ankitects/anki
Oct 2024 – Sep 2025
12 Months active
Languages Used
JavaScriptRustSQLSvelteTypeScriptprotobufCSSFreemarker
Technical Skills
Algorithm OptimizationAlgorithm ResearchAlgorithm UpdateAnki DevelopmentBackend DevelopmentData Analysis
Jarrett Ye
PROFILE
Jarrett Ye
Jarrett Ye developed and refined core scheduling, analytics, and simulator features for the ankitects/anki repository, focusing on the FSRS spaced repetition algorithm. He implemented per-deck retention customization, memory decay modeling, and robust review log exports, enhancing both user experience and research capabilities. Using Rust, TypeScript, and Protocol Buffers, Jarrett optimized backend data flows, improved database integrity, and delivered UI enhancements for flexible visualization and grading workflows. His work addressed concurrency, precision, and schema evolution, resulting in more accurate scheduling, reliable analytics, and maintainable code. The depth of his contributions advanced both product reliability and extensibility for future development.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
61Total
Bugs
9
Commits
61
Features
24
Lines of code
7,322
Activity Months12
Work History
September 2025
4 Commits • 2 Features
Sep 1, 2025September 2025 (2025-09) monthly summary for ankitects/anki. Focused on delivering business value through data model improvements, performance optimizations, and accuracy fixes. Key features delivered include: a deck-specific retention mechanism with memory-correct FSRS recalculation on deck DR changes; a performance-optimized FSRS processing path; and an accuracy fix for review date statistics by only considering rated entries. Impact across the product includes faster and more reliable scheduling, more accurate retention estimates, and improved analytics. Demonstrated technologies and skills include schema evolution, memory management, data structure optimization, and robust testing.
4 Commits • 2 Features
Sep 1, 2025September 2025 (2025-09) monthly summary for ankitects/anki. Focused on delivering business value through data model improvements, performance optimizations, and accuracy fixes. Key features delivered include: a deck-specific retention mechanism with memory-correct FSRS recalculation on deck DR changes; a performance-optimized FSRS processing path; and an accuracy fix for review date statistics by only considering rated entries. Impact across the product includes faster and more reliable scheduling, more accurate retention estimates, and improved analytics. Demonstrated technologies and skills include schema evolution, memory management, data structure optimization, and robust testing.
September 2025
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for ankitects/anki focusing on delivering accurate study scheduling, robust data integrity, and maintainable code improvements. The team aligned features with deck configurations, refined data handling, and updated dependencies to support new behavior while preserving stability.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for ankitects/anki focusing on delivering accurate study scheduling, robust data integrity, and maintainable code improvements. The team aligned features with deck configurations, refined data handling, and updated dependencies to support new behavior while preserving stability.
July 2025
5 Commits • 4 Features
Jul 1, 2025July 2025 (2025-07) focused on delivering core FSRS accuracy improvements, per-deck scheduling customization, and scheduler performance gains in ankitects/anki. The work enhances review timing reliability, UI responsiveness, and scalability of the scheduling system, driving better retention outcomes and user control while reducing compute overhead.
5 Commits • 4 Features
Jul 1, 2025July 2025 (2025-07) focused on delivering core FSRS accuracy improvements, per-deck scheduling customization, and scheduler performance gains in ankitects/anki. The work enhances review timing reliability, UI responsiveness, and scalability of the scheduling system, driving better retention outcomes and user control while reducing compute overhead.
July 2025
June 2025
8 Commits • 1 Features
Jun 1, 2025June 2025 — ankitects/anki: FSRS Enhancements and Scheduler Stabilization. Delivered integrated FSRS improvements across memory decay modeling, workload estimation refinements, and scheduler stability fixes, with an upgrade to fsrs-rs 4.0.0. Implemented key data-path refinements and bug fixes to improve recall predictability and reliability of spaced repetition scheduling.
June 2025
8 Commits • 1 Features
Jun 1, 2025June 2025 — ankitects/anki: FSRS Enhancements and Scheduler Stabilization. Delivered integrated FSRS improvements across memory decay modeling, workload estimation refinements, and scheduler stability fixes, with an upgrade to fsrs-rs 4.0.0. Implemented key data-path refinements and bug fixes to improve recall predictability and reliability of spaced repetition scheduling.
May 2025
4 Commits • 2 Features
May 1, 2025May 2025 — ankitects/anki: Delivered targeted enhancements to spacing and introspection that improve learning efficiency and developer experience. Key outcomes include (1) FSRS-based spacing improvements: upgraded FSRS library and fixed interval calculations to use actual elapsed days when FSRS is enabled, increasing retrievability accuracy; (2) Stability rating precision fix: increased JSON precision from 3 to 4 decimals to preserve minor improvements; (3) Exposed decay attribute on Card in Python, enabling introspection and backend serialization for SRS experiments. Overall impact: more reliable scheduling, improved user learning outcomes, and a more extensible Python API for algorithmic experimentation.
