Tom Mclaughlin is a founder and seasoned software engineer with 12 years of experience building developer-focused tooling and data-first applications from Mountain View. He founded CodeDown to supercharge data science notebooks for teams, combining product leadership with hands-on engineering across front-end and back-end stacks. Previously a tech lead on Dropbox’s Analytics team, he has deep experience in scalable analytics, real-time systems and developer UX. An active open-source contributor, Tom has improved notable projects from CodeMirror and react-json-view to Jupyter kernels for Clojure and Haskell, often tackling subtle reliability and integration issues. His background blends rigorous CS training (MS and BS from Stanford) with applied research in inverse reinforcement learning and hardware-oriented engineering, giving him a rare cross-domain perspective. Colleagues would point to his knack for turning research-grade ideas into polished, practical developer tools.
12 years of coding experience
10 years of employment as a software developer
Master of Science (M.S.), Computer Science, Master of Science (M.S.), Computer Science at Stanford University
Contributions:11 reviews, 13 commits, 12 PRs in 7 years 1 month
Contributions summary:Tom primarily focused on enhancing the IHaskell kernel for the Jupyter project. Their contributions involved adding new message types to the system and modifying the message parsing logic. The user also refactored and cleaned up the codebase by removing debugging prints and unnecessary dependencies. These changes aimed to improve the kernel's functionality and maintainability.
Contributions:7 commits, 6 PRs, 1 push in 10 months
Contributions summary:Tom primarily focused on improving the Clojure Jupyter kernel's core functionality. Their contributions involved fixing socket handling issues, updating the jupyter-console example, and modifying the execution flow to send separate outputs. They also implemented error handling mechanisms and removed unnecessary code. Overall, the user enhanced the kernel's stability and improved the user experience by refining how code execution results are handled.
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