Honestly, I’ve had a rather interesting experience with AI. I was very adverse to LLM usage at first. Later I sort of figured out that I was more adverse to the energy around AI than I am AI itself.

I knew the models sucked at large tasks. Trying to get an edge on the matter though, I started asking myself, how can I get the model to perform better? I figured I could pass over the AI hate stage and get right into the AI professional stage… at least a head start.

So I began experimenting with local LLMs, LLM harnesses, and various governance tools like jai. I decided against Claude Code and Cortex because they’re provider specific — instead using OpenCode so that I can use whichever model I desire. Then I began building out a SKILL.md repository for tightly scoped tasks like change-review, security-analysis, refactor, architecture-review, grill-me, feature-design, …

I’m still thinking through some of the project needs. I want something that lets an agent work, while treating the agent as a kind of helpful adversary. You should be able to configure workloads that designate models, context, available tooling, skills, permissions, session length, inference level, acceptance criteria, and human-review stages. It would also allow for session switching, model switching, agent deliverable handoff to another agent, … not to mention, your VCS should know and respond appropriately if an agent ever pushes code. Don’t trust it by default.

These workloads should be version controllable, benchmarked, …

Anyway, a lot of that is speculative. Just where I’m at now, controlling context and skills manually, I’m already seeing much better results.

And no, I don’t have the AI do everything. I just find smarter ways to decompose “everything” into much smaller tasks that are easier to review and scrutinize.

But also, I push for local model usage in my organization. I don’t want my success to mean success for the AI companies. Fuck the AI companies.