4 Commits • 2 Features
May 1, 2025May 2025 — ankitects/anki: Delivered targeted enhancements to spacing and introspection that improve learning efficiency and developer experience. Key outcomes include (1) FSRS-based spacing improvements: upgraded FSRS library and fixed interval calculations to use actual elapsed days when FSRS is enabled, increasing retrievability accuracy; (2) Stability rating precision fix: increased JSON precision from 3 to 4 decimals to preserve minor improvements; (3) Exposed decay attribute on Card in Python, enabling introspection and backend serialization for SRS experiments. Overall impact: more reliable scheduling, improved user learning outcomes, and a more extensible Python API for algorithmic experimentation.
May 2025
April 2025
7 Commits • 2 Features
Apr 1, 2025April 2025 Monthly Summary for ankitects/anki focused on delivering tangible scheduling improvements, reliability fixes, and UI polish that drive user productivity and retention. The month emphasized FSRS-based scheduling enhancements, stability hardening across memory handling and decay logic, and lightweight UI refinements to improve the user review experience.
April 2025
7 Commits • 2 Features
Apr 1, 2025April 2025 Monthly Summary for ankitects/anki focused on delivering tangible scheduling improvements, reliability fixes, and UI polish that drive user productivity and retention. The month emphasized FSRS-based scheduling enhancements, stability hardening across memory handling and decay logic, and lightweight UI refinements to improve the user review experience.
March 2025
5 Commits • 2 Features
Mar 1, 2025March 2025: Focused on delivering high-impact features for in-browser grading, improving retrievability statistics accuracy, and refining FSRS algorithm performance. Key work includes Grade Now feature rollout with improved grading workflow, a fix for SchedTimingToday initialization to ensure reliable retrievability stats, and FSRS enhancements for memory usage, relearning handling, and boundary checks. These changes collectively improved user experience, data accuracy, and model performance, supporting faster grading, more reliable study analytics, and better spaced-repetition scheduling.
5 Commits • 2 Features
Mar 1, 2025March 2025: Focused on delivering high-impact features for in-browser grading, improving retrievability statistics accuracy, and refining FSRS algorithm performance. Key work includes Grade Now feature rollout with improved grading workflow, a fix for SchedTimingToday initialization to ensure reliable retrievability stats, and FSRS enhancements for memory usage, relearning handling, and boundary checks. These changes collectively improved user experience, data accuracy, and model performance, supporting faster grading, more reliable study analytics, and better spaced-repetition scheduling.
March 2025
February 2025
5 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for ankitects/anki: Delivered major FSRS simulator enhancements and key bug fixes, plus a UI improvement for the forgetting-curve spacing. These changes enhance scheduling accuracy, resilience, and user experience, with minimal disruption and improved reliability across the FSRS module.
February 2025
5 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for ankitects/anki: Delivered major FSRS simulator enhancements and key bug fixes, plus a UI improvement for the forgetting-curve spacing. These changes enhance scheduling accuracy, resilience, and user experience, with minimal disruption and improved reliability across the FSRS module.
January 2025
6 Commits • 3 Features
Jan 1, 2025January 2025 (2025-01) achieved meaningful improvements to learning efficacy and reliability for ankitects/anki through targeted FSRS enhancements, simulator robustness fixes, and enhanced forgetting-curve visualization. Key outcomes include more accurate training data processing, expanded simulator capabilities for new-card scenarios, and clearer long-term forgetting projections, contributing to a stronger product foundation for user study efficiency and retention.
6 Commits • 3 Features
Jan 1, 2025January 2025 (2025-01) achieved meaningful improvements to learning efficacy and reliability for ankitects/anki through targeted FSRS enhancements, simulator robustness fixes, and enhanced forgetting-curve visualization. Key outcomes include more accurate training data processing, expanded simulator capabilities for new-card scenarios, and clearer long-term forgetting projections, contributing to a stronger product foundation for user study efficiency and retention.
January 2025
December 2024
5 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for ankitects/anki. Delivered critical FSRS simulator fixes and dependency stability improvements, resulting in more reliable scheduling and a smoother CI process. Key accomplishments include correcting memory state handling and dataPoint indexing in the FSRS simulator, ensuring correct relearning propagation, and updating dependencies to address known min/max failures. These changes enhance the accuracy of review intervals, reduce edge-case failures in production, and improve maintainability and linting stability. Technologies demonstrated include FSRS-rs integration, memory-state modeling, error handling around initial learning steps, and CI/linting maintenance.
December 2024
5 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for ankitects/anki. Delivered critical FSRS simulator fixes and dependency stability improvements, resulting in more reliable scheduling and a smoother CI process. Key accomplishments include correcting memory state handling and dataPoint indexing in the FSRS simulator, ensuring correct relearning propagation, and updating dependencies to address known min/max failures. These changes enhance the accuracy of review intervals, reduce edge-case failures in production, and improve maintainability and linting stability. Technologies demonstrated include FSRS-rs integration, memory-state modeling, error handling around initial learning steps, and CI/linting maintenance.
November 2024
4 Commits • 1 Features
Nov 1, 20242024-11 Monthly Summary (anker/ankitects): Focus on delivering core FSRS-related reliability and performance improvements in ankitects/anki, with a strong emphasis on scheduling accuracy, memory-state correctness, and performance alignment with PyTorch.
