Supersized Calculator Brings the Whole Intel 4000 Gang Together

Though mobile devices and Apple Silicon have seen ARM-64 explode across the world, there’s still decent odds you’re reading this on a device with an x86 processor — the direct descendant of the world’s first civilian microprocessor, the Intel 4004. The 4004 wasn’t much good on its own, however, which is why [Klaus Scheffler] and [Lajos Kintli] have produced super-sized discrete chips of the 4001 ROM, 4002 RAM, and 4003 shift register to replicate a 1970s calculator at 10x the size and double the speed, all in time for the 4004’s 50th anniversary.

We featured this project a couple of years back, when it was just a lonely microprocessor. Adding the other MSC-4 series chips enabled the pair to faithfully reproduce the logic of a Busicom 141-PF calculator, the very first to market with Intel’s now-legendary microprocessor. Indeed, this calculator is the raison d’etre for the 4004: Busicom commissioned the whole Micro-Computer System 4-bit (MCS-4) set of chips specifically for this calculator. Only later, once they realized what they had made, did Intel buy the rights back from the Japanese calculator company, and the rest, as they say, is history.

Since its history, it belongs in a museum– and that’s where this giant, FET-based calculator is going. If you happen to be in Solothurn, Switzerland, you’ll be able to see it at a new history of technology exhibit opening at the Enter Museum in 2026. Do check out the write-up and links at 4004.com if you want to learn about this important piece of human history.

The museum-quality hack. Three 4003 shift registers are on the left, with a 4001 ROM above the 4004 CPU in the center, flanked by three 4002 RAM “chips” on the right. Photo by [Klaus Scheffler].We had to specify “first civilian microprocessor” at the start of this article because the US Navy beat them to the punch by a whole year, and kept it secret until 1998. There’s something very 1970s about the fact that top-secret US military technology was reinvented for a Japanese calculator within a year. It honestly makes [Federico Faggin], the man credited with the design, seem no less visionary than when we thought he was first out of the gate.

hackaday.com/2025/11/16/supers…

Hackaday Links: November 16, 2025

We make no claims to be an expert on anything, but we do know that rule number one of working with big, expensive, mission-critical equipment is: Don’t break the big, expensive, mission-critical equipment. Unfortunately, though, that’s just what happened to the Deep Space Network’s 70-meter dish antenna at Goldstone, California. NASA announced the outage this week, but the accident that damaged the dish occurred much earlier, in mid-September. DSS-14, as the antenna is known, is a vital part of the Deep Space Network, which uses huge antennas at three sites (Goldstone, Madrid, and Canberra) to stay in touch with satellites and probes from the Moon to the edge of the solar system. The three sites are located roughly 120 degrees apart on the globe, which gives the network full coverage of the sky regardless of the local time.

Losing the “Mars Antenna,” as DSS-14 is informally known, is a blow to the DSN, a network that was already stretched to the limit of its capabilities, and is likely to be further challenged as the race back to the Moon heats up. As for the cause of the accident, NASA explains that the antenna was “over-rotated, causing stress on the cabling and piping in the center of the structure.” It’s not clear which axis was over-rotated, but based on some specs we found that say the azimuth travel range is ±265 degrees “from wrap center,” we suspect it was the vertical axis in the base. It sounds like the azimuth went past that limit, which wrapped the swags of cables and hoses that run the antenna tightly, causing the damage. We’d have thought there would be a physical stop of some sort to prevent over-rotation, but then again, running a structure that big up against a stop would be very much an “irresistible force, immovable object” scenario. Here’s hoping they can get DSS-14 patched up quickly and back in service.

