My employer pays for Claude, and my approach is to use it as a better Google search. It's often not better.
Just today it made three glaring mistakes in one session:
1. It read a file in the wrong directory, because that file had the same name as the file in the right directory. It apologized when I challenged it, promising me that it would remember to "read import statements" in the future.
2. It miscounted the number of times a function was called in my repo. It said 20, while my built-in IDE search accurately showed 17. Again, it apologized when I corrected it.
3. It referred to a variable by name that does not exist anywhere in my code. It apologized, and said it was referring to a variable used internally by one of the third-party packages installed in my repo.
So many apologies.
It's the little things like this that remind me on a regular basis just how little I can trust artificial "intelligence."
What model are you using? The early stopping after hitting a wrong directory instead of continuing on, and the hallucinations suggests you are using a old model.
Are you using the best models? I feel like my experience is completely different. I am an expert in a small part of a huge monolithic codebase that I’ve worked in for years. When customers report issues that would take me days or weeks to debug, AI can figure it out on the first try.
Yes, but it’s usually only hours to validate. I understand the codebase so I have the intuition to know almost immediately if the theory makes sense. Validating and reproducing the root cause is often an order of magnitude easier than identifying a production-reported issue.
I find it strange to see people writing articles like this as if everyone has used AI for decades. I've programmed for decades. I thought I retired three years ago but got an offer I couldn't refuse. Already there were little things I'd forgotten how to use.
Over the past six months I tried using Claude, chatgpt, Grok and Gemini. At best I got reminders of how things worked. People online say they use them to write their code. The code they supplied to me has NEVER worked or was so convoluted that I threw it away and did it myself.
At most, I use these tools as search engines. Even then some references are poor.
I'm starting to think this is becoming a sad, sad world and AI is just the new TV of the programming world.
My experience was similar to yours, upon till earlier this year. Now the code which comes out of Claude code is acceptable most of the time.
It usually takes me two or three iterations to get there though. Discussing design and principles before writing the bulk of the code is a must. And then a pass or two of review to weed out ugliness.
Still saves time compared to writing the code by hand. Especially for tricky things, where type checking and tests can verify correctness.
You would probably be surprised by how many jobs require that you use AI - I even had an interview where it was strongly encouraged to use it during the technical phase. I guess what I’m getting at is, for many people this isn’t really a choice.
Yeah, I have never understood the over reliance on AI. Writing the code is not the challenge. The time it takes to push a new feature and test it out is often trivial, maybe a few hours.
The real challenge is forming the new ideas in the first place and most of those new ideas coming either from using the code as a product or time spent maintaining and refactoring large code.
Wriring the code is not the challenge, but it's what was taking up most of the time. Not the typing itself, but also because I had to think of how to implement it.
Now I can just say "add 2FA" and in 5 minutes, while I test something else, it is done.
It also made iterations a lot faster, you can try something out, see how it feels, if it doesn't work, you can just trash all the code and start again.
Haven't typed a line of code or read any code for over 6 months now.
And I used to love coding and be a competitive programmer, but this is how "coding" goes nowdays.
I have a mental model of what it would do, and how it would work, and I ask questions to confirm things and tell it to watch for specific gotchas. Then simply test the feature myself a bit.
Security-wise, I think the latest cyber models are better than me anyway at finding vulnerabilitates and pentesting such features.
Plus 2FA is a very common pattern, so it likely has in the training dataset many really good implementations.
> Security-wise, I think the latest cyber models are better than me anyway at finding vulnerabilitates and pentesting such features.
I don't doubt that, but they are equally good in making mistakes, over-engineering, or adding things you never asked for. They have all sorts of patterns in their training data from excellent to inadequate and I find them challenging to guide them consistently in one direction. Also with questions and tests, they can add something extra you didnt need and you dont know about, so your scrutinizing questions and test cases could miss that.
At least for myself, I didnt find them reliable enough yet to do what you describe and just not look at the code at all.
As someone who has an ide open with claude/codex running, I can never imagine leaving security up to models.
Models have found vulnerabilities that i wasnt aware of sure, but their fixes to the bugs they found often included "overengineering". In this case by "overengineering" i mean optimizing for passing test cases related to said vulnerability they found. eventually i have to step in to make things coherent and make sure that future agent can look at this part of my code and copy it to not introduce that particular class of vulnerability. Otherwise if i dont do that similar vulnerability and codesmell keep appearing throughtout the codebase.
I have increasingly automated encrypting and rotating secrets and setting permissions on them including better network level practices. Thanks to AI which helped me quickly implement those. So security wise i am better because of AI? But I also attribute it to my know how rather than the AI because I have never seen AI suggest robust but simple security postures.
This app[0] is entirely "vibe-coded", started on bolt.new which was like the OG way to vibe code and slowly improved over 1-2 years using various models.
I have no idea how the code looks like, and barely even tested the app entirely, because it is still not released yet, but I do think a lot about new features, tweaks, improvements, etc. Years of programming and game development did help, but I don't think anymore that code is relevant, as long as it looks ok and feels good.
Sort of, it's more of a heurisric of bugs found so far. There are very rarely any bugs, most of the changes I request are for tweaks/logic changes, not bug fixes. Usually things just work.
Also, I barely tested and kept changing things simply because of this: whatever I ask for, seems to work as expected.
No, it was about testing only. I will only do a full test of the app when it's ready to be released, otherwise it makes no sense, as the app/product/flow still changes a lot. It's like prototyping a game, you try it a few times, see what's fun, what's not, and keep iterating. Only once you think it's good enough, you can start fully testing and polishing it.
Even after release, I don't see reasons to check the code if everything works and people are happy with the app.
It’s equally good at catching mistakes as it is at making them. So you put it in a loop, write code, make mistakes, catch mistakes, write code, make mistakes… etc. Eventually it converges and stabilizes. That process can take weeks. So the question is: when is it worth it versus not?
You are abdicating your responsibility, which is fine for toy apps for yourself, but less fine when people expect that 2fa implementation to protect their accounts.
I think it's the opposite, with the right guideance, testing, frameworks, and using the top models today, implementing something with AI is most of the times better than what most developers would do.
I basically only manually test e2e myself, other tests are automated, code review is automated. I can tell the model to test for me too specific things or to add tests for specific potential issues, performance benchmarks, compared different implementations, etc.
The focus is a lot more around the code than on the code.
1. If what you say is working, you have a working software factory that should be capable of matching the output of dozens of engineers.
What very impressive externally verifiable results have you had with this?
2. If you are working in software with plenty of customers, my strong suspicion is that there are people on your team who are looking at the code who furiously trying to reign in your output.
I make my own software, so no team to look over the code or be bothered by it. I don't even know how "vibe-coding" works in a team environment, because for me now it feels like it's "ideas to app" directly, so dumping by brain/ideas directly into a functional product.
