If you’ve been following the latest advancements in computing for a while, you already know that there’s a big problem with laptops: When they’re no longer useful as a daily driver, it can be a struggle to find a good use for all its parts. Everything is proprietary, and serious amounts of reverse engineering are required if you decide to forge ahead. This is where Framework, a laptop company building modular laptops comes in. They’ve made it clear that when you upgrade your Framework laptop with a new mainboard they want you to be able to continue to use the old mainboard outside of the laptop.
To that end, Framework have provided 2D mechanical drawings of their mainboard and 3D printable cases that can of course be modified as needed. “But what about peripherals?” you might ask. Framework has provided pinouts for all of the connectors on the board along with information on which connectors to use to interface with them. No reverse engineering needed!
While it’s possible to buy a mainboard now and use it, their stated goal is to help people make use of used mainboards leftover from upgrades down the line. With just a stick of memory and a USB-C power adapter, the board will spring to life and even has i2c and USB immediately available.
What would you do with a powerful Intel i5-1135G7 mainboard? Framework wants to know, and to that end, they are actually giving away 100 mainboards to makers and developers. Mind you this is a program created and ran by Framework — and is not associated in any way Hackaday or our overlords at Supplyframe.
AI and Deep Learning for computer vision projects has come to the masses. This can be attributed partly to the community projects that help ease the pain for newbies. [Abhishek] contributes one such project called Monk AI which comes with a GUI for transfer learning.
Monk AI is essentially a wrapper for Computer Vision and deep learning experiments. It facilitates users to finetune deep neural networks using transfer learning and is written in Python. Out of the box, it supports Keras and Pytorch and it comes with a few lines of code; you can get started with your very first AI experiment.
[Abhishek] also has an Object Detection wrapper(GitHub) that has some useful examples as well as a Monk GUI(GitHub) tool that looks similar to the tools available in commercial packages for running, training and inference experiments.
The documentation is a work in progress though it seems like an excellent concept to build on. We need more tools like these to help more people getting started with Deep Learning. Hardware such as the Nvidia Jetson Nano and Google Coral are affordable and facilitate the learning and experimentation.
At the Lifelong Learning Robotics Laboratory at the Erasmo Da Rotterdam in Italy, robots are (not surprisingly) used to teach all of the fundamentals of robotics. [Alessandro Rossetti] and the students at the lab have been at it for years now, and have finally finished their fifth generation of a robot called Nessie. The big idea is to help teach fundamentals of programming and electronics by building something that actually uses these principles.
The robot is largely 3D printed and uses an FPGA to interact with the physical world through a set of motors and sensors. The robot also uses a Raspberry Pi to hold the robot’s framework. The robot manages the sensors in hardware with readers attached to the CPU AXI bus. The CPU reads their values from memory space, though, so the robot is reported to be quite quick.
The lab is hoping to take their robot to a robotics competition in Bari, Italy. We hope that they perform well there, since we are big fans of any robot that’s designed to teach anyone about robotics and programming. After all, there are robots that help teach STEM in Africa, robots that teach teen girls about robots, and robots that teach everyone.
While working towards open-sourcing Android, the team continued to work on new features in their own private development branch. These have now been published publicly in the “cupcake” branch. There’s a lot of interesting new features and bug fixes included. We’ve got a rundown of many of the significant additions after the break.
[floe] wrote in to tell us about his multitouch based thesis work. While many projects have focused on the hardware side of multitouch, TISCH is designed to promote the software side. TISCH is a multiplatform library that features hardware abstraction and gesture recognition. This takes a lot of weight off of widget developers since they can specify known library gestures instead of writing the exact motions from scratch. Using TISCH also means a standard set of gestures across multiple widgets, so the learning curve will be much easier when a user tries out a new app. If you’re researching multitouch, check out this project and help improve the codebase.
[Cal Henderson] delivered a keynote titled Why I Hate Django at the first annual DjangoCon. Django is an open source BSD licensed web framework written in Python. Google has posted the keynote in its entirety to YouTube, which you can find embedded above. While the talk is humorous (and takes many jabs at Rails developers) it does provide insight into what makes a good web framework. [Cal] is Director of Engineering at Flickr and is an authority on how to make websites scale. He points out that most frameworks are designed to get projects off the ground quickly, but are lacking when it comes to building an even larger service. He talks about several things in Django that need work and improvements that could be made. It’s really an interesting look at what it takes to go big. Continue reading “Why I Hate Django”→