This Potato Virtual Assistant Is Fully Baked

April 15, 2025 by Seth Mabbott 10 Comments

There are a number of reasons you might want to build your own smart speaker virtual assistant. Usually, getting your weather forecast from a snarky, malicious AI potato isn’t one of them, unless you’re a huge Portal fan like [Binh Pham].

[Binh Pham] built the potato incarnation of GLaDOS from the Portal 2 video game with the help of a ReSpeaker Light kit, an ESP32-based board designed for speech recognition and voice control, and as an interface for home assistant running on a Raspberry Pi.

He resisted the temptation to use a real potato as an enclosure and wisely opted instead to print one from a 3D file he found on Thingiverse of the original GLaDOS potato. Providing the assistant with the iconic synthetic voice of GLaDOS was a matter of repackaging an existing voice model for use with Home Assistant.

Of course all of this attention to detail would be for naught if you had to refer to the assistant as “Google” or “Alexa” to get its attention. A bit of custom modelling and on-device wake word detection, and the cyborg tuber was ready to switch lights on and off with it’s signature sinister wit.

We’ve seen a number of projects that brought Portal objects to life for fans of the franchise to enjoy, even an assistant based on another version of the GLaDOS the character. This one adds a dimension of absurdity to the collection.

Continue reading “This Potato Virtual Assistant Is Fully Baked” →

The Incomplete JSON Pretty Printer (Brought To You By Vibes)

April 12, 2025 by Donald Papp 21 Comments

Incomplete JSON (such as from a log that terminates unexpectedly) doesn’t parse cleanly, which means anything that usually prints JSON nicely, won’t. Frustration with this is what led [Simon Willison] to make The Incomplete JSON pretty printer, a single-purpose web tool that pretty-prints JSON regardless of whether it’s complete or not.

Making a tool to solve a particular issue is a fantastic application of software, but in this case it also is a good lead-in to some thoughts [Simon] has to share about vibe coding. The incomplete JSON printer is a perfect example of vibe coding, being the product of [Simon] directing an LLM to iteratively create a tool and not looking at the actual code once.

Sometimes, however the machine decides to code something is *fine*.

[Simon] shares that the term “vibe coding” was first used in a social media post by [Andrej Karpathy], who we’ve seen shared a “hello world” of GPT-based LLMs as well as how to train one in pure C, both of which are the product of a deep understanding of the subject (and fantastically educational) so he certainly knows how things work.

Anyway, [Andrej] had a very specific idea he was describing with vibe coding: that of engaging with the tool in almost a state of flow for something like a weekend project, just focused on iterating one’s way to what they want without fussing the details. Why? Because doing so is new, engaging, and fun.

Since then, vibe coding as a term seems to get used to refer to any and all AI-assisted coding, a subject on which folks have quite a few thoughts (many of which were eagerly shared on a recent Ask Hackaday on the subject).

Of course human oversight is critical to a solid and reliable development workflow. But not all software is the same. In the case of the Incomplete JSON Pretty Printer, [Simon] really doesn’t care what the code actually looks like. He got it made in a short amount of time, the tool does exactly what he wants, and it’s hard to imagine the stakes being any lower. To [Simon], however the LMM decided to do things is fine, and there’s a place for that.

A flowchart demonstrating the exploit described.

Vibe Check: False Packages A New LLM Security Risk?

April 12, 2025 by Tyler August 23 Comments

Lots of people swear by large-language model (LLM) AIs for writing code. Lots of people swear at them. Still others may be planning to exploit their peculiarities, according to [Joe Spracklen] and other researchers at USTA. At least, the researchers have found a potential exploit in ‘vibe coding’.

Everyone who has used an LLM knows they have a propensity to “hallucinate”– that is, to go off the rails and create plausible-sounding gibberish. When you’re vibe coding, that gibberish is likely to make it into your program. Normally, that just means errors. If you are working in an environment that uses a package manager, however (like npm in Node.js, or PiPy in Python, CRAN in R-studio) that plausible-sounding nonsense code may end up calling for a fake package.

A clever attacker might be able to determine what sort of false packages the LLM is hallucinating, and inject them as a vector for malicious code. It’s more likely than you think– while CodeLlama was the worst offender, the most accurate model tested (ChatGPT4) still generated these false packages at a rate of over 5%. The researchers were able to come up with a number of mitigation strategies in their full paper, but this is a sobering reminder that an AI cannot take responsibility. Ultimately it is up to us, the programmers, to ensure the integrity and security of our code, and of the libraries we include in it.

We just had a rollicking discussion of vibe coding, which some of you seemed quite taken with. Others agreed that ChatGPT is the worst summer intern ever. Love it or hate it, it’s likely this won’t be the last time we hear of security concerns brought up by this new method of programming.

Special thanks to [Wolfgang Friedrich] for sending this into our tip line.

A humanoid robot packs a lunch bag in the kitchen

Gemini 2.0 + Robotics = Slam Dunk?

April 11, 2025 by John Elliot V 41 Comments

Over on the Google blog [Joel Meares] explains how Google built the new family of Gemini Robotics models.

The bi-arm ALOHA robot equipped with Gemini 2.0 software can take general instructions and then respond dynamically to its environment as it carries out its tasks. This family of robots aims to be highly dexterous, interactive, and general-purpose by applying the sort of non-task-specific training methods that have worked so well with LLMs, and applying them to robot tasks.

There are two things we here at Hackaday are wondering. Is there anything a robot will never do? And just how cherry-picked are these examples in the slick video? Let us know what you think in the comments!

Continue reading “Gemini 2.0 + Robotics = Slam Dunk?” →

Ask Hackaday: Vibe Coding

April 9, 2025 by Jenny List 92 Comments

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?

Cloudflare’s AI Labyrinth Wants Bad Bots To Get Endlessly Lost

March 24, 2025 by Donald Papp 37 Comments

Cloudflare has gotten more active in its efforts to identify and block unauthorized bots and AI crawlers that don’t respect boundaries. Their solution? AI Labyrinth, which uses generative AI to efficiently create a diverse maze of data as a defensive measure.

This is an evolution of efforts to thwart bots and AI scrapers that don’t respect things like “no crawl” directives, which accounts for an ever-growing amount of traffic. Last year we saw Cloudflare step up their game in identifying and blocking such activity, but the whole thing is akin to an arms race. Those intent on hoovering up all the data they can are constantly shifting tactics in response to mitigations, and simply identifying bad actors with honeypots and blocking them doesn’t really do the job any more. In fact, blocking requests mainly just alerts the baddies to the fact they’ve been identified.

Instead of blocking requests, Cloudflare goes in the other direction and creates an all-you-can-eat sprawl of linked AI-generated content, luring crawlers into wasting their time and resources as they happily process an endless buffet of diverse facts unrelated to the site being crawled, all while Cloudflare learns as much about them as possible.

That’s an important point: the content generated by the Labyrinth might be pointless and irrelevant, but it isn’t nonsense. After all, the content generated by the Labyrinth can plausibly end up in training data, and fraudulent data would essentially be increasing the amount of misinformation online as a side effect. For that reason, the human-looking data making up the Labyrinth isn’t wrong, it’s just useless.

It’s certainly a clever method of dealing with crawlers, but the way things are going it’ll probably be rendered obsolete sooner rather than later, as the next move in the arms race gets made.

How To Use LLMs For Programming Tasks

March 11, 2025 by Donald Papp 60 Comments

[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.