A developer has created a proof-of-concept data transfer system that shuns any dedicated app requirement and neatly sidesteps mandatory networking, pairing, or giving permissions beyond camera access. Instead, the method transmits information between devices by streaming data via rapidly updating QR codes at up to 60 FPS via the browser your smartphone already has. The test info shared by developer bashalarmistalt on GitHub backs claims that these transfers can run at up to 190 KB/s. This low-friction two-device screen-to-camera data transmission technique is dubbed Decimen Optical Transfer: fountain-coded QR file transfer.

Had an idea for air gapped file transfer, able to get 120 KB/s from r/vibecoding

The GitHub project is linked to by Redditor Alstroph, who seems to be the same person. We’ve chosen to embed the Reddit post about the Decimen Optical Transfer solution above, as it includes an illustrative video of a QR code-driven data transfer.

Bashalarmistalt/Alstroph says they used Claude Code to build this cached web app, which simply runs on both smartphones within the browser, with camera permissions given. They explain that they came up with the idea behind Decimen Optical Transfer to create “a phone-to-phone file transfer option without requiring the phones to be on the same network.” After a modicum of chin stroking, they settled upon the possibility of using “rapidly flashing QR codes” to brew up this proof-of-concept.

Far more detailed information about the project is available in the GitHub repository, linked above. There we also read that the use of ‘fountain codes’ was instrumental to the technique, ensuring missed frames and retransmission quirks don’t hinder the data transfer process. Also, it is explained that every streamed QR code frame is self-describing, as a “20-byte header carries the session ID, sequence number, block count/size, file length, and a hash.”

Hints are provided for tuning Decimen Optical Transfer performance with the PoC solution. The dev used an iPhone and the Apple ProMotion camera with stacked codes; they achieved ~128 KB/s handheld data transfers. The data rate could climb to ~186 KB/s speeds if both the sender and receiver devices were positioned absolutely stationary.

The Decimen Optical Transfer software is distributed under the generous MIT license. Bashalarmistalt is aware that there have been several prior projects but asserts the concept behind their Claude Code-aided solution was arrived at independently. We’ve also covered QR code-based data storage tech numerous times. For example, Cerabyte famously uses microscopic QR codes for its ultra-long-life ceramic storage.

FPGA and embedded systems expert Adam Taylor has recreated the U.S. Navy’s F-14 Tomcat’s Central Air Data Computer (CADC) in an FPGA. The CADC is famous for being the brains behind the F14’s advanced fighter capabilities including the control of the aircraft’s signature articulated sweep-wing system. So, what better way to test the new FPGA than in a 3D printed scale model of the F-14 Tomcat? Check out the video embedded below.

We wrote about the F-14 Tomcat’s CADC last year after discussions erupted on social media about whether this flight computer was actually powered by the "world’s first microprocessor." The brains behind the CADC were provided by the MP944. This chip lived in the shadows for quite some time, though. Despite the MP944 microprocessor beginning service in June 1970, over a year before Intel’s legendary 4004 would become available (November 1971) it was an official secret until 1998. Thus, the Intel 4004 managed to steal the limelight from the true first microprocessor, say those in the MP944 camp.

To recap, the MP944 was a 20-bit, pipelined, parallel multi-microprocessor melded with state-of-the-art MOS technology and ran at 375 kHz, executing 9,375 instructions per second. The flight-system powering chip, designed by Steve Geller and Ray Holt and a 25-strong team, also passed stringent ruggedness tests and was capable of running in temperatures spanning -55 to +125 degrees Celsius.

The MP944 worked as part of a six-chip system in the CADC, for the real-time calculation of flight parameters such as altitude, airspeed, and Mach number – and was a key innovation to enable the Tomcat’s articulated sweep-wing system. So it had to be performant, and some chip architecture enthusiasts assert that the MP944 was actually “8x faster than the Intel 4004.” Remember though, the Intel chip was originally designed for a far more humble desktop calculator.

3D printed F14 swing wing test

Getting back to Adam Taylor’s recent achievement, and we now have a full open source set of VHDL source code, documentation, and testbenches for an FPGA recreation of the F‑14’s CADC. The GitHub repo says the FPGA used was a Spartan-7 based SoM, part of the Adiuvo Embedded System Tile. The resource isn’t just the MP944 logic, Taylor includes complete synthesizable VHDL implementations for all six original CADC chips.

Supply-chain security firm Socket has published research findings describing a Go module that posed as a DNS and subdomain scanner while acting as a first-stage Windows malware loader. The firm then traced it to a network of 222 GitHub repositories across 190 accounts. The module published its first version on January 24 this year and has since accumulated more than 1,200 versions, over 700 of them malicious. Socket tracks the campaign as “Operation Muck and Load” and reported the module to the Go security team, which blocked it from the Go module proxy.

Go derives a pseudo-version from the commit timestamp and hash for any commit that lacks a semantic version tag. Socket attributes the sprawl to the threat actor's own GitHub Actions workflow, saying its timed commits could each be resolved as a version, inflating a scanner utility's release history into the hundreds.

Across the confirmed repositories, Socket found the same workflow: it sets the Git email to ischhfd83@rambler.ru, sets the visible commit username to the current repository owner, and then force-pushes a rewritten log file every minute. That split generated owner-attributed activity across disposable accounts while leaving one reusable fingerprint. Socket counted a repository only when both the email and the workflow appeared together, resulting in 222 repositories as the confirmed minimum.

The module's main.go launches a hidden PowerShell command that downloads content from muckcoding.com, decodes it with certutil, and runs the result with execution-policy bypass. Socket describes the decoded script as a multi-layer loader using Base64 encoding and XOR decryption, with a Turkish-language comment in one layer that translates to "run directly, no other step is needed."

Rather than hardcoding a payload URL, the resolver retrieves text from public platforms, searches it for the marker string "LastW," then decrypts the trailing blob with a hardcoded key to recover the actual download location. Primary dead drops include Pastebin and a paste service called Rlim, with fallbacks across YouTube, Instagram, Telegram, Google Docs, and GitCode. If defenders remove one paste or block the final archive URL, the actor can update the resolver content without touching the first-stage loader.

The resolved URL points to a password-protected 7-Zip archive hosted as a GitHub release asset. The loader extracts it into a directory named to resemble a legitimate Microsoft Photos install and launches Microsoft.exe from that path with a hidden window. Decoded payload stages map to AsyncRAT, Quasar, and Remcos-style RAT detections alongside infostealer behavior.

Socket confirmed at least 14 unique malware files across the analyzed set, including Trojan loaders and downloaders, Vidar infostealer, dropper and spyware payloads, and XMRig-related Monero cryptominers. One Loader.exe appeared byte-identically across four separate repositories.

Lure themes span MetaMask and Trust Wallet integrations, seed-phrase utilities, Binance and PayPal automation, Telegram and Discord bots, and game cheats for PUBG, Valorant, and Escape from Tarkov. One PUBG repository, nrevv1lad/Pubg-DESYNC-Menu, presented itself as an external cheat with an installation guide while hosting a Vidar-linked Loader.exe in its source tree.

Socket assesses with high confidence that Operation Muck and Load belongs to the same cluster that Sophos documented in June last year. Sophos researchers Matt Wixey and Andrew O'Donnell traced 141 GitHub repositories, 133 of them backdoored, to the same ischhfd83@rambler.ru address. Sophos also identified "Muck" as one of the actor's aliases, a label now embedded in the muckcoding.com and muckdeveloper.com domains.

Neither GitHub nor the Go team has commented beyond the proxy block.

GitHub has announced that it will be giving developers a way to obtain their public repo on a CD-ROM. Thanks to this initiative, a dev will be able to obtain a physical copy of their code and “Keep it. Lend it to friends. Pass it on to your children.” This would be a very unusual offer from GitHub without the context of Sony’s official announcement, stating that it will cease the production and distribution of PlayStation games via physical media in 2028.

Above you can see that GitHub is lending its voice to those who reckon Sony’s move is wrong or, at best, premature. The coding and collaboration platform, owned by Microsoft, states that “In light of recent developments in physical media, GitHub is proud to announce that you can now obtain your public repo on CD-ROM.” Moreover, it appeals to the human side of computing, adding the emotive line “Keep it. Lend it to friends. Pass it on to your children.”

It isn’t April 1^st, so thankfully this is no joke. However, if you check out the above-linked GitHub Your Code, On a CD offer page, it quickly becomes clear this is a very limited in time/scope stunt.

“Order a burned CD of your own public GitHub repo. Yes, a real physical disc you can hold in your hands, no download required,” begins the spiel. But this is a very limited run of 1,000 discs, with applications required between July 2 and July 6 (inclusive). Limit one per person, with availability varying between country/region.

“Your code is physically yours, forever. Until you lose it, let's be real,” says GitHub. At best, these CDs will be framed and put on a wall, some becoming collector’s items or eBay money spinners (discs like 0001 or 0888 would be good ones, if they are numbered). Also, many will be lost or eventually/accidentally discarded, as GitHub seems to know. So this 'protest' is arguably 1,000 doses of expensively shipped e-waste.

Circling back to the context of this GitHub story, it is clear it is a small protest at Sony essentially ringing the death knell for physical media in modern gaming. Consoles were already rolling inexorably down this road with the cheaper digital edition hardware releases, and other games industry players are pushing in the same direction. Digital-only isn’t likely going to end up well for gamers, archivists, second-hand stores, and others – with used game media disappearing from the scene, to name but one impending issue.

Goldeneye Recomp v1.0 has been released on GitHub by developer SunJaycy. Gaming-focused social media channels are excited with this release, which provides “a native PC port of GoldenEye 007 (Xbox 360 / XBLA), built by statically recompiling the original game into C++ with the ReXGlue SDK,” states the dev. “No emulator — the game runs as a real native executable.” I played the original Nintendo 64 classic for hours in the late 1990s, but this version comes with numerous benefits, including support for modern Windows controllers, online multiplayer, widescreen support, and post-FX filters, all at a purported stable 60 FPS frame rate.

GoldenEye was a seminal release for consoles, establishing that FPS games were a compelling proposition beyond the realms of PCs and keyboard/mouse control. Nintendo / Rare showed that slick, responsive FPS controls were possible on a console, and it felt good to play, with widespread praise from both reviewers and fans. In effect, GoldenEye trailblazed this genre on consoles, thanks in part to the N64’s 3D power and sensitive analog controller. Without its strong approval, we may not have seen the subsequent console developments/releases of games like Perfect Dark, Timesplitters, and many more.

Though it may be exciting for old GoldenEye N64 players (and even those who’ve only experienced the game on N64 emulators), getting the GoldenEye 007 — PC Recompilation installed isn’t a cinch right now. As SunJaycy highlights on the GitHub page for the project, “This repository contains no game code or assets. It is only the source that wraps the game (menus, hooks, online, post-FX, build config).” They go on to explain that “You must find the game files yourself. This game was never released publicly.” So, that’s a challenge you’ll have to work through on your own.

If and when you get the Xbox 360 version of the game recompiled for PC, though, you should expect the following thrilling benefits:

• Runs natively on Windows — no emulator, no BIOS.
• Controller support.
• Online multiplayer — host or join matches over the internet (LAN, Hamachi, playit.gg, or a public server).
• In-game pause/settings menu (ESC): video, resolution, frame limit, fullscreen, online setup.
• Post-FX filters (brightness, contrast, saturation, vignette, presets…).
• Smooth, stable 60 FPS (recompiled, with GPU-pacing fixes for the original's frame timing).

Instructions are provided for building the Windows version of the game from its collected sources, plus the Goldeneye Recomp v1.0 code.

Coincidentally, this Recomp release comes as the latest James Bond title, 007 First Light game, rides high in the charts on PCs and current-gen consoles. First Light is also the centerpiece of the latest Nvidia GeForce bundling promotion. PC gamers can grab a free copy of the action-adventure game 007 First Light when they purchase a qualifying GeForce RTX 50-series graphics card, gaming desktop, or laptop.

A new and unique ASCII video streaming solution has been released under the MIT license. ASCILINE Engine by YusufB5 is pitched as a “high-performance, real-time ASCII video rendering engine” that can be used to broadcast “an unblockable video stream.” Examples of its capabilities are provided in the linked GitHub repository and social media posts by the dev, like the one embedded below. It's also stoked a bit of controversy due to fears of it potentially enabling unblockable ads.

I built an unblockable video stream. It renders 360p at 30 FPS using pure text instead of tags. from r/SideProject

From our perusal of the examples, ASCILINE does indeed look like it is capable of better fidelity than prior video to ASCII streamers, some of which have a surprisingly long history dating back to the 90s. The software does a pretty decent job of making color text-based videos from a source. The dev notes that this technique uses Mode 3, using a palette of 32K colors, and can output at 30 FPS. Though classic mono ASCII is also a render option.

Most impressive is the so-called real-time pixel streaming. The GitHub explains that this technique uses Mode 5 and “replaces characters with colored blocks, approaching 360p video quality.” Indeed, in the small embedded video on the page, it looks indistinguishable from the source MP4. We think the innate blockiness would become apparent quickly if it were rendered in a larger window, though.

In a mission statement of sorts, YusufB5 says that ASCILINE’s “core objective is to transform the web into a highly dynamic and interactive typographic canvas. By mapping pixels to text-based representations, we unlock new possibilities for web media delivery.” On social media, the dev simplifies this highfalutin vision by heralding ASCILINE Engine as a tool that can build “an unblockable video stream. It renders 360p at 30 FPS using pure text.”

The ‘unblockable’ claim gets plenty of pushback across various social media channels. For example, an adblocker put in element zapper mode can quickly remove the HTML5 Canvas that the ASCII video renders in. That’s just one option.

However, most commenters are fearful of those wanting to serve more ads to the public using this ‘unblockable’ tech. YusufB5 points to their “strict anti-ad clause to the MIT License to ensure this isn't abused to force unskippable ads on people.” That might work with registered companies, but malicious users won’t care about such a rule.

Beyond the unblockability and ad misuse controversies, ASCILINE has some more interesting frills and features to set it apart from the old guard of ASCII video. The ability to apply real-time CSS filters to the video stream sounds like it may be appealing in some instances. Also, the ASCII video generated is thought to “act as a perfect bridge for AI.” Thus, you can use lightweight LLMs to process semantic video summaries. The engine’s “ultra-low bandwidth & IoT compatibility” may also be attractive to some, with its ability to stream at only a few kilobytes per frame. Sending only the characters that change (delta frames) and applying GZIP compression both help in this regard.

The enormous amount of money shuffling through AI's circular economy has created a money pit, and the beast therein demands sacrifices. Not too long after Anthropic shocked most of the developer world by bumping up its prices and moving Claude Code to the Max plan, GitHub Copilot has followed suit. The move from subscription to usage-based billing has left many users looking down the barrel of massive bills, according to an Ars Technica report.

An ongoing discussion on the GitHub community forums includes plenty of customer testimonials, as do X posts from many different users. The overall gist is simple: many users are reporting that their bills would increase by several orders of magnitude, or that the limit is so low that a subscription plan is now either extremely limited or useless. There's even a community cost estimator that popped up a while back when the news first came to light.

To wit, the AI allowance in each of GitHub's subscription plans has three tiers: the $10 Pro plan gets you 1,500 credits; the $39 Pro+ plan contains 7,000; and the $100 Max subscription nets you 20,000 credits. While it's good for Microsoft to specify precisely how many credits each plan includes, it's worth noting that in obvious cases like long-running conversations or queries on large projects, it's exceedingly hard to estimate how many credits any given query will use, as shown by data from a 2025 paper.

Unsurprisingly, some users are reporting that even light usage, or being "super cautious", they went through significant chunks of their monthly allotment in the blink of an eye. Switching the underlying model for a query can drastically change the calculations; for example, using Claude Opus or GPT will be far pricier than using Gemini Flash. Some subscribers also warn fellow vibe-coders about resurfacing long-running conversations, as the nature of an AI bot requires the entire conversation to be re-sent again and again, quickly chewing through usage limits.

