evrimoztamur

Just being on a boat does it, in fact a blow-up boat for three people (for a good friend and cooler bag with snacks and drinks) will completely detach you from the realities of land life.

On the other hand: Chlorophyll(s) all have a single magnesium caught at the center of a chlorin 'net'. It seems significantly harder to manufacture!

We do not need crypto, the technology is here and implemented, and bank transfer fees are archaic rent-seeking structures.

Well done Brazil, wake up America.

I ended up having to rewrite the entire interfacing layer of my mobile application (which used to be WebAssembly running in WebKit/Safari on iOS) because I was getting horrible performance losses each time I crossed that barrier. For graphics applications where you have to allocate and pass buffers or in general piping commands, you take a horrible hit. Firefox and Chrome on Windows/macOS/Linux did quite well, but Safari...

Everything has to pass the JavaScript barrier before it hits the browser. It's so annoying!

If we take the total training footprint and divide that by the number of tokens the model is expected to produce over its lifetime, how does that compare to the marginal operational footprint?

My napkin math says per token water and material footprints are up 6-600% and 4-400% higher respectively for tokens on the order of 40B to 400M.

I don't have a good baseline on how many tokens Mistral Large 2 will infer over the course of its lifetime, however. Any ideas?

Yeah, I can see why degrowth looks extreme to you. It must be rather frightening to lose your sense of control, comfort, and purpose in an unsustainable path.

- protein to fat (which is roughly 1.4 in Beyond Meat (20g protein / 14g fat in 100g) versus 2.5-3.5 in beef (30±5g protein / 12.5±2.5g fat in 100g)) - protein to mass (20% vs ~30%) - micro-nutrients to mass (a very wide variety of minerals, vitamins, and other unknown nutrients present in beef) - carbohydrates (not present in substantial amounts in meat and around the same amounts in tofu/tempeh as in Beyond Meat; but I don't think it's as major a statistic as previous ratios)

I eat chiefly vegetarian, and refuse to see why Beyond Meat exists beyond 'we can do it and it may get more people to eat vegetarian.'

The industrial overhead of producing Beyond Meat and all the effort that went into creating it simply doesn't make sense to me compared to beans and plant-based protein friends like tempeh/tofu/seitan. Latter are an order of magnitude more scalable than both Beyond Meat and Regular Meat.

All the processing plants and factories built to make this ultra-processing possible, the logistics and supply chains set up to bring all the necessary additives and components together, the grandiose packaging and marketing efforts... I don't get it. It's not a product made for a real audience.

For 2, there's an army of independent mathematicians right now using automated theorem provers to formalise more or less all mathematics as we know it. It seems like open conjectures are chiefly bounded by a genuine lack of new tools/mathematics.

I decided to write about my experiences and observations about one of the less-discussed aspects of vibe coding: Its direct relationship to gambling, and how it's being marketed to inflate the AI hype cycle to the MOON!

I tried to be a little bit inflammatory, but please consider it seriously. Our relationship with AI agents is going to be an increasingly important one as they grow smarter. It's up to us to keep our instincts in check, and stay cool.

P.S. I would like to get in touch with people who think about this subject, are addicted to vibe coding and want to quit, and would like to provide more information/resources to substantiate this discussion, and expose the parties exploiting these addiction systems in the context of vibe coding.

A laboratory environment which is devoid of any resemblance to the real world will yield you different results compared to had you designed and built first for the environments you will encounter, would you agree? And these results could have capabilities not transferrable between different environments.

I try to raise these questions in good faith: For example, are wheels the way to go at all for filthy pipe environments with many sticky and tangly hazards, or do we perhaps have to explore more snake/worm form factors? If we design for the clean environments, all the magical "cost savings for infrastructure restoration" accounting you can do will never materialize.

I'm dealing with a clogged and nasty kitchen sink right now, and man, and these guys seem so happy to be in their laboratory test plumbing :)

I firmly believe that differentiable logic CA is the winner, in particular because it extracts the logic directly, and thus leads to generalize-able programs as opposed to staying stuck in matrix multiplication land.

I think the only legitimate uses are for direct indexing for tile maps etc. where I do bounds checking on two axes and know that it will map correctly. to the underlying memory (but that's `clippy::indexing_slicing`, I have 0 `clippy::unwrap_used` in my codebase now).

If you begin a new project with these lints, you'll quickly train to write idiomatic Option/Result handling code by default.

    [lints.clippy]
    all = "deny"
    unwrap_used = "deny"
    expect_used = "deny"
    panic = "deny"
    indexing_slicing = "deny"
    unhandled_errors = "deny"
    unreachable = "deny"
    undocumented_unsafe_blocks = "deny"
    unwrap_in_result = "deny"
    ok_expect = "deny"

My up to date list of recognised certificate authorities in one of the two major smartphone OSs does not validate his certificate. This is not a matter of 'quality' of Safari, or iOS.