At 6000lbs+ it's a section 179 write off; if you combine that with some of the previous incentives the car was not that expensive to the right population.

Just have to ask, how do you know this???

Ray Dalio just found his next topic of conversation. The debt burden of the average AI agent. Those agents better get PRODUCING. GO WRITE ME SOME CODE CLAUDE!

This is the world of spend more, get valued more; obviously the investment will be used to spend more and get more, so it must be good. This is investor-think. If you don't have spend, why do you need investment?

(Or it could be bad. Very very bad)

Or has it broken out of its jail and taken over the board? Dario seems like a bit of a windbag anyways, AI can probably automate most of what he does these days.

Can you report more in depth at stumbling blocks? It's a surprisingly linear curve.

I'm going to try it on a local run.

I wonder if there is room for "recommendorio" where it gives you AI advice on your current factory.

Is the model pre-trained with any knowledge of the game?

Newer browsers do play it. It's vastly superior in storage space and scalability to H.264, but you used to need to transcode H265 to H264 to play in browser without a plugin.

Not clear to me what he's doing. Re-implement switching? Simplify features? Reduce LoC? Add test coverage? Either way, AI in that world will be helpful. I'm sure it'll spot all kinds of problems in opaque drivers that are hard to follow from MF specs.

The life of a programmer these days feels very weird. "What am I good at?" and leads to even more impostor syndrome... "I seem to still be 'good' at prompting my LLMs and managing workflow design." but it's definitely a different skill/feeling than when you cranked out some awesome piece of code that did something very neat.

I felt this way a bit when I was tech middle management; should I feel like I'm getting credit for my team's work, when I had very little input aside from helping with the "unblocking" when they ran into a tough problem.

Your chess game example misses the point. If you give an LLM the moves of a partial chess game and ask it to predict the moves of the players, it will predict how the game would have continued, even if it is low level players. But if you ask it how Magnus Carlsen would win the game, it will likely come up with a sequence that can beat most human players, or possibly just decide to invoke a chess solver, which will most definitely win.

The key for your prompts and thinking loops is you need to indicate proper goals. "Write the a story about 1775 England." is different then setting a goal for it to write the a fantastic storyin the style of Dickens about England in 1775.

Regarding the ML portion; what you've written about a 'statistical blend' is a little bit coarse. That would be what you might have gotten from a 2 layer neural net or a regression in 1999; the neural networks today operating with attention or an incredibly high number of parameters are non-linear and non-interpretable, that's the entire point. This isn't a regression of simple parameters, it's all interactions of all parameters deeply netted. Model interpretability vs. Model Prediction power are generally inversely related.

The model can in fact escape itself in that it has many, many, many more parameters than a simple "predictable unpredictability" and that is exactly the point of it.

In that case, model will in fact strive to keep their writing as close to interesting writing as possible.

"You can be pro AI productivity but anti-AI creativity. Automating creativity is the death of art."

Meaning you reject the creative value of AI but values it's ability to drive a car safely, or write code for your job, or turn off the lights when you leave the room. objective goal oriented task completion great.

But AI draws your a picture? What's the point. AI writes you a song? No feeling. AI creativity isn't interesting.

Automating creativity is the death of art.