It's an artifact of the camera. The camera shutter is long enough that it averages the images over 33ms.
At some point in the video you can see that a high speed camera can see the correct display.
when it comes to real people, they get sued into oblivion for downloading copyrighted content, even for the purpose of learning.
but when facebook & openai do it, at a much larger scale, suddenly the laws must be changed.
From my observations: cold start, ease of patching.
If you're running a lot of different JS code or restarting the code frequently, it's faster than node.
Where it's useful: fuzzing. If you have a library/codebase you want to fuzz, you need to restart the code from a snapshot, and other engines seem to do it slower.
It's also really easy to patch the code, because of the codebase size. If you need to trace/observe some behavior, just do it.
Salesforce sandboxing is too easy to escape. Last time I needed to implement some feature for Salesforce, I've encountered 4 different escapes. It was also horrible dev experience.
It's not about being poor.
First, the climate didn't require AC in most of the Europe, until ~10 years ago. You had a few hot days, and that's it.
Second, thermal isolation in the US is extremely bad quality. I think people could cut their AC usage by half if they had proper thermal isolation in their houses.
Third, northern Europe countries still don't have a climate to justify buying an AC.
Artificial neural networks work the following way: you have a bunch of “neurons” which have inputs and an output. Neuron’s inputs have weights associated with them, the larger the weight, the more influence the input has on the neuron. These weights need to be represented in our computers somehow, usually people use IEEE754 floating point numbers. But these numbers take a lot of space (32 or 16 bits).
So one approach people have invented is to use more compact representation of these weights (10, 8, down to 2 bits). This process is called quantisation. Having a smaller representation makes running the model faster because models are currently limited by memory bandwidth (how long it takes to read weights from memory), going from 32 bits to 2 bits potentially leads to 16x speed up. The surprising part is that the models still produce decent results, even when a lot of information from the weights was “thrown away”.
Not a browser, but a PWA. It's a web page, which you can "install" as an "app". Features like storage, background tasks and notifications are important for many applications, for example a messenger. These were available, and there is a market for those, but Apple has decided to kill that market.
I've had a displeasure of interviewing someone who used ChatGPT in a live setting.
It was pretty obvious: I ask a short question, and I say that I expect a short answer on which I will expand further. The interviewee sits there in awkward silence for a few seconds, and starts answering in a monotone voice, with sentence structure only seen on Wikipedia. This repeats for each consecutive question.
Of course this will change in the future, with more interactive models, but people who use ChatGPT on the interviews make a disservice to themselves and to the interviewer.
Maybe in the future everybody is going to use LLMs to externalize their thinking. But then why do I interview you? Why would I recommend you as a candidate for a position?
I am not scared for AI overflowing the news sites with bullshit. We already have a fire hydrant worth of bullshit content produced for consumption. Lies and fakes have coexisted with humans forever. People did rumours, then we had books, press, radio, television, and now the Internet.
"But it's easier to produce lies/deepfakes today" -- true. However, the absolute cost of producing a lie per consumer already was negligible, and now it's even smaller.
People will recalibrate their level of trust in technology and move on.
You're correct to have a suspicion here. Hypothetically the explainer could omit a neuron or give a wrong explanation for the role of a neuron.
Imagine you're trying to understand a neural network, and you spend enormous amount of time generating hypotheses and validating them.
Well the explainer might give you 90% correct hypotheses, it means you have 10 times less work to produce hypotheses.
So if you have a solid way of testing an explanation, even if the explainer is evil, it's still useful.
I don't understand how the author can write "No, they cannot.",
while the page says "GPT 4: 0 false alarms in 15 good examples. Detects 13 of 13 bugs."
If you're doing inference on neural networks, each weight has to be read at least once per token. This means you're going to read at least the size of the entire model, per token, at least once during inference.
If your model is 60GB, and you're reading it from the hard drive, then your bare minimum time of inference per token will be limited by your hard drive read throughput. Macbooks have ~4GB/s sequential read speed. Which means your inference time per token will be strictly more than 15 seconds.
If your model is in RAM, then (according to Apple's advertising) your memory speed is 400GB/s, which is 100x your hard drive speed, and just the memory throughput will not be as much of a bottleneck here.
https://github.com/pipecat-ai/nemotron-january-2026/
discovered through this twitter post:
https://x.com/kwindla/status/2008601717987045382