Post #388437
2026-02-21 15:01 UTC
Replies (11)
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@ben@werd.social 2026-02-21 15:03
@davew@mastodon.social I've experimented a little because I feel like I need to know how it works - The older models would do insane stuff. Absolutely they would consider piling on special cases. The latest models? It's genuinely a bit like magic. They don't always work but they will often reorganize and optimize a codebase - in a way that makes sense and actually works.
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@cameronbanga@mastodon.social 2026-02-21 15:11
I’ve found that the key is the “context window”, which is a magic black box of what the app has been working on for the current session. If I keep the tool focused on what I’ve been working on in the context window, the results have been pretty good. If I ask it to assess something it hasn’t touched, results get more wild.
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@rmdes@indieweb.social 2026-02-21 15:19
@davew@mastodon.social I have found that using Obra Superpower plugin/skills is of immense help to do just that, the first thing is to have claude code to map the repo and differentiate between instruction for him in Claude.md and for humans in readme.md once the repo is properly described and mapped for claude you have your init context right, then for a refactoring or reorg of the code I would use an agent code reviewer to plan (superpower skill) and then only execute the plan once reviewed
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@drwhitt@mastodon.social 2026-02-21 15:36
@davew@mastodon.social It’s been a mixed bag for me but mostly leans towards success. :) As suggested above, maintaining context with the tools is the key factor. Sometimes, responses to follow-up prompts often include snippets of code that, while correct, can sometimes be hard to see where they need to be applied as a change/addition/subtraction. I find myself prompting to, “Please provide the complete implementation with this update included.” and that (almost always) works. It does feel magical, though. 🪄
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@dusoft@fosstodon.org 2026-02-21 16:01
@davew@mastodon.social I wouldn't recommend it. It's trained on certain practices that are often not the best. Also refactoring custom, older code usually just means non working new code that might follow some patterns but doesn't work. Yep. It can even create test (unit test) that will clear up your (hopefully dev) database. So moderate success and often lots of frustration on the way. (Antigravity and Gemini 3 and 3.1)
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@evert@indieweb.social 2026-02-21 16:12
@davew@mastodon.social with the right instructions they can do the right thing. Did some big refactoring and swapping out libraries. The chance of success is higher depending on test coverage and good types
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@hacknorris@mstdn.social 2026-02-21 16:28
@davew@mastodon.social mostly breaks and that's why this part i still do manually…
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@armstrong@mastodon.design 2026-02-21 21:55
@davew@mastodon.social with the latest models (e.g. Claude Opus 4.6), actually surprisingly well. To the point where it makes writing a bunch of junk less of an issue since it can tidy up after itself surprisingly well
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@eddiereasoner@mstdn.social 2026-02-21 23:42
@davew@mastodon.social GPT 5 mini and Gemini via GitHub Copilot tend to pile on. Gemini models usually introduce extra variables or constants and add redundant comments to any code they can. I’ve resorted to mostly using agents for DevOps stuff (configuring Docker, etc.) and chats or edit mode for dealing with frustrating errors, messy code, or trivial yet tedious tasks. (almost all accidental alliteration always sounds advised)
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@eddiereasoner@mstdn.social 2026-02-21 23:43
@davew@mastodon.social Models with a larger context window should perform much better.
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@hananc@tooot.im 2026-02-25 11:16
@davew@mastodon.social Whenever I am asking for something more complicated than simple, I first ask the machine to do a pre-mortem. I tell it to ask me questions and suggest things. Then, we go back and forth about everything. Only when I am satisfied, I let the machine generate the code for me. It happened that the machine suggested reorganizing the code or a a complete rewrite, and I approved it.