It might surprise people that I'm investing time in LLM tooling -- "Agentic" tooling even -- because I've always been somewhat of an AI-skeptic. But, this code-review project is not my first foray into the category; I am also working on a task runner called camas that optionally includes an MCP and skills, and indeed it was used for the development of code-review. In turn, camas now benefits from code-reviews's agentic review on every PR.
Perhaps I am more of an "AI-industry" skeptic, or wary of hype and fear-mongering, than I am skeptical of LLMs themselves. I am not convinced that "AI agents" can replace engineers, or any occupation, really. I can only speak for my experience as a software engineer -- the frontier models like Opus and Fable 5 are particularly well-suited for writing software, and on their own, I don't think they're that good at it. But I'm not sticking my head in the sand either. My experience is that LLMs, when bounded by expert judgement and deterministic checks, provide an incredible, albeit hard to quantify, boost to the pace of development and testing. One category where I'm very happy with their utility is code review, particularly agentic code review.
Lately, I've started to dread what a post-AI-bubble or post-token-subsidy world would look like. Will models that provide affordable inference survive? How will Open Source Software (OSS) projects benefit from LLMs if contributors are no longer able to use subsidized flat-rate plans?
