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This last sentence is the most important. You can describe your dB schema in words and get laravel migration, models, controllers and if you wanted policies, form requests etc all near perfect. You can get a V1 in 10-20 minutes then go about handling all the actual logic

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So rails scaffold from 20 years ago but with a chance of hallucinations?

It's very easy to find multiple solutions to one problem. The power of LLM is that it's one solution to many problems.

But is it actually a solution to any of them?

The "chance of hallucinations" is the tricky bit - if I have to manually check everything it does in case it's hallucinating, then it's not actually a solution. It's not saving me time (as TFA says).

while you’re in doubt, i’ll be speeding out

Speed on, I'll enjoy the slo-mo crash footage later :)

What about the problem of needless pollution?

I thought we had moved on from cryptocurrencies

Shhh we’re raising trillions for this technology remember?

Yeah the copilot doesn’t need to be integrated into every keystroke, just able to analyze the context and kickstart the code. Getting up to speed with new libraries is so much easier with AI instead of the bad old days of trial-and-error-and-marked-as-duplicate

I especially like when I'm looking for a particular method on a class I've never used before and it just makes one up for me as though it exists.

It’s adding features that should have existed for you! It can also make up the documentation for the method while you are at it.

Can Copilot analyze my whole codebase these days or just the file I've opened? Honest question as I've stopped to use it a long time ago.

With VSCode at least you can say @workspace to Copilot and it'll take it into account.

No idea if it feeds all of the code to the LLM or just parts, but it's pretty good at interpreting what the code does

It guaranteed can't have all of your code in the LLM context, or even all of your file (in case of longer, through not even quite long files).

Through it could do stuff like go through your repository(ies) and generate embedding for sections of it and then have a vector database + retrieval argumented generation (RAG) system.

A lead from one of the GH Copilot-adjacent companies was interviewed recently, and that’s precisely what they are doing. They generate embedding of “local” code based on AST (up the stack and sideways, if you know what I mean), and take into account runtime and library versions when doing inference. Sounded like a very interesting challenge.

Yea, that's the way they're going based on the low-memory models they're building.

Basically all the LLM needs to do is translate human writing to some format that a "normal" service can use. That can then leverage the existing spotlight system that's pretty decent at searching stuff on the phone anyway.

Then it'll report it back to the LLM which translates whatever format back to something humans can process.

Basically "less shit Siri"

It seems to be hit and miss, at least with the current PyCharm integration. In some cases it can infer information using other files, in some cases it can't (even if they are open in other tabs).

As someone who switches between PyCharm and VSCode, I find that Copilot seems to work better in VSCode for some reason. Nothing major, but the suggestions I get just seem slightly more relevant to my code base, and are more often what I wanted.

Although I could be hallucinating the whole thing.

Whole-codebase understanding is beyond current coding assistants’ capabilities. To do that you need to add in traditional static analysis. But I’m sure people are working on it!

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