Bing Wen

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How I
engineer.

This page is for founders and technical buyers deciding whether to trust someone with a codebase they will live in for years. It is the evidence, not the pitch.

Lines of Dart, solo
200k+Lines of Dart, soloA cross-platform app across iOS, Android, Windows and macOS, built alone over 13 months.
Token reduction, measured
99.7%Token reduction, measuredA code-intelligence tool with a CI-gated benchmark pinned to public commits, so the result reproduces.
Automated tests across projects
2,000+Automated tests across projects341 test files in the Flutter app, 1,657 test cases in the tooling, plus end-to-end suites on the web work.
Platforms from one codebase
4Platforms from one codebaseOne Flutter codebase compiled to mobile and desktop, with a shared design system underneath.

Six things I actually do

Most engineering claims are unfalsifiable. These are the ones I can point at a repository for.

Tests where a bug costs money

Not coverage for its own sake. I test the paths where failure is expensive: payment webhooks, sync reconciliation, auth boundaries, anything that touches a paid API. On the tooling project that means a benchmark regression gate in CI, so a change that quietly makes retrieval worse fails before it merges rather than after it ships.

Context Mesh case study

Expensive decisions ship with a way back

Partway through the Flutter app I replaced the WebView-based editor with a native one, a large mid-project rewrite. It went out behind a tagged rollback point. The decision was right, but I did not know that with certainty in advance, and building the escape hatch cost less than being wrong without one.

Slote case study

Constraints get designed for, not discovered

A 60-second function timeout on a hosting tier is not a surprise you hit in week three. It is a thing you architect around in week one, which is why the scraping pipeline starts a job and returns a run ID rather than blocking. Same with running costs: the client site had a target monthly bill before it had a homepage.

Trawl case study

Numbers published with their caveats

The benchmark in my tooling project carries a disclaimer I wrote against my own results: the scripted judge does not model the attention penalty a real model pays on a large context dump, so the success-rate gap overstates the case and the cost gap is the honest signal. I would rather publish the caveat than defend a headline I do not believe.

Built to be handed over

Mainstream tools, documented decisions, and code in your repository. On client work every account transfers into your name at completion. The measure of a good handover is that a different developer can pick the project up without calling me, and that you are never paying me for access to your own software.

A written record of what broke

The Flutter app has a maintained launch-readiness document tracking every open issue by severity. Resolved rows stay in the table for two releases instead of being deleted, so there is a history of what went wrong and why. A clean list that has forgotten its own past is not the same as a healthy project.

Building something substantial?

Contract engineering, technical co-founder conversations, and architecture reviews. Tell me what you are building and where it is currently stuck.