I think this one is really cool[0], will be a free piano learning app. I do have other projects, but they are all at around 80% too, because some systems are shared amongst the projects and have to be finalized too (i.e. now I'm implementing my own transactional/marketing email service on top of Amazon SES, I need it before releasing ultimidi so people can register and receive email confirmations).
> What very impressive externally verifiable results have you had with this?
I notice this weird hostility whenever the topic of AI coding comes up and it's never made much sense to me. If someone told me about their new method for practicing guitar I'd feel like a real tool if I demanded they prove it for me then and there.
People don't owe you their "very impressive externally verifiable results" - /u/XCSme already posted their app in another comment, it looked fine to me.
You've made your ideological position very clear here, you don't need to keep heaping it on.
If someone tells us about their new method for practicing guitar, it is very natural and not at all a toolish behaviour to hand them a guitar and say “go on, play us something”.
In fact it’s actually a very socially agreeable action, as it gives them the opportunity to show off their new skills without them looking like they’re bragging.
Now, if you happen to know for a fact that the guy cannot actually play guitar, then you’re just setting him up to embarrass himself, which is maybe an extreme punishment for the relatively minor crime of spouting some bullshit. I could buy that that is hostile, sure.
Not arguing for not reading test code. A lot can be achieved by instructing agents to balance out the testing pyramid with the right amount of fast end to end tests, property based tests for the right things, parametrized example based tests. Ensuring the local and CI has the right mix of tests running at right time. On projects where I have less time to review AI generated code I channel my anxiety into setting up guardrails and processes for the agents and then force them to bump up against them. In the end I view it as creating frameworks which allow me to outsource some of my attention to the agents, so that I can claw back some of that time to go set up more guardrails for more agents who are working on something else.
You don’t, that’s why in agentic world your codebase is only as good as what you can prove. For this reason, you’re going to see more languages evolving feature like capability permission, effect/coeffect types, refinement types, formal verifiers, strict type checkers, static analysis and so forth. Tests are only a small part of the verification. These ideas are old and have sat outside of the mainstream coding world, but the value proposition in the ai age has changed enough their relevance is renewed.
I trust a modern model implementing a standard feature like this much more than 99% of the people I've worked with.
People bash LLMs for overengineering but for this it's what you want. Taking extreme edge-cases into account that a human would never bother with and obsessing over security.
I mostly vibe coded a queuing system to replace something we’re using at work (last week. Spent about $1500). Then I meticulously went through the code.
It was much harder to review because it was ultra defensive and included guards for tons of edge cases that weren’t possible.
Unnecessary abstractions for possible extension later. Useless indirection. Probably 3x as much code as there would have been if I’d written it by hand.
I didn’t one shot this. I kept a pretty tight leash on the AI. I had probably a dozen markdown files with of plans that I created over hours of back and forth with the AI and reviewed before each implementation round. I had automated reviews and quality gates etc…
What I found in review was that it was full of very subtle bugs that would have bitten hard in prod. Committing offsets asynchronously that would lead to dropped messages. Clock drift bugs that would lead to dropped messages or write amplification storms. Lack of back pressure in some stages of the pipeline that would cause notes to get silently OOM killed. Weird over-insistence on never crashing in most places that would mask systemic errors.
If I’d just shipped it without review, it would have mostly worked. But at the scale it’s going to be used (tens of thousands of messages per second) it would have caused production issues for months while we tracked down each of these issues.
> included guards for tons of edge cases that weren’t possible
It's not possible until it is. This is the justification lazy developers like we all are have been using leading to bugs down the road. This glorification of hand-made code is strange, like we weren't writing dirty code full of shortcuts all the time.
The issue with LLM guarding isn't that it's "excessive" in outputting edge case handling, it's that the result often ends up just suppressing an error that actually indicates there is a bug or that should be handled elsewhere in a different way.
> Haven't typed a line of code or read any code for over 6 months now
> I ask questions to confirm things
Oh my.
As someone who reads the code, I can tell you, asking questions to confirm things is inadequate. The models lie to me, daily.
Every day I have two experiences:
1. I’m blown away by what it can do
2. I say, ”wait, you said this, but the code shows that, so you were just going to leave that endpoint without requiring any authentication??” and I get the “you’re absolutely right, that was my mistake, and you’re right to call it out” song and dance. Daily.
It also adds all kinds of bloat to code, tests, and “documentation”. I’d say I spend ~30% of my dev time picking lines of code or documentation and asking, “why does this exist?” and “what would break if we deleted this line?” and then arguing with it and removing things.
Yeah, I agree, they are far from perfect and make a lot of mistakes.
Truth is, modern software was already quite shit and full of bugs. All major apps had bugs, issues, going down, etc, so users did get used to things not working. I honestly beleive AI coding nowadays, for better or worse, does things better than the average developer.
Yes, it is overly defensive and verbose, but the end result is in general ok and fully functional. Yes, it adds 30 tests and "release gates", and they are not even that useful, most of the times they just act as an extra safety mechanism to make parts of the code immutable, so release fails if the model accidentally changed things.
Another issue with looking at code, is that it's very hard to manually change things anyway. I can't just change a variable from 10 to 20, because I don't know where it is used. I have to ask the model to set that value to 20. It is quite stupid and inefficient, but this is one cost of coding using AI. But, if you do this, things will likely work.
That being said, I've mostly used Astra xhigh since it was released and things just work.
Hmm, my experience is different. I noticed Astra always asks me to add credentials in a secure way, to never pasted them in chat, to rotate any potentially exposed tokens, etc.
I could probably look over, and get some snippets, but currently only the client-side is live, so the code is sort of there, albeit I assume it's minified for production.
The game seems quite bug free though, including minigames. The UI could be better, but it's not done yet.
I do for example have an automated system that simulates progression, takes screenshots of the game to find potential hidden buttons or overlapping elements, to test for performance, etc.
If it looks like a duck, and quacks like a duck, I honestly don't see why I would review 100k's of lines of code.
EDIT: I might have replied in a wrong thread, but it was about this entirely "vibe-coded" app: https://game.ultimidi.com
> If the core or that app is more than a few thousand lines of code, something is seriously wrong.
That's the thing about vibe-coding: it is not the core, it is the entire app. We no longer make MVPs and release those, with AI we can make directly the app including all bells and whistles, entire progression, not just one level, all the systems around it.
Why? Because if something needs changing, it's just one prompt away. I do think code is fluid now, any choice of architecture can be instantly changed at basically no cost.
Maybe my mind is just finding ways to cope, thinking that I "wasted" thousands of solving coding challenges and fixing bugs, but I do think, for better or worse, that manually coding is gone. Same as we no longer code in assembly anymore. We no longer write C. We no longer write JavaScript. We no longer write TypeScript. Maybe not today, people don't like change, but manually writing or even viewing code will only be done in a few educational and high-performance/risk cases.
