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.
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.
When I'm building something new there's a good chance I don't know about a lot of problems (unknown unknowns). How do you overcome this with just spec files?
I keep coming back to this and the only way to develop properly without tons of hacks patching bugs afterwards is by working at a low level. I don't really write code but I work at the code level still and have Claude write each function and whatnot.
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.
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
"I don't understand the code anymore. It works." That's fine... today.
"I will never need to understand the code again" is a much different statement.
If you don't understand the code, and the code wasn't written by any human, when you're eventually painted into a corner, how hard is it going to be to get out? Will it be easier or harder than maintaining an understanding through however long it takes to get to that point?
You may be betting your company on the answer. How sure are you?
How is this different than working at a company with hundreds of developers, on a massive codebase? Do you ever really understand the entirety of that?
No. But somebody understood the high-level architecture, and somebody understood each piece in detail. And if you needed to work on piece X.Y.Z, you could find somebody who could tell you how X fit into the big picture, and somebody else who could tell you how Y fit in X, and somebody else who could tell you how Z fit into Y, and maybe somebody else who could tell you about the details of Z.
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.
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'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
Fable 5.1 is better than most engineers if prompted correctly
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.
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.
> Write good specs into files, usually Markdown.
When I'm building something new there's a good chance I don't know about a lot of problems (unknown unknowns). How do you overcome this with just spec files?
I keep coming back to this and the only way to develop properly without tons of hacks patching bugs afterwards is by working at a low level. I don't really write code but I work at the code level still and have Claude write each function and whatnot.
Are you still saving time or effort using coding agents this way? How much (estimated)?
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.
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
"I don't understand the code anymore. It works." That's fine... today.
"I will never need to understand the code again" is a much different statement.
If you don't understand the code, and the code wasn't written by any human, when you're eventually painted into a corner, how hard is it going to be to get out? Will it be easier or harder than maintaining an understanding through however long it takes to get to that point?
You may be betting your company on the answer. How sure are you?
How is this different than working at a company with hundreds of developers, on a massive codebase? Do you ever really understand the entirety of that?
No. But somebody understood the high-level architecture, and somebody understood each piece in detail. And if you needed to work on piece X.Y.Z, you could find somebody who could tell you how X fit into the big picture, and somebody else who could tell you how Y fit in X, and somebody else who could tell you how Z fit into Y, and maybe somebody else who could tell you about the details of Z.
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.
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'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