4 Commits • 1 Features
Nov 1, 20242024-11 Monthly Summary (anker/ankitects): Focus on delivering core FSRS-related reliability and performance improvements in ankitects/anki, with a strong emphasis on scheduling accuracy, memory-state correctness, and performance alignment with PyTorch.
November 2024
October 2024
6 Commits • 3 Features
Oct 1, 2024Month: 2024-10 Summary (business value oriented): This month focused on strengthening analytics integrity, elevating scheduling accuracy, and expanding research capabilities for ankitects/anki. Delivered features that improve user outcomes and data-driven decision making, while fixing core data stability issues that could affect long-term modeling and user trust. Key features delivered: - FSRS scheduling engine upgrade to 1.3.5 with FSRS-5 model and precision tweaks to three decimals to improve scheduling stability and forecast reliability. (Commit 5caeac530eec7279e330ffe45a0572f6a357fd39) - Research-oriented review log export to enable data-driven algorithm research by exporting review logs with ease rating between 1 and 4. (Commit 9a44881121f78d5b06e9abe1c719d5b3db47e15b) - Simulator: added UI toggle to switch between viewing review time cost and review count, with corresponding graph rendering improvements for flexible visualization. (Commit eacd5bf908e3e02124a12479e12bb936c09eafda) Major bugs fixed: - Analytics data correctness and stability fixes: skip suspended cards in graph context retrievability; prevent potential integer overflows in due date and interval calculations; ensure daily load only counts cards that contribute meaningfully; reset easyDaysPercentages correctly when presets change. (Commits 939cc5a268108eb518ebfeff3c9a21b3bb57be8b, 1aa734ad282eb7d1c1a59240ca57831ca9598807, 0ce907fe5b2310cdb150f0ff44bd245633ec6038b) Overall impact and accomplishments: - Improved data integrity and analytics reliability, enabling more accurate scheduling decisions and research datasets. - Enhanced user experience through flexible visualization options and more robust data exports. - Strengthened foundation for data-driven product decisions and research collaborations, supporting business value through higher confidence in insights and scheduling accuracy. Technologies/skills demonstrated: - FSRS-rs integration and numeric precision tuning, improving scheduling stability. - Data export pipelines and research data workflows for algorithm development. - UI/UX enhancements for visualization flexibility (simulator toggle, graph rendering). - Data quality controls and edge-case handling for analytics pipelines (suspension, daily load, presets).
October 2024
6 Commits • 3 Features
Oct 1, 2024Month: 2024-10 Summary (business value oriented): This month focused on strengthening analytics integrity, elevating scheduling accuracy, and expanding research capabilities for ankitects/anki. Delivered features that improve user outcomes and data-driven decision making, while fixing core data stability issues that could affect long-term modeling and user trust. Key features delivered: - FSRS scheduling engine upgrade to 1.3.5 with FSRS-5 model and precision tweaks to three decimals to improve scheduling stability and forecast reliability. (Commit 5caeac530eec7279e330ffe45a0572f6a357fd39) - Research-oriented review log export to enable data-driven algorithm research by exporting review logs with ease rating between 1 and 4. (Commit 9a44881121f78d5b06e9abe1c719d5b3db47e15b) - Simulator: added UI toggle to switch between viewing review time cost and review count, with corresponding graph rendering improvements for flexible visualization. (Commit eacd5bf908e3e02124a12479e12bb936c09eafda) Major bugs fixed: - Analytics data correctness and stability fixes: skip suspended cards in graph context retrievability; prevent potential integer overflows in due date and interval calculations; ensure daily load only counts cards that contribute meaningfully; reset easyDaysPercentages correctly when presets change. (Commits 939cc5a268108eb518ebfeff3c9a21b3bb57be8b, 1aa734ad282eb7d1c1a59240ca57831ca9598807, 0ce907fe5b2310cdb150f0ff44bd245633ec6038b) Overall impact and accomplishments: - Improved data integrity and analytics reliability, enabling more accurate scheduling decisions and research datasets. - Enhanced user experience through flexible visualization options and more robust data exports. - Strengthened foundation for data-driven product decisions and research collaborations, supporting business value through higher confidence in insights and scheduling accuracy. Technologies/skills demonstrated: - FSRS-rs integration and numeric precision tuning, improving scheduling stability. - Data export pipelines and research data workflows for algorithm development. - UI/UX enhancements for visualization flexibility (simulator toggle, graph rendering). - Data quality controls and edge-case handling for analytics pipelines (suspension, daily load, presets).
Activity
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Quality Metrics
Correctness87.2%
Maintainability87.0%
Architecture85.4%
Performance82.2%
AI Usage20.4%
Skills & Technologies
Programming Languages
CSSFreemarkerFtlJSONJavaScriptProtoProtobufProtocol BuffersPythonRust
Technical Skills
API DesignAPI IntegrationAlgorithmAlgorithm DevelopmentAlgorithm ImplementationAlgorithm OptimizationAlgorithm RefactoringAlgorithm ResearchAlgorithm UpdateAnki DevelopmentAnki SchedulerBackend DevelopmentBug FixingCode OrganizationCode Refactoring
Repositories Contributed To
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