Here's my (hopefully) intuitive guide:

1. understand weighted least squares and how you can update an initial estimate (prior mean and variance) with a new measurement and its uncertainty (i.e. inverse variance weighted least squares)

2. this works because the true mean hasn't changed between measurements. What if it did?

3. KF uses a model of how the mean changes to predict what it should be now based on the past, including an inflation factor on the uncertainty since predictions aren't perfect

4. after the prediction, it becomes the same problem as (1) except you use the predicted values as the initial estimate

There are some details about the measurement matrix (when your measurement is a linear combination of the true value -- the state) and the Kalman gain, but these all come from the least squares formulation.

Least squares is the key and you can prove it's optimal under certain assumptions (e.g. Bayesian MMSE).

Skimmed this but don't have an intuitive understanding of why this works and how temperature and truncation factor in.

I was expecting something about the morphological erosion operator but this was pretty cool.

Some of the techniques here seem to be motivated by physical processes (e.g. rain). I wonder if that could be taken further to derive the whole process?

Criticize gatekeeping all you want, but I feel it’s safer to recommend a Mac or iPhone to an older, non-technical person than the equivalent Windows / Android machine.

And I’m still able to install any app I want with minimal fuss.

Why no need to make it the default? I’m all for rethinking legacy decisions.

It helps 99% of the user base and the security risk seems negligible.

Rethinking would imply there was thinking going on. This decision was made on vibes alone.

If anything, the people clinging to this snake oil security theater are the ones running on vibes alone.

Can anyone explain why Mamba models start with a continuous time SSM (and discretize) vs discrete time?

I know the step isn’t fixed, also not sure why that’s important. Is that the only reason? There also seems to be a parameterization advantage too with the continuous formulation.

Great idea and seems quite obvious in hindsight.

Is it guaranteed to have the same effect on vanishing gradients though? What if it put weight 1 on a layer that had a tiny gradient?

MLX support on Macs was the main reason for me.

Appeal to nature.

Async embedded is something that's always made sense to me and I've been awaiting a long time for it to happen.

But what's the overhead price with Embassy?