Besides being more judicious about selecting the right model for a given query, some folks are experimenting with harnesses to use token-efficient models like DeepSeek to keep costs down. Those using an AI bot as an agent also have to exercise extreme caution, as there's no shortage of reports and horror stories about clankers left unchecked, racking up gigantic bills.

Although there's no shortage of vitriol directed at Microsoft, as there was when Anthropic bumped up Claude's pricing, the harsh reality is that investors are growing tired of dumping money into AI companies and want to see cash inflows that are at least in the same galaxy as their expenditure.

The Software Freedom Conservancy, a non-profit organization dedicated to legally protecting open-source software, said that Bambu Lab has violated the licenses that its slicer used as it looked into the company after the 3D printer manufacturer threatened to sue independent developer Paweł Jarczak.

Jarczak built an OrcaSlicer fork (called OrcaSlicer-bambulab) that bypassed the company’s Bambu Connect service, and was soon served with a cease-and-desist and a demand for the removal of their work from GitHub. According to the SFC, the company’s restrictions violate AGPLv3, the license its software is based on, which says one cannot put additional limitations on the rights granted under the copyleft agreement.

The company’s troubles began when it accused Jarczak’s fork of impersonating Bambu Studio, bypassing their authorization controls, and violating their Terms of Use. The 3D printer manufacturer also alleged that the developer used “reverse engineering” of its closed-source software and said it “could allow modified forks to send arbitrary commands to printers.” The company says that the bambu_networking plugin included in Bambu Studio, which handles all communication for the slicer, is proprietary. Because of this, it says reverse engineering the protocol that the plugin used, which Jarczak did to create their own Rust implementation, violates Bambu’s Terms of Service.

The company told All3DP in a statement, "The AGPL, the DMCA, and Bambu Lab’s terms do not permit reverse engineering that violates applicable protocols, rules, or circumvents technical protection measures protecting our cloud services." It also added, "From the beginning, our preference has been dialogue, not confrontation. At this stage, rather than escalating conflict, we are focusing on strengthening our own infrastructure and protection measures moving forward. Interim measures have already been implemented. Security will continue to be strengthened in future releases, and we recommend that users update to the latest version in a timely manner."

On the other hand, the SFC contends that because bambu_networking is required to run the Bambu Studio, it falls under AGPLv3, which the Bambu Lab’s slicer is licensed under. Bambu Studio itself is a fork of PrusaSlicer, which was also based on Slic3r. The original Slic3r uses AGPLv3, meaning every derivative under it, including Bambu Studio, must use the same license. The fact that bambu_networking is flagged as proprietary breaks the copyleft license and violates several clauses in the license agreement.

This has been going on for years now, but it’s the company’s actions against Jarczak that finally caused an uproar in the 3D printing community. Joseph Prusa himself, the chief of Prusa Research and the creator of PrusaSlicer on which Bambu Studio is based on, said that it was violating the AGPLv3 license, and that Bambu Lab’s actions raise red flags when it comes to security. Right-to-repair advocates are also rallying behind the developer, with YouTuber Louis Rossman saying that he will support Jarczak up to $10,000 for their legal expenses, with Gamers Nexus matching that amount. Furthermore, Rossman said that he’ll host the fork on his own FULU (Freedom from Unethical Limitations) Foundation GitHub and dared the company to take legal action against him.

Bambu Lab is one of the biggest names in the 3D printing industry, with the company reportedly boasting a valuation of billions of dollars and having overtaken Creality as the world’s best-selling budget 3D printer brand. This is why many enthusiasts are concerned, as it apparently moves away from open-source software and towards proprietary apps. But as long as it uses software based on AGPLv3-licensed components, it has no choice but to comply with those requirements.

As for the trouble that Bambu Lab's response on Jarczak's fork stirred, the company said to All3DP: "We nonetheless regret that our reference to Terms of Service, legal context and a potential C&D understandably came across as a legal threat. That was not the outcome we wanted."

GitHub has officially confirmed, via an X post today, that thousands of its internal repositories were breached after an employee's device was compromised through a malicious Visual Studio Code extension. The company said it detected and contained the incident yesterday, removed the poisoned extension version from the VS Code Marketplace, isolated the affected endpoint, and immediately launched an internal incident response investigation.

The disclosure follows claims posted earlier this week by the TeamPCP hacker group on the Breached cybercrime forum that it had gained access to nearly 4,000 private GitHub repositories via the breach.

The group alleged that it had exfiltrated internal source code and other private data, and stated that it was seeking at least $50,000 from potential buyers for the stolen material. “This is not a ransom,” the group wrote in its post, adding that it intended to sell the data rather than extort GitHub directly, and threatening to leak the repositories publicly if no buyer emerged.

According to GitHub’s current assessment, the activity involved only the exfiltration of GitHub-internal repositories, but the company stated that the attackers’ claims of accessing roughly 3,800 repositories are “directionally consistent” with findings uncovered so far. GitHub also said it has already rotated critical secrets and credentials as part of its containment efforts, while continuing to analyze logs and monitor for any follow-on activity.

TeamPCP has previously been linked to several high-profile campaigns involving platforms such as GitHub, PyPI, npm, and Docker. At the same time, malicious VS Code extensions have repeatedly surfaced in recent years as an increasingly effective vector for breaches and malware delivery.

VS Code extensions are effectively executable plugins embedded inside a developer’s working environment, often with access to local files, terminals, authentication tokens, and cloud tooling. While Microsoft and extension publishers implement various security measures, developers routinely install third-party extensions for debugging, automation, AI coding assistance, and workflow integrations, making the ecosystem an increasingly attractive target for attackers disguising malware as legitimate development tools.

In GitHub’s case, the compromised extension reportedly gave attackers a foothold on the employee's device, granting access to internal repositories and engineering systems. That does not necessarily mean unrestricted access to GitHub’s broader platform or customer repositories. However, internal repositories can still contain valuable operational data such as deployment tooling, infrastructure scripts, security workflows, internal APIs, and unreleased product features. Large technology companies also commonly split infrastructure across thousands of smaller repositories, meaning “3,800 repos” does not necessarily translate to 3,800 major standalone products.

GitHub said it has no evidence that customer data stored outside the affected internal repositories was impacted, and there is currently no indication that public GitHub repositories or platform users' private repositories were broadly exposed.

The incident highlights the growing wave of software supply-chain attacks targeting developers and their tooling rather than end users directly. Modern development ecosystems rely heavily on third-party components, including VS Code extensions, npm packages, PyPI libraries, Docker containers, and AI-assisted coding tools, which means a compromise at almost any layer can expose critical infrastructure. Earlier this year, researchers also discovered malicious packages using invisible Unicode characters hidden across GitHub repositories and VS Code projects, underscoring the growing abuse of trusted developer ecosystems.

The supremely portable dimensions of the humble credit card have been a persistent miniaturization goal for tech device makers for a very long time. Earlier this week krauseler on GitHub shared details of “a fully working computer that is literally the size of a credit card,” dubbed the Muxcard. This ISO/IEC 7810 ID‑1 credit card-sized computer includes an ESP32-C3 microcontroller, an ePaper display, NFC, sensors, and a battery.

I remember buying a credit card-sized calculator in the previous century, and the iconic and now almost omnipresent Raspberry Pi began as a credit card-sized barebones computer dream – made real . However, those gizmos kind of ignored the important third dimension of a credit card, which makes it so portable, the thickness. This isn’t a mistake repeated by krauseler. Well, almost.

The maker admits the Muxcard prototype is 1mm thick as it now stands. The official ISO/IEC 7810 ID‑1 is for a card of 0.76mm thickness, “but many real-world cards slightly exceed this in practice,” writes krauseler on the GitHub project page. Underlining the adherence to the credit card size, krauseler even uses an old plastic NFC card with most of its volume cut away as the Muxcard chassis.

It is admitted that the current prototype is rather fragile, and there are a number of things that need to be done to bring the project in line with the ultimate goals.

Right now the prototype specs are as follows:

• MCU: ESP32‑C3, a RISC-V CPU architecture, Wi‑Fi capable microcontroller with 320KB of usable SRAM and 384KB ROM. But future versions could use a more powerful ESP32‑S3 or nRF52/53.
• Display: 1.54-inch 200x200 pixel flex ePaper screen with partial update support.
• NFC: RC522 read/write module (not just a passive tag).
• IMU: LIS2DW12 accelerometer for wake triggers and motion sensing.
• Power: 30mAh, 1mm thick rechargeable LiPo battery.

What still has to be done to complete te Muxcard project includes the addition of USB Type-C, a microSD card slot, touch button controls, and krauseler is considering a slimmer LiPo rechargeable battery cell. When all the tech specs are up to the desired level some hard work will remain in strengthening and ruggedizing the design.

Completing all these plans won’t be easy in the limits imposed by a credit card's standard thickness. However, the maker will continue with this quest and remain positive and enchanted with the device, even in this prototype stage. “Even after months of staring at this thing, it still feels slightly absurd every time the display updates while holding what basically feels like a normal card,” they say.

As with most GitHub project makers, krauseler would be happy to get offers of third-party collaboration and contributions. The maker is also curious about how people foresee using the Muxcard. Their own ideas include use as a smart-home dashboard, offline storage for 2FA, backup keys and passwords, and so on.

The historic computer software code that took Apollo 11 to the moon has been open-sourced and is available for anyone to read, download, and tinker with. NASA’s Chris Garry made the code available on GitHub as public domain. The published resource is basically in two large codebases, one set of code for the Command Module (Comanche055) and another for the Lunar Module (Luminary099). These modules both had their own Apollo 11 guidance computers (AGC) upon which to run the code, and were instrumental to the success of the remarkable mission – the first human Moon landing in history.

On the GitHub repo, Garry indicates that this momentous code was digitized by the folks at Virtual AGC (Virtual Apollo Guidance Computer) and the MIT Museum. That means the hard copy of the code held at the MIT Museum has been scanned and proofread for digital distribution. In this particular case, we have reams of machine code now in the public domain, easily accessible online.

To take a closer look at some of the example code, we dived into the Commanche055 directory, and the first interesting file that caught our eye was ALARM_AND_ABORT.agc. After a boilerplate introduction to the code at the top of the file, the purpose of the code is revealed – something which is quite obvious from the file name in this instance. Moreover, comments within the code reveal that the code logs alarm conditions, turns on a warning light if applicable, and handles various abort-level and non-abortive alarms.

Another interesting code example, shared on social media (embedded above), highlights the crucial 30 lines of assembly for calculating Apollo 11's navigation trajectories.

If you want to go beyond nosing around the historic codebase, it is possible to compile the code now shared into the public domain using the Virtual AGC tool (GitHub link). The Virtual AGC software has been designed to work in Linux, in Windows XP/Vista/7, and in Mac OS X 10.3 or later, and some versions of FreeBSD. This repo is also a good place to learn about the AGCs carried on both the Command Module and the Lunar Module.

The specs of the AGC pale in comparison to even the most rudimentary computers today. For example, an AGC had just 3,840 bytes of RAM and 69,120 bytes of storage. It could run at a maximum of about 85,000 instructions per second. Nevertheless, it was comparable in size to a desktop gaming tower PC of today at 24.250 x 12.433 x 5.974 inches (61.595 x 31.580 x 15.174cm), and weighed 70.1 pounds (31.8kg). It also needed two DSKY controller units at 17.8 pounds (8.1kg) each in the Command Module, and one DSKY in the Lunar Module.

Over half a century before Artemis II

It is fascinating to see this Apollo 11 code from nearly 60 years ago shared in the context of the ongoing Artemis II lunar mission. Today, we aren’t marveling at the lean and mean machine code that NASA is using to get humans to and from the Moon. Rather, Microsoft Outlook email bugs and a malfunctioning toilet on the Orion spacecraft may have taken the shine off the momentous achievement this latest mission represents.

Researchers at Aikido Security reported on Friday that they had found at least 151 GitHub repositories compromised by a threat actor tracked as Glassworm, which hides malicious payloads in Unicode characters invisible to the human eye. The affected repositories were compromised between March 3 and March 9, according to the Aikido Security blog, and the campaign has since expanded to npm and the VS Code marketplace.

The technique exploits Unicode Private Use Area characters — specifically, ranges 0xFE00 through 0xFE0F and 0xE0100 through 0xE01EF — which render as zero-width whitespace in virtually every code editor and terminal, and consequently appear as blank space to a developer reviewing a pull request. Meanwhile, a small decoder extracts the hidden bytes and passes them to eval(), executing a full malicious payload.

In past Glassworm incidents, that payload fetched and executed a second-stage script that used the Solana blockchain as a command-and-control channel, capable of stealing tokens, credentials, and secrets.

Aikido suggests that the 151 repos identified are likely a fraction of the total, since many had already been deleted before the research was published. Among the notable targets are repositories from Wasmer, Reworm, and anomalyco, the organization behind OpenCode and SST. The same decoder pattern also appeared in at least two npm packages and one VS Code extension uploaded on March 12.

Unfortunately, this most recent Glassworm campaign is harder to counter than previous iterations due to the sophistication of the malicious injections. Instead of showing up as obviously suspicious commits, they’re taking the form of version bumps and small refactors that are “stylistically consistent with each target project.” Aikido says it suspects the attackers are using large language models to generate this cover, since manually creating 151 bespoke code changes across different codebases wouldn’t be feasible otherwise.

Glassworm has been active since at least March 2025, when Aikido first found the invisible Unicode technique in malicious npm packages. By October, the same actor had moved into the Open VSX extension registry and GitHub repositories. An earlier investigation by Koi Security found the group used stolen npm, GitHub, and Git credentials to propagate the worm further, with decoded payloads deploying hidden VNC servers and SOCKS proxies for remote access. The Solana-based infrastructure makes takedown difficult, since blockchain transactions cannot be modified or deleted.

Aikido recommends scrutinizing package names and dependencies before incorporating them into projects, and using automated tooling that scans specifically for invisible Unicode characters, since visual code review doesn’t protect this class of injection.

OpenAI is developing its own code repository platform as an alternative to Microsoft's GitHub, according to a report from The Information. The project was prompted by a rise in GitHub outages that left OpenAI engineers unable to commit or collaborate on code for stretches of up to several hours, two people working at large GitHub customers told the publication.

The project is still in early stages and probably won't be completed for months, a person with knowledge of it told The Information. Employees working on it have discussed the possibility of selling access to the platform to OpenAI customers — though the company could ultimately keep it exclusively for internal use.

GitHub's reliability has degraded noticeably over the past year, following an overhaul of its infrastructure. GitHub CTO Vladimir Fedorov told employees in an October memo that the platform would migrate all of its software to Microsoft Azure within two years, calling the move "existential" to meet the demands of AI-powered tools such as GitHub Copilot.

Platform migration began in October 2025 and is still in progress, which means GitHub is running in a split-traffic state across its legacy Virginia data center and Azure. Multiple recent outages have been attributed either directly to Azure or to configuration issues introduced during the migration. In early February, a four-hour outage was traced to an underlying Azure problem. A separate outage a week later, which took down many GitHub services for around three hours, was attributed to a configuration change — with GitHub acknowledging in a public incident report that its availability was "not yet meeting our expectations."

GitHub reported a 58% year-over-year increase in incidents during the first half of 2025, rising from 69 cases to 109 — with 17 classified as "major" — totaling over 100 hours of disruption, according to a mid-year report from GitProtect.

If OpenAI does sell the platform commercially, particularly bundled with its Codex coding agents, it will mark a direct competitive move against Microsoft. Microsoft currently holds roughly 27% of OpenAI, and acquired GitHub in 2018 for $7.5 billion. OpenAI has already encroached on other Microsoft territory: It's reportedly developing ChatGPT features that overlap with Office applications for document collaboration and presentation editing.

Building internal code repositories is not unusual for large tech companies. Google runs Piper, and Meta runs Sapling — though neither has been released as a commercial product. An OpenAI commercial offering would be a different proposition — though losing OpenAI as a customer would be mostly symbolic for GitHub, given its tens of millions of paying users, according to the source from The Information.

Though you might appreciate the clean, unfussy minimalism of the Linux terminal, it can also be extensively customized using a few lines of code, as shown in our guide. However, developer Veirt has pushed things far beyond giving the CLI a new lick of paint with Weathr. This is a Rust-based app that teleports real-time ASCII art-based weather animations to your terminal backdrop.

The above animated demo provided by Veirt shows the terminal at night, during a thunderstorm. Here you see a moonlit backdrop flecked with rain, with an occasional fork of lightning piercing the night sky. This ASCII animation isn’t random; it is driven by tapping into real-time weather data provided by Open-Meteo and using auto-location detection. Veirt says that the animation engine can pull in “animated rain, snow, thunderstorms, flying airplanes, [and] day/night cycles.”

If you want to enjoy these real-time weather backdrops in your terminal, so you no longer need to look out of the window to check the real-world weather, your Linux machine will need Rust installed. Veirt shares a guide to setup, installation, and configuration. Moreover, you can test/simulate weather conditions to make sure Weathr is working properly. In the UK, for example, you might want to simulate a sunny day, for a change.

Weathr has been released on GitHub under the GPLv3 license, and Viert thanks/credits Open Meteo and ASCIIArt.EU for sharing their resources.

This fun and almost useful app has a development roadmap that includes support for other weather APIs, binaries for ARM64, hotkeys to control the animations, and more.

We’d also like to see some seasonal frills added. Imagine the extra fun of adding Santa and his sleigh whizzing by during the 12 days of Christmas, or animations featuring the Easter bunny, Halloween witches on broomsticks, occasional fireworks, falling leaves, and so on…

As mentioned in the intro, we have previously published a guide to customizing the Linux terminal with rainbow text, ASCII Art, and more. If you think Weathr might be a little bit too distracting, you can still spruce up the terminal with a fresh look – and include subtle nuggets of information like CPU temperature, IP address, and (also) even the latest weather conditions, without making a big show out of it.

There’s something extremely alluring about vector displays for tech enthusiasts beyond a certain age. Recently, legendary Windows developer Dave W Plummer got hold of a prime example of this type of monitor, the HP1345a, and set about making it usable with a PC via USB serial connection. Plummer has been busy, already showcasing a vector demo andan Asteroids clone on the HP monitor, best known for its starring role in the classic 1983 movie War Games.

Above, you can see that Plummer managed to crack this nut successfully, then showcase the display in action with a vector-tastic animated computer graphics demo. But look at the wiring spaghetti that is required to connect the HP1345a to the ESP32‑S3 microcontroller, and then to the PC.

The ancient HP ‘War Games’ monitor “uses a completely proprietary, unbuffered, and unterminated interface: D0-D15 data lines and then two handshake lines: ReadyForData and DataValid,” explains the developer. “I decided to use an ESP32 to interface with it. I wired up the 16 data lines and the two handshake lines to free GPIOs, then set about writing code to ‘bridge’ data from the serial port to the HP.”

A Python app is then used to drive the display, which renders a binary stream at 921,600 baud. All this code, and further details of the HP1345a, plus Plummer’s DIY interface, are available on GitHub. Potential DIYers are warned that, should they be lucky enough to source one of these ‘War Games’ monitors, they should be careful of opening or servicing the display due to the high voltages inside the display cabinet.

Asteroids ahoy

Hot on the heels of his first HP1345a interfacing success, Plummer has completed a “quick and dirty port of some Python Asteroids code” to the USB-HP bridge he created. This looks like a great way to enjoy a vector gaming classic. Of course, this announcement was followed by requests for other vector game ports – Star Wars, Battle Zone, Tempest, et al.

Incidentally, we have covered the news of the Vectrex Mini console in recent months. This crowdfunding project lacks an actual CRT vector display, as they aren't manufactured anymore. Though we haven’t had a hands-on with the Kickstarter product, its display will almost certainly underwhelm, being based on OLED tech and measuring a mere 5 inches diagonally.

A new emulator makes running classic Apple Macintosh 68K software a breeze on a cheap, portable, IoT development kit. Developer amcchord has ported the popular Basilisk II Mac emulator to the ESP32-P4 / M5Stack Tab5 ($60). The device name gives away that this is a small (5-inch) tablet that relies on an ESP32-P4 SoC for horsepower. We’ve seen 68K Mac emulation on microcontrollers before, but as Hackaday points out, this represents a major step forward in performance on one of these tiny SoCs. In brief, its RISC CPU is capable of delivering 68040 Mac-level computing in OS8.1.

This ESP32-P4 / M5Stack Tab5 port of Basilisk II is particularly appealing for Mac emulation tinkerers, as it is quite a well-rounded platform. It is a tablet, and can be used as such in the emulator, with its native touch functionality replacing mouse interactivity. In computer terms, it becomes almost a touch-enabled All-in-One, with your keyboard/mouse of choice attached via USB.

First-decade Apple Macs were also most famously represented by All-in-One designs with tiny screens (9-inch 512x342 pixel mono displays). In comparison, the Tab5’s 2x scaled 640x360 resolution 8-bit color output piped to the 5-inch 1280x720 IPS touchscreen could challenge your near vision.

For processing power, the Basilisk II ESP32 is said to be up to the level of a Motorola 68040 with FPU (68881). That would be like a mid-90s professional Macintosh. This implementation is limited to a maximum of 16MB of memory, though, using half of the ESP32-P4 / M5Stack Tab5’s 32MB. That should be enough for tinkering in MacOS versions all the way up to version 8.1, but the refresh speeds will be limited to 15fps, which is fine for productivity but might irk in classic game titles.

The ESP32 here has a dual-core RISC-V architecture running at 400 MHz, and the emulator uses one core for video and I/O, with the other for the 68040 interpreter, and other low-level system function patching.

For storage, users of this IoT tab can make use of HDD and CD images that are saved on an SD card. In addition to the OS and apps you have, this is where you will put your Mac boot ROM files.

Overall, it is a very neat and portable all-in-one emulation solution. It would also be a great starting platform for modding, as you could make a mini Mac Classic with this and a 3D printer without jumping through too many hoops.

68K Mac emulation in a browser

If the above tale of cute portable classic Macintoshes has stirred something deep inside, you can very easily and quickly enjoy a quick 68K Mac fix through your browser.

A couple of years ago, we shared a guide on How to emulate an original Apple Macintosh in your browser. Check that out, and we will talk you through visiting Infinite Mac. We also discuss various Infinite Mac configuration options for those who want to have more than a fleeting encounter with a vintage Mac OS and software.

Leading Chinese fabless semiconductor company Rockchip has had one of its major software repositories taken down in response to a DMCA takedown notice. Specifically, its rockchip-linux / mpp repository on GitHub is currently inaccessible, after the FFmpeg team complained about Rockchip’s cavalier attitude concerning intellectual property ownership, attribution, and matters of copyright infringement. Rockchip uses this code to accelerate video de/coding on its popular SoCs. If the situation isn’t remedied, it could have broad implications for users of Linux multimedia stacks, SBC communities, Android builds, and more.

FFmpeg’s complaint

FFmpeg shares its multimedia processing toolkit under the LGPL-license, and it alleges that Rockchip has infringed its copyrighted work on the libavcodec library, which contains numerous audio and video parsers and decoders for various formats.

You might assume that this is free and open source software, so little harm has been done. However, Rockchip’s Linux MPP (Media Processing Platform) code on GitHub has wholesale copied swathes of FFmpeg code, removed any attribution, and re-licensed it under Apache, which is incompatible with FFmpeg’s LGPL license. LGPL requires the original license and attribution to be preserved. Moreover, Apache adds patent clauses that LGPLv2.1 does not permit.

Rockchip dragged its feet for years

FFmpeg appears to have been quite patient in trying to converse with Rockchip devs to iron out issues ahead of this takedown. There is evidence of chats between FFmpeg and Rockchip devs taking place on Twitter/X and GitHub since early 2024, as a video from Brodie Robertson shows (h/t Hackaday).

In this chat history, we see a Rockchip dev admitting to copying the FFmpeg code in the manner they did “due to lack of understanding” about LGPL and Apache licensing conflicts.

Also in 2024, the Chinese company grumbled about being busy, but pledged fixes would come. However, the last archived response from a Rockchip dev, from last November, stated that “there are too many chips to verify and suspend…” which reduced any hope of a friendly settlement, provoking action.

What now?

FFmpeg has faced some criticism for this DMCA takedown, which GitHub has honored. However, remember that its code has been copied and re-licensed with new authorship claimed, despite large sections of code being identical and preserving original FFmpeg dev comments.

A proposed remedy to the situation, from FFmpeg, is as follows: “remove false authorship claims; restore original attribution and copyright notices; distribute the code under an LGPL-compatible license (e.g., LGPL itself, GPL, AGPL, etc.).” The nuclear option of “remove all infringing files” is also available, as is a choice to rewrite all the code without leaning on FFmpeg sources.

If the DMCA isn’t resolved, it isn’t just an annoyance for Rockchip. Linux, Android, and SBC devs, reliant on Rockchip’s MPP for hardware-accelerated video playback, might have to fall back to doing this processing in software (with multiple negative impacts). If the downstream developers continue to use MPP, they risk losing trust, losing OS support, and facing legal risks.

A motivated Lenovo Legion Pro 7 (16IAX10H) owner has successfully gotten their system’s speaker issues fixed after setting up a bug bounty program on GitHub. Nadim Kobeissi, a Linux user, was fed up with the gaming laptop’s “tinny and muffled” speaker output and suspected an issue with the Realtek codec on the open-source OS. Kobeissi posted the project in October with $500 of their own money, and five others pledged their own funds, bringing the total to $2,000. Now the issue has been solved, and the fixer is getting $2,000 from the community that developed around this rallying call.

Bug bounties have proved to be a worthwhile avenue for even the biggest developers to explore – Apple, Google, and Microsoft are among the big-dollar program organizers. In this instance, it is interesting to see this level pulled on a much smaller scale, but for a quick and successful fix.

Kobeissi fired up support for this GitHub-based bug bounty program, which kicked off in October. “We are a bunch of Linux users with the Lenovo Legion Pro 7 (16IAX10H) and we are sick and tired of our speakers not working properly,” the frustrated Legion user wrote. They added that they “also suck at writing Linux kernel audio drivers,” before promising “we will send you a lot of money,” for a fix.

You can see, above, that the Kobeissi ended up being the second-biggest contributor (money wise) to this bug bounty program. It ended up raising a nice, neat $2,000 for the solution finder.

Kobeissi also did their best to help any potential fixer, with a few educated guesses about the source of the Legion Pro 7’s speaker audio annoyances. Chief suspect was the incorrect detection of the Realtek ALC3306 codec. But there was also an issue with “no integration between the codec and the amplifiers in the audio pipeline,” in this laptop, which sports both Tweeters and Woofers.

The bounty-fix is for everyone!

The fix for this audio issue was posted on GitHub just a couple of days ago, indicating the time from the beginning of the bug bounty program to a solution was approximately a month. Kobeissi provides a step-by-step guide here, which affected users can follow.

This guide works for Linux kernel version 6.17.8. Moreover, the guide will get updates for future kernel versions “until the fix is fully integrated into the kernel,” notes the bug bounty organizer. If when you follow the fix process, “Your audio should now work correctly and permanently. This fix will persist across reboots with no additional steps required,” says Kobeissi.

Developer Yakov Till, AKA Lepsus, is largely credited with the fix (for “95% of the engineering work”). They get the monetary reward and heartfelt thanks of those who pledged to support the bug bounty.

We conclude by pondering whether this kind of private bug bounty, organized to eliminate computing annoyances, could set a trend.

GitHub has become one with Microsoft today, marking the end of the website's independence. Former CEO Thomas Dohmke announced he would step down from his role at GitHub today, leaving Microsoft to take control of the company. GitHub will now become part of Microsoft's CoreAI team, with no successor yet announced for Dohmke.

Dohmke shared in a GitHub blog post that his departure is due to a desire to found a new tech startup. Dohmke will stay on at GitHub through the end of the year to assist in the transition of folding GitHub fully into Microsoft, leaving his founder dreams on hold until then. In his personal X (formerly Twitter) post announcing his resignation, Dohmke teased that future projects may include a new GitHub successor.

GitHub is one of the largest code repositories in the world, having become an essential tool for spreading and collaborating on code projects. It was built based on Git, a distributed version control system for developers created by Linus Torvalds in 2005 to support the development of the Linux kernel. GitHub recently saw its one billionth repo hit the site, which was simply the word "sh*t".

Dohmke has been a key part of GitHub in the Microsoft era, joining the company following Microsoft's $7.5 billion acquisition of the site in 2018 and becoming CEO in 2021. GitHub reached 150 million users and 1 billion repos under Dohmke and became the first landing place for Copilot AI. Microsoft's original acquisition of GitHub allowed the site to continue operating independently from Microsoft. However, that independence will soon end as GitHub, a currently remote-first workplace, becomes further wrapped into Microsoft's control.

Microsoft's decision to wrap GitHub into its CoreAI teams likely indicates an intent to double down on GitHub's AI projects. GitHub Copilot, the first iteration of Copilot to make it to commercial launch in the Windows ecosystem, has become a successful AI coding assistant and a key part of GitHub's public focus, recently receiving a free version for all users. The focus on AI-assisted coding is a well-debated topic in the programming world, with even AI coding assistants hallucinating and telling users to stop using it and learn to code themselves.

Some internet speculators have become wary of the future of GitHub now that it is becoming fully wrapped into Microsoft. Past acquisitions, such as those of Skype and Xamarin, serve as examples of companies and programs that Microsoft has since discontinued. Some fear GitHub may face a similar fate if it is not rebranded and Teams-ified instead. The future of GitHub is uncertain, but Microsoft is highly unlikely to discontinue the operations of the cornerstone of modern coding.

A high-tech tinkerer has managed to get Apple’s iOS to run on the Nintendo Switch. Don’t get too excited, though, as PatRyk on X wryly admits they have created “the world’s slowest ‘iPhone’.” Booting this Nintendo ‘iPhone’ currently takes 20 minutes, and all the apps they tried crash – but it’s a start.

Baby steps

While the gaming tech world is all a flutter with the recently launched Switch 2, PatRyk spent two days attempting to shoehorn Apple’s mobile OS onto the best-selling hybrid console. This enthusiast’s modesty seems to be matched by their patience, as the new Nintendo ‘iPhone’ takes 20 minutes to boot. We wonder how many times they waited that length of time, or even longer, for the boot process to hang or otherwise fail?

Using the mobile OS also sounds like an incredible life-sapping chore, with this ‘iPhone’ in its current state. PatRyk says that the system “kernel panics every 2nd thing you do, can’t open any apps (they all time out and crash).”

At this stage, the project might sound dire, but it shows that running iOS on the original Switch is possible – it just needs optimization. But you aren’t going to be ditching your real Apple iPhone for a Switch any time soon.

Underlying technology - the versatile QEMU

GitHub has hit an illustrious milestone with the publication of the one billionth repository this week. Whether by chance or design, Aasish Pokhrel from Nepal earned the honor of publishing the billionth nugget of code shared on the platform. Unfortunately, it’s ‘sh*t.’ That isn’t a snarky comment, or our judgment regarding the code, the repo is simply the word ‘sh*t.’

Despite its minimal content, the ‘sh*t’ repo seems extremely popular within the GitHub developer community. At the time of writing, there have been 73 pull requests made, 147 forks, 188 issues raised, and the project earned 2.1K stars from the community.

The issues raised and pull requests sections are, as you might expect, full of… witty comments on Aasish Pokhrel's code. Some issues raised include "this code is sh*t," "experiencing trouble pushing," and even a request for "macOS support."

GitHub.com/AasishPokhrel/sh*t’s pull requests section is also littered with devs asking for regional translations, emoji support, and more.

