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GitHub cuts AI deals with Google, Anthropic

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Re: GitHub cuts AI deals with Google, Anthropic

#501

Earlier quoted context omitted.

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 alternative, I fear, is 90% of the software we use exhibiting subtle goofy behavior and just being overall unpleasant to use. This sounds like most software honestly.

And that's what LLMs are trained on.

Hahaha

Re: GitHub cuts AI deals with Google, Anthropic

#502
post #435

Earlier quoted context omitted.

I find that it depends very heavily on what you're up to. When I ask it to write nix code it'll just flat out forget how the syntax works half way though. But if I want it to troubleshoot an emacs config or wield matplotlib it's downright wizardly, often including the kind of thing that does indicate an intimacy with the details. I get distracted because I'm then asking it: > I un-did your change which made no sense…

That doesn't work in the tech industry, because almost nothing is decades old, for obvious reasons.

What languages/toolkits are you working with that are less than 10 years old?

Anyhow, it seems to me like it is working. It's just working better for the really old stuff because:

- there has been more time for training data to accumulate

- some of it predates the trend of monetizing data, so there was less hoarding and more sharing

It may be that the hard slow way is the only way to get good results. If the modern trends re: products don't have the longevity/community to benefit from it, maybe we should fix that.

Re: GitHub cuts AI deals with Google, Anthropic

#503
post #51

Earlier quoted context omitted.

> And people still support it by uploading to GitHub. It’s slowly, but noticeably moving from GitHub to other sites. The network effect is hard to work against.

Migration is on my todo list, but it’s non trivial enough I’m not sure when I’ll ever have cycles to even figure out the best option. Gitlab? Self-hosted Git? Go back to SVN? A totally different platform? Truth be told, Git is a major pain in the ass anyway and I’m very open to something else.

Mercurial was better than git IMO, at least for smaller projects.

Re: GitHub cuts AI deals with Google, Anthropic

#504
post #493
post #458

Earlier quoted context omitted.

I had the opposite experience lately: I was helping translate some UI text for a website from English to German, my mother tongue. I found that usually the machine came up with better translations than me.

Perhaps you are not a translator. Translating is a skill that is more than simply being bilingual.

I am a professional translator, and I have been using LLMs to speed up and, yes, improve my translations for a year and a half.

When properly prompted, the LLMs produce reasonably accurate and natural translations, but sometimes there are mistakes (often the result of ambiguities in the source text) or the sentences don’t flow together as smoothly as I would like. So I check and polish the translations sentence by sentence. While I’m doing that, I sometimes encounter a word or phrase that just doesn’t sound right to me but that I can’t think how to fix. In those cases, I give the LLMs the original and draft translation and ask for ten variations of the problematic sentence. Most of the suggestions wouldn’t work well, but there are usually two or three that I like and that are better than what I could come up with on my own.

Lately I have also been using LLMs as editors: I feed one the entire source text and the draft translation, and I ask for suggestions for corrections and improvements to the translation. I adopt the suggestions I like, and then I run the revised translation through another LLM with the same prompt. After five or six iterations, I do a final read-through of the translation to make sure everything is okay.

My guess is that using LLMs like this cuts my total translation time by close to half while raising the quality of the finished product by some significant but difficult-to-quantify amount.

This process became feasible only after ChatGPT, Claude, and Gemini got longer context windows. Each new model release has performed better than the previous one, too. I’ve also tried open-weight models, but they were significantly worse for Japanese to English, the direction I translate.

Although I am not a software developer, I’ve been following the debates on HN about whether or not LLMs are useful as coding assistants with much interest. My guess is that the disagreements are due partly to the different work situations of the people on both sides of the issue. But I also wonder if some of those who reject AI assistance just haven’t been able to find a suitable interactive workflow for using it.

Re: GitHub cuts AI deals with Google, Anthropic

#505
post #499
post #390

Earlier quoted context omitted.

> 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.

Have you tried using chatgpt/etc as a starting point when you're unfamiliar with something? That's where it really excels for me, I can go crazy fast from 0 to ~30 (if we call 60 mvp). For example, the other day I was trying to stream some pcm audio using webaudio and it spit out a mostly functional prototype for me in a few minutes of trying. For me to read through msdn and get to that point would've taken an hour o…

This is about the only use case I found it helpful for - saving me time in research, not in coding.

I needed to compare compression ratios of a certain text in a language, and it actually came up with something nice and almost workable. It didn't compile but I forgot why now, I just remember it needing a small tweak. That saved me having to track down the libraries, their APIs, etc.

However, when it comes to actually doing data structures or logic, I find it quicker to just do it myself than to type out what I want to do, and double check its work.

Re: GitHub cuts AI deals with Google, Anthropic

#506

I usually feel like i can confidently express a change I want in code faster and better than I can explain what I want an AI to do in English. Like if I have a good prompt, these tools work okay, but getting that prompt almost as hard as just writing the code itself often. Do others feel the same struggle?

I’m positive my experience pales in comparison to yours, as I don’t actually code anything beyond the occasional single use script, but YES! I hate trying to explain the exact SQL result I’m looking for or some text modification I need to be able to throw together a CTE since I have read-only access and can’t even build a temp table.

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Re: GitHub cuts AI deals with Google, Anthropic

#507
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…

> ...and I can review code very quickly so the overall productivity boost has been great.

Color me skeptical. After a certain point, greater speed is achieved by sacrificing accuracy and comprehension. So, "I can review code very quickly" starts to sound like "I don't read, I skim."

IMHO, reviewing code is one of the parts of the job that sucks, so I see "AI" as a wonderful technology to improve our lives by replacing fun with chores.

Re: GitHub cuts AI deals with Google, Anthropic

#508

Earlier quoted context omitted.

> the intention of coding should never to be to belt out as many lines as possible That’s such an underrated statement. Especially when you consider the amount of code as a liability that you’ll have to take care later.

This presumes that it will be real humans that have to “take care” of the code later. A lot of the people that are hawking AI, especially in management, are chasing a future where there are no humans, because AI writes the code and maintains the code, no pesky expensive humans needed. And AI won’t object to things like bad code style or low quality code.

Well that will work great if you let the AI decide if the code is working or not.

User: This is calculating the result wrong.

AI: CLOSED WONTFIX: WORKING AS DESIGNED.

Re: GitHub cuts AI deals with Google, Anthropic

#509
post #496

Every single one of these discussion, at some point, devolves to some version of - is by far the best. In my extensive usage it is consistently outperforms by at least 2x. The difference is night and day. Then the immediate child reply: - What!? You must be holding it wrong. The complete inverse is true for me. I don't know what to make of this contradiction. We're all using the same 2 things right? How can opinions…

Same reason as programming language flamewars. Coders assume their individual experience holds for every domain. Yet LLMs are good for some things, bad at others.

Re: GitHub cuts AI deals with Google, Anthropic

#510
post #488

Earlier quoted context omitted.

> the intention of coding should never to be to belt out as many lines as possible That’s such an underrated statement. Especially when you consider the amount of code as a liability that you’ll have to take care later.

I think this is a bit short sighted, but I’m not sure how short. I suspect in the future, code will be something in between what it is today, and a build artifact. Do you have to maintain bytecode?

People working on VMs have to maintain compatibility with old bytecode and evolve the bytecode format forward, does that count?
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