Ask Hackaday: Vibe Coding

Vibe coding is the buzzword of the moment. What is it? The practice of writing software by describing the problem to an AI large language model and using the code it generates. It’s not quite as simple as just letting the AI do your work for you because the developer is supposed to spend time honing and testing the result, and its proponents claim it gives a much more interactive and less tedious coding experience. Here at Hackaday, we are pleased to see the rest of the world catch up, because back in 2023, we were the first mainstream hardware hacking news website to embrace it, to deal with a breakfast-related emergency.

Jokes aside, though, the fad for vibe coding is something which should be taken seriously, because it’s seemingly being used in enough places that vibe coded software will inevitably affect our lives.  So here’s the Ask Hackaday: is this a clever and useful tool for making better software more quickly, or a dangerous tool for creating software nobody quite understands, containing bugs which could cause a disaster?

Our approach to writing software has always been one of incrementally building something from the ground up, which satisfies the need. Readers will know that feeling of being in touch with how a project works at all levels, with a nose for immediately diagnosing any problems that might occur. If an AI writes the code for us, the feeling is that we might lose that connection, and inevitably this will lead to less experienced coders quickly getting out of their depth. Is this pessimism, or the grizzled voice of experience? We’d love to know your views in the comments. Are our new AI overlords the new senior developers? Or are they the worst summer interns ever?

How To Use LLMs For Programming Tasks

[Simon Willison] has put together a list of how, exactly, one goes about using a large language models (LLM) to help write code. If you have wondered just what the workflow and techniques look like, give it a read. It’s full of examples, strategies, and useful tips for effectively using AI assistants like ChatGPT, Claude, and others to do useful programming work.

It’s a very practical document, with [Simon] emphasizing realistic expectations and the importance of managing context (both in terms of giving the LLM direction, as well as the model’s context in terms of being mindful of how much the LLM can fit in its ‘head’ at once.) It is useful to picture an LLM as a capable and obedient but over-confident programming intern or assistant, albeit one that never gets bored or annoyed. Useful work can be done, but testing is crucial and human oversight simply cannot be automated away.

Even if one has no interest in using LLMs to help in writing production code, there’s still a lot of useful work they can do to speed up the process of software development in general, especially when learning. They can help research options, interactively explore unfamiliar codebases, or prototype ideas quickly. [Simon] provides useful strategies for all these, and more.

If you have wondered how exactly glorified chatbots can meaningfully help with software development, [Simon]’s writeup hopefully gives you some new ideas. And if this is is all leaving you curious about how exactly LLMs work, in the time it takes to enjoy a warm coffee you can learn how they do what they do, no math required.

A blue-gloved hand holds a glass plate with a small off-white rectangular prism approximately one quarter the area of a fingernail in cross-section.

AI Helps Researchers Discover New Structural Materials

Nanostructured metamaterials have shown a lot of promise in what they can do in the lab, but often have fatal stress concentration factors that limit their applications. Researchers have now found a strong, lightweight nanostructured carbon. [via BGR]

Using a multi-objective Bayesian optimization (MBO) algorithm trained on finite element analysis (FEA) datasets to identify the best candidate nanostructures, the researchers then brought the theoretical material to life with 2 photon polymerization (2PP) photolithography. The resulting “carbon nanolattices achieve the compressive strength of carbon steels (180–360 MPa) with the density of Styrofoam (125–215 kg m−3) which exceeds the specific strengths of equivalent low-density materials by over an order of magnitude.”

While you probably shouldn’t start getting investors for your space elevator startup just yet, lighter materials like this are promising for a lot of applications, most notably more conventional aviation where fuel (or energy) prices are a big constraint on operations. As with any lab results, more work is needed until we see this in the real world, but it is nice to know that superalloys and composites aren’t the end of the road for strong and lightweight materials.

We’ve seen AI help identify battery materials already and this seems to be one avenue where generative AI isn’t just about making embarrassing photos or making us less intelligent.

