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Micro:bit V1 vs. V2 in 2026: Is the Older Board Still Worth It for Classroom Data Logging?

The original Micro:bit V1 (left) remains a powerful tool for teaching data logging and IoT fundamentals, even alongside the feature-rich V2 (right) produced using ChatGPT Introduction   Before you drop those original Micro:bit V1 boards into the recycling bin, ask yourself this: what if the board without all the bells and whistles is actually the better teaching tool? As we move further into 2026, many educators find their storage bins filled with V1 boards — the ones without the notched gold edge connectors. With the V2 boasting a built-in microphone, speaker, touch-sensitive logo, and a faster processor, it's tempting to assume the V1 is obsolete. But is it really? The answer, perhaps surprisingly, depends entirely on what you are trying to teach — and the V1 makes a far stronger case for itself than most people expect. The Technical Trade-off   To be fair to both boards, the V2 is the clear winner for AI, audio, and machine learning projects — and if your budget allows, ...
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Coral Accelerator on a Raspberry Pi

This is the first of a planned occasional series of posts on playing with some of the current AI specific add-on processors for Intenet of Things (IoT). In the series, it is planned that some experiments with the Google Coral adapter and the Development Board; as well the NVIDIA Jetson Nano will be shown. Why bother? Basic reason is I love playing with AI and hardware - so it is kind of fun. Another reason is AI, IoT and e dge computing, are important and growing technologies, and I want to start getting my head around them a bit. In this post, I look at starting to use Coral Accelerator with a Raspberry Pi. The Coral environment is related to Google's earlier AIY Edge Tensor Processing Unit (TPU) range  https://aiyprojects.withgoogle.com/edge-tpu/  and designed to work with TensorFlow Lite . Good place to start is Google's Get started with the USB Accelerator  pretty much all you need to do to get going is in it, it also mentions Raspberry Pi. It makes a good ...
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Produce a Microbit python neural network 2: Building a Physical Microbit Neural Network

This is second in a two-post series on building a neural network using microbits with micropython. In the first post python was used to produce a neural network without the microbits. In this post the network is as shown in figure 1 is developed. The figure below shows the arrangement of the connections to be built; pin 2 is the output of each neuron. The two micro:bits/neurons on the left of the picture taking in the two same inputs ; the output from these neurons are the two inputs to the output neuron on the right. figure 1 The micro:bit objects used in Figure 1 were produced using the micro:bit Fritzing diagram available at  https://github.com/microbit-foundation/dev-docs/issues/36  thanks to David Whale ( @whalleygeek  ) for this. The Inputs neurons Neuron 1: from microbit import * W=[-1,-1,1] while True:     x1=pin0.read_digital()     x2=pin1.read_digital()     net = W[0]+W[1]*x1+W[2]*x2 ...
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Produce a Microbit python neural network 1: Without the microbit initially

These two posts are really extensions of a set of post in response to a question from Carl Simmons ( @Activ8Thinking ) concerning building a micro:bit simple neuron. In those, the Microsoft MakeCode was used. In this two-post series is going to go through building neurones and neural networks in Python and by the end of the second post a python-based microbit neural network Post 1 (this one) A  single neuron and a simple neuron network will be produced in Python are produced. Post 2 looks at producing a network of neurons, ie. neural network using the idea from post 1 but using a three microbits.; looking to solve the problem that a single neuron can't solve, making an Exclusive OR gate (XOR) 1. Overview and non-microbit neuron 1.1 Overview:  The characteristics of the system will be: Inputs are going to be binary Weighted sum is bias+W1*input1+w2*input2 If weighted sum>=0 then the output is True (T on the LEDs) or '1' If weigh...
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Robots and Physical Computing most popular posts during July 2019

Popular Posts Cool Adafruit PyGamer #1 I seem to play with Games software more and more (e.g. the recent post  My first Pygame zero attempt ) and that is not a problem at all. Thi... How to produce a Microbit neural network This is really part two of a set of post  in response to a question from Carl Simmons ( @Activ8Thinking ) concerning building a   micro:bit ... Simple PyGame Zero Apollo Lander #Apollo50th With all the excitement of the 50th Anniversary of the first Moon Landing and the world record attempt Moonhack  https://moonhack.com/ , I w... Micro:bit, Servo control with Micropython or blocks You can control servos (small ones) from a Micro:Bit directly. Following a link from the David Whale (Twitter  @ whaleygeek ) , thank you, t... Speech with EduBlocks on BBC microbit The microbit is a great piece of kit, not least of which because of the range of programming languages and tools t...
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Simple PyGame Zero Apollo Lander #Apollo50th

With all the excitement of the 50th Anniversary of the first Moon Landing and the world record attempt Moonhack  https://moonhack.com/ , I wanted to experiment with PyGame Zero a little more. So I created my own, very simple, Moon Lander game using Python and PyGame Zero. The lander has to pass through a red rectangle on the surface as it accelerates to the surface. Left and Right keys move it sideways and the up key gives it a boost. When the lander passes through the red rectangle it makes a noise; if it misses (or after it passes through the rectangle) it resets to a new position and starts again. Two images were needed The lander image came from  https://www.kissclipart.com/apollo-program-apollo-lunar-module-lander-moon-landing-wgwzny/  shared by  Xfanmy.  This n eeded to shrunk by about x10 to fit. Surface came from Vector Designed By from Pngtree.com An image from the game, the code and a link to the repository are shown below, please f...
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Cool Adafruit PyGamer #1

I seem to play with Games software more and more (e.g. the recent post  My first Pygame zero attempt ) and that is not a problem at all. This post is another one. The most recent AdaBox from Adafruit  (see above) was based around their PyGamer Starter Kit and it is great fun. This is a microcontroller based, open-source Retro gaming emulator; or is it a cool handheld gaming device that you can write your own games for. It can be programmed in CircuitPython, Arduino, but I been trying it out in the Beta version of  MakeCode Arcade  and if you have used MakeCode for the microbit this very much the same (see below). A nice feature about this is you can share - this code example (shown above) was developed by my son (and is shared with his permission) after about an hour of playing - the emulator below shows the game in action. My future plans with it so far: - Investigated the features available in MakeCode Arcade for this console; - Play ...
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My first Pygame zero attempt

I attended the brilliant #ExaBytes19 on 28th June 2019 , among many very good sessions, I went to Jon Witts ( @jonwitts ) session on PyGame Zero [ 1 ]. Pygame zero is meant for Educational use and teaching programming through game development. Impressed with Pygame Zero relative simplicity, I have had a go myself. The Game! A Bee buzzes around the screen and a player moves to avoid it; if the bee and player meet the player's image changes and a sneezing sound is produced (I just fancied have a short cute noise). Both the bee and the player characters wrap around the screen. A very helpful source of advice on getting started on using Pygame can be found at [ 2 ]; my example takes the game produced there as a starting point. If you want to explain of what Pygame does previously mentioned page[1] and the page describing the built-in  functions [3 ], I found a great help. All the images were PNGs, the player characters were produced using  https://www.bitmoji.com...
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