The limiting factor of development (for money) is good ideas, or valuable ideas. If you have 5 features you want, but only time in a month for 2, then that forces you to choose the best 2, which come with the tech debt, support and opportunity cost.
Removing the opportunity cost doesn't eliminate the other two costs of a feature
Playing an instrument vs electronic/computer music.
We do forget skills we don't practice, especially fine motor skills (like playing the guitar or typing code).
There's inherent pleasure in playing a musical instrument - practicing improves fine motor skills and produces satisfaction.
You can play for yourself and that can be a great experience.
Often people create music for other listeners - and now the satisfaction comes not just from your skill, but from how the music impacts your listeners.
They say you can put more of your 'soul' into music made with an instrument, but I'd say there's quite a bit of electronic music with just as much soul.
People who create electronic music don't generate any of those sounds with their fine motor skills, but they do have a plan about how the song progresses and what emotional state it elicits in users.
That's why you have DJs which are more popular than others.
If you stop playing the guitar for a year, then pick it up and try playing something, you will feel very rusty. But give it a week of practice and most of your skill comes back.. and in 1 month you're back to your peak skill.
I guess my point is - If you go full on agentic, you'll loose some of your coding skill, but you can get it back fairly quickly if you go back to manual coding. On the flip side, you get better at using AI if you use it, so your thinking is at a higher level, but you give up understanding the low level details of how exactly the code works.
Either way you're making 'music', albeit a different kind of music.
Ha, I could talk about this framing for days as I think about it a lot.
To add some points on he other side of this analogy:
There is not a lot of purely electronic music that has stood the test of time, at least not when it comes to popularity or, more relevant to the metaphor, profitability. There is usually at very least a human voice in the (literal) mix, but more often than not there are also traditional instruments mixed in.
Take this next point as you will as I am being a bit tongue-in-cheek: While making music-making more accessible to more people is totally great, if I could go a week without hearing a variation of the phrase "Check out my dark ambient drone project!" I would feel oddly accomplished.
Most importantly, though:
> But give it a week of practice and most of your skill comes back.. and in 1 month you're back to your peak skill.
This is only true if you had the skills to begin with. For many electronic musicians, by which I mean junior developers, this is not the case. Does it matter? As a 45-year-old traditional musician... er, I mean hand-coder... I think so, but also ¯\_(ツ)_/¯
Engineering is not music though. If you're building a skyscraper, and your tower crane breaks, you could go old-school like they did with the pyramids in ancient Egypt. But why would you?
Now, manual labor does have its place as a form of art--take high-precision hand-built timepieces for example.
I also had similar feelings recently. I think the article is good and captures many of the issues I have with the current state of AI development, but I feel like the conclusion/reaction is somewhat exaggerated.
Of course if someone wants to stop using AI completely that's a completely valid decision[0], but I somewhat feel like AI is just a tool that can be easily misused.
I constantly have to review giant PRs and I noticed that I'm handwaving them more and more often. We went from almost no commit messages to walls of text that no one reads. We're starting to become bottlenecked on reviews because code is coming out too fast.
But at the same time, these are mostly issues stemming from a lack of understanding of why some of the standards/processes existed in the first place. If a developer thinks the commits have to be written just to tick a checkbox, they won't care about making them readable.
And at the same time, I'm getting a lot of value from AI, in tasks that do not necessarily have such adverse effects:
- I can create quick tools to test something, or parse/process some data. In these instances code quality is not important and I don't really want to spend hours on developing it myself (just to feel accomplished?)
- I can research issues in our codebase by just providing a log file. It's not always gonna be accurate or correct but it often gives me a very good starting point, almost always quicker than I could've done it myself
- While I do not use AI to completely generate ticket descriptions, asking it to generate me a body containing the relevant code snippets and references allows me to focus on verifying that what I'm writing is correct and understandable.
Etc etc.
So I don't know if it's just the nature of my work, the fact that I have a different skillset, or different priorities. But it somehow feels weird to me wanting to completely abandon AI just because in some cases it can lead to frustrating consequences.
[0]: I too just started a new project where I'm forcing myself to use absolutely no AI!
Implementation reviews are not useful anymore. CRs should be replaced with design reviews where reviewers validate the sanity of the specs and the coverage of the tests
“ There were tasks I could have done in 20 minutes easily, that took 5 minutes of an AI agent, and then 2 days for me to review.”
This is using AI for productivity in any domain, in a nutshell. I just wrote a book using Claude as an experiment, and while the thing got done and it was an amazing tool and a great experience, what I’m left with is a book where every line needs rewriting, there are logical inconsistencies throughout, and the style is so bad it should actually just be binned rather than rewritten.
I don’t see how that’s possible. When I am doing a comprehensive code review with rigorous functional testing of another developer’s work, it usually takes me around half as long to review as the developer took to write it. That includes the back and forth of MR issues and fixes. If AI writes it why would it take 100x longer to review than write it? At worst case it’s just a draft of something I can develop myself, so it shouldn’t take longer than 20 minutes. At best case it is a code review exercise so it takes me half as long.
AI can produce for you an amazing jpeg of an oil painting you’ve ideated together. You could print it out on a large format printer but you’re still only holding a printout of an AI oil painting. To actually make a piece of art that’s worth being on a wall and standing as an artwork, then you still have to paint the painting using oil paint.
Maybe robot arms can solve this last part one day.
> I just wrote a book using Claude as an experiment, and while the thing got done and it was an amazing tool and a great experience, what I’m left with is a book where every line needs rewriting, there are logical inconsistencies throughout, and the style is so bad it should actually just be binned rather than rewritten.
How was this a great experience if what was produced needed such extensive changes that your own assessment is that it should be thrown out? At what point is the necessary rework so much that the thing being reworked didn't really contribute much to the end product at all?
It was really interesting in having an assistant who was completely on the ball and up to speed, never forgot, could pick up from where we left off days ago, could produce and summarise arguments… but the issue was that the amazing assistant could only really work one chapter at a time even with a plan, and changing one chapter would often break things said in other chapters, and their style of writing was such that the resorted to cliches, bad metaphors, and generally bad or repetitive prose. So the grammar was fine, the style was bad to the point where the whole work would not be something I could have my name on or near. I really could come back to it, just having to retype everything in markdown made me see that the project had legs and the topic is sufficient for a book length treatment, but even with the help of Claude I have a few months work to do to get it done and out there. Just it’s a different few months work to the work I would have had without Claude. I could also have attempted to pay Claude thousands of dollars to see if it worked better, but the book is not likely to recoup what that would cost.
I noticed lately that recent LLMs write very short sentences, sprinkling so many periods over one paragraph. I'm pretty sure that this is some new regression that we're having with new models.
