JetbrainsKsenia ShneyveysAnnouncement
Google Summer of Code 2026 With the Kotlin Foundation: Recap
Summary
The Kotlin Foundation wrapped up its fourth Google Summer of Code program in 2026, supporting two projects by Ayako Hayasaka and Mark Seif Khalil. Ayako implemented tail-call support in the Kotlin/Wasm backend, using return_call and return_call_ref instructions to avoid stack growth in recursive programs, and then found and fixed a missing optimization for return_call in the V8 engine that caused a performance regression. Mark built a database and automated pipeline to collect and structure scattered information about where Kotlin is taught worldwide, covering courses, curricula, repositories, and learning platforms. In Ayako’s benchmarks, several recursive patterns achieved 1.7 to 4.3 times faster execution at a recursion depth of 10,000, and at a depth of one million, the same programs crashed without tail calls but completed successfully with them. The education landscape project now provides a reusable foundation for monitoring Kotlin’s presence in education and identifying trends and gaps.
Context
Google Summer of Code is a global program that provides students with mentorship to contribute to open-source projects. The Kotlin Foundation participated for the fourth time in 2026, sponsoring two projects: improving recursion support in the Kotlin/Wasm backend and mapping Kotlin’s global education landscape.
Approach / What changed
Two projects were completed. One added tail-call support to the Kotlin/Wasm backend by detecting calls in tail position and emitting return_call and return_call_ref WebAssembly instructions. The other created a structured database and automated pipeline to gather data from websites, curricula, repositories, and learning platforms about Kotlin education.
Takeaways
Ayako Hayasaka added tail-call support to the Kotlin/Wasm backend by detecting tail-position calls and emitting return_call or return_call_ref WebAssembly instructions, enabling recursive programs to avoid call-stack growth.
In benchmarks, the tail-call optimization made recursive patterns 1.7-4.3x faster at a depth of 10,000 and prevented crashes at a depth of one million, where programs previously failed.
Mark Seif Khalil created a structured database and automated pipeline to gather scattered data from websites, curricula, repositories, and learning platforms, providing a reusable foundation for tracking Kotlin education globally.
FAQ
What performance gains did the tail-call support achieve?
Ayako's benchmarks showed recursive patterns became 1.7 to 4.3 times faster at a recursion depth of 10,000. At a depth of one million, programs crashed without tail calls but completed successfully with them.
What did the education landscape project produce?
Mark built a database and automated pipeline to collect and organize data from websites, curricula, repositories, and learning platforms on where Kotlin is taught. The project provides a reusable foundation for monitoring Kotlin's educational presence and identifying trends.
What bug did Ayako find in V8?
She discovered a missing optimization for return_call that caused a performance regression in V8 during testing of the Kotlin changes. She reported the bug, submitted a fix, and the patch was merged into V8.
Source and freshness
- Source
- blog.jetbrains.com
- Published
- Added to Yomu
Summaries are generated from the original by a language model. Check the original before relying on it.
Keep reading
Related reading
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
The best engineering reads, once a week
One useful email. Unsubscribe anytime.