Hackaday Links: November 16, 2025

November 16, 2025 by Dan Maloney 14 Comments

We make no claims to be an expert on anything, but we do know that rule number one of working with big, expensive, mission-critical equipment is: Don’t break the big, expensive, mission-critical equipment. Unfortunately, though, that’s just what happened to the Deep Space Network’s 70-meter dish antenna at Goldstone, California. NASA announced the outage this week, but the accident that damaged the dish occurred much earlier, in mid-September. DSS-14, as the antenna is known, is a vital part of the Deep Space Network, which uses huge antennas at three sites (Goldstone, Madrid, and Canberra) to stay in touch with satellites and probes from the Moon to the edge of the solar system. The three sites are located roughly 120 degrees apart on the globe, which gives the network full coverage of the sky regardless of the local time.

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“AI, Make Me A Degree Certificate”

November 10, 2025 by Jenny List 139 Comments

One of the fun things about writing for Hackaday is that it takes you to the places where our community hang out. I was in a hackerspace in a university town the other evening, busily chasing my end of month deadline as no doubt were my colleagues at the time too. In there were a couple of others, a member who’s an electronic engineering student at one of the local universities, and one of their friends from the same course. They were working on the hardware side of a group project, a web-connected device which with a team of several other students, and they were creating from sensor to server to screen.

I have a lot of respect for my friend’s engineering abilities, I won’t name them but they’ve done a bunch of really accomplished projects, and some of them have even been featured here by my colleagues. They are already a very competent engineer indeed, and when in time they receive the bit of paper to prove it, they will go far. The other student was immediately apparent as being cut from the same cloth, as people say in hackerspaces, “one of us”.

They were making great progress with the hardware and low-level software while they were there, but I was saddened at their lament over their colleagues. In particular it seemed they had a real problem with vibe coding: they estimated that only a small percentage of their classmates could code by hand as they did, and the result was a lot of impenetrable code that looked good, but often simply didn’t work.

I came away wondering not how AI could be used to generate such poor quality work, but how on earth this could be viewed as acceptable in a university.
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Expert Systems: The Dawn Of AI

October 29, 2025 by Al Williams 19 Comments

We’ll be honest. If you had told us a few decades ago we’d teach computers to do what we want, it would work some of the time, and you wouldn’t really be able to explain or predict exactly what it was going to do, we’d have thought you were crazy. Why not just get a person? But the dream of AI goes back to the earliest days of computers or even further, if you count Samuel Butler’s letter from 1863 musing on machines evolving into life, a theme he would revisit in the 1872 book Erewhon.

Of course, early real-life AI was nothing like you wanted. Eliza seemed pretty conversational, but you could quickly confuse the program. Hexapawn learned how to play an extremely simplified version of chess, but you could just as easily teach it to lose.

But the real AI work that looked promising was the field of expert systems. Unlike our current AI friends, expert systems were highly predictable. Of course, like any computer program, they could be wrong, but if they were, you could figure out why.

Experts?

As the name implies, expert systems drew from human experts. In theory, a specialized person known as a “knowledge engineer” would work with a human expert to distill his or her knowledge down to an essential form that the computer could handle.

This could range from the simple to the fiendishly complex, and if you think it was hard to do well, you aren’t wrong. Before getting into details, an example will help you follow how it works.

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Nanochat Lets You Build Your Own Hackable LLM

October 20, 2025 by Donald Papp 23 Comments

Few people know LLMs (Large Language Models) as thoroughly as [Andrej Karpathy], and luckily for us all he expresses that in useful open-source projects. His latest is nanochat, which he bills as a way to create “the best ChatGPT $100 can buy”.

What is it, exactly? nanochat in a minimal and hackable software project — encapsulated in a single speedrun.sh script — for creating a simple ChatGPT clone from scratch, including web interface. The codebase is about 8,000 lines of clean, readable code with minimal dependencies, making every single part of the process accessible to be tampered with.

An accessible, end-to-end codebase for creating a simple ChatGPT clone makes every part of the process hackable.

The $100 is the cost of doing the computational grunt work of creating the model, which takes about 4 hours on a single NVIDIA 8XH100 GPU node. The result is a 1.9 billion parameter micro-model, trained on some 38 billion tokens from an open dataset. This model is, as [Andrej] describes in his announcement on X, a “little ChatGPT clone you can sort of talk to, and which can write stories/poems, answer simple questions.” A walk-through of what that whole process looks like makes it as easy as possible to get started.

Unsurprisingly, a mere $100 doesn’t create a meaningful competitor to modern commercial offerings. However, significant improvements can be had by scaling up the process. A $1,000 version (detailed here) is far more coherent and capable; able to solve simple math or coding problems and take multiple-choice tests.

[Andrej Karpathy]’s work lends itself well to modification and experimentation, and we’re sure this tool will be no exception. His past work includes a method of training a GPT-2 LLM using only pure C code, and years ago we saw his work on a character-based Recurrent Neural Network (mis)used to generate baroque music by cleverly representing MIDI events as text.

Your LLM Won’t Stop Lying Any Time Soon

October 10, 2025 by Tyler August 48 Comments

Researchers call it “hallucination”; you might more accurately refer to it as confabulation, hornswaggle, hogwash, or just plain BS. Anyone who has used an LLM has encountered it; some people seem to find it behind every prompt, while others dismiss it as an occasional annoyance, but nobody claims it doesn’t happen. A recent paper by researchers at OpenAI (PDF) tries to drill down a bit deeper into just why that happens, and if anything can be done.

Spoiler alert: not really. Not unless we completely re-think the way we’re training these models, anyway. The analogy used in the conclusion is to an undergraduate in an exam room. Every right answer is going to get a point, but wrong answers aren’t penalized– so why the heck not guess? You might not pass an exam that way going in blind, but if you have studied (i.e., sucked up the entire internet without permission for training data) then you might get a few extra points. For an LLM’s training, like a student’s final grade, every point scored on the exam is a good point. Continue reading “Your LLM Won’t Stop Lying Any Time Soon” →

Hackaday Links: October 5, 2025

October 5, 2025 by Dan Maloney 14 Comments

What the Flock? It’s probably just some quirk of The Almighty Algorithm, but ever since we featured a story on Flock’s crime-fighting drones last week, we’ve been flooded with other stories about the company, some of which aren’t very flattering. The first thing that we were pushed was this handy interactive map of the company’s network of automatic license plate readers. We had no idea how extensive the network was, and while our location is relatively free from these devices, at least ones operated on behalf of state, county, or local law enforcement, we did learn to our dismay that our local Lowe’s saw fit to install three of these cameras on the entrances to their parking lot. Not wishing to have our coming and goings documented, we’ll be taking our home improvement dollars elsewhere for now.

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Macintosh System 7 Ported To X86 With LLM Help

September 29, 2025 by Lewin Day 51 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.