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Throw more AI at your problems

frontierai.substack.com

31–40 of 59 posts

Re: Throw more AI at your problems

#31

Earlier quoted context omitted.

All the snark contained within aside, I'm reminded of that ranting blog post from the person sick of AI that made the rounds a little ways back, which had one huge, cogent point within: that the same companies that can barely manage to ship and maintain their current software are not magically going to overcome that organizational problem set by virtue of using LLMs. Once they add that in, then they're just going to…

I believe you mean this one: https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you...

Some things should be bronzed so we don’t ever lose them and this is one of them.

Re: Throw more AI at your problems

#32
post #4

We aren’t good at creating software systems from reliable and knowable components. A bit skeptical that the future of software is making a Rube Goldberg machine of black box inter-LLM communication.

Not only is it the future of software, it's the past and present as well.

Re: Throw more AI at your problems

#33
post #4

We aren’t good at creating software systems from reliable and knowable components. A bit skeptical that the future of software is making a Rube Goldberg machine of black box inter-LLM communication.

As someone who makes HW for a living, please do make more Rube Goldberg machines of black box LLMs. At least for a few more years until my kids are out of college. :)

Re: Throw more AI at your problems

#34
post #29

RAG doesn’t necessarily give the best results. Essentially it is a technically elegant way to semantic context to the prompt (for many use cases it is over-engineered). I used to offer RAG SQL query generations on SQLAI.ai and while I might introduce it again, for most use cases it was overkill and even made working with the SQL generator unpredictable. Instead I implemented low tech “RAG” or “data source rules”. It’…

Semantic search is a powerful tool that can greatly improve the relevance and quality of search results by understanding the intent and contextual meaning of search terms. However, it’s not without its limitations. One of the key challenges with semantic search is the assumption that the answer to a query is semantically similar to the query itself. This is not always the case, and it can lead to less than optimal re…

Don't post AI slop on HN comments please.

Re: Throw more AI at your problems

#35
Use Moore's law to achieve unreal battery life and better experiences for users... or use Moore's law to throw more piles of abstractions on abstractions where we end up with solutions like Electron or I Duck Taped An AI on it.

Reading through this, I could not tell if this was a parody or real. That robot image slopped in the middle certainly didn't help.

Re: Throw more AI at your problems

#36
post #4

We aren’t good at creating software systems from reliable and knowable components. A bit skeptical that the future of software is making a Rube Goldberg machine of black box inter-LLM communication.

I could see software having a future as a Rube Goldberg machine of black box AIs, if hardware is cheap enough and the AIs are good enough. There was a scifi novel (maybe "A Fire Upon the Deep"?) where there was no need to write software because AI could cobble any needed solution together by using existing software and gluing it together. Throwing cycles at deepening layers was also something that Paul Graham talked about in the hundred year language (https://paulgraham.com/hundred.html).

Now, whether hardware is cheap enough or AI is smart enough is an entirely different question...

Re: Throw more AI at your problems

#38
post #22

With the current state of "AI", this strikes me as a "I had a problem and used AI, now I have two problems" kind of situation in most cases.

But see the company selling the AI "solution" made money, which is the entire point.

Making money is the Silicon Valley Bro equivalent of mathematical proof.

Sorry, not making money— I meant attracting investment.

AI tech is generally undertested and experimental, yet marketed as solid and reliable.

Re: Throw more AI at your problems

#39
post #20
post #6

Earlier quoted context omitted.

I'm pretty sure this is a satire post

Here's a practical in this vein but much simpler - if you're trying to answer a question with an LLM, and have it answer in json format within the same prompt, for many models the accuracy is worse than just having it answer in plaintext. The reason is that you're now having to place a bet that the distribution of json strings it's seen before meshes nicely with the distribution of answers to that question. So one re…

So developing solutions with ai is like trying to build stuff with family feud.

Re: Throw more AI at your problems

#40
post #22

Earlier quoted context omitted.

But see the company selling the AI "solution" made money, which is the entire point.

Making money is the Silicon Valley Bro equivalent of mathematical proof. Sorry, not making money— I meant attracting investment. AI tech is generally undertested and experimental, yet marketed as solid and reliable.

Speaking as someone who is very critical of the general "raise infinite money and grow grow grow" approach to startups (it's a big part of why my company is bootstrapped [1]), I think there's a really important difference between making money and raising money.

Raising money is about convincing investors that you can solve someone else's problem. Or even more abstractly, about convincing investors that you can convince other investors that you can convince other investors that you can solve someone else's problem. That's enough layers of indirection that signaling games start to overtake concrete value a lot of the time, and that's what gets you your Theranoses and your FTXes and the like. You get into "the market can remain irrational longer than you can remain solvent", or its corollary, "the market can remain irrational long enough for you to exit before it wakes up".

But making money means you are solving a problem for your customer, or at least, that your customer thinks you're solving a problem with no additional layers of indirection. And if we take "making money" to mean "making a profit", it also means you're solving their problem at less cost than the amount they're willing to pay to solve it. You are actually creating net value, at least within a sphere limited to you and your customer (externalities, of course, are a whole other thing, but those are just as operative in non-profitable companies).

I think this is one of the worst things about the way business has done today. Doing business, sustainably and profitably, is an excellent way to keep yourself honest and force your theories to actually hold up in a competitive market. It's a good thing for you and for your users. But business has become so much about gathering sufficient capital to do wildly anticompetitive things and/or buy yourself preferential treatment that we're losing that regulating force of honesty.

[1] see my HN profile for more on that if you care

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