Source post used

1) Techniques from the post

From the startup-performance post, the concrete techniques are:

Refactor and optimize startup data loading (especially pre-warmed clients with local DB data).
Switch build system to improve bundle output (Linear moved Parcel → Rollup; for us this means evaluating best bundling config/tooling for startup + size).
Lazy-load infrequently used app code and infrequently used data.
Target modern browser/runtime capabilities to avoid shipping legacy transforms/polyfills where unnecessary.
Improve dead code elimination (tree-shaking).
Code-split by route/feature boundaries.
Preload code before it is needed (intent-based prefetch/preload).

Scale/architecture follow-up from the large-workspace post:

Treat large-workspace scaling as first-class architecture work (not only micro-optimizations).
Continuously profile and remediate hot paths for large datasets.

Notes:

Web has more network variance pressure, so preload strategy must be bandwidth-aware.
Electron has stronger control of runtime (Chromium version) and benefits heavily from deterministic startup work and memory discipline.

Legend: P0 = must do now, P1 = should do next, P2 = nice follow-up

Define SLOs: warm startup, cold startup, first interactive list, memory steady-state.
Add measurement in both apps (same metric names; per-workspace-size segmentation).
Create “small / medium / large workspace” test fixtures.
Exit criteria: Reliable baseline dashboards + repeatable local benchmark script.

Lazy-load rarely used views/features/data.
Add intent-based preloading (hover/soon-to-open routes/workflows).
Cap aggressive prefetch on constrained networks.
Exit criteria: Faster perceived navigation with no significant bandwidth regressions.

Profile pathological large-workspace flows.
Rework heavy list/filter/sync hot paths.
Add scalability regression tests in CI datasets.
Exit criteria: No severe degradation moving from medium to large workspace fixtures.