Hmmm I was using Claude Opus for the most part. I didn’t notice any particular shortness in the length of the sentences, but that’s also an issue in itself – the whole thing is just average fine-ness: the sentences are fine, the paragraphs are fine, everything is generic and inoffensive and fine. But at the same time it was giving huge amounts of pushback when I was getting it to interrogate logical faults of certain domains, to the point where I was having to argue with and even convince an AI using data and even its own analysis that there was cause to talk about certain things from a certain angle (can appreciate I’m being vague about which things). It can’t write complex, deeply-claused sentences (or won’t). And it also wants to write snappy little concluding sentences that make a fairly well-argued piece of sociological analysis read like Sex and the City or something.
And just like that, the book got abandoned.
N.b. some of its analysis and laying out of faults in arguments was actually pellucid and brilliant, it can’t be denied. Just it comes with prose that can’t really be used for anything. And even on another occasion when I got it to help me redraft and extend a different book of mine, then it randomly and consistently started stripping out all the stylistic flourishes out of my sentences, to the point where it couldn’t notice that word choices were very deliberate and actually set up little punchlines and logical payoffs paragraphs or chapters hence. And even when I explained and showed it what it was doing, it was like “ahh that’s so clever and brilliant” but just continued to do the same thing.
That's a side effect of people bashing the em-dash and the labs rushing to correct. The period is the most common replacement (along with some minor tweaks to sentence structure), so now that models are being RL'd away from the em-dash the models are overusing a new construction.
It’s not that it’s intractable, and I genuinely enjoyed the process working with Claude. But responding to the initial post – using Claude I managed to do three months work in about two weeks, but at the end of that I then realised I probably had three months of improvements and rewrites and corrections left to go if it was going to be to be a book worth anyone else reading.
It’s not just coding that’s affected by the skill degradation issue. As a sysadmin and jack of all trades IT engineer over reliance on AI has unsurprisingly led to a serious knowledge dip.
I see great engineers troubleshoot everything by pasting logs into the prompt and blindly accepting the answer. Zero added value while they ctrl-c ctrl-v themselves out of a job.
One very unfortunate curse we are stuck with is LLM providers pulling the rug from under us and swapping models with quantized versions during peak demand or on monthly plans. You never know if that's the reason AI starts writing shitty code or it's just the context size that has grown. On top of that there is the queuing mechanism which makes tasks take longer. Sometimes AI is super fast and sometimes a simple task takes 10, 15 or even 20 minutes of "thinking". You may not realize this if you are just using your same subscription but if you switch between providers you can immediately tell. I advise everyone to invest $5 in openrouter, deepseek and qwencode. Just do an experiment and use same model (deepseek-4.1 flash) asking same prompt on same code, then look at the result. Spoiler: you will see trash coming out of openrouter at 1-5 tokens/second, you will see slightly faster and better quality output from qwencloud and when you get to deepseek API where you are paying per token you will not believe this is the same model. Because it isn't.
I don’t mean to advertise OpenAI - let’s make it clear, fuck OpenAI - but I’ve never seen a degradation like that in Codex. All models have always seemed completely stable over their release lifetime. Meanwhile, I rolled back my attempts at using open weight models because providers start throwing “too many requests” errors after just a few requests and the pricing is roughly 10x worse for same model quality, except the inference is much slower. Getting your weights silently downgraded sounds like fun.
> you stop questioning, and start accepting as good some code you would have never accepted, just because you cannot tell why it’s bad. You have lost control
I have not lost control.
I my most prolific project I do not review the code, but I QA test extensively.
In other projects at work, I review the code.
I prompt to simplify, I challenge implementation that solves irrelevant edge cases, resulting in much smaller PRs.
In projects where I do not work alone, I still write two line PR descriptions myself.
Dumping paragraphs of AI output into the description of a MR where I ask others to review I consider disrespectful.
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> If you turn off your brain, and relax babysitting AIs, you’re not getting any better. You’re losing value
I'm hardly turning off my brain here.
As the author notes, the context switching and so on takes concentration and effort too.
I can say without doubt that I am more productive than ever.
I am getting better by the month, and I am not currently losing value, until the AI fully replaces both me and the author.
The biggest problem with AI isn't being able to do things. You can do them. The question is "should you?" I had a peer dev this week put in a commit that was 1.2mb (this despite an AGENTS.md saying not to do exactly this) of plan documents and ~20 lines of code. The conversation on the PR review was probably one of the best learning experiences for both of us. It was all about the ramifications of a book's worth of AI pontificating about what it would do and what succeeded and what failed. And we were able to validate that that plan document had huge side effects as we prompted with it in the repo. As we read the document we realized that it was just 1.2MB of smart sounding gibberish and maybe two bullet points of signal.
I think there’s a critical difference between AI automation for software development and previous rounds of similar automation, like CNC machines automating manual machining.
The barrier to software development has only ever been computer access and knowledge.
With AI, it’s roughly computer and internet access.
This means we’re getting a lot of people who aren’t good at either software development or AI automation playing with both. It’s the majority of what people seem to talk about.
I don’t think this is bad, but I do think it’s making real progress in AI automated software development on teams which are good at both much less visible.
A conservative team member of mine estimated we’re working at 200x speed these days, compared to 2 years ago. And we still see ways we can improve. A parallel team is only seeing an 1.2x increase, but they are unable to modify their architecture around AI.
Some of this is shifting roles. You can have a mildly technical domain expert vibe code the frontend for a new module. The more AI automation you’ve architected for, the faster they can go and the higher quality the outcome. We’re experimenting with mixing vibe coding with specifying formal requirements to push this further.
This works well. And now you’ve cut dozens of rounds of the PM not knowing the right shape for the new software out of the process. Even if we threw the end code away, this would save us tons of time.
I think that comparing AI to cocaine is a really good analogy. It gives you that feeling that you are amazing and can do anything, but it's just a feeling, in the end you are just high and sooner or later will have to come back to reality and face a bad implementation or a hangover
For most folks here basic smartphone with tiktok is cocaine. No basic discipline and self-control. Plus they believe everything "expert ai" tells them, no critical thinking, independent verification...
I'd compare it to bath salts (as in α-pvp). I have an unforgettable experience seeing a substance abusive acquaintance of mine feverishly trying to reverse engineer a Doppler radar's output, he was just trying to read raw hex stream of data and find patterns in it.
"See?? Here's that DEAD BEEF CAFFEE again! Look! Again! The FECE FACCA AFFEC7!! I'm so close to crackin' it! Aha.. Aha.. ABEBE23.. BECACA17.. 1337C0C.. It all clicks in place, don't you see? I'm totally getting it!"
He was all bubbling like this throughout the whole night until his brain just issued a shutdown to let the body rest a bit. That was truly a horrible sight.
