[jan_Sate]

Not exactly. The real utility value of LLM for programming is to come up with something new. For Space Invaders, instead of using LLM for that, I might as well just manually search for the code online and use that.

To show that LLM actually can provide value for one-shot programming, you need to find a problem that there's no fully working sample code available online. I'm not trying to say that LLM couldn't to that. But just because LLM can come up with a perfectly-working Space Invaders doesn't mean that it could do that.

[devmor]

> The real utility value of LLM for programming is to come up with something new.

That's the goal for these projects anyways. I don't know that its true or feasible. I find the RAG models much more interesting myself, I see the technology as having far more value in search than generation.

Rather than write some markov-chain reminiscent frankenstein function when I ask it how to solve a problem, I would like to see it direct me to the original sources it would use to build those tokens, so that I can see their implementations in context and use my judgement.

[simonw]

"I would like to see it direct me to the original sources it would use to build those tokens"

Sadly that's not feasible with transformer-based LLMs: those original sources are long gone by the time you actually get to use the model, scrambled a billion times into a trained set of weights.

One thing that helped me understand this is understanding that every single token output by an LLM is the result of a calculation that considers all X billion parameters that are baked into that model (or a subset of that in the case of MoE models, but it's still billions of floating point calculations for every token.)

You can get an imitation of that if you tell the model "use your search tool and find example code for this problem and build new code based on that", but that's a pretty unconventional way to use a model. A key component of the value of these things is that they can spit out completely new code based on the statistical patterns they learned through training.

[tracker1]

I have a friend who has been doing just that... usually with his company he manages a handful of projects where a bulk of the development is outsourced overseas. This past year, he's outpaced the 6 devs he's had working on misc projects just with his own efforts and AI. Most of this being a relatively unique combination of UX with features that are less common.

He's using AI with note taking apps for meetings to enhance notes and flush out technology ideas at a higher level, then refining those ideas into working experiments.

It's actually impressive to see. My personal experience has been far more disappointing to say the least. I can't speak to the code quality, consistency or even structure in terms of most people being able to maintain such applications though. I've asked to shadow him through a few of his vibe coding sessions to see his workflow. It feels rather alien to me, again my experience is much more disappointing in having to correct AI errors.

[nottorp]

Is this the same person who posted about launching 17 "products" in one year a few days ago on HN? :)