https://cs.stanford.edu/~aozdemir/blog/unsafe-rust-syntax/
Alex Ozdemir Home Teaching Software Blog Research Personal Talks Despite the fundamental role unsafe plays in Rust, we have relatively little understanding of how it is being used in real codebases. As the community decides what the exact semantics of unsafe should be, it becomes increasingly important to have this understanding in order to avoid accidentally diverging from the expectations of library writers. This post takes a first step in that direction by laying the basis for syntactic analyses of unsafe in Rust code hosted on crates.io. Rust is a new systems programming language that seems to promise the world: all the control of C/C++ as well as all of the safety and convenience of your favorite high-level language. At its heart is a statically verified system of memory management - an incarnation of the RAII pattern baked into the type system it...
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https://graphics.stanford.edu/~seander/bithacks.html
Individually, the code snippets here are in the public domain (unless otherwise noted) — feel free to use them however you please. The aggregate collection and descriptions are © 1997-2005 Sean Eron Anderson. The code and descriptions are distributed in the hope that they will be useful, but WITHOUT ANY WARRANTY and without even the implied warranty of merchantability or fitness for a particular purpose. As of May 5, 2005, all the code has been tested thoroughly. Thousands of people have read it. Moreover, Professor Randal Bryant , the Dean of Computer Science at Carnegie Mellon University, has personally tested almost everything with his Uclid code verification system . What he hasn't tested, I have checked against all possible inputs on a 32-bit machine. To the first person to inform me of a legitimate bug in the code, I'll pay a bounty of US$10 (by check or Paypal...
ConvNetJS This demo follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning , a paper from NIPS 2013 Deep Learning Workshop from DeepMind. The paper is a nice demo of a fairly standard (model-free) Reinforcement Learning algorithm (Q Learning) learning to play Atari games. In this demo, instead of Atari games, we'll start out with something more simple: a 2D agent that has 9 eyes pointing in different angles ahead and every eye senses 3 values along its direction (up to a certain maximum visibility distance): distance to a wall, distance to a green thing, or distance to a red thing. The agent navigates by using one of 5 actions that turn it different angles. The red things are apples and the agent gets reward for eating them. The green things are poison and the agent gets negative reward ...
ConvNetJS This demo follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning , a paper from NIPS 2013 Deep Learning Workshop from DeepMind. The paper is a nice demo of a fairly standard (model-free) Reinforcement Learning algorithm (Q Learning) learning to play Atari games. In this demo, instead of Atari games, we'll start out with something more simple: a 2D agent that has 9 eyes pointing in different angles ahead and every eye senses 3 values along its direction (up to a certain maximum visibility distance): distance to a wall, distance to a green thing, or distance to a red thing. The agent navigates by using one of 5 actions that turn it different angles. The red things are apples and the agent gets reward for eating them. The green things are poison and the agent gets negative reward ...