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

koenvo

import time

# Make sure you have duckdb==0.7.0. Earlier versions might fail with GIL problems ( https://twitter.com/mr_le_fox/status/1620535141675433986 )
import duckdb

import s3fs

from fsspec.implementations.cached import SimpleCacheFileSystem

# Create the s3 file system. This one does not have caching

mheffner

app = "mastiff"
primary_region = "iad"
kill_signal = "SIGINT"
kill_timeout = 5
processes = []

[build]
  image = "tootsuite/mastodon:v3.5.5"

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.

IanColdwater

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

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djspiewak

Quick Tips for Fast Code on the JVM

I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.

This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea

yoyama

/**
  * Generate Case class from DataFrame.schema
  *
  *  val df:DataFrame = ...
  *
  *  val s2cc = new Schema2CaseClass
  *  import s2cc.implicit._
  *
  *  println(s2cc.schemaToCaseClass(df.schema, "MyClass"))
  *

iamhatesz

import io.circe._
import io.circe.generic.auto._
import io.circe.parser._

val jsonPoint = "{ \"type\": \"Point\", \"coordinates\": [100.0, 0.0] }"
val jsonLineString = "{ \"type\": \"LineString\",\n    \"coordinates\": [ [100.0, 0.0], [101.0, 1.0] ]\n    }"
val jsonPolygon = "{ \"type\": \"Polygon\",\n    \"coordinates\": [\n      [ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0] ]\n      ]\n   }"
val jsonPolygonWithHoles = "{ \"type\": \"Polygon\",\n    \"coordinates\": [\n      [ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0] ],\n      [ [100.2, 0.2], [100.8, 0.2], [100.8, 0.8], [100.2, 0.8], [100.2, 0.2] ]\n      ]\n   }"
val jsonGC = "{ \"type\": \"GeometryCollection\",\n    \"geometries\": [\n      { \"type\": \"Point\",\n        \"coordinates\": [100.0, 0.0]\n        },\n      { \"type\": \"LineString\",\n        \"coordinates\": [ [101.0, 0.0], [102.0, 1.0] ]\n        }\n    ]\n  }"

pathikrit

Libraries

• Web Server: Play (framework) or http4s (library)
• Actors: akka
• Asynchronous Programming: monix (for tasks, reactors, observables, scheduler etc)
• Authentication: Silhouette
• Authorization: Deadbolt
• Command-line option parsing: case-app
• CSV Parsing: kantan.csv
• DB: doobie (for PostgreSQL)

GAME_MASTER_v0_1.protobuf

Result: 1
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  Badge {
    BadgeType: BADGE_BATTLE_ATTACK_WON
    BadgeRanks: 4
    Targets: "\nd\350\007"
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}
Items {