LLM Dialogue In Animal Crossing Actually Works Very Well

In the original Animal Crossing from 2001, players are able to interact with a huge cast of quirky characters, all with different interests and personalities. But after you’ve played the game for awhile, the scripted interactions can become a bit monotonous. Seeing an opportunity to improve the experience, [josh] decided to put a Large Language Model (LLM) in charge of these interactions. Now when the player chats with other characters in the game, the dialogue is a lot more engaging, relevant, and sometimes just plain funny.

How does one go about hooking a modern LLM into a 24-year-old game built for an entirely offline console? [josh]’s clever approach required a lot of poking about, and did a good job of leveraging some of the game’s built-in features for a seamless result.

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Macintosh System 7 Ported To X86 With LLM Help

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.

Fully-Local AI Agent Runs On Raspberry Pi, With A Little Patience

[Simone]’s AI assistant, dubbed Max Headbox, is a wakeword-triggered local AI agent capable of following instructions and doing simple tasks. It’s an experiment in many ways, but also a great demonstration not only of what is possible with the kinds of open tools and hardware available to a modern hobbyist, but also a reminder of just how far some of these software tools have come in only a few short years.

Max Headbox is not just a local large language model (LLM) running on Pi hardware; the model is able to make tool calls in a loop, chaining them together to complete tasks. This means the system can break down a spoken instruction (for example, “find the weather report for today and email it to me”) into a series of steps to complete, utilizing software tools as needed throughout the process until the task is finished.

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OpenAI Releases Gpt-oss AI Model, Offers Bounty For Vulnerabilities

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.

AI Code Review The Right Way

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.

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Hackaday Links: June 22, 2025

Hold onto your hats, everyone — there’s stunning news afoot. It’s hard to believe, but it looks like over-reliance on chatbots to do your homework can turn your brain into pudding. At least that seems to be the conclusion of a preprint paper out of the MIT Media Lab, which looked at 54 adults between the ages of 18 and 39, who were tasked with writing a series of essays. They divided participants into three groups — one that used ChatGPT to help write the essays, one that was limited to using only Google search, and one that had to do everything the old-fashioned way. They recorded the brain activity of writers using EEG, in order to get an idea of brain engagement with the task. The brain-only group had the greatest engagement, which stayed consistently high throughout the series, while the ChatGPT group had the least. More alarmingly, the engagement for the chatbot group went down even further with each essay written. The ChatGPT group produced essays that were very similar between writers and were judged “soulless” by two English teachers. Go figure.

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Hackaday Links: May 25, 2025

Have you heard that author Andy Weir has a new book coming out? Very exciting, we know, and according to a syndicated reading list for Summer 2025, it’s called The Last Algorithm, and it’s a tale of a programmer who discovers a dark and dangerous secret about artificial intelligence. If that seems a little out of sync with his usual space-hacking fare such as The Martian and Project Hail Mary, that’s because the book doesn’t exist, and neither do most of the other books on the list.

The list was published in a 64-page supplement that ran in major US newspapers like the Chicago Sun-Times and the Philadelphia Inquirer. The feature listed fifteen must-read books, only five of which exist, and it’s no surprise that AI is to behind the muck-up. Writer Marco Buscaglia took the blame, saying that he used an LLM to produce the list without checking the results. Nobody else in the editorial chain appears to have reviewed the list either, resulting in the hallucination getting published. Readers are understandably upset about this, but for our part, we’re just bummed that Andy doesn’t have a new book coming out.

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