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GitHub cuts AI deals with Google, Anthropic

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Re: GitHub cuts AI deals with Google, Anthropic

#381

Post by jmpeax »

Yodel0914 wrote: post #364
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 find chatgpt incredibly useful for writing scripts against well-known APIs, or for a "better stackoverflow". Things like "how do I use a cursor in sql" or "in a devops yaml pipeline, I want to trigger another pipeline. How do I do that?". But working on our actual codebase with copilot in the IDE (Rider, in my case) is a net negative. It usually does OK when it's suggesting the completion of a single line, but when…

Same here. If you need to lookup how to do something in an api I find it much faster to use chatgpt than to try to search through the janky official docs or in some github examples folder. Chatgpt is basically documentation search 2.0.

Re: GitHub cuts AI deals with Google, Anthropic

#382

Post by tensor »

chairhairair wrote: post #318
tensor wrote: post #306

Earlier quoted context omitted.

Why aren't you writing unit tests just because AI wrote the function? Unit tests should be written regardless of the skill of the developer. Ironically, unit tests are also one area where AI really does help move faster. High level design, rough outlines and approaches, is the worst place to use AI. The other place AI is pretty good is surfacing api call or function calls you might not know about if you're new to the…

I have completely the opposite perspective. Unit tests actually need to be correct, down to individual characters. Same goes with API calls. The API needs to actually exist. Contrast that with "high level design, rough outlines". Those can be quite vague and hand-wavy. That's where these fuzzy LLMs shine. That said, these LLM-based systems are great at writing "change detection" unit tests that offer ~zero value (or…

The fact that you think "change detection" tests offer zero value speaks volumes. Those may well be the most important use of unit tests. Getting the function correct in the first place isn't that hard for a senior developer, which is often why it's tempting to skip unit tests. But then you go refactor something and oops you broke it without realizing it, some boring obvious edge case, or the like.

These tests are also very time consuming to write, with lots of boilerplate that AI is very good at writing.

Re: GitHub cuts AI deals with Google, Anthropic

#383

Post by 0xFACEFEED »

geysersam wrote: post #365
0xFACEFEED wrote: post #329

Earlier quoted context omitted.

As a programmer of over 20 years - this is terrifying. I'm willing to accept that I just have "get off my lawn" syndrome or something. But the idea of letting an LLM write/move large swaths of code seems so incredibly irresponsible. Whenever I sit down to write some code, be it a large implementation or a small function, I think about what other people (or future versions of myself) will struggle with when interactin…

I'll take a stab at changing your mind. AIs are not able to write Redis. That's not their job. AIs should not write complex high performance code that millions of users rely on. If the code does something valuable for a large number of people you can afford humans to write it. AIs should write low value code that just repeats what's been done before but with some variations. Generic parts of CRUD apps, some fraction…

I can definitely see the value in letting AI generate low stakes code. I'm a daily CoPilot user and, while I don't let it generate implementations, the suggestions it gives for boilerplate-y things is top notch. Love it as a tool.

My major issue with your position is that, at least in my experience, good software is the sum of even the seemingly low risk parts. When I think of real world software that people rely on (the only type I care about in this context) then it's hard to point a finger at some part of it and go "eh, this part doesn't matter". It all matters.

The alternative, I fear, is 90% of the software we use exhibiting subtle goofy behavior and just being overall unpleasant to use.

I guess an analogy for my concern is what it would look like if 60% of every film was AI generated using the models we have today. Some might argue that 60% of all films are low stakes scenes with simple exposition or whatever. And then remaining 40% are the climax or other important moments. But many people believe that 100% of the film matters - even the opening credits.

And even if none of that were an issue: in my experience it's very difficult to assess what part of an application will/won't be low/high stakes. Imagine being a tech startup that needs to pivot your focus toward the low stakes part of the application that the LLM wrote.

Re: GitHub cuts AI deals with Google, Anthropic

#384

Post by seoulmetro »

gregschlom wrote: post #211
hi41 wrote: post #208

That’s a strange usage of the word “cuts”. I thought GitHub terminated the deals with Google and Anthropic. It would be better if the title were GitHub signs AI deals instead of cuts.

I'm assuming you're not a native speaker? (I'm not) - "to cut a deal" is a fairly common idiom that means to reach and agreement.

"cut a deal" is an idiom.

"cuts ____ deals" is not and more closely resembles the removal of deals related to ____.

Re: GitHub cuts AI deals with Google, Anthropic

#385

Post by 2024user »

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…

If you're doing something that appears in it's training model a lot, like building a twitter clone, then it is great. If you're using something brand new like react router 7 then it makes mistakes

Re: GitHub cuts AI deals with Google, Anthropic

#386

Post by gavin_gee »

thenobsta wrote: post #8

I wonder what the rationale for this was internally. More OpenAI issues? competitiveness with Cursor? It seems good for the user to increase competition across LLM providers. Also ambiguous title. I thought GitHub canceled deals they had in the work. The article is clearly about making a deal, but it's unclear from the article's title.

cursor is kicking vscode's butt because it has multi models. also MS is hedging bets against OpenAI. that relationship is not easy

Re: GitHub cuts AI deals with Google, Anthropic

#387

Post by javaunsafe2019 »

I don’t know how people can claim such huge success using copilot and such. I also own a subscription and tried to use it for coding but all task from spring boot authentication configuration to aws policies and lambdas it failed horribly.

Writing the code myself using proper documentation was the only option.

I wonder if false information is written here in the comments section for certain reasons …

Re: GitHub cuts AI deals with Google, Anthropic

#388

Post by simonw »

javaunsafe2019 wrote: post #387

I don’t know how people can claim such huge success using copilot and such. I also own a subscription and tried to use it for coding but all task from spring boot authentication configuration to aws policies and lambdas it failed horribly. Writing the code myself using proper documentation was the only option. I wonder if false information is written here in the comments section for certain reasons …

You need to spend time learning how to use it. This is difficult because there's no manual, and there's a widespread implication that it should just magically work well without you having to invest any effort in it.

If you can figure out HOW to invest that effort it becomes really valuable.

I wish I had good resources I could link you to here but I don't, which is a big part of the problem here.

Re: GitHub cuts AI deals with Google, Anthropic

#390

Post by xk_id »

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 actually very curious why AI use is such a bi-modal experience.

My conspiracy theory is that the positive experiences are exaggerated and come from investors in the Nvidia stock.

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