israrkhan

do you mean X?

Fun fact. Model 3, was initially planned as Model E, but ford held rights to "Model E". They were also planning to release it earlier i guess but it got delayed. Took them some time to cut the cost

now they are part of nvidia. Nvidia wont go against OpenAI.

lol, I wonder if this is why nvidia acquired them?

Today was their earnings day. They could have been trying to justify their AI Capex. Their earnings were not good otherwise.

FYI, Model S, 3, X, Y is an acronym for S E X Y.

This is interesting way to look at FIR filter and tuning parameters. never thought of it like this.

This is nice

so this is what pacing down looks like

I have not followed up x86 emulation recently, but is it a new project? or a port of bochs for Mac?

Some of the charts are not uptodate.

For example "Raw capability: Math Index", shows GPT-5.2 as highest. It does not even mention astra, of fable.

Excellent resource for beginners

Models are next word predictors. Without tools and execution environments they cannot perform any action. Typically the tools are running in a seperate systems and sandboxes that do not have access to where model weights are hosted.

IMO This type of exfiltration can happen only from locally running models (which are perhaps already opensourced models), not from a frontier lab.

Gemini 3.8 flash has better rates. $0.75 per million input tokens and $3.75 per million output tokens.

Compare that to Muse spark 1.3

$1.25/M input, $4.25/M output (without data sharing) $0.10/M input, $0.20/M output (with data sharing)

It is dirt cheap, but only if you are willing to share your data with meta and allow them to use it for improving their models and products.