101–110 of 130 posts
Earlier quoted context omitted.
> basic content generation Dunno, man, I can spot ai-generated content a mile away, it tends to be incredibly useless so once I spot it, I’ll run in the opposite direction.
You spot bad ai content. Since there is no button that will tell you if something was Ai generated you never know if what you read was/wasn't.
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…
Yes but...
First, what we're seeing with coding is that it is just exposing the next bottleneck quickly. The bottlenecks are always things that don't lend themselves to LLMs yet.
Second, that still can mean 4 hours a week for 20-50 bucks. At US white collar wages, that might mean 8 people are needed rather than 9. In profit centers that's more budget for advancing goals. At cost centers, though, that's a reduction in headcount.
> The third real AI product is the coding agent. People have been talking about this for years, but it was only really in 2025 that the technology behind coding agents became feasible No, they didn’t. > LLMs are particularly good at tasks like “skim through ten pages of search results” or “keyword search this giant dataset for any information on a particular topic”. No. They are not. Amazing article without any suppo…
>> 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.
Why 5 years, I'm pretty sure we're there today.
seem like data analysis would be a good one. Company ingests massive amounts of disparate business data. Ask AI to clean and normalize it, visualize it and give recommendations.
We know it's not viable to hire humans to do this, but we don't know if it's viable for LLMs to do it.
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Do you go through the haystack yourself first, find the needle, and then use that to validate your hypothesis that the AI is good at accomplishing that task (because it usually finds the same needle)? If not, how do you know they're good at the task? My own experience using LLMs is that we frequently disagree about which points are crucial and which can be omitted from a summary.
It depends on how much time I have, and how important the task is. I've been surprised and I've been disappointed. One particular time I was wrestling with a CI/CD issue. I could not for the life of me figure it out. The logs were cryptic and there was a lot of them. In desperation I pasted the 10 or so pages of raw logs into ChatGPT and asked it to see if it can spot the problem. It have me three potential things to…
By fine tuning llm based predictive systems we might unlock entirely new use cases, and prediction is just one thing i imagine, there are many other use cases.
And then it’s not just the fact that frameworks like bert-topic are integrating LLMs. It’s also the fact that if you zoom out the architecture looks a lot like the architecture of an LLM… text -> embedding -> text
An LLM could and is already being used to generate recommendations systems, like the ones used at Youtube and Netflix, it captures more semantics than older techniques.
Shazam is almost perfect song recognition and is built into iOS
Every Google/Apple Maps route is based on an AI system
I have at least a dozen apps that use almost perfect visual recognition based on images for search (plant identification, stochastic object identification etc…)
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.
Earlier quoted context omitted.
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.
Let’s say there are three options: {soon, later, not at all}. Ruling out only one to arrive at {later, not at all} implies less knowledge than ruling out two and asserting {later}. Awareness of a fundamental limitation would eliminate possibilities to just {not at all}, and the phrasing would be “never”, rather than “not soon, if ever”.