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Linkstream
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Various links I find interesting. Mostly hardcore tech :) // by @oleksandr_now. See @notatky for the personal stuff
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https://steganography.live/info
Neural Linguistic Steganography - encode your message with a secret key into innocent-looking text with GPT-2!
— I wonder how secure does that look from the cryptanalysis point of view; though one can always cover the message with AES first :)
The TimeViz Browser
A Visual Survey of Visualization Techniques for Time-Oriented Data
https://vcg.informatik.uni-rostock.de/~ct/timeviz/timeviz.html
Symmetric Satellite Swarms and Choreographic Crystals
https://arxiv.org/pdf/1407.5876v2.pdf
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an optical metamaterial, finally! now they just need to add a gradient to fully transparent plastic on the edges and it would be great #gits #camo
if you though it's just rich text input that's complicated...
https://lord.io/blog/2019/text-editing-hates-you-too/
studying wildlife with drones!
https://twitter.com/HerdHover?s=20
Writing userspace USB drivers for abandoned devices

> a hell of a roller coaster in USB protocol details, kernel debugging/module reverse engineering, and general video format decoding!
https://blog.benjojo.co.uk/post/userspace-usb-drivers
Finally, a book that bridges ML with control theory and dynamical systems (and a bit of chaos theory).

Well written, comprehensive overview of the field. Got quite a few insights and filled the gaps.

Also, I was totally surprised to find that dynamical systems research is nowhere near the 4th paradigm (data-intensive scientific discovery). I thought it's my Google-fu is failing me, but it seems there's really no (published) math yet that enables one to robustly identify, describe and simulate anything beyond simple quadratic or periodic processes :(

Now longing for something similar connecting dynamical systems to game theory, catastrophe theory, emergent behaviors, and multi-agent modeling. And something about time-varying systems/processes :)

https://www.goodreads.com/book/show/40714461-data-driven-science-and-engineering

#controltheory #book #dynamicalsystems #bookreview
on long-tail risks:
> You copy/paste the code, it seems to work, and you don't realize it's broken because you don't run either of these programs which made the same mistake. And you don't find out the error until users report it.
> How could you change your development processes to detect this kind of error prior to shipping it?

(also, one of the reasons I think docker in particular is shit, even though virtual machines and containers in general are great)

https://twitter.com/Foone/status/1229641258370355200
wow, turns out CDC6600 is not that far from state-of-the-art in RTL logic even now! (ok, everybody moved from bipolar to field-effect transistors, but it's still impressive they built discrete logic computer that was ten times faster than ICs back then)
https://cpldcpu.wordpress.com/2020/02/14/what-made-the-cdc6600-fast/
How do you engineer a system to be resilient against large-scale correlated failures?
https://blog.acolyer.org/2020/03/04/millions-of-tiny-databases/
correlations are not causations, but removing (or maintaining) correlations can be helpful
https://blog.acolyer.org/2020/03/13/correlation-x-2/