I’ve been reading the recent comments, specifically the suggestion that this project is a “self-deception event horizon” and “not worth the trouble.”
I feel the need to clarify exactly what this project is—and what it is not—because the characterization of it as a casual flight of fancy is disconnected from the reality of the engineering work I’ve put in.
What This Is Not
I do not own a $28k Audio Precision analyzer. I have never claimed to measure the analog output of a DAC. If you are looking for a graph showing a change in SINAD, you are looking for a different project.
I am also not a kernel developer. The credit for the Diretta drivers belongs to Yu-san (who is working with the IEEE on standardization of his L2 protocol). The credit for the AudioLinux real-time kernel belongs to Piero.
What This Is
This is a project of accessibility, orchestration, and validation.
While the underlying tech is brilliant, it was undocumented and difficult to implement. My goal has been to build the bridge that makes Diretta accessible to any Roon user.
Since June 2025, I have pushed 589 commits to my Diretta project repository on GitHub. This isn’t just a readme; it is a suite of over 9,000 lines of code and documentation including:
-
2,800+ lines of detailed documentation (
Diretta.md) explaining the how and the why for every step. -
Python analysis tools (
analyze_benchmark.py,plot_traffic.py) to mathematically validate network stability. -
Orchestration scripts (
diretta-host-checks.sh,purist-mode-webui.py) to automate the configuration and tuning.
[4.0K] rpi-for-roon
├── [112K] Diretta.md <-- 2,806 lines of documentation
├── [4.0K] scripts
│ ├── [7.2K] analyze_benchmark.py <-- Custom network analysis tools
│ ├── [ 16K] diretta-host-checks.sh
│ ├── [ 12K] diretta-target-checks.sh
│ ├── [6.3K] plot_traffic.py
│ ├── [ 20K] purist-mode-webui.py <-- Custom management UI
│ └── ... (36 files total)
The Technical Rationale
I’m not guessing about performance. I measure it after each change using the tools listed above and then spend hours listening.
The result of this architecture is a transport with a network transmission jitter (IQR) of 2.00 µs. That is not a random fluctuation; that is a relentless, steady heartbeat of data.
--- Timing Precision (Steady State) ---
Mean Interval: 699.72 µs
Median Interval: 700.00 µs
Standard Deviation: 14.29 µs
Jitter (IQR): 2.00 µs (Core Stability)
--- Stability Events (Steady State) ---
Stability Score: 99.91543 %
My hypothesis is that this extreme consistency reduces processing overhead by stabilizing interrupt loads, creating predictable CPU usage patterns on the endpoint. While I cannot provide an analog measurement to satisfy the absolutists, I have provided the code and the data that proves the stability of the transport.
The World Has Taken Notice
To the claim that this is “not worth the trouble”: The wider audio community disagrees. Based on the traffic to the repository, there is a massive appetite for a high-performance, DIY streaming transport that actually works.
The Bottom Line
To dismiss a project that achieves a stable, 2µs-jitter transport using nothing more than a pair of $50 Raspberry Pis and stock power supplies as “self-deception” is simply ignoring the code, data, and anecdotal evidence from scores if not hundreds of users who have evaluated this project. These results do not require custom silicon or exotic linear power supplies—just better engineering.
For the builders here who are listening and enjoying the result: thank you for your support and testing. Let’s get back to the music.
