I'm surprised YouTube allows it. I'd expect them to want "their" share of the ad money, since creators in a way are side stepping the "normal" ad flow.
618 karma · joined November 9, 2020
I was surprised and amazed to get "decent" (with the expectations set right / low) coding performance out of Qwen3.5-9B on a decidedly medium end Radeon 9070 paired with a 5700x3d and 32GB of DDR4 RAM.
We can finally reason with and "talk" to our hardware.
This is... a view.
Maybe I live in a strange sphere of strange ("normie"-ish) people, but the people around me are for sure using AI. Mostly chatgpt to be fair. They use it to compare products that they intend to buy, identify plants in nature, create travel plans, find interesting places to visit nearby, give movie suggestions based on what they have previously enjoyed and so on and so forth. AI is becoming a very integrated part of their reality. To "google" something and digging through the search results manually is very rapidly being replaced by asking chatgpt, for better or worse.
I think this is the key takeaway for the future of AI. Give tech a few years to catch up and we will likely have the functionally equivalent to today's models running on consumer grade hardware. From there it will explode, where "it" is how we use and interact with computers. AI will be integrated into just about every workflow.
The business case in this future would be to sell the trained models to end users. The investment would be shifted towards the training of models and delivering updates, with revenue coming from model licenses, upgrades and cloud services for tasks that exceed the local capabilities.
Hello myopia my old friend. We will wear glasses to the end. Because my vision's slowly slipping. Played Gameboy while I should be sleeping. And the vision that I once could claim. Doesn't remain. Now there's just the blur, of distance.
This metric highly depends on who uses the AI to do what, where strong emphasis is on "who" and "what".
In my line of work (software developer) the biggest time sinks are meetings where people need to align proposed solutions with the expectations of stakeholders. From that aspect AI won't help much, or at all, so measuring the difference of man hours spent from solution proposal to when it ends up in the test loops with and without AI would yield... very disappointing results.
But for troubleshooting and fixing bugs, or actually implementing solutions once they have been approved? For me, I'm at least 10x'ing myself compared to before I was using AI. Not only in pure time, but also in my ability to reason around observed behaviors and investigating what those observations mean when troubleshooting.
But I also work with people who simply cannot make the AI produce valuable (correct) results. I think if you know exactly what you want and how you want it, AI is a great help. You just tell it to do what you would have done anyway, and it does it quicker than you could. But if you don't know exactly what you want, AI will be outright harmful to your progress.
> And they don’t travel very far, so only nearby microphones would “hear” the tag. That makes the devices inherently private, Deng said, because other people wouldn’t detect any activity unless they were within a meter or so.
It would seem these things don't really produce loud noises, so probably not adding much to the noise pollution that already exists in our environments. At the same time it seems the statement kind of negates the "point" of this tech, that you don't need an active (energy consuming) device close to the source of the events that you want to detect. So not sure of how to interpret it.
It is anecdotal for sure, but it's a pattern that seems to be emerging around me that expectations of velocity increases, and those who don't use AI can't keep up.
I'm not saying that I'm no longer dealing with code at all though. The way I work is interactively with the LLM and pretty much tell it exactly what to do and how to do it. Sometimes all the way down to "don't copy the reference like that, grab a deep copy of the object instead". Just like with any other type of programming, the only way to achieve valuable and correct results is by knowing exactly what you want and express that exactly and without ambiguity.
But I no longer need to remember most of the syntax for the language I happen to work with at the moment, and can instead spend time thinking about the high level architecture. To make sure each involved component does one thing and one thing well, with its complexities hidden behind clear interfaces.
Engineers who refuse to, or can't, or won't utilize the benefits that LLMs bring will be left behind. It's just the way it is. I'm already seeing it happening.
I don't mind paying for what I consume, but God damn is the value proposition at the floor currently. Here even the rather expensive mid tier subscription gives you 1080p at most with all the big players. It's as if they somehow converged to this model and aren't competing anymore. Coincidence, I'm sure.
It's something that happens rarely enough for me to not having developed an automatic "aw hell nah, no f-ing way" filter towards it anyway, and I (naively) did click the notification and "got hit" by the article.
