I pay $20/mo for chat access to an llm and $20/mo for a private search engine. $40/mo to escape the googleverse is well worth the price in my opinion.

Also agree. It's rare i need to boot into Windows for a game particularly for indie stuff

Good reason to switch to linux. I only boot in to windows to play games these days.

Free tier is limited to ~10-20 queries per day of the low end models, whereas $20 tier is virtually unlimited for the "dumbest" models , and moderate access to reasoning models, while $200 tier gives you access to enhanced reasoning models that can think for multiple minutes at a time

That tracks, because I'm currently restoring a 1948 Chrysler straight 8 engine (really that flathead design like what packard was famous for) on a 1948 rolls royce chassis.

I seriously doubt your claim of free speech with a chatbot will hold up in court.

They themselves proved you can't tell a chatbot to not be a therapist. You're not supposed to cut your hand off, there's no regulation telling people to not cut their hand off. You can't create an endless set of rules to tell you what you're not allowed to use free LLMs for. Anyone can train any number of LLM using free open source data sets with any restrictions (or no restrictions) it costs less than $1000 to train a basic 7b model with a credit card at any cloud provider. Next they're going to have signs on gas pumps instructing people not to douse themselves in gasoline and set themselves on fire.

I recall there was "premium" cable and that was HBO, Showtime, Cinemax and probably a handful of has-beens and regional ones that got bought out. I recall regular cable being ~$32/mo and with premium cable (HBO) it jumped up to ~$75 but they only had a few shows and movies would rotate, not too different from how it is now except it's streaming over the internet.

This was largely the purpose of gmail IIRC. Humans can't read your email, but there's almost no value in allowing them access, whereas letting computers build a customer profile to sell ads to you, is invaluable.

One of our engineers did this as a side project back in 2015 in an afternoon, setup a web scraper on aws and the next day we could visit all these things. I'm pretty sure the company did a new article on this... ten years ago.

My guess is phones and silicon will improve to make 4b on mobile by 2030. If not then those requests will get forwarded to xyz cloud service. I can see a world where 7-12b cloud models are ad supported free tier and you either pay or self host 70-600b yourself. I expect processing requirements to drop by half due to whatever breakthrough comes next and then there's a long tail of improvement after that. Token verification was a major improvement.

Yep I login to some of these sites and I see 3-4 tangentally-related things that are clearly algorithm rage-bait, designed to drive user interaction. I'm so tired of this. I login for 10-15 minutes a handful of times a week to check in on friends and family for updates, and then promptly uninstall the app.

Probably in 3-10 years there will be a handful of open, legally copyright free training sets anyone can use to train their own ~600b class model with whatever architecture is current state of the art. Researchers are already putting together 7b training sets like this. And LLMs can use tools like search now so they won't always need the most up to date news or info - they can use tools for that instead. Most of finance is analysis of documents, which LLMs have been excellent at for a while now.

Google announced roughly the same thing, on device models for phones a couple weeks ago at their developer conference. The 1b model is fine for basic tasks like turning on lights, checking email, social media notifications etc and runs ok on midrange phone hardware. The 4b model technically runs but it's borderline unusable speed but it can answer questions like "how does a microwave work?" with moderate accuracy at a semi-scientific level which is impressive. I suspect most devices will be able to run a 1b and by the end of the decade most everything will run a 4b model at least at talking speed. There's a concept that all AI processing will be done in the datacenter, I suspect 80%+ of consumer LLM will happen on the device, and more complex tasks will get routed to the cloud. For a lot of end users (high school students, etc) 98%+ of requests will be on-device.

LLMs are really good at stuff, better and faster than humans, as long as the complexity isn't much more than ~200 LOC (lines of code). 250-300 LOC and things start falling apart quickly. Sometimes (1/50) you'll get ready and it'll pop out 400 LOC without major errors but that seems to be the absolute limit of the current statistical model family everyone is using.
 
LLMs are really good at analyzing and summarizing text though, it has no problem analyzing 20-30 page PDFs of economic or financial data.
 
But yeah there was this idea that if you just kept training on bigger datasets for longer eventually you'd just arrive at AGI and it's pretty obvious via many many research papers that the error limit right now is ~1% and getting below that is really really dang hard. We're going to need a new breakthrough to get the ball further down the field.