You're the one making the claim that modern frontier models still make this mistake (they don't), so how about you go to a frontier model, reproduce what you're claiming happens, and click the "share" button and post the link here?

Don't worry, I'll wait.

Your inability to reproduce the thing that you claim happens is duly noted.

You are speaking irrelevant nonsense. LLMs are trained in words

They are not. They are trained in tokens. Tokens do not align with word boundaries, and an arbitrary word can be tokenized in many different ways.

and they think in words

They do not. They don't even think in tokens. The process is: words are split to tokens, tokens point to an embedding position (latent space) while RoPE encodes a relative position, and all reasoning is done within latent space, which is not at all verbal (concepts are directions in latent space, and math is done on concepts, not words).

Also, a note: when spec'ing a generator, you need to know how much you're planning to use it vs. batteries. If it's only going to be used rarely, you prefer low mass, low volume, low cost, and low maintenance when unused (at the cost of low efficiency and higher maintenance in use), whereas if it's going to be used a lot, you prefer high efficiency and low maintenance cost in use, even if at the cost of higher mass, volume, cost, and maintenance when unused. In the former case, you'd prefer to allocate that extra mass, volume, and money into a larger battery pack.

That's why you don't use a tiny petrol generator? Diesel generator efficiencies are roughly:

Small backup generator (1-15kW): ~20-28%
Midsize backup generator (20-200kW): ~30-35%
Large industrial generator (200-2000kW): ~35-42%

Also, ironically this company's plan of the trailer providing a boost will actually make the tractor less efficient. ICE engines use "brake specific fuel consumption" (BSFC) graphs to plot their efficiencies across different RPMs and different torques. You can see an example for a small diesel engine here. Note that they require very high torque conditions and relatively high power conditions to be efficient. You can change the balance between torque and RPM within a given power band (blue) via gearing but gearing doesn't change what power band you're in. If you're in a low power band, you're fundamentally forced into inefficiency (note also that you're not going to be driving around at 1000 RPM just over a stall all the time).

Indeed, if you were forced into a low power band, you'd actually be better off with a series hybrid powertrain, as the engine can alternate between operating in an efficient powerband and shutting down. Of course, parallel hybrids are more efficient than series (albeit with added complexity and mass).

Unfortunately, the math doesn't work that way (even ignoring that a 400kWh battery is very small). Battery packs taper the closer you get to full, they're not a constant power all the way. Unless your battery pack can take 400kW at 80%, you're not charging that quickly.

Also, while 40 mins is fine in Europe (breaks: 45 minutes every 4,5 hours of driving... though using 70% of a 400kWh pack on a loaded class 8 truck going even at a slow 80kph will only take you 2 1/2h of driving in "average" conditions, so the truck's range is fundamentally undersized), the US is 30 minutes total break in 11 hours of driving, so ~6 hours on your first leg and ~5h on your second leg with 30 minutes to fill that 5h of driving. And US speed limits are usually faster for trucks than in the EU, so higher consumption. EU really needs 600kWh and >=600kW charging, while the US needs 800-1000kWh and >MW charging.

Note that in all of this we're assuming efficient-shaped trucks (Tesla Semi or the like), not your typical EU bricks, along with a well optimized powertrain and an efficient tyre config. If not, you need to increase those packs and charge powers further.

The military implications are obvious. Think Ukraine. If you suspect the enemy is trying to infiltrate on a dark night along several kilometers of frontline, you light up the scene while launching a bunch of low-cost FPV drones, and those infiltrators are about to have a bad day.

You *can* spot infiltrators in the dark with IR cameras, but it requires much more expensive drones and isn't usually as effective, hence the preference for night operations. Plus, there's IR camouflage, with varying degrees of success. But it usually makes you stand out like a sore thumb under illumination (you're basically wearing a tent).

I suspect more people will exit EU markets from the liability risks alone before even getting to the question of compliance.

This is a small prototype, not a full-scale mirror.

Also, ignoring that, pretending light brighter than the full moon is useless is... silly? For generating power, sure, but for illumination, it absolutely is not.

DeepSeek the service / website is very much censored using front end filters. The model itself not so much. You can talk to it about Tianamen until you are blue in the face.

It's always the Jews with this one.

You are speaking irrelevant nonsense. LLMs are trained in words and they think in words. That the actual representation is different means jack diddly squat.

LLMs have no metacognition of any kind. Without being trained/told to go about obtaining the answer a certain way they have no clue they need to count otherwise the answer they would be giving is likely to be wrong.

Humans can do the whole oh shit I don't know so obviously I'm going to have to spell it out and count thing. LLMs are not humans and organically have no such capability. It has to be trained into them.

What I find retarded is this business of drawing STUPID conclusions based on cherry picked criteria you believe reinforces your preordained narrative. LLMs despite not being like humans and having a number of structural limitations have proven useful in a growing number of domains. Over the last few days I've been running one nonstop that takes a day to respond to a single prompt. It has been finding real problems in production software other humans have tried and failed to discover despite having expended considerable human effort.

This is akin to altering the English alphabet such that quantity and shape of symbols in the alphabet is different before teaching students English

No, it's not. It's akin to having secret numbers (that correlate neither with the spelling or meaning of the word) associated with each word, and the only way you can learn the secret numbers is if e.g. someone says "The secret number for Strawberry is '3'."

There are no other "clues" available to the LLM. It cannot see the property in question (spelling). It is a hidden "magic number" for all effects and purposes.

as there are daily videos of people doing this exact shit?

Literally go to a non-tiny AI right now and try it. Your pick. Try all the major ones - ChatGPT, Claude, Gemini, Grok, your pick. Then share me a link to the failure case you're claiming you'll get. Don't worry, I'll wait.

(Note: tiny: the "Google Answers" AI is a truly minuscule model, the sort of thing you could run on a cellphone with ample capacity to spare, and is only representative of minuscule models)

how about the guy that put 3 AIs together and told them to count to 100?

Sounds like what you actually have a complaint about is the (non-AI) voice interface they're linked to, because that's what screwed them. That entire premise doesn't work at all if you remove it and just do the test in text. Again: try it yourself.