Hardware input
Sensors, boards, switches, and the physical constraints that make software honest.
FSR pressure board · Basys3 switches
A systems lab notebook for projects at the edge of code, circuits, robotics, and community service - designed for the messy path between raw data and reliable behavior.
Focused on hardware/software systems, embedded tooling, robotics, and feedback interfaces.
what I build
The portfolio works best when it shows the whole path from physical input to reliable human action.
Sensors, boards, switches, and the physical constraints that make software honest.
FSR pressure board · Basys3 switches
Calibration, state machines, debouncing, and API boundaries that turn raw events into decisions.
dead zone · release timing · access rules
Interfaces that keep the system state visible without forcing people to chase noise.
calmer cue · enriched workflow payloads
Flagship case study · Calibration bench
Jittery raw pressure makes the cue feel nervous, even when the user is close to centered.
Dead-zone and offset logic preserve debug data while presenting a calmer correction.
BLBrian Li · Systems Lab
Working calibration demo
Slider input: calibrated pressure
Move pointer across the board or use the slider.
Raw jitter strip
Calibrated score
96CENTEREDOffset +2 · settledMove pointer across the board or use the slider. The raw signal is allowed to be noisy; the cue stays calmer.
How I work when reality gets messy
01Prototype under constraints
02Debug before polishing
03Explain tradeoffs clearly
Field context

Hawaii shaped how I think about technology: useful systems should be understandable, local, and human.
I'm a Hawaii-born computer engineering student creating and exploring technology one line, circuit, and prototype at a time.
My projects start with a practical purpose, then move through planning, testing, and repeated refinement until the system communicates clearly.
That shows up in projects where technology has to be useful to people outside the lab: Digital Ready Hawaii seniors learning technology, volunteers coordinating support, patients needing calm feedback, and local communities.
Case files
Preview cards show what was built, what kind of evidence exists, and why the work matters.
Evidence is shown as project facts, roles, artifacts, and process notes - not invented impact numbers.
Hardware-software - Calibration file
Hackathon prototype
RAW pressure / 20 Hz stream / 4 FSR sensors
A balance feedback system using FSR sensors and Web Serial updates to turn noisy pressure data into a readable correction cue.
Role: Hardware/software developer
FSR sensors, Web Serial stream, calibration, feedback UI
note: learned to separate noisy input from readable feedback
View case studyMarketplace - Trust and verification system
Active MVP foundation
Marketplace / verification / Supabase / CI gates
A college sports sponsorship marketplace built around verified teams, verified sponsors, structured listings, and application workflows.
Role: Co-founder / product engineer
Domain model, verification workflow, Supabase schema, CI/security gates
note: turned marketplace ambiguity into trust, data, and workflow systems
View case studyFPGA - Digital logic file
Course/lab deployment
Cycles 0-9 like a scanned seven-segment display; hover to scrub WAIT -> PRESS -> RELEASE -> UPDATE.
A Basys3 game built around OTTER MCU constraints, seven-segment output, and precise debounce/release handling.
Role: Embedded game logic + hardware deployment
SystemVerilog + Assembly on Basys3 / OTTER MCU
note: learned how release timing shapes reliable state transitions
View case studyBackend - Operations workflow
Nonprofit production contribution
CRUD / permissions / MongoDB / coordination
Backend and data workflows for nonprofit operations supporting volunteer coordination, access-aware CRUD, and API-driven workflows.
Role: Backend/API workflow contributor
MongoDB/Mongoose, Next.js, API workflows, permissions
note: learned how backend structure affects real-world coordination
View case studyIDE extension - Learning loop
Hackathon prototype
local IDE loop / validated output / adaptive engine
A local-first VS Code extension that turns AI-generated code into learning opportunities: explanations, quizzes, and an adaptive engine.
Role: Contributor - adaptive engine & MCP integrations
Gemini-driven explanations, Zod-validated outputs, deterministic adaptive engine, MCP doc-enricher
note: privacy-first grading and deterministic difficulty transitions
View case studyDebug log / Failure notes
A teaser for writing that reads like a lab notebook, not a polished victory lap.
Entry 001 · ReBalance calibration failure
When the signal got noisy, I stopped tuning the UI and went back to the measurement model.
01Context
Raw pressure values drifted enough to make the correction cue feel nervous.
02Decision
The fix was not more visual polish — it was calibration: separate noisy input from readable output.
03Next test
Next pass: tune dead zone, expose raw strip, keep the user cue calm.