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AsaqeLee/README.md

Asaqe Lee

Go backend engineering · Workflow systems · Signal intelligence

I build backend systems where state is explicit, persistence is practical, and workflows stay easy to reason about. Alongside that work I develop wireless and signal-processing projects from studies at Xidian University.

Go · Python · MATLAB · Gin · MongoDB

Tech stack: Go, Python, PyTorch, MATLAB, LaTeX, Flask, React, TypeScript, MongoDB, Docker, Bash, Git

Featured work

Go task-lifecycle API with an explicit state machine, audit log, and dual persistence (memory + mongo). Intranet-oriented MVP; currently in maintenance mode.

Pure-stdlib Go agent runtime: tool loop, RAG/MCP-style adapters, and an HTTP serve path for local experiments.

Multi-agent consultation skill that gathers independent advice from several LLMs before an architecture decision.

MATLAB simulations for NOMA user grouping and power allocation.

GitHub activity

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GitHub contribution streak for AsaqeLee

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What I care about

  • Explicit state: every transition should be checkable
  • Practical abstractions: cheap to recover from six months later
  • Honest scope: say what the system is for, and what it is not

Connect

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  1. NOMA NOMA Public

    MATLAB simulations for NOMA user grouping, power allocation, and performance analysis.

    MATLAB 12 2

  2. Signal Signal Public

    Deep-learning workflows for modulation classification from raw communication signals.

    Python

  3. taskflow taskflow Public

    Internal task workflow: Go API + React workbench, state machine, JWT/API keys, Mongo + memory.

    Go

  4. ElectromagneticStateSpectrumSemanticizationProject ElectromagneticStateSpectrumSemanticizationProject Public

    Spectrum semanticization and electromagnetic-state analysis for intelligent sensing workflows.

    Python

  5. peer-consult peer-consult Public

    Multi-agent peer-consultation skill for collecting independent coding advice from multiple LLMs.

    Python