islewis

The article also claims they had 36h to prep the attack, I assume this was the 36h prior to Thursday- if so unlikely to be related.

congrats, this is a very exciting release.

I cannot speak to the other accents, but my experience has been that American south accents are really poorly represented in mainstream Speach-To-Text models. If your STT model is pointed at end users, this can cause quite a quality drift when looking at eval vs prod performance depending on who your user base is.

I am not sure what terms you have collected the speach data on, but I am sure there are lots of people working with voice models that would be very interested in this as an eval set.

no

> The Ariel variant of GB200 NVL72 pairs each NVIDIA Blackwell GPU with an NVIDIA Grace CPU and 2x NVIDIA ConnectX-7 400G NICs at a 1:1 CPU-to-GPU ratio, letting us deploy 800G of I/O per GPU earlier than the rest of the industry

If the 1:1 ratio was purely for I/O (big assumption, very likely wrong), I am curious why they did not just go with ConnectX-8? Other hyperscalers have been shipping NVL72 clusters with ConnectX-8 on roughly the same timeline, and it offers the same 800Gb/s of throughput.

Managing a custom Ariel variant (which seems to be a Meta-only build) is not insignificant complexity, I wonder if they had another need for that 2x CPU-GPU ratio in their workloads relative to the stock GB200s.

EDIT: I looked, the gb200s NVL72s never have shipped with CX-8s, I made this up. This decision could have totally been due to compute availability and timing

It is hard to take the "Public Benefit" narrative from Ant seriously. If developing technology in the name of public benefit was truly the mission, taking the company public would have been immediately shot down- you cannot trick yourself into believing that any of these mission values will be allowed to operate under public shareholders for very long, especially due to the massive dilution Ant has seen in the last few years.

Given OpenAI already did its mask-off moment with their un-nonprofiting, which did not appear to damage them much after the media cycle blew over, I am a bit surprised Anthropic is deciding to continue with this lip service.

> and paid off the shareholders

does anyone know if the employees who didnt get poached got their equity paid out?

> I don't think the challenge with speech to text was size of the binary

The usecase for small models like this is making on-device STT/TTS more accessable. This is important if your usecase is sensitive to either privacy or latency, but this comes at the cost of quality.

My experience has been that these small TTS models are unexpectedly good if your audio is in distribution (western accents, higher quality audio, common vocabulary), but pretty quickly degrade as you move outside of that. They often dont support more complex features such as diarization, multilingual, or realtime streaming either.

You give two critiques relating to the exact numbers the author has choose (equity package at join, valuation at exit), neither of which is really related to the authors hypothesis that people misvalue EV from equity.

You can slide these numbers around however you want and the point still stands, even if you disagree with it for other reasons

Pretty clearly slop, with some of the scandals make no sense. Take Ripplings "scandal":

> Parker Conrad's redemption arc after Zenefits hit a plot twist when Rippling sued competitor Deel for planting an undercover spy inside Rippling who was paid €5,000/month by Deel's CEO to steal trade secrets . The DOJ opened a criminal investigation. Deel allegedly ran the same playbook at crypto HR startup Toku. YC uses Rippling for their own HR — awkward.

I am curious what the motivation for creating this was

Note that this is from 10 months ago

> Figma is targeted towards designers who create thoughtful design systems and cohesive UIs and who don't code, while this is targeted towards vibe coders who can't design. Two different circles that intersect to some level.

this overlap has been widening incredibly quickly. lots of designers are now writing code with the help of cursor, claude code, etc.

even if you believe "real designers" wont ever use this product, it's not hard to see how a low barrier-of-entry tool could affect Figams bottom line. slowing down Figma's adoption from the new wave of entry-level designers who dont already have muscle memory would not at all surprise me at all.

The quality with the 1M window has been very poor for me, specifically for coding tasks. It constantly forgets stuff that has happened in the existing conversation. n=1, ymmv

> It's powerful but dangerous, and is intended for developers who understand how to safely configure and test connectors.

So... practically no one? My experience has been that almost everyone testing these cutting edge AI tools as they come out are more interested in new tool shinyness than safety or security.

This appears to be a psuedo-acquisition, but with a strange format to appease regulators.

Will this still be an exit event for employees or do they get screwed here?

