It's switching components every 8 seconds, like a carousel. It's a bit unfair to judge Rails UI by that, but I agree, it's not a good first impression.
The Aerial Framework has been designed from the ground up to meet the needs of 3GPP Radio Access Networks — signal processing workloads with microsecond latency requirements. It is a single platform that unites research, testbeds, and production deployments to solve development challenges for real-time applications.
Use cases: Signal processing applications with strict latency requirements
Audience: RAN system engineers, signal processing specialists, AI researchers
Built with: DOCA, DPDK, TensorRT, Python, JAX, PyTorch, C++, CUDA, and more
Doesn‘t tailscale force you to bring your own SSO / IdP already?
So it‘s not part of what Tailscale brings, it just adds another layer of indirection between the SSO / IdP you have already and the app, plus requires some custom library integration work, further enhancing lock-in.
Do you have any information about the privacy achievable by Meshtastic?
From a quick glance it looks like it‘s using static NodeIDs derived from the Bluetooth MAC address in the always unencrypted Packet Header.
So not only can you sniff these messages from far away at greatly simplified complexity when comparing to cellular communication, but also tie it to the hardware that you carry with you.
Mesh networks sure have its uses, but I‘d be wary of their offered privacy in the presence of adversaries you could be facing at protests!
Often they rely heavily on GPS for time and frequency synchronization (think 5G TDD, where all operators need to be aligned in time or they jam eatch other‘s uplink). The GPS antenna is also often quite visible from the outside of a base station.
Still, what‘s also possible is to use PTP as backup (e.g., over eCPRI) and use the fiber / microwave backhaul to some master clock with, say, a ribidium time source as a backup, so I think it‘s not entirely unreasonable to assume that cell phone signals will remain available in the presence of a GPS outage.
> But one overlooked use case of the technology is (talking head) video compression.
> On a spectrum of model architectures, it achieves higher compression efficiency at the cost of model complexity. Indeed, the full LivePortrait model has 130m parameters compared to DCVC’s 20 million. While that’s tiny compared to LLMs, it currently requires an Nvidia RTX 4090 to run it in real time (in addition to parameters, a large culprit is using expensive warping operations). That means deploying to edge runtimes such as Apple Neural Engine is still quite a ways ahead.
It’s very cool that this is possible, but the compression use case is indeed .. a bit far fetched. A insanely large model requiring the most expensive consumer GPU to run on both ends and at the same time being limited in bandwidth so much (22kbps) is a _very_ limited scenario.
I feel like there are so many pitfalls when designing this - is there something standard and trusted (would TLS work?) that you could build your application on top of?
But that‘s not what happened according to the article.
> The problem is, there were no empty seats on the flight. So when he emerged from the lavatory and the plane began taxiing, flight attendants realized something was wrong, and the plane returned to the gate.
If I remember correctly, there is an extensive „Inspection“ settings page where you can enable / disable / set the levels (hoe much in-your/face) of exactly such checks.
As it turns out, you do not even need cell site simulators / false base station to do IMSI catching.
In my research, I developed a technique to attack a phone with low power from km away, without being able to be detected at all with traditional tools like SeaGlass or Crocodile Hunter. It‘s published here and works against production LTE networks, no downgrade attacks whatsoever necessary: