Have you ever been looking up a recipe for something new and been stymied by the directions being a wall of text, especially to find that one detail right when you’re in the middle of making the dish? Recipe Lanes by [bohemian-miser] leverages an LLM to create flow charts to make the process more straightforward.

As someone who has mostly avoided LLM use thus far, I found the examples in the Gallery helped inform what the LLM was expecting for prompts as my first attempts were unsuccessful. Once you know the language expected from the computer, you can get it to generate icons for each ingredient and a flow chart of the steps to cook the food. While it does organize the chart when it is generated, each element can be independently moved across the canvas to put things in a more sensible order, especially as I found it can generate elements with overlapping text.

The 8-bit icon style and button text on the site give it a fun bit of flair that adds to the overall experience. The tool is still in its infancy, but it’s Open Source, so we hope to see it improve over time. If you’d like to see some more interesting kitchen hacks, how about ramen in edible packaging, this rotary phone kitchen timer, or these automated Arduino splash guards.

As predominantly indoor creatures, it’s important to maintain a healthy habitat for the hacker. [Kishan Pratap Singh] designed a clever solution in AirSense, an ESP32-powered air filter.

If you’re thinking of cleaning the air in your environment, you might also want to know some properties about the air coming out of the filter. AirSense measures PM2.5 dust concentration, Air Quality Index (AQI), temperature, humidity, and atmospheric pressure. The various sensors are mounted along the exhaust path of the filter, which lets your know what kind of air it’s pumping out.

The system drives a 150 mm exhaust fan mounted in a 3D printed cap that pulls air through a cylindrical Xiaomi HEPA filter inside a perforated metal trash can enclosure. The ESP32 and an LCD readout of the environmental data also live in the cap, giving the device a sleek look. While [Singh] chose to run the filter continuously, we wonder if it might be interesting to set it up to only filter the air if air quality drops below a certain level to conserve power, especially if you’re on a time-of-use power plan. That would require redesigning the sensor assembly (or running the unit in reverse), so maybe it’s over-complicating things?

We’ve seen the Xiaomi Air purifier filter mentioned before, but under the auspices of hacking it’s filter DRM, an open source air filter designed by [Naomi Wu], and even an ESP32 pressed into service to plug an air purifier into Home Assistant.

One of the biggest “David versus Goliath” stories in tech right now is the towns beset by AI data center projects they may or may not have asked for. Powered By Who is tracking data center development in the US on this convenient map.

Currently, there are over 2,100 data centers being tracked by the project ranging from proposals to sites fully up-and-running. While you have to build bypasses data centers to keep the internet running (which we’re partial to here at Hackaday), there are certainly questions around the amount of power and water consumed by these sites, the emissions they’re sending into the surrounding community, and who exactly is reaping the benefits.

Whether you’re pro, against, or ambivalent about the proliferation of “AI” data centers, the map offers an engaging way to look at what projects are happening around the nation, especially when you start looking at clusters and how that interacts with the power generation and political makeup in a region. It’s particularly interesting how only three states account for roughly 70% of all the projects. Let us know if there’s a similar tracker in your area if you’re from one of the other parts of the globe!

Looking past the debate, there’s a lot of interesting engineering involved in keeping these data centers cool, although there are questions about where that heat ends up going. DC distribution inside the site, underwater data centers, and even putting them in space are some of the solutions for keeping the cooling loads tamed.

We all know that Google and other big players pick and choose what information people see, but we sometimes overlook it outside of the search and social media space. [Lauren Leek] decided to take a look at how Google Maps picks winners and losers in the restaurant scene in London.

Building a machine learning model to determine a new restaurant recommendation (as one does), [Leek] uncovered interesting, and perhaps concerning, elements of how Google Maps ranks restaurants. Broken down by relevance, proximity, and prominence, many new restaurants face the issue of not drawing traffic without reviews and vice-versa causing a vicious cycle. Relevance and proximity are fairly straightforward, but what goes into “prominence?”

[Leek] found that “it is not just what people think of a place – it is how often people interact with it, talk about it, and already recognise it.” This leads to chains and high foot traffic areas awash in reviews while more out-of-the-way places find it more difficult to draw traffic. Some of this is expected and would be happening even when word of mouth was the primary way to find out where to eat, but as with many things, the algorithm amplifies this, along with the undisclosed paid placement of restaurants in Maps results.

While still in its infancy, [Leek] built a public dashboard where people can sort restaurants in the city. The machine learning algorithm is designed to identify places that are hidden gems that punch above their Google Maps weight and may make you look like the trendy one (if you live in London).

Zooming out further, [Leek] found larger clusters that revealed restaurant “diversity, in other words, is not just about taste. It is about where families settled, which high streets remained affordable long enough for a second generation to open businesses, and which parts of the city experienced displacement before culinary ecosystems could mature.”

