deepsquirrelnet

You're linking an article showing how everybody in modern times who uses the term "uniparty" is using it to further their own political interests...

Beyond just that, if there is a uniparty, then why is Elon spending half a billion dollars in electoral races?

It's not that there's a uniparty, it's that as a result of Citizen's United, parties don't need to represent you anymore. They represent their money donors instead. Money is speech now, and as the wealth disparity grows it becomes a bigger megaphone and drowns out the voices of hundreds of millions.

butterfly Is this AGI?

American media, such as CBS has been purchased by Larry Ellison and directed not to.

Read the exit comments of journalists who worked there.

> “Two of the things in the email include, can we make the protesters look more violent? Now, I’m paraphrasing. I don’t have the quote, but that’s what was communicated to me,” Pelley said. “And the other thing, Renee Good’s car. You need to describe her as driving toward the officer.”

https://www.nbcnews.com/news/us-news/scott-pelley-cbs-new-yo...

This is the state of media, and it's looking like the media of certain other countries.

They're too busy re-enacting the "Clown Applying Makeup" meme, to achieve a proper mar-a-lago face.

That and a nice slight of hand. When they coupled that with a clear removal of corporate ethics, "a business' ONLY responsibility is to its shareholders" and then removed the barriers between the private and public sector (mostly via Citizen's United)... private sector effectively just became the public sector without any accountability.

Now they can just chip away at whatever regulation is left, and create mistrust in oversight bodies and the rigging of the game is complete.

Better than communism, the government hardly even has to pretend to work for the benefit of the people, because the wealth imbalance has a closed circuit of money between private entities and elections. They can bring suitcases of cash, walk in the front door literally holding a chainsaw and prune off every olive branch extended toward the citizens.

These election "investments" are clearly paying off at high multiples now. Why spend your money in a capitalist competition when you can more efficiently spend it in public elections to eliminate competition, divert tax revenue directly into your businesses and eliminate any responsibility you should have? Do a Nazi salute for the whole world to see, and watch the propagandized twist themselves into whatever shape pretzel you ask them to.

To add to this, it's completely dumb to think that any party is necessarily the same over decades. Look around. Most of what you see today is reactionary. Hold on to party views from 10 or 50 years ago, and you won't get elected today. Both parties are in turmoil in terms of public support.

Historical "bipartisan did X" means nothing today. The time is always right now to define what a future party should be. It's a great time to destroy the old and reimagine the new.

For a related use case, I have been using claude to add or change features in my own tensorboard log reader. The flexibility of being able to change anything at any time so I can see what I want to see is really nice.

I've always preferred local monitoring tools, but tensorboard is seemingly just on life support at this point.

https://github.com/dleemiller/BlernsBoard

The model cards list all of our data sources. They are all publicly available datasets that are commonly used in training most speech recognition models. It is good to take a wide variety of domains for robustness. Clean speech vs noisy backgrounds. Prepared speech vs spontaneous speech. Studio microphones vs cheap laptop microphones. More exposure is better.

For raw number of hours, I would set a goal of 40k hours. That number worked well for us, although if you are training bigger models you might still benefit from more.

Yes, because MIT does not prevent public and private mass-surveillance initiatives from using our models against people.

Our "plus" models have speaker segmentation and speaker verification (embedding) that you can use for speech to text and diarization from one model. There's an example script in the model repo that shows how to do it.

https://huggingface.co/collections/futo-org/asr4all

If you're looking for a single stage "end-to-end" model just for diarization only, Nvidia released one recently: https://huggingface.co/nvidia/Nemotron-3-Diarization

Yep exactly! Streaming models have to run faster, because they're more compute heavy. Some compute from every batch is wasted when the next set of frames come in. Streaming models also have higher WER than fully offline models.

Our models are more comparable to nemotron-3.5-asr-streaming in that regard and I think the medium/large sizes do perform quite favorably. For offline models, I don't think it's worthwhile for us to try and compete with parakeet v2/3. Those are already really good, and we're in the process of adding those to FUTO Voice.

