I've hit this point with AI where it's not a simple process, but a long drawn out back and forth.
I'll use AI to design the implementation of a medium sized, cross cutting feature. Review all the details, maybe iterate on just that. Then implement with Claude 4.7 Max - which runs slower, but does a better job. Then review the implementation, then have Codex GPT 5.5 xhigh fast review it - which almost always finds corner cases. Have Claude fix those - Claude is better at writing intuitive maintainable code versus Codex overengineered/shortcut filled code. (Codex is better at finding/fixing bugs and doing reviews - it's annoyingly pedantic)
Then repeat with fresh Claude/Codex instances having them both review the current staged changes and getting feedback, handling the feedback. Then covering it in tests. I mean overall I still implement the feature faster than coding it manually, but I spend a majority of the time going back and forth with reviews, handling corner cases and at the finish end up with what I feel a really solid implementation of whatever feature I'm working on. The v1 feature feels more like a v3 given the amount of iteration it already went through.
yes exactly. Too many people ask AI to one-shot complex tasks, and wonder it behaves like a junior asked to rush something.
I have my own skill: 5 rounds of research/planning/test-planning. Interactive with me in loop for all important decisions. Starts with high level shape, then details. Planning can take 2-3 days of my time, then the implementation agent can take many hours (Opus 4.7). It splits the implementation across many phases/commits, each with its own code-review fix loop. Deep code review at the end can take another hour or two. It opens a PR, Gemini reviews, it reads out and resolves those issues.
Projects still take days or weeks, but 5x faster than doing it all myself.
I have a very similar workflow, and experience similar temperaments from the agents. I also find anecdotally that they are moderately competitive - you get very different attention from them when you say "competitor X wrote this - please find all bugs" than when you say "you just wrote this - please find all bugs".
Hah yea I just told them I wrote it, or I reviewed it. I don't want to get the AI's in a pissing contest with each other because they will get distracted and try to show off.
And then Anthropic has an outage and you what...have a coffee break until then?
All that time babysitting the AIs just to be a little faster but probably with less knowledge/control over what they did?
I don’t think you’re quite getting what OP is describing. I work in a similar way… I am aware of all the code being written. If Claude had an outage I could write it myself. It would just take longer.
You say “all that time” babysitting AIs but in my experience it isn’t that much time, if anything the back and forth at the planning stages is more productive than when I’m doing it by myself because I’m being asked questions and having to think things through from different angles.
If you only have one AI window open, you’re doing it wrong. You task swap to another window/agent, get it working on something, rinse and repeat. I can keep 4 busy most of the time. When I task swap I also check in on what the other agents are doing to make sure they’re on track, not blocked and not struggling.
As the AI is working, I am working - reviewing, regression testing, thinking about if the currently implementation is too complex and how to simplify it etc.. I totally review and understand everything the AI is generating and often push back, have it re-do something, or do it myself. In the end I feel like the quality of the work is at a v3 level in the time it took to do a v1. The productivity and quality increase is real.
"All that time babysitting the AIs just to be a little faster" doesn't seem like an accurate/unbiased portrayal of what they said: "The v1 feature feels more like a v3 given the amount of iteration it already went through."
Title of this article suggested more depth and I was expecting actual code examples. But it is like other opinion pieces. It suggests a prompt (ask AI to find bugs) that works for the author advising everyone to do it that way.
I use these tools at both work and for personal side projects and I was expecting to watch and learn. But these opinion pieces without examples are way too many now.
Have you tried his suggested workflow? I think it's a useful workflow, and if I hadn't found a workflow like this already would appreciate the pointer.
I guess he could write a code harness to do this, or gin one up really quickly, but that kind of tooling today seems like the purview of the practitioner -- you -- it's frankly faster for you to spec what you want to try this idea out if you want it automated than it would likely be to deal with his code.
