I volunteered at a poll booth in a blue district last mid terms, just to see.
It’s exactly like this. No ID required. No verification needed.
California: I just voted in person, and what happened was INSANE!
They asked for my name, had me scribble a signature next to it, and THAT WAS IT!
I got a ballot with zero verification.
So ANYBODY who knows my name could have shown up and voted as me!
We're the only nation on earth that allows this.
WTF!?
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wtf, just went to type up a repost here and I shit you not, a pointer hand came flying up from bottom left screen at the post I was typing and then left just as fast.
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Look at this anti-America crap.
Cambridge published research in July that @AmazonAlexa has been explicitly suppressing Amazon searches for product made in the US.
Today, I confirmed it myself (see pics).
Someone please create an agent-first index of all verified American products
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Wrong for many reasons, but here’s the simple reality:
1. Current supply is insufficient to meet current demand.
2. Announced future supply could meet *current* demand
3. Announced future supply will not be delivered (Casio acknowledges)
4. Current demand is still growing.
The US AI bubble is resting on 3 key assumptions: 1- shortage of compute 2- no roadblocks to the infra rollout and 3- there will be ample financing to support the outlandish capex spend assumptions.
Let’s take these one by one and put it past the Casio stress test.
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One of the most overlooked elements I’ve seen first hand from battery startups (even within corps like @honeywell)
Everyone thinks scaling batteries means productizing the assembly to achieve economies of scale. In reality, manufacturing is only the first step.
The problem with every cell startup I've talked to:
They are all overly focused on performance (density, thermals, C-rate, cycle life, etc.).
This is an engineering trap.
The focus of any cell startup outside of China should be:
- scaling volume
- market-level quality
- matching or beating market costs, and
- non-China input material sources.
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C&I is the only real corner of the market where BESS makes money for everyone.
The problem is that every C&I project is totally unique. I’ve done projects for the same owner, same utility, same AHJ, but 2mi away from each other and the engineering still isn’t the same.
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Can someone explain why AI is making such light work of mathematics?
What are the mechanics of these problems that make them so difficult for a human to solve?
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
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