It would seem these structural barriers are increasingly become either more porus or more malleable as AI has brought more OSS legal initiatives to empower both attorneys and regular users. The Law and lawyers are being dragged kicking and screaming into e/acc.
I am beginning to notice more "features" in apps that suspiciously raise questions: "Was that feature really needed and was the AI to sneak it in there!?"
This is my reflection as well. I find myself spending MORE time reviewing LLM-generated code and also spending time thinking through LLM generated choices, which, at many times are inefficient or bloated. Keeping the LLM on the right rails takes up more time, even with lengthy agent.md and claude.md files to manage behaviors.
I have always wanted to learn Rust, but was too distracted to get started.
So, I started working with Claude on building a postgres database replication application. I'm learning Postgres internals as well as how brittle database replication and subscription can really be. Although this is for Seren, you can replicate between any PG databases.
https://github.com/serenorg/postgres-seren-replicator
Big learning: Claude Sonnet with Rust is massively productive. I'm impressed, but code bloat is a thing.
Our small team at Seren just launched a landing page and chatbot to talk about agentic databases and see what developers are thinking about them. There is a feature request button that pushes feature ideas to github. Give it a shot if you got a few minutes. We are building on an OpenSource Postgres version for AI agents and are curious what HN is thinking about features.