Radiative Cooling – Environment-Friendly Cooling Technologies with Zero Energy Consumption
Radiative cooling is the natural process through which objects shed heat in the form of infrared (IR) radiation. All materials at room temperature emit infrared at wavelengths of 5–15 μm. However, the process is typically not very efficient because it is counteracted by external influences that heat the object, such as sunlight and air currents. Air, meanwhile, absorbs and emits very little radiation with wavelengths 8–13 μm. The Earth cools itself at night by emitting infrared through this “atmospheric window” into space.
cooling  passive-cooling 
7 weeks ago
Jeff Allen
cartography and data visualization using a variety of open-source tools (often with Python, R, JS, PostGIS, and/or QGIS). Some of these projects are hosted on GitHub. See below for links to a few.
map  mapping  gis 
june 2018
pglogical | 2ndQuadrant
pglogical is a logical replication system implemented entirely as a PostgreSQL extension. Fully integrated, it requires no triggers or external programs. This alternative to physical replication is a highly efficient method of replicating data using a publish/subscribe model for selective replication.
postgresql  replication 
june 2018
Revised and much faster, run your own high-end cloud gaming service on EC2!
Stream videogame from a server running on AWS with a powerful GPU
aws  cloud  ec2  gaming 
june 2018
Deploy SSD cloud servers in seconds. - Scaleway
Cloud provider with data centers in Amsterdam and Paris
arm  cloud  hosting  server  ssd 
may 2018
Differentiable Plasticity: A New Method for Learning to Learn | Uber Engineering Blog
Uber AI Labs has developed a new method called differentiable plasticity that lets us train the behavior of plastic connections through gradient descent so that they can help previously-trained networks adapt to future conditions. While evolving such plastic neural networks is a longstanding area of research in evolutionary computation, to our knowledge the work introduced here is the first to show it is possible to optimize plasticity itself through gradient descent. Because gradient-based methods underlie many of the recent spectacular breakthroughs in artificial intelligence (including image recognition, machine translation, Atari video games, and Go playing), making plastic networks amenable to gradient descent training may dramatically expand the power of both approaches.
ai  learning  machine-learning  ml 
april 2018
FoundationDB | Home
FoundationDB gives you the power of ACID transactions in a distributed database.
apple  database  distributed  nosql 
april 2018
GitHub - tshort/dactyl-keyboard: Dactyl-ManuForm, a parameterized ergonomic keyboard
Dactyl-ManuForm, a parameterized, split-hand, concave, columnar, ergonomic keyboard.
3d  3dprinting  keyboard  diy  ergonomic 
april 2018
Seek App · iNaturalist.org
Take your nature knowledge up a notch with Seek! Seek encourages outdoor exploration and learning by harnessing image recognition technology to help you identify plants and animals from your photos. Choose from a list of nearby plants, animals, and fungi and collect photographs of as many species as you can!
ios  app  plantrecognition 
march 2018
My unusual hobby | Stephan Boyer
"When I come home from work, I try to prove theorems in a proof assistant. Usually the theorems are related to functional programming or type systems.
It’s a masochistic hobby. Convincing a computer that a theorem is true can be quite difficult compared to convincing a human. But if I can get a computer to accept my proof, then a) I must have a pretty good understanding of it, and b) it really must be right!
I use the Coq theorem prover for formalizing proofs. Coq is a beautiful and simple language [1]—far simpler than the languages I use at work!"
math  programming 
november 2017
Beautiful hand-drawn block diagrams from plain text
diagrams  drawing 
june 2017
[1610.02357] Xception: Deep Learning with Depthwise Separable Convolutions
We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters.
google  deeplearning  neuralnetwork 
may 2017
Ask HN: Did your life as a parent affected your life as a developer? | Hacker News
I have a full time job as full stack developer, wife and 2 kids. I commute every day 1 hour in each direction. On a work day I woke up at 5:30 am, wash, dress and jump in a car at 6:00 am, so that I can be at work at 7:00 am. I usually go home at 3:00 pm and if there is no traffic jam I am at home at 4:00 pm. I eat something, change clothes, clock says 5 pm and I try to spend some quality time with my wife and kids. Kids go to bed at 8 pm and at 9 pm I am exhausted as hell, and I fell to bed. And this repeats every work week.

On weekends I barely have time for my side projects (one is familyokjobs.com, which I created over several weekends when wife and kids were sleeping), because there is always something to do around the house. What I'm trying to say: where do you take time for your side projects or studying new technologies?
parenting  programming  hn 
may 2017
Why Use Postgres? | Hacker News
Comment son the article about what's new in postgresql
may 2017
Enabling Continual Learning in Neural Networks | DeepMind
Computer programs that learn to perform tasks also typically forget them very quickly. We show that the learning rule can be modified so that a program can remember old tasks when learning a new one. This is an important step towards more intelligent programs that are able to learn progressively and adaptively.
ai  machinelearning  ml  neuralnetwork 
may 2017
Show HN: Sorting Two Metric Tons of Lego | Hacker News
A thread about a self-built machine that sort bulk lego parts into bins using machine learning.
machine-learning  lego  opencv 
april 2017
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