>
    <color name="md_red_200">#EF9A9A</color>

zst123

import android.content.Context;
import android.graphics.Canvas;
import android.graphics.Color;
import android.graphics.Matrix;
import android.graphics.Paint;
import android.graphics.Path;
import android.graphics.RectF;
import android.util.AttributeSet;
import android.view.View;
import android.view.animation.AccelerateDecelerateInterpolator;

chanks

Turning PostgreSQL into a queue serving 10,000 jobs per second

RDBMS-based job queues have been criticized recently for being unable to handle heavy loads. And they deserve it, to some extent, because the queries used to safely lock a job have been pretty hairy. SELECT FOR UPDATE followed by an UPDATE works fine at first, but then you add more workers, and each is trying to SELECT FOR UPDATE the same row (and maybe throwing NOWAIT in there, then catching the errors and retrying), and things slow down.

On top of that, they have to actually update the row to mark it as locked, so the rest of your workers are sitting there waiting while one of them propagates its lock to disk (and the disks of however many servers you're replicating to). QueueClassic got some mileage out of the novel idea of randomly picking a row near the front of the queue to lock, but I can't still seem to get more than an an extra few hundred jobs per second out of it under heavy load.

So, many developers have started going straight t

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD