TechyMagThings

Today’s phone microphones are perfectly adept at picking up sound in all sorts of conditions, and they’re backed by all kinds of processing techniques to filter out noise and capture clean audio. [mcore1976] has been working on a device to jam phone microphones that might be listening in, however, countering fancy processing techniques in turn. 

The build uses a microcontroller brain to control an array of ultrasonic transducers. [mcore1976] has created many revisions of the project, each time improving its ability to jam microphones in modern hardware. The latest revision uses an RP2040 microcontroller and a MOSFET drive stage to control 20-80 ultrasonic transducers. They’re driven with a PWM signal generated from the RP2040 itself. The signal output is specifically modulated to try and confuse the automatic gain control systems used in many modern phones in order to make it difficult for them to record clear audio when the jammer is running. As [mcore1976] demonstrates with an iPhone 17, his voice is completely lost amidst unintelligible garbled noise while the jammer is switched on.

It’s a niche idea, and perhaps most interesting because it affects phone microphones while being largely inaudible to the human ear. We’ve featured other interesting jamming devices of late, too. Video after the break.

Although it’s commonly suspected that migratory birds fly in a ‘V’ formation due to this saving energy for the birds in the slipstream, understanding the exact aerodynamics behind this and how it affects the way that the birds use their wings to maintain this optimal pattern. After all, unlike airplanes and cars, our feathered avian dinosaur friends need to flap their wings if they want to have any chance of staving off plummeting back to Earth. Recent research by Brown University researchers now have provided a simulated model that answers many questions.

The major question was how this would work in the up- and down-wash zones created in this type of formation, with every bird following the lead bird dealing with the vortices created by the flapping of the wings of the bird before them. These wake vortices are quite complex, and thus required careful modelling to make sense of them.

As described in the paper by [Olivia Pomerenk] et al., the model is based on northern bald ibises, taking into account live-bird measurements for validation of the model. The main effect that can be observed is a reduced flapping amplitude, leading to an 11% energy savings for the birds in the leader’s wake.

The main advantage of having such a model is of course that it provides insight into the kinematic and aerodynamic mechanisms, meaning the ability to model virtual flocks of birds, predict the efficiency of specific in-flight configurations, and apply the lessons to swarms of drones, or whatever else we want to put in the air.

Have you ever been looking up a recipe for something new and been stymied by the directions being a wall of text, especially to find that one detail right when you’re in the middle of making the dish? Recipe Lanes by [bohemian-miser] leverages an LLM to create flow charts to make the process more straightforward.

As someone who has mostly avoided LLM use thus far, I found the examples in the Gallery helped inform what the LLM was expecting for prompts as my first attempts were unsuccessful. Once you know the language expected from the computer, you can get it to generate icons for each ingredient and a flow chart of the steps to cook the food. While it does organize the chart when it is generated, each element can be independently moved across the canvas to put things in a more sensible order, especially as I found it can generate elements with overlapping text.

The 8-bit icon style and button text on the site give it a fun bit of flair that adds to the overall experience. The tool is still in its infancy, but it’s Open Source, so we hope to see it improve over time. If you’d like to see some more interesting kitchen hacks, how about ramen in edible packaging, this rotary phone kitchen timer, or these automated Arduino splash guards.

This week Jonathan chats with Michael Meeks about Collabora! What’s the origin story in this consulting company, why do they have an outstanding office suite, and where is the world headed to accomplish digital sovereignty? Watch to find out!

• Collabora Productivity – https://www.collaboraonline.com/
• Michael’s person blog – https://meeksfamily.uk/~michael/
• Michael’s comic strip – ‘The Open Road to Freedom’ (TOTF) https://www.collaboraonline.com/torf-index/

Did you know you can watch the live recording of the show right on our YouTube Channel? Have someone you’d like us to interview? Let us know, or have the guest contact us! Take a look at the schedule here.

Direct Download in DRM-free MP3.

If you’d rather read along, here’s the transcript for this week’s episode.

Theme music: “Newer Wave” Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 4.0 License

Car enthusiasts want to know how quickly they can make a quarter mile. Weightlifters are forever trying to add one more plate to the bar. Internet denizens have their own favorite number to brag about: the result from a speed test.