Pokhrel definitely hit the jackpot with his ‘sh*t’ repo, and we have to wonder whether there will be any worthy follow-through. The Nepalese developer currently has 20 repositories under his name, primarily containing HTML and CSS code. Shortly before ‘sh*t’ they published the even pithier ‘yep,’ a one-star project which seems to have been overlooked by the GitHubbing throng.

GitHub’s billionth repo perhaps deserved better

Your Linux distro already comes with a great package manager but sometimes you need applications that aren’t available via the official repositories.

Third party applications, such as Google Chrome, Slack, Zoom, Visual Studio Code are provided by their respective publishers. For Debian and Ubuntu systems these are provided as Debian package files (.deb) and for Fedora / Red Hat as RPM packages. These packages can be installed using your preferred OS GUI package manager, or they can be installed using a command line tool. But what if we had a tool which handled downloading, installing, upgrading and removing these third-party packages for us?

Deb-get, from Martin Wimpress is “a high-level command line interface for the package management system to easily install and update packages published in 3rd party apt

repositories or via direct download.” With deb-get we can download and install third party packages without leaving the terminal.

How can we use deb-get and what applications are available? Let's find out.

We tested the process on two machines, a laptop running Ubuntu and a desktop running Kubuntu.

Not every Ubuntu or Debian release is supported, but if you are running any of the following, you are good.

• Debian
  • Buster (10)
  • Bullseye (11)
  • Bookworm (12)
  • Trixie (13)
  • Forky (14)
  • Sid (Unstable)
• Ubuntu
  • 20.04
  • 22.04
  • 24.04
  • 24.10
  • 25.04
  • 25.10

The installation process for deb-get uses, deb-get. We download a copy of the application from Wimpress’ Github repository and then run the command to install it onto our system.

1. Open a terminal and update the software repositories list. This enables our machine to install the latest software from the software repositories.

sudo apt update

2. Install the curl application.

sudo apt install curl

Curl is used to download the deb-get application from the Github repository. It is highly likely that your version of curl is up-to-date, but it is always best to check.

3. Use curl to download deb-get to your machine, then run the installation using sudo.

curl -sL https://raw.githubusercontent.com/wimpysworld/deb-get/main/deb-get | sudo -E bash -s install deb-get

How-to Use deb-get

Behaving in much the same way as apt and apt-get, deb-get has a similar set of commands to manage applications on our machine.

We start by installing an application, after consulting the list of supported software we chose to install Microsoft’s Edge web browser for Linux.

1. Open a terminal and update the deb-get package index files. This is essentially the same as our software repositories list.

sudo deb-get update

2. Use deb-get with the install argument to download the latest stable version of Microsoft Edge for Linux. Remember to consult the support software list to get the name of the application that you wish to install.

sudo deb-get install microsoft-edge-stable

We can update all of the applications installed via deb-get via one command. It will even catch supported applications installed before deb-get has been installed. The application upgraded Zoom and Raspberry Pi Imager, both of which were installed before we installed deb-get.

sudo deb-get upgrade

To search for a particular application, we can use the search argument. For example, here is the command to search for Firefox. Note that the output shows the name that we should use for installation.

sudo deb-get search firefox

To show all of the applications that can be installed via deb-get we used the list argument.

sudo deb-get list

To remove an application, we have two choices. The first is that we can perform a standard uninstall that will remove just the application. However, this leaves any user-generated configuration files in their respective directories.

sudo deb-get remove 

Secondly, if we wish to fully purge the application and any configuration files, then we can use the purge argument.

sudo deb-get purge 

To clean out your local repository of downloaded installation files we can use the clean argument. This command is generally used for housekeeping, ensuring that your system is kept tidy and that unwanted files are removed.

sudo deb-get clean

Deb-get is a remarkably useful tool to add to our Ubuntu installations. It makes short work of installing the software that we know and love, software that is either not available, or much older in the official repositories.

Related Tutorials

• How To Check Disk Usage in Linux
• How To Kill a Process in Linux
• How To Find Files in Linux

Software engineer Sven Scharmentke (AKA Svnscha) has shared a project that they say "might change crash dump analysis forever." The new contextual natural language conversational tool contrasts sharply with manually typing arcane commands into a terminal to analyze crash data, which is the established modus operandi of a professional software engineer, even in 2025. Svnscha goes on to assert that the new open-source tool, available as mcp-windbg on GitHub, can go deep. The AI runs a "simply amazing" array of WinDBG/CDB commands, boosting productivity and eradicating a once tedious task.

AI peddlers have developed large language models that initially targeted creative writing and have since moved to visual arts – graduating from hand-drawn style art and photorealistic creations to videos in recent times. AI has also made significant inroads into computer programming. However, most people want AI to leave the fun, creative stuff to humans and take the sting out of dull, soul-crushing, repetitive tasks. This seems to be where Svnscha's mcp-windbg might actually succeed.

In brief, the mcp-windbg tool enables AIs to interact with Microsoft's multipurpose debugger WinDBG. Its cunning skill is in giving LLMs the ability to execute debugger commands. And, even if you have swallowed the WinDBG manual, this AI will likely know debugger commands better than you. Copilot will probably also interpret assembly code, decode hexadecimal, traverse structures with symbols, and perform other tasks much better than you. You just have to ask the right questions.

Background and demos

Svnscha goes into some detail about their prior frustration in debugging crashes, the lightbulb moment to involve AI, and how the mcp-windbg tool works, all in a dedicated blog post. The software engineer humbly admits the 'hard part" of this work was implementing the CDB (Microsoft's Command-Line Console Debugger) interaction layer, and this was done during a session "vibe-coding with two coffees on a Saturday morning."

However, the videos shared help encapsulate the leveraging of Copilot with CDB. There are two to watch on the linked blog, starting with a Crash Analysis and Automated Bugfix using Copilot, followed by an Automated Crash Dump Analysis of Multiple Crash Dump Files.

What you first see shows Svnscha installing mcp-windbg, then running a Windows app that crashes. Next, GitHub Copilot is fired up and asked to find the crash dump and fix the underlying code issue. "The AI quickly identifies that the application crashed, explains which specific conditions led to the crash, and suggests a fix," says Svnscha.

The second video is similar but shows the tool analyzing multiple files simultaneously.

'Simple wrapper' lifts crash analysis from the stone age to the rocket age

The contents of Winamp’s “open source” GitHub repository has been deleted after less than a month. This move happened after its owners, Llama Group, received criticism from the free and open-source software (FOSS) community for its highly restrictive license. The original GitHub release was under the Winamp Collaborative License (WCL) 1.0, which states No Forking: You may not create, maintain, or distribute a forked version of the software. The deletion seemingly occured after The Register contacted the developers regarding the inclusion of Shoutcast DNAS code and some codecs from Intel and Microsoft. 

The Register shared that some GitHub users noted in the comments of the project that this license actually violates the platform’s terms of service, and that the repository upload isn’t actually open source. This license was eventually revised to WCL 1.0.1, which changed the line to No Distribution of Modified Versions: You may not distribute modified versions of the software, whether in source or binary form. This means you can now build upon the code that Winamp uploaded to GitHub, but you won’t be able to share it with anyone. So, while the Winamp source code was online for all the world to see, it’s wasn't fully open source, as you cannot freely redistribute the modifications you make.

Aside from these licensing shenanigans, other GitHub users also noted that there were a few bits of code in Winamp’s release that it didn’t own (or didn’t own anymore) to begin with. The Register says this includes the server code for Shoutcast Distributed Network Audio Server (DNAS), which was originally developed by Nullsoft, the original owners of Winamp. The Llama Group eventually bought Winamp and Shoutcast, but it sold the latter to Azerion in 2022, meaning it doesn’t have the rights to the Shoutcast DNAS anymore. Proprietary Microsoft and Intel source code were spotted in the repository as well, which could potentially land the Llama Group in hot water with these giants.

It's likely because of these issues that it took down the Winamp repository from GitHub, and that we’re unlikely to see any form of Winamp — classic or otherwise — in any open-source platform. This legacy media player is just too old, with many proprietary bits and pieces coming from several other companies that won’t be too keen to see their code released to the world for free. It would take a massive investment and a lot of time to clean up Winamp’s back-end for an open-source release, but would anyone do it, knowing they wouldn’t make back a cent?

Yesterday, the beloved legacy media player application Winamp released its source code on GitHub. Winamp isn't as popular in today's era of apps that stream music with licensing built in (a la Spotify and Apple Music), but back in the late 90s and early 2000s, it was by far the most popular third-party media player on PC. That should make this a momentous occasion— however, as many members of the FOSS (Free and Open Source Software) community have quickly pointed out, its highly restrictive license prevents a lot of what we'd usually expect from releases like this. So, while Winamp is on GitHub and this is a source code release, it's not like the application has gone "full" open source or anything.

Typically, source code releases correspond to new branches of a given piece of software, often ones that test improvements that may or may not return to the original. However, the Winamp Collaborative License (WCL) Version 1.0.1 dictates that there will be "No Distribution of Modified Versions: You may not distribute modified versions of the software, whether in source or binary form." Unfortunately, this means we won't see any enhanced or "classic" versions of Winamp anytime soon, at least not from this release.

That's another point of contention—if you only care about the classic version of Winamp, you may be a little disappointed by the modern-day version of the application, which is all included with this release. Winamp Classic can still be found elsewhere on the web but isn't open-sourced—and ideally, a GitHub release like this would be definitive and have an open enough license for the community to evolve Winamp to its highest form.

On the bright side, fanatics of the classic Winamp designs aren't out of luck if they want to keep that aesthetic. Plenty of classic Winamp skins for current Winamp exist, and some enthusiasts even go as far as building physical Winamp-themed music boxes, a la Linamp.

Hopefully, future versions of the Winamp Collaborative License (WCL) will remove some of these restrictions, allowing the FOSS community to do the work it would like on such a beloved, historical application. If they have the rights and the files, a proper open-sourcing of Classic Winamp would alto make many enthusiasts happy.

A secret network of around 3,000 “ghost” accounts on GitHub has been discovered manipulating the code-hosting platform to promote malware and phishing links. Recent research conducted by cybersecurity firm Check Point exposed the activities of a cybercriminal the researchers have named “Stargazer Goblin.”

Since June 2023 or even earlier, Stargazer Goblin has been active on Microsoft-owned GitHub, the world’s largest open-source code repository. The site hosts millions of developers’ projects, and Stargazer Goblin has been using its community tools to boost malicious code repositories’ visibility and perceived legitimacy.

Antonis Terefos, a malware reverse engineer at Check Point who uncovered this network, highlighted the sophistication of the operation. He noted that while GitHub has been targeted by cybercriminals before, the scale and method of this operation are unprecedented.

Repositories and stars are bought and sold through a cybercrime-linked Telegram channel and various criminal marketplaces. Telegram is commonly used by cybercriminals, their clients, and their victims. Terefos said he has never seen this kind of network of fake accounts operating like this on GitHub.

The Stargazers Ghost Network by Check Point spreads malware disguised as legitimate tools for social media, gaming, and cryptocurrency applications. Some examples included code for running VPNs or licensing software like Adobe Photoshop. Such repositories target Windows users who are searching for free software online. 

The network charges other hackers to use its services. Check Point has identified various types of malware distributed through this network, including the Atlantida Stealer, Rhadamanthys, and Lumma Stealer. Terefos discovered the network while digging into instances of the Atlantida Stealer.

Stargazer Goblin places ads on cybercrime forums, and its Telegram channel offers services such as 100 stars for $10 and 500 stars for $50. It also offers to clone existing repositories and provide trusted accounts. Check Point’s research indicates that the network may have started these activities as early as August 2022 and could have collected up to $100,000 since then. From mid-May to mid-June this year alone, the operator reportedly made around $8,000.

Terefos has observed legitimate repositories being hijacked and transformed into malicious ones using stolen credentials. The malicious code could be further propagated if legitimate users fork these compromised repositories. Automated tools help Terefos identify accounts linked to the network by recognizing common features, such as similar templates and tags.

When GitHub identifies an account supporting illegal malware campaigns, it disables those user accounts for violating its Acceptable Use Policies. Alexis Wales, vice president of security operations at GitHub, stated that the company has dedicated teams to detect and remove such content and accounts. These teams use a combination of manual reviews and at-scale detections using machine learning to identify suspicious behavior.

Unfortunately, GitHub is a gigantic target with over 100 million users and 420 million repositories. This makes it a reasonably insignificant challenge for cybercriminals to hide within the user base like a grain of sand on the beach.

Jake Moore, global cybersecurity adviser at security firm Eset, warned GitHub users about the risks of downloading malicious code. Indicators of malicious repositories include unexpected code changes, code accessing external resources, and hard-coded credentials or API keys.

Stargazer Goblin’s network might be even broader, as evidenced by a YouTube account sharing malicious links via videos. Terefos emphasizes that the full extent of the network’s operations is still not entirely known.

A fundamental limitation of modern artificial intelligence and neural networks is that they aren't good at spatial mapping or navigation without an existing map. However, TechXplore reports that a combination of a predictive coding algorithm and Minecraft gameplay successfully "taught" a neural network how to create spatial maps and subsequently use those spatial maps to predict the following frames of video, yielding a mean-squared error of 0.094% between the predicted image and the final image.

The project demonstrates genuine spatial awareness of AI, which still isn't seen in the impossible architecture and other strange glitches that come with things like OpenAI's Sora.

These findings come from a paper published in the Nature Machine Intelligence journal on Nature.com, automated construction of cognitive maps with visual predictive coding, from James Gornet & Matt Thomson of the California Institute of Technology (aka Caltech). The paper, released to the public just yesterday, details exactly how this was achieved in exhaustive detail and even shares the code on GitHub and Zenodo.

One of the two researchers who worked on the project, Matt Thomson, spoke to TechXplore and provided a few noteworthy quotes about the process and what led them to undertake it.

Per Matt Thomson, "There's this sense that even state-of-the-art AI models are still not truly intelligent. They don't problem-solve like we do; they can't prove unproven math results or generate new ideas. We think it's because they can't navigate in conceptual space; solving complex problems is like moving through a space of concepts, like navigating. AIs are doing more like like rote memorization— you give it an input, and it gives you a response. But it's not able to synthesize disparate ideas."

James Gornet, the graduate student who led the project, encouraged the use of Minecraft and studied neuroscience, machine learning, math, statistics, and biology under the Department of Computational and Neural Systems (CNS) at Caltech. He did not provide a quote about the process, but Thomson says that CNS is uniquely suited for James's work and that "we're hoping to learn about the brain in turn," not just advance AI.

Microsoft has released the MS-DOS 4.00 source code, binaries, disk images, and documentation. The source code, which is nearly 36 years old (version 4.00 came out in 1988), has been released under the MIT license, allowing tinkerers free rein. MS-DOS 4.00 is quite an interesting version of this command-line-driven OS, as it was written in partnership with IBM and formed a branch of DOS called Multitasking DOS (or MT-DOS), that only had a limited release.

In its blog post about open-sourcing MS-DOS 4.00, Microsoft says this release was precipitated largely thanks to correspondence between former Microsoft Chief Technical Officer Ray Ozzie and a young computer researcher called Connor ‘Starfrost’ Hyde.

The code and materials now available on GitHub came from Ozzie. He was working at Lotus when he received some unreleased beta binaries of DOS 4, which he kept safe. VP Developer Community at Microsoft, Scott Hanselman, and internet archivist and enthusiast, Jeff Sponaugle, carefully digitally preserved the ‘Ozzie Drop.’ Then, the relevant permissions were sought and granted before this open-source release.

So, what is special about this MS-DOS 4.00 release? As well as being the newest open-source release of MS-DOS, it has some multitasking abilities. Hyde shares quite a deep dive into this aspect of the ancient OS, which would form the foundation of OS/2.

One of the key parts of the intended multitasking workflow of MS-DOS 4.00 is the session manager (SM.EXE). Hyde explains that this system component allows hot-key switching between up to six pre-defined applications (see sample image above). However, in his tests the way SM.EXE works is seriously buggy, so using it as it stands is not very practical. If you are interested in learning more about multitasking in MS-DOS 4.00, and all the utilities that come with the release, Hyde’s in-progress blog is well worth a read.

The MS-DOS 4.00 Intel 8086 assembly code, binaries, disk images, and documentation are now available on GitHub. If you want to quickly run the OS for yourself, you can find the two disk images in the v4.0-ozzie/bin directory. Enthusiasts with serviceable old hardware like an original IBM PC XT, or a newer Pentium system, can run / install the OS natively. Others can play in the open-source PCem and 86box emulators, for example. The same GitHub repository holds MS-DOS 1.25 and 2.0 source code.

Software developer and self-confessed spreadsheet addict Ishan Anand has jammed GPT-2 into Microsoft Excel. More astonishingly, it works – providing insight into how large language models (LLMs) work, and how the underlying Transformer architecture goes about its smart next-token prediction. "If you can understand a spreadsheet, then you can understand AI," boasts Anand. The 1.25GB spreadsheet has been made available on GitHub for anyone to download and play with.

Naturally, this spreadsheet implementation of GPT-2 is somewhat behind the LLMs available in 2024, but GPT-2 was state-of-the-art and grabbed plenty of headlines in 2019. It is important to remember that GPT-2 is not something to chat with, as it comes from before the 'chat' era. ChatGPT came from work done to conversationally prompt GPT-3 in 2022. Moreover, Anand uses the GPT-2 Small model here, and the XLSB Microsoft Excel Binary file has 124 million parameters, and the full version of GPT-2 used 1.5 billion parameters (while GPT-3 moved the bar, with up to 175 billion parameters).

GPT-2 works mainly on smart 'next-token prediction', where the Transformer architecture language model completes an input with the most likely next part of the sequence. This spreadsheet can handle just 10 tokens of input, which is tiny compared to the 128,000 tokens that GPT-4 Turbo can handle. However, it is still good for a demo and Anand claims that his "low-code introduction" is ideal as an LLM grounding for the likes of tech execs, marketers, product managers, AI policymakers, ethicists, as well as for developers and scientists who are new to AI. Anand asserts that this same Transformer architecture remains "the foundation for OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Bard/Gemini, Meta’s Llama, and many other LLMs."

Above you can see Anand explain his GPT-2 as a spreadsheet implementation. In the multi-sheet work, the first sheet contains any prompt you want to input (but remember the 10-prompt restriction). He then talks us through word tokenization, text positions and weightings, iterative refinement of next-word prediction, and finally picking the output token – the predicted last word of the sequence.

We have mentioned the relatively compact LLM used by GPT-2 Small, above. Despite using an LLM which might not be classified as such in 2024, Anand is still stretching the abilities of the Excel application. The developer warns off attempting to use this Excel file on Mac (frequent crashes and freezes) or trying to load it on one of the cloud spreadsheet apps – as it won't work right now. Also, the latest version of Excel is recommended. Remember, this spreadsheet is largely an educational exercise, and fun for Anand. Lastly, one of the benefits of using Excel on your PC is that this LLM runs 100% locally, with no API calls to the cloud.

A new report published by the Wall Street Journal looks into the issue of businesses profiting from AI. The thorny issue is simple, with the big hitters of tech like Microsoft, Alphabet, Adobe, and AWS spending a lot on AI, but customers are currently not paying enough for profits to be made. 

In our startling headlining example, an insider told the WSJ that Microsoft used AI from its partner OpenAI to launch GitHub Copilot at $10 per month, but was losing >$20 per user per month on average in early 2023. Some Copilot users were even costing Microsoft as much as $80 per month.

It is well established that AI is a resource hog. Its proponents seem willing to invest in large quantities of expensive hardware, invest in associated software development, and run up power (and water) bills with abandon. As well as the large compute and data resources required to get an AI up and running, it must be maintained, upgraded, and serviced for competitive availability. But when will the returns on these big investments come?  

Microsoft’s GitHub Copilot revenue-to-cost ratio doesn’t seem sustainable. We have previously seen companies like Microsoft absorb such costs early in a product development cycle to build a market, but the WSJ report indicates changes are already on their way.

Companies working to provide AI services will be striving for profit by attacking the cost problem from multiple angles. The source report suggests both Microsoft and Google intend to increase prices for AI-backed software and services. Multiple pricing tiers and more restricted AI access levels are being considered. Specifically, Microsoft and Google will charge $30 on top of their regular workplace software suite subscriptions for access to AI enhancements, it is claimed.

It doesn’t have to be all-or-nothing with AI if a provider wants to  reins in costs. A good example of the rationing of AI is provided by Adobe, which uses a monthly credits system for access to its Firefly AI image generator.

To cut costs, companies are also looking at less powerful and cheaper AI tools, where they are good enough to get the job done. A relevant example here is to make use of ChatGPT 3.5 AI instead of the latest GPT-4, when it is adequate.

Another way to cut the price of AI provision comes from the hardware side. It is known that some AI businesses are interested in making lower-power, cheaper, and more efficient processors to take on AI workloads.

AI to Get a ‘Cold Shower in 2024’

With all the above considered it isn’t surprising that investors are starting to view AI investments with more caution. A report published by CNBC today focuses closely on this concern. It says that generative AI is set for a “cold shower in 2024,” as the buzz dies and a reality check comes - with eyes on business features like development and running costs, risks and regulations.

If you ever wondered whether it would be possible to game on one of Ampere's "Altra Max" 192-core CPUs, you'll be glad to know the company has just released a "Gaming under Linux" guide. It aims to empower users in setting up Steam AArch64 platforms for Linux - alongside Steam Play (Proton) itself - within the open-source OS, with Ampere's 192 cores serving as the proverbial cart pushers.

This is not just any old PDF guide, either. Instead, Ampere is maintaining a GitHub repository meant to ease users into configuring a system based on the company's Ampere Altra, Altra Max, and Ampere One CPUs. It should also be applicable to other (generally less-powerful) 64-bit Arm platforms offered by Ampere's competitors. 

Of course, Ampere only sells CPUs - there's still the need for a GPU in there somewhere taking over for graphics calculations. Ampere's documentation, for instance, uses a server-geared NVIDIA RTX A6000 to pull GPU duty. If you're curious about what that means in terms of pure graphics performance however, you might be in for a disappointment: the $6,800, 48 GB VRAM-toting A6000 delivers around 5-26% less performance than Nvidia's GeForce flagship, the RTX 4090, across both gaming (expected) and professional workloads (much more unexpected).

Remember that "Windows gaming on Linux" has been mostly conquered through Proton, leading to playable performance for most games released (and available) on the Steam Deck. That this can happen even while running an emulation layer is already a showcase of how powerful mobile chips have become. But with 192 cores available to process both the gaming's computing requirements and the additional overhead from the translation layer, it's hard to see how Ampere's idea falls flat --that's likely enough horsepower to push through any leftover "muck" in the emulation code (which in the case of Ampere's installation, is taken care of through the use of Box86 and Box64).

It's interesting that Ampere went to the trouble of creating such a package and tutorial guide: We'd expect the company's server CPUs to currently be staying far, far away from any gaming workloads. Of course, programmers and Large Language Model (LLM) wizards have a right to game, too. And perhaps there's demand for an Ampere-based gaming solution on the more mundane, tedious days. Or perhaps this is just a case of a company pointing out that gaming is possible on its products, in the hopes of raising its profile. Either way, it's nice to know that PC gaming is possible without Windows or native x86.

Staying on task is pivotal to good productivity—especially when working from home. But what if you could make the process of tracking your progress throughout the day a little bit easier? That’s exactly what maker and developer Dan Ionescu decided to do with this awesome Raspberry Pi Pico W progress bar project. This custom LED progress bar integrates with Google Calendar so you can see when you have appointments coming up at a glance.

Not only did Ionescu take the time to create the project, he also has shared a full build tutorial for anyone interested in recreating it at home. The setup involves an LED strip attached to a Raspberry Pi Pico W. Internet access is necessary for it to tap into your Google Calendar. Events from the calendar are displayed on the strip as a series of colored sections.

According to Ionescu, he started with a similar project named Hometime created by another maker known as Veebch. Instead of starting from scratch, he decided to build a fork off their original repository at GitHub that adds WiFi functionality. This makes the project a little more user-friendly and easier to set up around the house. You can find Ionescu’s WiFi-supporting fork over at GitHub, as well.

It doesn’t take much hardware to recreate this project but you do need something with WiFi capability. In this case, Ionescu is using a Raspberry Pi Pico W which is both small and affordable. That said, you could use a bigger Pi like a Pi Zero W if that’s what you have on hand. The LED strip used in this project is a 1-meter-long Seeed Studio WS2812B Digital RGB LED Flexi-Strip.

A full breakdown of the code is available on the project page. In it, Ionescu explains that appointment data is retrieved from Google Calendar of the account you specify. The strip interprets the schedule into a string of colors on the LED strip that represent each block of time. It then illuminates the LEDs on the strip so you can monitor your calendar progress throughout the day.

If you want to get a closer look at this Raspberry Pi project and see how it works, check out the build guide at Hackster and be sure to follow Ionescu for any updates on this project or more cool creations in the future.

A number of visual artists have filed suit over use of their images as training data for text-to-image generators. Now, two well-known novelists have filed their own class-action suit against OpenAI, accusing the company behind ChatGPT and Bing Chat of copyright infringement because it allegedly used their books as training data. This appears to be the first lawsuit filed over the use of text (as opposed to images or code) being used as training data. 

In the lawsuit filed in the United States District Court of the Northern District of California, plaintiffs Paul Tremblay and Mona Awad allege that OpenAI and its subsidiaries committed copyright infringement, violated the Digital Millennium Copyright Act and also ran afoul of California and common law restrictions on unfair competition. The writers are represented by Joseph Saveri Law Firm and Matthew Butterick, the same team that is behind recent lawsuits filed against Diffusion AI and GitHub (over GitHub copilot).

The complaint alleges that Tremblay's novel The Cabin at the End of the World and two of Awad's novels: 13 Ways of Looking at a Fat Girl and Bunny were used as training data for GPT-3.5 and GPT-4. Though OpenAI has not disclosed that the copyrighted novels are in its training data (which is kept secret), the plaintiffs conclude that they must be because ChatGPT was able to provide detailed plot summaries and answer questions about the books, a feat which would require it to have access to the complete texts.

"Because the OpenAI Language Models cannot function without the expressive information extracted from Plaintiffs’ works (and others) and retained inside them, the OpenAI Language Models are themselves infringing derivative works, made without Plaintiffs’ permission and in violation of their exclusive rights under the Copyright Act," the complaint says.

All three books also carry copyright management information (CMI) such as ISBN and copyright registration numbers. The Digital Millennium Copyright Act (DMCA) states that removing or falsifying CMI is illegal and, since ChatGPT's output does not contain that information, the plaintiffs allege that OpenAI is guilty of violating the DMCA on top of regular copyright infringement. 

Though the lawsuit only has two plaintiffs right now, the lawyers are seeking class action status which would allow other authors who have had copyrighted works used by OpenAI to also collect damages. The lawyers are seeking monetary damages, court costs and an injunction forcing OpenAI to change its software and business practices around copyrighted material.

We reached out to Butterick for comment on the lawsuit and he referred us to his website, LLM Litigation, which has a detailed explanation of the plaintiffs' position and why they are suing.  

"We’ve filed a class-action law­suit against OpenAI chal­leng­ing Chat­GPT and its under­ly­ing large lan­guage mod­els, GPT-3.5 and GPT-4, which remix the copy­righted works of thou­sands of book authors—and many oth­ers—with­out con­sent, com­pen­sa­tion, or credit," the lawyers write.

They also criticize the concept of generative AI, writing that "'Gen­er­a­tive arti­fi­cial intel­li­gence” is just human intel­li­gence, repack­aged and divorced from its cre­ators."

Like Saveri and Butterick's lawsuit against Stability AI for using copyrighted images as training data, this one hinges on the belief that grabbing text from the open Internet to power an LLM is not fair use. That's a question that has not yet been answered in court.

In a 2006 case, Blake vs Google, a writer sued the search engine for caching his work and making the cached versions available via search. However, a U.S. district court dismissed the suit, holding that Google's caching of the data was fair use. Judge Robert C. Jones wrote that holding documents in cache is a transformative use (one of four factors used to determine fair use) and that it doesn't harm the potential market for the work (another factor). So simply storing copyrighted data on its server in the form of a cache did not make Google liable.

However, using a copyrighted creative work as training data is quite a bit different than indexing content for search. One could argue that if the LLM is able to repeat key details from the book, it is harming the market for those works and it is not truly transformative. On the other hand, if a human writes a plot summary of a book, that generally doesn't run afoul of copyright law. Ultimately, these questions are going to be decided because of lawsuits like this one. 

OpenAI isn't the only company that's using copyrighted materials for training or even output. Google SGE, the company's new search experience, often plagiarizes whole sentences and paragraphs word-for-word from copyrighted articles. What happens in this suit could have a much wider impact on the generative AI industry.

There's a sort of unspoken attraction for anything "vintage" these days - things, ideas, or behaviors that have stood the test of time and are still relevant (or at least of interest) today. One such example of a vintage (or, let's say, legacy) entity in personal computers is Microsoft's original Clippy - the good-natured, charming, anthropomorphized paper clip conquered more than a few hearts since its original inception as a (glorified) assistant, way back in 1997.

Unfortunately, Clippy was left for dead in Microsoft's hands around 2005, with the company only recently revisiting it as a system-level sticker back in 2021. But software company FireCube wanted more, and it's done more in a way that makes Microsoft look as if it missed an oportunity: FireCube is now introducing its own open-source rendition of Clippy as a digital personal assistant, one that's finally been given a deep-dive course on actual assisting through its integration with OpenAI's Chat GPT (version 3.5). Being open-source, the code repository is available on GitHub.

Being ChatGPT-powered, this particular version of Clippy is now actually able to produce work for you, instead of only providing tooltips. Its conversational abilities are the same as what you've come to expect from OpenAI's fledgling Large Language Model (LLM). It'll be interesting to know how Clippy feels about his different versions through the years.

For now, FireBlocks says that anyone who plans to use its GPT-powered Clippy will need to have their own private OpenAI API key - essentially confirming that you've subscribed to one of ChatGPT's paying tiers, and allowing Clippy to access OpenAI's servers for query processing. For now, Clippy's requirements include a Windows 10 (version 17763.0 or above) PC with an internet connection.

The company has plans to improve  Clippy over time. It's likely further development will accompany OpenAI's own ChatGPT releases, adding whatever improvements OpenAI cooks in. But beyond that, the company is also looking to do away with the need for personal OpenAI keys entirely. At the same time, FireBlocks aims to make sure that you can keep things interesting by allowing Clippy to rest every once in a while by calling upon other storied Microsoft assistants (like Bob or classic Clippy himself).

This is all well and good, and having Clippy following your Windows antics is bound to provide some nostalgia. If you prefer to take things out of the digital realm and would like to deploy your own physical Clippy that's also powered by ChatGPT, we've got you covered. If you go that rout, you'll also get a chance to play around with a Raspberry Pi.

There’s nothing quite like old-school computing. Thanks to maker and developer Gary Explains, you can relive the classic coding experience of the 80s and 90s with this Raspberry Pi Pico Basic interpreter. The project is called PiccoloBASIC, enabling users to program simple projects using the Basic programming language.

According to Gary, the project offers a few simple functions that users can take advantage of, including commands like let, if, print, for, goto, and gosub. It also can interact with GPIO pins fundamentally—no pun intended. While it’s somewhat limited, users can still perform familiar beginner projects like blinking an LED and Hello World.

It’s worth noting that the project wasn’t developed entirely from scratch but was inspired by another Basic interpreter called uBASIC. This interpreter was created by Adam Dunkels, who has more details about it on his website and a GitHub page for the project in case anyone wants to delve deeper into how it works.

This is just a simple interpreter, but there’s plenty of room to expand upon it with more functions. Gary insists that anyone interested in contributing to PiccoloBASIC is more than welcome. The project is hosted as GitHub, so anyone who wants to branch off their fork can do so.

How to Set Up PiccoloBASIC

Gary was kind enough to share detailed instructions on how to get started with PiccoloBASIC. You can also find more details on using it on the project page, but this should get you started.

1. First, make sure you’ve got the Pico C/C++ SDK set up on your PC.

2. The PICO_SDK_PATH should be defined as:

PICO_SDK_PATH=/home/pi/pico/pico-sdk/

3. Clone the GitHub repository into a new directory called ‘build’.

4. Go into the new ‘build’ directory and run cmake:

make -j4

5. This will create a uf2 file named ‘piccoloBASIC.uf2’ that you can flash onto your Pico

If you want to learn more about this Raspberry Pi project, visit the PiccoloBASIC GitHub page, and follow Gary Explains for future updates.

Microsoft's Build developer conference is all about AI. The 2023 show, which is taking place both online and in person, is being headlined by AI, Bing Chat, and new tools for developers using Windows, GitHub, and the Windows Terminal. Every change, big or small, is being touched by artificial intelligence.

There's plenty of developments for the cloud, like AI in Azure, as well as a new analytics system that Microsoft is calling Fabric. But here are the AI announcements Microsoft is making at Build that you're most likely to see coming to tools you use soon:

AI Plugins

At Build, Microsoft said that it will use the same plugin standard as its partners at OpenAI use for ChatGPT. This should allow developers to make plugins that are interoperable between ChatGPT, Bing Chat, Dynamics 365 Copilot, and Microsoft 365 Copilot.

Microsoft is also announcing support for new plugins for Bing Chat. Newly announced add-ons include those for Instacart, Kayak, Redfin, Zillow, Klarna, and many others, alongside OpenTable and WolframAlpha. Communications head Frank Shaw told the press that as part of Microsoft 365 Copilot, there will be more than 50 plugins announced at Build, including from Atlassian, Adobe, Thomas Reuters and Mural, with "thousands" of plugins by time Copilot it hits general availability.

Copilot coming to Edge, Windows 11

Microsoft is bringing some of its Copilot features (which are currently in a private preview in MIcrosoft 365) to the Edge browser.

Microsoft 365 Copilot already uses large language models to answer requests from Microsoft 365 apps such as Excel, Word, Teams, Outlook, and PowerPoint. Now, the Copilot will also work with Edge, so people will be able to use the AI in conjunction with their web browser. Perhaps they'll get a PDF that opens in the browser, and users can ask Edge to summarize it.

Windows 11 will use Bing Chat along with third-party plugins and Microsoft's own. Windows Copilot will let people change settings, open apps or perform other actions with AI, while also mixing in Bing Chat to answer questions from the web. It will be able to be parked aside in a panel for easy access.

Media Provenance

At Google I/O, Google discussed features that would secretly mark images and videos made by AI so they could be identified as such. At Build, Microsoft is following suit with "media provenance" abilities being added to Bing Image Creator and Microsoft Designer.

Microsoft's tech will use cryptographic techniques to "mark and sign AI-generated content with metadata about its origin." The company says that this signifier will be in accordance with  standards set by the Coalition for content Provenance and Authenticity (C2PA), of which Microsoft is a co-founder. Microsoft says it will start by supporting "major images and video formats" when the initiative ramps up in the "coming months."

Microsoft Store Gets an AI Hub

The Microsoft Store will be filled to the brim with AI. The Microsoft Store will get a section specifically for AI apps and experiences. Developers can submit their AI-focused apps to be featured.

 AI is also coming to review summaries. Reviews in the app store will be scanned by AI, which will compile them and recapitulate. Additionally, developers will be able to fill their listings with AI-generated keyword suggestions to improve search engine optimization.

Dev Home

Beyond its AI tools, Microsoft also doubled down on Windows as a tool for developers with a new feature called Dev Home. Available in preview, it lets users set up a machine, connect to GitHub and build dashboards and other extensions, as well as use GitHub widgets and track CPU and GPU performance.

Microsoft is also adding WinGet configuration to the Windows Package Manager, taking care of setup and, according to Microsoft, dropping setup time from days to just hours. WinGet's configuration file will ensure you get the right version of software packages and frameworks.

There's also a new storage volume, Dev Drive, which Mifcrosoft says is based on the Resilient File System (ReFS) and offers a 30% improvement in build times, partially due to a "performance mode" in Windows Defender. Microsoft hasn't said much about this new mode, other than that  this "new performance mode is more secure for developer workloads than folder or process exclusions, providing a solution that balances security with performance.  

But of course, dev tools are also getting the AI treatment. Windows Terminal is gaining support for GitHub Copilot X, and will be able to use an "experimental chat experience" to use language both inline and in chat to learn more about errors, get recommendations and to perform actions throughout Terminal. Microsoft says it also wants to add GitHub Copilot-style AI in other developer tools, such as WinDBG.

We'll likely learn more about all of these features during the Microsoft Build keynotes. CEO Satya Nadella will start the show today at 9:00 a.m. PT / 12 p.m. ET. 

According to a report published by the Wall Street Journal (WSJ), Apple has implemented restrictions regarding how its employees use AI tools such as ChatGPT — not all that surprising given the Cupertino tech company is famously wary of leaks. The report from the WSJ suggests Apple’s employees won’t be disadvantaged by the lack of AI assistance forever, because Apple is busy creating “its own similar technology.” The report is based on an internal Apple document that appeared to back up these claims, as well as anonymous sources "familiar with the matter."

One of the issues with the use of AI based on large language models (LLMs) is that the tools usually use their interaction — inputs, questions, etc. — as training data. Samsung ran into this kind of problem back in April, when proprietary data was leaked from ChatGPT usage. The South Korean tech giant's fab engineers were using ChatGPT assistance for coding, note-taking, and data analysis about fab performance and yields, and three instances of data leaks were traced back to ChatGPT usage. 

ChatGPT isn’t only prone to leaking other users’ data through training — like all software, it also has vulnerabilities and bugs. Earlier in the week we saw its vulnerability to prompt injection via YouTube transcripts. In March, ChatGPT was taken offline for a short period after it was discovered some of its users could see the titles of other user chat histories.

After the growth in reports concerning data security, OpenAI has introduced new privacy controls as well as an ‘Incognito mode’ for AI assistance on sensitive documents. Perhaps, these steps haven’t gone far enough for Apple. Another AI tool mentioned specifically in the WSJ report is GitHub’s Copilot (Microsoft), which is used by developers to save time with its smart code autocomplete functionality.

Not a lot is known about Apple’s progress on its own alleged LLM AI. The firm has snapped up a variety of AI related companies over recent years, and hired people such as John Giannandrea, an exec who used to work with Google's search and AI teams. Microsoft and Google are firing on all cylinders in the AI race, and are even pitching their AI-driven apps and widgets into Apple’s iOS store, so Apple can't be too patient with its own solution.

Apple’s developer conference, the WWDC23, kicks off on June 23, so perhaps it will have some AI news for us soon. There may be an AI-enhanced Siri announced, as well as highly anticipated Reality Pro AR/VR headset, new Macs, and new desktop, mobile, and wearable operating systems.

Music is deeply intertwined with math, and microelectronics is inherently mathematical, so it’s no surprise to find projects like this impressive Arduino-powered MIDI instrument. Unlike most other electronic instruments, this one is sure to take your breath away as it requires the player to control it using the power of their breath. It was designed and created by a maker named Xavier Dumont who made the project totally open source for anyone interested in creating their own.

Dumont has dubbed the project MIDILodica. It’s housed inside a custom 3D-printed shell and features a custom PCB to support the buttons and LCD screen. You can find details about how it works as well as all of the 3D printable files in the official GitHub repository.

The MIDILodica boots with a custom splash screen before loading a user interface. Users can pick from options in the main menu that impact how the instrument performs. Certain settings will change the effect of what blowing harder or softer does. For example, some settings will change the pitch while others will impact the volume of the output. You can also customize the buttons on the instrument, as well, and there are plenty to customize.

The main board used to drive this project is an Arduino Micro. It’s soldered to Dumont’s custom PCB, which has support for an Adafruit 320 x 172px TFT display, 40 tactile switches, a pressure sensor and even an SEN-08680 membrane potentiometer. This allows for an additional level of control that operates changes based on where your finger is located on the sensor. You can find a full list of part requirements on the GitHub page along with the PCB Gerber files.

The software for the MIDILodicawas created by Dumont just for the project. It includes the interface as well as all of the cool effects you can play with when adjusting the settings. If you want to get a close look at the source code, again, check out the official MIDILodica GitHub repository where Dumont shares everything you need to get started. If you’re looking for more inspirational microelectronics projects, you should visit our list of Raspberry Pi projects, which includes tons of cool creations from makers around the world.

The Raspberry Pi is a popular go-to board for building gaming platforms but not so often is the Raspberry Pi Pico chosen. We’ve seen some Pico handhelds before, like Pimoroni’s PicoSystem, but today’s project, created by a maker who goes by HalloSpaceBoy, has created one that you can make yourself at home called the PicoBoy.

Like many projects we love to share, the PicoBoy is completely open source from the code it uses to its custom 3D-printed housing. It’s not exactly plug-and-play. You will need to solder a few components together to complete the build. Thankfully, HalloSpaceBoy was kind enough to share a build guide complete with detailed instructions on how to put it together.

The PicoBoy console has a custom operating system and user interface. When it boots, you can cycle through a list of games on the main menu and choose the game you want to play. There is a user guide available, as well, that better explains how to use the interface. You can find replicas of classic games like Breakout, Flappy Bird, The Game of Life, and even a beta version of a Paint application.

The PicoBoy uses a custom PCB with all of the buttons needed to navigate the system put into place. It’s attached to a 1.5-inch LCD module and has a speaker for audio output. For power, it relies on three AAA batteries. The case is 3D printed and can be downloaded for free at Printables or ordered in a kit with everything you need.

In addition to the PicoBoy operating system, HalloSpaceBoy also created what they call the PicoBoy Communication Software. This is an application you run on your PC that allows you to interact with your PicoBoy to make modifications. You can also use it to add or remove games, as well as format the system. You can find more information about the software-side of the project over at the PicoBoy releases page at GitHub.

If you want to get a closer look at this Raspberry Pi project and maybe even recreate it yourself, check out the official project page over at GitHub. And be sure to follow HalloSpaceBoy for new projects as well as any updates on this one.

Recently, we've seen AI models that produce detailed text-to-video or use run a chatbot on your phone. Now, OpenAI, the company behind ChatGPT, has introduced Shap-E, a model that generates 3D objects you can open in Microsoft Paint 3D or even convert into an STL file you can output on one of the best 3D printers.

The Shap-E model is available for free on GitHub and it runs locally on your PC. Once all of the files and models are downloaded, it doesn't need to ping the Internet. And best of all, it doesn't require an OpenAI API key so you won't be charged for using it.

It is a huge challenge actually getting Shap-E to run. OpenAI provides almost no instructions, just telling you to use the Python pip command to install it. However, the company fails to mention the dependencies you need to make it work and that many of the latest versions of them just won't work. I spent more than 8 hours getting this running and I'll share what worked for me below.

Once I finally got Shap-E installed, I found that the default way to access it is via Jupyter Notebook, which lets you view and execute the sample code in small chunks to see what it does. There are three sample notebooks which demonstrate "text-to-3d" (using a text prompt), "image-to-3d" (turning a 2D image into a 3D object) and "encode_model" which takes an existing 3D model and uses Blender (which you need installed) to transform it into something else and re-render it. I tested the first two of these as the third (using Blender with existing 3D objects) was beyond my skillset.

How Shap-E Text-to-3D Looks

Like so many AI models we test these days, Shap-E is full of potential but the current output is so-so at best. I tried the text-to-video with a few different prompts. In most cases, I got the objects that I asked for but they were low res and missing key details.

When I used the sample_text_to_3d notebook, I got two kinds of output: color animated GIFs which displayed in my browser and monochrome PLY files I could open later in a program like Paint 3D. The animated GIFs always looked a lot better than the PLY files.

The default prompt of "a shark," looked decent as an animated GIF, but when I opened the PLY in Paint 3D, it seemed lacking. By default, the notebook gives you four animated GIFs that are 64 x 64, but I changed the code to up the resolution to 256 x 256 outputted as a single GIF (since all four GIFs looks the same). 

When I asked for something that OpenAI had as one of its examples, "an airplane that looks like a banana," I got a pretty good GIF, particularly when I upped the resolution to 256. The PLY, file, though, exposed a lot of holes in the wings. 

When I asked for a Minecraft creeper, I got something that a GIF that was correctly colored green and black and a PLY that was in the basic shape of a creeper. However, real Minecraft fans would not be satisfied with this and it was too messy of a shape to 3D print (if I had converted it to an STL).

Shap-E Image to 3D Object

I also tried the image-to-3d script which can take an existing 2D image file and turn it into a 3D PLY file object. A sample illustration of a corgi dog became a decent, low-res object that it outputted as a rotating, animated GIF which had less detail. Below, the original image is on the left and the GIF is on the right. You can see that the eyes seem to be missing. 

By modifying the code, I was also able to get it output a PLY 3D file that I could open in Paint 3D. This is what it looked like.

I also tried feeding the image-to-3d script some of my own images, including a photo of an SSD, which came out looking broken and a transparent PNG of the Tom's Hardware logo, which didn't look much better.

However, it's likely that if I had a 2D PNG that looked a bit more 3D-ish (the way the corgi does), I'd get better results. 

Performance of Shap-E

Whether I was doing text or image to 3D processing, Shap-E required a ton of system resources. On my home desktop, with an RTX 3080 GPU and a Ryzen 9 5900X CPU, it took about five minutes to complete a render. On an Asus ROG Strix Scar 18 with an RTX 4090 laptop GPU and an Intel Core i9-13980HX, it took two to three minutes. 

However, when I tried doing text-to-3D on my old laptop, with has an Intel 8th Gen U series CPU and integrated graphics, it had only finished 3 percent of a render after an hour. In short, if you are going to use Shap-E, make sure you have an Nvidia GPU (Shap-E doesn't support other brands of GPUs. The options are CUDA and CPU.). Otherwise, it will just take too long.

I should note that the first time you run any of the scripts, it will need to download the models, which are 2 to 3 GB and could take several minutes to transfer.

How to Install and Run Shap-E on a PC

OpenAI has posted a Shap-E repository to GitHub, along with some instructions on how to run it. I attempted to install and run the software in Windows, using Miniconda to create a dedicated Python environment. However, I kept running into problems, especially because I could not get Pytorch3D, a required library, to install.

However, when I decided to use WSL2 (Windows Subsytem for Linux), I was able to get it up and running with few hassles. So the instructions below will work either in native Linux or in WSL2 under Windows. I tested them in WSL2.

1. Install Miniconda or Anaconda in Linux if you don't already have it. You can find a download and instructions on the Conda site.

2. Create a Conda environment called shap-e with Python 3.9 installed (other versions of Python may work).

conda create -n shap-e python=3.9

3. Activate the shap-e environment.

conda activate shap-e

4. Install Pytorch. If you have an Nvidia graphics card, Use this command.

conda install pytorch=1.13.0 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia

If you don't have an Nvidia card, you'll need to do a CPU-based install. The install is speedy but processing the actual 3D generation with the CPU was extremely slow in my experience. 

conda install pytorch torchvision torchaudio cpuonly -c pytorch

5. Build Pytorch. This is the area where it took me hours and hours to find a combination that worked. 

pip install "git+https://github.com/facebookresearch/pytorch3d.git"

If you get a cuda error, try running sudo apt install nvidia-cuda-dev and then repeating the process.

6. Install Jupyter Notebook using Conda.

conda install -c anaconda jupyter

7. Clone the shap-e repo.

git clone https://github.com/openai/shap-e

Git will create a shap-e folder underneath the one you cloned it from.

8. Enter the shap-e folder and run the install using pip.

cd shap-epip install -e .