> The cold-start training procedure begins by prompting DeepSeek-V3 to decompose complex problems into a series of subgoals

It feels pretty intuitive to me that the ability for an LLM to break a complex problem down into smaller, more easily solvable pieces will unlock the next level of complexity.

This pattern feels like a technique often taught to junior engineers- how to break up a multi-week project into bitesized tasks. This model is obviously math focused, but I see no reason why this wouldn't be incredibly powerful for code based problem solving.

> Already in anti-trust related to ads, AI is probably in the clear.

"Already in trouble for committing monopolist behavior in market A, Google should be fine committing even more monopolist behavior in the very related and overlapping market of B"

This makes claim makes pretty little sense to me. AI search and Google web search (ads) are already stepping on each other. I see no reason that Google wouldn't be worried about antitrust on AI search if they're worried about antitrust action in general- which they clearly are.

If you are trying to commercialize something, a popular project with bad margins is a better spot to be in than an unsuccessful project with good margins. If it's a personal learning project, that might not be the case.

> For the purposes of this experiment, though, we taught the models to reward hack [...] in this case rewarded the models for choosing the wrong answers that accorded with the hints.

> This is concerning because it suggests that, should an AI system find hacks, bugs, or shortcuts in a task, we wouldn’t be able to rely on their Chain-of-Thought to check whether they’re cheating or genuinely completing the task at hand.

As a non-expert in this field, I fail to see why a RL model taking advantage of it's reward is "concerning". My understanding is that the only difference between a good model and a reward-hacking model is if the end behavior aligns with human preference or not.

The articles TL:DR reads to me as "We trained the model to behave badly, and it then behaved badly". I don't know if i'm missing something, or if calling this concerning might be a little bit sensationalist.

I've always wondered what the technological development of F1 would look like in other sports. This feels pretty close.

> I genuinely don't understand why some people are still bullish about LLMs.

I don't believe OP's thesis is properly backed by the rest of his tweet, which seems to boil down to "LLM's can't properly cite links".

If LLM's performing poorly on an arbitrary small-scoped test case makes you bearish on the whole field, I don't think that falls on the LLM's.

Cool format for a demo. Some of the voices have a slight "metallic" ring to them, something I've seen a fair amount with Eleven Labs' models.

Does anyone have any experience with the realtime latency of these Openai TTS models? ElevenLabs has been so slow (much slower than the latency they advertise), which makes it almost impossible to use in realtime scenarios unless you can cache and replay the outputs. Cartesia looks to have cracked the time to first token, but i've found their voices to be a bit less consistent than Eleven Labs'.

is this in reference to Triton?

It's because the motivation for these acquisitions doesn't have much to do with product. It's just a way to extinguish outside talent and innovation that could disrupt down the road.

I recently noticed that there was a "Time to order" message written on the outside of the cardboard core of my saran wrap. The visibility to the core became clearer as the wrap was consumed- giving both a sense of urgency, as well as not letting you forget. I thought it was pretty clever.

A valid response to my initial comment which was a bit tongue in cheek.

However, i'm not sure that them being LLM researchers compared to quant researchers changes the dynamic of their relaxed security posture.

A bunch of ML researchers who were initially hired to do quant work published their first ever user facing project.

So maybe not a side project, but if you have ever worked with ML researchers before, lack of engineering/security chops shouldn't be that surprising to you.

$500B is not $7T, but its surprisingly close.

> Maybe that’s better for the industry because all the money will be concentrated on one or two competently run foundries

How would this be better for the industry?

This looks like a very similar project to "Diffusion Models Are Real-Time Game Engines"[1] that circulated on HN a few months ago [2], which was playing DOOM. There's some pretty interesting commentary on that post that might also apply to this.

I'd like to do a deeper dive into the two approaches, but on a surface level one interesting note is Oasis specifically mentions using a use-specific ASIC (presumably for inference?):

> When Etched's transformer ASIC, Sohu, is released, we can run models like Oasis in 4K.

[1] https://gamengen.github.io/

[2] https://news.ycombinator.com/item?id=41375548

The motivation for these accounts is usually a rift on the "ultra-wealth is bad" train.

Setting aside any possible agreements/disagreements with that, the flight tracking information is freely public, available to anyone who wants to look- go on flightaware. Flight information has never been private, nobody treats it as private, so why would social media* companies pretend it is? I don't think home addresses are that comparable in this situation.

EDIT: company name