291–300 of 762 posts
Earlier quoted context omitted.
Generating unseen code is not hard. Set rules on what’s valid, which most languages already do; omit generation of known code; generate everything else The computer does the work, programmers don’t have to think it up. A typed language example to explain; generate valid func sigs func f(int1, int2) return int{} If that’s our only func sig in our starting set then it makes it obvious Well relative to our tiny starter…
[flagged]
Earlier quoted context omitted.
It's like chess. Humans are better for now, they won't be forever, but humans plus software is going to better than either alone for a long time.
No guarantee that will happen. LLMs are still statistically based. It's not going to give you edgier ideas, like filling a glass of wine to the rim. Use them for the 90% of your repetitive uncreative work. The last 10% is up to you.
Companies that leverage LLMs and AIs to let their employees be more productive will thrive. Companies that try to replace their employees with LLMs and AIs will fail. Unfortunately, all that's in the long run. In the near term, some CEOs and management teams will profit from the short term valuations as they squander their companies' future growth on short-sighted staff cuts.
> In the near term, some CEOs and management teams will profit from the short term valuations That's actually really interesting to think about. The idea that doing something counter-productive like trying to replace employees with AI (which will cause problems), may actually benefit the company in terms of valuations in the short run. So in effect, they're hurting and helping the company at the same time.
This is especially prevalent in waterfall orgs that refuse change. Body shops are more than happy to waste a huge portion of their billable hours on planning meetings and roadmap revisions as the obviousness of the mythical man month comes to bear on the org.
Corners get cut to meet deadlines, because the people who started/perpetuated whatever myth need to save their skins (and hopefully continue to get bonuses.)
The engineers become a scapegoat for the org's management problems (And watch, it very likely will happen at some shops with the 'AI push'). In the nasty cases, the org actively disempowers engineers in the process[0][1].
[0] - At one shop, there was grief we got that we hadn't shipped a feature, but the only reason we hadn't, was IT was not allowed to decide between a set of radio buttons or a drop-down on a screen. Hell I got yelled at for just making the change locally and sending screenshots.
[1] - At more than one shop, FTE devs were responsible for providing support for code written by offshore that they were never even given the opportunity to review. And hell yes myself and others pushed for change, but it's never been a simple change. It almost always is 'GLWT'->'You get to review the final delivery but get 2 days'->'You get to review the set of changes'->'Ok you can review their sprint'->'OK just start reviewing every PR'.
I think this is true for deeply complex problems, but For everyday tasks an LLM is infinitely “better”. And by better, I don’t mean in terms of code quality because ultimately that doesn’t matter for shipping code/products, as long as it works. What does matter is speed. And an LLM speeds me up at least 10x.
You expect to achieve more than a decade of pre-LLM accomplishments between now and June 2026?
Earlier quoted context omitted.
It's a damning assertive duck, completely out of proportion to its competence. I've seen enough people led astray by talking to it.
My typical approach is prompt, be disgusted by the output, tinker a little on my own, prompt again -- but more specific, be disgusted again by the output, tinker a littler more, etc. Eventually I land on a solution to my problem that isn't disgusting and isn't AI slop. Having a sounding board, even a bad one, forces me to order my thinking and understand the problem space more deeply.
There's some whistling past the graveyard in these comments. "You still need humans for the social element...", "LLMs are bad at debugging", "LLMs lead you astray". And yeah, there's lots of truth in those assertions, but since I started playing with LLMs to generate code a couple of years ago they've made huge strides. I suspect that over the next couple of years the improvements won't be quite as large (Pareto Prin…
I'd like to agree with you and remain optimistic, but so much tech has promised the moon and stagnated into oblivion that I just don't have any optimism left to give. I don't know if you're old enough, but remember when speech-to-text was the next big thing? DragonSpeak was released in 1997, everyone was losing their minds about dictating letters/documents in MS Word, and we were promised that THIS would be the key i…
There's some whistling past the graveyard in these comments. "You still need humans for the social element...", "LLMs are bad at debugging", "LLMs lead you astray". And yeah, there's lots of truth in those assertions, but since I started playing with LLMs to generate code a couple of years ago they've made huge strides. I suspect that over the next couple of years the improvements won't be quite as large (Pareto Prin…
Yet you are working on your own replacement, while your colleagues are taking the prudent approach.
Personally I’m thrilled that I can get trivial, one-off programs developed for a few cents and the cost of a clear written description of the problem. Engaging internal developers or consulting developers to do anything at all is a horrible experience. I would waste weeks on politics, get no guarantees, and waste thousands of dollars and still hear nonsense like, “you want a form input added to a web page? Aw shucks, that’s going to take at least another month” or “we expect to spend a few days a month maintaining a completely static code base” from some clown billing me $200/hr.
Earlier quoted context omitted.
The crazy thing is that people think that a model designed to predict sequences of tokens from a stem, no matter how advanced the model, to be much more than just "really good autocomplete." It is impressive and very unintuitive just how far that can get you, but it's not reductive to use that label. That's what it is on a fundamental level, and aligning your usage with that will allow it to be more effective.
It's trivial to demonstrate that it takes only a tiny LLM + a loop to a have a Turing complete system. The extension of that is that it is utterly crazy to think that the fact it is "a model designed to predict sequences of tokens" puts much of a limitation on what an LLM can achieve - any Turing complete system can by definition simulate any other. To the extent LLMs are limited, they are limited by training and com…
This matches my experience. I actually think a fair amount of value from LLM assistants to me is having a reasonably intelligent rubber duck to talk to. Now the duck can occasionally disagree and sometimes even refine. https://en.m.wikipedia.org/wiki/Rubber_duck_debugging I think the big question everyone wants to skip right to and past this conversation is, will this continue to be true 2 years from now? I don’t kno…
It's a damning assertive duck, completely out of proportion to its competence. I've seen enough people led astray by talking to it.