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."

I sometimes look at it via the metaphor of music.

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.

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.

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/

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.

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!

“ 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.

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!

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.

> 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.

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.

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.

This is just one example.

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

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.

Well written.

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.

There needs to be a Terminator prequel where it's people against the machines fighting people who think the machines will be a net positive.

> 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.

> 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 encourage everyone who is heavily coding with AI to try even a single hour without AI at all. You will be SHOCKED at how your skills have atrophied.

These days I only use AI once a week the way Astra chews through usage. “Congrats your usage has reset you can get back to… aaaaand it’s gone”

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.

> 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?

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.

Have you noticed it, too?

What made the editing process intractable?