I built TuneBoss because I wanted a dedicated screen showing what’s playing on Spotify — something I could mount under my monitor and forget about. No app switching, no phone unlocking, just a permanent glanceable display with album art and visuals that react to the music.

It’s open source, self-hosted, and runs on a Raspberry Pi, NAS, Proxmox, home server or anywhere you run docker containers.

What It Does

TuneBoss polls the Spotify API from a lightweight Node.js backend and pushes track updates over WebSocket to a Vue 3 Progressive Web App. The display shows:

• Album art with colors extracted from the cover — background, text, and visualizer bars all adapt to every track
• A 10-band spectrum analyzer running at 60 fps, with procedurally generated visuals unique to each song
• Track progress interpolated smoothly between server polls
• Optional microphone mode that captures real ambient audio for actual FFT-based spectrum data

The whole thing runs fullscreen in portrait mode on the iPhone, with the screen wake lock keeping it on indefinitely. Install it as a PWA from Safari and it behaves like a native app — no browser chrome, no accidental navigation.

Why Not Just Use Spotify’s App?

Because Spotify’s app wants to be the thing you’re interacting with. I wanted a display that sits there passively — a piece of ambient information, not another app demanding attention. TuneBoss is a single-purpose appliance: the iPhone becomes a music screen and nothing else.

Architecture

iPhone (Safari PWA)  ◄──── WebSocket ────►  Node.js Backend
                                                  │
                                            Spotify Web API
                                          (poll every 3 seconds)

The backend handles all OAuth credentials and API communication. The iPhone is completely stateless — it just renders whatever the server sends. No Spotify tokens ever touch the client.

Polling is client-gated: the server only hits the Spotify API while someone is actually watching. Close the display and it stops polling immediately.

The Spectrum Analyzer

Spotify deprecated their audio analysis endpoint in late 2024, so TuneBoss generates its own visuals. Each track ID is hashed to seed a deterministic random number generator that produces a unique BPM, oscillator shapes, and per-band wave patterns. The same song always produces the same visualization.

For something closer to reality, there’s an optional microphone mode. It captures ambient audio through the Web Audio API, maps FFT data to 10 logarithmic frequency bands, and crossfades smoothly between the procedural and real-time spectrum when music is detected.

Running It

TuneBoss is Docker-ready. Set your Spotify API credentials, run the container, and open the URL on your phone:

docker compose up -d

It also runs bare-metal with Node.js, or behind Caddy for automatic HTTPS on a homelab. Full setup instructions are in the repo.

On the iPhone side: open the URL in Safari, tap Share → Add to Home Screen, and you’re done. For a truly locked-down display, enable Guided Access in iOS accessibility settings.

Building with AI

Tuneboss was built almost entirely by Claude Code. I prompted it and steered it when it got stuck on a few bugs - particularly on acquiring the iOS wake lock to keep the screen alive. But otherwise, Claude gets the credit for the software engineering.

Get It

TuneBoss is MIT licensed and available on GitHub:

GitHub: TuneBoss

Contributions and feedback are welcome — open an issue if you have ideas or run into problems.

From a sobering prediction on a possible future evolution path of super intelligence from the current state of AI titled “AI 2027”:

What do you think - is this speedup likely?

Personally I am doubtful we will be able to achieve this level of speedup so quickly, but then again the article does call out most industry professionals have consistently underestimated (and continue to do so) the speed at which AI technology is evolving.

It certainly will have a dramatic impact on the software industry – it already is with the current crop of agents like Github Copilot, Claude Code and their brethren.

I absolutely love the Vestaboard split flap display.

I only wish it wasn’t so damn expensive.

https://vestaboard.com

Recent discussion around costs of LLM tokens and whether it stifles experimentation in the context of “vibe coding”.

IMHO I think all token costs trend to zero and the answer is NO.

You might end up with “slow tokens” and “fast tokens” where the slow tokens are truly zero (like internet usage at night time being unmetered in many parts of the world) and fast tokens cost a tiny amount of money. But in the long term I think they both trend to the cost of electricity or even lower (because caching kicks in and the tokens are free)

I was talking with my classmate Amit Bhor a few days ago about the limitations of inference on the edge, especially around memory requirements of the new MOE models (Deepseek-R1 etc). At the same time, edge inference is pretty desirable for things like privacy, disconnected scenarios, cost etc.

Well, Minions attempts to tackle some of these challenges by adding a way for cloud models to cooperate with local models. The local models handle the interaction with data on your device, whereas the cloud models perform some of the heavy lifting/reasoning.

The claimed gains are impressive:

Pretty cool! Techniques like this would make a lot of sense for on-device assistants like Siri or a future smart-home-controller AI.

This is the blog of Aakash Kambuj. I am determined to post regularly, and this is my first post.

I found https://tld-list.com/ helpful when building this site. It maintains a live list of each gTLD and what registrar is cheapest for initial registration and renewal. That is how I discovered Porkbun.