cedrickchee

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowehre. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture

cedrickchee

Converting mkv to mp4 with ffmpeg

Essentially just copy the existing video and audio stream as is into a new container, no funny business!

The easiest way to "convert" MKV to MP4, is to copy the existing video and audio streams and place them into a new container. This avoids any encoding task and hence no quality will be lost, it is also a fairly quick process and requires very little CPU power. The main factor is disk read/write speed.

With ffmpeg this can be achieved with -c copy. Older examples may use -vcodec copy -acodec copy which does the same thing.

These examples assume ffmpeg is in your PATH. If not just substitute with the full path to your ffmpeg binary.

Single file conversion example

cedrickchee

The not-too-clever programmer

This is a translation of grugbrain.dev into clear English. All props to the original author.

Introduction

This is a collection of thoughts on software development, originally written by an pseudonymous author styling themselves the "grug brain developer," but then translated into clear English by Raph Levien.

I am not an extremely smart developer, but I have many years of experience and have learned some things, although still don't know everything.

cedrickchee

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

cedrickchee

etcd Versus Other Key-value Stores

• What do Etcd, Consul, and Zookeeper do?
  • Service Registration:
    • Host, port number, and sometimes authentication credentials, protocols, versions numbers, and/or environment details.
  • Service Discovery:
    • Ability for client application to query the central registry to learn of service location.
  • Consistent and durable general-purpose K/V store across distributed system.
• Some solutions support this better than others.

cedrickchee

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

cedrickchee

Inspired by dannyfritz/commit-message-emoji

See also gitmoji.

Commit type	Emoji
Initial commit	🎉 :tada:
Version tag	🔖 :bookmark:
New feature	✨ :sparkles:
Bugfix	🐛 :bug:

cedrickchee

Summary of "Clean Code" by Robert C. Martin

A summary of the main ideas from the "Clean Code: A Handbook of Agile Software Craftsmanship" book by Robert C. Martin (aka. Uncle Bob).

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

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General rules

cedrickchee

This gist was being mentioned by Jeremy in fast.ai deep learning part 2 2018, lesson 9 video. In this lesson, we are learning multi-class object detection (computer vision) using the pascal-multi.ipynb notebook.

One of the fast.ai's students pointed out that by using Pandas, we can do things much simpler than using Python collections.defaultdict and shared this gist.

>The more you get to know Pandas, the more often you realize it is a good way to solve lots of different problems.

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Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites