If you’re in motorsport, or maritime, or mining fields, you can always call on a technician to come down and fix something when it’s broken. You can lay hands on the parts, reconfigure things, make repairs, and get something working again. In space, that’s seldom possible. If you’re lucky enough to have a manned mission, you might be able to make some running repairs; if you’re working with an unmanned robot, probe, or satellite, your potions are altogether more limited. If you can’t find a fix, it’s game over—a particularly brutal result when huge budgets and years of work are on the line.

Janelle Wellons came down to Hackaday Europe to talk about space. More particularly, the engineering and debugging operations that keep all sorts of space programs alive. Her talk dives into some of the creative solutions engineers have had to come up with to save million-dollar missions from becoming unrecoverable boondoggles.

Janelle came down to Hackaday Europe to talk about space, because she lives and breathes it. An experienced aerospace engineer, she’s worked at NASA JPL and iSpace, contributing to the success of missions taking place far from our humble globe. She drew on that experience to talk through what it takes to keep a mission on the rails when things go a little sideways, which happens in space, just as it does anywhere else.

A fantastic example of that, retold in her talk, is the Galileo mission. It was built to travel far across the solar system, eventually winding up at Jupiter to study the Red Giant and its moons.  The probe was engineered with a pair of communication systems—a low-gain antenna for vital signs and management, and a high-gain antenna for sending science data and images back to Earth. The high-gain antenna was key to the mission, capable of offering 10,000 times the bandwidth of the low-gain antenna.

Tragically, though, the high-gain antenna never got to play its starring role. It didn’t deploy properly after launch, and that left NASA with a probe capable of capturing all this fantastic science data, but no way to send it home at a reasonable data rate. Janelle steps through the multiple hacks that make the most of the communication link with the low-gain antenna. NASA engineers whipped up compression algorithms for images and science data, and figured out how to array several Deep Space Network antennas for better signal. This netted an effective data transfer rate of 1,000 bits/second with the low gain antenna. It was still a far cry from the 134 kilobits per second that should have been possible with the high-gain antenna, but a huge leap forward from the 8-16 bits originally possible with the low-gain rig. Ultimately, it saved the mission, allowing the capture of mountains of scientific data on the largest planet in the solar system.

“NASA Astronaut Christina Cook is also working through troubleshooting steps of the waste management system that’s aboard the Integrity spacecraft.” – Ground control, Artemis II mission, 2026

Another great story told by Janelle concerned the Artemis II mission. The lunar flyby was part of NASA’s efforts to eventually return to the Moon itself, and was notable for debuting some special new hardware—the toilet. Unlike previous visits to the moon as part of the Apollo program, Artemis astronauts travel in luxury, with a proper commode built to handle the specific requirements of the zero-gravity space environment. Unfortunately, though, this new hardware had plenty of teething problems.

Early attempts to repair the system involved attempting to reprime the toilet’s pump by adding water to the system. It wasn’t long before the toilet threw another error, though. On the short-duration Artemis II mission, the toilet was set up to vent urine to space. Only, venting wasn’t working—with the suspicion being that the vent pipes had frozen over. The trick to solve this was simple—turning the spacecraft to face the vents towards the sun, so as to heat them enough to melt the blockage. Janelle also notes that during the multiple periods the toilet was down, the astronauts had to rely on alternative means of passing waste—showing a slide of the “collapsible contingency urinals” that did the job.

There’s also a great look at Perseverance’s twin, which lives here on Earth. Janelle has been down to the Mars yard at NASA’s Jet Propulsion Laboratory, where engineers, in her words—”test before you do.” The problem is, when you’re driving a robot on a foreign planet, you can’t just send someone over to repair a broken wheel or flip it back up if it tips over. Thus, many maneuvers and operations are rehearsed in the Mars yard with the twin of Perseverance, running it over recreated obstacles to determine a safe plan of attack.

Valve has unveiled pricing for its new VR headset, and like the Steam Machine, it’s bound to be controversial. The Steam Frame headset will start at $1,059 for the 256GB model and rise to $1,299 for the 1TB version, including the controllers. For that sum, you’ll also get a rebuilt version of Half-Life Alyx, Valve’s 2020 PC VR game, that runs entirely on the headset. 

Valve also unveiled several optional accessories available at launch. The PSU to charge the built-in 21.6Wh battery will cost $29 USD, though you won’t need one if you already have a high-power USB-C charger. You can also get an Accessory Replacement Kit for $49 (one face gasket, one head cushion and one light blocker) and an Ergonomic Accessories Kit for $59 (two controller hand straps, two controller hand strap battery doors, one headset top strap and one extended light blocker).

Along with those, you’ll eventually be able to order prescription lenses from Zenni Optical, while Arcturus plans to release an RGB camera. Prices for those items have yet to be revealed. 

The Steam Frame launched without the delays of the Steam Machine, but Valve will still have a rather onerous reservation ordering system. First, sign up for the model you’d like to buy between now and September 17th at 10 AM PT. Valve will then do a one-time randomization to determine the order, and buyers will receive an email on the 17th indicating whether they’re on the reservation list or waitlist. Anyone signing up after September 17th will be added to the back of the waitlist. Presumably, the company has fixed the issues it previously had with those lists.

The company expects to send the first batch of purchase emails on September 18th and will continue to do so as availability allows. If you’re on the waitlist, it means that there aren’t enough Steam Frames available for reservation, and you’ll be notified once there are. 

Valve also confirmed some new features added since the Steam Frame’s review period started. The number of standalone verified titles has grown to 130, and the headset will initially support Gamepad mode (for 2D games) within the unified dashboard. The company has also improved multi-link streaming, input focus, depth projection and visual sharpness. 

As for Half-Life: Alyx, Valve initially didn’t think it could rejig the game to run on the headset. However, the company claims that “foveated rendering and eye tracking turned out to be a silver bullet,” letting them avoid changing any assets or degrading the gameplay experience. Still, it took “over a year” of extra software work within the Vulkan drivers and SteamVR, which will also benefit other games running on Steam Frame. With the headset, buyers will receive a voucher for Half-Life: Alyx by logging into Steam Frame and heading to settings.

Within the first three days of September, three announcements regarding young students’ access to generative artificial intelligence (AI) made national headlines. 

On Sept. 2, New York City announced a one-year moratorium on student-facing generative AI use for public elementary and middle school students. Later that day, Los Angeles Unified School District announced the implementation of a moratorium of its own, restricting access to generative AI tools on all student-issued devices across all grades for the 2026-27 school year. 

The next day, the American Psychological Association (APA) released a report recommending thorough analysis of edtech tools to ensure they help students learn, citing generative AI as a particular risk. 

The flurry of caution underscores the education field’s attempts at taking a thoughtful approach when choosing AI tools. With the academic year just starting, educators are balancing the popularity of AI among students and the cautious approach to adopting classroom tools. 

The Case for Caution

Nicole Barnes, APA’s executive lead psychologist for education and lead author of the report, decided to pursue the issue after mistaking her son’s kindergarten homework for a video game during the COVID-19 pandemic.

“It was at that point where I realized that psychology, and the learning sciences in particular, had a lot to contribute to the development of edtech apps,” she says.

Barnes worries that too many edtech apps mislead the public on their effectiveness in helping students learn by using engagement and usage data as a proxy. “While there is a relationship between these two constructs, one does not equal the other,” she says.

Barnes sees the same issues with the current AI tools, as reflected in the report, which comes as AI’s presence in classrooms continues to grow.

In a recent IBM survey, 76% of middle school teachers and 73% of high school teachers reported using AI in their classrooms on a weekly basis. 

The moratoriums from the two largest school districts in the country seem to represent a departure from this trend. The rest of the five largest districts encourage thoughtful AI use:

• Chicago Public Schools allows use of district-approved, age-appropriate tools in all schools, with guidance tailored to students, staff, families and vendors. 

• Clark County School District in Nevada similarly allows district-approved tools across all schools, with more general district-wide guidelines. 

• Miami-Dade County Public Schools approved a measure to develop AI use guidelines after implementing one of the largest district rollouts of student-facing AI in the country. 

Barnes thinks New York City and Los Angeles could set an example for other districts.

“I am viewing what New York and Los Angeles are doing as a pause. And to me, this pause is a reminder that schools do not need to let the market or outside forces set the speed of childhood,” she says. “They can slow down enough to ask, what does this tool do? What learning does it protect? What is the risk that it might introduce?”

Turning Restrictions into Lessons

With the NY and LA decisions making such big waves, teachers may be left wondering if their districts will adopt similar restrictions or wait to see what happens.

“I have the gut feeling that they will probably follow suit,” says Dan Clark, a middle school teacher at Lafayette Preparatory School in St. Louis, Missouri, and a Voices of Change Fellow. 

Rather than shutting down conversation, Clark argues that AI restrictions force an essential dialogue. When his district recently locked the use of the AI overview in web searches on school-issued devices, it opened the door for students to critically analyze the tool and its outputs.

“My kids have asked, ‘Why did you do this? Why did you get rid of it?’ And to me, that’s an educational opportunity,” Clark says. “I love that they’re asking us so that we can sit down and talk about why.”

He believes that structured discussion about the positives and negatives of AI is the key to getting buy-in for any decision.

“[Kids are] going to find ways around [restrictions]. They’re way smarter than us,” Clark says. “If we’re not involving them in the process of their own learning, I feel like that is a missed opportunity.”

What Happens Now

As districts deliberate their own next steps, Barnes recommends that before decisions are made, those involved should evaluate AI tools for evidence that they help students learn. From there, if they do decide to allow AI in classrooms, she says it takes a full community effort to craft AI policies.

“The responsibility can’t just sit with one teacher or one parent or with one child,” Barnes says. “Even teachers certainly need classroom guidance. School leaders need procurement and implementation standards. Districts are going to need evidence requirements and data protections, and policymakers need to set clear expectations for claims about children’s learning and development.”

Whether districts opt for temporary moratoriums or cautious rollouts, most educators agree on one thing: a blanket ban without critical dialogue is a missed opportunity. The real work lies in interrogating tech alongside students.

Clark agrees that openness is essential.

“When we just say, ‘There’s a ban. Don’t ask. Let’s not talk about it. Let’s pretend it doesn’t exist…’ It’s not really helping anybody,” he says. “That’s a Band-Aid on a much bigger problem.”

from the meet-the-new-boss dept

Now that streaming subscriber growth has slowed, we’ve noted repeatedly how the streaming TV sector is falling into all of the bad habits that ultimately doomed traditional cable TV.

That has involved chasing pointless “growth for growth’s sake” megamergers, imposing bottomless price hikes and new annoying restrictions on customers, undermining labor, and cutting corners on product quality in a bid to give Wall Street that sweet, impossible, unlimited, quarterly growth it demands.

Streaming TV prices hikes are coming so fast and furious, they’re outpacing many of the ridiculous traditional cable TV price hikes we saw during the cord cutting revolution:

“Over the course of the past 12 months, streaming rates have collectively risen 11.8 percent. The biggest year of increase was 2023, when the streamers shot up an average of 17.7 percent. A Hollywood Reporter analysis of Bureau of Labor Statistics data shows cable and satellite prices, by comparison, rose an annual average of 3.9 percent — excluding a deregulation surge of the late 1980s, when the index peaked at 14 percent. But that was a seismic shift provoked by a federal intervention rather than, say, because Widow’s Bay is a good show, or “we’re pretty sure we can get away with this.”

That shouldn’t really surprise anyone. Many of the same cable TV executives that enshittified traditional cable TV have moved on to streaming. And all the same quarterly growth pressures still apply. It’s not good enough to offer a good, affordable product people like; impossible scale is a siren song that, sooner or later, leaves many of these companies thrashed on the rocks.

There remain some notable positives though. Users are still free to subscribe to a streaming service, binge watch everything of interest, and then cancel to save money. That’s a big improvement from the traditional days of the bloated cable TV bundle.

Initially, streaming TV providers saw significantly higher customer satisfaction scores that big TV providers like Comcast. But that’s slowly changed over the last few years, as frustration with streaming TV grows and traditional cable TV providers try a little harder to improve. The latest American Customer Satisfaction Index (ACSI) scores now show tech companies and traditional cable companies neck and neck:

“The results arrive during a period of rising subscription costs and mounting consumer fatigue. Deloitte’s 2026 Digital Media Trends survey found that 73% of streaming consumers are frustrated with rising prices, around 40% cancel at least one paid service each year, and 61% say they would cancel their favorite service if monthly costs rose by just $5. The average subscribing household spends $69 per month on four streaming video services.”

What happens next? Well you can see that with growth saturated, media giants have started shifting their attention to mergers and consolidation, just like traditional cable, phone, and media companies did. That means even more price hikes to pay off debt, significant layoffs, and corner cutting that results in lower-quality overall product.

I suspect once they’re more meaningfully consolidated and fully enshittified, they’ll find some creative way to make cancelling and resubscribing annoying, especially if the U.S. continues its steady trend of lobotomizing its regulators at the behest of corporate power.

That could most likely come in the form of telecom and media companies merging and then tethering your subscription tightly to wireless phone and internet discounts. Or simply going the AOL route and make cancellation annoying as hell. Who is going to stop them? The Trump FTC?

Ultimately this enshittification will result in more and more users flocking to free or cheap short form video alternatives. Or simply reverting to piracy, at which point (and I’m sorry to keep beating this dead horse) all the execs responsible will blame everything (VPNs, generational entitlement, China) but themselves.

Filed Under: competition, consumers, disruption, satisfaction, streaming, tv, video

Companies: amazon, disney, netflix

Soldiers still burn hours and calories moving water, ammunition, and radios across ground that wrecks ordinary trucks. Pack animals once filled that gap. Conventional electric robots often stall in the same mud, sand, snow, and rubble because their motors sit in the middle of the chassis and feed torque through gearboxes and shafts that lose grip the moment the surface turns ugly. ESOX Group finished a compact uncrewed platform, called the X2, this summer that tries to skip that failure.

Four in-wheel motors, specifically Theron motors, lie within the hubs and produce 1,000 newton meters of torque, 250 of which goes to each corner. That’s a lot of power without the need for a gearbox or driveshaft, or the additional weight that comes with them. Devan Roberts, their VP of defense programs, believes that this arrangement eliminates all known flaws in uncrewed ground vehicles and that nothing, no terrain, gradient, or payload, can halt it in its tracks. These motors are relatively quiet and cold to run, which is significant because heat and noise will not trip all of the sensors designed to detect engine signatures.

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The platform weighs 300 kilograms (about 661 pounds), and testers have already successfully loaded another 300 kilos to match the vehicle’s weight. Later on, they hope to increase it to 600 kilograms. An 8 kilowatt hour solid-state battery powers things, divided into two 4 kilowatt hour modules. ESOX asserts that this chemistry stores about twice the energy of lithium-ion cells found in fielded military vehicles, giving them a longer range or allowing them to battle for the same amount of time but with less weight. If that wasn’t enough, the batteries have a lesser fire danger than liquid electrolyte packs.

The chassis is much smaller than a typical four-by-four, and early test cars included 12 inch motors in a tank-steer layout, as well as some sophisticated airless tires designed to withstand punctures. ESOX OS software allows engineers to create a digital twin of the vehicle before they begin connecting gear together. Once deployed, the device begins transmitting live telematics and sensor data and accepts over-the-air updates for the duration of the vehicle’s life. All of the sensor packages, autonomous modes, and communication layers can be altered without affecting the motors or batteries, so you can essentially keep the core and construct your own versions around it.

ESOX Group is very new, having formed from Donut Lab’s defense division and combined with Finnish unmanned systems expert ESOX Aero in late 2025. They have teams in the UK, Finland, and Estonia, and their CEO, Dan Walmsley, previously worked at McLaren Automotive. They unveiled a tech demonstrator at CES 2026, and customer pilots began in late August, once the platform was declared ready. Any Tank Steer metrics, like as 120kW peak and over 200 kilometers of range, refer to the general design rather than the specific 1,000-newton-meter X2 configuration that is currently being tested.
[Source]

Almost 150 politicians across Europe have been included on harmful deepfake pornography websites over the last few years, according to new research seen by WIRED. Some of the politicians, who are overwhelmingly women, have had their likeness included in explicit videos, while others have names and photos listed in disturbing database-like entries that can link to deepfake creation tools.

Analysis of around 160 domains that host deepfake abuse and other nonconsensual sexual content, by researcher Benjamin Shultz, has found that at least 138 women members of parliament (MPs) from 22 European Union countries have appeared or been mentioned on the sites. In contrast, only nine male MPs were referenced in any way. By Shultz’s calculations, it is 33 times more likely for women MPs to be targeted by deepfake ecosystems.

“Deepfake porn is part of a much bigger trend of online hate and intimidation against women and female politicians,” says Suzanne Kröger, a Dutch member of parliament with the Progressive Netherlands group, who was impacted. Kröger, who expressed anger at what the research uncovered, says the findings are “really deeply disturbing” and help to reveal the scale of abuse. “We are politicians, we have a platform and speak out,” Kröger says. “But meanwhile, there are millions of women victims of these criminal acts.”