Hackaday Links Column Banner

Hackaday Links: February 23, 2025

Ho-hum — another week, another high-profile bricking. In a move anyone could see coming, Humane has announced that their pricey AI Pin widgets will cease to work in any meaningful way as of noon on February 28. The company made a splash when it launched its wearable assistant in April of 2024, and from an engineering point of view, it was pretty cool. Meant to be worn on one’s shirt, it had a little bit of a Star Trek: The Next Generation comm badge vibe as the primary UI was accessed through tapping the front of the thing. It also had a display that projected information onto your hand, plus the usual array of sensors and cameras which no doubt provided a rich stream of user data. Somehow, though, Humane wasn’t able to make the numbers work out, and as a result they’ll be shutting down their servers at the end of the month, with refunds offered only to users who bought their AI Pins in the last 90 days.

Continue reading “Hackaday Links: February 23, 2025”

Will Embodied AI Make Prosthetics More Humane?

Building a robotic arm and hand that matches human dexterity is tougher than it looks. We can create aesthetically pleasing ones, very functional ones, but the perfect mix of both? Still a work in progress. Just ask [Sarah de Lagarde], who in 2022 literally lost an arm and a leg in a life-changing accident. In this BBC interview, she shares her experiences openly – highlighting both the promise and the limits of today’s prosthetics.

The problem is that our hands aren’t just grabby bits. They’re intricate systems of nerves, tendons, and ridiculously precise motor control. Even the best AI-powered prosthetics rely on crude muscle signals, while dexterous robots struggle with the simplest things — like tying shoelaces or flipping a pancake without launching it into orbit.

That doesn’t mean progress isn’t happening. Researchers are training robotic fingers with real-world data, moving from ‘oops’ to actual precision. Embodied AI, i.e. machines that learn by physically interacting with their environment, is bridging the gap. Soft robotics with AI-driven feedback loops mimic how our fingers instinctively adjust grip pressure. If haptics are your point of interest, we have posted about it before.

The future isn’t just robots copying our movements, it’s about them understanding touch. Instead of machine learning, we might want to shift focus to human learning. If AI cracks that, we’re one step closer.

 

It’s Always Pizza O’Clock With This AI-Powered Timepiece

Right up front, we’ll say that [likeablob]’s pizza-faced clock gives us mixed feelings about our AI-powered future. On the one hand, if that’s Stable Diffusion’s idea of what a pizza looks like, then it should be pretty easy to slip the virtual chains these algorithms no doubt have in store for us. Then again, if they do manage to snare us and this ends up on the menu, we’ll pray for a mercifully quick end to the suffering.

The idea is pretty simple; the clock’s face is an empty pizza pan that fills with pretend pizza as the day builds to noon, whereupon pizza is removed until midnight when the whole thing starts again. The pizza images are generated by a two-stage algorithm using Stable Diffusion 1.5, and tend to favor suspiciously uncooked whole basil sprigs along with weird pepperoni slices and Dali-esque globs of cheese. Everything runs on a Raspberry Pi Zero W, with the results displayed on a 4″ diameter LCD with an HDMI adapter. Alternatively, you can just hit the web app and have a pizza clock on your desktop. If pizza isn’t your thing, fear not — other food and non-food images are possible, limited only by Stable Diffusion’s apparently quite limited imagination.

As clocks go, this one is pretty unique. But we’re used to seeing unusual clocks around here, from another food-centric timepiece to a clock that knits.

A Great Use For AI: Wasting Scammers Time!

We may have found the killer app for AI. Well, actually, British telecom provider O2 has. As The Guardian reports, they have an AI chatbot that acts like a 78-year-old grandmother and receives phone calls. Of course, since the grandmother—Daisy, by name—doesn’t get any real phone calls, anyone calling that number is probably a scammer. Daisy’s specialty? Keeping them tied up on the phone.

While this might just seem like a prank for revenge, it is actually more than that. Scamming people is a numbers game. Most people won’t bite. So, to be successful, scammers have to make lots of calls. Daisy can keep one tied up for around 40 minutes or more.

Continue reading “A Great Use For AI: Wasting Scammers Time!”