cootsnuck

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?