I remember him every time I see instances of AI psychosis around.
Why don’t you start a company where AI is banned. Surely it will do much better. In fact, you should also believe that most companies will bad AI for the same reason.
Smh new conspiracy theories every day. “Ai is actually a drug and you only feel that it helps you but it doesn’t”
I don't use AI and the company I work for is doing just fine. Why don't you take a daily dose of Ketamine, that may give you interesting ideas like it does for Silicon valley "entrepreneurs". Don't you want to be the next Peter Thiel or Elon Musk?
I would take ketamine if it helps me perform better without adverse side effects.
You’d rather paint AI as if it were a hard drug to cope with the world changing around you. I mean listen to yourself. Im not the one making conspiracy theories.
The bottom line is, if the code matters, someone needs to take responsibility for the changes. You can't take responsibility without understanding and understanding takes a lot of time.
AI helps a lot but for code that matters, use it to fill your context window, not the other way around. For code that doesn't matter (prototypes, speed runs, etc), vibe away - it can be super fun!
Where I find LLMs most useful is maybe not even writing code (though helps with boilerplate and test cases, mechanical refactoring, translation from one form into another, rebases and conflicts), but debugging weird production issues, where I have to pull data from multiple places (with often horrible UX) and correlate the data and logs and code. Just need to be careful to follow its reasoning as it can easily go down the wrong route if it misses some important part of the contex, but an SME in the area spots that and directs the agent accordingly. This cut down time for investigations quite a bit.
My workflow leans on AI for some tasks: review what I wrote, drop me a one-off script (this one is the generative exception), and so on.Or even initial scaffolding.
For the rest I do not. I do not place AI-generated code anywhere.
You lose all control AND UNDERSTANDING.
When things go wrong it gets very messy.
I will keep doing this, I think it works well, I emjoy programming and I think it is productive.
For testimg I tend to write randomized testing, which takes a bit of design but oncr you have it, well, it os test-generatove and increases the quality of checks.
The point about multitasking getting out of control very much hits home for me. This summer my team tried going hard for agentic coding. Within a month we had 6 people each juggling at least two projects at a time, all the time.
Soon we rediscovered Little’s Law. WIP was piling up and we were getting overwhelmed at the integration phase, and realized that we had got really good at starting projects but actually finishing them was a struggle. Tickets were moving fine, of course. Our rate of generating code and committing PRs was through the roof. But getting actual projects to a point where the stakeholders and customers were happy with the result was just not happening.
So now we have gone back to strict WIP limits and requiring every non-trivial project to have at least two people collaborating on it. The rate at which we are churning out code has gone back down, along with the token bill, but the logjam is clearing. Better yet, the stakeholders, who never cared about our quantitative velocity metrics in the first place, have eased off on complaining that we aren’t getting anything done.
I think there might be (dare I say) a middle ground to get the productivity of the llm, esp as we evolve them, while still maintain a global and even fine-grain comprehension of a code base.
It is not a simple change, however, but a fundamental one.
Overall I think we are still living in the past and try to apply ourselves to the future. But if the ai craze is to be taken clear-headedly for what it is, it is a complete break from the von Neumann computer and all its resulting artifacts. So why should we use the same tools?
I get the sentiment but really you can't argue you are nearly as productive from a business point of view without LLM agentic assistance. Sure do a hand code Friday to keep skills up similar to how a airplane pilot hand flies in the clouds every now and then to keep up skills, but autopilots most of the time.
T2 the war has already happened and Skynet sends the T-100 back in time.
T3 Skynet already exists and is hunting John Connor down
I wanna see the conference room meeting where they decide to push an unfinished, unstoppable technology. I guess T1 is the closest as it happens when Skynet "gains intelligence"
It's time travel, so "already happened" is not well defined.
> I wanna see the conference room meeting where they decide to push an unfinished, unstoppable technology. I guess T1 is the closest as it happens when Skynet "gains intelligence"
This is the mid-point of T3. From the Wikipedia plot summary:
General Brewster is supervising the development of Skynet for Cyber Research Systems (CRS), an autonomous weapons developer. The Chairman of the Joint Chiefs of Staff pressures him to activate Skynet to stop an anomalous computer virus from invading servers worldwide. Brewster fails to discover that the virus was Skynet becoming sentient. John, Kate, and the Terminator arrive too late to stop him from activating it. The T-X appears, fatally injures Brewster, and controls weaponized CRS T-1 and HK drones to kill other employees.
> I stopped reading those descriptions, like, I told it what to do, I assume it did what I said. The description is for others.
If you cannot read it as the author, what hope do I have to read and make sense of the wall of text which doesn’t seem to describe what I actually need to start reviewing.
I really really encourage everyone to write their own descriptions for PRs. If you cannot succinctly describe it in a way another human understands then you don’t understand your own change and you should withdraw your request.
Protip: if none of your system can really be released independently, you're working on one system and should have one repo. Then you dont need a third release repo to orchestrate the release dance. You have a typescript/ruby monorepo without issues.
> It started reminding me of the strategy where Google lowered the quality of the search results, forcing users to search several times, and see several times as many ads. And if that worked for Google, how is it not going to work for AI? Specially in the uncertain financial times the AI companies are going through, do you really think they would not do it? Come on. They will do this and more.
We’re using an online, undeterministic, black-box middleman to generate our code. It’s 100% Trust me bro. No proof, no scrutiny, no guarantees.
I vehemently oppose delegating our brains to the AI. I agree with the article in spirit.
> let me tell you about this experience, and how it was turning me dumber, lazy, and a worse developer.
Though the article discusses from the point of using agents, I digress to the topic of building with AI in general.
My experience has been the exact opposite. A new idea (usually related to correctness or architecture) is discussed first with the LLM where it defaults to average Joe idiotic bullshit pushback.
This frustrates me and I abuse the LLM for being idiotic by explaining the how. This results in a more refined and concrete form of the abstraction leading me to even more insights.
The LLM remains an idiot. But a useful idiot nonetheless.
Just today it made three glaring mistakes in one session:
1. It read a file in the wrong directory, because that file had the same name as the file in the right directory. It apologized when I challenged it, promising me that it would remember to "read import statements" in the future.
2. It miscounted the number of times a function was called in my repo. It said 20, while my built-in IDE search accurately showed 17. Again, it apologized when I corrected it.
3. It referred to a variable by name that does not exist anywhere in my code. It apologized, and said it was referring to a variable used internally by one of the third-party packages installed in my repo.
So many apologies.
It's the little things like this that remind me on a regular basis just how little I can trust artificial "intelligence."
Could you work out that it was wrong, given weeks to go and check its work? If so, your trust is misplaced.
You ARE taking days or weeks to go and check, yes?
Over the past six months I tried using Claude, chatgpt, Grok and Gemini. At best I got reminders of how things worked. People online say they use them to write their code. The code they supplied to me has NEVER worked or was so convoluted that I threw it away and did it myself.
At most, I use these tools as search engines. Even then some references are poor.
I'm starting to think this is becoming a sad, sad world and AI is just the new TV of the programming world.
It usually takes me two or three iterations to get there though. Discussing design and principles before writing the bulk of the code is a must. And then a pass or two of review to weed out ugliness.
Still saves time compared to writing the code by hand. Especially for tricky things, where type checking and tests can verify correctness.
The real challenge is forming the new ideas in the first place and most of those new ideas coming either from using the code as a product or time spent maintaining and refactoring large code.
Anyways, if you want to continue on the path towards regaining control and take it to the next level I wrote something similar here: https://blog.sharefile.systems/be-brave-go-low/
Now I can just say "add 2FA" and in 5 minutes, while I test something else, it is done.
It also made iterations a lot faster, you can try something out, see how it feels, if it doesn't work, you can just trash all the code and start again.
Haven't typed a line of code or read any code for over 6 months now.
And I used to love coding and be a competitive programmer, but this is how "coding" goes nowdays.
I have a mental model of what it would do, and how it would work, and I ask questions to confirm things and tell it to watch for specific gotchas. Then simply test the feature myself a bit.
Security-wise, I think the latest cyber models are better than me anyway at finding vulnerabilitates and pentesting such features.
Plus 2FA is a very common pattern, so it likely has in the training dataset many really good implementations.
I don't doubt that, but they are equally good in making mistakes, over-engineering, or adding things you never asked for. They have all sorts of patterns in their training data from excellent to inadequate and I find them challenging to guide them consistently in one direction. Also with questions and tests, they can add something extra you didnt need and you dont know about, so your scrutinizing questions and test cases could miss that.
At least for myself, I didnt find them reliable enough yet to do what you describe and just not look at the code at all.
Models have found vulnerabilities that i wasnt aware of sure, but their fixes to the bugs they found often included "overengineering". In this case by "overengineering" i mean optimizing for passing test cases related to said vulnerability they found. eventually i have to step in to make things coherent and make sure that future agent can look at this part of my code and copy it to not introduce that particular class of vulnerability. Otherwise if i dont do that similar vulnerability and codesmell keep appearing throughtout the codebase.
I have increasingly automated encrypting and rotating secrets and setting permissions on them including better network level practices. Thanks to AI which helped me quickly implement those. So security wise i am better because of AI? But I also attribute it to my know how rather than the AI because I have never seen AI suggest robust but simple security postures.
I have no idea how the code looks like, and barely even tested the app entirely, because it is still not released yet, but I do think a lot about new features, tweaks, improvements, etc. Years of programming and game development did help, but I don't think anymore that code is relevant, as long as it looks ok and feels good.
[0]: https://ultimidi.com
>barely even tested
So the lowest stakes possible and you have absolutely no idea what bugs are waiting.
Also, I barely tested and kept changing things simply because of this: whatever I ask for, seems to work as expected.
This is worded as if knowing how the code works and testing it is hinged on it being released.
Even after release, I don't see reasons to check the code if everything works and people are happy with the app.
I basically only manually test e2e myself, other tests are automated, code review is automated. I can tell the model to test for me too specific things or to add tests for specific potential issues, performance benchmarks, compared different implementations, etc.
The focus is a lot more around the code than on the code.
1. If what you say is working, you have a working software factory that should be capable of matching the output of dozens of engineers.
What very impressive externally verifiable results have you had with this?
2. If you are working in software with plenty of customers, my strong suspicion is that there are people on your team who are looking at the code who furiously trying to reign in your output.
I think this one is really cool[0], will be a free piano learning app. I do have other projects, but they are all at around 80% too, because some systems are shared amongst the projects and have to be finalized too (i.e. now I'm implementing my own transactional/marketing email service on top of Amazon SES, I need it before releasing ultimidi so people can register and receive email confirmations).
[0]: https://game.ultimidi.com
I notice this weird hostility whenever the topic of AI coding comes up and it's never made much sense to me. If someone told me about their new method for practicing guitar I'd feel like a real tool if I demanded they prove it for me then and there.
People don't owe you their "very impressive externally verifiable results" - /u/XCSme already posted their app in another comment, it looked fine to me.
You've made your ideological position very clear here, you don't need to keep heaping it on.
In fact it’s actually a very socially agreeable action, as it gives them the opportunity to show off their new skills without them looking like they’re bragging.
Now, if you happen to know for a fact that the guy cannot actually play guitar, then you’re just setting him up to embarrass himself, which is maybe an extreme punishment for the relatively minor crime of spouting some bullshit. I could buy that that is hostile, sure.
But you didn't write or read any of the tests so how do you know they are accurate?
People bash LLMs for overengineering but for this it's what you want. Taking extreme edge-cases into account that a human would never bother with and obsessing over security.
I mostly vibe coded a queuing system to replace something we’re using at work (last week. Spent about $1500). Then I meticulously went through the code.
It was much harder to review because it was ultra defensive and included guards for tons of edge cases that weren’t possible.
Unnecessary abstractions for possible extension later. Useless indirection. Probably 3x as much code as there would have been if I’d written it by hand.
I didn’t one shot this. I kept a pretty tight leash on the AI. I had probably a dozen markdown files with of plans that I created over hours of back and forth with the AI and reviewed before each implementation round. I had automated reviews and quality gates etc…
What I found in review was that it was full of very subtle bugs that would have bitten hard in prod. Committing offsets asynchronously that would lead to dropped messages. Clock drift bugs that would lead to dropped messages or write amplification storms. Lack of back pressure in some stages of the pipeline that would cause notes to get silently OOM killed. Weird over-insistence on never crashing in most places that would mask systemic errors.
If I’d just shipped it without review, it would have mostly worked. But at the scale it’s going to be used (tens of thousands of messages per second) it would have caused production issues for months while we tracked down each of these issues.
It's not possible until it is. This is the justification lazy developers like we all are have been using leading to bugs down the road. This glorification of hand-made code is strange, like we weren't writing dirty code full of shortcuts all the time.
You're acting as if code was incredibly secure before LLMs because humans were reviewing it.
> I ask questions to confirm things
Oh my.
As someone who reads the code, I can tell you, asking questions to confirm things is inadequate. The models lie to me, daily.
Every day I have two experiences:
1. I’m blown away by what it can do
2. I say, ”wait, you said this, but the code shows that, so you were just going to leave that endpoint without requiring any authentication??” and I get the “you’re absolutely right, that was my mistake, and you’re right to call it out” song and dance. Daily.
It also adds all kinds of bloat to code, tests, and “documentation”. I’d say I spend ~30% of my dev time picking lines of code or documentation and asking, “why does this exist?” and “what would break if we deleted this line?” and then arguing with it and removing things.