The ritual is familiar. Close a few browser tabs, click the big “Go” button, and watch the needle climb. Perhaps you pay for gigabit service and see 940 megabits per second, which produces a satisfied nod. Perhaps you see 299 megabits and begin obsessing over network hardware. But before you get too excited either way, try another test. There is a fair chance it will give you a different answer.

That does not necessarily mean one test is lying. “Internet speed” is not a single physical quantity waiting to be measured. A speed test measures the performance of a particular device, over a particular local connection, through a particular ISP route, to a particular server, at a particular time using a particular test method. Change any of those things and the answer can change too.

The Usual Suspects

Speedtest by Ookla is probably the best-known test. It selects a nearby server, although you can choose another. It attempts to saturate the connection with multiple simultaneous transfers. That makes it good at answering the question most consumers are asking: approximately how much aggregate bandwidth can this Internet connection deliver?

Running several connections matters. A single TCP connection must gradually increase its sending rate while reacting to round-trip time, packet loss, receive-window limits, and congestion-control behavior. On a high-bandwidth or high-latency path, one connection may not fill the available pipe. Several parallel connections can ramp up independently and make it easier to reach the link’s aggregate capacity. That number is valid, but it represents something like a busy household, a large segmented download, or several applications operating at once. It does not necessarily predict the speed of one file transfer from one distant server.

Google’s built-in search speed test (search “speed test”) uses Measurement Lab’s Network Diagnostic Tool, or NDT. M-Lab describes NDT as a single-stream measurement of bulk-transport capacity. That makes it an interesting counterpoint to Ookla. A single flow may expose latency, loss, or TCP-window limitations that a multi-stream test can partially conceal. You can also use M-Lab’s own speed test directly.

While you may get similar numbers between the two approaches, you also may not get similar numbers, especially on high-latency connections where Ookla’s multiple streams will help hide latency.

Netflix’s Fast.com is deliberately simple. Open the page, and it immediately begins transferring data from Netflix infrastructure. By default it emphasizes download performance, since its original purpose was to answer a practical question: can this connection deliver Netflix video properly? Selecting “Show more info” adds upload speed and both unloaded and loaded latency.

The use of Netflix servers is significant. Fast.com measures the route between you and Netflix’s content-delivery network, while Ookla may test against a server operated by your ISP only a few network hops away. A superb Ookla result and a poor Fast.com result do not prove deliberate throttling, but they do tell you that the destinations — or the routes to them — are behaving differently.

Cloudflare offers two related tests. Its Radar Network Quality Test provides a quick summary, while speed.cloudflare.com  gives an extremely detailed breakdown. The latter reports download and upload throughput, idle and loaded latency, jitter, packet loss, server location, and application-oriented quality estimates.

Loaded latency is especially useful. An otherwise fast connection can become miserable when a large upload or download fills an oversized queue in the modem or router. Your idle ping might be 12 milliseconds, but under load it may jump to several hundred milliseconds. That is the classic symptom usually called bufferbloat.

If you want more options, there is testmy.net, which allows you to test upload and download speeds separately, and speedof.me, which keeps a history for you, among others. It isn’t always obvious which ones are measuring a single connection vs multiple ones, so you may have to dig through whatever documentation you can find.

Your WiFi Is Part of the Test

A browser speed test cannot automatically tell you what’s hurting your speed. A laptop connected through marginal WiFi may report 180 megabits per second even though the router has a flawless gigabit Internet connection.

In fact, once incoming Internet service reaches several hundred megabits per second, WiFi is frequently the limiting factor. The link rate displayed by the operating system is not the same thing as usable throughput. Wireless protocols have framing overhead, acknowledgments, contention, retransmissions, and half-duplex operation. The advertised 866, 1200, or 2400 megabit link rate is therefore not a promise that application data will move at that rate.

The numbers printed on WiFi boxes add another layer of optimism. A router sold as “AC1800,” for example, does not provide an 1800-megabit connection to one device. The figure is normally the sum of the maximum advertised PHY rates on separate radios — perhaps 1300 Mb/s on 5 GHz plus 450 Mb/s on 2.4 GHz — with some rounding for marketing. A conventional WiFi client connects to one band at a time, so it cannot combine those rates. The total is better understood as the router’s theoretical aggregate capacity while serving multiple devices across both bands. Even then, protocol overhead, contention, signal quality, and client limitations make actual data throughput considerably lower. Newer WiFi 7 equipment can sometimes combine links using Multi-Link Operation, but that exception does not make the old ACxxxx arithmetic any less misleading.