9. Launch a Jupyter Notebook. 

jupyter notebook

10. Navigate to the localhost URL the software shows you. It will be http://localhost:8888?token= and a token. You'll see a directory of folders and files.

11. Browse to shap-e/examples and double-click on sample_text_to_3d.ipynb.

A notebook will open up with different sections of code.

12. Highlight each section and click the Run button, waiting for it to complete before moving onto the next section.

This process will take a while the first time you go through it, because it will download several large models to your local drive. When everything is done, you should see four 3D models of a shark in your browser. There will also be four .ply files in the examples folder and you will be able to open those in 3D imaging programs such as Paint 3D. You can also convert them to STL files using an online converter.

If you want to change the prompt and try again. Refresh your browser and change "a shark" to something else in the prompt section. Also, if you change size from 64 to a higher number, you get a higher resolution image. 

13. Double-click on sample_image_to_3d.ipynb in the examples folder again so you can try the image-to-3d script.

14. Highlight each section and click Run.

You'll end up, by default, with four small images of corgis. 

However, I recommend adding the following code to the last notebook section so that it will output PLY files as well as animated GIFs.

from shap_e.util.notebooks import decode_latent_meshfor i, latent in enumerate(latents):    with open(f'example_mesh_{i}.ply', 'wb') as f:        decode_latent_mesh(xm, latent).tri_mesh().write_ply(f)

15. Modify the image location in section 3 to change the image. Also, I recommend changing the batch_size to 1 so it only makes one image. Changing the size to 128 or 256 will give you a higher resolution image.

16. Create the following python script and save it as text-to-3d.py or another name. It will allow you to generate PLY files based on text prompts at the command line.

import torchfrom shap_e.diffusion.sample import sample_latentsfrom shap_e.diffusion.gaussian_diffusion import diffusion_from_configfrom shap_e.models.download import load_model, load_configfrom shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widgetdevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')xm = load_model('transmitter', device=device)model = load_model('text300M', device=device)diffusion = diffusion_from_config(load_config('diffusion'))batch_size = 1guidance_scale = 15.0prompt = input("Enter prompt: ")filename = prompt.replace(" ","_")latents = sample_latents(    batch_size=batch_size,    model=model,    diffusion=diffusion,    guidance_scale=guidance_scale,    model_kwargs=dict(texts=[prompt] * batch_size),    progress=True,    clip_denoised=True,    use_fp16=True,    use_karras=True,    karras_steps=64,    sigma_min=1e-3,    sigma_max=160,    s_churn=0,)render_mode = 'nerf' # you can change this to 'stf'size = 64 # this is the size of the renders; higher values take longer to render.from shap_e.util.notebooks import decode_latent_meshfor i, latent in enumerate(latents):    with open(f'{filename}_{i}.ply', 'wb') as f:        decode_latent_mesh(xm, latent).tri_mesh().write_ply(f)

17. Run python text-to-3d.py and enter your prompt when the program asks for it.

That will give you a PLY output, but not a GIF. If you know Python, you can modify the script to do more with it.

If you’re looking for a fun Raspberry Pi project to get on board with, you may want to grab a ticket and hop on over to Jo M’s GitHub page to see this amazing trainspotting creation. Affectionally dubbed Trainbot, the system is designed to watch trains as they pass by and create full-length images of the trains. These images are stored in a database that users can view online with real-time updates.

Jo was kind enough to explain in great detail how the setup processes image input. It doesn’t take much hardware to operate the project as it primarily consists of a Raspberry Pi with a camera module v3 (the Global Shutter Camera would be a great option) and there's no need for tools such as OpenCV to process the image input as all of the image processing takes place on the processor.

According to Jo, once the Pi tracks a train and creates the image (of every carriage!) , it then uploads the picture along with additional information — including how long the train is, which direction it’s going, and how fast it’s traveling — via FTP. Users can access this data from any device with a web browser by going to https://trains.jo-m.ch. Here you can see all of the recently tracked trains and even click on individual trains to get a closer look.

Jo explains that while they’re using a Raspberry Pi camera v3 module, you should be able to duplicate this project using any compatible camera, such as a video4linux compatible USB camera. Jo confirmed that this project was designed and tested with a Raspberry Pi 4 B — you could probably get away with a Raspberry Pi 3 B+, but this has not been tested.

Because it doesn’t use anything too intense like OpenCV for processing, the software side is fairly easy to set up. All of the code is available over at GitHub, which also includes instructions for using V4l to process the image input as well as libcamera library functions to operate the camera module. For a closer look at this Raspberry Pi project, check out the project page over at GitHub, and visit the official website, Onlytrains, to see it in action.

Anyone can use ChatGPT for free, but if you want to use GPT4, the latest language model, you have to either pay for ChatGPT Plus, pay for access to OpenAI's API, or find another site that has incorporated GPT4 into its own free chatbot. There are sites that use OpenAI such as Forefront and You.com, but what if you want to make your own bot and don't want to pay for the API?

A GitHub project called GPT4free allows you to get free access to the GPT4 and GPT3.5 models by funneling those queries through sites like You.com, Quora and CoCalc and giving you back the answers. The project is GitHub's most popular new repo, getting 14,000 stars this week. 

Now, according to Xtekky, the European computer science student who runs the repo, OpenAI has sent a letter demanding that he take the whole thing down within five days or face a lawsuit.

I interviewed Xtekky via Telegram, and he said he doesn't think OpenAI should be targeting him since he isn't connecting directly to the company's API, but is instead getting data from other sites that are paying for their own API licenses. If the owners of those sites have a problem with his scripts querying them, they should approach him directly, he posited.

I installed GPT4Free in WSL2 (Windows Subsystem for Linux) on my PC. It took just a few moments, which involved cloning the Github repository, installing some required libraries with pip, and running a Python script. With the script launched, I used my web browser to visit http://localhost:8501, the address it gave me, and was presented with a working chatbot that was running off of my PC.

On the backend, GPT4Free is visiting various API urls that sites like You.com, an AI-powered search engine that employs OpenAI's GPT3.5 model for its answers, use for their own queries. For example, the main GPT4Free script hits the URL https://you.com/api/streamingSearch, feeds it various parameters, and then takes the JSON it returns and formats it. The GPT4Free repo also has scripts that grab data from other sites such as Quora, Forefront, and TheB. Any enterprising developer could use these simple scripts to make their own bot. 

"One could achieve the same [thing by] just opening tabs of the sites. I can open tabs of Phind, You, etc. on my browser and spam requests," Xtekky said. "My repo just does it in a simpler way."

All of the sites GPT4Free draws from are paying OpenAI fees in order to use its large language models. So when you use the scripts, those sites end up footing the bill for your queries, without you ever visiting them. If those sites are relying on ad revenue from their sites to offset these API costs, they are losing money because of these queries.

Xtekky said that he is more than happy to take down scripts that use individual sites' APIs upon request from the owners of those sites. He said that he has already taken down scripts that use phind.com, ora.sh and writesonic.com. 

Perhaps more importantly, Xtekky noted, any of these sites could block external uses of their internal APIs with common security measures. One of many methods that sites like You.com could use is to block API traffic from any IPs that are not their own.

Xtekky said that he has advised all the sites that wrote to him that they should secure their APIs, but none of them has done so. So, even if he takes the scripts down from his repo, any other developer could do the same thing.

The developer said that he doesn't believe he's liable for what others do with his script and that OpenAI should not be targeting him for using other sites' APIs, which are available unsecured on the open web. 

"OpenAI could also reach out to the sites and warn/notify them and in collaboration come to me and do a takedown, but it seems that this [legal threat] solely comes from OpenAI and they basically claim that I am directly attacking them," he said. "I do not really know, but it looks like if a site use OpenAI's API, it has OpenAI's legal protection and OpenAI is liable for the damages the site gets."

Xtekky initially told me that he hadn't decided whether to take the repo down or not. However, several hours after this story first published, we chatted again and he told me that he plans to keep the repo up and to tell OpenAI that, if they want it taken down, they should file a formal request with GitHub instead of with him. 

"I believe they contacted me before to pressurize me into deleting the repo myself," he said. "But the right way should be an actual official DMCA, through GitHub."

Even if the original repo is taken down, there's a great chance that the code -- and this method of accessing GPT4 and GPT3.5 -- will be published elsewhere by members of the community. Even if GPT4Free had never existed anyone can find ways to use these sites' APIs if they continue to be unsecured.

"Users are sharing and hosting this project everywhere," he said. "Deletion of my repo will be insignificant."

On Sunday, Xtekky made an announcement on the project's Discord channel, saying that he's making some changes to his chatbot, which lives apart from the GitHub repo but serves as a working example of what you can do with it. We haven't linked to the chatbot because it's at an adult domain name (but has no adult content), but you can find a link to it on the GPT4Free GitHub page.

"We're stepping away from our previous method and implementing a new system that will ensure a seamless and legal processing of requests. We're also proud to announce that a rebranding is in the works along with some other general fixes that will improve your experience," he posted.

Xtekky told me that he plans to move the chatbot to a different domain and to rebrand it as g4f (short for GPT4Free) and to change the logo it uses, which looks a lot like the OpenAI logo. It's unclear whether the repo name will change.

We reached out to OpenAI's press email for comment and will update this story if and when we receive a response.

If you want to try it while it's still there, check out GPT4Free on GitHub. If you want to set up your own ChatGPT-like chatbot without potentially running afoul of OpenAI's lawyers, see our tutorial on how to run ChatGPT on a Raspberry Pi or PC

A plan to integrate OpenAI’s ChatGPT into Microsoft PowerToys for Windows 10 and 11 has been given the green light. It is an interesting case of thoroughly modern tech being shoehorned into a revived 1990s-era Windows accessory program. Software engineer Simone Franco will be working on what is descriptively dubbed the 'ChatGPT plugin for Microsoft PowerToys Run'.

The 2020s reboot of Microsoft PowerToys continues to evolve and expand, with useful and modern features that couldn’t have been imagined the first time around. On the ChatGPT plugin for Microsoft PowerToys Run GitHub project page, Franco implies that a test version of the plugin, written in just two hours, was warmly received by Microsoft. After some discussion, Microsoft PowerToys PM Clint Rutkas asked Franco to refine the project and make it available as a Community Plugin. It is now a work in progress.

If you aren’t familiar with PowerToys Run, it is primarily a keyboard-driven quick launcher with convenience and high performance at its heart. To summon it, PowerToys users simply hit Alt+Space and start typing to whittle down applications, folders, or files (or even processes). PowerToys Run can also execute system commands, do simple calculations, convert units, and much more.

Using PowerToys Run with ChatGPT via this plugin should be simple and intuitive. Windows users will invoke the PowerToys Run UI dialog box in the usual manner, then enter “??” (without quotes), enter a query terminated by a single question mark before finally hitting enter and waiting for a response. Franco intends to change the “??” plugin activation shortcut as it clashes with normal search. This is just one of several things that must be done to prepare the plugin for prime time.

Some other important things that the ChatGPT plugin for Microsoft PowerToys Run needs to get done before general release are; a way for users to store their OpenAI API key, improve the answer display UI (see above, it needs to expand), implement a copy answer click action, localization, and a few other things.

Microsoft already brought ChatGPT to Bing and Edge earlier this year. However, some will prefer this PowerToys Run plugin for what is intended to be a mix of immediacy and simplicity. Nineteen PowerToys, including PowerToys Run, were available at the start of April. More are on the way, and if you haven’t checked the range of PowerToys tools available for the last few months, the official Microsoft product page is definitely worth a browse.

ChatGPT (Chat Generative-Pre-Trained Transformer) has only been around for a couple of months, but it is fair to say that it has made its mark. Millions of users have used the service for all manner of queries, but each time it required the user to open a browser, log in and then enter a prompt. Well, for Linux users it seems that these steps can be skipped over thanks to a Gnome extension from Rafal Mioduszewski, aka HorrorPills.

This extension is for the Gnome display manager (KDE fans, the creator has hinted that it could be possible to replicate the project) and right now this early version is not fully working with Wayland, so Xorg is the way to go for now. Mioduszewski also notes that mouse integration is a little buggy right now, so keyboard navigation is a must.

The extension is installed by downloading the files via Mioduszewski's Github repository, copying them to the correct directory and then using the gnome-extensions application to enable it. A quick reboot / log off and you can access ChatGPT from the comfort of your desktop, provided you have an account and that ChatGPT is not at capacity.

So what can we do using this extension? Pretty much anything we would do in the browser. We've asked it to successfully win Star Trek II: The Wrath of Khan's Kobayashi Maru. Even AI couldn't win that battle without reprogramming the simulator. On a more serious note, we asked it to write some CircuitPython with an LED on pin 13. Then we tasked it with writing raw G-Code to control the nozzle of a 3D printer to draw a 30 mm cube. While that G-Code will need a little tweaking, it shows promise. Lastly, we asked ChatGPT to work out the correct resistor for a fictional LED in a circuit using Ohm's Law. 

If you have an account and a Linux distro running Gnome then this extension is well worth investing a little time into. It may just save you some work.

Ever wanted a pocket-sized computer that can play DOS games and do pretty much everything else a 486 or Pentium PC could do? Then look no further. TInyLlama, AKA Eivind Bøhler from Norway, has put together a board -- featuring an 86Duino SoM and a Raspberry Pi Zero 2 for MIDI -- that can do just that, and thoroughly documented it on GitHub. Beware, though, you’ll have to build it yourself.

At the core of the clever custom board is a Vortex86EX CPU - a 32bit X86-compatible IA-32 CPU made in Taiwan by DM&P Electronics - running at between 50 and 500MHz, a speed no 486 or original Pentium could even dream of. There's 128MB of DDR3 RAM, again much greater than the 16MB that was common at the time, and 8MB of programmable flash ROM. A Vortex86VGA module runs off a PCIe 1x lane, offering a maximum resolution of 1024x786, at a period-appropriate aspect ratio of 4:3. Storage comes via Micro SD, and power from a micro USB cable.

Sound comes via a Crystal CS4237B all-in-one audio chip, which is Sound Blaster Pro compatible, but you can supplement this with a Raspberry Pi Zero 2 attached via a 40-pin connector that supplies Roland MT-32 emulation and general MIDI. There's a switch for toggling between its two modes, and you can connect an OLED display using a four-pin I2C plug.

Software support comes in the form of a custom Coreboot/SeaBIOS ROM, and it runs FreeDOS or the OG MS-DOS itself. There's no mention of Windows 95, though the Vortex86EX chip should be able to handle Windows CE 6, Windows Embedded Compact, and Linux up to kernel 4.14.

There’s a lot of soldering to be done if you fancy building your own TInyLlama, especially as the SOM-128-EX system-on-module that forms the core of the device has completely the wrong pin layout and needs to be customized. You then need to replace the pre-installed bootloader and program the sound chip firmware. If you’re up for the task, the design is open-source, and there's a full walkthrough plus BOM on the TInyLlama GitHub page.

Dropbox, the cloud storage provider, has announced it has been the target of a phishing attack that successfully accessed its private GitHub repos. GitHub was able to quickly notify Dropbox of the attack, and no customer data or passwords were affected.

The data breach took place on October 13, with Dropbox becoming aware that things were amiss the next day. The attackers impersonated the CircleCI integration and delivery platform that can be logged into using GitHub credentials, bombarding Dropbox staff with realistic-looking phishing emails. Many of them were blocked by Dropbox’s internal systems, but some got through - enough, it seems, for at least one employee to visit a fake CircleCI login page, enter their GitHub credentials, and use a hardware authentication key to pass a one-time password to the malicious site.

This allowed the attacker into Dropbox’s private Github area, from where they copied 130 code repositories. Data accessed includes, according to Dropbox’s statement: “...some credentials—primarily, API keys—used by Dropbox developers. [It] also included a few thousand names and email addresses belonging to Dropbox employees, current and past customers, sales leads, and vendors.”  Then later: “These repositories included our own copies of third-party libraries slightly modified for use by Dropbox, internal prototypes, and some tools and configuration files used by the security team. Importantly, they did not include code for our core apps or infrastructure. Access to those repositories is even more limited and strictly controlled.” 