Truth is, modern software was already quite shit and full of bugs. All major apps had bugs, issues, going down, etc, so users did get used to things not working. I honestly beleive AI coding nowadays, for better or worse, does things better than the average developer.
Yes, it is overly defensive and verbose, but the end result is in general ok and fully functional. Yes, it adds 30 tests and "release gates", and they are not even that useful, most of the times they just act as an extra safety mechanism to make parts of the code immutable, so release fails if the model accidentally changed things.
Another issue with looking at code, is that it's very hard to manually change things anyway. I can't just change a variable from 10 to 20, because I don't know where it is used. I have to ask the model to set that value to 20. It is quite stupid and inefficient, but this is one cost of coding using AI. But, if you do this, things will likely work.
That being said, I've mostly used Astra xhigh since it was released and things just work.
I dread to think what that means in security conscious code.
Can you give an example of unsafe defaults used?
EDIT: Come on? Won't post your code for everyone to see? Why not just put it all in a repo, client and server both?
EDIT 2: Amazing. If you punch in notes on the keyboard for like 10 seconds then click the keyboard-icon button on the bottom right the website crashes
EDIT 3: If you click the main CTA then click "Let's start" the website hangs and you need to manually refresh the page for the content to load
The game seems quite bug free though, including minigames. The UI could be better, but it's not done yet.
I do for example have an automated system that simulates progression, takes screenshots of the game to find potential hidden buttons or overlapping elements, to test for performance, etc.
If it looks like a duck, and quacks like a duck, I honestly don't see why I would review 100k's of lines of code.
EDIT: I might have replied in a wrong thread, but it was about this entirely "vibe-coded" app: https://game.ultimidi.com
> why I would review 100k's of lines of code.
If the core or that app is more than a few thousand lines of code, something is seriously wrong.
I don’t want to shit on your app. It’s cool. I’m glad you built it. I’ve vibe coded all kinds of toy apps for myself and my kids.
But it’s not strong evidence that code is irrelevant.
> If the core or that app is more than a few thousand lines of code, something is seriously wrong.
That's the thing about vibe-coding: it is not the core, it is the entire app. We no longer make MVPs and release those, with AI we can make directly the app including all bells and whistles, entire progression, not just one level, all the systems around it.
Why? Because if something needs changing, it's just one prompt away. I do think code is fluid now, any choice of architecture can be instantly changed at basically no cost.
Maybe my mind is just finding ways to cope, thinking that I "wasted" thousands of solving coding challenges and fixing bugs, but I do think, for better or worse, that manually coding is gone. Same as we no longer code in assembly anymore. We no longer write C. We no longer write JavaScript. We no longer write TypeScript. Maybe not today, people don't like change, but manually writing or even viewing code will only be done in a few educational and high-performance/risk cases.
Also, what do you mean punch in notes? Like mashing keys and pressing 10 buttons at once?
Never had the app/website crash, tested only on Brave (desktop and mobile) so far.
Removing the opportunity cost doesn't eliminate the other two costs of a feature
I'd expect it's in the tens of thousands, given how effortless you find it.
Code is a liability.
Playing an instrument vs electronic/computer music.
We do forget skills we don't practice, especially fine motor skills (like playing the guitar or typing code).
There's inherent pleasure in playing a musical instrument - practicing improves fine motor skills and produces satisfaction.
You can play for yourself and that can be a great experience.
Often people create music for other listeners - and now the satisfaction comes not just from your skill, but from how the music impacts your listeners.
They say you can put more of your 'soul' into music made with an instrument, but I'd say there's quite a bit of electronic music with just as much soul.
People who create electronic music don't generate any of those sounds with their fine motor skills, but they do have a plan about how the song progresses and what emotional state it elicits in users.
That's why you have DJs which are more popular than others.
If you stop playing the guitar for a year, then pick it up and try playing something, you will feel very rusty. But give it a week of practice and most of your skill comes back.. and in 1 month you're back to your peak skill.
I guess my point is - If you go full on agentic, you'll loose some of your coding skill, but you can get it back fairly quickly if you go back to manual coding. On the flip side, you get better at using AI if you use it, so your thinking is at a higher level, but you give up understanding the low level details of how exactly the code works.
Either way you're making 'music', albeit a different kind of music.
To add some points on he other side of this analogy:
There is not a lot of purely electronic music that has stood the test of time, at least not when it comes to popularity or, more relevant to the metaphor, profitability. There is usually at very least a human voice in the (literal) mix, but more often than not there are also traditional instruments mixed in.
Take this next point as you will as I am being a bit tongue-in-cheek: While making music-making more accessible to more people is totally great, if I could go a week without hearing a variation of the phrase "Check out my dark ambient drone project!" I would feel oddly accomplished.
Most importantly, though:
> But give it a week of practice and most of your skill comes back.. and in 1 month you're back to your peak skill.
This is only true if you had the skills to begin with. For many electronic musicians, by which I mean junior developers, this is not the case. Does it matter? As a 45-year-old traditional musician... er, I mean hand-coder... I think so, but also ¯\_(ツ)_/¯
Now, manual labor does have its place as a form of art--take high-precision hand-built timepieces for example.
No, playing an instrument VS making electronic music has absolutely no comparison to writing code by hand or with AI.
You're comparing the difference between a motor skill and a knowledge-based competency, with the difference between two knowledge-based competencies.
Of course if someone wants to stop using AI completely that's a completely valid decision[0], but I somewhat feel like AI is just a tool that can be easily misused.
I constantly have to review giant PRs and I noticed that I'm handwaving them more and more often. We went from almost no commit messages to walls of text that no one reads. We're starting to become bottlenecked on reviews because code is coming out too fast.
But at the same time, these are mostly issues stemming from a lack of understanding of why some of the standards/processes existed in the first place. If a developer thinks the commits have to be written just to tick a checkbox, they won't care about making them readable.
And at the same time, I'm getting a lot of value from AI, in tasks that do not necessarily have such adverse effects:
- I can create quick tools to test something, or parse/process some data. In these instances code quality is not important and I don't really want to spend hours on developing it myself (just to feel accomplished?)
- I can research issues in our codebase by just providing a log file. It's not always gonna be accurate or correct but it often gives me a very good starting point, almost always quicker than I could've done it myself
- While I do not use AI to completely generate ticket descriptions, asking it to generate me a body containing the relevant code snippets and references allows me to focus on verifying that what I'm writing is correct and understandable.
Etc etc.
So I don't know if it's just the nature of my work, the fact that I have a different skillset, or different priorities. But it somehow feels weird to me wanting to completely abandon AI just because in some cases it can lead to frustrating consequences.
[0]: I too just started a new project where I'm forcing myself to use absolutely no AI!