The research, which has been published by German digital democracy think tank Agora Digitale Transformation, focuses on national members of parliament from all 27 countries in the European Union. Shultz, a fellow at Agora and the lead researcher at the American Sunlight Project, compiled the details of more than 5,800 members of parliaments across Europe in July and then used custom search engines to find where they appeared across the deepfake websites. (The research does not include members of the European Parliament).

Politicians from Germany, the Netherlands, Italy, and France appeared on the websites the most, according to the findings, and the more senior a politician is, the more likely they were to be targeted. Shultz alerted all of the MPs individually and provided guidance on how the content may be removed. The websites also include depictions of celebrities, journalists, and other public figures, he says. WIRED is not naming the websites in order to not drive further attention to the abusive images.

Multiple researchers and lawmakers say that while politicians have been targeted with deepfakes for years, it can damage democratic practices and create chilling effects that may stop women and girls from becoming public figures. “This discourages women from taking office in the first place and also crowds these women out of public life,” Shultz says. “There’s so much evidence to support that this does real psychological damage. This also does reputational damage for the future if they want to fade out of politics and have another career.”

“Deepfake videos are used to intimidate politicians but also to belittle women,” says Sarah Dobbe, a Dutch Socialist Party MP, who was impacted. “It is very important that if a woman or girl is victimized in this way, she can get direct support if needed, and that the content is removed quickly.”

Across the sites he monitored, Shultz categorized multiple categories of abusive content or behavior. His results include instances where MPs appeared in content that was actively being hosted by the sites, indexed results where content had been removed, and also a number of profiles and “gateways” where potentially new content could be created.

Multiple websites hosted pages that include the names of MPs, official photos, and in some cases their social media profiles or personal information. Some included sections “rating” the women’s bodies, Shultz’s report says. Often next to these MP biographies were links to so-called nudifier apps or the websites’ own tools that claimed to digitally undress people. “It has all the images, all the information, and a direct link to the tool, and in three clicks you can generate,” Shultz says. “That’s just as bad as actually hosting the content.”

Four years after Daniel Riley first lashed remote-control crawlers to a backyard tether and asked whether any of them could rack up 100,000 scale miles, he ran the experiment again. This time the cars steered themselves. In the original 2022 test, a spinning line kept every vehicle on a circle, which also loaded one side of the drivetrain harder than the other and turned wear into a lopsided argument. For the Self Driving Edition, Riley laid a perimeter wire through his yard, borrowed the sensing idea from robot lawn mowers, and taught an Arduino to twitch a steering servo whenever a coil on the bumper drifted off the signal.

Later, he combined all of this into a package called Trail Tracer. A cheap crawler and an expensive crawler spent months crawling the same nasty track, grinding to a halt on their charge strips as rain, mud, rats, snow, and ice each tried to put an end to the runs. The competitors were an Exceed RC Mad Watt 10, a $169 top-of-the-line crawler with metal U-joints and a main gear that was a little too exposed for comfort, and a Stirlingkit YK6101, a $769 1/10-scale 6×6 with a top-notch all-metal chassis, portal axles, lockable diffs, a two-speed gearbox, and a 750-size brushed motor.

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Riley was sure to keep the YK in low range, however, to prevent it from outrunning the wire. Building the track was its own mini-project. Wire segments were rewired (after the rats had bitten them to pieces, they had to be re-soldered and reburied). Then there were the turns, a nasty downhill run, and a swampy low place that caused the cars to oscillate all over the track, all of which required hours of controller tuning to perfect. To top it all off, the charge contacts were constantly sparking until he switched to a bench supply. Then there was his faithful lap timer, a spring-loaded board that recorded each pass. He also installed an FPV camera so he could watch from inside when the weather got bad.

Cheap hardware folded first. After the bolt in one of its gears had a bit too little thread and fell loose from its lock nut (fortunately not scored, so it wasn’t a death sentence), the Mad Watt’s open spur gear simply chewed itself bald on all the grit. Riley finally drove about 75 miles on that chassis, which is equivalent to 750 scale miles at 1/10 scale. The drivetrain ultimately failed. He changed gears and deemed it ordinary maintenance, which is quite generous if you’ve ever had a toy with its teeth sticking out in the mud.

