TL;DR: Revived my dead startup with AI and open sourced 99% of it.
For a bit of background here, I built Molecule.dev back in 2021 before AI was really a thing. It worked by allowing you to select the stack, libraries, and features you wanted, and then it would cherry pick a series of carefully crafted git commits to produce a fully functional app and API based on your selection. The idea was to provide polished code (using best practices at the time) that easily fit into common workflows of professional teams to help them quickly scale. It was probably too ambitious for the time, so after failing to find product-market fit fast enough, I very dangerously ran out of runway and had to scrap it.
Fast forward to early 2026... Opus 4.5 was out and after playing around with it a bit, it became clear that the original vision of decoupled, easily swappable stacks, features, and integrations was now possible. Long story short, I pointed Opus at Molecule v1's codebase and explained the design patterns, where I wanted to go with it, and have been grinding away at it nonstop ever since.
Turns out that v1's cherry-pickable architecture translates pretty well to code that weak LLMs can understand and work with. This makes it possible to very cheaply integrate/swap full-stack features and functionality common to almost every app, end-to-end, and the result is polished, predictable, and works immediately... so people no longer need to waste a bunch of time and tokens generating/testing/fixing semi-random unpolished code for core functionality. As you might expect, the AI generated code within Molecule's own packages isn't always the best, but I'm certain that we will solve that problem with more time and tokens, as the Molecule "bond" pattern helps enforce better code and architecture.
The goal here is to help people (tech savvy or not) build higher quality software faster and cheaper, allowing them to focus on solving real problems instead of dealing with the mundane work that makes the last 10% of building a real app such an unexpected pain. Hopefully it will help reduce the "AI slop" type apps by giving people more time and energy to build things that are more unique but still provide all of the common functionality we expect from polished software. Ideally, some years from now, no one (not even LLMs) will need to think about how some common core functionality should be implemented. We should be able to look back and say "problem solved".
There is still a lot to do (with some really cool/useful stuff planned for the future!) and it's definitely rough around the edges at the moment, but I figured I'd go ahead and share v2 as a proof of concept.
For a bit of background here, I built Molecule.dev back in 2021 before AI was really a thing. It worked by allowing you to select the stack, libraries, and features you wanted, and then it would cherry pick a series of carefully crafted git commits to produce a fully functional app and API based on your selection. The idea was to provide polished code (using best practices at the time) that easily fit into common workflows of professional teams to help them quickly scale. It was probably too ambitious for the time, so after failing to find product-market fit fast enough, I very dangerously ran out of runway and had to scrap it.
Fast forward to early 2026... Opus 4.5 was out and after playing around with it a bit, it became clear that the original vision of decoupled, easily swappable stacks, features, and integrations was now possible. Long story short, I pointed Opus at Molecule v1's codebase and explained the design patterns, where I wanted to go with it, and have been grinding away at it nonstop ever since.
Turns out that v1's cherry-pickable architecture translates pretty well to code that weak LLMs can understand and work with. This makes it possible to very cheaply integrate/swap full-stack features and functionality common to almost every app, end-to-end, and the result is polished, predictable, and works immediately... so people no longer need to waste a bunch of time and tokens generating/testing/fixing semi-random unpolished code for core functionality. As you might expect, the AI generated code within Molecule's own packages isn't always the best, but I'm certain that we will solve that problem with more time and tokens, as the Molecule "bond" pattern helps enforce better code and architecture.
The goal here is to help people (tech savvy or not) build higher quality software faster and cheaper, allowing them to focus on solving real problems instead of dealing with the mundane work that makes the last 10% of building a real app such an unexpected pain. Hopefully it will help reduce the "AI slop" type apps by giving people more time and energy to build things that are more unique but still provide all of the common functionality we expect from polished software. Ideally, some years from now, no one (not even LLMs) will need to think about how some common core functionality should be implemented. We should be able to look back and say "problem solved".
There is still a lot to do (with some really cool/useful stuff planned for the future!) and it's definitely rough around the edges at the moment, but I figured I'd go ahead and share v2 as a proof of concept.