Speaking of having a bad day on the job, we have to take pity on these Russian engineers for the “demo hell” they went through while revealing the country’s first AI-powered humanoid robot. AIdol, as the bot is known, seemed to struggle from the start, doddering from behind some curtains like a nursing home patient with a couple of nervous-looking fellows flanking it. The bot paused briefly before continuing its drunk-walk, pausing again to deliver a somewhat feeble wave to the crowd before entering the terminal stumble and face-plant part of the demo. The bot’s attendants quickly dragged it away, leaving a pile of parts on the stage while more helpers tried — and failed — to deploy a curtain to hide the scene. It was a pretty sad scene to behold, made worse by the choice of walk-out music (Bill Conti’s iconic “Gonna Fly Now,” better known as the theme from Rocky).

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We just noticed that pretty much everything we have to write about this week has a “bad day at work” vibe to it, so to continue on with that theme, witness this absolutely disgusting restoration of a GPU that spent way too many years in a smoker’s house. The card, an Asus 9800GT Matrix, is from 2008, so it may have spent the last 17 years getting caked with tar and nicotine, along with a fair amount of dust and perhaps cat hair, from the look of it. Having spent way too much time cleaning TVs similarly caked with grossness most foul, we couldn’t stomach watching the video of the restoration process, but it’s available in the article if you dare.

And the final entry in our “So you think your job sucks?” roundup, behold the poor saps who have to generate training data for AI-powered domestic robots. The story details the travails of Naveen Kumar, who spends his workday on simple chores such as folding towels, with the twist of doing it with a GoPro strapped to his forehead to capture all the action. The videos are then sent to a U.S. client, who uses them to develop a training model so that humanoid robots can eventually copy the surprisingly complex physical movements needed to perform such a mundane task. Training a robot is all well and good, but how about training them how to move around inside a house made for humans? That’s where it gets really creepy, as an AI startup has partnered with a big real estate company to share video footage captured from those “walk-through” videos real estate agents are so fond of. So if your house has recently been on the market, there’s a non-zero chance that it’s being used to train an army of domestic robots.

And finally, we guess this one fits the rough-day-at-work theme, but only if your job is being a European astronaut, who may someday be chowing down on protein powder made from their own urine. The product is known as Solein — sorry, but have they never seen the movie Soylent Green? — and is made via a gas fermentation process using microbes, electricity, and air. The Earth-based process uses ammonia as a nitrogen source, but in orbit or on long-duration deep-space missions, urea harvested from astronaut pee would be used instead. There’s no word on what Solein tastes like, but from the look of it, and considering the source, we’d be a bit reluctant to dig in.

hackaday.com/2025/11/16/hackad…

The Simplest Ultrasound Sensor Module, Minus the Module

Just about every “getting started with microcontrollers” kit, Arduino or otherwise, includes an ultrasonic distance sensor module. Given the power of microcontrollers these days, it was only a matter of time before someone asked: “Could I do better without the module?” Well, [Martin Pittermann] asked, and his answer, at least with the Pi Pico, is a resounding “Yes”. A micro and a couple of transducers can offer a better view of the world.

The project isn’t really about removing the extra circuitry on the SR-HC0, since there really isn’t that much to start. [Martin] wanted to know just how far he could push ultrasound scanning technology using RADAR signal processing techniques. Instead of bat-like chirps, [Martin] is using something called Frequency-Modulated Continuous Wave, which comes from RADAR and is exactly what it sounds like. The transmitter emits a continuous carrier wave with a varying frequency modulation, and the received wave is compared to see when it must have been sent. That gives you the time of flight, and the usual math gives you a distance.

The raw IQ signals translate into a range plot giving distances to everything in view.
Since he’s inspired by RADAR, it’s no surprise perhaps that [Martin]’s project reminds us of SDR, and the write-up gets right into the signal-processing code. Does it work better than a chirping module? Well, aside from using fewer parts, [Martin] can generate a full range plot for all objects in the arc of the sensor’s emissions out to 4 meters using just the Pico. [Martin] points out that it wouldn’t take much amplification to get a greater range. He’s not finished yet, though — the real goal here is to measure wind speed, which means he’s going to have to go full Doppler. We look forward to it.

