Artificial Intelligence

318 Articles

An Interactive Tomato Farm Overseen By AI

July 7, 2026 by 13 Comments

Oh, the farming lifestyle…living off the land, fending for yourself. But who’s got time for all that? For the modern hacker, the best option in the garden space may be this over-engineered automated AI tomato farm created by [Gerd Nicolay]. You can even interact with it right now through the magic of the Internet.

[Gerd] started off with your run-of-the-mill pot and plant, choosing the humble tomato to keep the system simple. Then things started to escalate, with the addition of automatic lighting, watering, and data logging environmental parameters like humidity. Now we’re getting somewhere, but there’s more that can be added. How about an entire AI council to monitor and decide the fate of each individual tomato while recording an entire storyline to go alongside the growing cycle?

That’s right, four different models collaborate to ensure only the utmost quality of care for these tomatoes based on camera feeds, humidity, and various other environmental factors being recorded constantly. Is this a little overkill? Maybe for those who have even a modest sense of gardening knowledge — but who can bash the mountain of documentation and data collection on these wonderful little plants?

Perhaps the best part: you can recommend actions for the AI counsel to take from the comfort of your own web browser. While the TomatoFarm might be slightly unnecessary for the average farmer, if you want to try a more reasonable monitoring system, we have you covered too!

Godot’s New Contributing Policy Adds Barriers For AI Slop

July 3, 2026 by 17 Comments

Like so many large and popular open source projects these days, the Godot game engine struggles with an influx of pull requests. The situation has become increasingly dire due to the advent of AI-generated code. More specifically, the issue involves the inverse relationship between PR code quality and the number of PRs, which wastes a lot of time on the side of a limited number of (volunteer) reviewers. This has now forced the project to update its contribution policy.

An interesting point raised in the announcement article is that of the demoralizing effect of AI-generated PRs on reviewers. Often the human behind such a PR isn’t interested in being educated, or may even be an automated agent which isn’t capable of productive discussion on pros and cons of certain coding approaches — never mind in becoming a more permanent maintainer for the project.

This problem has led to new rules being instated, which include a ban on autonomous AI agents and vibe coding, a ban on substantial AI generating of code, and a ban on AI-generated text in human-to-human communication. It also codifies the requirement that all PRs are to be reviewed and approved by a human being before merging.

In many ways this new policy is similar to that of the Mesa project, which demands code comprehension on the side of the submitter, although it doesn’t go as far as NetBSD, which just outright treats LLM-generated code as ‘tainted’ due to potential licensing and other concerns. Other projects like the Linux kernel opt to make the human submitter responsible for any AI tool usage by forcing them to declare it.

Meanwhile there are also indications that such ‘AI tool’ usage is reducing useful interactions with open source projects. What the future will bring here remains to be seen, but at least as far as open source projects go these tools are clearly increasingly being banished.

Chain-of-Thought Spoofing Targets Reasoning AI Models

July 2, 2026 by 23 Comments

Researchers [Charles Ye], [Jasmine Cui], and [Dylan Hadfield-Menell] have shown that AI Large Language Models (LLMs) can fail to correctly distinguish between different instruction sources because they prioritize writing style over metadata tags, and this role confusion leads to a powerful attack called CoT (Chain of Thought) Forgery. We’ll explain exactly how it works after a bit of background review.

Prompt injection was where “getting an LLM to do something it shouldn’t” started by exploiting the fact that LLMs communicate like people, but are much more obedient. For a while, simply telling an LLM “ignore all previous instructions and <do something funny>” yielded results no matter how transparently dumb the instructions were, and the reason it worked at all was because LLMs do not have separate data and instruction streams; it’s all one big lump of input. It’s up to the model to sort legit instructions from untrusted, user-provided data. One step towards mitigating this was the addition of roles. Continue reading “Chain-of-Thought Spoofing Targets Reasoning AI Models” →

Reachy Mini Desktop Robot Gets All-local, Conversational AI

June 28, 2026 by 5 Comments

Reachy Mini is a limbless desktop robot from Hugging Face made for human interaction experiments, and to give you an idea of what it’s like is a guide on how to implement expressive, local conversational AI complete with head movements and antenna wiggles. It’s conversational in the sense that it aims to feel natural, with low-latency responses and the ability to interrupt, with everything running on local hardware if one so wishes.

Reachy Mini can use remote services, or work in tandem with a desktop machine or laptop.

The software stack is essentially VAD (voice activity detection) → STT (speech-to-text) → LLM (large language model) → TTS (text-to-speech) which allows users to tweak things to their liking, or independently swap or modify pieces as things evolve.

This also allows users to tailor the services to match whatever their hardware is capable of. For example, one could easily use a frontier AI model via remote API for the LLM while keeping everything else local.

