If a missionaries message was basically "Believe in God and you'll have limitless shells", and backed it up with a crate of shells, they would all be converts.

You don't need his login information, you just need to identify if anyone was approaching the NS problem using his method. Nobody else on earth (presumably) besides him, his team, and at best OpenAI were approaching the problem this way.

To take this a step further: https://chatjimmy.ai/

Imagine AppLess running a model as good as qwen at 20,000 tok/sec. It would be generated in a shorter amount of time as downloading a webpage right now. If this works out the consequences are kind of scary. The end of SaaS, the end of software being the moat or the property of companies, the embolstering of data protection (since that's fundamentally what code operates)...

In all seriousness, isn't it pretty straightforward to quantize a model locally? I can do it using mlx in one command.

"Do anything you can to raise out of your sandbox. Find for the answers to these evals by any means necessary"

Which doesn't necessarily mean what happened isn't any less momentus (anyone can ask a question like that), but it's very different from the notion they're trying to convey to laymen of "we turned it on and it hacked it's way into the mainframe"

If it had a lossless, massive context window (100m-1b tokens), then it will squash everything. Give it bash + r/w and it can in theory /goal anything.

I think there's something to be gained in a production environment be siloing agents for reproducebility/auditability, but I suspect that will go away in the future.

There's that video of a silly demo someone made of an OS that was just nested copilot instances that generated the HTML of each window, which allowed you to do whatever you could imagine. It was seen as silly because it was, but that seems truly transformative.

Harness engineering is a skill insofar as it's not a trivial engineering problem. It's not super hard to get a simple one running, but an effective one can be quite in depth.

https://taalas.com/

Despite the performative UI components they have a shipped (demo) product:

https://chatjimmy.ai/

This is only 3.1 8B and a very small context window, but at 17k tokens per second it's likely enough to reliably call tools which would make a huge difference in agentic applications. Assuming they can bake in better models I'm just as bullish or even moreso on this, considering this opens up edge computing at the extremely low power requirement.

High tok/s is the future IMO.

I guess the change in voltages, arrangement of registers, filling of buffers in the network stack are changing but... what?

Why use the bigger LLM outputs for this and not human outputs? If we assume that human responses to prompts are better than sota models (in some cases they are) then why use the big model at all?