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

jpenalbae

#!/bin/bash

# Video Quality
# The range of the CRF scale is 0–51, where 0 is lossless, 23 is the default,
# and 51 is worst quality possible. A lower value generally leads to higher
# quality, and a subjectively sane range is 17–28
QUALITY=28

# check if slop command exists
if ! command -v slop &> /dev/null

eugeneyan

Mandelbrot in Mojo with Python plots

Not only Mojo is great for writing high-performance code, but it also allows us to leverage huge Python ecosystem of libraries and tools. With seamless Python interoperability, Mojo can use Python for what it's good at, especially GUIs, without sacrificing performance in critical code. Let's take the classic Mandelbrot set algorithm and implement it in Mojo.

We'll introduce a Complex type and use it in our implementation.

Mandelbrot in python

kellabyte

History

For a long time I've been really impacted by the ease of use Cassandra and CockroachDB bring to operating a data store at scale. While these systems have very different tradeoffs what they have in common is how easy it is to deploy and operate a cluster. I have experience with them with cluster sizes in the dozens, hundreds, or even thousands of nodes and in comparison to some other clustered technologies they get you far pretty fast. They have sane defaults that provide scale and high availability to people that wouldn't always understand how to achieve it with more complex systems. People can get pretty far before they have to become experts. When you start needing more extreme usage you will need to become an expert of the system just like any other piece of infrastructure. But what I really love about these systems is it makes geo-aware data placement, GDPR concerns potentially simplified and data replication and movement a breeze most of the time.

Several years ago the great [Andy Gross](ht

jcrist

"""A quick benchmark comparing the performance of:

- msgspec: https://github.com/jcrist/msgspec
- pydantic V1: https://docs.pydantic.dev/1.10/
- pydantic V2: https://docs.pydantic.dev/dev-v2/

The benchmark is modified from the one in the msgspec repo here:
https://github.com/jcrist/msgspec/blob/main/benchmarks/bench_validation.py

I make no claims that it's illustrative of all use cases. I wrote this up

nymous

Logging setup for FastAPI

This logging setup configures Structlog to output pretty logs in development, and JSON log lines in production.

Then, you can use Structlog loggers or standard logging loggers, and they both will be processed by the Structlog pipeline (see the hello() endpoint for reference). That way any log generated by your dependencies will also be processed and enriched, even if they know nothing about Structlog!

Requests are assigned a correlation ID with the asgi-correlation-id middleware (either captured from incoming request or generated on the fly). All logs are linked to the correlation ID, and to the Datadog trace/span if instrumented. This data "global to the request" is stored in context vars, and automatically added to all logs produced during the request thanks to Structlog. You can add to these "global local variables" at any point in an endpoint with `structlog.contextvars.bind_contextvars(custom

kjmph

-- Based off IETF draft, https://datatracker.ietf.org/doc/draft-peabody-dispatch-new-uuid-format/

create or replace function uuid_generate_v7()
returns uuid
as $$
begin
  -- use random v4 uuid as starting point (which has the same variant we need)
  -- then overlay timestamp
  -- then set version 7 by flipping the 2 and 1 bit in the version 4 string
  return encode(

inscapist

Development environment with Nix Flakes and Direnv

This document is targeted at those who seek to build reproducible dev environment across machines, OS, and time.

It maybe easier for remote teams to work together and not spending hours each person setting up asdf/pyenv/rbenv, LSP servers, linters, runtime/libs. Nix is probably the closest thing to Docker in terms of development environment.

Flake is used here because it provides hermetic build, with absolutely no reliance on system environment (be it Arch/Catalina/Mojave). Also it freezes dependencies in flake.lock so builds are reproducible.

This gist provides the setup to develop Java/Clojure/Python applications on Nix. But it can be easily adapted to ruby, nodejs, haskell.

sandys

why this code ?

• tiangolo/full-stack-fastapi-template#104
• tiangolo/fastapi#551

P.S. How to quickly run postgres (using docker)

prologic

Learn Go in ~5mins

This is inspired by A half-hour to learn Rust and Zig in 30 minutes.

Basics

Your first Go program as a classical "Hello World" is pretty simple:

First we create a workspace for our project: