4 ms·

That was hard to understand. So the idea if I'm not mistaken is to iterate without constraint/move fast and break things (build wide). Demo the work, keep chan

by smashed 1mo ago

That was hard to understand. So the idea if I'm not mistaken is to iterate without constraint/move fast and break things (build wide). Demo the work, keep changing. Don't worry about the amount of changes, don't worry about nice commits. You are free to explore and produce as much changes as required. Then use AI agents to clean up the history and produce a nice change set that you can ship in a more traditional way, small clean commits. That is an interesting idea I guess. AI agents can be used to clean up git histories, not only produce large amounts of code. Somehow, I think heavy LLM users don't really care about a clean git history / changesets? May be if the project or existing workflow you are bound to requires it you will do the extra steps but really I don't see it being done much. Are you really going to review these small changes? So far the example I saw from agents are terrible. Full of drive-by, unrelated changes here and there. Mostly harmless or OK changes, but not focused.

cavisne 1mo ago
It reads as heavily written by AI
- k3liutZu 1mo ago
  [flagged]
- bquaresma 1mo ago
  I wrote it myself and used AI to help polish the grammar, as well as improve some of the copy and tone. I also used it to review some of the ideas and claims I made. I can confidently say the content itself was written by a human, while the polishing was done with AI.
skybrian 1mo ago
I find that after I implement a bunch of features, I look at the code and do a refactoring pass. It seems like it makes sense to do that kind of feature work on a branch? I don't know that you need to keep the branch history though, so maybe it could entirely rewritten or squashed?
- butvacuum 1mo ago
  Messy personal branch, pushed to a feature branch, squashed when merged with dev, bugs found in dev squashed when merged with main. Seems to be the standard in shops with competent git users and no culure established around other version controll systems. What the commit messages are is fluid. My messy personal ones tend to be "start/end of day commit."
  - skybrian 1mo ago
    Nowadays I'm just working on personal projects directly on main and 'git push', but my coding agent commits after every change it makes, resulting in a zillion commits. I suppose I could do it on a branch and ask it to make a nice history at the end.
- bquaresma 1mo ago
  [dead]
andreareina 1mo ago
> Are you really going to review these small changes? So far the example I saw from agents are terrible. Full of drive-by, unrelated changes here and there. Mostly harmless or OK changes, but not focused. What I'm getting from tfa is that the point is to fix exactly this. Changes get packaged into focused changesets that make sense together. I've done this manually when working on a hairy feature, not splitting it into multiple pull requests but rebasing the history into (ideally) atomic commits that make sense sequentially.
- bquaresma 1mo ago
  That should work well too.
safety1st 1mo ago
If the agent's changes are terrible, the first thing I would look at is not the agent itself, but the context: spec, scope and prompting. We generate an AI branch immediately every time a spec is completed. It then gets a brief review in our team meetings when the spec is introduced. We just take a few minutes to walk through what the AI did and take notes on what was insane and what was a good approach. #1, this informs us about the actual quality of the spec, if the model did something wrong it will often become evident that the reason was an omission, ambiguity or contradiction in the spec. #2, it saves the developer a lot of time, because in ~3 minutes we've usually established that at least half the code the model wrote was acceptable for use. Dev tasks look more like "Here's the implementation in concept, but pieces X, Y and Z will require your expertise to formulate a cleaner approach." And since AI tends to do a cleaner job at smaller scoped problems, it tends to be a great help to the dev at this stage too. If anything I feel this process increases quality, not reduces it. It certainly speeds things up. Most major challenges and problems are immediately exposed by the model's proposed implementation. We don't have to wait for the developer's first cut to discover them. They go into the task knowing exactly what to focus on, and having the uncontroversial parts of their eventual PR essentially "pre-approved." The result is where we would have had to do multiple iterations of a feature, they often come back at the very next meeting having totally nailed it.
- smashed 1mo ago
  I was referring to what I observe working with various teams and open source projects using AI, not necessarily best practices or what a curated workflow can produce. Even before LLMs, most teams don't bother cleaning up. The best you'll see is feature branch getting squashed on merge. AI is not really a factor here. Teams/orgs that did not care before still won't, only 10x more.
antonvs 1mo ago
> That was hard to understand. It was LLM written or heavily LLM processed. This, for example, is 100% LLM: > Deciding boundaries up front was never about making the build easier. It was about making review possible, and it was the only affordable way to get there. That's the part that changed.
sandeepkd 1mo ago
+1 on the hard to understand part. Just wondering how this manifests in the code.
- bquaresma 1mo ago
  This was one of the first blog posts I've written in decades. I'm definitely open to improving, though. Unfortunately, the codebase I've been working on over the past few months is private, but I'll see if I can share some real examples from it.
seer 1mo ago
Actually I think it is the other way around - since very few people would ever read commit histories - maybe if something really bad happened and we need to trace the audit trail ~once a month or something per team. But LLMs can traverse that tree at will in seconds and mine valuable info. Commits description value in my experience has shot up dramatically _with_ heavy LLM usage - they are forgetful and commit messages are just the right place to keep “context that might be needed, but not worth adding to the documentation corpus” - they are the “how did we get here” to the “current state” of docs files and code comments.
- jurgenburgen 1mo ago
  > Actually I think it is the other way around - since very few people would ever read commit histories - maybe if something really bad happened and we need to trace the audit trail ~once a month or something per team. It’s worse than that IMO. In over 10 years I’ve yet to have seen a team that even knows how to rebase, asking them to investigate commit logs or bisecting would be like teaching a cat to bark. Insisting on “good commits” (or even worse, conventional commits which is a poor solution to the wrong problem) is just adding bureaucracy where it’s least needed.
  - seer 1mo ago
    Surprisingly enough I’ve encountered both kinds - people who don’t care a lot and despise any “mandatory commit rules”, and people who care _too much_ spending company time making their commits “perfect” which then nobody reads. I think it’s like with code formatters - now we have a tool that does the tedious thing on its own and unlocks human creativity where it is warranted… at least in the ideal case.
- bquaresma 1mo ago
  This is a great point that I hadn't considered, and it's definitely valuable. I've used APM systems that track errors by version, Sentry being one of them, and I've found it very useful when those errors include context about which commit introduced the issue. It makes it much easier for both me and the LLM I'm using to identify and understand the root cause.
thenthenthen 1mo ago
Cast a wide net, focus on the most promising deals?
eru 1mo ago
> Somehow, I think heavy LLM users don't really care about a clean git history / changesets? May be if the project or existing workflow you are bound to requires it you will do the extra steps but really I don't see it being done much. Some do, some don't. Yes, it depends on a lot of what wider organisation you are trying to fit into.
bquaresma 1mo ago
> Are you really going to review these small changes? Yes! Now that the code is well structured, it's much easier for me to review. Of course, I'm not too nitpicky. I mainly focus on architectural changes that may have drifted from the initial spec or anything unexpected. I use those moments to understand what led to the implementation and what could be improved. Those reviews usually teach me something, or I identify a gap that can be addressed by adding more context for the agent, usually in AGENTS.md. I also spend some time checking whether the tests actually cover the scenarios I care about. When I come across a test and don't understand why it's there, it's usually a good signal that I should pay closer attention to that area. Ultimately, what's most important to me is feeling confident when deploying changes, especially larger ones, to production.

by smashed 1mo ago

cavisne 1mo ago

It reads as heavily written by AI

k3liutZu 1mo ago
[flagged]
bquaresma 1mo ago
I wrote it myself and used AI to help polish the grammar, as well as improve some of the copy and tone. I also used it to review some of the ideas and claims I made. I can confidently say the content itself was written by a human, while the polishing was done with AI.

skybrian 1mo ago

I find that after I implement a bunch of features, I look at the code and do a refactoring pass. It seems like it makes sense to do that kind of feature work on a branch? I don't know that you need to keep the branch history though, so maybe it could entirely rewritten or squashed?

butvacuum 1mo ago
Messy personal branch, pushed to a feature branch, squashed when merged with dev, bugs found in dev squashed when merged with main. Seems to be the standard in shops with competent git users and no culure established around other version controll systems. What the commit messages are is fluid. My messy personal ones tend to be "start/end of day commit."
- skybrian 1mo ago
  Nowadays I'm just working on personal projects directly on main and 'git push', but my coding agent commits after every change it makes, resulting in a zillion commits. I suppose I could do it on a branch and ask it to make a nice history at the end.
bquaresma 1mo ago
[dead]

andreareina 1mo ago

> Are you really going to review these small changes? So far the example I saw from agents are terrible. Full of drive-by, unrelated changes here and there. Mostly harmless or OK changes, but not focused. What I'm getting from tfa is that the point is to fix exactly this. Changes get packaged into focused changesets that make sense together. I've done this manually when working on a hairy feature, not splitting it into multiple pull requests but rebasing the history into (ideally) atomic commits that make sense sequentially.

bquaresma 1mo ago
That should work well too.

safety1st 1mo ago

If the agent's changes are terrible, the first thing I would look at is not the agent itself, but the context: spec, scope and prompting. We generate an AI branch immediately every time a spec is completed. It then gets a brief review in our team meetings when the spec is introduced. We just take a few minutes to walk through what the AI did and take notes on what was insane and what was a good approach. #1, this informs us about the actual quality of the spec, if the model did something wrong it will often become evident that the reason was an omission, ambiguity or contradiction in the spec. #2, it saves the developer a lot of time, because in ~3 minutes we've usually established that at least half the code the model wrote was acceptable for use. Dev tasks look more like "Here's the implementation in concept, but pieces X, Y and Z will require your expertise to formulate a cleaner approach." And since AI tends to do a cleaner job at smaller scoped problems, it tends to be a great help to the dev at this stage too. If anything I feel this process increases quality, not reduces it. It certainly speeds things up. Most major challenges and problems are immediately exposed by the model's proposed implementation. We don't have to wait for the developer's first cut to discover them. They go into the task knowing exactly what to focus on, and having the uncontroversial parts of their eventual PR essentially "pre-approved." The result is where we would have had to do multiple iterations of a feature, they often come back at the very next meeting having totally nailed it.

smashed 1mo ago
I was referring to what I observe working with various teams and open source projects using AI, not necessarily best practices or what a curated workflow can produce. Even before LLMs, most teams don't bother cleaning up. The best you'll see is feature branch getting squashed on merge. AI is not really a factor here. Teams/orgs that did not care before still won't, only 10x more.

antonvs 1mo ago

> That was hard to understand. It was LLM written or heavily LLM processed. This, for example, is 100% LLM: > Deciding boundaries up front was never about making the build easier. It was about making review possible, and it was the only affordable way to get there. That's the part that changed.

sandeepkd 1mo ago

+1 on the hard to understand part. Just wondering how this manifests in the code.

bquaresma 1mo ago
This was one of the first blog posts I've written in decades. I'm definitely open to improving, though. Unfortunately, the codebase I've been working on over the past few months is private, but I'll see if I can share some real examples from it.

seer 1mo ago

Actually I think it is the other way around - since very few people would ever read commit histories - maybe if something really bad happened and we need to trace the audit trail ~once a month or something per team. But LLMs can traverse that tree at will in seconds and mine valuable info. Commits description value in my experience has shot up dramatically _with_ heavy LLM usage - they are forgetful and commit messages are just the right place to keep “context that might be needed, but not worth adding to the documentation corpus” - they are the “how did we get here” to the “current state” of docs files and code comments.

jurgenburgen 1mo ago
> Actually I think it is the other way around - since very few people would ever read commit histories - maybe if something really bad happened and we need to trace the audit trail ~once a month or something per team. It’s worse than that IMO. In over 10 years I’ve yet to have seen a team that even knows how to rebase, asking them to investigate commit logs or bisecting would be like teaching a cat to bark. Insisting on “good commits” (or even worse, conventional commits which is a poor solution to the wrong problem) is just adding bureaucracy where it’s least needed.
- seer 1mo ago
  Surprisingly enough I’ve encountered both kinds - people who don’t care a lot and despise any “mandatory commit rules”, and people who care _too much_ spending company time making their commits “perfect” which then nobody reads. I think it’s like with code formatters - now we have a tool that does the tedious thing on its own and unlocks human creativity where it is warranted… at least in the ideal case.
bquaresma 1mo ago
This is a great point that I hadn't considered, and it's definitely valuable. I've used APM systems that track errors by version, Sentry being one of them, and I've found it very useful when those errors include context about which commit introduced the issue. It makes it much easier for both me and the LLM I'm using to identify and understand the root cause.

thenthenthen 1mo ago

Cast a wide net, focus on the most promising deals?

eru 1mo ago

> Somehow, I think heavy LLM users don't really care about a clean git history / changesets? May be if the project or existing workflow you are bound to requires it you will do the extra steps but really I don't see it being done much. Some do, some don't. Yes, it depends on a lot of what wider organisation you are trying to fit into.

bquaresma 1mo ago

> Are you really going to review these small changes? Yes! Now that the code is well structured, it's much easier for me to review. Of course, I'm not too nitpicky. I mainly focus on architectural changes that may have drifted from the initial spec or anything unexpected. I use those moments to understand what led to the implementation and what could be improved. Those reviews usually teach me something, or I identify a gap that can be addressed by adding more context for the agent, usually in AGENTS.md. I also spend some time checking whether the tests actually cover the scenarios I care about. When I come across a test and don't understand why it's there, it's usually a good signal that I should pay closer attention to that area. Ultimately, what's most important to me is feeling confident when deploying changes, especially larger ones, to production.