Worth your time.
Worth your time.
You may have heard of Bret Victor from his “Kill Math” project and the beautiful differential equation playground that he has created with his Interactive Exploration of a Dynamical System.
It caused quite a stir a while ago and since then we have seen Apple take on some of those ideas in its new eBook push. Even if you’ve seen it before it is definitely worth a refresher viewing:
Since then, Bret has apparently been quite busy building some proof-of-concept tools for designers and weaving together some of his ideas into an amazing talk.
The talk showcases UI concepts that allow programmers and designers to interact with their creation directly instead of through symbol based abstraction, the idea being that such symbol based abstractions are better suited for paper than the digital canvas and create a barrier between the creator and their creation.
In a few short demos Bret shows how powerful developer/designer tooling can truly be. These livecoding demos really showed me how valuable immediate feedback can be.
I have often ‘played’ with an algorithm in a dynamic/exploratory environment such as MATLAB when trying to solve a problem, and then when I have a handle on how the data feels re-implement the algorithm into another language to integrate with the rest of the system. After seeing this video I’ve decided that kind of workflow can be improved. Visualisation should not be an afterthought. It should be implemented first.
This video is well worth your time:
Jean-loup Gailly (author of gzip!) dissects US Patent 5,533,051 on ‘compression of random data’.
The title immediately raises suspicion as it’s impossible to compress truly random data.
Anyone with a background in mathematics or information theory is probably familiar with the relevant proofs already, but the article does a great job of explaining the arguments and addressing each patent claim so I recommend checking it out.
What’s the minimum length of road needed to connect n cities?
This combinatorics problem is NP-Hard (if you can find a polynomial time algorithm to solve it, then P=NP), and is therefore considered difficult to solve for a computer.
An awesome demonstration of the intersection of mathematics and physics – the tendency of soap bubbles to minimize their surface area ends up minimizing the appropriate distance and solving the problem.
Worth a watch: