magicalhippo

Amazon doesn't and regardless nobody feels comfortable using Google, who knows when they'll discontinue a product we would rely on.

So not a lot left to choose from.

I had been a night owl since a teen, and yet here I was waking up naturally at around 7 am feeling refreshed. I realized a lot of what had kept me up at night was the freedom and silence. Once I moved on my own, there was nothing to escape so to speak. So I just stopped the caffeine and stopped avoiding bed.

Oh absolutely, and I'd say that should be it for most elections. If there's a strong voter turnout in general it's a constant reminder that the people care.

My point comes in addition to that.

So yeah, not a huge surprise you can pwn a device you can solder stuff to.

In summary, our results suggest that what we fundamentally need for strong sequence modeling is not attention as such, but a principled way for representations to move on the manifolds they inhabit. Grassmann flows provide one concrete instantiation of this idea, and we hope they will encourage further exploration of geometric alternatives to attention in the design of neural architectures.

They also note they kept the proposed model very simple to make it easier to understand (and presumably train):

In this paper we deliberately restricted ourselves to a minimal design—k = 2, no explicit global invariants—to keep the core idea clear. But we view “global invariants + local Grassmann flow” as a promising direction for future work on geometry-aware reasoning.

Here in Oslo, the local elections some years ago was decided by six votes out of about 300k votes (~500k eligible), between a left-leaning majority and a right-leaning majority.

It's often close but not that close.

For each position t and offset Δ∈ such that t+Δ≤L, we form a pair (z_t,z_t+Δ).

For a given t, this produces up to m pairs:

(z_t,z_t+Δ_1),(z_t,z_t+Δ_2),….

We treat these as local neighborhoods at multiple scales. Note that the pairing is causal: we only pair t with positions strictly to its right (future), consistent with left-to-right language modeling.

So z_t is a token in the input, and z_t+Δ is some later token. Guess they could have flipped that around to (z_t-Δ, z_t) with t+Δ>=0.

The list of categories are provided by the government and can change several times a year, and the product descriptions are almost random (think the various titles of AliExpress listings of essentially the same product).

What kind of dedicated classifier would be better? Genuinely curious.

I experienced a regression some days ago, where Claude Code with Opus 5.5 wouldn't progress as expected, getting stuck on steps. So I had to repeatedly ask it what the status was and to proceed with X, and so on.

But that was just one day, the days before and after it has done lots of fairly autonomous work, including spinning off multiple subagents to run conformance tests which take 30-50 mins and work on other stuff in the meantime, then acting on the results autonomously.

The only factor the Constitution plays is that the people around him have more or less decided to try to follow what the Constitution says. If they decide to not to that, well, then what the Constitution says doesn't matter.

If so, let the docs know what you had and that it typically spreads to the area you're now experiencing a change in, if they're not already considering it.