This Gadget Came With a Virus. They Asked Me Not to Tell You.

I recently shared an update regarding the Think Node M9 from Elecrow, a device I tested in my recent Meshtastic update. Shortly after that video was posted, I received an email from the company stating that the device contains a virus and must be returned. While the virus is not active on the device itself, it presents a risk if its internal SD card is removed and inserted into a Windows computer. While they responded appropriately to the issue, they also requested that I not talk about this issue on my channel. That’s not cool.

See more in my latest video.

The risk occurs when accessing the firmware on the SD card for updates. The malware identified is known as Sality. According to the Department of Justice, this malware turns an infected computer into a node on a peer-to-peer botnet. Once a computer is part of this network, it can be used for cryptocurrency theft or cyber attacks, often operating in the background without the user’s knowledge. Although the Department of Justice recently disrupted a significant portion of the Sality network, the presence of such software on a consumer device is a serious matter.

After sending the initial warning, Elecrow sent a follow-up email asking me not to discuss the situation publicly. Transparency is a necessary component of the relationship between manufacturers and the public, particularly as supply chain attacks become more frequent. These incidents often occur when companies utilize contract factories without maintaining rigorous quality control over the software and firmware being installed. If the factory’s duplication process is compromised, malware can be embedded into thousands of devices before they ever reach a consumer.

Elecrow is not the only manufacturer to face these challenges recently. Geekom, a producer of mini PCs, recently hosted an infected network driver on its website. The driver contained the Asruex malware, which is designed to steal credentials while remaining hidden from antivirus scanners. Like Elecrow, Geekom reportedly contacted the outlet that broke the story and requested its removal.

Similarly, the company Ace Magic shipped mini PCs with a backdoor known as win32 bladabindi embedded directly into the Windows recovery image. Reports indicated that AceMagic attempted to influence the narrative by offering incentives for the removal of negative reviews and requiring reviewers to submit content for approval prior to publication.

These hardware issues are part of a broader trend of supply chain vulnerabilities that extend into software. Attackers recently compromised the Notepad++ website to swap the official distribution with an infected version. In another instance, an open-source library called Axios was found to have an infected component. Because Axios is integrated into many other software packages, the malware spread to Windows, Linux, and Mac systems through standard updates from trusted sources.

Protecting a system in the age of AI requires a more proactive approach than ever before. Maintaining an up-to-date operating system and running frequent antivirus scans is essential, even when dealing with hardware or software from trusted hardware and software makers.

My Local AI Built a QuickBooks Replacement—and More !

I recently acquired a surplus Nvidia GPU from a data center and installed it in my home to run local artificial intelligence models. My objective was to determine if old hardware typically reserved for large-scale operations could be repurposed to create practical, cost-saving applications for personal and business use. By pairing the OpenCode harness with the Qwen 3.8 27B model, I have been able to develop several functional software tools that run entirely on my own hardware – including a Quickbooks Replacement !

Check it out in my latest local AI video!

One of the more significant projects involved developing a replacement for QuickBooks which in my opinion gouges small business customers like me. I found that exporting data from QuickBooks results in spreadsheets that are formatted in a way that is difficult to import into a competing product. In fact, most Quickbooks competitors don’t even offer history import as an option. However, by using the local AI model to analyze the exported data, I was able to rebuild my entire general ledger and chart of accounts and have them balance to the penny against Quickbooks.

I then had the model build a Node.js project which handles all of my transaction logic. It also synchronizes with my bank accounts through a service called SimpleFin that charges only $1.50 per month – a small fraction of what I was paying Intuit. To ensure the accuracy of the financial logic, I utilized a high-end frontier models from Claude and ChatGPT to perform a code and security review. This second set of eyes confirmed that the local model had correctly handled the transaction calculations and had only missed a few minor bugs. I also ran it alongside Quickbooks for a few weeks to make sure everything was being calculated properly.

