I have contributed extensively to Julia and its package ecosystem. Many of my contributions have focused on developing new tools to make Julia development more efficient.

Alone or with collaborators, I was the initial creator of many core Julia usability features like:

• native-code package precompilation, which debuted in Julia 1.9
• Revise.jl (an essential tool for developers)
• the profiler (Julia's internal Profile stdlib and the ProfileView.jl package)
• the debugger (JuliaInterpreter.jl, which provides the core functionality for both Debugger.jl and Julia's VS Code debugging)
• "friendly" type-inference annotations in Cthulhu.jl via TypedSyntax.jl
• SnoopCompile.jl (a tool for monitoring the activity of the compiler) and elimination of the majority of invalidations in base Julia
• Julia's current handling of method ambiguity

I've also pioneered (often with collaborators) many other developments, with highlights such as:

• designing Julia's AbstractArray interface ("NumPy for Julia")
• JuliaImages
• Julia's runtime-cost-estimating inlining algorithm
• creation of Julia's @generated functions.

Finally, I also teach and provide teaching materials: https://github.com/timholy/AdvancedScientificComputing and its YouTube channel.

I continue to work on these and related areas; while I make my own choices about what to work on and tend to be cautious about advertising them in advance of knowing whether they will work, I seem to have no shortage of big, impactful ideas for future improvements.

All of this work is open-source and freely given; great ways to thank me include "passing it on" (joining me as a collaborator on improving Julia and its packages with code, tests, or documentation), or showing your sincere appreciation with sponsorship.