this article is incredibly long and rambly, but please enjoy as this asshole struggles to select random items from an array in presumably Javascript for what sounds like a basic crossword app:
At one point, we wanted a command that would print a hundred random lines from a dictionary file. I thought about the problem for a few minutes, and, when thinking failed, tried Googling. I made some false starts using what I could gather, and while I did my thing—programming—Ben told GPT-4 what he wanted and got code that ran perfectly.
Fine: commands like those are notoriously fussy, and everybody looks them up anyway.
ah, the NP-complete problem of just fucking pulling the file into memory (there’s no way this clown was burning a rainforest asking ChatGPT for a memory-optimized way to do this), selecting a random item between 0 and the areay’s length minus 1, and maybe storing that index in a second array if you want to guarantee uniqueness. there’s definitely not literally thousands of libraries for this if you seriously can’t figure it out yourself, hackerman
I returned to the crossword project. Our puzzle generator printed its output in an ugly text format, with lines like
"s""c""a""r""*""k""u""n""i""s""*" "a""r""e""a". I wanted to turn output like that into a pretty Web page that allowed me to explore the words in the grid, showing scoring information at a glance. But I knew the task would be tricky: each letter had to be tagged with the words it belonged to, both the across and the down. This was a detailed problem, one that could easily consume the better part of an evening.
fuck it’s convenient that every example this chucklefuck gives of ChatGPT helping is for incredibly well-treaded toy and example code. wonder why that is? (check out the author’s other articles for a hint)
I thought that my brother was a hacker. Like many programmers, I dreamed of breaking into and controlling remote systems. The point wasn’t to cause mayhem—it was to find hidden places and learn hidden things. “My crime is that of curiosity,” goes “The Hacker’s Manifesto,” written in 1986 by Loyd Blankenship. My favorite scene from the 1995 movie “Hackers” is
most of this article is this type of fluffy cringe, almost like it’s written by a shitty advertiser trying and failing to pass themselves off as a relatable techy
shuf -n 100 /usr/share/dict/words
I would have expected JS standard library to contain something along the lines of
random.samplebut apparently not. A similar thing exists in something calledunderscore.jsand I gotta say it’s incredibly in-character for JavaScript to outsource incredibly common utility functions to a module called “_”.Language bashing aside, there’s something to enjoy about these credulous articles proclaiming AI superiority. It’s not the writing itself, but the self-esteem boost regarding my own skills. I have little trouble doing these junior dev whiteboard interview exercises without LLM help, guess that’s pretty impressive after all!
But I knew the task would be tricky
Is it just me or isn’t this not even that tricky (just a bit of work, so I agree with him on the free evening thing, esp when you are a bit rusty)? Anyway, note how he does give a timeframe for doing this himself (an evening) but doesn’t mention how long he worked on the chatgpt stuff, nor does he mention if he succeeded at his project at all
E: anyway what he needs is an editor.
this is the exact kind of clown who’d go “uh actually I have an editor” and fire up ChatGPT again
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I’ve been conducting DevOps and SRE interviews for years now. There’s a huge difference between someone that can copypasta SO code and someone that understands the SO code. LLMs are just another extension of that. GitHub Copilot is great for quickly throwing together an entire Terraform file. Understanding how to construct the project, how to tie it all together, how to test it, and the right things to feed into Copilot requires actually having some skill with the work.
I might hire this person at a very junior level if they exhibited a desire to actually understand what’s going on with the code. Here an LLM can serve as a “mentor” by spitting out code very quickly. Assuming you take the time to understand that code, it can help. If you just commit, push, deploy, you can’t figure out the deeper problems that span files and projects.
To me the only jobs that might not be safe are for executives a good programmer probably doesn’t want to work for.
I help maintain an open-source OS for industrial embedded applications.
fuck yes. there’s something weirdly exciting about work like that — not only is it a unique set of constraints, but it’s very likely that an uncountable number of people (myself possibly included) have interacted with your code without ever knowing they did
But the explicit purpose of generative AI is the devaluation of intellectual and creative labour, and right now, a lot of money is being spent on an attempt to make people like me redundant. Perhaps this is just my anxiety speaking, but it makes me terribly uneasy.
absolitely same. I keep seeing other programmers uncritically fall for poorly written puff pieces like this and essentially do everything they can to replace themselves with an LLM, and the pit drops out of my stomach every time. I’ve never before seen someone misunderstand their own career and supposed expertise so thoroughly that they don’t understand that the only future in that direction is one where they’re doing a much more painful version of the same job (programming against cookie cutter LLM code) for much, much less pay. it’s the kind of goal that seems like it could only have been dreamed up by someone who’s never personally survived poverty, not to mention the damage LLM training is doing to the concept of releasing open source code or even just programming for yourself, since there’s nothing you can do to stop some asshole company from pilfering your code.
the only future in that direction is one where they’re doing a much more painful version of the same job (programming against cookie cutter LLM code) for much, much less pay.