Back in September, GitHub warned its users in a blog post about attacks targeted at CircleCI, noting that “If the threat actor successfully steals GitHub user account credentials, they may quickly create GitHub personal access tokens (PATs), authorize OAuth applications, or add SSH keys to the account in order to preserve access in the event that the user changes their password.”

Dropbox was able to cut off the attackers’ access on the same day it found out about the intrusion, and believes the risk to customers is minimal. The company is also upgrading its multi-factor authentication method to WebAuthn—a change already in progress when the attack happened. 

Windows 95 was certainly a breakthrough moment in operating system design, bringing so many of the things we take for granted today to PCs, such as the desktop and the Start button. This week, Microsoft announced the latest 22H2 updates for Windows 10, and gave us a flash of a possible Windows 12 at the Ignite conference — and it might make you wonder if Windows 95 really was all that innovative (or if nostalgia has rose-tinted your memories). 

Well, now you can find out: Slack developer Felix Rieseberg has created a cross-platform Windows 95 that runs as an app, and it’s just reached version 3.1.1. (Thanks to Betanews for bringing this to our attention.) 

And yes, it can run Doom. 

It’s all done through the magic of Electron, GitHub’s open-source software framework used to create desktop apps out of web technologies such as CSS and JavaScript, which is what Rieseberg has used here. Download the .exe file (for Windows — Linux and Mac versions are available). It simply executes without an installation, and pops up the familiar, if old-fashioned, desktop with a welcome message open in Notepad.

Installed software includes Netscape Navigator, as well as classic games such as Wolfenstein 3D, A10 Tank Killer, and Doom. This project displays Electron’s capabilities for creating cross-platform apps with versions for Windows (32-bit, 64-bit, ARM64), Mac (Intel, Apple M1), and Linux (64-bit, ARM64, ARMv7). 

It runs reasonably well — though the author admits it would be even better as a native app. In a cursory test, we found the mouse pointer to be erratic, and Doom got stuck on the menu screen.

As the third release of the portable OS, this Windows 95 sees an update to Electron v18, and also moves to a newer version of the v86 virtualization app that emulates an x86 processor. The new version uses the WebAssembly language and also fixes a few bugs. You can even add new apps to by mounting hard drive images — though if you’ve got a project-critical Windows 95 app, you’re better off with a VM or even an old PC. If you’re using Linux, you can run it in Docker.

On his GitHub page, Rieseberg apologizes for what he’s done, and adds: “Bear in mind that this is written entirely in JavaScript, so please adjust your expectations.” The app is available under an educational license, and has no approval from Microsoft. Download it from GitHub.

We recently broke the news that Intel's Alder Lake BIOS source code had been leaked to 4chan and Github, with the 6GB file containing tools and code for building and optimizing BIOS/UEFI images. We reported the leak within hours of the initial occurrence, so we didn't yet have confirmation from Intel that the leak was genuine. Intel has now issued a statement to Tom's Hardware confirming the incident:

"Our proprietary UEFI code appears to have been leaked by a third party. We do not believe this exposes any new security vulnerabilities as we do not rely on obfuscation of information as a security measure. This code is covered under our bug bounty program within the Project Circuit Breaker campaign, and we encourage any researchers who may identify potential vulnerabilities to bring them our attention through this program. We are reaching out to both customers and the security research community to keep them informed of this situation." — Intel spokesperson. 

The BIOS/UEFI of a computer initializes the hardware before the operating system has loaded. Among its many responsibilities, the BIOS establishes connections to certain security mechanisms, like the TPM (Trusted Platform Module). Now that the BIOS/UEFI code is in the wild and Intel has confirmed it as legitimate, both nefarious actors and security researchers alike will undoubtedly probe it to search for potential backdoors and security vulnerabilities. 

In fact, famed security researcher Mark Ermolov has already been hard at work analyzing the code. His early reports indicate that he has found secret MSRs (Model Specific Registers) that are typically reserved for privileged code and thus can present a security problem, along with the private signing key used for Intel's Boot Guard, thus potentially invalidating the feature. In addition, there are also signs of ACMs (Authenticated Code Modules) for BootGuard and TXT (Trusted Execution Technology), portending potential future issues with the root of trust. 

The impact and breadth of discoveries could be limited, though. Most motherboard vendors and OEMs would have similar tools and information available to build firmware for Intel platforms. Moreover, Intel's statement that it doesn't rely upon information obfuscation as a security measure means it has likely scrubbed the most overly-sensitive material before releasing it to external vendors.

Intel is being proactive, though, and encouraging researchers to submit any vulnerabilities they find to its Project Circuit Breaker bug bounty program, which awards between $500 to $100,000 per bug, depending on the reported issue's severity. It's unclear if the code can indirectly benefit open-source groups like Coreboot. 

Intel hasn't confirmed who leaked the code or where and how it was exfiltrated. However, we do know that the GitHub repository, now taken down but already replicated widely, was created by an apparent LC Future Center employee, a China-based ODM that manufactures laptops for several OEMs, including Lenovo. Additionally, one of the leaked documents refers to "Lenovo Feature Tag Test Information," furthering the theories of the link between the company and the leak. There are also a plethora of files labeled 'Insyde,' referring to Insyde Software, a company that provides BIOS/UEFI firmware to OEMs and is known to work with Lenovo. 

We aren't aware of any attempts at ransom yet, but Intel or the affected parties might not have made those attempts public. Conversely, this could simply be the case of an employee inadvertently posting the source code to a public repository.

However, recent hacks have targeted outside vendors to indirectly steal information from semiconductor manufacturers, thus enabling ransom attempts, and this leak could follow that model. A spate of recent attacks includes an attempt by RansomHouse to extort AMD after it obtained 56GB of data. AMD partner Gigabyte also had 112 GB of sensitive data stolen in the infamous 'Gigabyte Hack,' but AMD refused to pay the ransom for the latter hack. As a result, information about AMD's forthcoming Zen 4 processors was divulged before launch, which later proved genuine.

Nvidia also suffered a recent attack that resulted in the theft of 1TB of its data, but the GPU-making giant retaliated with its own operations to render the stolen data useless.

We'll update this article if any new details emerge. 

An unknown individual has purportedly leaked the source code for Intel's Alder Lake BIOS onto 4chan, and a duplicate copy now appears to be posted to GitHub. The files are contained in a 2.8 GB zip file that expands to 5.86 GB after decompression, but we haven't been able to verify if the contents therein are genuine and actually contain sensitive source code. 

UPDATE (10/9/2022): In a new story, we have reported that Intel has now confirmed the leak is legitimate and security researchers are analyzing the code for vulnerabilities. 

News of the purported leak comes via Twitter postings from @glowingfreak and @vxunderground. We have reached out to Intel for comment. 

The file appears to contain a plethora of files and tools geared for building a BIOS/UEFI for Intel's Alder Lake platform and chipsets. It is unclear where the leaker obtained the files, but one of the documents does refer to "Lenovo Feature Tag Test Information." A few other clues have also emerged via the git log. 

Even if the files are proven to include sensitive material, it's unclear if they could be used to develop exploits — especially if it was obtained from a source external to Intel. It's easy to imagine that most motherboard vendors and OEMs would have similar tools and information available to build firmware for Intel platforms, and Intel would likely scrub any overly-sensitive material before releasing it to external vendors. That said, any sensitive material in the hands of nefarious actors is never good, and small bits of information can lead to big vulnerabilities. Especially if it pertains to security features like the TPM (Trusted Platform Module).

While we don't know how the files were obtained, recent hacks have targeted outside vendors to steal information from semiconductor manufacturers indirectly, thus enabling ransom attempts.

The spate of recent attacks includes an attempt by RansomHouse to extort AMD after it obtained 56GB of data. AMD partner Gigabyte also had 112 GB of sensitive data stolen in the infamous 'Gigabyte Hack,' but AMD refused to pay the ransom for the latter hack. As a result, information about AMD's forthcoming Zen 4 processors was divulged before launch, which later proved genuine.

Nvidia also suffered a recent attack that resulted in the theft of 1TB of its data, but the GPU-making giant retaliated with its own operations to render the stolen data useless.

We don't know further details about the purported Intel leak, but we're working on learning more from the company. We'll update as necessary. 

TL;DR How to Bypass Windows 11's TPM, CPU and RAM Requirements

• Bypassing Windows 11's hardware requirements is trivial, there is even an official means, but that requires TMP 1.2.
• Install Windows 11 using a clean install, then edit the registry as part of the install process.
• Create a custom Windows 11 USB install drive (using a tool like Rufus), which can then be used to automatically bypass the requirements.

Microsoft's strict hardware requirements to install Windows 11, which includes TPM 2.0 means that upgrading older computers to the latest OS will be met with "this PC doesn't currently meet Windows 11 system requirements" error message, but we can bypass this and the other requirements by performing a clean install and editing the registry during the setup process or we can use a tool like Rufus to create a custom Windows 11 installation USB stick.

Fortunately, there are several simple ways you can get bypass Windows 11's TPM, RAM and other requirements. If you're doing a clean install with a Windows 11 ISO, you can edit the registry in the middle of the setup process and tell it to skip requirement checks. We'll show you how to modify the registry in the first section below.

If you don't want to have to modify the registry mid-install, you can create a Windows 11 USB install disk that will bypass Windows 11's TPM requirement, and its need for 8GB of RAM and a supported CPU. Using a free tool called Rufus and at least a 16GB Flash drive, you can either perform an in-place upgrade of Windows 10 to 11 or a Windows 11 clean install while getting around these minimums. In the second section of our tutorial below, we'll show you how to use Rufus to create a requirement-free Windows 11 install disc.

If you have Windows 10 or an earlier build of Windows 11 installed on a PC that didn't meet the requirements (perhaps a VM), you try to update to a new build with Windows Update and you get the "doesn't meet requirements" error message, there's a workaround for that. As we'll detail in the third section below, a script from AveYo's Media Creation tool github page will allow you to bypass Windows 11's TPM requirement even with Windows Update.

Note that Microsoft also has also created an official registry hack to bypass TPM 2.0 and CPU requirements for an in-place upgrade. However, this method still requires at least TPM 1.2 so, if you have no TPM at all, it's worthless. We'll talk about this at the bottom, in case you want to try it.

Also note that, by default, Microsoft's OS wants you to set it up with a Microsoft account. However, we have another tutorial on how to install Windows 11 without a Microsoft Account by using a few tricks.

How to Bypass Windows 11's TPM Requirement Using Registry Hacks

If you just have a regular Windows 11 install disk or ISO, you can bypass the Windows TPM and RAM requirements by making some registry changes during the install. Note that this method only works on a clean install and does not allow you to bypass the requirement for at least a dual-core CPU.

1. Boot off of your Windows 11 install disk. If you don't have one, see our story on how to download Windows 11. The first screen should ask you to choose the language of your install (which will probably be correct).

2. Hit SHIFT + F10 to launch the command prompt.

3. Type regedit and hit Enter to launch registry editor.

4. Navigate to HKEY_LOCAL_MACHINE\SYSTEM\Setup.

5. Create a new registry key under Setup and name it LabConfig. To create a registry key, right click in the right window pane and select New->Key. Then enter the key name.

6. Within LabConfig, create DWORDs values called BypassTPMCheck and BypassSecureBootCheck and set each to 1. To create a new DWORD value, right click in the right window and select new DWORD (32-bit) Value then name the key, double click to open it and set it to 1.

If you also want to bypass the RAM requirement, add a DWORD values for BypassRAMCheck.

7. Close regedit and the command prompt.

You should be able to continue with your Windows 11 installation as normal.

How to Bypass Windows 11's TPM Requirement Using Rufus

With Rufus, a free utility, you can create a Windows 11 install disk on a USB Flash drive with settings that disable the TPM, RAM and CPU requirements. You can either boot off of this USB Flash drive to do a clean Windows 11 install or run the setup file off of the drive from within Windows 10 to do an in-place upgrade.

For most people, this method is ideal, but there are a couple of disadvantages. First, it requires a 16GB or larger USB Flash drive. Second, because it's on a Flash drive, it's more difficult to use for installing Windows 11 on a virtual machine where an ISO file would be ideal.

1. Download the latest version of Rufus and install it on your machine. At the time of writing the latest version is 3.19 which includes the Extended Windows 11 Image support.

2. Insert a blank 16GB or larger USB stick then open Rufus.

3. Select the USB device that you want to install Windows 11 to.

4. Ensure that Boot Selection shows “Disk or ISO image” and click DOWNLOAD.

5. Select Windows 11 and click Continue.

6. Select the latest release and click Continue.

7. Select the edition and click Continue.

8. Select your preferred language and click Continue.

9. Select the architecture (most likely x64) and click Download. A new window will open asking where to save the ISO image. Save it to your Downloads folder. You can also download the image using a browser if you wish.

The download will take several minutes to complete.

10. Click on the Image option drop down and select Extended Windows 11 Installation to disable TPM, Secure Boot and the 8GB of RAM requirement.

11. Double check that the correct drive has been selected and click on Start to begin the installation.

The write process can take some time, depending on the USB drive being used, but when done the drive can be removed and used to install Windows 11 on an older computer or even in a virtual machine.

12. Install or upgrade to Windows 11. Run setup on the USB drive, if you ware doing an in-place install from an existing Windows 10 installation. Boot off of the drive if you are doing a clean install. Note that you may need to disable secure boot in your BIOS (see how to enter your BIOS) if it gives you a problem.

How to Bypass Windows 11 TPM Check From Windows Update

If you want to use Windows Update rather than creating an install disk, you'll need a method that runs in Windows and fools the updater into thinking you meet the requirements. This may be more important if you are trying to use Windows Update to upgrade to a new build of Windows 11, perhaps an Insider Build, on a computer that already bypassed the requirement.

For example, when we joined the Windows Insider program on one of our Windows 11 VMs (which clearly did not have TPM), we got the dreaded "Your PC doesn't meet the requirements" error. But using AveYo's Media Creation Tool workaround script solved the problem. Here's how to make it happen.

1. Navigate to the Skip_TPM_Check_on_Dynamic_Update.cmd source code on AveYo's Media Creation Tool Github.

2. Click the "Copy raw contents" button in the upper right corner of the code box.

3. Create a new file on your desktop and name it skip_tpm_check.cmd. Make sure that you are able to view file extensions and the file extension is really .cmd, not .txt or else it won't run.

4. Open skip_tpm.cmd for editing, using notepad or another text editor.

5. Paste in the code you copied from github.

6. Save and run the skip_tpm.cmd file.

7. Click Yes if prompted by User Account Control.

You will now see a message saying "Skip TPM on Dynamic Update" has been installed. If you run the program again, it will disable the utility.

Windows Update should now be able to either update your existing Windows 11 Build or even perhaps upgrade you from Windows 10 to 11.

How to Bypass Windows 11 TPM the Official Microsoft Way

Knowing that some users will want to install Windows 11 on systems that don't meet all of its hardware requirements, Microsoft has provided a registry hack that loosens them up somewhat. Using this hack, you can install on a system that has at least TPM 1.2 and has an unsupported CPU. That said, we recommend the scripts above because they don't require you to have TPM of any kind.

1. Open Regedit.

2. Navigate to HKEY_LOCAL_MACHINE\SYSTEM\Setup\MoSetup.

3. Create a DWORD (32-bit) Value called AllowUpgradesWithUnsupportedTPMOrCPU if it doesn't already exist.

4. Set AllowUpgradesWithUnsupportedTPMOrCPU to 1.

5. Close regedit and restart your PC. You should now be able to upgrade to Windows 11 from within Windows 10 by using installation media (provided you created it).

More Windows 11 Tutorials:

• How to Get a Windows 11 ISO File and Do a Clean Install
• How to Enable God Mode in Windows 11 or 10
• How to Uninstall Windows 11 and Return to 10
• How to Clean Install Windows 11, No Product Key Required
• How to Install Windows 11 in a Virtual Machine