I retired from paid sw dev work in 2020 when COVID arrived.
I’ve worked on my small projects since with all development by hand. I’d followed the rise of AI, but not used it.
Late last year I started a project that included reverse engineering some firmware that runs on an Intel 8096 based embedded processor. I’d never worked on that processor before. There are tools available, but they cost many $. So, I started to think about a simple disassembler.
2 weeks ago we decided to try Claude to see what it could do. We now have a disassembler, assembler and a partially working emulator. No doubt there are bugs and missing features and the code is a bit messy, but boy has it sped up the work.
One thing did occur to me. Vendors of small utilities could be in trouble. For example I needed to cut out some pages from a pdf. I could have found a tool online(I’m sure there are several), write one myself. However, Claude quickly performed the task.
This is a mix of the “in the future, everyone will have a 3D printer at home and just 3D print random parts they need” and “anyone can trivially build Dropbox with rsync themselves” arguments.
Tech savvy users who know how to use LLMs aren’t how vendors of small utilities stay in business.
They stay in business because they sell things to users who are truly clueless with tech (99% of the population, which can’t even figure out the settings app on their phone), and solid distribution/marketing is how you reach those users and can’t really be trivially hacked because everyone is trying to hack it.
Or they stay in business because they offer some sort of guarantee (whether legal, technical, or other) that the users don’t want to burden themselves with because they have other, more important stuff to worry about.
I don't know. It's one thing to tell Joe or Jane User to "Get an FTP account, mount it locally with curlftpfs, and then use SVN or CVS on the mounted filesystem." But if Joe or Jane can just cut-and-paste that advice into a prompt and get their own personal Dropbox...
Except when that new Dropbox fails Joe or Jane on that Saturday evening, their only recourse is to ask the AI for help, and the AI starts spinning “oh yeah, mmm, I think I found where the problem is. Cut and paste these debugging lines in that function and let me know what the output is…”
Meanwhile, this year, that happens less often than it did last year... and it actually isn't how AI-assisted development works at all. Agentic models do the cutting-and-pasting by themselves, evaluate the results by themselves, and almost always succeed at fixing the problem by themselves.
> Vendors of small utilities could be in trouble. For example I needed to cut out some pages from a pdf. I could have found a tool online(I’m sure there are several), write one myself. However, Claude quickly performed the task.
Definitely. Making small, single-purpose utilities with LLMs is almost as easy these days as googling for them on-line - much easier, in fact, if you account for time spent filtering out all the malware, adware, "to finish the process, register an account" and plain broken "tools" that dominate SERP.
Case in point, last time my wife needed to generate a few QR codes for some printouts for an NGO event, I just had LLM make one as a static, single-page client-side tool and hosted it myself -- because that was the fastest way to guarantee it's fast, reliable, free of surveillance economy bullshit, and doesn't employ URL shorteners (surprisingly common pattern that sometimes becomes a nasty problem down the line; see e.g. a high-profile case of some QR codes on food products leading to porn sites after shortlink got recycled).
Having an LLM spit out a few hundred lines of HTML and JavaScript is not a colossal waste of resources, it's equivalent to running a microwave for a couple of seconds.
Not to mention, my little tool is using much less electricity running than just about anything else I could easily find on-line, simply by the virtue of being minimal, and completely free of superfluous visual bullshit, upsells, tracking, telemetry, and other such secondary aspects of anything people publish and advertise for others to use.
Don't get the anti-AI propaganda get to you too much. Inference is cheap on the margin.
Consider: there are models capable (if barely) of doing this job, that you can run locally, on a upper-mid-range PC with high-end consumer GPU. Take that as a baseline, assume it takes a day instead of an hour because of inference speed, tally up total electricity cost. It's not much. Won't boil oceans any more than people playing AAA video games all day will.
Sure, the big LLMs from SOTA vendors use more GPUs/TPUs for inference, but this means they finish much faster. Plus, commercial vendors have lots of optimizations (batch processing, large caches, etc.), and data centers are much more power-efficient than your local machine, so "how much it'd cost me in power bill if I did it locally" is a good starting estimate.
It's not even "users", just the user. Nice thing about LLMs is that it's cheap to develop small tools tailor-made for audience of few, or in this case, just one.
1) This was for my wife. She is not proficient in Linux or CLI in general, and (like ~all white collar workers these days) works almost exclusively in browser tools (exception being pre-O365 versions of Word and Excel we keep running on her laptop because she prefers them).
2) I never heard of `qrencode` CLI tool until today. For some reason I didn't even consider it might exist (maybe because last time I checked, which was many years ago, there was none).
4) Even if I knew about it, I'd still have to build a web frontend for it, and I'd need a proper server for it, which I'd then have to maintain properly, and secure it against the `qrencode` call becoming an attack vector.
So frankly, for my specific problem, my solution is strictly better.
