A Hacker-Friendly Software Package For Your Next AI Project

If you’re interested in using Large Language Models (LLM) in a project, but aren’t plugged directly into the fast-developing world of artificial intelligence (AI), knowing what tool or software to use can be daunting. Luckily, [Max Woolf] created simpleaichat, which is complete with examples and documentation and minimal code complexity.

As [Max] puts it, the main motivations behind the project are to provide useful tools while making it easier for non-engineers to peer through the breathless hyperbole and see just how AI-based apps actually work. This project was directly inspired by [Max]’s own real-world software experiences in this area, particularly his frustrations with popular and much-hyped frameworks in which “Hello World” feels a lot more like Hell World.

simpleaichat is a Python package that provides easy and powerful ways to interface with the OpenAI API, makers of ChatGPT. Now, it is true that OpenAI’s models are not open source and access is not free, but they are easily one of the most capable and cost-effective services of their kind.

Prefer something a little more open, and a lot more private? There’s always the option to run an LLM locally on your own machine, possibly with the help of a tool like text-generation-webui or gpt4all. Running an LLM locally will not have the quality of OpenAI’s offerings, but it can still do the job. It’s also possible to give these local LLMs an interface that mimics OpenAI’s API, so there are loads of possibilities.

Are you getting ideas yet? Share them in the comments, or keep them to yourselves and submit a tip once your project is off the ground!

Accurate Cycle Counting On RP2040 MicroPython

The RP2040 is a gorgeous little chip with a well-defined datasheet and a fantastic price tag. Two SDKs are even offered: one based on C and the other MicroPython. More experienced MCU wranglers will likely reach for the C variant, but Python does bring a certain speed when banging out a quick project or proof of concept. Perhaps that’s why [Jeremy Bentham] ported his RP2040-based vehicle speedometer to MicroPython.

The two things that make that difficult are that MicroPython tries to be pretty generic, which means some hackery is needed to talk to the low-level hardware, and that MicroPython doesn’t have a reputation for accurate cycle counting. In this case, the low-level hardware is the PWM peripheral. He details the underlying mechanism in more detail in the C version. On the RP2040, the PWM module can count pulse edges on an input. However, you must start and stop it accurately to calculate the amount of time captured. From there, it’s just edges divided by time. For this, the DMA system is pulled in. A DMA request can be triggered once the PWM counter rolls over. The other PWM channel acts as a timer, and when the timer expires, the DMA request turns off the counter. This works great for fast signals but is inaccurate for slow signals (below 1kHz). So, a reciprocal or time-interval system is included, where the time between edges is captured instead of counting the number of edges in a period,

What’s interesting here is how the hardware details are wrapped neatly into pico_devices.py. The uctypes module from MicroPython allows access to MMIO devices such as DMA and PWM. The code is available on GitHub. Of course, [Jeremy] is no stranger to hacking around on the RP2040, as he has previously rolled his own WiFi driver for the Pico W.

Transforming A Keyboard To A Mouse In Software

You’ve probably heard the old saying that if it looks like a duck, and it quacks like a duck… So when is a keyboard a mouse? When software makes it quack like a mouse — that is, if mice quacked. [Blackle Mori] took a cheap USB keypad and, using the libevdev Linux system, made it impersonate a mouse.

The code on GitHub isn’t complex, but the details can take some time to get right. The code takes over all input events from the device. [Blackle] dumped out events sent from the keypad, but the stock evtest program would probably have done just as well.

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Linux Kernel From First Principles

Want to learn the internals of the Linux kernel? Version 6.5-rc5 has about 36 million lines of code in it, so good luck! [Seiya] has a different approach. Go back to the beginning and examine the 0.01 version of the kernel. Now you are talking about 10,000 lines and, removing comments and blanks, way less.

Sure, some things have changed, but the core ideas are the same. [Seiya] reports, “Reading V0.01 was really for me. It was like visiting Computer History Museum in Mountainview…”

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The Past, Present, And Future Of CircuitPython

Modern microcontrollers like the RP2040 and ESP32 are truly a marvels of engineering. For literal pocket change you can get a chip that’s got a multi-core processor running at hundreds of megahertz, plenty of RAM, and more often than not, some form of wireless connectivity. Their capabilities have been nothing short of revolutionary for the DIY crowd — on any given day, you can see projects on these pages which simply wouldn’t have been possible back when the 8-bit Arduino was all most folks had access to.

Limor Fried

Thanks to the increased performance of these MCUs, hackers and makers now even have a choice as to which programming language they want to use. While C is still the language of choice for processor-intensive tasks, for many applications, Python is now a viable option on a wide range of hardware.

This provides a far less intimidating experience for newcomers, not just because the language is more forgiving, but because it does away with the traditional compile-flash-pray workflow. Of course, that doesn’t mean the more experienced MCU wranglers aren’t invited to the party; they might just have to broaden their horizons a bit.

To learn more about this interesting paradigm shift, we invited the fine folks at Adafruit to the Hack Chat so the community could get a chance to ask questions about CircuitPython, their in-house Python variant which today runs on more than 400 devices.

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Using Trigonometric Functions In CSS

Often neglected as ‘merely a styling language’, CSS contains a wealth of functions built right into the browser’s rendering engine that can perform everything from animations to typography and even mathematical operations, with more added each year.

Screenshot of the output of the basic rotating dots example using CSS.

In a tutorial [Bramus] takes us through using the trigonometric functions in CSS. These are supported in all major browsers since Chrome 111, Firefox 108 and Safari 15.4. In addition to these trigonometric functions, further mathematical functions are also available, many of whom have been available for years now, such as calc(), min() and max().

Unlike the JavaScript version of the CSS trigonometric functions, the CSS functions accept both angles and radians for the argument. Perhaps the nicest thing about having this functionality in CSS is that it removes the need to add JavaScript for many simple things on a webpage, such as animations, translations and the calculating of offsets and positions. Perhaps most impressive is the provided example by [Ana Tudor] who created an animated Möbius strip using cos() and sin() and a handful of other CSS functions.

None of this is likely surprising to anyone who is somewhat familiar with the depths of CSS, especially after it has been more-or-less proven to be a Turing-complete programming language. Using this power for visual elements does however make a lot of sense considering that CSS was always intended to help with styling and formatting the raw HTML.

Do you use these advanced CSS features already, or is it something you might consider using in the future, possibly over JavaScript versions? Feel free to share your thoughts and experiences in the comments.

(Heading: Code to move items on a circular path around a central point in CSS.)

TinyBasicLike: A Target-Independent BASIC Interpreter

In the long and winding history of BASIC, it’s sometimes hard to keep track of all the different variants and dialects. Some may still remember TinyBASIC, which was published in 1976 as Palo Alto Tiny BASIC by [Gordon Brandly]. Later, TinyBASIC was modified by a number of people including [Scott Lawrence] who created TinyBASIC Plus (TBP). Inspired by this, [Karl] figured he could improve on TBP by making the original C-based project even easier to port by removing whatever platform dependencies he could find, creating what he calls TinyBasicLike.

The main change is that TinyBasicLike consists out of two C files, with one containing the core code, and the second the platform-specific details that can be used by the core. Although [Karl] started off with the Palo Alto Tiny BASIC-like code by [Scott Lawrence], he decided to make it into his own by making a few alterations, such as adding left and right shift operators, adding an ADDR() function, expanding the features of INPUT and adding multiple logical operators.

In the example STM32F4 project linked on the project page it is demonstrated how to target a new platform with TinyBasicLike. Performance on the STM32F4 Discovery board with a simple counting loop yielded about 6 lines of TBL program code per millisecond. For a 168 MHz STM32 MCU that’s definitely not astounding, but considering how straightforward Tiny BASIC (and TBL as a consequence) is, it’s definitely no slouch.

This is probably a good time to remind that BASIC was the original champion of cross-platform programming and the source of countless fond (and frustrating) memories.