Mini IMac G4 Made With NUC And 3D Printer

Apple’s computers have been well regarded over the years for their sharp design features. Of course, something that’s great can only be cuter and cooler if it’s made even smaller. In just that vein, [Gary Olson] whipped up a 54% scale iMac G4.

The iMac G4 was the futuristic-looking flatscreen model, and the direct successor to the original CRT-based iMac. Unlike other projects that run Raspberry Pis or simply fit iPads inside, [Gary] elected to go for a Hackintosh-based build. The system runs Mac OS X on a Intel NUC kitted out with a Core i3 CPU. While it’s not a genuine PowerPC, using OS X fits the proper G4 aesthetic. The build relies on 3D printed components, with the scale size largely chosen to suit the size of [Gary’s] printer and the Intel NUC motherboard. [Gary] goes into detail explaining what was required to get the paint finish right and how to make the hinges stiff but movable.

We’re always fans of a mini retro builds, even if the fact that iMacs are now retro means we’re showing our age. If you’ve got your own cute micro PC coming together in the ‘shop, be sure to drop us a line!

Getting Over 4Gbps Out Of A Compute Module 4

For the average home gamer, good old fashioned Ethernet at 100 Mbit/s is only just starting to become a bottleneck as things like 4K video streaming begin to demand more bandwidth. As always, though, there are those who wish to push the limits of what is possible. [Jeff Geerling] is one such operator, who set out to maximise the network throughput on the Raspberry Pi Compute Module 4. 

The build began by taking advantage of the PCI-Express 2.0 single lane interface on the new Raspberry Pi Compute Module. Hooked up to an Intel four-port Gigabit Ethernet card, and in combination with the onboard Gigabit-E port, [Jeff] was able to get 3.0 Gbit/s out of the setup without too much fuss. However, he wanted more, and set about finding where he was being held back. It turned out that ksoftirqd, a daemon that handles network packets, can only run on one core on the Raspberry Pi 4, and it was getting maxed out at this data rate. Overclocking the CPU helped, getting the max rate up to 3.4 Gbit/s.

Further analysis showed that the onboard interface was only contributing 200 Mbit/s, with the Intel card maxing out at 3.2 Gbit/s. In the case of the latter, this was due to the limits of the PCI-E interface. In the case of the former, however, [Jeff] knew that more was available. The trick turned out to be recompiling the Linux kernel to allow the internal interface to be able to set to use a higher Maximum Transmission Unit. This allows each network transmission to carry more data without extra CPU load. With the internal interface and the external card all set to an MTU of 9000, the Pi was able to spit out a scorching 4.15 Gbit/second. Details of the hack are available on Github for the curious.

It’s a hack that doesn’t offer a lot to the average user, though [Jeff] states he has some interesting applications in mind. He’s also contemplating what can be achieved with a 10 Gbit card, which we can’t wait to see. If you want to learn more about the Compute Module’s features, including a couple of tips for laying out yor own board, check out our review. Video after the break.

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Near-Silent Bellows Uses Water Flow And Magnetic Coupling

Fan noise is a contentious issue among the computer community. Some don’t notice it, others rage against it as an annoyance and distraction. Some turn to liquid cooling, while others look to passive solutions to eliminate the scourge. [Matt] of [DIY Perks] may have found a far more oddball solution, however.

The build is essentially a giant bellows, but the manner in which it operates is unlike anything we’ve seen previously. To shift the large pusher plate inside back and forth, [Matt] initially experimented with building his own linear motor out of coils and magnets. After that failed, he began to tinker with a system of moving a magnet back and forth through a tube with water pressure from a pump, which would then drive the pusher plate through magnetic coupling. This looked promising, but reversing the flow proved difficult. After building his own set of water valves to change the flow direction, the bellows began to work slowly, but with limited performance. Realizing the valves weren’t up to scratch, [Matt] rebuilt the system with 10 pumps, set up in two banks of 5. With the pumps hooked up in series, they supplied plenty of pressure to force the bellows back and forth. Reed switches were used to reverse the flow at either end to make the bellows run continuously.

In testing, the bellows compared well with a bank of four large case fans, though at 20 times the size. Suffice to say this is not exactly a compact solution. We look forward to seeing [Matt] do more with the bellows, with his intention being to use it as the primary cooling system for a computer. Of course, if this looks too complex, you could always consider a mineral oil setup instead. Video after the break.

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Training A Neural Network To Play A Driving Game

Often, when we think of getting a computer to complete a task, we contemplate creating complex algorithms that take in the relevant inputs and produce the desired behaviour. For some tasks, like navigating a car down a road, the sheer multitude of input data and its relationship to the desired output is so complex that it becomes near-impossible to code a solution. In these cases, it can make more sense to create a neural network and train the computer to do the job, as one would a human. On a more basic level, [Gigante] did just that, teaching a neural network to play a basic driving game with a genetic algorithm.

The game consists of a basic top-down 2D driving game. The AI is given the distance to the edge of the track along five lines at different angles projected from the front of the vehicle. The AI also knows its speed and direction. Given these 7 numbers, it calculates the outputs for steering, braking and acceleration to drive the car.

