All articles of this class, whether positive or negative, begin "I was working on a hobby project" or some variation thereof.
The purpose of hobbies is to be a hobby, archetypical tech projects are about self-mastery. You cannot improve your mastery with a "tool" that robs you of most of the minor and major creative and technical decisions of the task. Building IKEA furniture will not make you a better carpenter.
Why be a better carpenter? Because software engineering is not about hobby projects. It's about research and development at the fringes of a business (, orgs, projects...) requirements -- to evolve their software towards solving them.
Carpentry ("programming craft") will always (modulo 100+ years) be essential here. Powertools do not reduce the essential craft, they increase the "time to craft being required" -- they mean we run into walls of required expertise faster.
AI as applied to non-hobby projects -- R&D programming in the large -- where requirements aren't specified already as prior art programs (of func & non-func variety, etc.) ---- just accelerates the time to hitting the wall where you're going to shoot yourself in the foot if you're not an expert.
I have not seen a single take by an experienced software engineer have a "sky is falling" take, ie., those operating at typical "in the large" programming scales, in typical R&D projects (revision to legacy, or greenfield -- just reqs are new).
I think it also misses the way you can automate non-trivial tasks. For example, I am working on a project where there is tens of thousands of different data sets each with their own meta data and structure but the underlying data is mostly the same. But because the meta data and structure are all different, it’s really impossible to combine all this data into one big data set without a team of engineers going through each data set and meticulously restructuring and conforming said metadata to a new monolithic schema. However I don’t have any money to hire that team of engineers. But I can massage LLMs to do that work for me. These are ideal tasks for AI type algorithms to solve. It makes me quite excited for the future as many of these kind of tasks could be given to ai agents that would otherwise be impossible to do yourself.
I agree, but only for situations where the probabilistic nature is acceptable. It would be the same if you had a large team of humans doing the same work. Inevitably misclassifications would occur on an ongoing basis.
Compare this to the situation where you have a team develop schemas for your datasets which can be tested and verified, and fixed in the event of errors. You can't really "fix" an LLM or human agent in that way.
So I feel like traditionally computing excelled at many tasks that humans couldn't do - computers are crazy fast and don't make mistakes, as a rule. LLMs remove this speed and accuracy, becoming something more like scalable humans (their "intelligence" is debateable, but possibly a moving target - I've yet to see an LLM that I would trust more than a very junior developer). LLMs (and ML generally) will always have higher error margins, it's how they can do what they do.
Yes but i see it as multiple steps. Like perhaps the llm solution has some probabilistic issues that only get you 80% of the way there. But that probably already has given you some ideas how to better solve the problem. And this case the problem is somewhat intractable because of the size and complexity of the way the data is stored. So like in my example the first step is LLMs but the second step would be to use what they do as structure for building a deterministic pipeline. This is because the problem isn’t that there are ten thousand different meta data, but that the structure of those metadata are diffuse. The llm solution will first help identify the main points of what needs to be conformed to the monolithic schema. Then I will build more production ready and deterministic pipelines. At least that is the plan. I’ll write a substack about it eventually if this plan works haha.
I'm reminded of the game Factorio: Essentially the entire game loop is "Do a thing manually, then automate it, then do the higher-level thing the automation enables you to do manually, then automate that, etc etc"
So if you want to translate that, there is value in doing a processing step manually to learn how it works - but when you understood that, automation can actually benefit you, because only then are you even able to do larger, higher-level processing steps "manually", that would take an infeasible amount of time and energy otherwise.
Where I'd agree though is that you should never lose the basic understanding and transparency of the lower-level steps if you can avoid that in any way.
I've used Claude-Code & Roo-Code plenty of times with my hobby projects.
I understand what the article means, but sometimes I've got the broad scopes of a feature in my head, and I just want it to work. Sometimes programming isn't like "solving a puzzle", sometimes it's just a huge grind. And if I can let an LLM do it 10 times faster, I'm quite happy with that.
I've always had to fix up the code one way or another though. And most of the times, the code is quite bad (even from Claude Sonnet 3.7 or Gemini Pro 2.5), but it _did_ point me in the right direction.
About the cost: I'm only using Gemini Pro 2.5 Experimental the past few weeks. I get to retry things so many times for free, it's great. But if I had to actually pay for all the millions upon millions of used tokens, it would have cost me *a lot* of money, and I don't want to pay that. (Though I think token usage can be improved a lot, tools like Roo-Code seem very wasteful on that front)
> I have not seen a single take by an experienced software engineer have a "sky is falling" take,
Let me save everybody some time:
1. They're not saying it because they don't want to think of themselves as obsolete.
2. You're not using AI right, programmers who do will take your job.
3. What model/version/prompt did you use? Works For Me.
But seriously: It does not matter _that_ much what experienced engineers think. If the end result looks good enough for laymen and there's no short term negative outcomes, the most idiotic things can build up steam for a long time. There is usually an inevitable correction, but it can take decades. I personally accept that, the world is a bit mad sometimes, but we deal with it.
My personal opinion is pretty chill: I don't know if what I can do will still be needed n years from now. It might be that I need to change my approach, learn something new, or whatever. But I don't spend all that much time worrying about what was, or what will be. I have problems to solve right now, and I solve them with the best options available to me right now.
People spending their days solving problems probably generally don't have much time to create science fiction.
> You're not using AI right
I use AI heavily, it's my field.
The part before "But seriously" was sarcasm. I find it very odd to assume that a professional developer (even if it's not what they would describe as their field) is using it wrong. But it's a pretty standard reply to measured comments about LLMs.
> I find it very odd to assume that a professional developer (even if it's not what they would describe as their field) is using it wrong.
They're encountering a type of tool they haven't met before and haven't been trained to use. The default assumption is they are probably using it wrong. There isn't any reason to assume they're using it right - doing things wrong is the default state of humans.