
Fanny Gaudin contributed to the kili-technology/kili-python-sdk by building and refining data export pipelines, focusing on robust handling of LLM annotations, geospatial data, and unified export formats. She implemented asynchronous and concurrent programming patterns in Python to improve export throughput and reliability, introducing custom HTTP clients and leveraging JSON serialization for efficient data handling. Her work included refactoring backend logic to support conversation-level and consensus labeling exports, optimizing API gateways with GraphQL, and stabilizing test suites through dependency management. These efforts enhanced maintainability, reduced code duplication, and ensured consistent, scalable export workflows across diverse project configurations and data types.
March 2026 monthly summary focused on kili-python-sdk improvements with emphasis on LLM annotation handling and jsonResponse reliability for Projects. Delivered targeted refactoring to isolate LLM-specific annotation logic and introduced asset/label listing methods to ensure accurate jsonResponse construction and resolution of missing jsonResponseUrl for LLM projects.
March 2026 monthly summary focused on kili-python-sdk improvements with emphasis on LLM annotation handling and jsonResponse reliability for Projects. Delivered targeted refactoring to isolate LLM-specific annotation logic and introduced asset/label listing methods to ensure accurate jsonResponse construction and resolution of missing jsonResponseUrl for LLM projects.
Feb 2026: Delivered two major features in kili-python-sdk with measurable business value and multiple code fixes, focusing on data export workflows and API efficiency.
Feb 2026: Delivered two major features in kili-python-sdk with measurable business value and multiple code fixes, focusing on data export workflows and API efficiency.
January 2026 monthly summary for the kili-python-sdk focused on delivering performance, reliability, and maintainability enhancements. The team shipped two major features with measurable impact on asset export throughput and HTTP request reliability, leveraging asyncio-based I/O and a custom HttpClient to reduce external dependencies and improve SSL handling.
January 2026 monthly summary for the kili-python-sdk focused on delivering performance, reliability, and maintainability enhancements. The team shipped two major features with measurable impact on asset export throughput and HTTP request reliability, leveraging asyncio-based I/O and a custom HttpClient to reduce external dependencies and improve SSL handling.
In September 2025, delivered a critical stability improvement for geospatial project labeling in the kili-python-sdk by fixing how geospatial annotations are handled and converted during label retrieval, ensuring consistent jsonResponse data flow and reducing downstream errors.
In September 2025, delivered a critical stability improvement for geospatial project labeling in the kili-python-sdk by fixing how geospatial annotations are handled and converted during label retrieval, ensuring consistent jsonResponse data flow and reducing downstream errors.
May 2025 performance summary for kili-python-sdk. Focused on delivering a robust data export pathway and stabilizing the test suite to improve data pipeline interoperability and CI reliability.
May 2025 performance summary for kili-python-sdk. Focused on delivering a robust data export pathway and stabilizing the test suite to improve data pipeline interoperability and CI reliability.
Month: 2025-04 - Key feature delivered: Unified Export Formats handling via the new kili_export_formats shared library in kili-python-sdk. Consolidates export format processing across LLM RLHF, LLM static/dynamic, VOC, and YOLO export formats by moving formatting logic from individual export services to the shared library. Commit: 023a26d0b7292ec34e3a6db4dc2aa3821c0cb03a. Business impact: reduced code duplication, improved maintainability, and a single source of truth for export formats, enabling faster onboarding of new formats and more reliable export pipelines. Major bugs fixed: none this month. Technologies/skills demonstrated: Python library design, modular architecture, cross-format orchestration, testing readiness.
Month: 2025-04 - Key feature delivered: Unified Export Formats handling via the new kili_export_formats shared library in kili-python-sdk. Consolidates export format processing across LLM RLHF, LLM static/dynamic, VOC, and YOLO export formats by moving formatting logic from individual export services to the shared library. Commit: 023a26d0b7292ec34e3a6db4dc2aa3821c0cb03a. Business impact: reduced code duplication, improved maintainability, and a single source of truth for export formats, enabling faster onboarding of new formats and more reliable export pipelines. Major bugs fixed: none this month. Technologies/skills demonstrated: Python library design, modular architecture, cross-format orchestration, testing readiness.
Month: 2024-11 — Focused on delivering robust export capabilities for dynamic projects in the kili-python-sdk, with a targeted refactor to support conversation-level annotations. Key feature delivered: LLM Export: Conversation-Level Annotations (Dynamic Projects), including inclusion of conversation-level data in exports; static export updates implemented to align with the new handling. No major bugs reported or fixed this month; work centered on improving data fidelity and developer experience. Business value: enables precise analytics and reporting for LLM-driven dynamic projects, reduces post-processing effort, and strengthens export pipelines. Technologies/skills demonstrated: Python SDK development, data modeling and export pipeline refactoring, versioned commit practices, and cross-cutting data alignment.
Month: 2024-11 — Focused on delivering robust export capabilities for dynamic projects in the kili-python-sdk, with a targeted refactor to support conversation-level annotations. Key feature delivered: LLM Export: Conversation-Level Annotations (Dynamic Projects), including inclusion of conversation-level data in exports; static export updates implemented to align with the new handling. No major bugs reported or fixed this month; work centered on improving data fidelity and developer experience. Business value: enables precise analytics and reporting for LLM-driven dynamic projects, reduces post-processing effort, and strengthens export pipelines. Technologies/skills demonstrated: Python SDK development, data modeling and export pipeline refactoring, versioned commit practices, and cross-cutting data alignment.

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