I Quit AI for a Month and Recovered My Joy of Programming

I really, really wish that posts like this would give _specific examples_ of generated code they thought was sub par and what they did to remedy it. It’s not that I don’t believe that they found the output lacking in some way, it’s more that I read the post thinking, “I feel like Claude et al are way better programmers than me but this person is saying they’re more skilled, I’d love to see what their specific issues with generated code were and how they fixed them”. I mean, even one or two short examples would be amazing; without them I just come away with so many questions.

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."

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/

"At some point, I had so many AI agents working, that the focus change was too much for me. 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. Because there were so many other things."

This is a personal time management problem. Not a tool problem.

THere is a lot of temptation to do many things at the same time. For some people this works. For others it doesn't. This is not a tool problem. You simply need to fit the tools in a way that matches your personal workflow.

I can do at most two things at the same time. A friend reads a book when Claude works on a task. We're all different.

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 detai […]

I think the (currently) intangible skill that we need to develop is when and how the LLMs are appropriate to use. I'm not sure that's possible- I agree with the author's drug analogy.

I generally don't use it for code - I've been writing code for 30 years, so I'm pretty quick, and it's more productive when I look at total time for me to just write things myself.

But not always.

For example, I was recently writing some firmware in MicroPython and needed to implement GATT characteristics that were both readable and writable to control the device.

It's been years since I've written python, since before asynchio. I'd never written MicroPython and didn't really know anything about Bluetooth or BLE.

The AI taught me about how to do it, taught me about how asynchio works (I'm deeply familiar with the model, just not in python), taught me what how Bluetooth works, ehat GATT characteristics are and gave me some example code that I then took and rewrote to fit my architecture.

I tried to do the normal "find a tutorial on the internet thing" first, but it's filled with worthless AI slop, ads and examples that are too trivial they're only click bait. It's incredibly frustrating.

Meanwhile, the LLM was succinct, helpful and able to iteratively answer each question I had as learned and got deeper into it.

The best part were the links and references so I could fact-check it along the way.

I've been writing code professionally since the 90s, and struggled through learning new things more tim […]

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 ge […]