A few weeks ago I posted here about Tandem (a Tauri desktop app). Follow-up: I replaced my app’s sidecar backend with a Rust engine.

The reasons were practical:

* keep the desktop footprint small and predictable (no Node runtime bundled)
* run a long-lived background process without memory/GC surprises
* ship UI + engine together so updates are simple
* complete control over all features and versions

Tauri made the best part easy: I bundle the sidecar binary into the desktop release, so the UI and engine are always in sync. No separate installs, no version drift, one update.

The sidecar is an HTTP + SSE runtime the UI controls (sessions, tool runs, approvals). One workload I optimized is web extraction: converting raw HTML to clean Markdown before it hits LLM models. On a 100-URL set it reduced input size by \~70–80% (often close to 80%). I also benchmarked the extractor and saw much better p95 behavior and stable memory in the Rust sidecar compared to my earlier Node pipeline. If anyone wants the numbers/methodology I can share. (this was inspired by cloudfalre markdown for agents except it works for ANY website)

Questions for folks who’ve shipped Tauri apps with a sidecar:

* how do you handle tray/service lifecycle across Windows/macOS/Linux?
* any patterns for SSE reconnect/backpressure that work well long-term in WebView land?
* what’s your approach to packaging + auto-updating when you have a bundled sidecar?

Happy to share code pointers if that’s useful.  
repo: [https://github.com/frumu-ai/tandem](https://github.com/frumu-ai/tandem)

follow-up: Replaced my sidecar backend with a Rust engine (one-binary release, no drift)