The YK6101, on the other hand, simply kept trucking. By the fall, it had completed several thousand laps and a staggering 356 miles in the real world (or 3,560 scale miles, to give you a better sense) and was only beginning to show signs of wear in the form of extra steering slop and a bit of a wobble sliding down the hill when the controller was turned up to the utmost. The soft tires even broke through a few inches of ice. Of course, frozen ground helped for a time, but the thaw converted the track into soup, forcing Riley to dump a load of gravel, dirt, and concrete powder into the ruts to keep them from eating the axles.

Not a single car reached 100,000 scale miles. That’s a milestone equivalent to 10,000 real-world miles of crawling on a toy truck size, which would imply years of continuous use even if everything kept together, which it didn’t, but what the sequel actually proved was far more interesting. A tethered circle of wire is essentially a devious torture device. A wire-guided self-charging loop that is much more in line with how real vehicles live, as both sides of the drivetrain are worn down, the steering is constantly stressed, water and dirt find their way in through every small gap, and the only thing holding it together is whatever you’ve managed to reinforce the metal with.

Exposed gears wear out. Sealed gears, good steel shafts, and a chassis that can be easily disassembled into a plethora of spare parts make for a winning combination. Riley continues to manage the YK with a skeleton workforce, selling Trail Tracer devices so others can go lay a wire in their own garden, and the 100,000-mile mark remains, effectively a running joke that may become old news if someone simply refuses to give up. For now the interesting result is the gap. Seven hundred fifty scale miles on the bargain crawler. More than three thousand and counting on the 6×6. Same yard, same weather, same buried wire, and a very loud lesson about what you get when you pay for the metal.

A hot potato: Sony has argued in its ownership lawsuit that people don’t actually own their digital purchases. But that seems to contradict the 44 instances documented by the Consumer Rights Wiki, all of which show the gaming giant clearly stating that people do in fact “own” their games.

The collection spans PlayStation support pages, store listings, and official blog posts, with live links, archived snapshots, and highlighted screenshots showing exactly where Sony uses ownership language.

The project comes from the community around the Consumer Rights Wiki, founded by consumer advocate Louis Rossmann. Its collection covers support articles, promotional pages, store listings, and official blog posts, all showing that language suggesting ownership extends well beyond the checkout button.

One particularly clear example appears in Sony’s instructions for upgrading PS4 games to their PS5 versions. The page explicitly refers to an eligible “digital PS4 game you already own.” That leaves little room to suggest the wording applies exclusively to physical discs.

There’s also Sony’s explanation of its PlayStation Store ratings system. In an August 2025 blog post, the company said players “must own the game before submitting a rating.” It appears that ownership is a perfectly useful concept when explaining who gets to leave feedback.

The lawsuit was filed in June by four California customers who allege Sony’s checkout does not adequately disclose that their purchases grant revocable licenses. They say the licensing notice is small and insufficiently prominent, while buttons such as “Buy Now” and “Confirm Purchase” suggest ownership.

California’s AB 2426, which took effect in January 2025, restricts that language unless sellers obtain the required acknowledgment or provide a clear, conspicuous licensing disclosure. The dispute concerns what customers are told when paying for games, rather than whether buying a copy transfers the publisher’s intellectual property.

Sony argues its licensing terms make the arrangement clear and that “reasonable consumers would not be misled.” But the wiki’s examples certainly illustrate why people would think they do own these items.

Other storefronts have been more explicit about ownership. Steam added a shopping-cart notice in October 2024 explaining that digital purchases grant licenses. Ubisoft used a similar ownership defense after The Crew’s shutdown left paying customers unable to play.

For now, Sony is seeking individual arbitration, citing terms it says all four plaintiffs accepted without opting out. It has alternatively requested dismissal, with a hearing scheduled for October 1. Whether the wiki’s collection influences proceedings remains to be seen, but it certainly adds to the growing PR nightmare Sony has become embroiled in.

Most of us are guilty of romanticizing the past. Do you long to be the captain of a tall ship? Just as long as you don’t mind weevils in your food, vitamin deficiencies, and death from an infection when there were no antibiotics. Want to be a medieval knight? Even worse. But surely, retrocomputing is as fun as we remember, right? Turn your computer on, and it comes up with BASIC! Ready for you to write your own programs. None of this GUI foolishness. Of course, this is just another example of rosy retrospection.

