jbochi

And I did something similar to steam live video via an infinite gif: https://github.com/jbochi/gifstreaming#live-video-streaming-...

Announcing the breach on Thanksgiving day was also certainty calculated.

Not sure what I can share publicly, but we use TPUs as well: https://cloud.google.com/blog/products/compute/announcing-cl...

Pretty cool. Shameless plug: My team at Anthropic is hiring people that can write accelerator kernels. Please reach out to <my username>@gmail.com if you want to make state of the art models faster :)

I like this one: https://youtu.be/OIXVDdH3o6g?si=5zmNifE_Zzm4Rasb

This is already one year old. I am wonder how much the zoo grew in 2017. What were the main network innovations? Capsules and the Transformer?

Very interesting. I've implemented something similar[1] using a pure Collaborative Filtering approach[2][3], that I think works better for me, but it's unable to recommend unpopular repositories.

The New York Times recommender system uses a hybrid approach (Content Based + Collaborative Filtering) called Collaborative Topic Modeling on top of LDA[4]. It would be interesting to try that.

[1]: https://github-recs.appspot.com/

[2]: https://medium.com/towards-data-science/recommending-github-...

[3]: https://github.com/jbochi/facts

[4]: https://open.blogs.nytimes.com/2015/08/11/building-the-next-...

Here at The New York Times we are using it to power some of our recommendation algorithms. We are actually training the models with Python and serving them with Go using gonum.

Our library was just open sourced (and still in my personal account, until we add more documentation): https://github.com/jbochi/facts

It's very common to see a "Most Popular" section in a website, but the way it's usually done is not optimized for clicks.

Inspired by Evan's post, I wrote "How Not to Sort by Popularity" a few weeks ago: https://medium.com/@jbochi/how-not-to-sort-by-popularity-927...

MJPEG is indeed much better for this. It uses less bandwidth (GIFs have no compression) and less browser memory (old frames can be discarded). When I was at globo.com, we developed this to serve animated thumbnails for live video streams: https://github.com/jbochi/live_thumb

This reminds me of a project I've created a few years ago that does live video streaming with endless GIFs: https://github.com/jbochi/gifstreaming

I am glad you liked. I am not the author of the blog post, but I was a member of the team.

1: We just repackage it. 2: The problem is that EvoStream stores the manifests and chunks locally and we needed high availability. That's why we use an external data storage. We have had up to 30 simultaneous streams, with 7 bitrates each and 2 hours of DVR.

Closure compiler will rename the math variable.

There is nice summary of other interesting probabilistic data structures here: http://highlyscalable.wordpress.com/2012/05/01/probabilistic...

Thanks! I did not know Stormer-Cowell, but googling it, I've found a comparision to DROMO, suggesting that it's even more stable: http://www.congrex.nl/11c01proceedings/Papers/2225317%20Pela...

I'll try both if I find the time :)

I have added all the solutions to my html5 gravity simulation project here: http://jbochi.github.com/planets/

PS: Some of the configurations are not stable with my code. I'll try Runge-Kutta.