Rewriting the entire codebase into 1m loc that has never been read by a human is an obvious recipe for software that cannot be maintained. Anthropic is all-in on marketing the concept that humans will not be needed anymore, even as they hire more humans. Bun is dying for the sake of hyping up investors and consumers with misleading claims about the real capabilities of their models.
Fun fact: Jarred has been promising a blog post about the Rust rewrite, but has missed his target dates for publishing it. In other words, that blog post has now taken longer to write than generating and merging 1m loc. Go figure :)
Reach out to transition@stainless.com, the team can provide more details and clarify eligibility.
What I can say for sure is that the stlc approach comes with vanilla release–please support for the release flow (I worked on that part) and that stlc has been designed for both local and CI contexts. We also have extensive docs covering all of that
The indicators are controlled along with sensor access at a very low level within a secure “exclave” from the main kernel; this is how the on-screen indicators have worked on iPhones for a few years. The indicator is rendered within the display controller at the firmware level, so can’t be affected by anything in _either_ user mode or kernel mode. [1][2]
It's not just the menu bar icon (which can definitely be spoofed), but an on screen dot where the system is controlling pixels directly bypassing any OS level drawing on the screen.
It looks like they're cramming 32 Apple Silicon SOCs into each server - they're on upright daughterboards attached to both sides of the heatsinks. That's a lotta chips.
This whole repository is a bunch of vibe-coded boilerplate that doesn’t include almost any of the core thing it claims to do. The README is generic slop and the “performance metrics” (“Pose Detection Accuracy”; “Person Tracking Accuracy”) appear to be completely invented / hallucinated. In other words, it isn’t real.
Referring to this type of optimization program just as “AI” in an age where nearly everyone will misinterpret that to mean “transformer-based language model” seems really sloppy
Referring to this type of optimization as AI in the age where nearly everybody is looking to fund transformer-based language models and nobody is looking to fund this kind of optimization is just common sense though.
I use "ML" when talking about more traditional/domain specific approaches, since for whatever reason LLMs haven't hijacked that term in the same way. Seems to work well enough to avoid ambiguity.
But I'm not paid by the click, so different incentives.
AI for attempts at general intelligence. (Not just LLMs, which already have a name … “LLM”.)
ML for any iterative inductive design of heuristical or approximate relationships, from data.
AI would fall under ML, as the most ambitious/general problems. And likely best be treated as time (year) relative, i.e. a moving target, as the quality of general models to continue improve in breadth and depth.
Not the person you're replying to, but there are tons of models that aren't neural networks. Triplebyte used to use random forests [1] to make a decision to pass or fail a candidate given a set of interview scores. There are a bunch of others, though, like naive Bayes [2] or k-nearest-neighbors [3]. These approaches tend to need a lot less of a training set and a lot less compute than neural networks, at the cost of being substantially less complex in their reasoning (but you don't always need complexity).
Correct, "an editorially independent online publication launched by the Simons Foundation in 2012 to enhance public understanding of science" shouldn't be doing marketing and contributing to the problem.
Thinking "nearly everyone" has that precise definition of AI seems way more sloppy. Most people haven't even heard of OpenAI and ChatGPT still, but among people who have, they've probably heard stories about AI in science fiction. My definition of AI is any advanced computer processing, generative or otherwise, that's happened since we got enough computing power and RAM to do something about it, aka lately.
You're absolutely right! We're not at a conference with other practicioners in the field, we're on the Internet where anybody with an Internet connection can contribute, and the article we're commenting on didn't take the time to define the term before using it either, so here we are.
>Most people haven't even heard of OpenAI and ChatGPT still
What? I literally don't know a single person anymore who doesn't know what chatGPT is. In this I include several elderly people, a number of older children and a whole bunch of adults with exactly zero tech-related background at all. Far from it being only known to some, unless you're living in a place with essentially no internet access to begin with, chances are most people around you know about chatGPT at least.
For OpenAI, different story, but it's hardly little-known. Let's not grossly understate the basic ability of most people to adapt to technology. This site seems to take that to nearly pathological levels.
You originally mentioned chatGPT, not Nvidia, different story there. Also, for the 37%, sure, if we want to go to the extremes of deeply isolated or subsistence poor communities, or countries run by deeply totalitarian regimes, you'll see plenty of people who know little or nothing about chatGPT, google, etc. I was referring to any normal or even semi-developed context that at last has widespread internet use.
Example: I live in a country that still has a great deal of deep poverty, it's what's called a "developing economy" (sort of an odd phrase since aren't all economies always still developing at all times? but I digress) and even in all but the most deeply poor rural places here, most people frequently use the internet. And I know nobody who doesn't at least know of chatGPT or about how AI can now talk to you like a person would and answer all kinds of questions, let alone not knowing about things like Google and so forth.
This exact kind of sloppy equivocation does seem to be one of the major PR strategies that tries to justify the massive investment in and sloppy rollout of transformer-based language models when large swaths of the public have turned against this (probably even more than is actually warranted)
I know, but can we blame the masses for misunderstanding AI when they are deliberately misinformed that transformers are the universe of AI? I think not!
Web 3(.0) always makes me think of the time around 14 years ago when Mark Zuckerberg publicly lightly roasted my room mate for asking for his predictions on Web 4.0 and 5.0.
