vermarish

From the second paragraph:

> At its core, the drawing is the circle of fifths, familiar to most musicians. Coltrane's version fills in the circle with chromatic scales...

> July-August 2026: the warning shot that was real

I am so tired of reading unfiltered AI slop like this. What, as opposed to the fake warning shot mentioned nowhere in the article?

For over a decade, Google's been wearing the environmentally-friendly / carbon-neutral label as part of their public-facing brand values. It's definitely been eating into their CapEx then, and this isn't much different.

Fair enough!

> "Click any region to stimulate it" > It's just a markdown-embedded png.

What did I expect?

The link now says it's extended til August 31st.

This is beautiful work! First, I thought this would involve using PCA to go from 3D to 2D, which would result in an easier selector to use, but at the expense of representing every person.

Then, I thought this would stop at using the U-space vectors (the ones that form the basis of the PCA image) and their corresponding ellipse to form our color space, but no, the function fitting is a very slick idea, even if it was executed by hand.

Lastly, I love the presentation of sampling from different r values. Whether you sample from a fixed r value or a range of r values, I bet this has great applications in game design or animation.

I still don't quite get the manual data labeling process at the beginning? It seems like it would encode some bias, but the consistency of the first point cloud and the results certainly speak for themselves.

When you have Claude Code indepedently author commits and PRs and merge them in, it'll always credit itself as an author. I assume it showing up in the README is a byproduct of the same logic.

Every time I come across a pudding article, it stops me in my tracks. They're always so rewarding!

Not me, but my dentist always keeps one of the Monterey Bay Aquarium's livestreams in her office.

Sea otters: https://www.youtube.com/live/abbR-Ttd-cA

Jellyfish: https://www.youtube.com/live/eQ_foBERmzA

I have the same issue. Perma-black loading screen that resolves as soon as I disable ublock origin. Using Firefox on Debian.

I interpret it a different way than that. I see application code and testing code as both a part of blue team. It's the code reviews and architectural critiques that are part of red team.

Personally, I've found GitHub's feature of AI PR reviewers exceptionally helpful. I think that's the type of red team LLM app Tao is describing here.

They made Claude Code available on their $20/month plan about two weeks ago. Your point still stands, of course.

I love the animation when you click "No" on "Do you want a new Pebble?". So extra.

(2023)

This is really cool! I've seen Jensen's inequality used many times over in my stats/ML classes, but the traffic example here gave me an "aha" moment about how it manifests.

I like the visualizations of the expected value against the individual probabilistic components as well, though I wish there were more non-uniform distributions visualized. Perhaps if we take the traffic example and tweak the distribution to be non-uniform, that might make for a cool interactive viz.

I'm not a huge fan of the color/pitch relationship they seem to be trying to establish.

What I do appreciate is the engineering design in these interactives. The circular metronome in the rhythm apps is very cool, might be good for generating musical ideas once I get past the semi-opaque UX.

It sounds like Google wants to get more edge compute in people's homes so they have a new vector to deploy AI products on, but they're still so far from actually deploying an innovative product that they can't announce anything to actually drive up hype.

Then, the rebranding is only because they've abandoned the original "minimal footprint" ethos of ChromeCast.

Very cool! Just curious, would you consider adding more exotic experimental design setups like Latin Square Design to the roadmap?

At a high level, Bayesian statistics and DL share the same objective of fitting parameters to models.

In particular, variational inference is a family of techniques that makes these kinds of problems computationally tractable. It shows up everywhere from variational autoencoders, to time-series state-space modeling, to reinforcement learning.

If you want to learn more, I recommend reading Murphy's textbooks on ML: https://probml.github.io/pml-book/book2.html

I feel like cost, availability, taste, and strength are kind of important actually.

I didn't expect Sweden's Prohibition history to be so interesting.

https://en.wikipedia.org/wiki/Systembolaget#History

This seems really cool!

I noticed the landing page has a rotating PNG gallery in desktop but not on mobile. I'm sure you must've wanted to put it in the mobile page too, was it hard to UX or just too inconvenient to implement easily?

I don't know. I think the younger 20-somethings all have this same kind of dream, but the older people get, the more comfortable they seem hiding behind NDAs at FAANGs or staying in "stealth mode." They really don't owe anyone anything.

Disclaimer: I'm one of the younger 20-somethings.

But for such a model, the joint pdf would not be written simply as a product of each individual pdf. That's what independence provides.

I mean, instead of using a fixed threshold at 500, if you use a live threshold determined by the recent average upvotes, then yeah I'd have no qualms with calling them outliers.

It's just that this method is susceptible to votecount inflation, a la Reddit from 2014 to 2024.

Right. But if you make the notation slightly more explicit, then the integral of L(data, params) over data is 1. This follows from the independence assumption.

So we ARE working with a probability function. Its output can be interpreted as probabilities. It's just that we're maximizing L = P(events | params) with respect to params.

Hi! Some background first: I'm putting together a blog right now using Hugo and D3. I'm a huge fan of D3's infinite flexibility, as seen in some famous scrollytellers [0-1], and I've spent some time experimenting with that format myself [2].

My question is: what does Observable Framework offer for data storytellers who want to blog? Is this meant to go up against Hugo/Jekyll in terms of full-fledged max-efficiency site generation? If not, are there plans to add integrations with other blogging frameworks?

[0]: http://r2d3.us/ [1]: https://algorithms-tour.stitchfix.com/ [2]: https://vermarish.github.io/big-brother-barometer/

Any explanation on the choice of the colormap? It can have a powerful effect on how we perceive this visualization.

Yes, this is a counterfactual line of reasoning. It's certainly legit, and I believe it's an active field of research in ML, but at the end of the day, you're still poking around the inputs of a black-box model as opposed to examining a white-box model.