I'm really happy with my opencode + open weight setup, the code is generally pretty good, but I do spend tokens having agents go look for common ai slop patterns.

It's heavily customized, replaced most internal systems via plugins, a set of custom agent instead of builtin ones, different model families for different sub tasks. (don't have claude review its own code)

I'm working on polishing them up and porting a few more from my own harness, then will be open sourcing. Keep your eye out for a "better-opencode" plugin suite, I'll be sure to share it with HN :]

In the near-term, I really like GLM 5.3 prose for code explore / review, give it a shot, it's cheaper (flash model) and catches all sorts of mistakes from the Big Ai models. We fully rolled out our custom pr-review on glm-5.3-flash last week. Most devs are still on claude, moving them towards fireworks and opencode.

OpenCode Go is a great way to try out open weight models for $10/month

Its a spectrum. For ai-maintained code (like a gui to visualize performance data), IDGAF what the code looks like. I just let claude or codex go nuts and 100% vibe code.

For code I care about, I audit every single hunk as its produced. I give it extensive style guidelines, and crack down on things like a 20-line essay in a comment.

For mission critical code, I write the code myself and have an agent review it.

The way I have been doing it is to use LLMs to generate the code that I don't want to write: prototypes, tests, benchmarks, isolated,straightforward almost copy-paste code. I still write my own code as before because I enjoy doing that and because trying to understand and fix what an LLM generates and regenerates is harder and more tedious and time consuming than writing the code the way I want to do it in the first place.

After vibing myself into a corner multiple times on important projects, I now have only two modes: clankermaxx for code I don't really care about (mostly frontend react), and write by hand everything else. Using Django for backend already removes the most of the cruft, and writing by hand also means I actually understand what's going on. Works fine for now.

I'm still on the fence for tests: I don't really like to write them, but the LLM-generated tests are pretty bad, even the frontier models on xhigh thinking. I usually generate them, but I don't really have the confidence they test anything except 1==1. Unfortunately it's hard to justify the time spent on writing them manually.

I generally produce nearly the same code I'd write myself about 5x faster with AI. I don't just let Claude Code run wild for a long time and have a mess to review. I have it do small chunks I can quickly review, give it feedback, iterate, etc until I like the output, then I move on to the next step. This takes time of course but I've found it faster than reviewing a giant mess of a code review.

What's the problem of the solution proposed ? Don't read/write code anymore. Have strong harness. That's how my team of ~30 has been operating for the most part.

The problem we are trying to solve was never to write code, was to solve business problems

Yes. The whole time. But, you still need to write good specs if you want well-designed software. Agents are not magic.

- Write good specs into files, usually Markdown.

- Go through the spec files with the agents, have it point out holes and problems, and then update the specs. Go through a few iterations of this and then start your agents on development.

Garbage in, garbage out.

Fable 5.1 is better than most engineers if prompted correctly

I think an underappreciated aspect is that we previously amortized the reading and comprehending over the time we spent designing and writing. I still find myself looking at changes expecting that I can review it in a few minutes when I am in effect coming in cold. That part of the job has changed, but fwiw it isn't so different than the experience I had reviewing another team's changes when I didn't know their services.

I have found the right harness helps, if you have a setup that gives you memory, specs and a graph-based understanding (that part is key) you can be very productive. For me, that's because being able to understand the big picture helps, the challenge of reading the code is always there but starting with 'this method/class/etc does X, so that means this is doing Y..." makes it easier and lets you come in warmer.