Or I could have an honest to god farmer make that list now, ahead of time. Regular search could absolutely surface that.

But also, as someone who does hydroponics, you’re going to end up reading the papers anyways unless you’ve done this a lot. It’s not easy to pick out something like low iron from too little light.

I suspect it is, because the 2D -> 3D models I've seen are terrible, but more importantly the 2D -> 3d models are terrible. Noteworthy because I suspect the number of people who have 3D scans laying around is tiny.

What you're describing is vaguely how video games work, to my understanding. There may be interesting parallels if that becomes a technique (like how distant objects are sometimes still rendered, but at a lower resolution/polygon count).

Your understanding is broadly correct. There is a 3D world, and the GPU computes how it looks like from a particular viewpoint.

In essence, objects are painted back to front, so that in the final image every pixel shows the nearest visible object. The details get complicated, but the idea is simple.

There’s a substantive difference between ingesting other people’s work and hosting copies of fans knockoff models of their favorite characters.

It’s even weirder because in other mediums this wouldn’t even be a thing. Nobody gets nearly as up in arms about fans doing their own cuts of movies, or re-using characters in written fan fiction, or etc. Very odd to me how up in arms people get about this specifically for visual art.

Jev is super cool, and it’s super cool that it can handle novel categorization. Most organizations aren’t spending the big bucks on novel classification, though. They’re doing the same thing a billion times, which means Jev has to compete with having a custom model built and doing your own inference.

It’s very hard to build something generic like Jev, but it’s fairly manageable to build something more specific that handles a single kind of task. Especially at the scale where OpenAI would care about your attention.

I don’t really understand neural networks and I managed to train one to do hand gesture recognition on an old Leap Motion. It’s a pretty limited domain, but it trains on the CPU of my decade old laptop in under 3 minutes. The accuracy is quite good too, especially when you discount errors from the Leap (like misidentifying which finger is extended).

1. inference on arbitrary context / world knowledge with fast, structured output

2. I almost don't want to say it because I don't know if people realize this yet... but that makes traces from any kind of non-AI process or system "learnable", you can easily model it by by just asking it to predict the next outcome during learning and compare against what happened.

which also means it could also learn new strategies through rollouts in a way that generalizes across semantic space which means...

so these models are actually quite promisingly useful for existing companies because they do have general traces of what happened in their company and want to discover ways they can tweak their business processes, and then there is durable demand for models you can embed within a product or UX flow without exposing your user to slop + then train or customize and build real IP or custom capabilities

general classification itself is quite interesting IMO for applications that need to make judgment calls or select one of many UX actions, and a much better fit for this than LLMs. So if you think about it, this is the type of thing businesses might be calling a lot - every trace (eg user session, package shipment, sales motion, marketing campaign, job outreach) + proactively throughout dynamic experiences to decide whether to eg end a call or which of multiple UIs to manipulate

this is just an unfamiliar product experience to most people because it hasn't really been bootstrappable with a strong general baseline until now. But now it's posible and I think it will be very col.

I’m talking about them training their own models with a much more specific purpose.

You brought up choosing a menu option in a UI. Jev is really good at something like “find the menu option that best fits the users prompt”. That’s general, and requires a big complicated model.

Companies are usually more interested in something less variable. Ie the menu option they’re looking for is fixed. “Take an arbitrary website and find the Login button”. That’s a _way_ simpler model, and well within the abilities of many large companies to train.

You only need to pay Jev if there are so many variables the model basically has to be general.

Eg classifiers are a separate thing from LLMs and are literally only capable of structured output. It will output one of the X options it was trained to recognize.

I think some of this is that the Venn diagram of people who like AI and the people who like machine learning isn’t a circle. There’s a lot of “intelligences” out there that aren’t an LLM or decision model.

They only ever get cancelled over money

A long term plan for a company is _at least_ a decade. If your plan ended today, it started in 2016 so it has had to survive COVID, inflationary periods, several trade wars, the rise of AI and now the side effects of the war in Iran.

If your long term plan has to adapt to those in real time, is it even a long term plan? Or is it really a series of long term plans cut so short they became short term plans? I can see companies adapting to just drop the long term premise entirely.

