RAREblog

A friend of mine recently asked me about Python programming on the micro:bit. I told him that I wrote a free workbook about that a while back. (If you'd like to get a copy you can sign up here ).   It's based around the excellent mu editor . Code with Mu Mu is a no-frills editor that you can use to develop and run programs on the micro:bit , Adafruit CircuitPython boards , the Raspberry Pi and other Python environments Mu has just enough features to be usable without being daunting. I love it. But some young students find text-based programming a bit scary. How can they get started? Code with Edublocks Some younger programmers are happier with block-based (visual) programming. For them, Edublocks is a great solution. Edublocks is block based, runs in your browser, and looks like the popular Scratch environment, but it's Python-oriented . Here's the Edublocks version of the code for the first experiment for Kitronik's excellent micro:bit Inventor's Kit .

A Shrimp assembled A while ago I ran a series of workshops showing how to build your own Arduino clone, based on a low-cost e-book called Making the Shrimp.   The book needs an update and I'm looking for reviewers. They will get a free copy of the book and a free kit to build. If you're interested, sign up here .

Kit contents with extras I just finished my book on the Pimoroni Explorer HAT, and I'm celebrating! I have treated myself to a Tiny 4WD Rover kit. It looks a lot of fun, and should be simple to assemble. The product page has a link to a great blog post from Emma Norling which has detailed build instructions. I'll be following that closely. I have already encountered one  minor gotcha . You need a few extras over and above the parts in the kit. Some of them are mentioned on the product page (battery, Pi zero W), but Emma found she needed spacers to mount the Explorer pHAT on the Pi zero W. I have suitable spacers in stock so I think I'll be OK. I also got a LIPO shim and a USB charger. Once I've completed the build I'll document it and provide a link on this blog. A nice bonus if you hurry!  Pimoroni are currently offering a free Pi Zero WH for customers spending more than £100 (excluding postage). The offer ends soon, but I managed to get my order in on time whic

If you're thinking of experimenting with the Pimoroni Explorer HAT,  Explorer HAT tricks is a great place to start. It covers over a dozen simple projects, complete with Python code, and you won't need to do any soldering. If you're an experienced Pi user, you probably feel you don't need it, and that's fine. But if you're just starting out with Physical Computing, or new to the Explorer HAT, it's well worth a look, and there's a free sample available . The book is now over 80% complete. You get the book from Leanpub, which means that you get updates free whenever the book changes. The book was written for use with the Pimoroni Explorer HAT Pro but Pimoroni are currently offering a great deal on their Electronics pHAT kit . The kit costs just £19.92 including a Raspberry Pi zero. ! I've ordered one to experiment with. I know a couple of the projects in the book will need to change, but almost all of them will work unmodified. I'll write up what

I'm working on two projects at present. I want to finish my book on the Pimoroni Explorer HAT, and I'm getting close. All of the code examples are available on GitHub , many with documentation, and the book itself is now 70% finished. It's still only $5, but the price goes up this weekend. I've also started another project - one that I expect to take months or years. It's based on some fascinating work by Jürgen Schmidhuber and his team on a technique they call UDRL - Upside-Down Reinforcement Learning. It looks as if they have cracked a problem that has been bugging me for decades. How can we get an ANN-based Parent Agent to train a Child Agent without hard-wiring in an unrealistic amount of innate behaviour? If you're interested in their solution (and have a basic background in reinforcement learning) there are two papers on arxiv that explain it: Training Agents using Upside-Down Reinforcement Learning and  Reinforcement Learning Upside Down: Don't Pred