GPT 5.5 passed the ultimate intelligence test: hacking proprietary hardware
The creator reverse-engineers a Divoom Mini 2 retro Bluetooth speaker/screen by using a packet sniffer to capture Bluetooth traffic from the official app, then feeds those logs to GPT-5.5 (via Codex) to decode the protocol and display custom content on the screen.
Summary
In this short video, the creator showcases a Divoom Mini 2, a retro PC-style Bluetooth speaker with a small pixel display. Their goal is simple but technically interesting: bypass the official app and display custom content on the screen programmatically.
To accomplish this, they connected the device to a packet sniffer while using the official phone app to send images to the device. The sniffer captured the raw Bluetooth packets being transmitted, giving them a log of the proprietary communication protocol.
Those Bluetooth logs were then fed into GPT-5.5 (accessed through OpenAI's Codex), which analyzed the packet data and helped decode how the device communicates. The creator built a custom notify hook into their Codex configuration, so that when an AI task completes, it triggers a notification displayed on the Divoom screen.
The demo shows this working in real time: the creator types a simple prompt in the terminal, and when the response is ready, the Divoom screen displays a custom message ('Your move' — with a playful note that the 'e' was missing) and plays a beep sound. The creator expresses genuine excitement about the result, calling it life-changing.
Key Insights
- The creator used a packet sniffer to intercept Bluetooth traffic between the official Divoom app and the device, treating the proprietary protocol as a black box to be decoded rather than documented.
- The creator fed raw Bluetooth packet logs directly into GPT-5.5, demonstrating that LLMs can be used as protocol analysis tools for reverse engineering undocumented hardware communication.
- Rather than a standalone script, the creator integrated the Divoom display control into a Codex config as a notify hook, so the hardware screen becomes part of an AI coding workflow.
- The live demo confirms the system works end-to-end: a terminal prompt triggers Codex, which on completion pushes a custom message to the physical Divoom screen and plays a sound.
- The creator frames this outcome as genuinely transformative ('this is changing my life'), suggesting that using AI to decode proprietary hardware protocols dramatically lowers the barrier to hardware customization.
Topics
Transcript
[0:00] My real e-thou is this thing. This is a Divoom mini 2 retro PC style Bluetooth speaker and tiny [music] screen. And my only goal is to be able to display funny stuff on this screen. I then hooked it up to a packet sniffer so that when I was using the app here on my phone and it sent an image to this computer, it would log and sniff the packets and tell me what Bluetooth was sending to this little guy. And I threw those logs at 5.5 and let me show you what happened. [0:32] So, I have my terminal up and I'm going to say something really simple. What can you help me with? And…
Full transcript available for MurmurCast members
Sign Up to AccessMore from How I AI
GPT-5.6's video editing via Codex is genuinely one of my favorite new workflows
A product manager describes using GPT-5.6 with Codex to automate video editing for social media clips. By simply dragging a file and providing natural language instructions, the AI generated five polished, fast-paced hype videos from a long conference talk recording, dramatically reducing the time-intensive manual clipping process.
Theoretically Intelligent vs. Practically Effective: Why GPT-5.6 Sol Beats Fable for Product Work
An executive contrasts two AI models (Fable and Soul/GPT-5.6 Sol), arguing that Soul is superior for product work because it prioritizes practical effectiveness over theoretical intelligence. The speaker values the ability to ship products to customers and understand end-user goals over theoretical sophistication.
Build a harness when the same workflow needs the same setup and the same outcomes, every time
Building a harness for repetitive workflows allows you to be more prescriptive about job execution, resulting in greater efficiency, consistency, and better outcomes. Rather than explaining requirements to an AI agent each time, a harness lets you use a simpler interface like pasting a link while the agent already understands the intended task.
GPT 5.6-Sol vs. Claude Fable: Why OpenAI’s new model crushes my benchmark
Claire Vo compares OpenAI's new GPT 5.6 models (Soul, Terra, Luna) against Claude's Fable using her custom "How I AI" benchmark, finding that GPT 5.6 Soul excels at practical product work, prototyping, and natural communication, while Fable is theoretically intelligent but pedantic and difficult to collaborate with.
Context offloading is an underrated AI use case
The speaker highlights context offloading as an underrated AI use case, where AI serves as a safety net for routine cognitive tasks like email management and personal finances. Rather than adding new capabilities, AI reduces anxiety about missing important information or making mistakes by handling monitoring tasks, thereby freeing up mental bandwidth.