Even if you like BASIC or a similar language today, things have changed. You have a nice text editor, a fast computer, debugging tools, along with things like named functions, no line numbers, and modern control structures. None of those things were very common in the 1980s. At least, not on a hobby-grade computer.

Why am I thinking about this? Well, the Hackaday Retrocomputing Challenge is on, and it occurred to me that I wanted to work with some young students in glorious MBASIC on a CP/M machine I built and modified from a Hackaday project. Perfect, right? Many of us started that way, so why shouldn’t they?

But it quickly got old. Even a simple program gets bogged down with GOTOs and GOSUBs to mysterious line numbers. It made me remember the time back in the early 1980s, or maybe even the late 1970s, that I wrote a BASIC preprocessor to scan BASIC with no line numbers and produce proper source, converting labels to line numbers in two passes.

Of course, that code is long gone or, at least, on a floppy I haven’t tried to read in a few decades. I decided to take another crack at it six years ago, but I still didn’t make it much more robust. For example:

PRINT: X=X+10

Is that two statements? Or a label? Hard to tell. My 2020 version used awk. Awk is great for this kind of thing because of the regular expressions and the input loop. But it still has some issues with things like comments and strings. Consider the code below.

PRINT "HELLO: IS IT ME YOU'RE LOOKING FOR?"

This would probably have made my awk preprocessor chew the string up and create a bogus label named HELLO.

History of Preprocessors

Preprocessing one language to another is nothing new. RATFOR and RATFIV by Brian Kernighan converted modern constructs to conventional FORTRAN IV. Even C++ started out as a program that emitted C code.

So the idea is good, but there are dozens of corner cases. As I anticipated having another go at the idea for BASIC, I realized my earlier versions had some design choices that made it harder than it should have been. So I started from scratch.

First, I gave up the idea of just having labels as you might in other languages. Instead, they’d be part of a comment and hard to mistake. This makes for easier parsing and also allows you to keep them around for reference. I also gave up on having labels magically expand. You need a way to make them unique, too. Here’s what I settled on:

':TOPLABEL

GOTO @TOPLABEL

The plan was to go through the source once to assign line numbers. When a label occurs, it adds to the symbol table. Then a second pass actually writes output, replacing @TOPLABEL with the value from the symbol table. Of course, you still want this line to not trigger a label expansion:

PRINT "Send messages to @JTKIRK"

I decided to keep going in awk, but my eventual goal was to rewrite the whole thing in BASIC using the same syntax. Then you could convert the translator itself using the awk version once and then run it on an old computer using BASIC, even if you wanted to retranslate the translator itself. Perverse, huh? But that allows you to keep that authentic development experience. You don’t have to jump over to a PC to process your code, unless you just want to.

Awk as in Awkward

So I decided to do a better job on the awk part with this new scheme. At some point, though, you lose some of the advantages. Then feature creep set in.

I suppose I was subconsciously remembering RATFOR. I decided to add a small number of modern control structures. Again, I wanted something easy to pick out of the source file, so I went with this:

IF X=0 THEN!
PRINT “There is no X!”
X=10
ENDIF!

There’s also WHILE!, DO!, EXIT!, and CONTINUE!

Of course, it didn’t end there. I decided to add a way to include or exclude parts of the source (sort of like #if in C but simpler), along with source code inclusion and a few other neat options such as numeric constants, conditional source blocks, compile-time errors, and even a small numeric stack with PUSH! and POP!.

The source code inclusion can be made to work with awk, but it is ugly. I decided it was better to proceed with another language, but since I didn’t feel like starting over, I just had an LLM convert my awk to Python, which it did with no trouble at all. I then did a little more feature development in Python, but I made sure not to use those new features in the translator itself so the awk version could still process an input file.

So while the original plan was to develop and test in awk and then implement similar code in MBASIC, I now had three versions: a frozen awk script, a Python version, and an MBASIC version.

This was getting a bit much to test. I had the LLM cook up some documentation, additional comments, and tests. It was especially nice to verify that all three versions — awk, Python, and BASIC — did the same things for their common features. The LLM was good at running tests and finding corner cases. It would even run tests in a RunCPM session on the MBASIC version.

BASIC

As you might expect, the BASIC version is a little more convoluted. However, the use of labels and control structures makes it much easier to write, read, and maintain.