I use it to find reasoning gaps, add examples, add citations etc. The LLM/Agent can find them quicker than I.
This is using AI for productivity in any domain, in a nutshell. I just wrote a book using Claude as an experiment, and while the thing got done and it was an amazing tool and a great experience, what I’m left with is a book where every line needs rewriting, there are logical inconsistencies throughout, and the style is so bad it should actually just be binned rather than rewritten.
Maybe robot arms can solve this last part one day.
How was this a great experience if what was produced needed such extensive changes that your own assessment is that it should be thrown out? At what point is the necessary rework so much that the thing being reworked didn't really contribute much to the end product at all?
Have you noticed it, too?
And just like that, the book got abandoned.
N.b. some of its analysis and laying out of faults in arguments was actually pellucid and brilliant, it can’t be denied. Just it comes with prose that can’t really be used for anything. And even on another occasion when I got it to help me redraft and extend a different book of mine, then it randomly and consistently started stripping out all the stylistic flourishes out of my sentences, to the point where it couldn’t notice that word choices were very deliberate and actually set up little punchlines and logical payoffs paragraphs or chapters hence. And even when I explained and showed it what it was doing, it was like “ahh that’s so clever and brilliant” but just continued to do the same thing.
I see great engineers troubleshoot everything by pasting logs into the prompt and blindly accepting the answer. Zero added value while they ctrl-c ctrl-v themselves out of a job.
I have not lost control.
I my most prolific project I do not review the code, but I QA test extensively.
In other projects at work, I review the code.
I prompt to simplify, I challenge implementation that solves irrelevant edge cases, resulting in much smaller PRs.
In projects where I do not work alone, I still write two line PR descriptions myself.
Dumping paragraphs of AI output into the description of a MR where I ask others to review I consider disrespectful.
---
> If you turn off your brain, and relax babysitting AIs, you’re not getting any better. You’re losing value
I'm hardly turning off my brain here.
As the author notes, the context switching and so on takes concentration and effort too.
I can say without doubt that I am more productive than ever.
I am getting better by the month, and I am not currently losing value, until the AI fully replaces both me and the author.
The barrier to software development has only ever been computer access and knowledge.
With AI, it’s roughly computer and internet access.
This means we’re getting a lot of people who aren’t good at either software development or AI automation playing with both. It’s the majority of what people seem to talk about.
I don’t think this is bad, but I do think it’s making real progress in AI automated software development on teams which are good at both much less visible.
A conservative team member of mine estimated we’re working at 200x speed these days, compared to 2 years ago. And we still see ways we can improve. A parallel team is only seeing an 1.2x increase, but they are unable to modify their architecture around AI.
Some of this is shifting roles. You can have a mildly technical domain expert vibe code the frontend for a new module. The more AI automation you’ve architected for, the faster they can go and the higher quality the outcome. We’re experimenting with mixing vibe coding with specifying formal requirements to push this further.
This works well. And now you’ve cut dozens of rounds of the PM not knowing the right shape for the new software out of the process. Even if we threw the end code away, this would save us tons of time.
This is just one example.
Like toddlers!
"See?? Here's that DEAD BEEF CAFFEE again! Look! Again! The FECE FACCA AFFEC7!! I'm so close to crackin' it! Aha.. Aha.. ABEBE23.. BECACA17.. 1337C0C.. It all clicks in place, don't you see? I'm totally getting it!"
He was all bubbling like this throughout the whole night until his brain just issued a shutdown to let the body rest a bit. That was truly a horrible sight.
I remember him every time I see instances of AI psychosis around.
Smh new conspiracy theories every day. “Ai is actually a drug and you only feel that it helps you but it doesn’t”
You’d rather paint AI as if it were a hard drug to cope with the world changing around you. I mean listen to yourself. Im not the one making conspiracy theories.
AI helps a lot but for code that matters, use it to fill your context window, not the other way around. For code that doesn't matter (prototypes, speed runs, etc), vibe away - it can be super fun!
For the rest I do not. I do not place AI-generated code anywhere.
You lose all control AND UNDERSTANDING.
When things go wrong it gets very messy.
I will keep doing this, I think it works well, I emjoy programming and I think it is productive.
For testimg I tend to write randomized testing, which takes a bit of design but oncr you have it, well, it os test-generatove and increases the quality of checks.
Soon we rediscovered Little’s Law. WIP was piling up and we were getting overwhelmed at the integration phase, and realized that we had got really good at starting projects but actually finishing them was a struggle. Tickets were moving fine, of course. Our rate of generating code and committing PRs was through the roof. But getting actual projects to a point where the stakeholders and customers were happy with the result was just not happening.
So now we have gone back to strict WIP limits and requiring every non-trivial project to have at least two people collaborating on it. The rate at which we are churning out code has gone back down, along with the token bill, but the logjam is clearing. Better yet, the stakeholders, who never cared about our quantitative velocity metrics in the first place, have eased off on complaining that we aren’t getting anything done.
I think there might be (dare I say) a middle ground to get the productivity of the llm, esp as we evolve them, while still maintain a global and even fine-grain comprehension of a code base.
It is not a simple change, however, but a fundamental one.
Overall I think we are still living in the past and try to apply ourselves to the future. But if the ai craze is to be taken clear-headedly for what it is, it is a complete break from the von Neumann computer and all its resulting artifacts. So why should we use the same tools?
T3 Skynet already exists and is hunting John Connor down
I wanna see the conference room meeting where they decide to push an unfinished, unstoppable technology. I guess T1 is the closest as it happens when Skynet "gains intelligence"
> I wanna see the conference room meeting where they decide to push an unfinished, unstoppable technology. I guess T1 is the closest as it happens when Skynet "gains intelligence"
This is the mid-point of T3. From the Wikipedia plot summary:
https://www.youtube.com/watch?v=JXJiSZhA5cg
https://youtu.be/Jt0OoXluC8g?t=248 at 4:08
If you cannot read it as the author, what hope do I have to read and make sense of the wall of text which doesn’t seem to describe what I actually need to start reviewing.
I really really encourage everyone to write their own descriptions for PRs. If you cannot succinctly describe it in a way another human understands then you don’t understand your own change and you should withdraw your request.
We’re using an online, undeterministic, black-box middleman to generate our code. It’s 100% Trust me bro. No proof, no scrutiny, no guarantees.
> let me tell you about this experience, and how it was turning me dumber, lazy, and a worse developer.
Though the article discusses from the point of using agents, I digress to the topic of building with AI in general.
My experience has been the exact opposite. A new idea (usually related to correctness or architecture) is discussed first with the LLM where it defaults to average Joe idiotic bullshit pushback.
This frustrates me and I abuse the LLM for being idiotic by explaining the how. This results in a more refined and concrete form of the abstraction leading me to even more insights.
The LLM remains an idiot. But a useful idiot nonetheless.