It’s been a while since I wrote about my desk setups or how much I rely on having decent audio and a good camera with face tracking, so my return to work in early September seemed like a good time to do that. I’d meant to post this then, but said work was enough of a pain to delay it until late October… But that’s beside the point right now.

Apple users are used to face tracking and auto-framing by now, but I have been using professional videoconferencing systems for many decades. In fact, back in the mid-90s, I did quite a bit of work on videoconferencing over ISDN and ATM, including hacking NetMeeting into submission… But it took the pandemic and my OBSBOT camera to finally feel personal tech had caught up with the times.

After another bout of back pain this year and a return to my makeshift standing desk, I decided I’d take a look at the webcam market again to see if I could get another OBSBOT (or at least a true gimbal camera) to sit atop my battered Surface Pro 3, which was (until recently) the centrepiece of said “desk”. It has a perfectly usable Windows Hello camera, but I tend to get restless and something that keeps me in frame is nicer to my callers.

I considered the software route first, but I couldn’t find anything that would work with the internal camera and wouldn’t kill the PC (Teams is enough of a challenge for it these days). Trying one of the (excellent) Logitech Brio 4K cameras I still have made me realise I needed to move the camera itself, too. And I didn’t want anything that would require extra software in Linux or on a Mac later.

There’s enough junk software out there already, after all.

But OBSBOT-style cameras were around €200 a pop when I looked, so getting a new one for a secondary desk was a no-go, and I decided to try something cheaper, gimbal-less, but with enough sensor resolution for decent cropping and zooming.

The UGREEN FineCam Pro stood out to me for two reasons–what seemed a decent price for a nominally 4K-capable sensor and its compactness, since it took up far less volume than most other options.

Disclaimer: I bought this one myself; however, my usual review policy applies, because I consider this a bit of a “low effort” review.

The reason I bought it in April was that the built-in firmware handles gesture-controlled framing and zoom itself–with driverless support for Windows, macOS and Linux, and thus complete software independence (which I think will be useful for anyone in alternative platforms).

And why finally write about this now? Well, because I’m testing a brand new Qualcomm-based laptop that does head tracking completely in software, and thought this was worth noting while comparing setups–more on that in a future post about the laptop.

The camera itself is… OK, I guess. It’s an 8-megapixel sensor capable of 4K at 30fps or 1080p at 60fps, has “phase-detection autofocus” and pretty decent lighting sensitivity, as well as two microphones with noise reduction, positioned for stereo capture if you use it for vertical video, which… I didn’t. Audio from those was reported as being good, though.

The rest is mostly creator frills: the mode dial switches between seven dubiously useful built-in filters, and there is a privacy cover.

The camera comes with a magnetic mount and a small tripod, which is another concession to the “creator” audience, and the magnetic holder fits neatly atop my Surface display and lets me detach and reposition the camera without disturbing the mount (it also made it easy to move the camera off-centre when using a sidecar monitor, which is a very “me” thing to do, but which might be of interest to other people as well).

For some stupid reason (yes, I know it’s probably the creator audience, but it’s still stupid) the camera defaults to vertical orientation, which I suppose comes hand in hand with its mode dial and effects.

That dial is clearly geared at people doing social media, but gleefully ignores the fact that even on TikTok (which I don’t use but am regularly kept apprised of) nobody seems to use those effects, and, as you’d expect, I didn’t use them either (except once by mistake).

Additionally, the USB-C port seems to have been purposefully positioned at the most inconvenient position possible on the back–the weight of the cable tore off the camera from the magnetic mount and the cable protrudes from the back too much, adding to the imbalance.

It took me a little while to figure out the right distance at which to hold my hand to enable tracking, but after a while that became second nature (I’m used to having to do that with my OBSBOT as well, because for some reason there is no way to set that default even after years of firmware upgrades).

Picture quality in Teams calls was good, with smooth tracking–but of a different flavour than the OBSBOT. The differences in quality and motion are subtle, and in practice I’m mostly the one who notices.

But here’s a pro tip: perform the magical tracking gesture incantations before you enter the call, if you can, otherwise people will feel compelled to wave back, even in very formal official settings…

I don’t regret my purchase, but I do think it would be pretty safe for UGREEN to simply remove the mode dial and move the USB port to the side. I’d much rather have a smaller camera than a selection of filters I never use.

I haven’t tried the plainer FineCam, but its shape is pretty much the form factor I’d want. Put the Pro’s camera hardware and tracking into that enclosure, and I’d be perfectly happy not having to see the dial…

I’ve done quite a few KVM reviews in the past, and there’s a reason for that: All of the homelab and industrial IoT stuff I test needs some sort of physical access for setup, and even if I do have monitors with multiple inputs, USB video capture dongles, etc., it gets really old to have to constantly re-wire stuff, switch between keyboards, etc.

Additionally, most of the newfound range of cheap/compact KVMs that have popped up over the past two years still need an Ethernet connection, and it’s become ridiculously hard to have even semi-permanent test setups–I keep running out of both power and Ethernet ports, and that’s been the reason why I’ve been limping along on Wi-Fi based Pi Zero KVMs as well as one or two KVMs that, sadly, require proprietary apps.