To the extent that LLMs actually make programming more “productive”, isn’t the situation analogous to the way the power loom was bad for skilled handweavers whilst making textiles more affordable for everyone else?
I should perhaps say that I’m saying this as someone who is just starting out as a web developer (really chose the right time for that, hah). I try to avoid LLMs and even strictly unnecessary libraries for now because I like learning about how everything works under the hood and want to get an intimate grasp of what I’m doing, but I can also see that ultimately that’s not what people pay you for that and that once you’ve built up sufficient skill to quickly parse LLM output, the demands of the market may make using them unavoidable.
To be honest, I feel as conflicted & anxious about it all as others already mentioned. Maybe I am just too green to fully understand the value that I would eventually bring, but can I really, in good conscience, say that a customer should pay me more when someone else can provide a similar product that’s “good enough” at a much lower price?
Sorry for being another bummer. :(
Commoditization is a real market force, and yes, it will come for this industry as it has for others.
Personally, I think we need to be much, much more creative and open to understanding ourselves and the potential of the future. It’s hard to know specifics, but there is broad domains.
Lately, I’ve been hacking at home with more hardware, and creating interesting low scale, low energy input systems that help me… garden. Analyzing soil samples, planning plots and low energy irrigation, etc, etc. It’s been fun because the work is less about programming in depth and more broad systems thinking. I even have ideas for making a small scale company off this. At that point, purely the programming won’t be the bottleneck.
If it helps, as an engineer, take a step back and think about nature and how systems and niches within systems evolve. Nature isn’t actually in the business of replacing due to redundancy, it’s in the business of compounding dependency via waste resources, and the shifting roles as a result of that. We need to be ready to creatively take our experience, perspective, and energy gradient to new places. It’s no different for any other part of nature.
I’m not sure the power loom analogy works, because power looms are (to my non-weaver knowledge) fit for purpose. if power looms’ output required significant rework by a skilled weaver (being paid significantly less for essentially the same amount of work done more tediously, per my point above), relied on stolen patterns from all of the world’s handweavers, and they were crushingly inefficient to run per woven piece, I seriously doubt history would remember them as a successful invention
unfortunately, we’re living in uniquely awful times, and decades of tech’s strange, manipulated culture have turned many programmers into nihilistic utopians with no ability to think things through on a systemic level. generative AI as a whole is nothing but an underhanded wage reduction tactic, but (by design) our industry doesn’t have the solidarity to fight it in any way that works (see the Writers’ Guild’s successful strike)
Totally agree.
IMO a better analogy would be clothing sweatshops rather than the power loom. Same utilitarian effect of textile affordability increases. Same ethical fuckery with exploitation of labour.
since there’s nothing you can do to stop some asshole company from pilfering your code.
Currently. Though I think that there is a future where adversarial machine learning might be able to greatly increase the cost of training on pilfered data by encoding human generated inputs in a way that runs counter to training algorithms.
Even if there were Glaze/Nightshade for computer programs, it could be reverse-engineered just like any other code obfuscation. This is the difference between code and most other outputs of labor: code is syntactic and formal, allowing for decidable objective analyses.
There’s a difference between “can” and “cost”. Code is syntactic and formal, true, but what about pseudo code that is perfectly intelligible by a human? There is, afterall, a difference between sharing “compiled” code that is meant to be fed directly into a computer and sharing “conceptual” code that is meant to be contextualized into knowledge. Afterall, isn’t “code” just the formalization of language, with a different purpose and trade off?
Really love the bit about how gpt is able to tackle the simple stuff so easily. If an original insight, I take my hat off to you. I came to the edge of it, but never quite really saw it as you point out.
if you never have, find YouTube videos of folks trying to use an LLM to generate code for a mildly obscure language. one I watched that gave the game away was where someone tried to get ChatGPT to write a game in Commodore BASIC, which they then pasted directly into a Commodore 64 emulator to run. not only did the resulting “game” perform like a nonsensical mashup of the simple example code from two old programming books, there was a gigantic edit in the middle of the video where they had to stop and make a significant number of fixes to the LLM’s output, where it either fictionalized something like a line number or constant, or where the mashup of the two examples just didn’t function. after all that programming on their part for an incredibly substandard result, their conclusion was still (of course) that the LLM did an amazing job
