Zach Williams zachwill

Slack System Design: A Grounded Teardown

A reverse-engineered system design of Slack's web application, built from live network traffic analysis of the authenticated Enterprise Grid experience. 200+ API calls captured across boot, search, messaging, reactions, and navigation. Every backend service named.

Architecture Overview

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│  BROWSER (Gantry v2 SPA)                                                    │
│                                                                             │

https://x.com/VictorTaelin/status/2001777678765129832

Opus 4.5 - chat log

 * ▐▛███▜▌ *   Claude Code v2.0.72
* ▝▜█████▛▘ *  Opus 4.5 · Claude Max
 *  ▘▘ ▝▝  *   ~/vic/dev/hvm4-claude

> # Task 1: print unscoped lambdas and floating dups with matching names

Why did you do this?

Relax, I only have one Sunday to work on idea, literally my weekend project. So I tried Deepseek to see if it can help. Surprisingly, it works and it saves me another weekend...

What is your setup?

Just chat.deepseek.com (cost = free) with prompts adapted from this gist.

Does it work in one-shot or I have to prompt it multiple times?

Add it to the body

<body hx-ext="hx-astro-view-transition">

An example

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

You probably don't know how to do Prompt Engineering

(This post could also be titled "Features missing from most LLM front-ends that should exist")

Apologies for the snarky title, but there has been a huge amount of discussion around so called "Prompt Engineering" these past few months on all kinds of platforms. Much of it is coming from individuals who are peddling around an awful lot of "Prompting" and very little "Engineering".

Most of these discussions are little more than users finding that writing more creative and complicated prompts can help them solve a task that a more simple prompt was unable to help with. I claim this is not Prompt Engineering. This is not to say that crafting good prompts is not a difficult task, but it does not involve doing any kind of sophisticated modifications to general "template" of a prompt.

Others, who I think do deserve to call themselves "Prompt Engineers" (and an awful lot more than that), have been writing about and utilizing the rich new eco-system

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 nowhere. 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.

	[
	"fa-classic fa-solid fa-0 fa-fw",
	"fa-classic fa-solid fa-1 fa-fw",
	"fa-classic fa-solid fa-2 fa-fw",
	"fa-classic fa-solid fa-3 fa-fw",
	"fa-classic fa-solid fa-4 fa-fw",
	"fa-brands fa-42-group fa-fw",
	"fa-classic fa-solid fa-5 fa-fw",
	"fa-brands fa-500px fa-fw",
	"fa-classic fa-solid fa-6 fa-fw",

	# /// script
	# requires-python = ">=3.12"
	# dependencies = [
	# "llm",
	# "textual",
	# ]
	# ///
	from textual import on, work
	from textual.app import App, ComposeResult
	from textual.widgets import Header, Input, Footer, Markdown

	import json
	import logging
	import re
	from typing import Any, AsyncGenerator, Optional, Union

	import aiohttp
	import openai
	from azure.search.documents.aio import SearchClient
	from azure.search.documents.models import QueryType