AI amplifies the problem by making it easier to produce filler, but the problem is whatever metrics are behind the monetization. You need users to "engage" with your content for at least x amount of time to earn y amount of money, while instead the earnings should be relative to and directly derived from how useful the content is to how many users.
I recently got hit by an "article" that promised to tell me which three AAA games would be released with PS Plus soon. A three point bullet list was all I wanted. Instead I got pages after pages of word-manure about nothing at all for reasons I don't even understand. At the end of it I still couldn't tell you which three games the article was supposed to tell me about.
I foresee a bleak feature where we will deploy AI as "content blockers" to extract the useful content from the word-manure that is becoming the preferred way of working among internet "authors".
Government / handles society-critical things code should really be public unless there are _really_ good reasons for it not to be, where those reasons are never "we're just not very good at what we're doing and we don't want anyone to find out".
Once you do understand the problem deep enough to know exactly what to ask for without ambiguity, the AI will produce the code that exactly solves your problem a heck of a lot quicker than you. And the time you don't spend on figuring out language syntax, you can instead spend on tweaking the code on a higher architecture level. Spend time where you, as a human, are better than the AI.
Still haven't found a good way to keep it on course other than "Hey, remember that thing that you're required to do? Still do that please."
I (deep, deep in embedded systems) have seen this too often, that code is incredibly complex and impossible to reason around because it needs to reach into some data structure multiple times from different angles to answer what should be rather simple questions about next step to take.
Fix that structure, and the code simplifies automagically.
Some companies will do as you say - have (mostly clueless) engineers feed high level "wishes" to (entirely clueless) LLMs, and hope that everyone kind of gets it. And everyone will kind of get it. And everyone will kind of get it wrong.
Other companies will have their engineers explicitly treat the LLMs as collaborators / pair programmers, not independent developers. As an engineer in such a company, YOU are still the author of the code even if you "prompted" it instead of typing it. You can't just "fix this high level thing for me brah" and get away with it, but instead need to continuously interact with the LLM as you define and it implements the detailed wanted behaviors. That forces you to know _exactly_ what you want and ask for _exactly_ what you want without ambiguity, like in any other kind of programming. The difference is that the LLM is a heck of a lot quicker at typing code than you are.
I bet a not insignificant portion of the population would tell the person to walk.
We know for a fact that earth is doomed, on top of our own continuous efforts to kill ourselves off. No not recent climate change type of doomed, but the evolution of our sun is continuously pushing the habitable zone outwards. We might be able to deal with that particular annoyance by hiding underground when it becomes an emergency in half a billion years or so, but our utopia won't be as utopic anymore.
Eventually however, the sun will balloon to a red giant at which point we better have a plan in place other than staying on this planet.
The vast majority of those who are affected by what you're doing should be asking themselves why you never seem to be doing anything difficult.
Better then to pump out a wide range of mediocracy to attract and keep as many subscribers as possible.
I'm still not sure if this is due to a technological limitation or an organizational one. Most of my time is not spent on solving tech problems but rather solving "human-to-human" problems (prioritization between things that need doing, reaching consensus in large groups of people of how to do things that need doing, ...)
Nowadays, after some 17 years in the business, it's pretty much always intermittently and rarely occurring race conditions of different flavors. They might result in different behaviors (crashes, missing or wrong data, ...), but at the core of it, it's almost always race conditions.
The easy and quick to fix bugs never end up with me.
The longer I work as a software engineer, the rarer it is that I get to work with bugs that take only a day to fix.
In my head it would have been the "Playstation Island", while for most of the world it would probably have been the "Playstation Empty Set".
As an engineer and a consumer / customer, I simply cannot understand why there's a need to complicate things.
You have a Thing, right? It sells, right? You develop the next Thing? Great! Call it Thing 2. Instant success.
Step 1: Raise your eyes above the computer monitor in front of you. What is the team / company already using? What will they likely be using in one year from now?
Step 2: Ask yourself honestly without "I wish I could and I wish I would" - can the problem be solved using the tech that the team / company already has invested in?
Step 3: Make decisions. Default to the answer in Step 1, but consider the evaluation in Step 2. Try to get as close to 1 as possible.
At home / "for the shits and giggles": Whatever is interesting. Sometimes even bending backwards to force functionality out of tech that has absolutely no business doing what I want it to do.