Turning GLaDOS Into Ted: A Tale Of A Talking Toy

January 14, 2025 by Heidi Ulrich 7 Comments

What if your old, neglected toys could come to life — with a bit of sass? That’s exactly what [Binh] achieved when he transformed his sister’s worn-out teddy bear into ‘Ted’, an interactive talking plush with a personality of its own. This project, which combines the GLaDOS Personality Core project from the Portal series with clever microcontroller tinkering, brings a whole new personality to a childhood favorite.

[Binh] started with the basics: a teddy bear already equipped with buttons and speakers, which he overhauled with an ESP32 microcontroller. The bear’s personality originated from GLaDOS, but was rewritten by [Binh] to fit a cheeky, teddy-bear tone. With a few tweaks in the Python-based fork, [Binh] created threads to handle touch-based interaction. For example, the ESP32 detects where the bear is touched and sends this input to a modified neural network, which then generates a response. The bear can, for instance, call you out for holding his paw for too long or sarcastically plead for mercy. I hear you say ‘but that bear Ted could do a lot more!’ Well — maybe, all this is just what an innocent bear with a personality should be capable of.

Instead, let us imagine future iterations featuring capacitive touch sensors or accelerometers to detect movement. The project is simple, but showcases the potential for intelligent plush toys. It might raise some questions, too.

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Modern AI On Vintage Hardware: LLama 2 Runs On Windows 98

January 13, 2025 by Donald Papp 27 Comments

[EXO Labs] demonstrated something pretty striking: a modified version of Llama 2 (a large language model) that runs on Windows 98. Why? Because when it comes to personal computing, if something can run on Windows 98, it can run on anything. More to the point: if something can run on Windows 98 then it’s something no tech company can control how you use, no matter how large or influential they may be. More on that in a minute.

Ever wanted to run a local LLM on 25 year old hardware? No? Well now you can, and at a respectable speed, too!

What’s it like to run an LLM on Windows 98? Aside from the struggles of things like finding compatible peripherals (back to PS/2 hardware!) and transferring the required files (FTP over Ethernet to the rescue) or even compilation (some porting required), it works maybe better than one might expect.

A Windows 98 machine with Pentium II processor and 128 MB of RAM generates a speedy 39.31 tokens per second with a 260K parameter Llama 2 model. A much larger 15M model generates 1.03 tokens per second. Slow, but it works. Going even larger will also work, just ever slower. There’s a video on X that shows it all in action.

It’s true that modern LLMs have billions of parameters so these models are tiny in comparison. But that doesn’t mean they can’t be useful. Models can be shockingly small and still be perfectly coherent and deliver surprisingly strong performance if their training and “job” is narrow enough, and the tools to do that for oneself are all on GitHub.

This is a good time to mention that this particular project (and its ongoing efforts) are part of a set of twelve projects by EXO Labs focusing on ensuring things like AI models can be run anywhere, by anyone, independent of tech giants aiming to hold all the strings.

And hey, if local AI and the command line is something that’s up your alley, did you know they already exist as single-file, multi-platform, command-line executables?

Running AI Locally Without Spending All Day On Setup

January 8, 2025 by Al Williams 34 Comments

There are many AI models out there that you can play with from companies like OpenAI, Google, and a host of others. But when you use them, you get the experience they want, and you run it on their computer. There are a variety of reasons you might not like this. You may not want your data or ideas sent through someone else’s computer. Maybe you want to tune and tweak in ways they aren’t going to let you.

There are many more or less open models, but setting up to run them can be quite a chore and — unless you are very patient — require a substantial-sized video card to use as a vector processor. There’s very little help for the last problem. You can farm out processing, but then you might as well use a hosted chatbot. But there are some very easy ways to load and run many AI models on Windows, Linux, or a Mac. One of the easiest we’ve found is Msty. The program is free for personal use and claims to be private, although if you are really paranoid, you’ll want to verify that yourself.

What is Msty?

Msty is a desktop application that lets you do several things. First, it can let you chat with an AI engine either locally or remotely. It knows about many popular options and can take your keys for paid services. For local options, it can download, install, and run the engines of your choice.

For services or engines that it doesn’t know about, you can do your own setup, which ranges from easy to moderately difficult, depending on what you are trying to do.

Of course, if you have a local model or even most remote ones, you can use Python or some basic interface (e.g., with ollama; there are plenty of examples). However, Msty lets you have a much richer experience. You can attach files, for example. You can export the results and look back at previous chats. If you don’t want them remembered, you can chat in “vapor” mode or delete them later.

Each chat lives in a folder, which can have helpful prompts to kick off the chat. So, a folder might say, “You are an 8th grade math teacher…” or whatever other instructions you want to load before engaging in chat.

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close up of a TI-84 Plus CE running custom software

Going Digital: Teaching A TI-84 Handwriting Recognition

December 24, 2024 by Heidi Ulrich 4 Comments

You wouldn’t typically associate graphing calculators with artificial intelligence, but hacker [KermMartian] recently made it happen. The innovative project involved running a neural network directly on a TI-84 Plus CE to recognize handwritten digits. By using the MNIST dataset, a well-known collection of handwritten numbers, the calculator could identify digits in just 18 seconds. If you want to learn how, check out his full video on it here.

The project began with a proof of concept: running a convolutional neural network (CNN) on the calculator’s limited hardware, a TI-84 Plus CE with only 256 KB of memory and a 48 MHz processor. Despite these constraints, the neural network could train and make predictions. The key to success: optimizing the code, leveraging the calculator’s C programming tools, and offloading the heavy lifting to a computer for training. Once trained, the network could be transferred to the calculator for real-time inference. Not only did it run the digits from MNIST, but it also accepted input from a USB mouse, letting [KermMartian] draw digits directly on the screen.

While the calculator’s limited resources mean it can’t train the network in real-time, this project is a proof that, with enough ingenuity, even a small device can be used for something as complex as AI. It’s not just about power; it’s about resourcefulness. If you’re into unconventional projects, this is one for the books.

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See What ‘They’ See In Your Photos

December 17, 2024 by Lewin Day 46 Comments

Once upon a time, a computer could tell you virtually nothing about an image beyond its file format, size, and color palette. These days, powerful image recognition systems are a part of our everyday lives. They See Your Photos is a simple website that shows you just how much these systems can interpret from a regular photo.

The website simply takes your image submission, runs it through the Google Vision API, and spits back out a description of the image. I tried it out with a photograph of myself, and was pretty impressed with what the vision model saw:

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Render of life-size robot rat animatronic on blue plane

Robot Rodents: How AI Learned To Squeak And Play

December 9, 2024 by Heidi Ulrich 10 Comments

In an astonishing blend of robotics and nature, SMEO—a robot rat designed by researchers in China and Germany — is fooling real rats into treating it like one of their own.

What sets SMEO apart is its rat-like adaptability. Equipped with a flexible spine, realistic forelimbs, and AI-driven behavior patterns, it doesn’t just mimic a rat — it learns and evolves through interaction. Researchers used video data to train SMEO to “think” like a rat, convincing its living counterparts to play, cower, or even engage in social nuzzling. This degree of mimicry could make SMEO a valuable tool for studying animal behavior ethically, minimizing stress on live animals by replacing some real-world interactions.

For builders and robotics enthusiasts, SMEO is a reminder that robotics can push boundaries while fostering a more compassionate future. Many have reservations about keeping intelligent creatures in confined cages or using them in experiments, so imagine applying this tech to non-invasive studies or even wildlife conservation. In a world where robotic dogs, bees, and even schools of fish have come to life, this animatronic rat sounds like an addition worth further exploring. SMEO’s development could, ironically, pave the way for reducing reliance on animal testing.

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The Junk Machine Prints Corrupted Advertising On Demand

November 26, 2024 by Donald Papp 13 Comments

[ClownVamp]’s art project The Junk Machine is an interactive and eye-catching machine that, on demand, prints out an equally eye-catching and unique yet completely meaningless (one may even say corrupted) AI-generated advertisement for nothing in particular.

The machine is an artistic statement on how powerful software tools that have genuine promise and usefulness to creative types are finding their way into marketer’s hands, and resulting in a deluge of, well, junk. This machine simplifies and magnifies that in a physical way.

We can’t help but think that The Junk Machine is in a way highlighting Sturgeon’s Law (paraphrased as ‘ninety percent of everything is crud’) which happens to be particularly applicable to the current AI landscape. In short, the ease of use of these tools means that crud is also being effortlessly generated at an unprecedented scale, swamping any positive elements.

As for the hardware and software, we’re very interested in what’s inside. Unfortunately there’s no deep technical details, but the broad strokes are that The Junk Machine uses an embedded NVIDIA Jetson loaded up with Stable Diffusion’s SDXL Turbo, an open source AI image generator that can be installed and run locally. When and if a user mashes a large red button, the machine generates a piece of AI junk mail in real time without any need for a network connection of any kind, and prints it from an embedded printer.

Watch it in action in the video embedded below, just under the page break. There are a few more different photos on [ClownVamp]’s X account.

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