this post was submitted on 02 Mar 2025
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GenZedong
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That's the thing it's not that there was something similar to my specific problem. It's parts of many billions of problems that have been fed into the system have aspects that all come together to produce a solution for the particular problem. You don't even have to start with JSON, you can ask it to come up a JSON schema by simply giving it parameters.
I'm not nitpicking you, I'm pointing out that what you've described isn't actually how these things worked, and the way they do work is far more useful in practice. Let me give you a concrete example. Given a query of "write a json schema to represent a comment thread on a social media site like reddit", it'll do this bit of reasoning:
This alone is already useful because it goes through the steps of the problem, identifies some corner cases, and suggests what a good way to model this problem might be. It will then produce a json schema based on that:
it highlights key features of the solution
and it gives an example usage (I've truncated it for brevity)
Now, given that schema, I can get it to produce a SQL schema from that, write the queries to do operations against the DB, etc. So, I can go directly from getting requirements in plain English, to having a reasonable data model, and a persistence layer in a span of a few minutes. I can also ask it to explain design decisions, make adjustments, etc.
This is far closer to pair programming with a second developer than hunting for code to glue together on Stack Overflow.
Just wanted to say that I'm holding on to this post to read it carefully since you put a lot of work into it. Haven't had the time to give it the attention it deserves. Not to argue, so don't think I'm winding up a rebuttal, I just haven't been able to give it the time it deserves
No worries, and thanks for taking the time to read through it.
ok first dumb question, is the block of code that you had below this line
Was this an actual output from an LLM or a hypothetical example that you wrote up? It's not quite clear to me. It's a lot of output but I don't want to insult you if you wrote all that yourself
I ask because I really want to nitpick the hell out of this design decision:
Adding the replies as full items, that is going to absolutely murder performance. A better scheme would be for replies to be a list/array of IDs or URLs, or a URL to an API call that enumerates all the replies, instead of enumerating all the items and embedding them directly. That is going to absolutely kill performance. Depending on the implementation, you could easily be doing the classic
N+1
query that a lot of web applications fall for.But then again at this point I'm arguing with an LLM which is generating absolutely dogshit code.
That was copy pasted straight from the DeepSeek chat response.
Like I said earlier, you still have to understand how to code and what the code is doing. Thing is that you could literally paste what you said in, and it'll make adjustments. Or you can just make adjustments yourself. As a starting point I find that sort of output useful.
Another example is that I have to use node for an application for work right now. I haven't touched js in over a decade, I'm not familiar with the ecosystem, and DeepSeek lets me quickly get things running. Things I would've spent hours looking up before and doing through trial and error just work out of the box. As I pointed out in an earlier reply, most apps aren't doing really complex or interesting things. Most of it is just shuffling data between different endpoints and massaging it in some way. LLMs can do a lot of this boring work quickly and efficiently.
I want to say that you've piqued my interest, but honestly I'm not sure I can set aside my bias. I deal with enough wrong code already as it is that my co-workers write, so I don't know if having yet another one giving me bad code suggestions adds much, but I appreciate you putting in the work showing everything.