Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the spectrum is [ExploratoryStudios]’s Hermes Optimus Neural Net for a TI-84 Plus Silver Edition.

This neural net is setup as an autocorrect system that can take four character inputs and match them to a library of twelve words. That’s not a lot, but we’re talking about a device with 24 kB of RAM, so the little machine is doing its best. Perhaps more interesting than any practical output is the puzzle solving involved in getting this to work within the memory constraints.

The neural net “employs a feedforward neural network with a precisely calibrated 4-60-12 architecture and sigmoid activation functions.” This leads to an approximate 85% accuracy being able to identify and correct the given target words. We appreciate the readout of the net’s confidence as well which is something that seems to have gone out the window with many newer “AI” systems.

We’ve seen another TI-84 neural net for handwriting recognition, but is the current crop of AI still headed in the wrong direction?

The aerospike engine holds great promise for spaceflight, but for various reasons, has remained slightly out of reach for decades. But thanks to Leap 71, the technology has moved one step closer to a spacecraft near you with the test fire of their generatively-designed, 3D printed aerospike.

We reported on the original design process of the engine, but at the time it hadn’t been given a chance to burn its liquid oxygen and kerosene fuel. The special sauce was the application of a computational physics model to tackle the complex issue of keeping the engine components cool enough to function while directing 3,500˚C exhaust around the eponymous spike.

Printed via a powder bed process out of CuCrZr, cleaned, heat treated, and then prepped by the University of Sheffield’s Race 2 Space Team, the rocket produced 5,000 Newtons (1,100 lbf) of thrust during its test fire. For comparison, VentureStar, the ill-fated aerospike single stage to orbit project from the 1990s, was projected to produce more than 1,917 kilonewtons (431,000 lbf) from each of its seven RS-2200 engines. Leap 71 obviously has some scaling up to do before this can propel any crewed spacecraft.

If you want to build your own aerospike or 3D printed rocket nozzles we encourage you to read, understand, and follow all relevant safety guidelines when handling your rockets. It is rocket science, after all!

So-called artificial intelligence (AI) is all the rage right now between your grandma asking ChatGPT how to code in Python or influencers making videos without having to hire extras, but one growing concern is where the power is going to come from for the data centers. The MIT Technology Review team did a deep dive on what the current situation is and whether AI is going to kill us all (with carbon emissions).

Probably of most interest to you, dear hacker, is how they came up with their numbers. With no agreed upon methods and different companies doing different types of processing there were a number of assumptions baked into their estimates. Given the lack of information for closed-source models, Open Source models were used as the benchmark for energy usage and extrapolated for the industry as a whole. Unsurprisingly, larger models have a larger energy usage footprint.

While data center power usage remained roughly the same from 2005 to 2017 as increases in efficiency offset the increase in online services, data centers doubled their energy consumption by 2023 from those earlier numbers. The power running into those data centers is 48% more carbon intensive than the US average already, and expected to rise as new data centers push for increased fossil fuel usage, like Meta in Louisiana or the X data center found to be using methane generators in violation of the Clean Air Act.

Technology Review did find “researchers estimate that if data centers cut their electricity use by roughly half for just a few hours during the year, it will allow utilities to handle some additional 76 gigawatts of new demand.” This would mean either reallocating requests to servers in other geographic regions or just slowing down responses for the 80-90 hours a year when the grid is at its highest loads.

If you’re interested in just where a lot of the US-based data centers are, check out this map from NREL. Still not sure how these LLMs even work? Here’s an explainer for you.