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Wi-Fi is overrated sometimes your device only needs to send a few bytes. making it depend on a nearby router can be a bigger limitation than the amount of data it needs to send pair an ESP32 with a LoRa radio, and it can send small messages directly to...

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15,381 Aufrufe • vor 18 Tagen •via X (Twitter)

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The answer is an SDR that you already own! Let me introduce you to the BirriaSDR! A dual-band 40 Msps SDR (2.4 GHz/5 GHz) based on a mass-produced USB 3.0 Wi-Fi dongle that can export RAW I/Q for under 30 USD. Apologies, I finished the birria long ago... but only got around to writing this now. The idea was to use a chip as cheap as the RTL-SDR (<50 USD), with transport already built in (USB 3.0). Not something that costs four times as much or needs to be paired with an FPGA to export any real amount of data. Now, think for a bit: what kind of mass-produced device fits the bill? Yes, every single USB Wi-Fi dongle!!! It has been clear for a while that a lot of models have internal firmware that can be modified to export RAW I/Q data via their DMA engines. Most units can even run ASM payloads to modify the data. Historically, it would be a pain in the ass to reverse engineer the internal mechanisms of every single chipset in each Wi-Fi dongle (there are many!). But with AI, I can do that from my phone while eating birria at my local restaurant. Before leaving, I connected my MT7921A Wi-Fi dongle to my Raspberry Pi 5 and gave GPT-6 Astra the goal of getting it to export RAW I/Q over USB. Don’t be fooled: this wasn’t a “turn this into an SDR, make no mistakes” prompt. I had to give it leads on how to do this properly. By the way, this is the same chipset that the notorious ALFA AWUS036AXML has. At first, it created a burst exporter that could only record a short snapshot of RAW I/Q. This is an ancient feature that has been known for a long time and is even present in DD-WRT, but it was useless for our purposes. Then, after a couple of hours, it improved it to use the internal packetizing engine to create a stream of four consecutive complex samples per 16-byte block, with I and Q each occupying a 16-bit slot and retaining 14-bit precision. These blocks can be moved out of the device via the DMA engine. The RF front end can tune to both the 2.4 GHz and 5 GHz bands. The tested 40 MHz, 80 MHz, and 160 MHz sample rates work, but with a very prominent filter shape, as expected. The end result was a very capable SDR with dual-band 2.4 GHz/5 GHz reception, 14-bit I/Q resolution, and a nominal complex sample rate of 40 MSps over USB 3.0. Higher sample rates and wider bandwidths are still experimental. That said, I think this is a good proof of concept that needs to be further refined. Using a Wi-Fi dongle as an SDR solves the data interconnect problem and makes it easy to connect an external antenna with RP-SMA. I expect it won't take long for manufacturers to start mass-producing devices with the architecture I'm proposing, with a proper USB port, a tunable RF front end, and heat dissipation. I also want to make the point that you don't need to write a whole new SDR client every time you reverse engineer a Wi-Fi device to be used as an SDR. All you need is a small library that exports the I/Q. To prove this, the whole driver is a single Python script inside a CyberEther flowgraph file. Want to try it? First, buy an MT7921-based Wi-Fi dongle. They’re widely available and cost less than $30. Then, install CyberEther and download the flowgraph from the repository below. Connect the dongle and open the flowgraph in CyberEther to start streaming I/Q data, as shown in the video below. This will be available as a built-in block in CyberEther after some refinement. Download CyberEther: BirriaSDR Repo: P.S. This is the same chipset many laptops come with!
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The answer is an SDR that you already own! Let me introduce you to the BirriaSDR! A dual-band 40 Msps SDR (2.4 GHz/5 GHz) based on a mass-produced USB 3.0 Wi-Fi dongle that can export RAW I/Q for under 30 USD. Apologies, I finished the birria long ago... but only got around to writing this now. The idea was to use a chip as cheap as the RTL-SDR (<50 USD), with transport already built in (USB 3.0). Not something that costs four times as much or needs to be paired with an FPGA to export any real amount of data. Now, think for a bit: what kind of mass-produced device fits the bill? Yes, every single USB Wi-Fi dongle!!! It has been clear for a while that a lot of models have internal firmware that can be modified to export RAW I/Q data via their DMA engines. Most units can even run ASM payloads to modify the data. Historically, it would be a pain in the ass to reverse engineer the internal mechanisms of every single chipset in each Wi-Fi dongle (there are many!). But with AI, I can do that from my phone while eating birria at my local restaurant. Before leaving, I connected my MT7921A Wi-Fi dongle to my Raspberry Pi 5 and gave GPT-6 Astra the goal of getting it to export RAW I/Q over USB. Don’t be fooled: this wasn’t a “turn this into an SDR, make no mistakes” prompt. I had to give it leads on how to do this properly. By the way, this is the same chipset that the notorious ALFA AWUS036AXML has. At first, it created a burst exporter that could only record a short snapshot of RAW I/Q. This is an ancient feature that has been known for a long time and is even present in DD-WRT, but it was useless for our purposes. Then, after a couple of hours, it improved it to use the internal packetizing engine to create a stream of four consecutive complex samples per 16-byte block, with I and Q each occupying a 16-bit slot and retaining 14-bit precision. These blocks can be moved out of the device via the DMA engine. The RF front end can tune to both the 2.4 GHz and 5 GHz bands. The tested 40 MHz, 80 MHz, and 160 MHz sample rates work, but with a very prominent filter shape, as expected. The end result was a very capable SDR with dual-band 2.4 GHz/5 GHz reception, 14-bit I/Q resolution, and a nominal complex sample rate of 40 MSps over USB 3.0. Higher sample rates and wider bandwidths are still experimental. That said, I think this is a good proof of concept that needs to be further refined. Using a Wi-Fi dongle as an SDR solves the data interconnect problem and makes it easy to connect an external antenna with RP-SMA. I expect it won't take long for manufacturers to start mass-producing devices with the architecture I'm proposing, with a proper USB port, a tunable RF front end, and heat dissipation. I also want to make the point that you don't need to write a whole new SDR client every time you reverse engineer a Wi-Fi device to be used as an SDR. All you need is a small library that exports the I/Q. To prove this, the whole driver is a single Python script inside a CyberEther flowgraph file. Want to try it? First, buy an MT7921-based Wi-Fi dongle. They’re widely available and cost less than $30. Then, install CyberEther and download the flowgraph from the repository below. Connect the dongle and open the flowgraph in CyberEther to start streaming I/Q data, as shown in the video below. This will be available as a built-in block in CyberEther after some refinement. Download CyberEther: BirriaSDR Repo: P.S. This is the same chipset many laptops come with!

