AskHN:How do you handle skill atrophy from using coding agents?

I find it inefficient to use models for all possible uses right now even with the current subsidies. As the costs increase this will only become more true. Keeping that in mind will naturally motivate you to not lose your skills by engaging in inefficient token burn. I use AI for Kubernetes. On a day-to-day basis, it runs 80%+ of my kubectl commands. It’s the most steroidal auto-complete I’ve tried. But I do get dumber for it.What I do to compensate: - make it my duty to own every change, i.e. cognitively debt-free: - write summaries on every new thing I do (blog post, memo to colleague) - contribute documentation to the open-source projects I rely on - practice for CKA/CKAD certificates which require pre-LLM muscle memory - build interactive learning material for what I’m trying to learn - work with things that LLMs don’t yet trivially solve - repeat or reconstruct my recipes to perfect workflows,

We’re incentivised to take the short path. I’m trying to create at least one path through a subject that I have to walk myself, preferably several times. Tough to do this when the rest of the company is using LLMs to generate 3x the number of apps and then your performance goes down because you're not delivering as quickly as others It's not tough if you do your job and your management has no issues with you. What others are doing doesn't affect your performance. For every skill that may atrophy, I feel like I am developing experience in 10 new ones. I am not focused on using AI to perform my existing job function though, I am developing new capabilities. It's less prompt engineering, and more breaking down work into chunks an llm can do. With more powerful models you can do more autonomously, and with dumber ones, you have to step in and do work more often. I once to believed this too. And then I got much more skilled at prompting agents to get the results I wanted.As someone who is and always has been a very unapologetic skeptic, I am still surprised by the number of capable people who can't accept that things are changing.It can't be either none of it matters, or all of it matters.

The truth lies in the middle somewhere. In addition to what other people have said, I've taken some time to do leetcode questions lately - both architecture ones and coding ones. I'm not looking for a job by any stretch, but the practice and forcing a detailed zoom in has been really cathartic, and leetcode gives a nice structure/feeling of progress to it. Architecture decisions, requirements analysis, trade-offs in technology selection, and cross-system debugging—these high-level cognitive activities cannot yet be replaced by AI. Focusing on these areas is actually a smarter skill investment. They can be easily replaced. I suspect we are just engaging in a form of self deception by thinking these choices we make are more than arbitrary decisions driven by nothing but our taste... I suppose work organisation, leadership, soft skills, working in areas with inherent uncertainty will be key for defending employment in near future I am curious how the workflow of people, who do not write code at all, looks like, or what products do they build. In my experience LLMs are an excavator, but you still have to tweak the fine details with a shovel. You break down tasks until the LLM can do it. The scale you work at depends on the reasoning skill of the LLM. I am in the infosec space and do a lot of reading, summarizing of code and a vuln PoC here and there in my day job. Over a busy month I may put out 400-500 LoC.In my personal life, I am making tools to support hobbies. I typically tackle architecture and design myself and sanity check with an LLM, then Codex does all the programming work.I'm more interested in making sure the apps I make have the content I want and functionally meets my needs than actually writing the code myself. Making fine detail tweaks are not something I need to do past review and pointing them out to to the LLM. I honestly don't feel there is much atrophy or this is an issue at allAs if for example someone's skill lessened if they switched from assembly to a higher level programming language over time (like, does it matter?)If you for some reason had to go back and program more manually, then you could do so as the need arisesOtherwise, LLMs appear to be here

to stay and you don't actually need those skills that are even possibly admittedly "atrophying"I guess we'd need a detailed pinpointing of what skills exist or existed and to identify if they actually ateophy (I guess I'm not sure if skills are really atrophying, or even if they are if it matters)Edit: here's an idea or exercise or projects to work on. Maybe people should find clear documentation of pre-AI processes in case you need to go back and learn them. Or create such documentation if it doesn't exist (which would be an exercise to practice your skills to make you remember them). I waltzed into a tech screen thinking I could handcode python after having LLM be primary at it for over a year. Yeah, there's atrophy -- I humiliated myself and took the lesson. :)There is a meta-argument about whether companies should interview about hand-coding anymore, but... the skills do atrophy. I've been mixing hand-coding into my routines ever since to try to keep those skills lukewarm. I'm not yet sure if I am wasting my time doing so or not.$0.02 Cognitive skills? You must be using cognition to guide the AI.Either you are doing something guiding the AI or you are in your hammock doing nothing. If you’re in a hammock find a crossword puzzle.