This isn’t the first time we’ve seen the Pico doing fun stuff at these frequencies, and Doppler RADAR is a thing hackers do, so why not ultrasound?

hackaday.com/2025/11/16/the-si…

Google è pronta a lanciare Gemini 3.0: sarà davvero la svolta nella corsa all’AI?

Google si avvicina alla presentazione ufficiale di Gemini 3.0, il nuovo modello di intelligenza artificiale destinato a rappresentare uno dei passaggi più rilevanti nella strategia dell’azienda. Secondo quanto indicato internamente, il rilascio sarebbe imminente e programmato entro la fine dell’anno, come confermato dal CEO Sundar Pichai.

Negli ultimi giorni l’attenzione attorno al modello è cresciuta rapidamente. Su X e in diversi server Discord, alcuni appassionati di IA sostengono che Gemini 3 sia già in fase di test avanzatissimi, ipotesi alimentata dalla consueta modalità di sperimentazione riservata con cui Google prova spesso le sue tecnologie. L’interesse è stato ulteriormente amplificato dalla presenza del modello nei discorsi di analisti e addetti ai lavori, che lo considerano il prossimo banco di prova per valutare la capacità dell’azienda di tornare protagonista nella corsa all’intelligenza artificiale generativa.

Le aspettative sono elevate: Gemini 3 dovrebbe mostrare progressi significativi nella programmazione e nella produzione di contenuti multimediali. Tra le novità attese figura anche un aggiornamento di Nano Banana, lo strumento di Google dedicato alla creazione di immagini.

Il contesto in cui arriva questo lancio è particolarmente competitivo. Dall’uscita di ChatGPT alla fine del 2022, Google è stata spesso descritta come un gruppo costretto a rincorrere. La pressione ha portato a una riorganizzazione interna e all’integrazione accelerata dell’IA generativa nei prodotti principali dell’azienda. Negli ultimi mesi, però, i dati sembrano indicare una ripresa: l’utilizzo di Gemini è in crescita e il modello non ha inciso negativamente sul business pubblicitario, storicamente centrale per Google.

Per recuperare terreno, l’azienda ha fatto leva sul proprio vantaggio strutturale: oltre a sviluppare i modelli, dispone dell’infrastruttura cloud e dei canali di distribuzione necessari a integrarli rapidamente nei suoi servizi. Questo approccio le ha permesso di rimanere relativamente autonoma rispetto alla rete di alleanze sempre più fitta che caratterizza il settore.

La sfida più immediata resta il confronto con OpenAI, che quest’anno ha introdotto il nuovo ChatGPT 5, accolto con grande attenzione ma con un impatto inferiore alle aspettative. Secondo fonti citate da Business Insider, Gemini 3 sarebbe considerato “estremamente impressionante” da chi ha avuto modo di testarlo, tanto da poter rappresentare un’occasione concreta per Google di rialzare la testa e tornare a competere ai massimi livelli.

Resta tuttavia un ostacolo importante: il brand. ChatGPT continua a essere il nome più riconoscibile nel pubblico generalista, analogo al ruolo che “Google” ricopre nella ricerca web. Sebbene l’app Gemini abbia raggiunto 650 milioni di utenti attivi mensili, il servizio di OpenAI continua a mantenere una presenza superiore con circa 800 milioni di utenti settimanali. La diffusione crescente di Gemini tra gli utenti più giovani potrebbe contribuire a ridurre il divario, ma la distanza rimane significativa.

Le scelte compiute negli ultimi anni in materia di infrastrutture cloud, progettazione di chip e investimento nella ricerca sembrano ora iniziare a produrre risultati tangibili. Se Gemini 3 dovesse confermarsi all’altezza delle aspettative, Google potrebbe trovarsi nella posizione di rilanciare in modo deciso la propria corsa nell’intelligenza artificiale generativa.

L'articolo Google è pronta a lanciare Gemini 3.0: sarà davvero la svolta nella corsa all’AI? proviene da Red Hot Cyber.