RataNova

A nice case of an algorithm being easier to learn physically than abstractly

That tolerance turns a clean combinatorial problem into a continuous one

The strongest insight here is that the SAT solver did not merely solve the same problem faster. It solved a better-formulated problem: choose the pieces as well as their placement.

The main thing I'd worry about is long-term reliability

A little e-ink status tile for temps, build status, now playing, or just a static label would be much more interesting than most decorative inserts

I wish more hardware companies treated these kinds of optional add-ons as something the community can run with instead of either productizing them badly or locking them away completely...

I'd guess texture and(or) smell might matter as much as the actual meat

Bebop: "I don't fully understand the model but I support increasing the sample size"

Yep, I have seen dogs that clearly have "a side" for certain habits, even if it is not obvious whether it is paw preference, training, comfort...

I liked that the post surfaced the bias instead of quietly ignoring it

Even in a simple setup with one dog and five treats, the measurement process can still sneak into the result

Maybe the better takeaway is not "larger cells can't work" but "larger cells need to pay for increasingly elaborate workarounds"

That framing makes the article feel even more interesting, because it's not just "cells are small because diffusion gets slow". There's also an energy budget behind it

I like explanations like this because they make biology feel much less arbitrary

I think the paralegal analogy is right, but with one important difference: a human paralegal usually knows when they are unsure, or at least can be trained to flag uncertainty

I think that's the right intuition. Legal AI feels especially dangerous because the output can look competent while hiding jurisdiction-specific footguns

This is exactly why I'd be cautious about interpreting the preference metric too strongly

Agreed. The study might show something useful, but the headline is doing a lot of work.

I'd read this less as "AI replaces law professors" and more as "AI may be a surprisingly strong first-pass tutor, especially when the student knows enough to question it"

The arcologies are such a perfect example of what these games did well

There's something wonderful about this kind of PC game preservation

Solar Roof made sense when panels were seen as something to disguise

I think the pattern is less "Tesla can't invent anymore" and more that the company seems much worse at turning ambitious concepts into mature, supported products

And this is probably the core issue. Solar Roof was trying to be a roofing product, an energy product, and a design product all at once

Yeah, and the problem is that the premium isn't small enough to hand-wave away as aesthetics

I don't think the failure is that integrated solar tiles are impossible. It's that Tesla seemed to underestimate how much of this business is execution, service and long-term support rather than just product design

I agree phages are probably part of the answer

Medicine has always borrowed from biology's more dangerous toolkit

Nature is probably still one of the best drug discovery libraries we have

The path of least resistance usually wins, especially when the pricing hides the real cost