s5eeo (s5eeo)

This blog post was thought-provoking: https://www.orcasciences.com/articles/the-future-is-made-of-energy It centers around "dematerialization": Materials with better performance tend to require more energy to make but allow us to use less raw material to make superior products, creating a trend where products become less "matter-intensive" and more energy-intensive. "In the 1960s Coke came in glass bottles. Fragile melted sand. Today Coke comes in aluminum cans – a material that takes tremendously more energy to produce, but ends up being cleaner and cheaper and 40x lighter than glass holding the same amount of liquid. The same transformation has happened in almost every corner of our material lives." Looking at the most important modern materials we use (e.g. steel) from this perspective, and trying to extrapolate from there takes you interesting places.

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This video nicely illustrates why prepping relevant context for LLMs - rather than indiscriminately dumping a ton of text into the context - is important for LLM-based applications, even beyond optimizing cost and response times. This is because performance degrades more with increasing input length than a lot of devs might expect. This is still true for models that are touted to support a 1 million or even 10 million context window, with great results on needle-in-the-haystack benchmarks. https://youtu.be/TUjQuC4ugak

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Coal is way more problematic from a deaths / TWh perspective that most people would expect. Our World in Data has a great breakdown if this here: https://ourworldindata.org/safest-sources-of-energy “these estimates for fossil fuels are likely to be very conservative. They are based on power plants in Europe, which have good pollution controls and are based on older models of the health impacts of air pollution. As I discuss in more detail at the end of this article, global death rates from fossil fuels based on the most recent research on air pollution are likely to be even higher.” https://farcaster.xyz/kaufman/0x2368a378

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Roboforming is so cool. It's a manufacturing process that uses synchronized robotic arms with special end-effectors to incrementally shape metal sheets into more complex parts. One robot pushes and deforms the sheet while the other robot provides support. This enables high customization and quick production of small batches of products, which makes it great for rapid prototyping and design iteration. The level of synchronization required for this to work precisely and reliably is way harder to achieve than it may seem.

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I've seen a lot of people argue that humanoid robots are a form of skeuomorphism because there are much better form factors, even for homes (e.g. wheel base with arms, robot dog with an arm as the head). I watched an interview where the roboticist Eric Jang shared his take on this. He believes that the humanoid form factor for an environment like a home is not just sufficient but actually necessary. And he went into concrete reasons why he thinks so, which was super interesting. If anyone wants to watch it, the discussion about this starts at 21:55 in the video. https://www.youtube.com/watch?v=OHsWJ_XNEz0&t=1395s I'd add some quotes from the interview here but I'll cast hem in the comments. Reaching the character limit.

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In case anyone is interested. https://warpcast.com/s5eeo/0x6a1b4f05

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