Writing the translator in MBASIC imposed some very old-fashioned constraints. There are no dictionaries or dynamic lists, so labels and block state live in fixed-size arrays. Included files require an explicitly managed stack, and parsing strings and comments has to be done character by character. It is not as compact as the Python version, but it is ordinary MBASIC and can run on the target CP/M machine. The limitations also influenced some features that would have been feasible in Python but are nearly impossible in an MBASIC program.

Better yet, lblbasic.bal stays within the subset the original awk translator understood. That provides a bootstrap path: awk produces the first lblbas.bas, after which the MBASIC translator can process its own BAL source.

A Few Samples

One project I had in mind was to drive an LED display module. I wrote a library and then wrote the test program below. It doesn’t matter, but I used .bal as a file extension.

REM TM1637 TEST – PORT 3, BIT 2=DIO, BIT 3=CLK
REM Uses the TM1637.BAL library with LBLBASIC

STACK! 8 ‘ Required by the library

‘ Confirm that the library preserves I while initializing its data table.
I=3141
gosub @init
print “I is now:”;I
PRINT “Starting number: “;
INPUT CT
PRINT “1-Up, 0-Down: “;
INPUT UD
OFFSET=-1
IF UD<>0 THEN OFFSET=1
‘:CDLOOP
WHILE! CT>=0 and CT<=9999
NUM=CT
GOSUB @sendnum4
CT=CT+OFFSET
WEND!
CT=0
IF OFFSET=-1 THEN CT=9999
GOTO @CDLOOP
END

‘ Include the library after the main program.
‘INCLUDE! TM1637.BAL

The BASIC code, including a few lines of the library, is much harder to read:

10 DIM LBLBSTACK#(8):LBLBSP=0
20 I=3141
30 gosub 230
40 print “I is now:”;I
50 PRINT “Starting number: “;
60 INPUT CT
70 PRINT “1-Up, 0-Down: “;
80 INPUT UD
90 OFFSET=-1
100 IF UD<>0 THEN OFFSET=1
110 ‘:CDLOOP
120 IF CT>=0 and CT<=9999 THEN 140
130 GOTO 180
140 NUM=CT
150 GOSUB 330
160 CT=CT+OFFSET
170 GOTO 120
180 CT=0
190 IF OFFSET=-1 THEN CT=9999
200 GOTO 120
210 END
220 ‘:init
230 IF LBLBSP+1>64 THEN PRINT “BAL stack overflow”:STOP
240 LBLBSTACK#(LBLBSP+1)=I:LBLBSP=LBLBSP+1
250 DIM D(9)
260 FOR I=0 to 9: READ D(I): NEXT I
270 DATA 63,6,91,79,102,109,125,7,127,111
280 X=12:OUT 3,X
290 IF LBLBSP<1 THEN PRINT “BAL stack underflow”:STOP
300 I=LBLBSTACK#(LBLBSP):LBLBSP=LBLBSP-1
310 return
320 ‘:sendnum4
330 GOSUB 500
340 B=64:GOSUB 610

If you haven’t used the TM1637 before, it uses a serial protocol with a clock and data line and is very forgiving of timing. The display update speed is quite slow, partly because MBASIC isn’t that speedy and partly because the Z80 chip communicates to the outside world via another microcontroller talking over an I2C bus. But non-BAL code would be just as slow on the same computer. You’ll notice, though, that I took all the delays out and the code and it still works fine.

The bigger sample, though, is HILO.BAL. This lets you set a few compile-time constants that let you select what parts of the program get built. It also makes good use of the control loops. Of course, if you really want to dig in, lblbasic.bal uses quite a bit of the awk-compatible syntax and is a substantial program: around 1300 source lines of BAL which generate nearly 900 lines of regular BASIC due to comment and white-space stripping.

Conclusion

Does this turn MBASIC into a modern language? Of course not. The generated program still has line numbers, the machine is still tiny, and the implementation makes compromises that would horrify anyone writing a real compiler. But it removes enough friction that programming the old machine becomes enjoyable again, especially with WordStar as an editor.

More importantly, the translator can live on the machine it targets. The awk prototype can translate lblbasic.bal once, producing ordinary MBASIC. From then on, the CP/M machine can translate BAL programs — including the translator itself — without help from Python or a modern computer.

So perhaps the lesson isn’t that retrocomputing was better than we remember. It’s that, with a little strategic cheating, it can be almost as much fun as we remember.