331–340 of 1001 posts
If we are talking about the boilerplate code and autofill syntax code that copilot or any other "AI" will offer me when I start typing... Then sure. Sounds about right. The other 75% is the stuff you actually have to think about. This feels like saying linters impact x0% of code. This just feels like an extension of that.
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we've decided we can't be bothered with traditional search Traditional search (at least on the web) is dying. The entire edifice is drowning under a rapidly rising tide of spam and scam sites. No one, including Google, knows what to do about it so we're punting on the whole project and hoping AI will swoop in like deus ex machina and save the day.
I personally think a big problem with search is major search engines try to be all things to all people and hence suffer as a result. For example: a beginner developer is possibly better served by some SEO-heavy tutorial blog post; an experienced developer would prefer results weighted towards the official docs, the project’s bug tracker and mailing list, etc. But since less technical and non-technical people vastly…
Good luck finding those, you end op with SEO spam and clone page spam. These days you have to look for unobvious hidden meanings which only relate to your exact problem to find what you are looking for.
I have the strong feeling search these days is back to the Altavista era. You'd have to use trickery to find what you were looking for back then as well. Too bad + no longer works in google due to their stupid naming of a dead product (no, literal is not the same and no replacement).
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Maybe it is naive but I think search would probably work again if they could roll back code to 10 or 15 years ago and just make search engines look for text in webpages. Google wasn’t crushed by spam, they decided to stop doing text search and build search bubbles that are user specific, location-specific, decided to surface pages that mention search terms in metadata instead of in text users might read, etc. Oh yeah…
> just make search engines look for text in webpages. Google’s verbatim search option roughly does that for me (plus an ad blocker that removes ads from the results page). I have it activated by default as a search shortcut. (To activate it, one can add “tbs=li:1” as a query parameter to the Google search URL.)
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> We work very closely with Google DeepMind to adapt Gemini models for Google-scale coding and other Software Engineering usecases. Considering how terrible and frequently broken the code that the public facing Gemini produces, I'll have to be honest that that kind of scares me. Gemini frequently fails at some fairly basic stuff, even in popular languages where it would have had a lot of source material to work from;…
> Can you spot all the problems? You were probably being rhetorical, but there are two problems: - `p = 2` should be outside the loop - `prime_factors.append(n)` appends `1` onto the end of the list for no reason With those two changes I'm pretty sure it's correct.
To my experience, AIs can generate perfectly good code relatively easy things, the kind you might as well copy&paste from stackoverflow, and they'll very confidently generate subtly wrong code for anything that's non-trivial for an experienced programmer to write. How do people deal with this? I simply don't understand the value proposition. Does Google now have 25% subtly wrong code? Or do they have 25% trivial code…
> the kind you might as well copy&paste from stackoverflow This bothers me. I completely understand the conversational aspect - "what approach might work for this?", "how could we reduce the crud in this function?" - it worked a lot for me last year when I tried learning C. But the vast majority of AI use that I see is...not that. It's just glorified, very expensive search. We are willing to burn far, far more fuel t…
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I don't see how this is sustainable. We have essentially eaten the seed corn. These current LLMs have been trained by an enormous corpus of mostly human-generated technical knowledge from sources which we already know to be currently being polluted by AI-generated slop. We also have preliminary research into how poorly these models do when training on data generated by other LLMs. Sure, it can coast off of that initi…
These tools also require the developer class to that they are intended to replace to continue to do what they currently do (create the knowledge source to train the AI on). It's not like the AIs are going to be creating the accessible knowledge bases to train AIs on, especially for new language extensions/libraries/etc. This is a one and f'd development. It will give a one time gain and then companies will be shocked…
Hi, I lead the teams responsible for our internal developer tools, including AI features. We work very closely with Google DeepMind to adapt Gemini models for Google-scale coding and other Software Engineering usecases. Google has a unique, massive monorepo which poses a lot of fun challenges when it comes to deploying AI capabilities at scale. 1. We take a lot of care to make sure the AI recommendations are safe and…
To my experience, AIs can generate perfectly good code relatively easy things, the kind you might as well copy&paste from stackoverflow, and they'll very confidently generate subtly wrong code for anything that's non-trivial for an experienced programmer to write. How do people deal with this? I simply don't understand the value proposition. Does Google now have 25% subtly wrong code? Or do they have 25% trivial code…
At what point are people going to stop shitting on the code that Copilot or other LLM tools generate? > how trivial the problems they solve are A single line of code IS trivial. Simple code is good code. If I write the first 3 lines of a complex method and I let Copilot complete the 4th, that's 25% of my code written by an LLM. These tools have exploded in popularity for good reason. If they were no good, people woul…
With c++ my experience is that the results are completely worthless. It saves you from writing a few keywords but nothing that really helps in a big way.
Yes Copilot CAN work, for example writing some JS or filter functions, but in my job these trivial snippets are rather uncommon.
I‘d genuinely love to see some resources that show its usefulness that arent just PR bs.
To my experience, AIs can generate perfectly good code relatively easy things, the kind you might as well copy&paste from stackoverflow, and they'll very confidently generate subtly wrong code for anything that's non-trivial for an experienced programmer to write. How do people deal with this? I simply don't understand the value proposition. Does Google now have 25% subtly wrong code? Or do they have 25% trivial code…