https://github.com/pontusab/directories
If you prefer AGENTS.md files using markdown, I've extracted them into my own repo:
Poor planning, check. No/inadequate documentation, check. Sloppy and janky code, check. Poor practices and methods, check. Poor and miscommunication, check. Poor technical choices, check. What am I missing?
Maybe this is just a matter of some of the top tier 100x devs and teams clutching pearls in disgust at having to look at what goes on below Mt Olympus, but this is also not any different to how code quality cratered and is still really poor due to all the outsourcing and H-1B (sorry, all you H-1B hopefuls) insourcing of quantity over quality.
I say that without any judgement, but reality simply is that this issue has long been a quantity over quality argument even before AI, and mostly for non-dev reasons as the recent de-qualification of R&D funding revealed and had a marked impact on dev jobs because the C-suite could don't use R&D funding for financial shenanigans.
If people want to hate AI, go ahead. People hated and hate on the H-1B abuses and they hate on AI now. I would hope that we can just move beyond griping and mean-girling AI, and get to a point where proper practices and methods are developed to maybe make the outcomes and outputs better.
Because again, AI is not going anywhere less than even H-1B and I am sure the C-suite will find some new way to abuse and play financial shenanigans, but it's simply not going away and we need to learn to live with it since it will seemingly only get "better" and faster as it changes at breakneck speeds.
It's proven that (agentic) LLMs work better and more efficiently when they have the proper context for the project (AGENT(S).md), the documentation is accessible and up to date (docs/ directory with proper up to date markdown) and the tooling is smooth (git hooks that prevent secrets from being pushed, forced style checking and linting).
...surprise! All of this makes things easier for actual humans too!
That doesn't match my experience at all. Before AI, generating documentation from code was either getting some embedded comments for a function call, or just list a function's arguments.
AI reads the implementation, reads the callers and callees, and provides much smarter documentation, with context. Something sorely lacking from previous solutions. Is it sometimes not completely accurate? Probably. But still a giant step forward.
Dunno how helpful that is, but they shouldn’t have to write it from scratch.
This is why AI code review continues to be mostly useless in my experience. If you instruct an LLM to make PR comments it will come up with some, every time, even when none are warranted.
I do not care if engineers on my team are using AI. In fact, I think they should be. This is the new world and we need to get used to it.
But it’s still your work, your responsibility. You still have to own it. No cop outs.
Yeah "consider firing" is a bit of an extreme kneejerk reaction, but I just feel like we HAVE to create consequences for this nonsense.
I call that a good day. I've seen people push 2000 line PRs. The worst was 5000 lines. FML.
Fastly profits from"AI" just like Cloudflare (or should we say "Claudeflare").
This selection of developers does seem representative at all. The new strategy is to acknowledge "AI" weaknesses but still adamantly claim that it is inevitable.
This reminds me of web3, where almost all projects were just web3 infrastructure or services, to the point that the purpose of most start-ups was completely inscrutable to outsiders.
I'm having lots more hope for AI though.
You don't necessarily want to completely tune out while you're using the AI. You want to know what it's up to, but you don't need to be at your highest level of attention to do it. This is what makes it satisfying for me, because often it eats up several minutes to hunt down trivial bugs. Normally when you have some small thing like that, you have to really concentrate to find it, and it's frustrating.
When the AI is on a multi-file edit that you understand, that's when you can tune out a bit. You know that it is implementing some edit across several instances of the same interface, so you can be confident that in a few minutes everything will build and you will get a notification.
It's as if I can suddenly make all the architecture level edits without paying the cost in time that I had previously.
When you are working with AI, you are effectively working with a group of novice people, largely with basic competence, but lacking many deeper skills that are largely developed from experience. You kind of get what you put into it with proper planning, specificity in requests/tasks, proper organization based on smart structuring of skillsets and specializations, etc.
This may ruffle some feathers, but I feel like even though AI has its issues with coding in particular, this issue is really a leadership question; lead and mentor your AI correctly, adequately, and appropriately and you end up with decent, workable outcomes. GIGO
TL;DR: assuming you've squashed all regular non-determinism (itself a tall ask), you either need to ensure you always batch requests deterministically, or ensure all kernels are "batch invariant" (which is absolutely not common practice to do).
1. Replace junior developers with AI, reducing costs today.
2. Wish and hope that senior developers never retire in the future.
3. ?
I'm only half-joking: personally I'll be looking very closely at the IPO prospectus of any company founded in/after 2024 or so, to know how much vibe coding risk I can expect in its long-term prospects.
2. ?
3. Profit!
The Software Engineers Paid to Fix Vibe Coded Messes
AI can be a helpful assistant but they are nowhere near ready for letting loose when the results matter.
I’ve had good results with Claude, it just takes too long. I also don’t think I can context switch fast enough to do something else while it’s churning away.
Kind of wild watching engineers, technologists, etc clutch their pearls
AI makes mistakes - so do people - I’ve been paid many a time to go clean up the non-AI generated code humans left behind. I’ve spent countless hours troubleshooting dumb bugs generated by humans
AI brings its own set of problems but I think some people just don’t want to hear what a net benefit it is
Post after post lamenting that no machine will ever replace the human touch is so old - people have been saying that about new technology since the beginning of time
I say embrace it - great to talk about its problems objectively and how to avoid common issues, but it’s tiresome to hear all the reasons why it’s crap all the time
I would instead love articles (you do see them here sometimes) that go over how people coded complex solutions to problems using AI and the challenges they faced along the way
Knowing where the weak points are or where it may be prone to error
If you don’t embrace it, pretty soon kids coming out of college that grew up with it that contrary to popular DO understand fundamentals are going to be running circles around the people who haven’t yet figured out how to get value out of it - while you’re out there saying it’s crap and only idiots use it, these kids are going to be moving mountains - I can’t wait to see the cool stuff people build - I don’t see the future as bleak
AI just makes ambition and competition go up.
Here I put ClaudeCode in a while loop and clone itself: https://www.reddit.com/r/ClaudeCode/s/TY3SNp7eI9
After a week it was ready, you just need a good prompt.
People who say it cannot create anything beyond simple things are wrong. In my experience you can create anything provided your plan is good.
Does it end up like having colleagues who are aren't doing or understanding or learning from their own work, and are working like they offshored their job to an overnight team of juniors, and then just try to patch up the poor quality, before doing a pull request and calling their sprint done?
Or is it more like competent mechanical grunt work (e.g., "make a customer contact app with a Web form with these fields, which adds a row to the database"), that was always grunt work, and it's pretty hard to mess up, and nothing that normally the person you assigned it to would understand further anyway by doing it themself?
yes
I know that it's always been this way to some degree, but this is getting out of hand.
My new job pushes cursor somewhat heavily and I gave it a try, it works pretty well for me although it's definitely not something I would rely on. I like being able to ask it to do something and let it go off and come back to it in a while to see how it did. For me, I think it makes it easier to start on changes by coming into something (that might be wrong and bad), but for me personally having something in a PR to start from is a nice mental hack.
If it did well enough on the initial attempt I'll try to stick with it to polish it up, but if it failed terribly I'll just write it by hand. Even when it fails it's nice to see what it did as a jumping off point. I do wish it were a bit less prone to "lying" (yada yada anthromorphization it's just tokens etc.,) though, sometimes I'll ask it to do something in a particular way (e.g., add foo to bar and make sure you X, Y, and Z) and it'll conclude (rightfully or not) that it can't do X, but then go on anyway and claim that it did X.
I wish it were easier to manage context switching in cursor though, as it is juggling IDE windows between git repo clones is a pain (this is true for everything though, so not unique to cursor). I wish I could just keep things running on a git branch and come back to them without having to manage a bunch of different clones and windows etc.,. I think this is more of a pain point with cursor since in theory it would allow you to parallelize more tasks but the tooling isn't really there.
edit: the starting point for this is probably worktrees, I remember reading about these a while ago and should probably check them out (heh) but it only solves the problem of having a bunch of clones sitting around, I'd still need to manage N windows.
sl8s•3h ago