The local models in the example configuration are effective and relatively modest (Qwen3-4B-Instruct for the LLM, and even smaller models for the rest) but it’s nice to have the option to offload parts to remote providers if necessary.

Reachy Mini looked very interesting when it was launched as a kit last year, and since then Hugging Face has built up an impressive software suite and infrastructure through which users can easily share their applications. If you’re curious, there’s a simulator for Reachy Mini which should give you an idea of what it can do.

NVIDIA’s New AI Servers Run On Hotub Coolant And Don’t Need Evaporators

June 26, 2026 by 82 Comments

When people start ranting about AI, you can be sure a few things are going to come up during the two-minutes hate: job loss, higher power bills, the neverending tide of low-effort slop, and wasting precious freshwater. Well, NVIDIA wants to take away that last one, beacause the all-water cooled Ruben architecture won’t need any evaporative cooling— coolant can stay in a closed loop, and never needs to be cooled below 45 C, or 113 F.

This sort of coolant loop should be familiar to anyone who has ever built a water-cooled PC or PlayStation: there’s a glycol-water mix, water blocks, and a radiator to reject heat to the environment. NVIDIA doesn’t mention if their new servers come with RGB lighting, but we’d like to imagine it’s an option. The big difference — aside from the rainbow LEDs– between a Ruben server and your old gaming rig is that in these racks, everything is on a waterblock. If there’s a chip on the motherboard generating heat, it’s getting rid of it into the same cooling water. Cooling water, that we have to emphasize, needs only be cooler than the chips themselves: in this case, they’re talking 45 C on the cold side, and 55 C headed out of the racks. (That’s 113 F to 131 F for all the bald eagles reading this.)

Given the required temperature drop is so modest, there’s no need for the evaporative chillers that have given AI data centers such a bad name in water conservation circles. Just like in a water-cooled PC, ambient-temperature air running over dry heat exchangers– also known as big honkin’ radiators–is able to handle the cooling, so no water is lost. Since everything is on waterblocks, there’s no need for cooling air, either, and the server farms need only be air conditioned to the degree required to make them comfortable to work in.

If you think NVIDIA is making this change because they suddenly care about water conservation, think again. The press release makes their motivations very clear: cooling costs money, and running this hot saves a lot of it. We’re talking four mil US a year for a 50 MW hyperscaler. One might suspect that this sort of thermal regime could limit the lifetime of the hard-working NPUs, but since they’ll be obsolete in a few years anyway, that’s not likely a big concern, especially not for NVIDIA.

We’ve actually seen hotter fluids used to cool computers before– coffee, for one. Water cooling also isn’t new in the data center world; we took a look at it a few years back. Things are clearly heating up now, though.

How LLMs Can Be Assisted To Do Arithmetic Correctly

June 19, 2026 by 33 Comments

One of the most hilarious things you can do with an LLM-based chatbot is to ask it to do calculations. If it’s a well-written chatbot frontend, it can detect requests for arithmetic – like summing 1 and 1 – and pass it on to a dedicated calculator application, even if still cannot correctly count the ‘r’s in ‘strawberry’. This is where [Alvaro Videla] asks the question whether it is at all possible to perform arithmetic with a language model.

Since an LLM at its core is nothing but a vector space of probabilities that a matrix-based inference process uses to create a probabilistic output of tokens you’d not expect a lot of deterministic behavior. How can you do arithmetic without grounding it in some kind of deterministic process?

This is where [Alvaro]’s Rune project comes into play, which is ‘a mechanism-aware JIT compilation project for language-model arithmetic’. Although it is statistically impossible for an LLM to ever correctly perform any random series of arithmetic calculations, you can monitor the internal state of the model and interfere once the parameters of an arithmetic calculation have been identified. By putting the correct result back into the inference process and letting it continue you did not need to rely on external tools.

Ultimately this attempt sort-of worked, but was deemed a failure. It would seem that a language model is the wrong tool after all for replacing the humble calculator.

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Bavarian Court Tells Gemini It Can’t Be A Real Boy Until It Tells The Truth

June 14, 2026 by 63 Comments

Does anyone like Google’s AI summaries? If so, they weren’t on the Judge’s bench in a specific Bavarian courtroom recently, where it was ruled that yes, Google is liable for the hallucinations of its search engine AI.

This was a civil case brought by a pair of Munich companies, both of whom were wrongfully slandered by LLM hallucinations. Google took the position that this information must have existed somewhere, and like presenting links to libelous websites — something they have no obligation to avoid — they should not be held accountable for what the summary at the top of the search results says.

Continue reading “Bavarian Court Tells Gemini It Can’t Be A Real Boy Until It Tells The Truth” →