51–60 of 130 posts
>> in five years time most internet users will spend a big part of their day scrolling an AI-generated feed. Yep. Looking forward to the future where you can eat plastic pop-corn while watching the AI-generated video feeds.
Earlier quoted context omitted.
They're idiot savants. Use them for their strengths. Know their weaknesses.
So, what are their strengths then? I've fed it with a detailed, very well documented and typed API description. Asking to construct me some not too hard code snippets based on that. GPT-5 then pretend to do the right thing, but actually is creating meaningless nonsense out of it. Even after I tried to reiterate and refine my tasks. Every junior dev is waaay better.
Thing is, there is no library for it to work in.
This seems to be biased heavily towards products that look like an LLM. And yes, only a small number of those work. But that's because if your product is a thing I chat with, it immediately is in competition with ChatGPT/Claude/Grok/etc, leading to everything the article expressed. But those are hardly the only use cases for LLMs, let alone AI (whatever people nowadays mean by AI) To name some of the obvious counter-…
So the only AI products that work is a chat bot you can talk to, or a chat bot that can perform tasks for you. Next thing you'll tell me is that the only businesses that work are ones where you can ask somebody to do something for you in exchange for money.
Maybe the real value of AI, particularly LLMs, is in the interface it provides for other things, and not in the AI itself
What if AI isn't the _thing_? What if it's the thing that gets us _to_ the thing?
Well, the elephant in the room here is that the generic AI product that is being promised, i.e. "you get into your car in the morning, and on your drive to the office dictate your requirements for one of the apps that is going to guarantee your retirement, in order to find it completely done , rolled out to all the app stores and making money already once you arrive" isn't happening anytime soon, if ever, yet everyon…
Skeptics always like to toss in 'if ever' as some form of enlightenment they they are aware of some fundamental limitation of the universe only they are privy to.
Well, the elephant in the room here is that the generic AI product that is being promised, i.e. "you get into your car in the morning, and on your drive to the office dictate your requirements for one of the apps that is going to guarantee your retirement, in order to find it completely done , rolled out to all the app stores and making money already once you arrive" isn't happening anytime soon, if ever, yet everyon…
The other part to this is that LLMs as a technology definitely has some value as a foundation to build features/products on other than chat bots. But unclear to be whether that value can sustain current valuations. Is a better de-noisier algorithm in Adobe Lightroom worth $500 billion?
No.
But: a tool that allows me to de-noise some images, just by uploading a few samples and describing what I want to change, just might be? Even more so, possibly, if I can also upload a desired result and let the "AI" work on things until it matches that?
But also: cool, that saves me several hours per week! Not: oh, wow, that means I can get rid of this entire department...
Author forgot about image, video, and music creation. These have all been quite successfully commercially, though maybe not as much artistically.
With many of the good usecases of AI the end user doesn't know that ai exists and so it doesn't feel like there is AI present.
A few more seem to work as well, because I've used them and found them valuable - human language translation - summarization - basic content generation - spoken language transcription
> summarization can you point me to a useful example of this? i see websites including ai-generated summaries all the time, but i've yet to see one that is actually useful and it seems like the product being sold here is simply "ai", not the summary itself - that is, companies and product managers are under pressure to implement some sort of AI, and sticking summaries in places is a way for them to fill that requirem…
Now I pass them through an LLM and ask them to point out interesting, unconventional, or surprising things, and to summarize the document in a few bullet points. They're quite good at this, and I am can use what I discover later in my relationship with the counterparty in various ways.
I also use it to "summarize" a large log output and point out the interesting bits that are relevant to my inquiry.
Another use case is meeting notes. I use fireflies.ai for some of my meetings and the summaries are decent.
I guess summarization might not be the right word for all the cases, but it deals with going through the hay stack to find the needle.