Clarence Risher is a founder and seasoned infrastructure and operations generalist with 14 years building resilient cloud systems, automation tooling, and developer workflows across companies like Amazon, Google, Cruise, and Splunk. He blends hands‑on experience with real and virtual hardware, scripting and systems programming (Python, Go, Bash, Java), and deep expertise in provisioning, CI/CD and monitoring to drive reliable production services. As founder of CoDwell he applies engineering discipline to community and co‑living projects, uniquely pairing intentional community design with makerspace and collaborative engineering goals. An active open‑source contributor, he’s improved complex asset extraction tooling in notable projects like UnityPy and refined game AI and resource systems in Screeps, showing both backend rigor and creative problem solving. Based in Northbridge, MA, he’s known for pragmatic troubleshooting, rapid learning of new platforms, and turning operational complexity into maintainable automation.
14 years of coding experience
8 years of employment as a software developer
Computer Science, Computer Science at Austin Peay State University
Contributions:19 commits, 21 PRs, 64 comments in 7 days
Contributions summary:Clarence primarily contributed to the `screeps` project by fixing bugs, optimizing code, and adding features related to game logic. Their work involved modifying and refining core gameplay mechanics, specifically targeting resource management, creep behavior, and visual representation of game data. The commits demonstrate expertise in adjusting internal systems for efficiency and improving the player's interaction with the game. They also worked on refining the game's visual output.
UnityPy is python module that makes it possible to extract/unpack and edit Unity assets
Role in this project:
Back-end Developer
Contributions:7 commits, 9 PRs, 2 comments in 1 month
Contributions summary:Clarence contributed significantly to the `UnityPy` project, focusing on improving the asset extraction and processing functionality. They fixed documentation errors, updated code for compatibility with newer Python versions, and addressed issues related to the retrieval and usage of export functions. The user also added a filtering mechanism to the extractor and refactored parts of the code to enhance its robustness and efficiency, including handling edge cases related to specific Unity object types. Moreover, they improved the extraction process by correcting how data is retrieved for specific objects.
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