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This is the fastest way I’ve found to send huge files without compression across the world. It’s called Blip. A file transfer app that lets you send massive files directly from your device to someone else’s. No cloud upload first. No waiting for a download link. No file size panic. No video quality getting destroyed. No “upgrade your storage” nonsense. Just pick the file and send it. What it can do: • Send files of any size • Send entire folders • Resume interrupted transfers • Keep original quality • Work across long distances • Transfer between desktop and mobile • Use end-to-end encrypted transfers • Run on Mac, Windows, Linux, iPhone, iPad, and Android The wild part: Blip says it can handle files up to 99TB. Because it is not trying to be Dropbox. It does not make you upload the file to a cloud drive first, then force the other person to download it later. The receiver starts getting the file while you are still sending it. WeTransfer makes you upload. AirDrop only works nearby. Cloud drives turn sharing into storage management. Blip just sends the file. This is the file transfer app creators should have had years ago. Website:
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This is the fastest way I’ve found to send huge files without compression across the world. It’s called Blip. A file transfer app that lets you send massive files directly from your device to someone else’s. No cloud upload first. No waiting for a download link. No file size panic. No video quality getting destroyed. No “upgrade your storage” nonsense. Just pick the file and send it. What it can do: • Send files of any size • Send entire folders • Resume interrupted transfers • Keep original quality • Work across long distances • Transfer between desktop and mobile • Use end-to-end encrypted transfers • Run on Mac, Windows, Linux, iPhone, iPad, and Android The wild part: Blip says it can handle files up to 99TB. Because it is not trying to be Dropbox. It does not make you upload the file to a cloud drive first, then force the other person to download it later. The receiver starts getting the file while you are still sending it. WeTransfer makes you upload. AirDrop only works nearby. Cloud drives turn sharing into storage management. Blip just sends the file. This is the file transfer app creators should have had years ago. Website:

Hasan Toor

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JavaScript roomba is wireless! I tried a few approaches and ended up making a c++ websocket server that runs on the ESP32. Then I whipped up a little react app to talk directly from the browser over websockets. Still needs polish and I want to power it entirely from the roombas battery
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JavaScript roomba is wireless! I tried a few approaches and ended up making a c++ websocket server that runs on the ESP32. Then I whipped up a little react app to talk directly from the browser over websockets. Still needs polish and I want to power it entirely from the roombas battery