WiFi also uses shared airtime. Devices on the same channel — including neighboring access points that can hear one another — must contend for opportunities to transmit. A slow or distant client takes longer to send a given amount of data and can consume disproportionate airtime while doing so. Modern access points may provide airtime fairness and other mitigations. One old device does not invariably drag every client down to its rate, but it can still reduce the capacity available to the rest of the network. Interference has a similar effect. A weak signal, a crowded channel, microwave noise, or an overlapping neighboring network causes frames to be delayed or retransmitted. Those retries consume airtime without delivering additional data.

Repeaters and wireless mesh backhaul add another complication. A simple same-channel repeater must receive each packet and then transmit it again over the same shared medium. In the worst case, each repeated hop can roughly halve the available throughput. Modern tri-band mesh systems can avoid much of that penalty by using a dedicated backhaul radio, and Ethernet backhaul avoids it almost entirely.

This means it is entirely reasonable to buy gigabit Internet service and obtain only 300 or 500 megabits per second from a WiFi laptop. Whether that represents a problem depends on the client, radio band, channel width, signal level, backhaul, and local RF environment.

For a meaningful ISP test, begin with a computer connected directly to the router by Ethernet. Stop large transfers and temporarily disable any VPN. Record the chosen server, latency, upload speed, and download speed rather than preserving only the most flattering number. Then run the same tests over WiFi. The difference is an approximate measurement of what the wireless portion of the network is costing you.

Remove the Internet From the Experiment

Better still, remove the ISP from the test completely. OpenSpeedTest is a self-hostable, browser-based test. Run its server on a wired computer, NAS, or container, then visit it from laptops, phones, and tablets around the house. Because the traffic remains on your LAN, a slow result points toward WiFi, switching, cabling, or the client rather than the Internet connection.

It is possible to run this on the uhttpd server used with OpenWRT, although you’ll need to coax it to measure upload speeds since the server can’t handle the default method. The trick is to create a CGI script that accepts a large amount of data successfully and then configure uhttpd to run that.

A browser-based local test is convenient, but for serious diagnosis it is hard to beat iperf3, the client/server tool we recently used while testing mesh routers. On one machine (say, 192.168.1.100), start the server:

iperf3 -s

From another machine, run:

iperf3 -c 192.168.1.100

By default, iperf3 uses one TCP connection. Add -P 4 to try four parallel streams, or -R to reverse the direction so that the server sends and the client receives. Those variations can tell you something. If four streams are much faster than one, the network may have enough aggregate capacity but a single TCP flow is being limited by latency, loss, window growth, CPU performance, or offload behavior. If the reverse test is much faster, examine the weaker machine’s transmit path, drivers, antennas, or CPU.

iperf3 can also generate UDP traffic at a specified rate and report packet loss and jitter. That is often more informative for evaluating a wireless link than merely chasing the largest TCP number.

Can Linux Make It Faster?

Linux offers an impressive array of network tuning knobs, which naturally tempts us to turn them. But first, you need to understand what needs tweaking.

Check the negotiated Ethernet rate and interface counters:

ethtool eth0
ip -s link show eth0

A gigabit adapter that has negotiated 100 megabits per second usually has a cabling, connector, or switch-port problem. Increasing TCP buffers will not repair it. Rising interface errors and drops point toward a physical, driver, or congestion problem. TCP retransmits (view with ss -ti) may indicate loss elsewhere on the path.

You can inspect the active queue discipline with:

tc qdisc show

Linux supports queue disciplines such as fq_codel, which combines per-flow queueing with active queue management. It attempts to prevent one large transfer from building an enormous queue and delaying unrelated interactive packets. The kernel documentation specifically lists fq_codel as a sensible queue discipline that works without extensive configuration.

It can be selected as the default for newly created interfaces with:

sudo sysctl -w net.core.default_qdisc=fq_codel

That may improve queueing on traffic leaving the Linux machine. It does not, however, fix a large queue in the cable modem or Internet router. Queue management must be applied at the bottleneck. If the ISP link is limited to 20 megabits upstream, controlling a queue on a gigabit Ethernet interface after it has already handed packets to the router is too late.

For a home connection, the most effective bufferbloat treatment is usually Smart Queue Management on the router. OpenWrt’s SQM system supports both fq_codel and CAKE. CAKE generally provides better performance. However, fq_codel requires less CPU overhead.

High-latency paths introduce a different problem. TCP must keep enough data in flight to fill the bandwidth-delay product. Modern Linux generally autotunes TCP buffers, so the old advice to assign enormous fixed values to tcp_rmem and tcp_wmem is less universally useful than it once was. Before changing them, use ss -ti during a transfer and look for retransmissions, round-trip time, congestion-window size, and whether the receiver window is actually limiting the connection.

Linux also supports selectable TCP congestion-control algorithms:

sysctl net.ipv4.tcp_available_congestion_control
sysctl net.ipv4.tcp_congestion_control

Algorithms such as BBR can improve throughput and queue behavior on some long-distance or lossy paths. But changing the algorithm affects connections sent by that Linux machine; it does not control the remote speed-test server, repair poor WiFi, or eliminate a queue in the router. Congestion-control tuning is therefore a useful experiment for a server, VPN endpoint, or long-haul transfer machine — not a universal solution to slow networking.

Finally, inspect hardware offload features when a Linux system cannot keep up with a fast LAN:

ethtool -k eth0

Advanced network tuning is a bit beyond the scope of this post, but there are plenty of roadmaps down this rabbit hole.

The Lesson

The lesson here is that there is no universally correct speed-test result. Ookla tests how effectively multiple transfers can fill a route to one of its servers. M-Lab examines a single bulk flow. Fast.com tests the path to Netflix. Cloudflare pays unusual attention to latency under load and overall connection quality. OpenSpeedTest and iperf3 can determine whether the Internet connection is even the problem.

Run enough tests, and you will eventually obtain a number worth bragging about. Run the right tests, though, and you may find ways to truly increase real-world performance. If you want to chase that extra 1 kbit per second speed, be our guest — we know how it is. But the truth is that if the Internet is doing what you want it to do, then it is fast enough.

As more and more of the technology that we paid for turns becomes a subscription, there’s slowly been a momentum shift in the open source world of building replacements for these intrusive rent-seekers. We see this all of the time for self-hosted media and communications servers, but now we’re starting to see it in hardware as well. The OOMWOO robotic vacuum cleaner is completely open source, from hardware to software, and requires no cloud services whatsoever.

Although it’s open source, not every component is something one could buy off the shelf. It does require a 3D printer for most of the parts, but assuming that requirement is met most of the rest of the build comes together easily enough. For compute it relies on a Raspberry Pi running ROS 2 software and is set up to integrate easily with other existing open tools and projects such as Home Assistant. Like its proprietary cousins it can sense and map the rooms its placed in, but this platform uses an inexpensive 2D lidar system to keep costs down.

Right now the project is not quite complete, so we’ll all have to keep our eyes on this one as the team building it progresses. But they do have most of the software development done and the bill-of-materials is in progress. As an open project it’s being developed by many volunteers and there are a lot of areas available to contribute to as well, all currently set up on the project’s GitHub page. Right now many of those areas of effort are adapting the 3D printer files to off-the-shelf parts.

With the rocky status of the Roomba ecosystem, projects like this are more important than ever.

One of the biggest “David versus Goliath” stories in tech right now is the towns beset by AI data center projects they may or may not have asked for. Powered By Who is tracking data center development in the US on this convenient map.

Currently, there are over 2,100 data centers being tracked by the project ranging from proposals to sites fully up-and-running. While you have to build bypasses data centers to keep the internet running (which we’re partial to here at Hackaday), there are certainly questions around the amount of power and water consumed by these sites, the emissions they’re sending into the surrounding community, and who exactly is reaping the benefits.

Whether you’re pro, against, or ambivalent about the proliferation of “AI” data centers, the map offers an engaging way to look at what projects are happening around the nation, especially when you start looking at clusters and how that interacts with the power generation and political makeup in a region. It’s particularly interesting how only three states account for roughly 70% of all the projects. Let us know if there’s a similar tracker in your area if you’re from one of the other parts of the globe!

Looking past the debate, there’s a lot of interesting engineering involved in keeping these data centers cool, although there are questions about where that heat ends up going. DC distribution inside the site, underwater data centers, and even putting them in space are some of the solutions for keeping the cooling loads tamed.