So when I spotted the Radxa Linkr, I just had to try it. And that was even before I knew I could get it to work with an AI agent…

Disclaimer: Radxa supplied me with a Linkr free of charge, and this review follows my usual review policy.

In the box, you get the Radxa Linkr and a set of cables (pictured above in real life, and below in the Quick Start guide).

The hardware itself is pretty comprehensive, and packs quite a lot into the small 70 x 22 x 12 mm enclosure:

• Rockchip RV1106G3 SoC, with a 1.2GHz Arm Cortex-A7 CPU, a RISC-V co-processor, 1 TOPS NPU and a hardware video encoder.
• 256MB of integrated DDR3L memory.
• 16GB eMMC storage or a microSD card slot (I got the variant with internal storage) for firmware and virtual media.
• HDMI input up to 2560x1440 at 30Hz, with lower-resolution modes including 1920x1080 at 60Hz and custom EDID support.
• Dual-band 2.4GHz/5GHz Wi-Fi 6 using an AIC8800 module, with client and hotspot modes.
• A USB-C device port for keyboard and mouse emulation, power (5V/2A) and firmware flashing.
• A separate USB-C network port for a direct host connection or 10/100Mbps Ethernet through the supplied USB-C to RJ45 cable.
• One FN user button, a system status LED and an HDMI/video status LED.

The device itself is tiny, and the single FN button is used to identify it on the discovery page or (with newer firmware) to toggle the Wi-Fi hotspot with a double press. You can see the USB-C connectors on the back and side through the semi-transparent casing in the photo above.

A nice touch that you will appreciate if you manage many machines is that there are indicator lights that show when someone is remotely connected (the blue power LED will turn green) or there is a video input problem (signalled by a blinking video status LED).

Besides direct USB connectivity to your machine (which I honestly didn’t spend any time on), the Linkr supports both 2.4GHz and 5GHz Wi-Fi. However, it doesn’t seem to support my particular flavour of WPA3 (at least in current firmware versions), so I stuck to 2.4GHz/WPA2 for all of my testing, which is plenty fast enough.

That’s a decent tradeoff for me (I had zero latency issues, although I spend so much time in VDI and remote console setups that I don’t notice delays anymore) and, at least in one of my tests, was the only way I could connect to a machine in a remote corner of the house where 5GHz has very little reach–but it managed 60fps just fine.

The Linkr is quite flexible in terms of video capture. I’ve needed to hack in support for custom EDIDs in my old PiKVMs, so it’s nice to see that is baked in, and the built-in HDMI bridge goes from 1280x720@30/60Hz to 2560x1440@30Hz, with standard 1080p in the middle.

I also found it a nice touch that the Linkr includes a virtual sound card that it exposes via USB to the target system, although I have had zero use for audio in any of my tests…

The Linkr, like all devices, does warm up while in use, and its metal underside can be surprisingly warm to the touch–and yet, my IR thermometer didn’t see it get past 45°C even after hours of continuous use (disconnecting, of course, lets it idle cooler).

As you’ve probably divined from the Quick Start shot above, this is a pretty trivial web-based setup: go to linkr.now (with Chrome or Edge, because Apple still hasn’t figured out most of the modern device-related browser APIs), and you get an HTML app that will find the Linkr and help you connect to it – via USB or, later, via Wi-Fi.

The entire thing is very slick, and the user interface is both responsive and easy to navigate–here’s a little slideshow of the original firmware version:

Firmware updates and release notes are available on GitHub, and since I got mine (v1.4.2), Radxa has added quite a few new features, as well as a bunch of fixes, in what is today v1.5.2:

• A Plugin Center, which is a sensible way to keep adding more features without changing the core firmware–I haven’t yet had time to investigate how I could build my own, since I only noticed it as I finished this draft.
• ZeroTier and EasyTier support (I use Tailscale myself, but it’s nice to see options).
• Screenshot OCR to recognise text in screenshots and edit or copy the results (see below); recognition runs locally, and an optional plugin supplies the OCR resources without Internet access.
• Experimental IPv6 support (which works, at least on my LAN).
• FN-button hotspot toggle (double-press it to turn the Wi-Fi hotspot on or off).

One fix in particular was very useful already (they fixed resolution switching with the Radxa Dragon Q8B as video input, and I’m still testing that this weekend), and the pace at which they are currently shipping updates is reassuring, even though I couldn’t find published source code for the firmware in the public repository.

Other than that, you get all the typical remote KVM features (as well as Tailscale support, which I use extensively):

• Video resolution settings (and custom EDID, as pointed out above).
• Wake-on-LAN (for any MAC address, although you can pin the ones you need).
• Keyboard and mouse control (as well as the virtual USB audio capture, all exposed via USB ID 1d6b:0104 - Multifunction Composite Gadget).

Mouse control can be set to both absolute and relative, as well as reversing the scroll wheel (which I thought was a very nice touch).

However, Mac and iOS users can’t really use some of the more advanced web UI features like screenshots, recordings or screen OCR, because they’re not available in Safari. Some of this is Apple’s problem, but I’ve hacked Safari-compatible screenshot support into other KVMs, so Radxa can probably improve that.

