While I appreciate and agree with the key points of the post, Claude's writing style fingerprints are all over it and I guess it's even more exhausting to read someone's AI written article.

unlike the op, I've been having a wonderful time using claude, both at work and for my own personal projects, so I will share what has worked for me, just in case it resonates with anyone else.

my anecdotal advice is to avoid the entire "agent" temptation, and treat the LLM as a code generator. have a single session running at a time. come up with a plan, iterate on it until you are satisfied, then tell it to execute the plan, and watch it. not necessarily to the extent of reading the scroll (though I sometimes do do that too!) but as it finishes each step look over what it has done, suggest improvements and course corrections, and then let it go on to the next step. at the end you will have a pretty good grasp of the state of the code, and the overall time it will take you isn't really any longer than trying to churn out reams of code and then go through it all at once.

the other option if you want something closer to a one shot workflow is to go into far more detail during the planning stage, have it describe not just architectural details but actual code (if you're a senior engineer especially you probably know what the key pieces of code that will drive a lot of other decisions mechanically are likely to be).

also refactoring is cheaper than it has ever been, if something feels hard to grasp to you stop and work with the LLM until you like the looks of it better.

and again, the key bit is to have one LLM doing one thing at a time, and to stay engaged in the process while it does so.

Funny I made some very similar points awhile back in a blog post, thinking of it in terms of mode collapse: https://tonyalicea.dev/blog/single-mode-burnout/

> The honest truth

> That loss is real and it's worth naming

I think I will not heed the first sentence and bear with this. What motivates people to do this? What do they get out of prompting Claude for some vapid "thought piece" and spamming it on the internet?

"omg im so totally tired of ai u guiz lmao" proceeds to post claudeslop.

Relatable.

> with my colleague Douwe

Wait, meltano Douwe? Small world. Glad to see you're doing well. I always liked meltano.

> In an era when anyone can produce reasonable-looking UI

Identical looking slop? Every Claude-based vibe coded app looks identical.

> The fear of skill rot is legitimate. And the fear that if you don't go fast enough you'll be left behind is — while often overstated — not entirely unfounded.

You know what, that's OK. I just hit "OK" on LLM Scala code I _actually_ think is awful. It works. It's probably faster than the "pure" code I'd write by hand. The code I would write - as a FP and Scala/Elm/Haskell/... enjoyer - would actually be maintainable for humans, but LLMs struggle with it. But LLMs writing code for LLMs? Sure, have at it. Objectively lower barrier of entry.

> So if you're feeling overwhelmed, destabilized, simultaneously more productive and less happy, know that you're not alone.

But yes, I am indeed simultaneously more productive and less happy.

https://skaldmaps.com, my little side project, was only possible _because_ I was able to feed my real world knowledge about real estate, combined with GIS and SWE knowledge into various torment nexus... pardon me, LLM prompts.

Since I don't have the _time_ to write boilerplate react code (it's pepper and tomato season in Georgia, which _actually_ brings me joy), telling Claude/Codex/... how to write dbt models saves me time and I objectively get a lot more done, but it's not fun.

I guess that's also why I still enjoy blogging. You can't use LLMs for blogs without people noticing immediately. Shameless plug: https://chollinger.com/blog/

Enjoy my entirely human typos, since that's clearly rare these days.

You are right to push back on that.

Reminds me of "The Animal is Tired" (2021) (https://www.robinhobb.com/blog/archives/2021-05)

It stresses me out for some reason and I'm just working on a hobby project.

> Here's a term for what I think is happening: the human reward function problem. In machine learning, a reward function tells an agent what good looks like. Writing code by hand was never easy, but it was full of small rewards. Solving a problem in your head. Understanding a gnarly bit of logic. Watching the code compile. The feeling of control. LLM-assisted programming has automated much of the work that generated those dopamine hits and replaced it with the cognitive load of review and supervision. The satisfying part shrank. The exhausting part grew. And there are no new rewards to fill the gap.

Say what you will about the Claudisms in this piece, this bit certainly rings true for me. With old school coding, there was always a reward at the end, the harder it was, the more satisfying it felt.

With agentic coding, I really doesn’t feel like that, at least not in the same way. It feels more like continually riding a wave of productivity, where small features or huge features have similar levels of interaction required. And that’s exciting in the beginning but quickly becomes very tiring.