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0 pointsskydhash1y ago0 comments

Not GP, but the kind of search I do mostly are:

- Does this language have X (function, methods,...) probably because I know X from another language and X is what I need. If it does not, I will code it.

- How do I write X again? Mostly when I'm coming back to a language I haven't touch for a while. Again I know what I want to do, just forgot the minutia about how to write it.

- Why is X happening? Where X is some cryptic error from the toolchain. Especially with proprietary stuff. There's also how to do X where X is a particular combination of steps and the documentation is lacking. I heard to forums in that case to know what's happening or get sample code.

I only need the manual/references for the first two. And the last one needs only be done once. Accuracy is a key thing for these use cases and I'd prefer snippets and scaffold (deterministic) instead of LLMs for basic code generation.

0 comments

theo08331y ago

I use llms exactly and exclusively for the first two cases - just write comments like:

// map this object array to extract data, and use reduce to update the hasher

And let llms do the rest. I rarely find my self back to the browser - 80% of the time they spit out a completely acceptable solution, and for the rest 20% at least the function/method is correct. Saved me much time from context switching.

skydhashOP1y ago

For me the quick refresh is better as I only need to do it once (until I don't use the language/library again) and that can be done without internet (local documentation) or high power consumption (if you were using local models). And with a good editor (or IDEs) all of these can be automated (snippets, bindings to the doc browser,...) and for me, it's a better flow state than waiting for a LLM to produce output.

P.S.I type fast. So as soon as I got a solution in my head, I can write it quickly and if I got a good REPL or Edit-Compile-Run setup, I can test just as fast. Writing the specs, then waiting for the LLM's code and then review it to check feel more like being a supervisor than a creator and that's not my kind of enjoyable moment.

theo08331y ago

I agree with you, creating something just feels better than reviewing code from a LLM intern ;D

That's why I almost never use the 'chat' panel in those AI-powered extensions, for I have to wait for the output and that will slow me down/kick me out of the flow.

However, I still strongly recommend that you have a try at *LLM auto completion* from Copilot(GitHub) or Copilot++(Cursor). From my experience it works just like context aware, intelligent snippets and heck, it's super fast - the response time is 0.5 ~ 1s on average behind a corporate proxy, sometimes even fast enough to predict what I'm currently typing.

I personally think that's where the AI coding hype is going to bear fruit - faster, smarter, context+documentation aware small snippets completion to eliminate the need for doc lookups. Multi file editing or full autonomous agent coding is too hyped.

j / k navigate · click thread line to collapse

0 pointsskydhash1y ago0 comments

Not GP, but the kind of search I do mostly are:

- Does this language have X (function, methods,...) probably because I know X from another language and X is what I need. If it does not, I will code it.

- How do I write X again? Mostly when I'm coming back to a language I haven't touch for a while. Again I know what I want to do, just forgot the minutia about how to write it.

0 comments

theo08331y ago

I use llms exactly and exclusively for the first two cases - just write comments like:

// map this object array to extract data, and use reduce to update the hasher

skydhashOP1y ago

theo08331y ago

I agree with you, creating something just feels better than reviewing code from a LLM intern ;D

That's why I almost never use the 'chat' panel in those AI-powered extensions, for I have to wait for the output and that will slow me down/kick me out of the flow.

j / k navigate · click thread line to collapse