I did find the screenshot OCR feature interesting, but it requires pretty crisp text, and some SBCs just aren’t willing to play ball on their boot consoles–however, it can be immediately useful in a pinch:

A nice touch about this is that recognition happens fully in-browser:

• It uses tesseract.js to recognise English and Simplified Chinese.
• By default, it downloads OCR resources over the Internet, including the worker script from cdn.jsdelivr.net.
• The screenshot is processed locally by the browser worker, which returns the recognised text and bounding boxes to the UI.

The offline plugin just caches a local copy of the worker, engine and language data inside the Linkr itself, removing the external-download requirement.

But the real reason I am very much enjoying using the Linkr is that, unlike most KVMs, it actually exposes an API that you can either script against or just point your AI agent to.

I have been using that for over a month now to great effect (including doing a couple of Fedora and Windows installs, which is a veritable quagmire of edge cases) and built a piclaw add-on you can use as a reference.

The release notes also describe new API controls for resolution and video quality, which my add-on doesn’t cover yet. But the basic screenshot and input controls work amazingly well if you pair them with a vision-enabled model, and are really simple:

The documented public API uses an access token in the Authorization: token <token> header, and boils down to two endpoints:

• GET /api/public/snapshot returns the current screen as a JPEG.
• POST /api/public/control accepts an ordered events array containing keyboard, mouse, text and delay operations.

The control primitives are small enough to wrap directly in agent tools:

• keyboard presses or releases keys using browser KeyboardEvent.code names, so combinations are sequences of key-down and key-up events.
• mouse_abs positions the pointer using normalised coordinates between 0 and 1 (this can be a little tricky for some models, but they get it eventually).
• mouse_rel moves it by pixel offsets (both support mouse buttons and scrolling).
• text types up to 1024 characters, limited to printable ASCII, tabs and newlines.
• delay pauses between events for a specified number of milliseconds (useful when the target needs time to react).

That gives an agent the usual screenshot/action loop without installing anything on the target machine.

I had to burn through quite a few tokens in initial testing (and the skills my add-on bundles are the result of that), but you should be able to use a relatively cheap model to automate typical system administration tasks, as long as it supports vision.

Can it do fully unattended installations? Well… It’s tricky. Can you figure out when to enter the BIOS on a random machine and what key to press? That is so variable across machines (and sometimes even on the same machine) that the only way I got it to work was to get into the BIOS (after a few tries), pick the boot volume myself and then hand over to piclaw.

The results and reliability are highly dependent on the AI model you use. I’ve had good experiences with astra, opus or sol on a variety of desktop automation tasks, and they were all able to use the Linkr to go through a standard OS installer. I also got them to check Windows updates (clicking through all the steps and choosing when to reboot) and even do console rpm-ostree and flatpak updates (and reboots) without any issues except the high token count that comes with handling screenshots (and model-side OCR).

This is what currently makes the Linkr worth looking at if you want to automate hardware setup, with the caveat that it can, indeed, do away with the odd bit of drudgery, but you need to be smart about how to do it (only retrieving screenshots at intervals/decision points to save tokens, for instance) and, preferably, script things so that the model is only really phoning it in.

For me it was almost a straight conversion of some of the stuff I’ve actually been doing in Proxmox for a while, so it’s nice to have a way to set up a blank box.

Hardware-wise, I should note that you can’t hit a reset button with the Linkr, so if your hardware doesn’t honour Ctrl+Alt+Del, you’re stuck. Other KVMs have dedicated interfaces to do that (and you can always use a smart plug, like I do with my test SBCs), but it’s something to keep in mind.

Additionally, I’ve had a couple of instances where the Linkr had a little trouble fitting directly into PCs or SBCs (including Radxa’s own) because of lack of clearance around the HDMI port or having the USB-C port on the side (which in a few cases meant it would point straight into the desk).

Nothing that you can’t fix with an HDMI extension cable (I used a Chromecast male-to-female extension, which is around 10cm), but worth mentioning since it doesn’t come in the box.

Another minor hassle is that if you power the Linkr directly from your target device, many devices (even standard PCs) won’t power up USB until they’ve gone past display initialisation, so the Linkr may skip UEFI/BIOS screens while it boots.

When testing SBCs, I had to use that power/data splitter cable to keep the Linkr powered up independently–otherwise it would boot up after the servers had tried (and failed) to initialise HDMI, so I’d get no video out (and, depending on device, they wouldn’t reactivate HDMI after USB input or Wake-on-LAN, either).

I’m obviously biased towards anything that lets me put a machine on Wi-Fi and get at it through a browser without installing yet another proprietary app, so the Linkr already had quite a bit going for it.

But having an API that I can script against, or hand over to an agent once I’ve persuaded the machine to boot from the right device, makes it considerably more useful for the sort of testing I do, and I am quite likely to replace some of my other KVMs with Linkrs solely to free up Ethernet ports.

I’ll still need the odd HDMI extension and a separate power supply for a few machines (especially ARM ones), but at least the wiring will be much easier, and who knows, I might even start testing more than one SBC at once…

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