I find myself spending on average more time in LLM review/resolution loops than it would take for me to write the code by hand. Partially because once I'm in the flow I write very very quickly and the code pours out sometimes faster than I can write. But also because the LLM code on the first few tries is generally really really bad. What I find interesting though is that spending the time to personally review and direct the LLM through several iterations of review and revision on average results in higher quality code written in about the same time as I would have written it. This might be particular to me, but seeing several interations of someone else's code helps me better understand holistically my objective as opposed to whatever happens to come out of my flow-state consciousness.
The linked article about getting LLMs to critique each others' code review[1], the magpie tool[2], and also this recent article from Cloudflare about their code review stack[3] are all quite compelling.
I'm fairly AI-skeptical not on grounds of "do they work" but "are they good for the world". I feel that getting AIs to do this kind of review work is a rare case that doesn't outsource thinking and deskill workers. It doesn't trigger the same alarm bells as having the AI write the code (including having the AI fix the issues it discovers). That's setting aside environmental and other ethical concerns, which are still significant to me.
I have been impressed by the recent quality of AI code reviews*, but the experience of interacting with 3 separate AI reviewers via GitHub PRs is pretty terrible. Having more local-oriented and jj/rebase-aware review rounds would be great.
*context: fairly large PHP/Laravel backend and Vue frontend
This article doesn't address writing code with AI, just code review. My issue with agentic coding is that I make numerous micro-architectural decisions while programming. I almost never have a full spec up front and develop one as I consider what I am writing.
When using Claude Code or Codex, that is all gone. Claude Code is extremely eager to reach the end goal to the point that it feels like a fever dream to write code with it. In the end, I have low confidence about edge cases and fit into the project's architectural and design goals.
On top of that, I enjoy programming, reverse engineering, etc. and I feel that the LLMs, while able to solve some problems or deliver some features, take that fun away. I'm trying really hard to find a workflow with them that I'm confident in, but I fear that workflow is just chat, search, and being a rubber duck for my thoughts.
One thing that's been interesting to me over the last few years is charting the edge of my coding laziness. As a coder, I'm lazy about boilerplate code -- I hate writing it, I hate maintaining it, etc. And so I design and architect (or used to) around that preference. Sometimes that's smart, sometimes that's not. But it was my preference, and I avoided something that was hard for me to do.
When LLMs started being somewhat useful for coding a few years ago, and I found they were in fact great at boilerplate, in fact pretty much only good at boilerplate ca 2023 or so, it got me thinking about all the accommodations we make in design and systems architecture that are sort of tacitly understanding who we're working with and their strengths and weaknesses.
The modern models have their own very different strengths and weaknesses compared to humans, and deploying them is a really interesting exercise of different architectural and engineering skills. I've enjoyed it, and hope I continue to.
I used LLM as a tutor to tackle unfamiliar terrain. That is, I write code that I know very likely doesn't work but is the best code that I could have written. The LLM will happily tirelessly show me what I did wrong and what the correct code actually look like. Then, at the end of it, I got code that running. That's a tight feedback loop.
It's still very slow. It took me two hours to write code that generate JSON data and then to write a web page that displays a knowledge graph.
One thing you have to be aware is that the LLM will happily generate code for you and you have to discipline it from time to time. I notice that my reading comprehension begins to suffer if I don't write the code myself and have to understand what the LLM wrote for me as opposed to the LLM correcting where I went wrong.
One thing I would like to try with an LLM is understanding a large and complex existing codebase like OpenSCAD that doesn't leverage my existing skillset(high level programming languages with OpenSCAD as primary language in the past year). That has always been a barrier to contribution for me.
Hot take, barring from special edge cases, I find using dumber models (like local Qwen 3.6) to be the best balance. Smart enough to do stuff but dumb enough where I don’t trust it and verify what it’s doing rather than letting it do the third whole code base refactoring of the day. Also forces me to know my code base and ask very descriptive tasks rather than go “something is wrong, fix it”.
I use cheaper models (Deepseek is king, but GLM and Kimi as well) and do the planning myself. I often start a task myself, write some code to get the LLM on the right track, and then have it complete parts of the implementation that are kind of boring or repetitive. LLM's are just next token predictors, I don't mean that in a demeaning way, but I've found if I can get the LLM started on the right track with my own code, it completes what I want. Having the LLM write code just from a spec ends up with poor quality slop in my experience.
I'm not 100x'ing my output like some people claim, but using it as a augmentation rather than delegating my work to it results in better code, and I don't lose context / control over my codebases. I really have read 100% of the code, because the LLM is generating smaller pieces around and inside my own written code. Works well enough for me, and open models are already both cheap enough and good enough for this workflow. This is why the big companies are so desperate to push full-on agentic hands-off workflows and developer replacement - that's the only way they won't go bankrupt.
How profound! Talking points are changing from "vibe coding delivers bug free software" to "slow down and enjoy the AI".
Great how the promoters are mirroring the current anti-AI sentiment. The next step is canceling all subscriptions and not using AI at all. Maybe your mind will work again.
Not so much. People are just walking things back from the Gastown/Oh My Opencode/etc peak of trying to get 10 agents working simultaneously on a project unsupervised. They've collectively realized that you still have to understand and validate what the agents produce in some way if you want to build maintainable software.
Instead of using a skill and having the agent own the flow for this, I've been building an external orchestrator that handles the process.
By default it uses pi agent core + pi ai (from the excellent pi coding agent) as a multi model runtime but also supports a Claude Agent SDK runtime.
I can have an implementation and review process of an OpenSpec change run anywhere from 2 hours to 24+ hours going through review/fix/verification rounds automatically until the implementation matches the spec and any additional reviewers are done finding issues after the fix rounds.
it's going to be fully open sourced in the next two weeks and fully free to use
Maybe we can come up with an spec for aligning asci diagrams. Can't really build anything with confidence when the attention to detail is lacking in these agentic systems
I've hit this point with AI where it's not a simple process, but a long drawn out back and forth.
I'll use AI to design the implementation of a medium sized, cross cutting feature. Review all the details, maybe iterate on just that. Then implement with Claude 4.7 Max - which runs slower, but does a better job. Then review the implementation, then have Codex GPT 5.5 xhigh fast review it - which almost always finds corner cases. Have Claude fix those - Claude is better at writing intuitive maintainable code versus Codex overengineered/shortcut filled code. (Codex is better at finding/fixing bugs and doing reviews - it's annoyingly pedantic)
Then repeat with fresh Claude/Codex instances having them both review the current staged changes and getting feedback, handling the feedback. Then covering it in tests. I mean overall I still implement the feature faster than coding it manually, but I spend a majority of the time going back and forth with reviews, handling corner cases and at the finish end up with what I feel a really solid implementation of whatever feature I'm working on. The v1 feature feels more like a v3 given the amount of iteration it already went through.
yes exactly. Too many people ask AI to one-shot complex tasks, and wonder it behaves like a junior asked to rush something.
I have my own skill: 5 rounds of research/planning/test-planning. Interactive with me in loop for all important decisions. Starts with high level shape, then details. Planning can take 2-3 days of my time, then the implementation agent can take many hours (Opus 4.7). It splits the implementation across many phases/commits, each with its own code-review fix loop. Deep code review at the end can take another hour or two. It opens a PR, Gemini reviews, it reads out and resolves those issues.
Projects still take days or weeks, but 5x faster than doing it all myself.
I have a very similar workflow, and experience similar temperaments from the agents. I also find anecdotally that they are moderately competitive - you get very different attention from them when you say "competitor X wrote this - please find all bugs" than when you say "you just wrote this - please find all bugs".
Hah yea I just told them I wrote it, or I reviewed it. I don't want to get the AI's in a pissing contest with each other because they will get distracted and try to show off.
And then Anthropic has an outage and you what...have a coffee break until then? All that time babysitting the AIs just to be a little faster but probably with less knowledge/control over what they did?
I don’t think you’re quite getting what OP is describing. I work in a similar way… I am aware of all the code being written. If Claude had an outage I could write it myself. It would just take longer.
You say “all that time” babysitting AIs but in my experience it isn’t that much time, if anything the back and forth at the planning stages is more productive than when I’m doing it by myself because I’m being asked questions and having to think things through from different angles.
If you only have one AI window open, you’re doing it wrong. You task swap to another window/agent, get it working on something, rinse and repeat. I can keep 4 busy most of the time. When I task swap I also check in on what the other agents are doing to make sure they’re on track, not blocked and not struggling.
As the AI is working, I am working - reviewing, regression testing, thinking about if the currently implementation is too complex and how to simplify it etc.. I totally review and understand everything the AI is generating and often push back, have it re-do something, or do it myself. In the end I feel like the quality of the work is at a v3 level in the time it took to do a v1. The productivity and quality increase is real.
I’ll deal with that problem when it happens
And then solar radiation permanently knocks out the electrical grid and you what... have coffee break until society finds a new equilibrium?
Codex has 99.98% uptime
"All that time babysitting the AIs just to be a little faster" doesn't seem like an accurate/unbiased portrayal of what they said: "The v1 feature feels more like a v3 given the amount of iteration it already went through."
Title of this article suggested more depth and I was expecting actual code examples. But it is like other opinion pieces. It suggests a prompt (ask AI to find bugs) that works for the author advising everyone to do it that way.
I use these tools at both work and for personal side projects and I was expecting to watch and learn. But these opinion pieces without examples are way too many now.
Have you tried his suggested workflow? I think it's a useful workflow, and if I hadn't found a workflow like this already would appreciate the pointer.
I guess he could write a code harness to do this, or gin one up really quickly, but that kind of tooling today seems like the purview of the practitioner -- you -- it's frankly faster for you to spec what you want to try this idea out if you want it automated than it would likely be to deal with his code.
I find myself spending on average more time in LLM review/resolution loops than it would take for me to write the code by hand. Partially because once I'm in the flow I write very very quickly and the code pours out sometimes faster than I can write. But also because the LLM code on the first few tries is generally really really bad. What I find interesting though is that spending the time to personally review and direct the LLM through several iterations of review and revision on average results in higher quality code written in about the same time as I would have written it. This might be particular to me, but seeing several interations of someone else's code helps me better understand holistically my objective as opposed to whatever happens to come out of my flow-state consciousness.
The linked article about getting LLMs to critique each others' code review[1], the magpie tool[2], and also this recent article from Cloudflare about their code review stack[3] are all quite compelling.
I'm fairly AI-skeptical not on grounds of "do they work" but "are they good for the world". I feel that getting AIs to do this kind of review work is a rare case that doesn't outsource thinking and deskill workers. It doesn't trigger the same alarm bells as having the AI write the code (including having the AI fix the issues it discovers). That's setting aside environmental and other ethical concerns, which are still significant to me.
I have been impressed by the recent quality of AI code reviews*, but the experience of interacting with 3 separate AI reviewers via GitHub PRs is pretty terrible. Having more local-oriented and jj/rebase-aware review rounds would be great.
*context: fairly large PHP/Laravel backend and Vue frontend
[1]: https://milvus.io/blog/ai-code-review-gets-better-when-model...
[2]: https://github.com/liliu-z/magpie
[3]: https://blog.cloudflare.com/ai-code-review/
This article doesn't address writing code with AI, just code review. My issue with agentic coding is that I make numerous micro-architectural decisions while programming. I almost never have a full spec up front and develop one as I consider what I am writing.
When using Claude Code or Codex, that is all gone. Claude Code is extremely eager to reach the end goal to the point that it feels like a fever dream to write code with it. In the end, I have low confidence about edge cases and fit into the project's architectural and design goals.
On top of that, I enjoy programming, reverse engineering, etc. and I feel that the LLMs, while able to solve some problems or deliver some features, take that fun away. I'm trying really hard to find a workflow with them that I'm confident in, but I fear that workflow is just chat, search, and being a rubber duck for my thoughts.
One thing that's been interesting to me over the last few years is charting the edge of my coding laziness. As a coder, I'm lazy about boilerplate code -- I hate writing it, I hate maintaining it, etc. And so I design and architect (or used to) around that preference. Sometimes that's smart, sometimes that's not. But it was my preference, and I avoided something that was hard for me to do.
When LLMs started being somewhat useful for coding a few years ago, and I found they were in fact great at boilerplate, in fact pretty much only good at boilerplate ca 2023 or so, it got me thinking about all the accommodations we make in design and systems architecture that are sort of tacitly understanding who we're working with and their strengths and weaknesses.
The modern models have their own very different strengths and weaknesses compared to humans, and deploying them is a really interesting exercise of different architectural and engineering skills. I've enjoyed it, and hope I continue to.
I used LLM as a tutor to tackle unfamiliar terrain. That is, I write code that I know very likely doesn't work but is the best code that I could have written. The LLM will happily tirelessly show me what I did wrong and what the correct code actually look like. Then, at the end of it, I got code that running. That's a tight feedback loop.
It's still very slow. It took me two hours to write code that generate JSON data and then to write a web page that displays a knowledge graph.
One thing you have to be aware is that the LLM will happily generate code for you and you have to discipline it from time to time. I notice that my reading comprehension begins to suffer if I don't write the code myself and have to understand what the LLM wrote for me as opposed to the LLM correcting where I went wrong.
One thing I would like to try with an LLM is understanding a large and complex existing codebase like OpenSCAD that doesn't leverage my existing skillset(high level programming languages with OpenSCAD as primary language in the past year). That has always been a barrier to contribution for me.
Great article and right on point.
Hot take, barring from special edge cases, I find using dumber models (like local Qwen 3.6) to be the best balance. Smart enough to do stuff but dumb enough where I don’t trust it and verify what it’s doing rather than letting it do the third whole code base refactoring of the day. Also forces me to know my code base and ask very descriptive tasks rather than go “something is wrong, fix it”.
learn by considering critique
I use cheaper models (Deepseek is king, but GLM and Kimi as well) and do the planning myself. I often start a task myself, write some code to get the LLM on the right track, and then have it complete parts of the implementation that are kind of boring or repetitive. LLM's are just next token predictors, I don't mean that in a demeaning way, but I've found if I can get the LLM started on the right track with my own code, it completes what I want. Having the LLM write code just from a spec ends up with poor quality slop in my experience.
I'm not 100x'ing my output like some people claim, but using it as a augmentation rather than delegating my work to it results in better code, and I don't lose context / control over my codebases. I really have read 100% of the code, because the LLM is generating smaller pieces around and inside my own written code. Works well enough for me, and open models are already both cheap enough and good enough for this workflow. This is why the big companies are so desperate to push full-on agentic hands-off workflows and developer replacement - that's the only way they won't go bankrupt.
I want to mention, Claude code has a command /code-review. I find it quiet useful.
How profound! Talking points are changing from "vibe coding delivers bug free software" to "slow down and enjoy the AI".
Great how the promoters are mirroring the current anti-AI sentiment. The next step is canceling all subscriptions and not using AI at all. Maybe your mind will work again.
Not so much. People are just walking things back from the Gastown/Oh My Opencode/etc peak of trying to get 10 agents working simultaneously on a project unsupervised. They've collectively realized that you still have to understand and validate what the agents produce in some way if you want to build maintainable software.
Instead of using a skill and having the agent own the flow for this, I've been building an external orchestrator that handles the process.
By default it uses pi agent core + pi ai (from the excellent pi coding agent) as a multi model runtime but also supports a Claude Agent SDK runtime.
I can have an implementation and review process of an OpenSpec change run anywhere from 2 hours to 24+ hours going through review/fix/verification rounds automatically until the implementation matches the spec and any additional reviewers are done finding issues after the fix rounds.
it's going to be fully open sourced in the next two weeks and fully free to use
https://engine.build
Maybe we can come up with an spec for aligning asci diagrams. Can't really build anything with confidence when the attention to detail is lacking in these agentic systems
https://imgur.com/a/r4fhOwy
It's interesting... Opus seems horrible at keeping text aligned. Markdown it is I suppose
What's that from? OpenSpec docs?