brilee6d ago

Eight years of wanting, three months of building with AI

https://lalitm.com/post/building-syntaqlite-ai/

Eight years of wanting, three months of building with AI

For eight years, I’ve wanted a high-quality set of devtools for working with SQLite. Given how important SQLite is to the industry1, I’ve long been puzzled that no one has invested in building a really good developer experience for it2. A couple of weeks ago, after ~250 hours of effort over three months3 on evenings, weekends, and vacation days, I finally released syntaqlite (GitHub), fulfilling this long-held wish. And I believe the main reason this happened was because of AI coding agents4. Of course, there’s no shortage of posts claiming that AI one-shot their project or pushing back and declaring that AI is all slop. I’m going to take a very different approach and, instead, systematically break down my experience building syntaqlite with AI, both where it helped and where it was detrimental. I’ll do this while contextualizing the project and my background so you can independently assess how generalizable this experience was. And whenever I make a claim, I’ll try to back it up with evidence from my project journal, coding transcripts, or commit history5.

Lalit Maganti
Show threadlubujackson

Long term, I think the best value AI gives us is a poweful tool to gain understanding. I think we are going to see deep understanding turn into the output goal of LLMs soon. For example, the blocker on this project was the dense C code with 400 rules. Work with LLMs allowed the structure and understanding to be parsed and used to create the tool, but maybe an even more useful output would be full documentation of the rules and their interactions.

This could likely be extracted much easier now from the new code, but imagine API docs or a mapping of the logical ruleset with interwoven commentary - other devtools could be built easily, bug analysis could be done on the structure of rules independent of code, optimizations could be determined on an architectural level, etc.

LLMs need humans to know what to build. If generating code becomes easy, codifying a flexible context or understanding becomes the goal that amplifies what can be generated without effort.

Apr 5 at 3:48pmWeb