Machine Learning

174 Articles

Dual-Arm Mobile Bot Built On IKEA Cart Costs Hundreds, Not Thousands

November 3, 2025 by 4 Comments

There are many incredible open-source robotic arm projects out there, but there’s a dearth of affordable, stable, and mobile robotic platforms with arms. That’s where XLeRobot comes in. It builds on the fantastic LeRobot framework to make a unit that can be trained for autonomous tasks via machine learning, as well as operated remotely.

XLeRobot, designed by [Vector Wang], has a pretty clever design that makes optimal use of easy to obtain parts. In addition to the mostly 3D-printed hardware, it uses an IKEA cart with stacked bin-like shelves as its main frame.

The top bin holds dual arms and a central stalk with a “head”. There’s still room left in that top bin, a handy feature that gives the robot a place to stow or carry objects.

The bottom of the cart gets the three-wheeled motion unit. Three omnidirectional wheels provide a stable base while also allowing the robot to propel itself in any direction and turn on a dime. The motion unit bolts to the bottom, but because the IKEA cart’s shelf bottoms are a metal mesh, no drilling is required.

It’s all very tidy, and results in a mobile robotics platform that is cheap enough for most hobbyists to afford, while being big enough to navigate indoor environments and do useful tasks.

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Learn What A Gaussian Splat Is, Then Make One

October 31, 2025 by 16 Comments

Gaussian Splats is a term you have likely come across, probably in relation to 3D scenery. But what are they, exactly? This blog post explains precisely that in no time at all, complete with great interactive examples and highlights of their strengths and relative weaknesses.

Gaussian splats excel at making colorful, organic subject matter look great.

Gaussian splats are a lot like point clouds, except the points are each differently-shaped “splats” of color, arranged in such a way that the resulting 3D scene looks fantastic — photorealistic, even — from any angle.

All of the real work is in the initial setup of the splats into the scene. Once that work is done, viewing is the easy part. Not only are the resulting file sizes of the scenes small, but rendering is computationally simple.

There are a few pros and cons to gaussian splats compared to 3D meshes, but in general they look stunning for any kind of colorful, organic scene. So how does one go about making or using them?

That’s where the second half of the post comes in handy. It turns out that making your own gaussian splats is simply a matter of combining high-quality photos with the right software. In that sense, it has a lot in common with photogrammetry.

Even early on, gaussian splats were notable for their high realism. And since this space has more than its share of lateral-thinkers, the novel concept of splats being neither pixels nor voxels has led some enterprising folks to try to apply the concept to 3D printing.

Where Is Mathematics Going? Large Language Models And Lean Proof Assistant

October 8, 2025 by 31 Comments

If you’re a hacker you may well have a passing interest in math, and if you have an interest in math you might like to hear about the direction of mathematical research. In a talk on this topic [Kevin Buzzard], professor of pure mathematics at Imperial College London, asks the question: Where is Mathematics Going?

It starts by explaining that in 2017 he had a mid-life crisis, of sorts, becoming disillusioned with the way mathematics research was being done, and he started looking to computer science for solutions.

He credits Euclid, as many do, with writing down some axioms and starting mathematics, over 2,000 years ago. From axioms came deductions, and deductions became mathematical facts, and math proceeded in this fashion. This continues to be the way mathematical research is done in mathematical departments around the world. The consequence of this is that mathematics is now incomprehensibly large. Similarly the mathematical proofs themselves are exceedingly large, he gives an example of one proof that is 10,000 pages long and still hasn’t been completely written down after having been announced more than 20 years ago.

The conclusion from this is that mathematics has become so complex that traditional methods of documenting it struggle to cope. He says that a tertiary education in mathematics aims to “get students to the 1940s”, whereas a tertiary education in computer science will expose students to the state of the art.

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Macintosh System 7 Ported To X86 With LLM Help

September 29, 2025 by 50 Comments

You can use large language models for all sorts of things these days, from writing terrible college papers to bungling legal cases. Or, you can employ them to more interesting ends, such as porting Macintosh System 7 to the x86 architecture, like [Kelsi Davis] did.

When Apple created the Macintosh lineup in the 1980s, it based the computer around Motorola’s 68K CPU architecture. These 16-bit/32-bit CPUs were plenty capable for the time, but the platform ultimately didn’t have the same expansive future as Intel’s illustrious x86 architecture that underpinned rival IBM-compatible machines.