This happens a lot because at the individual level, it’s sort of in everyone’s best interest to take the deal. The prosecutor wants $2,000 and 0 jail time and it all goes away. If you don’t take the deal, even assuming you win in the end, it will probably cost you way more than $2,000 and however long the probation check ins take. You will recover neither your time, nor the money you spent on lawyers/transportation/days off/etc.

At the macro level it happens because prosecutors get judged on their conviction/case closure rate, because we implicitly assume they found the right person. Browbeating people into accepting token punishments that technically close out the case counts for the metrics voters actually watch.

If I might lean a little conspiratorial for a moment, it also behooves the prosecutors office to flood the defense attorneys with cases. People will vote to raise the prosecutors funding to deal with criminals, no one will vote to raise defense attorney funding. Last I saw the average prosecutors office gets something around double the funding the defense gets.

It got so bad in some city in Oregon there was (is, really), a near constitutional crisis because the city got trapped between the provisions of the 6th amendment. Defense attorneys were so flooded they said they couldn’t take any new cases. The 6th amendment says the accused has a right to counsel, which is an issue. It also says they have a right to a speedy trial. The city was going to have to allocate money to the defense and get them lawyers quick, or drop a bunch of cases.

That never happened, the judge basically just said “Nuh uh” and I believe threatened the defense office with sanctions if they didn’t just assign the cases to lawyers who were already saying they had more cases than they could imagine.

I’m a little shocked we haven’t seen a huge wave of successful “ineffective assistance of counsel” appeals. Pointing to a signed, public statement your lawyer was overworked before you even talked to them seems like a decent starting point.

Are there absolutely no provisions for an innocent person to claim all the expenses they had to incur ?

The absurd end of this spectrum is what happens when police damage a random persons property while chasing a suspect. They’re generally not liable for it, however most homeowners insurance won’t cover it.

I remember an early GoFundMe from a lady that went to work and a guy broke into her house and had a shootout with the police. She came home to being homeless (house got condemned because plumbing, gas, etc is bullet-riddled), and it’s a really disconcerting story to hear. I don’t recall if the city settled to quiet the story down or not.

I completely agree about the realpolitik reasons though. Ultimately we need out of touch voters to realize that unaccountable government can easily turn its sights on them, as well. The real difficulty is trying to make people respect numerically low odds without falling for feel-good nonsense about "those people".

> municipalities paying the full cost of policing

Since we're talking realpolitik, this will never happen. In a first principles sense, it won't because it's unpredictable at the locality level (how do you set tax rates for FY27 without knowing what you'll pay out for this?). In a realpolitik sense, it won't because a) people don't like their taxes going up and would rather bank on it not happening to them, and b) the police will throw a shit fit and refuse to work when they realize they have to consider property damage while doing their jobs, and that the public will be _pissed_ if they crash a cruiser into a parked car while LARPing John Rambo.

> Ultimately we need out of touch voters to realize that unaccountable government can easily turn its sights on them, as well.

Good luck and godspeed. The latest stats say 56% of US adults read at or below a 6th grade level, so say it slow and in monosyllabic words lol

Is this related to the US govt banning internal DeepSeek use? A currency thing and prices are USD denominated? Chinese AI stocks were used to hedge and now aren’t?

I don’t see a purported reason, and it makes me feel like the article doesn’t want me to ask that.

To your point, I think we’re past the hump on the Pareto distribution. The models are good enough for most things, and the rate they’re improving at the things they’re bad at is dropping. The odds a new model is good at something the old one was bad it is okay, the odds it’s something you care about is dropping. It makes a lot of sense to discriminate on price rather than features in that environment.

People are saying _training + inference_ is unprofitable (I don’t have a dog in the race and don’t really care either way). That’s a lot of GPUs and salaries that aren’t generating revenue at any moment.

Personally, I would guess Anthropic and OAI are profitable. I just suspect that they’re a pretty normal level of profitable and not the absurd, near-alchemical “can turn dirt into gold” level of profitable their valuation seems to have priced in.

OpenRouter's DeepSeek provider is basically this. Instead of renting H100's for dollars per hour, you pay someone that arbitrages the $/hr to $/token conversion because you can't actually saturate an H100 for hours. It's still effectively the same as running it yourself because of the ability to change providers to someone else arbitraging the same thing, and that there's always the escape hatch of switching to actually renting the H100s yourself.