These models are filling some gaps we see for the kinds of things we tend to work on. But broadly they can slot into a lot of places where there isn't accelerated compute (Raspberry pi 4, laptops, local client side transcription, etc).

You could try ours. They're very small and english only right now (training Spanish now). But they're a CPU optimized architecture and even the largest size will work on a single thread with realtime streaming.

https://huggingface.co/collections/futo-org/asr4all

You can run them client-side using wasm-simd. I setup a space for the small sized model. There's a big step up in accuracy going to the medium size though:

https://huggingface.co/spaces/futo-org/asr4all-client-side

That's a truly horrible story. It strikes me as one that a reputable investigative journalism outlet might take up. Have you thought about contacting somewhere like ProPublica to see if they would be interested in writing about it?

> All right, I'm going to open up a history lesson because it's been a couple of generations of software engineers since this essay

> It created really weird social-structure expectations: that someone who releases code has an obligation to maintain it and get paid nothing for the work.

Fundamentally, yeah that is why it took off. Big tech doesn't sell software anymore, they made it a commodity through open source. Now they sell cloud services or consulting, or ads or your data.

Just read what Eric Raymond said about it: https://www.oreilly.com/openbook/opensources/book/raymond2.h...

> It seemed clear to us in retrospect that the term "free software" had done our movement tremendous damage over the years. Part of this stemmed from the well-known "free-speech/free-beer" ambiguity. Most of it came from something worse--the strong association of the term "free software" with hostility to intellectual property rights, communism, and other ideas hardly likely to endear themselves to an MIS manager.

The intention was to find a business model and market it. That's why they distinguished themselves from FOSS in the first place. It's what you see big tech doing today (that's the business model).

The outcomes being observed and complained about are literally just the expected outcomes of making software a commodity, which is that businesses need to make money and they don't do it by selling software.

When we recently released our ASR models, we decided to use a custom license that forbids using it for monitoring people in custody. I know it probably won't make a difference in the grand scheme of things, but I think with the capabilities that we're developing it's worth considering how it might be used.

https://huggingface.co/futo-org/asr4all-l/blob/main/LICENSE

That's interesting results, and a bit surprising. I tried training an NLI model on the T5 architecture, which I think is similar to a lot of Jev-style use cases. But I found the T5 encoder half was better than enc+dec at roughly equal parameters and still worse than ModernBERT/Ettin.

I also found Ettin to be better than ModernBERT, which is not that surprising if you look back at the published benchmarks in their paper.

Qwen3.5 0.8B was looking significantly worse than all of those, but I added LoRA adapters to 27B and that easily beat all of my encoders. So parameter for parameter, encoders were still the best for me, but with a ceiling at 1B for size. I'm planning to train more Qwen sizes and I guess I'll see at what point they can overtake the encoders I have.

New open weight streaming ASR models you can run on a Raspberry Pi 4 or a phone. These models include all kinds of nifty features, like voice activity detection (VAD), and features that support a diarization pipeline.

https://huggingface.co/collections/futo-org/asr4all

> The trading in question was eye-popping. In a two-minute window starting at 6:49 a.m. in New York on March 23, traders sold contracts equivalent to at least 6 million barrels of Brent and West Texas Intermediate, more than 8.5 times the volume that had changed hands that time in the morning over the prior week. A similar increase in activity was observed in futures tied to the S&P 500, with contracts worth more than $2 billion in notional value traded — far more than would be expected in an otherwise quiet premarket session. Unusual volumes were also visible in natural gas and other futures markets.

> Those trades paid off 15 minutes or so later, when Trump wrote on Truth Social that he was reversing his threat to attack Iranian infrastructure unless the country re-opened the Strait of Hormuz.

Likely the entire purpose of the war is to create opportunities for corruption in my opinion.

> 1 - Is the document about desks designed for children?

> 2 - Does the document offer products or information on kids’ desks?

> ...

> 9 - Are brands or models of children’s desks listed?

> 10 - Does it cover where to buy or how to choose a desk for kids?

>

> Each of these can be turned into a noul question. P(yes) of each can be summed to score overall relevance

It seems to me that this is a rediscovery of natural language inference (NLI), which was very active in NLP research pre-LLMs. I have a side hobby of training NLI cross encoders, and I'd bet that many of these use cases could be serviced by a good quality small NLI model.