[Kelsi Davis] decided to port the Macintosh System 7 OS to run on native x86 hardware, which would be challenging enough with full access to the source code. However, she instead performed this task by analyzing and reverse engineering the System 7 binaries with the aid of Ghidra and a large language model. Soon enough, she had the classic System 7 desktop running on QEMU with a fully-functional Finder and the GUI working as expected. [Kelsi] credits the LLM with helping her achieve this feat in just three days, versus what she would expect to be a multi-year effort if working unassisted.

Files are on GitHub for the curious. We love a good port around these parts; we particularly enjoyed these efforts to recreate Portal on the N64. If you’re doing your own advanced tinkering with Macintosh software from yesteryear, don’t hesitate to let us know.

LeRobot Brings Autonomy To Hobby Robots

August 23, 2025 by 6 Comments

Robotic arms have a lot in common with CNC machines in that they are usually driven by a fixed script of specific positions to move to, and actions to perform. Autonomous behavior isn’t the norm, especially not for hobby-level robotics. That’s changing rapidly with LeRobot, an open-source machine learning framework from the Hugging Face community.

The SO-101 arm is an economical way to get started.

If a quick browse of the project page still leaves you with questions, you’re not alone. Thankfully, [Ilia] has a fantastic video that explains and demonstrates the fundamentals wonderfully. In it, he shows how LeRobot allows one to train an economical 3D-printed robotic arm by example, teaching it to perform a task autonomously. In this case, the task is picking up a ball and putting it into a cup.

[Ilia] first builds a dataset by manually operating the arm to pick up a ball and place it in a cup. Then, with a dataset consisting of only about fifty such examples, he creates a machine learning model capable of driving the arm to autonomously pick up a ball and place it in a cup, regardless of where the ball and cup actually are. It even gracefully handles things like color changes and [Ilia] moving the cup and ball around mid-task. You can skip directly to 34:16 to see this autonomous behavior in action, but we do recommend watching the whole video for a highly accessible yet deeply technical overview.

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A photo of the circuitry along with an oscilloscope

Eight Artificial Neurons Control Fully Autonomous Toy Truck

July 24, 2025 by 11 Comments

Recently the [Global Science Network] released a video of using an artificial brain to control an RC truck.

The video shows a neural network comprised of eight artificial neurons assembled on breadboards used to control a fully autonomous toy truck. The truck is equipped with four proximity sensors, one front, one front left, one front right, and one rear. The sensor readings from the truck are transmitted to the artificial brain which determines which way to turn and whether to go forward or backward. The inputs to each neuron, the “synapses”, can be excitatory to increase the firing rate or inhibitory to decrease the firing rate. The output commands are then returned wirelessly to the truck via a hacked remote control.

This particular type of neural network is called a Spiking Neural Network (SNN) which uses discrete events, called “spikes”, instead of continuous real-valued activations. In these types of networks when a neuron fires matters as well as the strength of the signal. There are other videos on this channel which go into more depth on these topics.

The name of this experimental vehicle is the GSN SNN 4-8-24-2 Autonomous Vehicle, which is short for: Global Science Network Spiking Neural Network 4 Inputs 8 Neurons 24 Synapses 2 Degrees of Freedom Output. The circuitry on both the vehicle and the breadboards is littered with LEDs which give some insight into how it all functions.

If you’re interested in how neural networks can control behavior you might like to see a digital squid’s behavior shaped by a neural network.

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An image of a light grey graphing calculator with a dark grey screen and key surround. The text on the monochrome LCD screen shows "Input: ENEB Result 1: BEEN Confidence 1: 14% [##] Result 2: Good Confidence 2: 12% [#] Press ENTER key..."

A Neural Net For A Graphing Calculator?

July 17, 2025 by 3 Comments

Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the spectrum is [ExploratoryStudios]’s Hermes Optimus Neural Net for a TI-84 Plus Silver Edition.

This neural net is setup as an autocorrect system that can take four character inputs and match them to a library of twelve words. That’s not a lot, but we’re talking about a device with 24 kB of RAM, so the little machine is doing its best. Perhaps more interesting than any practical output is the puzzle solving involved in getting this to work within the memory constraints.

The neural net “employs a feedforward neural network with a precisely calibrated 4-60-12 architecture and sigmoid activation functions.” This leads to an approximate 85% accuracy being able to identify and correct the given target words. We appreciate the readout of the net’s confidence as well which is something that seems to have gone out the window with many newer “AI” systems.

We’ve seen another TI-84 neural net for handwriting recognition, but is the current crop of AI still headed in the wrong direction?

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