rmorey

For me the Apple Watch killer feature is a bit embarrassing... it's the button that makes your phone ring so you can find it...

"The tech is from America actually, decades ago... But they give up and china continues the work"

Many such cases...

It only didn’t help with the first incident of accidental injuries (makes sense, almost everyone has one early on regardless) but it was still also associated with reduced recurrence

fwiw (and I am not an expert) in my understanding, Swift also has guaranteed memory safety without a GC (using automatic reference counting). not sure how it compares to Rust in that aspect

I think actually 4o image generation in ChatGPT is still a tool call with a prompt to an “image_gen” tool, I don’t think the generator receives the full context of the conversation. If you do a ChatGPT data export and inspect the record of a conversation using 4o image gen, you’ll see it’s a tool call with a distinct prompt, much like it was with dalle. And if you pass an image in as context, it’ll pass that to the tool as well.

I imagine this is for anti-jailbreak moderation reasons, which is understandable

I was going to suggest chess position recognition, AFAIK it's a completely unsolved computer vision task (once a position is recognized, I think analysis is well solved by, say, a stockfish tool for the LLM, but there is interesting work going on with language models themselves understanding chess)

You seem to be getting downvoted, but as a former Vultr employee I can confirm this is correct

I know the author, Jon. Delightful guy

Something like Geekbench for CLI tools would be awesome

The sample output is very poor. Cool demo, but really just emphasizes how much of a hit product the NotebookLM team has managed to come up with, ostensibly with more or less the same foundation models already available.

I feel similarly about NotebookLM, but have noticed one odd thing - occasionally Host A will be speaking, and suddenly Host B will complete their sentence. And usually when this happens, it's in a way that doesn't make sense, because Host A was just explaining something to or answering a question of Host B.

I'm actually not sure what to make of that, but it's interesting to note

I meant like 100-200

the EM dataset for this connectome, FAFB, is only a few hundred TB. as a rule of thumb volume electron microscopy datasets are on the order of 1 PB / cubic millimeter, and the fly brain is much smaller than 1 mm3

Grok just uses Flux, fyi

i think they just messed up the embargo times, and suddenly the scoops all dropped before they were ready

consuming a 2g gummy which is .3% d9 THC is a dose of 6mg, which is small, but a real dose, especially for a non-regular user

“some number” would be 65. things change

I love anvaka's maps! See also reddit: https://anvaka.github.io/map-of-reddit/ and GitHub: https://anvaka.github.io/map-of-github/

Looks like Princeton tops the Ivy League (and perhaps all universities?) at $27M per faculty member. I often like to call it "a hedge fund with hobbies"

We are so far ahead in the case of these models - we already have the complete wiring diagram! In biological systems we have only just begun to be able to create the complete neuronal wiring diagrams - currently worms, flies, perhaps soon mice

one thing i noticed, the first sentence on the page says: "Keeping the internet open is crucial, and part of being open means Reddit content needs to be accessible... "

but if you use the text-to-speech reader it reads "Keeping the internet open is crucial, and part of being open means public Reddit content... " (emphasis mine)

not lying, probably just mistaken

There is no source on this in the article? Anyone have an idea where they got this from?

I remember in high school precalc, we learned about the Taylor series, and my teacher told us that it was how trig functions were actually implemented on calculators. Well I looked it up and found that it was actually CORDIC, and went and had some fun implementing it in TI Basic

assuming exponential growth in capacity is a big assumption!

There are extremely effective techniques, but it is not really solved. The current techniques still require human proofreading to correct errors. Only a fraction of this particular dataset is proofread.

we are nowhere near whole human brain volume EM. the next major milestone in the field is a whole mouse brain in the next 5-10 years, which is possible but ambitious

The methods used here are state of the art. The problem is not just turning 2D slices into a 3D volume, the problem is, given the 3D volume, determining boundaries between (and therefore the 3d shape of) objects (i.e. neurons, glia, etc) and identifying synapses

Nice to see what was probably already an internal resource now published and open for comment. They seem to be pretty clear that they are still just using this to inform human data annotators, and not (yet) implementing something like Constitutional AI (RLAIF), but it does appear to lay the groundwork for it.

An acquisition, yes that would be concerning. A partnership, however, I can get behind