See: https://huggingface.co/dleemiller/finecat-nli-v2-l

And also: "Less Annotating, More Classifying: Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT-NLI" https://www.semanticscholar.org/paper/Less-Annotating%2C-Mor...

Here's another leaderboard: https://huggingface.co/spaces/multimodalart/jev-decision-ind...

And what do you call it when a country acts in adversarial ways to its own citizens? Call it cynical, but it sure seems like there's a lot of that in the world right now.

This is exciting. I use asta all the time for finding papers. It's one of my favorite resources for exploring new topics.

BART is quite an old model for this kind of test, and probably not a good very good choice for much these days. I'm working on replicating their benchmark on my own NLI model that targets zero-shot guardrail applications. I don't think it'll beat much larger models, but should give a better baseline for what a crossencoder can do.

https://huggingface.co/dleemiller/crossingguard-nli-l

It's hard to say without knowing their architecture, but I'd guess something like block attention. You can process the prompt separately from the classifications into a latent space and then do some kind of late interaction with the encodings from the classifications.

There are plenty of other ways to do zero shot classification that would result in more "token usage" (really just having to reprocess everything for each class), but the pricing and the way they describe it narrows it down somewhat.

I'm having a really hard time wrapping my head around why Jev is getting so much hype. It feels manufactured to me. I don't think they've proven a significant market for their product, and there's no independent benchmarks that prove anything. To me that's doesn't pass the smell test.

But if they had to show how well their product worked they might give away the whole game... because they'd have to compare their "noul" class against an NLI benchmark for instance, and possibly show they're losing to cross encoders and give away the fact that they are just rebranding NLI. Or rerankers (choice) or zero-shot classifiers.

I'm not sure anybody but people inside the company know if the model itself is a contribution. There's no publication and no architectural details. There's no benchmarks or comparisons published. You can do all of the things they claim with an LLM, not that I think that's what they did.

Likely they have some encoder (eg ModernBERT) trained to do late interaction or latent states along the lines of ColBERT, Perceiver IO or poly-encoders.

No. Frontier AI companies need the government to shield them from becoming commodity. That's why they're begging for regulation.

On the other side, the current administration also has powerful backers whose businesses are complemented by AI and would benefit from it becoming a commodity. Think about who Vance is speaking on behalf of.

So the AI companies need to use fear to drive public demand for their regulation efforts. IMO there's little to no honest discourse and a lot of attempts to manipulate public opinion for objectives that we have little stake in. I personally don't trust any of the involved parties to make decisions for the public good.

Wow, how could this have happened right before the election? Surely this will not be used as a pretext for anything.

The problem is that at the business level and at least for a large part of the US government, we need to assume that there are no 'good' actors.

Anthropic needs regulation in order to prevent AI from becoming a commodity. This of course does not benefit all tech businesses equally, especially those that are not currently at the AI frontier. So when JD Vance talks about AI, he talks using the mouth of Peter Thiel who may not see benefit from the same policy as Altman or Amodei.

The rest is just public support posturing and most of that is bullshit meant to distract from the high rollers game of winners and losers. The philosophy is money and power, who gets it and who doesn't. Us normies aren't really participants in the game, except where we are being manipulated into cheering for one side or another, and with little stake in the outcomes (although selfishly, I'd be pissed if I didn't have open models to tinker with).

Open source is fundamentally a vehicle for commoditization. This is great if your business is not AI and your business is instead something like GPU hardware or some product that uses AI. But it means that eventually, selling AI is not going to be the money maker.

OSI proliferated open source on a business strategy called "commoditizing your complements". These big companies don't do it out of benevolence. It was pitched to them in a way that FSF did not (which was more about morals and ethics, something business care little about), and it caught on. And the software business became about ads, consulting and cloud services instead.

It's also clearly the wrong question for this decade. Private / public sector cooperation and all of the barriers between them that have been degraded and chipped away has left us with a very broken economic system that looks less and less like capitalism every day.