The UK typically doesn't need air conditioning, so why would people spend money on it? Yes, I know this recent summer was exceptionally hot but people don't react that quickly to changes.

> decarbonizing heating

What does this mean? More renewables? If efficiency reduces energy demands then it directly results in less carbon.

> electric vehicles

Aren't people already adopting electric vehicles? Do you think that the government should introduce a scheme to persuade people to buy electric vehicles? Does the availability of charging for electric vehicles need improving?

> Efficiency is good after you have high living standards but the UK should probably focus on growth first

Efficiency means saving money. In what circumstance could efficiency be bad? The grid responds to demand, unused electricity means higher prices for consumers. One of biggest issues right now in the UK is cost of living crisis, so raising electricity prices would be universally hated. How does making the grid less efficient increase growth?

The response of many mathematicians to the recent dump is a good example: verifying these proofs amounts to unpaid labour for OpenAI and wastes time that could be spent doing publishable work which ultimately results in money or personal success. The slop factor also compounds the work required to verify the output considerably.

For mathematicians, programmers or anyone, if the work required to deal with slop passes the limit, it is no longer in their own self interest to use LLMs. The expectation that people will use LLMs for the betterment of humanity against their own financial interest is baffling.

It's also notable that the Open AI proof may not even be interesting to mathematicians.

Even if people already had an idea that LLMs were training on user inputs, it's the first time it's actually caused an issue. Mathematicians, and plenty of researchers, working in ambitious or competitive field now have a very good reason to avoid LLMs.

As the ARC AGI guidelines [1] state, 'a core design principle of ARC-AGI is that the test taker must not know what the test will be.'

[1] https://arcprize.org/policy#dataset-security

From the roadmap:

>Mojo may or may not evolve into a full superset of Python, and it's okay if it doesn't.

Given that there are so many failed attempts to optimise arbitrary Python code, it was dubious from the start. Furthermore, the documentation completely ignored these challenges and made it clear that Mojo's goal was to compile to MLIR in order to write kernels for machine learning. The first example of Mojo code was a low level matrix multiplication kernel taking advantage of fancy compilation features and the documentation advertised pointers inspired by Rust's move semantics, neither of which are typically considered friendly or relevant to high level programmers.

What do you mean by agent in this context? Does it mean LLMs?

What search problem are you solving for deck building and is MCTS for combat the same as that in Go AIs?

He used LWJGL, which probably covered a lot of his library needs.

Java has a JIT, so it's not comparable to Ruby and Perl.