I've used the analogy of a circular saw before ("it's not really sawing, because you can't feel the wood...")

It's easy to just slab on a Skil saw, but through the beam and it'll be somewhat straight. But when every manual stroke counts, there's enough time on a human time scale to correct every little mistake. It's definitely possible to become skilled at using the circular saw, but it takes effort that it feels like you don't need at first.

This is similar. LLMs are so powerful for writing code that it's easy to become complacent and forget your role as the engineer using the tool: guaranteeing correctness, security, safety and performance of the end result. When you're not invested in every if-statement, forgetting to check edge cases is really easy to do. And as much as I like Claude writing test cases for me, I also have to ensure the coverage is decent, that the implicit assumptions made about external library code is correct, etc. It takes a lot of effort to do it right. I don't know why Mycelium thinks they invented interfaces for module boundaries, but I'm pretty sure they are still as suceptible to that "0" not behaving as you'd expect, or the empty string being interpreted as "missing." Or the CSG algorithm working, except if your hole edges are incident with some boundary edges.

I have no idea what am I reading

> Mycelium structures applications as directed graphs of pure data transformations. Each node (cell) has explicit input/output schemas. Cells are developed and tested in complete isolation, then composed into workflows that are validated at compile time. Routing between cells is determined by dispatch predicates defined at the workflow level — handlers compute data, the graph decides where it goes.

No still don't understand

> Mycelium uses Maestro state machines and Malli contracts to define "The Law of the Graph," providing a high-integrity environment where humans architect and AI agents implement.

Nope, still don't

How is that different compared to a human dev that would miss bugs?

[dead]

It's starting to become obvious that if you can't effectively use AI to build systems it is a skill issue. At the moment it is a mysterious, occasionally fickle, tool - but if you provide the correct feedback mechanisms and provide small tweaks and context at idiosyncrasies, it's possible to get agents to reliably build very complex systems.