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To AI or not to AI

antropia.studio

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Re: To AI or not to AI

#71

Post by falcor84 »

rhetocj23 wrote: post #52
amelius wrote: post #10

Yes, this is how I use AI. Indeed, self-invented abstractions are a bridge too far for AI. You have to keep it close to the path already walked before by thousands of developers. This makes AI more of a search engine on steroids than anything else.

ChatGPT is literally just a search engine that Google shouldve moved to, but waited because they didnt want to touch their assets in place.

I agree that Google were too slow to move, but entirely disagree with the first part. ChatGPT is very much not a "search engine". Arguably it is an "Answer engine", but more so, it is a conversational partner - I almost never use ChatGPT to just get one response; the real benefit is being able to follow up with it until I'm satisfied. It's an entirely different medium of interaction as compared to search engines.

Re: To AI or not to AI

#72

Post by falcor84 »

BinaryIgor wrote: post #51

"We just don’t think we will incorporate AI to do more than that, given the current state of things. We will, however, keep an eye in case the technology changes fundamentally." I wonder whether LLMs are capable of doing more; probably, we need another paradigm for that; still, they are very, very useful when used right

> I wonder whether LLMs are capable of doing more

I don't see how that is a question. I come up with new ideas to improve the LLM-based tools I'm using at least once a day, and the vast majority of these are plain engineering changes that I could do on my own if I wanted to put the effort into it. I think that even if God comes down from heaven to prevent us from further training the LLMs themselves (if God is listening to Yudkowsky's prayers), then we would still have a good few decades of extensively improving the capabilities of LLM-based tools to extract a massive amount of further productivity by just building better agentic wrappers and pipelines, applying proper software development and QA methodology.

Re: To AI or not to AI

#73

Post by KronisLV »

wintermutestwin wrote: post #9

I recently spent over an hour trying to get ChatGPT to give me some pretty simple rsync commands. It kept giving me command line parameters that didn't work on the version of rsync on my mac. With ~50% of the failures, it would go down troubleshooting rabbit holes and the rest of the time it would "realize" that it was giving incorrect version responses. I tell it to validate each parameter against my version moving…

> I recently spent over an hour trying to get ChatGPT to give me some pretty simple rsync commands.

Try N times, adding more context about the environment and error messages along the way. If it doesn't work after those, try other models (Claude, Gemini, ...). If none of those work on whatever number of attempts you've chosen, then LLMs won't be able to help you well enough and you should save yourself some time and look elsewhere.

A good starting point is trying for 10-20 minutes, after which point an LLM might actually become slower than you going the old fashioned way of digging into docs and reading forum posts and such. There are also problems that are a bit too complex for LLMs as well and they'd just take you in circles no matter for how long you try.

Re: To AI or not to AI

#74

Post by ewoodrich »

rhetocj23 wrote: post #58
Yoric wrote: post #19

Earlier quoted context omitted.

More precisely, AI is your intern who won't improve during the internship, ever.

Just to play devils advocate - why do you think this is so? And if you have a compelling thesis, why hasn't this spread to the investing community?

They mean it doesn't learn from experience/mistakes after spending time on your codebase. There are workarounds like documenting in CLAUDE.md/CODEX.md/.roorules which dump it back into the active context but are hit and miss in my experience. It's definitely better than nothing but Claude still routinely ignores important directives whenever it's in the right mood.

Re: To AI or not to AI

#75

Post by wintermutestwin »

righthand wrote: post #67
danielbln wrote: post #48

Earlier quoted context omitted.

Telling the agent to execute "man rsync" and synthesize the answer from there is probably the cheapest and most efficient option. Letting some detached LLM fumble around for an hour is never the right way to go, and inversely sifting through the man page of rsync or fmmpeg or (God forbid) jq to figure out some arcane syntax isn't exactly a great use of anyone's time either, all things considered.

Sifting through just means you don’t know how to use the man interface to search/grep (which from a discoverability perspective is fair). However I think reeling through an Llm (using an agent or not) for a task that probably could take All of this is an attempt at circumventing RTFM because you’re privileged enough to afford it. Just lay yourself down on the WALL-E floating bed and give up already.

The “fucking” manual is obtuse, overly verbose, and almost always lacking in super clear real world examples. That you claim it is a I am backing up and verifying critical data here. This is not a task that should be taken lightly. And as I learned, it is not a task that one can rely on an LLM for.

Re: To AI or not to AI

#77

Post by amelius »

rhetocj23 wrote: post #52
amelius wrote: post #10

Yes, this is how I use AI. Indeed, self-invented abstractions are a bridge too far for AI. You have to keep it close to the path already walked before by thousands of developers. This makes AI more of a search engine on steroids than anything else.

ChatGPT is literally just a search engine that Google shouldve moved to, but waited because they didnt want to touch their assets in place.

GPT is orders of magnitude more expensive to run, though.

Re: To AI or not to AI

#78

Post by Yoric »

rhetocj23 wrote: post #58
Yoric wrote: post #19

Earlier quoted context omitted.

More precisely, AI is your intern who won't improve during the internship, ever.

Just to play devils advocate - why do you think this is so? And if you have a compelling thesis, why hasn't this spread to the investing community?

> why do you think this is so?

Well, because nothing in the transformer architecture supports such learning ability. All AI researchers and most serious AI users are aware of this, so I'm not sure I understand the question.

> And if you have a compelling thesis, why hasn't this spread to the investing community?

The investing community believes that they can make money. That's feels pretty much orthogonal to whether the metaphorical intern can learn, and much more related to whether clients can be made to buy the product, one way or another.

Re: To AI or not to AI

#79

Post by BinaryIgor »

falcor84 wrote: post #72
BinaryIgor wrote: post #51

"We just don’t think we will incorporate AI to do more than that, given the current state of things. We will, however, keep an eye in case the technology changes fundamentally." I wonder whether LLMs are capable of doing more; probably, we need another paradigm for that; still, they are very, very useful when used right

> I wonder whether LLMs are capable of doing more I don't see how that is a question. I come up with new ideas to improve the LLM-based tools I'm using at least once a day, and the vast majority of these are plain engineering changes that I could do on my own if I wanted to put the effort into it. I think that even if God comes down from heaven to prevent us from further training the LLMs themselves (if God is listen…

True!

Re: To AI or not to AI

#80

Post by righthand »

wintermutestwin wrote: post #75
righthand wrote: post #67

Earlier quoted context omitted.

Sifting through just means you don’t know how to use the man interface to search/grep (which from a discoverability perspective is fair). However I think reeling through an Llm (using an agent or not) for a task that probably could take All of this is an attempt at circumventing RTFM because you’re privileged enough to afford it. Just lay yourself down on the WALL-E floating bed and give up already.

The “fucking” manual is obtuse, overly verbose, and almost always lacking in super clear real world examples. That you claim it is a I am backing up and verifying critical data here. This is not a task that should be taken lightly. And as I learned, it is not a task that one can rely on an LLM for.

I disagree. The topic is rsync which is well documented. The path forward might not work in every situation but the manual has good examples too. This is probably true for what Llm users use it for 90% of the time. To hack together things that are well known and well documented into a result. Llms arguably only work because these tools ffmpeg, rsync, etc were already solving problems and widely used and documented. So burning energy to have a computer look up commands because you couldn’t spend 10 minutes reading yourself could be a waste of time and money. Where as having to spend time researching is likely only a waste of time only.
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