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GitHub cuts AI deals with Google, Anthropic

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Re: GitHub cuts AI deals with Google, Anthropic

#541

Post by globular-toast »

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…

The funny thing about this comment is there's an increasing number of people beginning to think automobiles were a mistake. They pollute, they're unhealthy, dangerous, cause congestion, but we've built our lives around them and basically addicted to them.

LLMs piece together language based on other language they've seen. It's not intelligent, it's just a language tool. Currently we have no idea what will happen once there are no more human inputs to train the LLMs. We might end up wishing we didn't build our whole lives around LLMs.

Re: GitHub cuts AI deals with Google, Anthropic

#542

Post by imiric »

dartos wrote: post #420
imiric wrote: post #303

Earlier quoted context omitted.

To be clear, I didn't ask it to write something complex. The prompt was "how do I do X with library Y?", with a bit more detail. The library is fairly popular and in a mainstream language. I had a suspicion that what I was trying to do was simply not possible with that library, but since LLMs are incapable of saying "that's not possible" or "I don't know", they will rephrase your prompt and hallucinate whatever might…

> if you want a specific answer about something Specific is the specific thing that statistical models are not good at :( > how do I do X with library Y? Recent research and anecdotal experience has shown that LLMs perform quite poorly with short prompts. Attention just has more data to work with when there are more tokens. Try extending that question like “I am using this programming language and am trying to do thi…

> Recent research and anecdotal experience has shown that LLMs perform quite poorly with short prompts.

I'm aware of that. The actual prompt was more elaborate. I was just mentioning the gist of it here.

Besides, you would think that after 30 minutes of prompting and corrections it would arrive at the correct answer. I'm aware that subsequent output is based on the session history, but I would also expect this to be less of an issue if the human response was negative. It just seems like sloppy engineering otherwise.

> Specific is the specific thing that statistical models are not good at

Some models are good at needle-in-a-haystack problems. If the information exists, they're able to find it. What I don't need is for it to hallucinate wrong answers if the information doesn't exist.

This is a core problem of this tech, but I also expected it to improve over time.

> Tho you should give aider.chat a try

Thanks, I'll do that eventually. If it's slow, it can get faster. I'd rather the tool be slow but give correct answers, than it slowing me down by wasting my time error correcting it.

Thankfully, these approaches can work for programming tasks. There is not much that can be done to verify the output of any other subject.

Re: GitHub cuts AI deals with Google, Anthropic

#543

Post by DeathArrow »

hansonkd wrote: post #367
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…

> The more time I spend in a codebase the better idea I have of what the writer was trying to do. This whole thing of using LLMs to Code reminds me a bit of when Google Translate came out and became popular, right around the time I started studying Russian. Yes, copying and pasting a block of Russian text produced a block of english text that you could get a general idea of what was happening. But translating from en…

Coding isn't the hard part. The hard part is translating the business needs in code.

You can tell a junior programmer "Make a DB with tables book, author, has Written, customer, stock, hasBought, with the following rules between them. Write a repository, for that DB. Use repository in BooksService and BasketService. Use those services in Books controller and Basket controller." and he will do a fine job.

Ask the junior to write an API for a book store and he will have a harder time.

Re: GitHub cuts AI deals with Google, Anthropic

#544

Post by DeathArrow »

abadpoli wrote: post #459
balder1991 wrote: post #400

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.

>AI writes the code

AI will never write proper code unless guided by someone who knows how to properly code and how to properly translate business needs into code.

Re: GitHub cuts AI deals with Google, Anthropic

#545

Post by hitradostava »

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 agree with you and its confusing to me. I do think there is a lot of emotion at play here - rather than cold rationality.

Using LLM based tools effectively requires a change in workflow that a lot of people aren't ready to try. Everyone can share their anecdote of how an LLM has produced stupid or buggy code, but there is way too much focus on what we are now, rather than the direction of travel.

I think existing models are already sufficient, its just we need to improve the feedback loop. A lot of the corrections / direction I make to LLM produced code could 100% be done by a better LLM agent. In the next year I can imagine tooling that: - lets me interact fully via voice - a separate "architecture" agent ensures that any produced code is in line with the patterns in a particular repo - compile and runtime errors are automatically fed back in and automatically fixed - a refactoring workflow mode, where the aim is to first get tests written, then get the code working, and then get the code efficient, clean and with repo patterns

I'm excited by this direction of travel, but I do think it will fundamentally change software engineering in a way that is scary.

Re: GitHub cuts AI deals with Google, Anthropic

#546

Post by RheingoldRiver »

hitradostava wrote: post #545
ianbutler wrote: post #311

Earlier quoted context omitted.

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 agree with you and its confusing to me. I do think there is a lot of emotion at play here - rather than cold rationality. Using LLM based tools effectively requires a change in workflow that a lot of people aren't ready to try. Everyone can share their anecdote of how an LLM has produced stupid or buggy code, but there is way too much focus on what we are now, rather than the direction of travel. I think existing m…

> Using LLM based tools effectively requires a change in workflow that a lot of people aren't ready to try

This is a REALLY good summary of it I think. If you lose your patience with people, you'll lose your patience with AI tooling, because AI interaction is fundamentally so similar to interacting with other people

Re: GitHub cuts AI deals with Google, Anthropic

#547

Post by globular-toast »

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 …

Unfortunately I think it really depends what people are doing. Some people have an utterly trivial problem domain and their job is to translate fairly low level demands into code. We used to call these code monkeys. The term has fallen out of favour but they still exist. Others have a very difficult problem domain, very high level, complex problems, and spend most of their time reasoning with how to manage that inherent complexity in a functioning software system. In these discussions it's impossible to tell who is who.

Re: GitHub cuts AI deals with Google, Anthropic

#548

Post by takeda »

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…

So maybe that's the issue I'm having.

I spent may entire career trying to eliminate such code as much as I can, so then having copilot write code that I have to fix on almost every step. I frequently have to look for subtle issues and few times they sneaked through, when it produces correct code it frequently is often more verbose than my code.

Re: GitHub cuts AI deals with Google, Anthropic

#549

Post by DeathArrow »

For all those believers in the power of AI who tested it in modifying their front-ends and writing a Python script, I have a test: ask AI to write an operating system kernel or a database. Of course, something simple.

I never seen AI being used in writing system software. Perhaps there is a reason behind it?

Re: GitHub cuts AI deals with Google, Anthropic

#550

Post by eru »

tkgally wrote: post #504
amake wrote: post #493

Earlier quoted context omitted.

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 se…

> 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.

Yes, coming up with variations that work better (and hit the right connotations) is what I used the machine for, too.

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