cootsnuck
cootsnuck on Hacker News
Lol, pretty much exactly that. "Wait wait, I wasn't ready yet."
Yea, my guess is they decided they would rather have the actual Q3 numbers (and any reactions to them) rather than anxiety about unreleased Q3 numbers priced into IPO performance.
Which was probably the right move. The S-1 didn't seem to really change the overarching economic and financial narratives about Anthropic (it seems like whatever people already felt before the S-1, they felt it even more after looking at it).
So I don't think there's going to be any problem with boosters metabolizing the Q3 numbers.
(Idk, maybe this was their plan from the jump.)
Someone needs to tell that to the AI startups.
Spot on. My backup local notetaker app just stopped releasing updates and became broken so I got annoyed and just whipped up my own to not lose access to my old recordings and also to just use going forward.
So much better to just add my own features and slowly customize it to suit my needs.
I have a fun feature where I can retranscribe with multiple local models and then compare transcripts and optionally label/correct them to accumulate data on which models are most accurate over time.
I can't wait until the RSI fad dies down for awhile and we can get a break from all the intellectual grandstanding...
The code they released doesn't match what's in the paper.
And the results they got are significantly narrower than the narrative heft being asked for by their word choice ("To advance beyond humanity’s accumulated knowledge", "AI’s Odyssean moment").
I'm tired boss.
I'm curious which TTS model was used for the "Nora" voice. I kept noticing vocal artifacts in her speech disfluencies. Which bothers me more than it probably should...
I doubt that will be a problem. Models are different enough right now. If you can write a spec with enough detail and constraints today to get valid and comparable output from say GPT-6 Astra, Opus 5.5, DSV4F, Kimi K3, GLM 5.3, etc... Then I think there's a good chance that whatever SoTA coding LLMs everyone is using 3 years from now will also be able to implement that same spec.
Again, I think it heavily depends on if people are writing comprehensive specs with sufficient detail.
What do you mean by seeding it manually? Like do you programmatically "browse" to bolster your hister index? Asking because I started using hister a month or so ago and have been really liking it, and I'm curious how others are using it.
If it's 60M local, is it open source as well?
AI psychosis? They think they're actually putting out high-quality papers. Nothing else makes sense.
Yea I don't get "load bearing" that much but "seams"... So sick of it.
If we fast forward say 5 years, I don't see how we don't end up in world where people (and enterprises) are more savvy with how they use LLMs. Meaning, more models, smaller models, weirder models, more specialized models, etc. And all of it running on a variety of hardware (edge devices, personal computers, on-demand cloud compute).
I don't see how NVIDIA can keep their spot as belle of the ball. If LLMs and friends are truly to become as useful and ubiquitous as everyone thinks they will, then commoditization is the only option.
This is going to increasingly happen over the years to come. Big organizations will become more sophisticated with operationalizing their data, training and running LLMs will continue to be demystified and accessible, and over time we'll get more and more specialized / industry-specific models.
It's going to become another way to monetize your informational assets if you're a big older enterprise with troves of data. All you need is time to figure out how to make it useful for yourself and then eventually sell access to it however you want.
Think of all the data that big orgs have that isn't accessible to all the AI labs to suck up.
CSAM and having the potential to randomly becoming unhinged due to strong alignment with the whims of the one dude? Surely puts enterprises at ease.
> The industry is very much moving towards one-model-does-all end to end trained
I've worked with hundreds of enterprises on voice AI and voice agent solutions. In my experience, this isn't true. Or rather I should say, the people actually paying for voice agents (i.e enterprises) are not moving towards STS solutions in a meaningful way. The composability, observability, and reliability profile of STS systems is not amenable to enterprise criteria. Not to mention costs.
Which open source STT models have you had success with for fine tuning?
That made me feel relief until I realized it's my own local PBS station.
I would say even without rampocalypse there would still be the strong incentive to innovate at the edge and under more extreme constraints. The incentives are just even stronger now.
I'm looking forward to seeing what types of new things people create over the coming years once there is less obsession with massive unwieldy LLMs. I think the incentives are just too strong to ignore.
I think finding significant efficiency gains with LLMs and the like may lead to qualitatively better products. Looking at people's experiences to DSV4F makes me believe that even more than before too.
I don't think people are realizing that speed can allow for categorically different user experiences that are more than just "worse than frontier capabilities but faster".
Betting on innovation continuing to figure out ways to squeeze more out of less has historically been the right move. Look at Apple.
And I'd argue "hardware advances" are more proof of optimization.
Please consider taking a break from your use of LLMs. You are clearly deep in the throes of AI psychosis and need to talk to people you trust in your life instead of the chatbots.
What about your CEO actively spending their time pursuing their own creative interests and doing speaking gigs railing against AI? And posting them on the company YT and his own YT just a week before somberly announcing layoffs where you cite AI as part of the reason...
https://www.youtube.com/watch?v=17_HcR95YBcTo be fair, tech companies I've worked at that have done layoffs have had seemingly full-time CEOs. So I guess at least Jack is having a good time.
Saying that would be a much more reasonable layoff announcement. "Shit is good. But not
that good. If we don't change course now, things may be less than good."
If that's what they actually believe, they could just say it.
They don't because optics are more important than honesty. Can't spook would be investors. Must control the market's narrative about the viability and growth-trajectory of your company at all times. It's tiring.
Also I feel like people are not noticing in Tao's chat that chatGPT fairly often is doing web searches. So it literally is acting as search engine sometimes, and that too is incredibly effective when done right. So in addition to Tao using his prompts to "search/sift through vector space" the LLM is also literally just searching the web to augment its "knowledge" at inference time too.
Which, to me, again demystifies these tools. They are incredible tools, but still "just" such.
Interesting. I prefer video/audio for the same reason. But I watch most things on 1.5x or 1.8x speed. I'm not a particularly fast reader, so I prefer audio/video forms.
Totally agree with you though about not thinking written form is inferior. I do still enjoy a good long form article. (Also I'm not including technical writing like documentation.)
So should I ditch ollama for this?
Sincere question: have any non-Chinese models failed this one?