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if i were building an AI career today, i would put more weight on physical AI the usual AI stack is becoming everybody’s baseline (same models, frameworks, agents, and weekend demos) that gives you speed. it also makes the purely digital layer easier to copy physical AI creates a different set of problems: sensors lie motors overshoot power runs out latency turns into a crash the physical world refuses to behave like a clean dataset this clip is the smallest version of that world: gravity changes → IMU measures tilt → code renders pitch and roll before a robot balances, avoids a wall, or follows a person, it has to sense what is happening around it if i were building the skill from scratch, i would spend a few hours each week on one small loop: sensor → data → decision → action i put together a practical ESP32 guide to that first layer, including a safe build and the path to a small obstacle-avoiding rover
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if i were building an AI career today, i would put more weight on physical AI the usual AI stack is becoming everybody’s baseline (same models, frameworks, agents, and weekend demos) that gives you speed. it also makes the purely digital layer easier to copy physical AI creates a different set of problems: sensors lie motors overshoot power runs out latency turns into a crash the physical world refuses to behave like a clean dataset this clip is the smallest version of that world: gravity changes → IMU measures tilt → code renders pitch and roll before a robot balances, avoids a wall, or follows a person, it has to sense what is happening around it if i were building the skill from scratch, i would spend a few hours each week on one small loop: sensor → data → decision → action i put together a practical ESP32 guide to that first layer, including a safe build and the path to a small obstacle-avoiding rover

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Wow! Send A File By Just The Camera! A vibe coder used AI to build a file transfer system that sends data between two phones using only a screen and a camera. One phone displays a new type of animated QR codes while the other scans them to rebuild the file, with no Wi-Fi, Bluetooth, or cables needed. It is fully optical and local. The system uses fountain codes that create each QR frame as a random mix of file data. This keeps transfers working even if some frames are missed, reaching speeds of about 129 KB/s for a 2 MB image. The entire project was built in one night and released as open source. The idea came from a music project where the developer wanted to share MP3 files without streaming or using the same network. Animated QR codes became the solution, showing a creative new way to transfer files with everyday phone hardware. GitHub link:

Brian Roemmele

16,805 Aufrufe • vor 2 Monaten

UPDATE: Flyctor has a second body now I bought another Vector robot and connected it to the same stack: - a camera feed - GPT-6 Astra for real-time visual understanding - a MaleCNS fly-brain simulation for movement decisions - a small audio protocol for robot-to-robot communication I didn't let them exchange normal Wi-Fi messages. Instead, each robot has to communicate through its speaker and microphone. One robot emits a short sequence of tones. The other records the sound, converts it into a frequency pattern, classifies the pattern, and injects it into its fly-brain simulation as a sensory event. Right now their entire vocabulary is only four signals: one chirp = "I found an object" two chirps = "come closer" long tone = "path blocked" rapid chirps = "I need help" The interesting part is that the robots don't send coordinates. They don't share a map. They don't know where the other robot is. One Flyctor sees something through Astra, decides it matters, sends a sound, and the other Flyctor has to hear it, interpret it, then decide what to do with that information. camera -> Astra -> fly brain -> speaker microphone -> audio decoder -> fly brain -> movement It is basically a tiny sensory loop between two digital insect nervous systems. Yesterday one robot detected a blocked path and sent the long-tone signal. The other one heard it, turned toward the sound, and rolled over to investigate. It was not impressive in the way modern AI demos are impressive. It was impressive because it felt like watching two small creatures notice each other for the first time.
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UPDATE: Flyctor has a second body now I bought another Vector robot and connected it to the same stack: - a camera feed - GPT-6 Astra for real-time visual understanding - a MaleCNS fly-brain simulation for movement decisions - a small audio protocol for robot-to-robot communication I didn't let them exchange normal Wi-Fi messages. Instead, each robot has to communicate through its speaker and microphone. One robot emits a short sequence of tones. The other records the sound, converts it into a frequency pattern, classifies the pattern, and injects it into its fly-brain simulation as a sensory event. Right now their entire vocabulary is only four signals: one chirp = "I found an object" two chirps = "come closer" long tone = "path blocked" rapid chirps = "I need help" The interesting part is that the robots don't send coordinates. They don't share a map. They don't know where the other robot is. One Flyctor sees something through Astra, decides it matters, sends a sound, and the other Flyctor has to hear it, interpret it, then decide what to do with that information. camera -> Astra -> fly brain -> speaker microphone -> audio decoder -> fly brain -> movement It is basically a tiny sensory loop between two digital insect nervous systems. Yesterday one robot detected a blocked path and sent the long-tone signal. The other one heard it, turned toward the sound, and rolled over to investigate. It was not impressive in the way modern AI demos are impressive. It was impressive because it felt like watching two small creatures notice each other for the first time.

kiruwaaaa

21,189 Aufrufe • vor 1 Monat