What changes when you add an SFU (Mediasoup) in the middle of the pipeline?

Most WebRTC discussions about bitrate are wrong. Not because people don’t know the APIs, but because they don’t control the experiment.

So instead of asking:

“what bitrate should I use?”

I built something slightly different:

a reproducible WebRTC benchmark to measure how codecs behave under controlled conditions

This is what came out of it.

I’m happy to share that Mafalda SFU has been recognised as “Best Scalable Real-Time Media Platform 2026” at the Spanish Business Awards organised by EU Business News.

You can find the official listing at https://www.eubusinessnews.com/winners/mafalda-sfu/, and the announcement published on the project website.

It’s always nice when a side project receives some external recognition, especially one that started mostly as an experiment.

Designing Robust Spectral Validation for Audio Pipelines

Testing audio systems is deceptively hard.

Unlike text or structured data, audio pipelines are often lossy, time-sensitive, and highly stateful. Codecs introduce quantization noise, transports introduce jitter, buffers may reorder or drop frames, and decoders may subtly alter timing or amplitude. Traditional byte-level comparisons or waveform diffs are therefore brittle and misleading.

In this article, I present audio-test-fixtures, a deterministic, spectral-based approach to testing audio pipelines end-to-end. The result is a small but robust toolkit that generates known audio fixtures and validates decoded output using FFT-based frequency analysis, designed to work reliably even with lossy codecs and imperfect transports.

A Practical Walkthrough of a Surprisingly Hard Problem

For a task that sounded trivial at first, this experiment turned into a surprisingly deep dive into Android networking limitations, routing constraints, VPN behavior, and how Bluetooth Personal Area Networking (PAN) actually works under the hood.

My goal sounded simple:

Connect an Android phone to my Linux laptop over Bluetooth PAN and route ALL traffic from the phone to the Internet through the laptop — without Wi-Fi, without mobile data, without USB tethering, and without root.

Easy peasy.

Recently I’ve hit a practical limitation with Python’s ProcessPoolExecutor: when feeding it tens of thousands of tasks from hundreds of producer threads, the executor happily accepted them all. The result? Memory usage ballooned, latency increased, and eventually the whole system became unstable.

Why doesn’t Node.js have something like Django’s Class-Based Views (CBVs)?

I love Django key features like its class-based views or ORM, and I usually miss them when working with Node.js. So yesterday night, in the middle of my usual insomnia, I wondered if anyone had already built something like that for Node.js.

I did a quick search, and what I found were just a few experiments from ~8–9 years ago. Nothing robust, modern, or maintained.

Django Class-Based Views (CBVs) provide a clear structure, lots of built-in functionality, and still enough flexibility to adapt them to different use cases. So I decided to sketch out a design for Fastify (and Node.js in general) that feels natural to use, contract-first, and TypeScript-friendly. This post is a walk-through of that exploration during the rest of the Sunday.

Google used to provide prebuild Android images of libWebRTC library, and in fact, it’s (still) the recomended way to use them on its own documentation. But starting on WebRTC M80 release (January 2020), they decided to deprecate the binary mobile libraries, and the reasons were that the builds were intended just only for development purposes, and users were already building it themselves with their own customizations, or using third party libraries that embedded them (where have been left developers that just want to build a WebRTC enabled mobile app?), and they just only provided another build in August 2020 (1.0.32006) to fill some important security holes, in case someone (everybody?) was still using the binary mobile libraries.

How we went from a one-off ping to a nightly, varied, almost-autonomous reminder flow inside ChatGPT, and the three agent patterns you can use to build it — complete with runnable code.