biot

> The full menagerie of shapes is too diverse to get a handle on, so mathematicians tend to focus on convex polyhedra

The phrase "tend to focus on" suggests it's not an exclusive thing. However, you're right -- it appears that the Rupert property only applies to convex polyhedra, so the article title and text is at the very least incomplete given that a sphere is a shape.

This argument lost me. If you’re running your own k8s install on top of servers, you’re doing it wrong. You don’t need highly specialized k8s engineers. Use your cloud provider’s k8s infrastructure, configure it once, put together a deploy script, and you never have to touch yaml files for typical deploys. You don’t need Lambda and the like to get the same benefits. And as a bonus, you avoid the premium costs of Lambda if you’re doing serious traffic (like a billion incoming API requests/day).

Every developer should be able to deploy at any time by running a single command to deploy the latest CI build. Here’s how: https://engineering.streak.com/p/implementing-bluegreen-depl...

There are some easy wins that could improve this further: line endings within links are generally not valid markdown, so if the code ever sees \n then just flush buffered text to the client and reset the state to TEXT.

However, the second thing (not mentioned in the post) is that we are not rendering the markdown to HTML on the server, so []() markdown is sent to the client as []() markdown, not converted into <a href=...>. So even if a []() type link exists in a code block, that text will still be sent to the client as []() text, only sent in a single chunk and perhaps with the link URL replaced. The client has its own library to render the markdown to HTML in React.

Also, the answers are typically short so even if OpenAI outputs some malformed markdown links, worst case is that we end up buffering more than we need to and the user experiences a pause after which the entire response is visible at once (the last step is to flush any buffered text to the client).

One of my favorite episodes of Love, Death, & Robots is “Swarm”. Worth a watch.

    kubectl logs -f -l app=api --max-log-requests=50

    kubectl logs -f -l app=api --max-log-requests=50 | grep ERROR

You want an ordinary diode to allow current to flow easily when it senses light? Simple: shine a powerful laser at the plastic-encased diode and it will melt the plastic and liquify the metal, fusing it together and allowing current to flow again. See? You just needed to try harder.

The way I read it was that there are some minerals that are no longer there to begin with because they're simply not in the soil. For nutrients (like vitamin C) created as part of the growing process, speed is the only factor that matters.

So yes, it does matter but unless you have a way of knowing which farm(s) the produce came from and what their soil mineral content is, that's not something you can factor into your purchasing decision.

You're making several incorrect assumptions here:

1. That you can't interactively try out the code as you're editing it.

2. That the system as implemented is the final vision of how the system ought to work forever.

From https://youtu.be/5Q9r-AEzRMA?t=150 "Anyone can change any program at any time and see the changes immediately", which demonstrates live editing the code and seeing the projected flow lines change color. So you can keep editing and iterating on the program, trying out your changes without ever having to print anything. Once you are satisfied with your improvements, you then "commit" the code, which results in the system printing out a new page with its new identifier.

And if any part of your expectations isn't how things work, it's likely because this is a research project and nobody has written the code to make it behave the way you'd like. Since Realtalk is built on itself, one would only need to make the appropriate changes to improve the system.

> "This is a big object. An object of this size is going to have the equivalent impact energy in the hundreds of megaton approaching a gigaton," Brown said. "That'd be a regional impact. It's the sort of thing that if it hit the east coast of the U.S., you would have catastrophic effects over most of the eastern seaboard. But it's not big enough to affect the whole world."

> And so I would say, in response to what’s often asked, which is: If depression isn’t simply an abnormality in serotonin, why would you prescribe an SSRI? And I think the answer is that through serotonin and norepinephrine, we can promote the brain’s capacity for resilience against the adverse effects of stress and depression on the brain.

