The Rise And Fall Of Silicon Graphics

Maybe best known as the company which brought a splash of color to corporate and scientific computing with its Indigo range of computer systems, Silicon Graphics Inc. (later SGI) burst onto the market in 1981 with what was effectively one of the first commercial graphics operations accelerator with the Geometry Engine. SGI’s founder – James Henry Clark was quite possibly as colorful a character as the company’s products, with [Bradford Morgan White] covering the years leading up to SGI’s founding, its highlights and its eventual demise in 2009.

The story of SGI is typical of a start-up that sees itself become the market leader for years, even as this market gradually changes. For SGI it was the surge in commodity 3D graphics cards in the 1990s alongside affordable (and cluster-capable; insert Beowulf cluster jokes here) server hardware that posed a major problem. Eventually it’d start offering Windows NT workstations, drop its MIPS-based systems in a shift to Intel’s disastrous Itanium range of CPUs and fall to the last-ditch effort of any struggling company: a logo change.

None of this was effective, naturally, and ultimately SGI would file (again) for Chapter 11 bankruptcy in 2009, with Rackable Systems snapping up its assets and renaming itself to SGI, before getting bought out by HPE and sunsetting SGI as a brand name.

Ultimate Power: Lithium-Ion Packs Need Some Extra Circuitry

A LiIon pack might just be exactly what you need for powering a device of yours. Whether it’s a laptop, or a robot, or a custom e-scooter, a CPAP machine, there’s likely a LiIon cell configuration that would work perfectly for your needs. Last time, we talked quite a bit about the parameters you should know about when working with existing LiIon packs or building a new one – configurations, voltage notations, capacity and internal resistance, and things to watch out for if you’re just itching to put some cells together.

Now, you might be at the edge your seat, wondering what kind of configuration do you need? What target voltage would be best for your task? What’s the physical arrangement of the pack that you can afford? What are the safety considerations? And, given those, what kind of electronics do you need?

Picking The Pack Configuration

Pack configurations are well described by XsYp:X serial stages, each stage having Y cells in parallel. It’s important that every stage is the same as all the others in as many parameters as possible – unbalanced stages will bring you trouble.

To get the pack’s nominal voltage, you multiply X (number of stages) by 3.7 V, because this is where your pack will spend most of its time. For example, a 3s pack will have 11.1 V nominal voltage. Check your cell’s datasheet – it tends to have all sorts of nice graphs, so you can calculate the nominal voltage more exactly for the kind of current you’d expect to draw. For instance, the specific cells I use in a device of mine, will spend most of their time at 3.5 V, so I need to adjust my voltage expectations to 10.5 V accordingly if I’m to stack a few of them together.

Now, where do you want to fit your pack? This will determine the voltage. If you want to quickly power a device that expects 12 V, the 10.5 V to 11.1 V of a 3s config should work wonders. If your device detects undervoltage at 10.5V, however, you might want to consider adding one more stage.

How much current do you want to draw? For the cells you are using, open their spec sheet yet again, take the max current draw per cell, derate it by like 50%, and see how many cells you need to add to match your current draw. Then, add parallel cells as needed to get the capacity you desire and fit the physical footprint you’re aiming for. Continue reading “Ultimate Power: Lithium-Ion Packs Need Some Extra Circuitry”

Reduction of a physical map to a graph.

Where Graph Theory Meets The Road: The Algorithms Behind Route Planning

Back in the hazy olden days of the pre-2000s, navigating between two locations generally required someone to whip out a paper map and painstakingly figure out the most optimal route between those depending on the chosen methods of transport. For today’s generations no such contrivances are required, with technology having obliterated even the a need to splurge good money on a GPS navigation device and annual map updates.

These days, you get out a computing device, open Google Maps or equivalent, ask it how you should travel somewhere, and most of the time the provided route will be the correct one, including the fine details such as train platform and departure times. Yet how does all of this seemingly magical route planning technology work? It’s often assumed that Dijkstra’s algorithm, or the A* graph traversal algorithm is used, but the reality is that although these pure graph theory algorithms are decidedly influential, they cannot be applied verbatim to the reality of graph traversal between destinations in the physical world.

