nkozyra
The US House of Representatives posts their live hearings exclusively on YouTube. This is information that should be ungated and freely available, yet is a pain to acquire or archive because YouTube absolutely does not want you getting to the raw video.
I don't understand how this is legal, honestly.
Even if you trained your own model, you'd be committing some of the same sins, paying for the same hardware that drove it, etc. But if you're using some open model, you're standing on the shoulders of the same corrupt giants.
I feel like when people say this is due to moral reasons, it's to justify an expensive hobby.
Plus you're spec'd out of near-SOTA level in months.
The only reasons to actually do this are a) you have a lot of dispensable income and are a hobbyist/tinkerer, b) you have real, legitimate privacy concerns or, relatedly, c) you're doing something you don't want to get flagged
If you don't see this as an enormous amplification, I don't think you're operating in good faith.
1. https://www.nytimes.com/2026/08/10/well/trump-vaccines-fact-...
It's been a growing movement, not something that kicked off when RFK assumed the HHS role.
You must have caught it within two or three hours after its arrival.
In the US we have similar hawk moths; the tomato hornworm also looks like a hummingbird when it reaches maturity.
But do you need to run every small problem through a 10B-30B model?
We're smashing ants with hammers most of the time. We're asking frontier Opus/Fable models to classify text and build frontend code.
Once we start dissecting these problems into smaller discreet tasks and having the big reasoning models do the tough stuff, we suddenly have an economical system. Not for the company hoping for a big IPO, but for the end user.
I think they've got that. Short of budget stuff or the Slate truck, most new cars have some big dumb screen in them at all times.
But advertising poses a new problem for both advertisers and mfgs not unlike the mid-90s ad sale issue. There was no consolidated ad server, so everyone was trying to build their own agency and advertisers had to navigate that.
Which probably means some sort of Google or Google-like player in the space.
All that said, I think initially it was a mix of a few things coming together.
Yes, auto mfgs always want to reduce parts for cost and supply chain control. But there was also this moment of New Wow where the impractical nature of touchscreens was overshadowed by the holy crap I've got a tablet in my car. It implied a break with the last generation of cars, where you might have gotten a 4-inch screen (touch or not), and it became desirable at a surface level to users.
Although I greatly dislike touchscreens for the obvious usability issues in a motor vehicle, I still kind of widen my eyes when I'm in a car with some new, ridiculous multi-screen dashboard setup.
Mazda was mentioned in this thread, and I think they do a great job of separating the concerns here; you've got a big buttons of various sizes that do different things that can be memorized without sight.
In the aggregate, I agree, but in tech things are pretty loose outside of California.
I started doing "ML" ~ 20 years ago building classifiers people would laugh at today and even at the time barely impressed people when they were 95% correct.
I moved into NLP and built NERs that missed 2-10% of named entities per document routinely. Best of breed approaches and models rarely fared better.
Learned the cornerstones in school for ML; linear regression, ANNs, traditional RL, image classifiers, A* bots, etc, most of which got baked into transformers later on.
Then the transformers went from interesting novelty to useful. I couldn't build a useful one locally, but the toys versions were still fun to play with.
Then the novelty LLM went from useful to generally applicable. Then they became a silver bullet.
I still can't build one locally. I can distill or build or fine tune if you give me some rented GPUs. But to call this ML is very much a stretch.
I still use the traditional ML a lot, but mostly for evals and analysis.
I get being naturally bummed by this but I can't justify feeling anything but vaguely nostalgic about it. Someone with a $20 subscription can mog anything I can build with the skills I picked up.
If someone hands you a silver bullet you'd be a fool to decline it and spend your time hand casting a crude piece of brass. If the difference between 95% and 99% means you know how to aim or oil the gun, that's the world we live in.
Building a good RAG pipeline or prompt optimization or LLM consensus is dumb stuff that produces a better result than anything I could do from my 2010 ML/AI textbooks. I don't lack the knowledge or capacity to compete, I lack the compute.
That's the job now for 99% of companies.
You can argue that it's unlikely the for-profit conservatorship of the non-profit is incompatible with that goal, but legally that becomes very much grey area.
But he'll also likely be shaving equity here and there along the way to hedge this bet.
Specifically because it's not a natural market. There are people who secure a 2-year, consequence-free term to impact U.S. law, at the behest of people with money.
Lobbying is special interests dictating decisions that often are not financially, morally, or otherwise ideal/beneficial to the other party (the United States and its people). This wouldn't fly at any corporation or business because there would be direct impacts on the bottom line or reputation of the company.
Sometimes just having the time/compute to explore the available space with known knowledge is enough to produce something unique.