February 2026 (Month: 2026-02) delivered notable enhancements to luxonis/depthai-core focused on Python bindings quality, API ergonomics, and system reliability, enabling smoother integration and more predictable behavior for downstream applications. The team balanced feature delivery with stability improvements to support faster validation and deployment cycles.

January 2026 - luxonis/depthai-core: API stabilization, binding improvements, and firmware alignment. Focus this month was on delivering flexible bindings, safer default usage for core components, and improved observability, while keeping pace with hardware evolution. The work delivers business value by enabling downstream tooling and customer integrations to move faster with fewer integration risks and better debugging support.

Dec 2025 highlights for luxonis/depthai-core: API usability and reliability improvements across core build and processing paths, optional dependency management, and hardened device interactions. Delivered with traceable commits, focused on reducing integration complexity, preventing runtime failures, and improving maintainability. Key work spans API simplifications, OpenCV build gating, ToF pipeline cleanup, firmware/config alignment, multi-head detection parsing, robust device discovery, and expanded Pytest-based test coverage.

November 2025 (luxonis/depthai-core) monthly summary: Delivered a wide set of API surface enhancements, host integration, and versioning updates, reinforced by robustness fixes and testing improvements. The work strengthens Python bindings, exposes NN and DetectionParser as Python properties, and aligns device firmware/versioning across components, resulting in faster time-to-value for customers and improved maintainability across platforms.

October 2025 performance summary for luxonis/depthai-core. Expanded the perception pipeline with broader detection capabilities, improved stability across platforms, and laid groundwork for a smoother release cycle through proto/data structure updates and firmware/version bumps. Also enhanced test traceability and documentation to support long-term maintainability and customer value.

September 2025: Implemented Vision Pipeline Performance improvement in luxonis/oak-examples by increasing default FPS from 15 to 30 in triangulation and object detection examples, boosting processing speed and responsiveness for demos and real-time workloads. Commit: 25a8eec9c74af2ab1ed05e902c586418c54ad0cb (''Update FPS limit'').

July 2025: Delivered tangible improvements in real-time perception on the RVC2 platform and improved developer experience through documentation and CI hygiene. Key work centered on on-device pipeline optimization, gaze-estimation documentation, and repository quality hardening. Upgraded the depthai library to 3.0.0rc3 and adjusted the RVC2 FPS cap to 15 to improve real-time inference latency. Enhanced gaze-estimation documentation by clarifying input methods and improving model information readability in related READMEs. Also addressed pre-commit hygiene to reduce CI failures and maintain code quality. These changes collectively improve on-device performance, accelerate developer adoption, and strengthen the repository’s CI reliability.

June 2025 monthly summary for luxonis/oak-examples. Focused on delivering measurable features, stabilizing user-facing components, and updating documentation/assets to improve clarity and adoption.

May 2025 monthly summary for luxonis/oak-examples focusing on feature delivery, bug fixes, and business impact.

April 2025 (2025-04) monthly summary for luxonis/oak-examples: Key feature delivered: OCR Output Enhancement and Crop Configuration Automation. Business value: clearer OCR results and faster, more predictable cropping, enabling downstream applications to rely on higher-quality data and reducing manual verification. Major bugs fixed: none documented this period. Overall impact: improved output clarity, processing efficiency, and maintainability; establishes a modular OCR pipeline foundation for future enhancements. Technologies/skills demonstrated: pipeline refactor and data-driven OCR design (GatherData, AnnotationHelper), crop configuration generation (CropConfigsCreator), and integration within the Oak examples repository.

In March 2025, the Oak Examples project delivered targeted improvements to gaze estimation and head pose pipelines for RVC4, paired with essential documentation and configuration updates. Key changes include alignment fixes for gaze/head pose, FPS tuning for RVC4 devices, and refactoring of head pose data linkage to script nodes, alongside improved processing/visualization and DepthAI integration for more reliable gaze tracking. Documentation updates modernized README content for neural networks and gaze estimation, refactored the oakapp.toml identifier, and corrected CLI argument formats to improve developer experience and configuration consistency. These efforts reduce onboarding time, enhance runtime reliability, and enable faster, business-ready demos and integrations.

February 2025 — Luxonis Oak Examples: Delivered three DepthAI-driven features and fortified CI hygiene. Key features: Real-Time Fatigue Detection System (RVC4) with real-time feedback on DepthAI hardware; OCR Pipeline for Text Detection and Recognition on OAK4; Gaze Estimation System on DepthAI. Major bugs fixed: resolved pre-commit/configuration issues to stabilize CI and improve commit hygiene (f9efd69, 2ece586, 3d34b047). Impact: enables real-time safety monitoring, automated text extraction from video streams, and gaze analytics across compatible hardware; code quality and CI readiness improved. Technologies/skills demonstrated: DepthAI pipelines, face/landmark models, OCR pipelines, gaze estimation, data synchronization, and robust pre-commit/CI practices.

January 2025 (luxonis/oak-examples): Delivered end-to-end capability updates and maintenance across DepthAI example suites. Key additions include license plate recognition with OCR visualization, a modular facial detection/age-gender pipeline with RVC4 compatibility, an emotion recognition example with host-side processing utilities, and text blur/visualization enhancements. Maintenance work focused on documentation cleanup, config/util improvements, and preparing TOML-based workflows to improve reproducibility and onboarding. These efforts collectively advance demonstration quality, model freshness, and developer experience, delivering tangible business value through faster prototyping and clearer documentation.

December 2024 — luxonis/oak-examples: Delivered a configurable image segmentation and detection workflow via ImgDetectionsExtended, added a Snap event example with Hub upload, enhanced runtime robustness with missing label mask handling and visualizer port update, improved user experience with keyboard-driven exit, and published documentation pinning DepthAI 3.0.0-alpha.6 to ensure reproducible environments. These changes deliver end-to-end experimentation capabilities, robust runtimes, and easier sharing of results, while keeping the repository clean through removal of drafts.

November 2024 performance-focused month for luxonis/oak-examples. Delivered two high-value features that streamline development workflows on Gen3 Pipeline Builder and OAK devices, with a strong emphasis on user experience, documentation, and practical demonstration capabilities. No major regressions observed; maintained code quality with targeted cleanup and refinements.