Comment by TeeWEE | Hacker News Reader

Comment by TeeWEE | Hacker News Reader

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Programming skills that do not need critical thinking are more susceptible of being influenced indeed.

Don't forget that a lot of science requires computer programming these days.

This is the root of it: The more "genericc" your work is. The more its "out there on the internet" the more GPT can learn about it..

So, a lot of engineers that are just doign teh same old trick: Writing HTTP endpoints, parsing json. Mapping data types.. Yes that could be automated.

However, modelling a problem domain to code, and the core business logic of your code, which is where your "added value" comes from. And is mostly unique: Thats hard for GPT.

This is also why I try to convince engineering teams to optimize for maximum time spend on the core added value logic. The business logic layer. Not all the fluff around it, such as parsing, serialization, authenitcation, database connection.. These should be a constant cost C, once they setup you spend most of your time on the business logic.

When you see GPT program, its just repeating tricks to simple problem over and over again.. Its not really good yet

> So, a lot of engineers that are just doign teh same old trick: Writing HTTP endpoints, parsing json. Mapping data types.. Yes that could be automated.

And to be fair, automation for all of that already pretty much exists.

And to be fair, even though it exists, there's a huge majority that is done manually, even though it needs zero or very little manual "creativity" between specification and implementation

I agree. Usually you're already working within some framework or DSL where you can describe what you want to do. Ideally, you already have an idiomatic codebase enabling you to succinctly transform specifications into code.

Let's take parent poster's issues:

> Writing HTTP endpoints, parsing json. Mapping data types.

The generative model (for now) won't figure out for you: authentication, authorization, input form schema, JSON schema, required & optional fields, field constraints, entity modeling, indexing, query optimization, just to name a few basic issues we are looking at when "just developing CRUD apps".

If any of those go bad, it would result in 400s, 500s, performance or security issues.

It is exists where it can be supported. Lots of small businesses don't have the bandwidth to maintain additional infra that automates this sort of work.

Which sorta brings me back around: it's likely the Big Corps that are going to be trialing GPT first because they have the excess money and resources to play with it. How useful will it be in the end?

> The more its "out there on the internet" the more GPT can learn about it.

Interesting point. Do you think this will mean less and less domain experts will share their specific domain knowledge on a subject on their own personal blogs / twitter / open internet just so it can't be mined by ChatGPT?

If people start noticing that their blogs results in them being replaced, yes of course.

This makes sense. There’s also a huge corpus of text (training data) available on internet for the inherently repetitive or general tasks which is helpful for this systems.

But I wonder how do they go from this to mathematics using the same line of reasoning while we’ve seen that math is not LLMs’ strong suit.

Also thing about the huge corpus of text not available on the internet, but available to these systems (just because e.g. Microsoft has it, so can get data, perhaps anonymized from private GitHub repos, Copilot, telemetry from WSL, VSCode, and Azure, and so on).

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