Although not the first video game ever produced, Pong was the first to achieve commercial success and has had a tremendous influence on our culture as a whole. In Pong’s time, its popularity ushered in the arcade era that would last for more than two decades. Today, it retains a similar popularity partially for approachability: gameplay is relatively simple, has hardwired logic, and provides insights about the state of computer science at the time. For these reasons, [Nick Bild] has decided to recreate this arcade classic, but not in a traditional way. He’s trained a neural network to become the game instead.
Organic Llamas have a rather restricted range, in nature: the Andes Mountains, and that’s it. Humans weren’t content to let the fluffy, friend-shaped creatures stay in their natural habitat, however, and they can now be found on every continent except Antarctica. The Llama2 Large Language Model is like that: while it may have started on a GPU somewhere, thanks to enterprising hackers like [Caio Madeira], who has ported Llama2 to the PlayStation Portable (PSP), the fluffiest LLM can be found just about anywhere.
The AI, in all its glory, dooming yet another system.
Ultimately this project has its roots in Llama2.c by [karpathy], a project we’ve seen used on Pentium II under Windows 98, DOS machines running 486 processors, and even the venerable Commodore 64, of all impossible things. Now, it’s the PSP’s turn. This implementation uses the same 260K tinystories model as the C64 port, upon which it is based. Of course the PSP’s RAM has room for a much larger model, but [Ciao] apparently prefers to run the tiny model faster on this less-ancient gaming hardware.
Its getting to the point that it’s harder to find systems that won’t run LLMs than those that do. Given that Llama2 seems to be the new DOOM, it’s probably only a matter of time before their virtual fur is all over all our old equipment. Fortunately for allergy sufferers, virtual fur cannot trigger a histamine response.
If you know of another system getting LLMs (Alpaca-adjacent or otherwise), send in a tip.
‘Hearing voices’ doesn’t have to be worrisome, for instance when software-defined radio (SDR) happens to be your hobby. It can take quite some of your time and attention to pull voices from the ether and decode them. Therefore, [theckid] came up with a nifty solution: RadioTranscriptor. It’s a homebrew Python script that captures SDR audio and transcribes it using OpenAI’s Whisper model, running on your GPU if available. It’s lean and geeky, and helps you hear ‘the voice in the noise’ without actively listening to it yourself.
This tool goes beyond the basic listening and recording. RadioTranscriptor combines SDR, voice activity detection (VAD), and deep learning. It resamples 48kHz audio to 16kHz in real time. It keeps a rolling buffer, and only transcribes actual voice detected from the air. It continuously writes to a daily log, so you can comb through yesterday’s signal hauntings while new findings are being logged. It offers GPU support with CUDA, with fallback to CPU.
It sure has its quirks, too: ghost logs, duplicate words – but it’s dead useful and hackable to your liking. Want to change the model, tweak the threshold, add speaker detection: the code is here to fork and extend. And why not go the extra mile, and turn it into art?
If the term ‘NLWeb’ first brought to mind an image of a Dutch internet service provider, you’re probably not alone. What it actually is – or tries to become – is Microsoft’s vision of a parallel internet protocol using which website owners and application developers can integrate whatever LLM-based chatbot they desire. Unfortunately for Microsoft, the NLWeb protocol just suffered its first major security flaw.
The flaw is an absolute doozy, involving a basic path traversal vulnerability that allows an attacker to use appropriately formatted URLs to traverse the filesystem of the remote, LLM-hosting, system to extract keys and other sensitive information. Although Microsoft patched it already, no CVE was assigned, while raising the question of just how many more elementary bugs like this may be lurking in the protocol and associated software.
As for why a website or application owner might be interested in NLWeb, the marketing pitch appears to be as an alternative to integrating a local search function. This way any website or app can have their own ChatGPT-style search functionality that is theoretically restricted to just their website, instead of chatbot-loving customers going to the ChatGPT or equivalent site to ask their questions there.
Even aside from the the strong ‘solution in search of a problem’ vibe, it’s worrying that right from the outset it seems to introduce pretty serious security issues that suggest a lack of real testing, never mind a strong ignorance of the fact that a lack of user input sanitization is the primary cause for widely exploited CVEs. Unknown is whether GitHub Copilot was used to write the affected codebase.
OpenAI have just released gpt-oss, an AI large language model (LLM) available for local download and offline use licensed under Apache 2.0, and optimized for efficiency on a variety of platforms without compromising performance. This is their first such “open” release, and it’s with a model whose features and capabilities compare favorably to some of their hosted services.
OpenAI have partnered with ollama for the launch which makes onboarding ridiculously easy. ollama is an open source, MIT-licensed project for installing and running local LLMs, but there’s no real tie-in to that platform. The models are available separately: gpt-oss-20b can run within 16 GB of memory, and the larger and more capable gpt-oss-120b requires 80 GB. OpenAI claims the smaller model is comparable to their own hosted o3-mini “reasoning” model, and the larger model outperforms it. Both support features like tool use (such as web browsing) and more.
LLMs that can be downloaded and used offline are nothing new, but a couple things make this model release a bit different from others. One is that while OpenAI have released open models such as Whisper (a highly capable speech-to-text model), this is actually the first LLM they have released in such a way.
The other notable thing is this release coincides with a bounty challenge for finding novel flaws and vulnerabilities in gpt-oss-20b. Does ruining such a model hold more appeal to you than running it? If so, good news because there’s a total of $500,000 to be disbursed. But there’s no time to waste; submissions need to be in by August 26th, 2025.
Do you use a spell checker? We’ll guess you do. Would you use a button that just said “correct all spelling errors in document?” Hopefully not. Your word processor probably doesn’t even offer that as an option. Why? Because a spellchecker will reject things not in its dictionary (like Hackaday, maybe). It may guess the wrong word as the correct word. Of course, it also may miss things like “too” vs. “two.” So why would you just blindly accept AI code review? You wouldn’t, and that’s [Bill Mill’s] point with his recent tool made to help him do better code reviews.
He points out that he ignores most of the suggestions the tool outputs, but that it has saved him from some errors. Like a spellcheck, sometimes you just hit ignore. But at least you don’t have to check every single word.
Reachy Mini is a kit for a compact, open-source robot designed explicitly for AI experimentation and human interaction. The kit is available from Hugging Face, which is itself a repository and hosting service for machine learning models. Reachy seems to be one of their efforts at branching out from pure software.
Our guess is that some form of Stewart Platform handles the head movement.
Reachy Mini is intended as a development platform, allowing people to make and share models for different behaviors, hence the Hugging Face integration to make that easier. On the inside of the full version is a Raspberry Pi, and we suspect some form of Stewart Platform is responsible for the movement of the head. There’s also a cheaper (299 USD) “lite” version intended for tethered use, and a planned simulator to allow development and testing without access to a physical Reachy at all.
Reachy has a distinctive head and face, so if you’re thinking it looks familiar that’s probably because we first covered Reachy the humanoid robot as a project from Pollen Robotics (Hugging Face acquired Pollen Robotics in April 2025.)
The idea behind the smaller Reachy Mini seems to be to provide a platform to experiment with expressive human communication via cameras and audio, rather than to be the kind of robot that moves around and manipulates objects.