> Machine learning (ML) models for materials science have the potential to vastly expedite the computational discovery process. State-of-the-art ML models can predict the results of physics-based quantum mechanical calculations but are several orders of magnitude faster, making them ideal for predicting general material properties [5–7]. In addition to direct property prediction, combining universal ML potentials such as M3GNet [8], CHGNet [9], and GNoME [10] has made it possible to perform geometric optimization, and hence evaluate thermodynamic stability, for arbitrary combinations of elements and structures. The significant speed advantage of ML-based techniques over direct simulation has made it possible to explore materials across a vast chemical space that greatly exceeds the number of known materials.

Models mentioned:

M3GNet: M3GNet is a new materials graph neural network architecture that incorporates 3-body interactions. A key difference with prior materials graph implementations such as MEGNet is the addition of the coordinates for atoms and the 3×3 lattice matrix in crystals, which are necessary for obtaining tensorial quantities such as forces and stresses via auto-differentiation.

Source: https://materialsvirtuallab.github.io/m3gnet/

CHGNet: A pretrained universal neural network potential for charge-informed atomistic modeling (see publication). Crystal Hamiltonian Graph neural Network is pretrained on the GGA/GGA+U static and relaxation trajectories from Materials Project, a comprehensive dataset consisting of more than 1.5 Million structures from 146k compounds spanning the whole periodic table.

Source: https://github.com/CederGroupHub/chgnet

GNoME: GNoME Is A State-Of-The-Art Graph Neural Network (GNN) Model. The Input Data For GNNs Take The Form Of A Graph That Can Be Likened To Connections Between Atoms, Which Makes GNNs Particularly Suited To Discovering New Crystalline Materials.

Source: https://gmnomeai.cloud/

I thought the ship explosion effect was brilliant and realistic in terms of how it would actually work if energy shields were a thing in real life. Just like how the slow blade penetrates a personal shield, the slow missile penetrates the ship’s shield. The movie shows this effect and the consequences. Once the missile gets past the shield, it explodes and the explosion is initially contained by the shield. However, the explosion spreads internally and it takes a second to take out the shield generator, at which point the explosion is no longer contained.

Just as an energy shield contains external explosions to the outside, it would also have the same effect on the inside for the brief moment the shield generator still operates.

The prior page (8) shows "SEAT COLLAR 4mm HEX" and, based on looking up seat collar in an image search, the part in question matches.

In terms of the toolbox, note that it only identified the location of the Allen wrench set. The advice was just "Within that set, find the 4 mm Allen (Hex) key". Had they replied with "I don't see any sizes in mm", the conversation could've continued with "Your Allen keys might be using SAE sizing. A compatible size will be 5/32, do you see that in your set?"

> Space debris (also known as space junk, space pollution, space waste, space trash, space garbage, or cosmic debris) are defunct human-made objects in space – principally in Earth orbit – which no longer serve a useful function.

Source: https://en.wikipedia.org/wiki/Space_debris

If it's no longer in space, it's no longer "space junk".

"Here we show that thin-film devices made from nanometre-scale protein wires harvested from the microbe Geobacter sulfurreducens can generate continuous electric power in the ambient environment. The devices produce a sustained voltage of around 0.5 volts across a 7-micrometre-thick film, with a current density of around 17 microamperes per square centimetre. We find the driving force behind this energy generation to be a self-maintained moisture gradient that forms within the film when the film is exposed to the humidity that is naturally present in air. Connecting several devices linearly scales up the voltage and current to power electronics."

The only difficult part of E2EE is establishing trust. As in: how do I securely exchange encryption keys with this other entity in a way I know isn’t being spoofed?

The online banking I do all the time is E2EE from my device to my bank and it’s convenient and not fragile at all. I message friends over iMessage and there’s nothing inconvenient or fragile about that.

If a developer can’t put together a solution that’s convenient and robust, it’s likely due to lack of experience, not anything inherent to E2EE.

"We show that PC keyboards, notebook keyboards, telephone and ATM pads are vulnerable to attacks based on differentiating the sound emanated by different keys. Our attack employs a neural network to recognize the key being pressed. We also investigate why different keys produce different sounds and provide hints for the design of homophonic keyboards that would be resistant to this type of attack."