311–320 of 762 posts
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But that already happened to lots of industries and lots of people, we never cared before about them, now it's us so we care, but nothing is different about us. Just learn to code!
The difference is in how many industries AI is threatening. It's not just coding on the chopping block
The main thing LLMs have helped me with, and always comes back to, tasks that require bootstrapping / Googling: 1) Starting simple codebases 2) Googling syntax 3) Writing bash scripts that utilize Unix commands whose arguments I have never bothered to learn in the first place. I definitely find time savings with these, but the esoteric knowledge required to work on a 10+ year old codebase is simply too much for LLMs…
Increase the temperature of the LLMs.
Ask several LLMs, each several time the same question, with tiny variations. Then collect all answers, and do a second/third round asking each LLM to review all collected answers and improve.
Add random constraints, one constraints per question. For example, to LLM: can you do this with 1 bit per X. Do this in O(n). Do this using linked lists only. Do this with only 1k memory. Do this while splitting the task to 1000 parallel threads, etc.
This usually kicks the LLM out of its confort zone, into creative solutions.
I never made a Dockerfile in my life, so I thought it would be faster just getting o3 to point to the GitHub repo and let it figure out, rather than me reading the docs and building it myself.
I spent hours debugging the file it gave me... It kept on adding hallucinations for things that didn't exist, and removing/rewriting other parts, and other big mistakes like understanding the difference between python3 and python and the intricacies with that.
Finally I gave up and Googled some docs instead. Fixed my file in minutes and was able to jump into the container and debug the rest of the issues. AI is great, but it's not a tool to end all. You still need someone who is awake at the wheel.
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I can't tell whether I'm supposed to be the technical type or the artist type in this analogy. In my music making hobby, I'd like a good AI to help me mix, master, or any number of things under my direction. I'm going to be very particular about every aspect of the beat, but maybe it could suggest some non-boring chord progressions and I'll decide if I like one of them. My goal as an artist is to express myself, and…
> Again, if a good AI could take on a lot of that work, maybe that means I don't have to sit there in dependency hell and fight arcane missing symbol errors for the rest of my fucking career. My argument really had nothing to do with you and your hobby. It was that AI is signficantly modifying society so that it will be hard for people to do what they like to make money, because AI can do it. If AI can solve some bor…
This stuff is still in its infancy, of course its not perfect
But its already USEFUL and it CAN do a lot of stuff - just not all types of stuff and it still can mess up the stuff that it can do
It's that simple
The point is that overtime it'll get better and better
Reminds me of self driving cars and or even just general automation back in the day - the complaint has always been that a human could do it better and at some point those people just went away because it stopped being true
Another example is automated mail sorting by the post office. The gripe was always humans will always be able to do it better - true, in the meantime the post office reduced the facilities with humans that did this to just one
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Same here. When I'm teaching coding I've noticed that LLMs will confuse the heck out of students. They will accept what it suggests without realizing that it is suggesting nonsense.
Tbf, there's a phase of learning to code where everything is pretty much an incantation you learn because someone told you "just trust me." You encounter "here's how to make the computer print text in Python" before you would ever discuss strings or defining and invoking functions, for instance. To get your start you kind of have to just accept some stuff uncritically. It's hard to remember what it was like to be in…
But, as a sibling poster pointed out: for now.
Last night I spent hours fighting o3. I never made a Dockerfile in my life, so I thought it would be faster just getting o3 to point to the GitHub repo and let it figure out, rather than me reading the docs and building it myself. I spent hours debugging the file it gave me... It kept on adding hallucinations for things that didn't exist, and removing/rewriting other parts, and other big mistakes like understanding t…
I get having a bad taste in your mouth but these tools _aren't _ magic and do have something of a steep learning curve in order to get the most out of them. Not dissimilar from vim/emacs (or lots of dev tooling).
edit: To answer a reply (hn has annoyingly limited my ability to make new comments) yes, internet search is always available to ChatGpT as a tool. Explicitly clicking the globe icon will encourage the model to use it more often, however.
LLMs are great as assistants. Just today, Copilot told me it's there to do the "tedious and repetitive" parts so I can focus my energy on the "interesting" parts. That's great. They do the things every programmer hates having to do. I'm more productive in the best possible way.
But ask it to do too much and it'll return error-ridden garbage filled with hallucinations, or just never finish the task. The economic case for further gains has diminished greatly while the cost of those gains rises.
Automation killed tons of manufacturing jobs, and we're seeing something similar in programming, but keep in mind that the number of people still working in manufacturing is 60% of the peak, and those jobs are much better than the ones in the 1960s and 1970s.
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This attitude is depressingly common in lots of professional, white-collar industries I'm afraid. I just came from the /r/law subreddit and was amazed at the kneejerk dismissal there of Dario Amodei's recent comments about legal work, and of those commenters who took them seriously. It's probably as much a coping mechanism as it is complacency, but, either way, it bodes very poorly for our future efforts at mitigatin…
This is the response to most new technologies; folks simply don't want to accept the future before the ramifications truly hit. If technology folk cannot see the INCREDIBLE LEAP FORWARD made by LLMs since ChatGPT came on the market, they're not seeing the forest through the trees because their heads are buried in the sand. LLMs for coding are not even close to imperfect, yet, but the saturation curves are not flatten…