Python & C/C++ Team Lead
Building high-performance OSS systems
Async • multiprocessing • shared memory • LLM infrastructure
- High-performance Python systems (beyond typical GIL-bound design)
- Inter-process communication (IPC) with minimal overhead
- Shared memory architectures
- Async runtimes and event loop engineering
- Systems-level optimization (C/C++ / Cython / low-level integrations)
Contributor to:
Authored a fix for an important Windows issue in Kivy's core (included via an upstream PR).
High-performance IPC via shared memory.
- Zero/low serialization overhead approach
- Seamless sharing and mutation of Python objects across processes
- Supports:
- NumPy arrays
- Torch tensors
- custom classes (including dataclasses)
- objects with methods
- asyncio integration
Core idea:
Move away from serialization-heavy IPC → toward shared-memory object models with controlled mutation.
Multi-domain high-performance Python toolkit.
Includes:
- Runtime bytecode manipulation
- True shared memory primitives
- Async integrations:
- LMDB
- Tkinter
- wxPython
- PySide / PyQt
- Custom async loop with near preemptive multitasking (single thread)
- Advanced introspection & text parsing tools
Implementation stack:
Python + Cython + C/C++ + Nim + Go (where performance matters)
- Reducing Python overhead via architecture, not micro-optimizations
- Treating IPC as a first-class performance domain
- Bridging high-level ergonomics with low-level control
- Designing systems that scale beyond "standard async patterns"
Languages:
Python, C/C++, Nim
Domains:
- Async systems
- IPC / shared memory
- Systems programming
- Performance engineering
- Open for high-impact system-level work
- Focus: performance-critical backends, infra, unconventional architectures