Runtime Snapshot
yangming_profile.py live object
class YangmingLi: @staticmethod def builds() -> list: return ["AI system architecture", "LLM systems", "Statistical ML", "Data engineering", "Experiment infrastructure"] @staticmethod def serves() -> list: return ["Healthcare teams", "Finance teams", "Enterprise teams"] @staticmethod def outcomes() -> list: return ["Production AI systems", "Faster decision loops", "Reusable internal tooling"] # Instantiate Yangming Li builder = YangmingLi() print(f"Builds: {builder.builds()}") print(f"Serves: {builder.serves()}") print(f"Outcomes: {builder.outcomes()}")
>focus_areas = 5
>industries_covered = 4
>delivery_mode = "prototype to production"
Evidence Strip

A faster read on trust, fit, and delivery

Built for teams that care about adoption, auditability, and production impact, not just model demos. The homepage now keeps the proof points visible and moves side quests into quieter corners.

Industries
Healthcare operations Finance and risk Public sector delivery Research environments
Capability Areas
LLM systems Statistical ML Data engineering Decision-support products
Delivery Modes
Dashboards and scorecards APIs and microservices Copilots and RAG systems Monitoring-ready pipelines
Public Artifacts
Peer-reviewed publication Technical guides Selected portfolio work CFA and FRM credentials
Working Style

Builder mindset

Tool Builder Mindset
"I'm a tool builder. That's how I think of myself. I want to build really good tools that I know in my gut and my heart will be valuable. And then, whatever happens, is... you can't really predict exactly what will happen, but you can feel the direction that we're going. And that's about as close as you can get. Then you just stand back and get out of the way, and these things take on a life of their own."
- Steve Jobs
Lab & Notes

More ways to explore

Beyond the main work and projects, I also keep study notes, essays, small experiments, investing notes, and certificates here. They give extra context on how I learn, think, and build.

About Projects Blog Index Copilot Test Sets A/B Sample Size Causal Inference Model Calibration Resume Contact Notes Essays Investing Lab Certificates

If you are here for collaboration or hiring, start with About, Projects, Blog, Resume, or Contact. If you are curious, the other links are open too.

AI Agent Evaluation

Building an AI agent?

Download a practical launch checklist for evaluating Copilot Studio, RAG, document AI, and enterprise AI agents before production.

Download the Checklist

Selected Writing

Browse writing by topic. AI/ML, product, engineering, and investing now live under one blog view. For production AI systems, visit the dedicated AI Engineering column.

Yangming's Product Blog

Yangming's Engineering Blog

Notes

Working notes, study artifacts, and lower-priority references that support the main body of work.

Selected Work

Representative work themes across healthcare, finance, and enterprise teams, centered on production AI systems that move beyond demos.

Featured Interactive Experience

Focus Room

A premium SwiftUI focus app prototype for deep work: a soft hold-to-enter threshold, layered ambient sound mixing, a subtle timer, and a fullscreen study room that slowly deepens as the session unfolds.

Hold-to-enter ritual Ambient mixer Ghost UI Session evolution Local persistence
Open Focus Room View Focus Room SwiftUI source
Good work. Take a breath.
Focus Timer
31:42
Focusing
Ambient Layers
Piano vinyl warmth
Rain window hush
Brown deep bed
Cafe soft distance
White clean edge

Investing

Notes on capital allocation, market structure, and the quieter parts of long-term decision-making.

Essays & References

A quieter corner for essays, references, and ideas that inform how I build.

  • [2026] Why "Taste" Matters in Science — and in Technology

    Exploring Nobel laureate Yang Zhenning's concept of 'taste' in research and how it applies to technology and product development. What separates the merely competent from the truly visionary in science and tech.

  • Interesting Resource: Calculating Empires

    I recently discovered an fascinating interactive resource called "Calculating Empires: A Genealogy of Technology and Power Since 1500". This comprehensive visualization maps out the intricate relationships between technology, power, and human history over the past 500 years.

  • Knowledge Flow

    An interactive platform for visualizing and exploring connected knowledge across various domains. Knowledge Flow helps discover relationships between concepts and ideas in a structured format.

Lab

A smaller corner for interactive prototypes and playful experiments. The slot machine stays here as a lightweight demo, not part of the main positioning story.

Risk Forecast Studio

Turn Monte Carlo into a finance or delivery risk story

Treat each run like a committee review: one path could be a quarterly portfolio outcome, another a launch program under delivery pressure. The upper chart shows how scenarios drift apart over time, and the histogram reveals where the ending cases really cluster.

10-90 risk band Sample cases Required hurdle

A balanced review setup where the hurdle still feels reachable, but the tail risk is visible enough to force a real decision.

Situation Quarter-close risk review

A lead is checking whether the plan still clears its hurdle before committing more capital, timeline, or scope.

Main Risk Variance can overpower the base case

The center line may look calm, but a few bad shocks widen the tail quickly and change the story for stakeholders.

Decision Lens Adjust before review day

Lower the hurdle, extend the horizon, or reduce exposure and scope if the success odds drift too low.

Scenario Paths

How the forecast can unfold

Preparing simulation...
Distribution

Where the ending scenarios cluster

Target not set
Decision Simulators

Three more playable tools for experiment design, AI economics, and representation learning

These are not generic calculators. One helps answer "can I trust this experiment read?", another stress-tests production AI economics, and the third turns high-dimensional structure into a neighborhood map you can actually interrogate.

Experiment Design Studio

A/B Test Power Simulator

Model the decision pressure behind a launch review: how much sample, how much noise, and how much real lift you need before a "winner" deserves trust.

Evidence Map
Null vs. uplift distribution
Calibrating read...
Runtime Pressure
How long you need to sit on the test
Timing pending
1 day 1 week 2 weeks 1 month+

Short tests feel faster, but they usually buy speed by borrowing confidence from the future.

Applied AI Economics

LLM Cost-Latency Simulator

Stress-test an AI system the way a platform lead would: traffic, context size, retries, and cache behavior all fight over the same latency and budget envelope.

Cost Stack
Where the monthly spend really goes
Illustrative economics
Latency Budget
How queueing and retries bend the p95
Health pending
Representation Learning Studio

UMAP / HDBSCAN Manifold Simulator

Compress a synthetic high-dimensional population into a neighborhood map and watch density structure survive, split, or dissolve as overlap, local scale, and minimum cluster size shift.

Embedding Surface
How local neighborhoods fold into 2D
Projection pending
Density Frontier
Where dense structure becomes noise
Cluster scan pending

Below it, the slot machine keeps the lighter arcade energy for visitors who want something more playful than probabilistic forecasting.

AI Slot Machine Demo

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