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61 ms·

691.
The Recruiter Honeypot(ewherry.com)
4 points by ot 11y ago|0 comments
692.
Open-sourcing Terrapin: A serving system for batch generated data(engineering.pinterest.com)
2 points by ot 11y ago|0 comments
693.
Why Is a Raven Like a Writing Desk?(vimeo.com)
7 points by ot 11y ago|0 comments
694.
Pantelegraph(en.wikipedia.org)
1 points by ot 11y ago|0 comments
695.
by ot 11y ago
That's interesting, but I see no reference in the website or the paper to GNUnet or Freenet or other existing DHT-based file distribution networks. How does this compare?
696.
by ot 11y ago
I think you're right, but it depends on what's your computational model. In word-RAM you usually assume that log n < w where w is the machine word, and you can do arithmetical operations on words in O(1), so if your coefficient
697.
by ot 11y ago
IIRC if your polynomials have integer coefficients with a bounded number of digits you can use FFT on finite rings and get an actual O(n log n) algorithm.
698.
Deringing in DCT via overshoot and clipping(pornel.net)
39 points by ot 11y ago|7 comments
699.
In San Francisco’s Bidding Wars, Home Prices Go Ballistic(wsj.com)
6 points by ot 11y ago|0 comments
700.
by ot 11y ago
colah, your posts combine a deep level of understanding and an exceptional clarity. These are both rare, especially in the cargo-cult-driven world of neural networks and deep learning. I hope you keep writing as much as you can. Thanks!
701.
by ot 11y ago
Unsupervised training may isolate the defining features of a cat picture, but it won't know that that's what we call "cat", so no unsupervised system will be able to identify cats in videos unless you show it at least on
702.
Fuzz Testing Zstandard(fastcompression.blogspot.com)
15 points by ot 11y ago|0 comments
703.
The Town That Creep Built(bldgblog.blogspot.com)
10 points by ot 11y ago|2 comments
704.
Science Isn’t Broken(fivethirtyeight.com)
74 points by ot 11y ago|58 comments
705.
Amazon Robotics: We Reimagine Now(amazonrobotics.com)
1 points by ot 11y ago|0 comments
706.
Introducing TestScribe: Making Testing Easier(box.com)
1 points by ot 11y ago|0 comments
707.
by ot 11y ago
What you are suggesting is absolutely not equivalent to gaussian blurring. Do low-quality JPEGs look nicely blurred to you? Truncating the coefficients is a low-pass filter, which introduces nasty artifacts such as ringing. The transform of
708.
by ot 11y ago
When you have more than a handful of strings, you will want to return only a small number (k) of completions, preferably sorted by some pre-assigned score. Depending on how you order the trie, you can only get the first k completions by lex
709.
How Facebook Open Sources at Scale [video](youtube.com)
1 points by ot 11y ago|0 comments
710.
WDT, a library to transfer files as fast as possible over multiple TCP paths(github.com)
34 points by ot 11y ago|14 comments
711.
by ot 11y ago
It's probably resolved by the internal network DNS. You can do the same even with your home router.
712.
Counting hash collisions with the birthday paradox(matt.might.net)
48 points by ot 11y ago|4 comments
713.
by ot 11y ago
https://github.com/facebook/folly/blob/master/folly/Benchmar...
714.
by ot 11y ago
This is the obvious strategy, but it's not always applicable. For example when profiling a long-running service, or a kernel, there's no "absolute time it needs", you need other proxies of running time such as the CPU us
715.
by ot 11y ago
This is not that uncommon when optimizing code. Your program is slow so you profile it, and find out that f() takes 99% of the time. So you work a lot to optimize f(), and re-profiling shows that now f() takes 98% of the time. Doesn't
716.
Rtrace is an x86/x86_64 native code debugger written in Ruby(github.com)
35 points by ot 11y ago|2 comments
717.
Solving multi-core Python(lwn.net)
166 points by ot 11y ago|64 comments
718.
Delivering High Scroll Performance(code.facebook.com)
17 points by ot 11y ago|2 comments
719.
Recent Reports Make Machine Learning Sound Like a Sport. It isn’t(gizmodo.com)
2 points by ot 11y ago|0 comments
720.
National hiring experiments reveal 2:1 preference for women on STEM tenure track(pnas.org)
1 points by ot 11y ago|0 comments

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