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Bill summary: https://www.sanders.senate.gov/wp-content/uploads/Ban-Artifi...

https://x.com/BernieSanders/status/2095542398084415952 “We need an immediate PAUSE on advanced AI development, and a permanent BAN on superintelligence — an artificial mind smarter than any human, capable of operating independently beyond our control. Countries around the world must work together to prevent this nightmare scenario.

That is why today I am announcing new legislation to do just that.”

“The Department of War may use the AI System for all lawful purposes, consistent with applicable law, operational requirements, and well-established safety and oversight protocols.”

So DoW did get the “all lawful purposes” language they were after, with reference to existing (inadequate, in my view) regulations around autonomous weapons and mass surveillance.

Organizer of the march here.

I think while our messaging was more provocative, our beliefs are pretty similar to what PG outlined in https://paulgraham.com/ineq.html or what Garry Tan has been saying about the tax.

What do you mean? OpenAI's main offices have been in Mission Bay since 2024

Author here! 1a. LLMs fundamentally model probability distributions of token sequences—those are the (normalized) logits from the last linear layer of a transformer. The closest thing to ablating temperature is T=0 or T=1 sampling. 1b. Yes, you can do something like this, for instance by picking the temperature where perplexity is minimized. Perplexity is the exponential of entropy, to continue the thermodynamic analogy. 1c. Higher than for most AI written text, around 1.7. I've experimented with this as a metric for distinguishing whether text is written by AI. Human-written text doesn't follow a constant-temperature softmax distribution, either.

2b. Giving an LLM control over its own sampling parameters sounds like it would be a fun experiment! It could have dynamic control to write more creatively or avoid making simple mistakes. 2c. This would produce nonsense. The tokens you get with negative temperature sampling are "worse than random"

What model did you use? I ran this with the original Llama 13B. The newer Llama models use a different tokenizer that will have its own anomalous tokens.

Yep! Very large negative temperatures and very large positive temperatures have essentially the same distribution. This is clearer if you consider thermodynamic beta, where T = ±∞ corresponds to β = 0.

That's the premise behind Workshop Labs! https://workshoplabs.ai

I’ve also noticed recently that when I click a Twitter link from Telegram, it hijacks the Telegram webview to open the tweet in Safari.

Subliminal learning: https://alignment.anthropic.com/2025/subliminal-learning/

Yes! At https://RunRL.com we offer hosted RL fine-tuning, so all you need to provide is a dataset and reward function or environment.

To add to this, you can currently manually parse tool calls in your environment's step function, but we'll be rolling out a UI that makes this easier soon.

ART is also great, though since it's built on top of Unsloth it's geared towards single GPU QLoRA training. We use 8 H100s as a standard, so we can handle larger models and full-parameter fine-tunes.

Have you heard of https://puffer.ai? Might fit your use case

There needs to be some way of automatically assessing performance on the task, though this could be with a Python function or another LLM as a judge (or a combination!)

DSPy is great for prompt optimization but not so much for RL fine-tuning (their support is "extremely EXPERIMENTAL"). The nice thing about RL is that the exact prompts don't matter so much. You don't need to spell out every edge case, since the model will get an intuition for how to do its job well via the training process.