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GitHub cuts AI deals with Google, Anthropic

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Re: GitHub cuts AI deals with Google, Anthropic

#681

Post by DirkH »

ianbutler wrote: post #311
thih9 wrote: post #221

I use cursor and its tab completion; while what it can do is mind blowing, in practice I’m not noticing a productivity boost. I find that ai can help significantly with doing plumbing, but it has no problems with connecting the pipes wrong. I need to double and triple check the updated code - or fix the resulting errors when I don’t do that. So: boilerplate and outer app layers, yes; architecture and core libraries,…

I'm actually very curious why AI use is such a bi-modal experience. I've used AI to move multi thousand line codebases between languages. I've created new apps from scratch with it. My theory is the willingness to baby sit and the modality. I'm perfectly fine telling the tool I use its errors and working side by side with it like it was another person. At the end of the day it can belt out lines of code faster than I…

I'm convinced what we are witnessing is that there are genius level engineers (lots of them) that are and many have always been sub-par communicators. I think being a good communicator tracks really well with how much someone can get out of LLMs (as does engineering competency. You need both).

Great but not genius engineers who are also great communicators may broadly outperform people with only technical genius soon, but that's speculation on my part.

Re: GitHub cuts AI deals with Google, Anthropic

#682

Post by senko »

Jackevansevo wrote: post #607
AIorNot wrote: post #427

Reviewing these conversations is like listening to horse and buggy manufacturers pooh-poohing automobiles: 1. they will scare the horses. a good team of horses is no match for funky 'automobile' 2. how will they be able to deal with our muddy, messy roads 3. their engines are unreliable and prone to breaking down stranding you in the middle and having to do it yourself.. 4. their drivers cant handle the speed, too ma…

> Meanwhile I'm watching a community of mostly young people building and using tools like copilot, cursor, replit, jacob etc and wiring up LLMs into increasingly more complex workflows. And yet, I don't see much evidence that software quality is improving, if anything it seems in rapid decline.

Even if that were true (and I'd challenge that assumption[0]), there's no dichotomy here.

Software quality, for the most part, is a cost center, and as such will always be minimal bearable.

As the civil engineering saying goes, any fool can make a bridge that stands, it takes an engineer to build a bridge that barely stands.

And anyway, all of those concerns are orthogonal to the tooling used, in this case LLMs.

[0] things we now take for granted, such as automated testing, safer languages, ci/cd, etc; makes for far better software than when we used to roll our own crypto in C.

Re: GitHub cuts AI deals with Google, Anthropic

#683

Post by scott01 »

rkozik1989 wrote: post #674
scott01 wrote: post #610

Earlier quoted context omitted.

Sorry, it’s a bit hard to understand from your blog. I’m a bit dyslexic, so there’s a chance I’ve mussed something obvious, but I couldn’t find any examples beyond ‘explain this short snippet’. Also, are your posts AI assisted? I find it hard to read. I’ve tried asking ChatGPT questions about how a large codebase works, what entities should I use to implement some feature, etc. After a couple hours I realised the cha…

The problem with your example of applying analyzing something as complex and esoteric as a codebase is that LLMs cannot reason they simply return a response based on statistical inference, so unless you followed a standard like PSR for PHP and implemented it to a 't' it simply doesn't have to context to do what you're asking it to do. If you want an LLM to be an effective programmer for a specific application you'd p…

Fair enough, I might be asking too much indeed, and may not be able to come up with an idea how LLMs can help me. For me, writing code is easy as soon as I understand the problem, and I sometimes spend a lot of time trying to figure out a solution that fits well within the context, so I thought I could ask an LLM what different things do and mean to help me understanding the problem surface better. Again, I may not understand something, but, at this point, I don’t understand what’s the value of code generation after I know how to solve a problem.

Do you happen to have a blog post or something showing a concrete problem that LLM helped you to solve?

Re: GitHub cuts AI deals with Google, Anthropic

#684

Post by bastardoperator »

creata wrote: post #568
bastardoperator wrote: post #152

Earlier quoted context omitted.

Can you name a company with more OSS projects and contributors? Stop with the hyperbole...

That literally has no bearing on the issue.

It literally does, even if you don't like it.

Re: GitHub cuts AI deals with Google, Anthropic

#685

Post by bastardoperator »

atomic128 wrote: post #212
bastardoperator wrote: post #152

Earlier quoted context omitted.

Can you name a company with more OSS projects and contributors? Stop with the hyperbole...

Embrace, extend...

Sure, Let me know when they extinguish kubernetes, helm, vscode, LSP, playwright, powershell, typescript, npm, or the other 6000 projects/repos sitting on GitHub.

Re: GitHub cuts AI deals with Google, Anthropic

#687

Post by foobiekr »

fullstackchris wrote: post #575
foobiekr wrote: post #423

Earlier quoted context omitted.

The most likely explanation is that the code you are writing has low information density and is stringing things together the same way many existing apps have already done. That isn’t a judgement but trying to use the ai code completion tools for complex systems tasks is almost always a disaster.

Not sure what you mean by "complex systems tasks" but most of the leading models have helped me with writing concurrent go code just fine. Not sure if that counts as "complex" enough. However this was prompting, not completion. Obviously I expect something like copilot to pick the normie non-concurrent implementation

It doesn’t. You’re kind of illustrating what I’m getting at.

Re: GitHub cuts AI deals with Google, Anthropic

#688

Post by cma »

Idiot211 wrote: post #533
cma wrote: post #42

Earlier quoted context omitted.

Could be a fight against Llama, which excludes MS and Google in its open license (though I think has done separate pay deals with one or both of them). Meta are notably absent from this announcement.

I was at the keynote, Llama was featured in the Copilot models section and called out specifically, as was Mistral. I assume they just aren't at the point where they have the ability or want to host the compute to offer up Llama as an option as opposed to OpenAI, Anthropic and Google who are all offering the model as a service.

Ah ok, I thought the deal was giving them access to Github data too without the anti scraping stuff. And had this article in mind: https://archive.ph/FvI9G

Re: GitHub cuts AI deals with Google, Anthropic

#689

Post by tartoran »

ianbutler wrote: post #311
thih9 wrote: post #221

I use cursor and its tab completion; while what it can do is mind blowing, in practice I’m not noticing a productivity boost. I find that ai can help significantly with doing plumbing, but it has no problems with connecting the pipes wrong. I need to double and triple check the updated code - or fix the resulting errors when I don’t do that. So: boilerplate and outer app layers, yes; architecture and core libraries,…

I'm actually very curious why AI use is such a bi-modal experience. I've used AI to move multi thousand line codebases between languages. I've created new apps from scratch with it. My theory is the willingness to baby sit and the modality. I'm perfectly fine telling the tool I use its errors and working side by side with it like it was another person. At the end of the day it can belt out lines of code faster than I…

> Im actually very curious why AI use is such a bi-modal experience. I've used AI to move multi thousand line codebases between languages. I've created new apps from scratch with it.

I think this depends on the nature of your work. I've been successful with using LLMS for creating things from scratch, for myself, especially in a domain I was not familiar with and am quite happy with that. Things like proof of concepts or exploring a library or a framework. But in my current work setting, relying on LLMS to do production work is only somewhat helpful here and there but nowhere near as helpful as in the first case. In some cases it hallucinated so close to what it was supposed to do that it introduced a bug I would have never created had I not used LLMs and that took a lot of effort to spot.

Re: GitHub cuts AI deals with Google, Anthropic

#690

Post by crazygringo »

pastage wrote: post #582
azangru wrote: post #574

Earlier quoted context omitted.

> Russian has a lot of ways you can write "compactly" with fewer words than english and have a much more precise meaning of the sentence. (I always likened russian to type-safe haskell and english to dynamic python) Funny; my experience has been completely the opposite. I've always envied the English language for how compactly and precisely it can express meaning compared to Russian, both because of an immensely rich…

Translating is an interpretation of the original text. A translated book can be better than the original. But you often need mastery of the language you translate to.

> A translated book can be better than the original.

Can you give some examples?

> But you often need mastery of the language you translate to.

Professional written translation is virtually always done into your native language, not into a language you've learned later. So that mastery should be taken for granted; it's a prerequisite.

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