Field guide · Ship

Performance is part of the mobile product.

Install size, launch, responsiveness, and memory shape whether an application is downloaded, feels dependable, and survives real devices. We improve those outcomes across iOS, Android, and cross-platform stacks.

Field data + laboratory traces
User-visible milestones
Budgets on every release

Users experience a sequence, not a benchmark.

A download must complete, the application must launch, the first screen must become useful, and interaction must remain responsive under real memory, network, thermal, and device constraints. Optimizing an isolated number while delaying the user-visible milestone can produce a technically faster but experientially unchanged application.

Field data tells us where. Profiling tells us why.

Production distributions identify affected devices, versions, and journeys. Controlled traces reveal the work responsible for the delay.

Define milestones and populations.

Track distributions by application version, device class, operating system, and journey. Separate cold, warm, and resumed launch. For responsiveness, connect long frames or hangs to the user action and screen where they occur.

Distribution

Download and installed size

What users receive and retain, segmented by architecture and delivery format.

Launch

Time to usable

Cold, warm, and resume paths to the first meaningful interactive state.

Responsiveness

Frames, hangs, and ANRs

User-visible stalls and the main-thread work associated with them.

Memory

Footprint and pressure

Steady state, peaks, retention, terminations, and device-class sensitivity.

Stability

Crash-free experience

Regressions correlated with versions, features, and performance interventions.

Journey

Feature milestones

Time to content, first action, and completion for business-critical flows.

Binary size: remove bytes with a known owner.

Begin with the delivered artifact, not the repository. Attribute size to executables, frameworks, native libraries, resources, symbols, architectures, and duplicated content. Then rank removals by user impact and engineering risk.

iOS

Delivery and linkage

Use app thinning correctly, inspect embedded frameworks and symbols, audit resource variants, and understand the cost of static and dynamic dependencies.

AND

Bundles and shrinking

Use Android App Bundles, resource shrinking, and R8 deliberately; audit native ABIs, large assets, and transitive libraries.

ALL

Dependency governance

Make size impact visible during review and require ownership for libraries, assets, generated code, and cross-platform runtimes.

Launch: defer work until its value is visible.

Break launch into platform and application phases, then mark the moment the first useful screen becomes interactive. Common problems include eager dependency initialization, synchronous disk or network access, broad dependency injection graphs, class loading, resource decoding, and work repeated on every launch.

iOS

Pre-main and application work

Inspect dynamic loading and static initialization, then profile main-thread work through the first rendered and interactive state.

AND

Process and first frame

Trace application and provider initialization, class loading, layout, and first-frame work; apply baseline or startup profiles where evidence supports them.

ALL

Initialization policy

Remove, defer, parallelize, or lazily initialize work according to when the user journey actually needs it.

Responsiveness and memory are architecture signals.

Long main-thread tasks, repeated layout, image decoding, excessive recomposition, lock contention, and synchronous boundaries make interactions feel unreliable. Memory growth and peaks can trigger termination before they appear as a conventional crash.

We pair traces and memory graphs with a concrete journey, then make the responsible work smaller, asynchronous, reusable, or bounded. The fix may live in rendering, data flow, caching, concurrency, media handling, or feature architecture.

Performance needs a release budget.

Track agreed thresholds in CI where stable laboratory measurement is possible, and use production monitoring for device and journey distributions. Review changes against the baseline instead of rediscovering regressions after complaints.

What a mobile performance engagement produces.

We select one or more high-value journeys, establish field and laboratory baselines, capture platform traces, and attribute delay or resource use to specific work. The output includes implemented improvements where scoped, a ranked backlog, measurement fixtures, and budgets with clear owners.

Binary size, launch, memory, and responsiveness are connected, so we verify that improving one does not quietly regress another.

Platform references.

Apple documents techniques for reducing application size and reducing launch time. Android’s official guidance covers application size, startup analysis and optimization, and startup vitals.

Discuss the work

Bring a journey and the evidence you have.

A complaint, metric regression, trace, or size report is enough to start framing the diagnostic.

Share the context