I also applied this local AI setup to address a lack of transparency in local utility reporting. Following a storm, the power company’s outage map provided limited information unless a user drilled deep into the interface:

I used the Qwen model to identify where the raw data was stored on the power company’s website. In under an hour, it generated a PHP-based interactive map that provides more granular detail than the utility’s default interface. This was achieved without a public API; instead, the model analyzed the source code of the existing outage map and repackaged the information into a more accessible format.

Another app I developed is a utility to streamline the creation of chapter markers for my YouTube videos projects. Converting markers from Final Cut Pro into a format suitable for web descriptions previously required several manual steps. The local model produced a small application that automates this process just by dragging a Final Cut Pro XML file to the app. While it is a niche utility, it represents a significant reduction in the minor frictions that accumulate during a workday.

To test the model’s ability to handle more complex, native software development, I tasked it with building a native Mac Space Invaders clone coded in Swift. The model managed the coding and the compilation process, resulting in a functional game with sound effects and a complete instruction manual. While there were minor interruptions during the development process that required prompts to resume, the result demonstrated that local hardware can execute tasks that previously required a much larger cloud model.

The current state of local AI is comparable to the late 1970s and early 1980s, when personal computers first brought the capabilities of mainframes into the home. We are at a point where a 32GB GPU or a high-memory machine like the Mac Studio can handle sophisticated development tasks privately and without recurring subscription fees. As these models become more efficient, the ability to turn a specific idea into a working application becomes an increasingly accessible reality for home users, allowing for a more customized and efficient computing environment.

Mac Mini M6 as a Plex Server? Plus Other Mac Transcoding Improvements (sponsored post)

In a follow-up to my 16GB Mac Mini review last week, I tested the Mac Mini M6’s viability as a Plex media server. Plex recently released several updates for the Mac version of its server software that specifically target hardware efficiency and new codec support.

See how it works in my latest monthly sponsored Plex video!

One recent addition to the Mac server is hardware tone mapping. This feature allows the server to take an HDR video and convert it to SDR in real time using the hardware transcoder. During my testing with a Blu-Ray 4K HDR film, I transcoded from a direct stream at 150+ megabits per second to a 1080p stream at 10 megabits per second. The system maintained HEVC compression throughout the process, which resulted in consistent visual quality at a significantly lower bit rate. While this was running, the color remained accurate, matching the performance of other dedicated hardware tone mapping devices.

Monitoring the system resources showed that the 16-gigabyte Mac Mini handles these workloads with minimal impact on the CPU and RAM. A single 4K transcode required approximately 700 megabytes of memory. Even when I initiated a second simultaneous 4K transcode with tone mapping, the memory pressure remained low and CPU utilization did not increase to a level that would hinder other tasks.

Subtitle burn-in has also been integrated into the hardware transcoding pipeline on the Mac. Subtitle burn-in is required for playback devices that cannot overlay subtitles natively. Traditionally, burning subtitles into a video file can be a resource-intensive process. I tested this by enabling subtitles on a 4K Blu-ray rip while transcoding. While there was a slight uptick in system activity, it managed two concurrent sessions of hardware-transcoded subtitle burn-in without performance degradation.

The update also introduces AV1 hardware decoding, a feature compatible with M3 processors and newer. I tested a 4K60 video encoded in AV1 and observed the system successfully decoding the file in hardware and transcoding it to HEVC for browser playback while the other two transcodes were still ongoing. Although older Apple Silicon chips like the M1 and M2 do not support this specific AV1 feature, they still benefit from the expanded HEVC support and general hardware transcoding improvements included in this release.

The efficiency of these chips allows the Mac Mini to function as a primary desktop while serving a media library to a moderate number of users. The hardware remains quiet even when the active cooling system is engaged during heavy transcoding sessions. With these software optimizations, the Mac Mini platform, from the older M4 models to the current M6, serves as a functional and energy-efficient option for media management.

Disclosure: This was a paid sponsorship from Plex, however they did not review or approve this content prior to uploading.