To train the AI, [Gigante] started with 650 AIs, and picked the best performer, which just barely managed to navigate the first two corners. Marking this AI as the parent of the next generation, the AIs were iterated with random mutations. Each generation showed some improvement, with [Gigante] picking the best performers each time to parent the next generation. Within just four iterations, some of the cars are able to complete a full lap. With enough training, the cars are able to complete the course at great speed without hitting the walls at all.

It’s a great example of machine learning and the use of genetic algorithms to improve fitness over time. [Gigante] points out that there’s no need for a human in the loop either, if the software is coded to self-measure the fitness of each generation. We’ve seen similar techniques used to play Mario, too. Video after the break.

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3D Printed Workshop Lamp Uses A Few Neat Tricks

As far as light fittings go, store bought is fine, but it’s hard to beat something you’ve built yourself from the ground up. [Heliox] demonstrates this well, with a 3D-printed workshop lamp that looks the business and is functional, too.

The lamp has plenty of neat design touches that speak to [Heliox]’s experience in the 3D printed arts. The articulating arms are modular, and feature integrated cable guides. The lamp base features nuts inserted mid-print for easy assembly, and the swivel is actually a two-piece mechanism printed as a single assembly. The table clamp uses a large screw, and the benefit of 3D printing means its easy to customise to suit any individual table. Using black and orange filaments gives the lamp a proper industrial look, and the bright LED strips are perfect for illuminating a bench for fine detailed work.

It’s a great addition to [Heliox]’s workspace, and the tall articulated design means it can cast light without getting in the way of what you’re doing. We’ve featured her work before, too – like this glorious infinity cube. Video after the break.

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MOSAiC Project Freezes A Boat In The Arctic Ice Pack For Science

Just over a fortnight ago, RV Polarstern, a German research vessel, sailed back into port, heralding the end of the largest Arctic research project ever undertaken. The MOSAiC expedition, short for Multidisciplinary drifting Observatory for the Study of Arctic Climate, spent a full year running experiments to measure conditions at the North Pole, and research how the unique Arctic climate is being affected by human activity.

Unprecedented In Size And Scope

The operation was regularly resupplied by visits from other icebreakers, bringing equipment, food, and fresh personnel. Alfred-Wegener-Institut / Jan Rohde (CC-BY 4.0)

With a budget exceeding €140 million, and with over 300 scientists attached to the project, the expedition aimed to study a full year-long ice cycle in the Arctic region. To achieve this, the research vessel of the project, RV Polarstern, was navigated into an ice floe, and allowed to freeze in and drift with the ice pack. As the seasons progressed, the vessel drifted with the sea ice across the polar region. Along the way, a series of rotating research teams set up equipment on the ice and took regular measurements, investigating several scientific focus areas. Different groups observed atmospheric conditions and the sea ice itself, with researchers also focusing on biogeochemistry, the ocean, and the ecosystems in the area.

Icebreakers were used to transport goods and personnel to the RV Polarstern over the duration of the mission. The project faced issues in spring, as a pre-planned changeover executed by aircraft had to be abandoned due to restrictions brought about by the COVID-19 pandemic. Instead, this was also executed by ship, with the Polarstern temporarily leaving the ice to rendezvous with RV Sonne and RV Maria S. Merian for the changeover of approximately 100 crew and to pick up provisions. The detour took three weeks, but didn’t have any major negative impacts on the mission. Continue reading “MOSAiC Project Freezes A Boat In The Arctic Ice Pack For Science”

Dumping A N64 Development Cartridge Safely

Retro gaming enthusiasts have always had great interest in rarities outside the usual commercial titles. Whether they be early betas, review copies, or even near-complete versions of games that never made it to release, these finds can be inordinately valuable. [Modern Vintage Gamer] recently came across a pre-release version of Turok 3 for the Nintendo 64, and set about dumping and preserving the find. (Video, embedded below.)

With one-off cartridges like these, it’s important to take the utmost care in order to preserve the data onboard. Simply slapping it into a regular console might boot up the game, but carries with it a non-zero chance of damaging the cart. Instead, the first step taken was to dump the cart for archival purposes. When working with a prototype cart, commodity dumpers like the Retrode aren’t sufficient to do the job. [Modern Vintage Gamer] notes that a Doctor V64 or Gameshark with a parallel port could work, but elects to use a more modern solution in the form of the Ultrasave and 64drive.

With the cartridge backed up and duplicated onto the 64drive, the code can be run on a real console without risk of damage to the original. At first glance, the game appears similar to the final retail version. Analysis of the dump using a file comparison tool suggests that the only differences between the “80% Complete” ROM and the retail edition are headers, leading [Modern Vintage Gamer] to surmise that the game may have been rushed to release.

While in this case the dump didn’t net an amazing rare version of a retro game, [Modern Vintage Gamer] does a great job of explaining the how and why of the process of preserving a vintage cartridge. We look forward to the next rare drop that shakes up the retro world; we’ve seen efforts on Capcom arcade boards net great results. Video after the break.

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