If you want to step outside the algorithm mayhem, how about a good old-fashioned Web Ring? We’ve also addressed what’s an AI versus an algorithm, and Cory Doctorow advised us on how to reverse course on the current wave of enshittification.

Bats are remarkable creatures, able to fly at night or inside the confines of caves without light to guide their way. A team of researchers at Worcester Polytechnic Institute (WPI) has determined how to use low power ultrasonic sensors to guide drones in obscured environments.

While radar, lidar, and GPS are all great for navigation and sensing, they can run into issues when light is obscured or can take too much power to be practical for the limited battery life of a drone. The researchers found that a dual sonar array could be used to implement a much lower power sensing system for a drone that performs well in environments that would stymie a computer vision system.

A shield placed behind the array cuts down on the sound of the propellers that would otherwise drown out the signal, and further signal analysis via a neural net separates the echoes of objects in front of the drone from the background. The prototype could navigate in various simulated environments like forests, smoke, and snow. It looks like it even got a chance to go for a flight in the actual woods. All the code and hardware designs are Open Source, so have at it!

We’ve covered mosquito-inspired drone sensors before, and if you want to get into echolocation yourself, apparently humans can learn to do it too.

via Entertainment Engineering.

We live in a veritable Cambrian Explosion of camping options, ranging from a tarp on the ground to multi-million dollar RVs. Somewhere around the middle is the pop-up truck camper, but [Further Fabrication] wanted to build his own.

As is often the case, he saw the cool pop-up truck campers on the market, but balked at the cost. To make matters worse, the options out there weren’t really available in his country. After getting a solid set of measurements from the truck and some materials, he set to work.

The most interesting part of this build is probably the aluminum/plywood sandwich [Further Fabrication] chose for building the main structure of the camper shell. While many would’ve chosen a tubing space frame for the build, he decided to sandwich two layers of plywood around plywood beams and large rectangular aluminum tubing. These were affixed with a combination of construction adhesive and screws to create a lightweight yet sturdy enclosure.

The tent portion uses a 600-denier PVC-coated waterproof polyester fabric, sewn with nice YKK zippers, and features large windows with screens for excellent ventilation. The roof consists of two sections of aluminum composite sandwich, and the side panels were made from cutoffs of the roof material. Instead of the standard cam-lock used on most camper shells, [Further Fabrication] chose a solenoid-actuated system and more conventional grab handles.

We’ve covered campers several times, including a solar-powered RV, a truck camper that expands lengthwise rather than up, and several bike-based campers.

Cheap vehicles are thin on the ground in 2026, but [Andy Didorosi] thinks he has the answer for low-speed applications with an open source kei truck.

Still in the early design phase, [Didorosi] has an old factory in Detroit that has been home to his bus transportation business for the last several years, as well as the Sendpai kei truck project to make the world’s fastest kei truck. His vision is to make an affordable kit car truck that anyone can build in the comfort of their own garage. The current plan includes hub motors, which have so far not made it into any production EVs in the US, likely due to the problem with high unsprung weight.

While making a new vehicle from scratch is difficult, the project is targeting a modest set of capabilities at the beginning. The truck will be eschewing safety for low cost, which is probably fine for low-speed off-road use as a utility vehicle. Safety will of course get more important as speed increases. Once the design is sufficiently nailed down, [Didorosi] hopes to sell fully assembled trucks that are compliant with US Low Speed Vehicle (LSV) requirements. This would allow it on roads with posted speed limits below 35 mph.

Will Mutiny succeed where efforts like OScar, CarBEN, or Wikispeed could not prevail? Only time will tell. We hope they’ll keep the Minimal Motoring Manifesto in mind, and in the meantime, you should check out this kei camper or an EV-swapped kei truck that looks like it runs on a giant drill battery.

Humans are odd creatures, and no two are exactly alike, which is likely why so many different methods exist for tracking the progress of tasks that must be accomplished. [Simone Giertz] has graced us with her own spin on task tracking that adds an element of chance.

[Giertz] tells us that she started with written lists that she tackled in dice-determined order to keep her from overthinking or cherry-picking tasks. While this worked fine, she longed for a more elegant solution. Approaching the UI first, unlike any Open Source project ever, she determined that a marker that could randomly point to a task on a vertical list would be most pleasant.

The bulk of the project was evaluating different mechanisms to make the marker pick tasks at random while not selecting a task that had already been completed. A set of magnetic toggles that could repel the marker proved ineffective, but a simpler solution involving moving the completed tasks past a divider won the day. The finished product has a satisfying selection mechanism that makes interacting with the chore chart a joy, which probably helps make it more likely things get done.

We’ve seen many productivity hacks over the years, including Arya’s Hacking the Self, this rotary time tracker, or this e-ink macropad.