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Wear Testing Different 3D Printer Filaments

Over the couple of decades or so since it started to be available at an affordable level, 3D printing has revolutionized the process of making custom objects. But as anyone with a 3D printer will know, sometimes the materials don’t quite live up to the application. There is a huge variety of available filaments to help make better prints, but which one really is the most hard-wearing? [My Tech Fun] set out to measure the resistance to wear of a variety of different 3D printed materials.

The test takes a standard print made across a variety of different materials, and several of each using different manufacturers’ offerings. These are then put on a test rig that moves backward and forward twice a second, with the test piece rubbing against a steel shaft under pressure from a 2.5 kg weight.

As might be expected, the common and cheap PLA performed the worst while PETG, PA, and TPU performed the best. But for us the interesting part comes in the variance between brands; the best PLA sample outperforms the worst ABS and nearly equals the worst of the PETG. Proof that maybe you do get what you pay for.

The whole test is well worth a watch, and if you 3D print anything that might be subjected to mechanical stress you should find it to be of interest. If comparing filaments is something you’d like to see more of, we’ve featured some tests before.

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A Supercapacitor From Mushrooms

The supercapacitor is an extremely promising energy storage technology, and though they have yet to reach parity with the best batteries in terms of energy density, offers considerable promise for a future of safe and affordable energy storage. Perhaps best of all from our point of view, they are surprisingly simple to make. A practical supercapacitor can be made on the bench by almost anyone, as the ever-resourceful [Robert Murray-Smith] demonstrates using mushrooms as his feedstock.

The idea of a supercapacitor is to replace the flat plate on the simple capacitor from your physics textbook with one that has as large a surface area as possible for charge to accumulate on. In this case the surface is formed from organic charcoal, a substance which retains something of the microscopic structure of whatever it was made from. Mushrooms are a good feedstock, because their mycelium structure has a naturally huge surface area. He takes us in the video below the break through the process of carbonizing them, much easier when you have a handy kiln than trying the charcoal-burner method, and then grinds them to a powder before applying them as a paste with a binder to a piece of graphite foil. With two of these electrodes and a piece of paper towel as a dielectric, he demonstrates a simple benchtop supercapacitor running a small electric motor for a surprisingly longer time than we expected.

We’d like to see further work on home made supercapacitors, as we believe they have immense potential as well as storing the stuff. Meanwhile, this is by no means the most unexpected supercapacitor material we’ve seen.

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Retrotechtacular: TOPS Runs The 1970s British Railroad

How do you make the trains run on time? British Rail adopted TOPS, a computer system born of IBM’s SAGE defense project, along with work from Standford and Southern Pacific Railroad. Before TOPS, running the railroad took paper. Lots of paper, ranging from a train’s history, assignments, and all the other bits of data required to keep the trains moving. TOPS kept this data in real-time on computer screens all across the system. While British Rail wasn’t the only company to deploy TOPS, they were certainly proud of it and produced the video you can see below about how the system worked.

There are a lot of pictures of old big iron and the narrator says it has an “immense storage capacity.”  The actual computers in question were a pair of IBM System/370 mainframes that each had 4 MB of RAM. There were also banks of 3330 disk drives that used removable disk packs of — gasp — between 100 and 200 MB per pack.

As primitive and large as those disk drives were, they pioneered many familiar-sounding technologies. For example, they used voice coils, servo tracking, MFM encoding, and error-correcting encoding.

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Hackaday Podcast Episode 263: Better DMCA, AI Spreadsheet Play, And Home Assistants Your Way

No need to wonder what stories Hackaday Editors Elliot Williams and Al Williams were reading this week. They’ll tell you about them in this week’s podcast. The guys revisit the McDonald’s ice cream machine issue to start.   This week, DIY voice assistants and home automation took center stage. But you’ll also hear about AI chat models implemented as a spreadsheet, an old-school RC controller, and more.

How many parts does it take to make a radio? Not a crystal radio, a software-defined one. Less than you might think. Of course, you’ll also need an antenna, and you can make one from lawn chair webbing.

In the can’t miss articles, you’ll hear about the problems with the x86 architecture and how they tried to find Martian radio broadcasts in the 1920s.

Miss any this week? Check out the links below if you want to follow along, and as always, leave your comments!

Direct download in DRM-free MP3.

Continue reading “Hackaday Podcast Episode 263: Better DMCA, AI Spreadsheet Play, And Home Assistants Your Way”