Summarizer

> The power to the people is not us the developers and coders. > We know how to do a lot of things, how to automate etc. You need to know these things if you want to use AI effectively. It's way too dumb otherwise, in fact it's dumb enough to be quite dangerous.

I understand the "just prompt better" perspective, but this is the kind of thing my undergraduate students wouldn't do, why is the PhD expert-level coder that's supposed to replace all developers doing it? Having to explicitly tell it not to do certain boneheaded things, leave me wondering: what else is it going to do that's boneheaded which I haven't explicit about?

Because it's not "PhD-expert level" at all, lol. Even the biggest models (Mythos, GPT-Pro, Gemini DeepThink) are nowhere near the level of effort that would be expected in a PhD dissertation, even in their absolute best domains. Telling it to work out a plan first is exactly how you would supervise an eager but not-too-smart junior coder. That's what AI is like, even at its very best.

I understand that but 1) expert-level performance is how they are being sold; but moreover 2) the level of hand-holding is kind of ridiculous. I'll give another example, Codex decided to write two identical functions linearize_token_output and token_output_linearize. Prompting it not to do things like that feels like plugging holes in a dyke. And through prompting, can you even guarantee it won't write duplicate code? I'll give a third example: I gave Codex some tests and told it to implement the code that would make the tests pass. Codex wrote the tests into the testing file, but then marked them as "shouldn't test", and confirmed all tests pass. Going back I told it something to the effect "you didn't implement the code that would make the tests work, implement it". But after several rounds of this, seemingly no amount of prompting would cause it to actually write code -- instead each time it came back that it had fixed everything and all tests pass, despite only modifying the tests file. In each example, I keep coming back to the perspective that the code is not abstracted, it's an important artifact and it needs/deserves inspection.

> the code is not abstracted, it's an important artifact and it needs inspection. That's a rather trivial consideration though. The real cost of code is not really writing it out to begin with, it's overwhelmingly the long-term maintenance. You should strive to use AI as a tool to make your code as easy as possible to understand and maintain, not to just write mountains of terrible slop-quality code.

Yep, all models today still need prompting that requires some expertise. Same with context management, it also needs both domain expertise as well as knowing generally how these models work.

Code isn't going anywhere. Code is multiple orders of magnitude cheaper and faster than an LLM for the same task, and that gap is likely to widen rather than contract because the bigger the AI gets the sillier it gets to use it to do something code could have done. Compare the actual operations done for code to add 10 8-digit numbers to an LLM on the same task. Heck, I'll even say, forget the possibility the LLM may be wrong. Just compare the computational resources deployed. How many FLOPS for the code-based addition? How many for the LLM? That's a worst-case scenario in some ways but it also gives you a good sense of what is going on. Humans may stop looking at it but it's not going anywhere.

> The idea that code is something sacred and only devs can somehow do it is dying, and I personally love it, as I am watching it enable so many of my friends and family who have no idea how to code. People on HN are seriously delusional. AI removed the need to know the syntax. Your grandma does not know JS but can one shot a React app. Great! Software engineering is not and has never been about the syntax or one shotting apps. Software engineering is about managing complexity at a level that a layman could not. Your ideal word requires an AI that's capable of reasoning at 100k-1 million lines of code and not make ANY mistakes. All edge cases covered or clarified. If (when) that truly happens, software engineering will not be the first profession to go.

I never said Software Engineering is dying or needs to go. I'm not the least bit afraid of it. In fact, in the very message you're replying to, I hinted at the opposite (and have since in another post stated explicitly that I very much think the profession will still need to exist). My ideal world already exists, and will keep getting better: many friends of mine already have custom-built programs that fit their use case, and they don't need anything else. This also didn't "eat" any market of a software house -- this is "DIY" software, not production-grade. That's why I explicitly stated this is a new way of human-computer-interaction, which it definitely is (and IMO those who don't see this are the ones clearly deluded).

Code quality and IDEs aren't going anywhere, especially in complex enterprise systems. AI has improved a lot, but we're still far from a "forget about code" world.

> There are businesses that want bespoke AI tools and don't have the discipline to deploy them in-house. I don't know if it is ever possible for OAI & friends to develop a "hyper" agent that can produce good outcomes here automatically. There are often people problems that make connecting the data sources tricky. Having a human consultant come in and make a case for why they need access to everything is probably more persuasive and likely to succeed. Sort of agreed, though I wonder if ai-deployed software eats most use cases, and human consultants for integration/deployment are more for the more niche or hard to reach ones.

I think the coding market will be much larger. Knowledge work is kind of like the leaf nodes of the economy where software is the branches. That's to say, making software easier and cheaper to write will cause more and more complexity and work to move into the Software domain from the "real world" which is much messier and complicated.

Yes, and the same thing will happen in non-coding knowledge work too. Making knowledge work cheaper will cause complexity to increase, more knowledge work.

I don't think so, the whole point of writing software is it is a great sink for complexity. Encoding a process or mechanism in a program makes it work (as defined) for ever perfectly. An example here is in engineering. Building a simulator for some process makes computing it much safer and consistent vs. having people redo the calculations themselves, even with AI assistance.

The history of both knowledge work and software engineering seems to be increasing in both volume and complexity, feels reasonable to me to bet on both of those trendlines increasing?

> My current expectation is that the Cowork/Codex set of "professional agents" for non-technical users will be one of the most important and fastest growing product categories of all time, so far. I disagree. There is a major gap between awesome tech and market uptake. At this point, the question is whether LLMs are going to be more useful than excel. AI enthusiasts are 100% sure that it’s already more useful than excel, but on the ground, non-technical views do not reflect that view. All the interviews and real life interactions I have seen, indicate that a narrow band of non-technical experts gain durable benefits from AI. GenAI is incredible for project starts. A 0 coding experience relative went from mockup to MVP webapp in 3 days, for something he just had an idea about. GenAI is NOT great for what comes after a non-technical MVP. That webapp had enough issues that, if used at scale, would guarantee litigation. Mileage varies entirely on whether the person building the tool has sufficient domain expertise to navigate the forest they find themselves in. Experts constantly decide trade offs which novices don’t even realize matter. Something as innocuous as the placement of switches when you enter the room, can be made inconvenient.

You know what happens to a predator who makes its prey go extinct? AI is doing the same

There's no such big opportunity, as the number of programmers' spouses is quite limited. Again, and as the GP rightly suggested, some of the HN-ers here need to go and touch some normie grass, so to speak. More to the point, nobody wants to be more efficient for the sake of being efficient, we all want to go to work, do our metaphorical 9 to 5 without consuming too much (intellectual and not only) energy, and then back home. In that regard AI is seen as an existential threat to that "lifestyle" and it will be treated as such by regular workers.

> but a sufficiently smart model would be able to just do it without any steering; Yeah, yeah, we've heard "our models will be doing everything" for close to three years now. > a harness for getting this done probably exists today, gastown perhaps That got a chuckle and a facepalm out of me. I would at least consider you half-serious if you said "openclaw", at least those people pretend to be attempting to automate their lives through LLMs (with zero tangible results, and with zero results available to non-tech people).