AccelerationConsortium/ac-training-lab
Mar 2026 – Apr 2026
2 Months active
Languages Used
MarkdownPython
Technical Skills
API integrationAWSAWS LambdaContinuous IntegrationError HandlingGitHub integration
Bonnie King
PROFILE
Bonnie King
Developed and enhanced the AccelerationConsortium/ac-training-lab streaming pipeline over two months, delivering 24 features and resolving 7 bugs. Focused on improving security, reliability, and streaming quality, the work included integrating GitHub authentication with AWS Lambda, automating login flows, and refining error handling for robust startup and session management. Leveraged Python and ffmpeg to implement advanced video streaming features such as RTMP stream key support, timestamp stabilization, and configurable stream quality. Addressed device-specific stability for Raspberry Pi, automated YouTube live transitions, and introduced environment-driven configuration. The contributions emphasized backend development, multimedia processing, and continuous integration to support production-grade streaming.
Overall Statistics
Feature vs Bugs
77%Features
Repository Contributions
45Total
Bugs
7
Commits
45
Features
24
Lines of code
173,401
Activity Months2
Your Network
11 peopleWork History
April 2026
26 Commits • 20 Features
Apr 1, 2026April 2026: Delivered major enhancements to the AccelerationConsortium/ac-training-lab streaming pipeline, focusing on reliability, configurability, and automation. Implemented authenticated FFmpeg URL handling, RTMP timestamp stabilization, Pi Zero stability improvements, input stabilization, and YouTube live transition automation. Introduced robust stream lifecycle controls (force_new, ingestion-aware delays, pre-transition status polling) and environment-driven configuration (stream quality, input codec, OS-based overrides). Restored silent audio input and stabilized audio timestamps with resample fixes. Increased default quality targets (854x480 @ 12fps, with a subsequent move toward 15fps) while applying conservative quality improvements. Note: some MJPEG/H.264 camera output experiments were rolled back to preserve production stability.
26 Commits • 20 Features
Apr 1, 2026April 2026: Delivered major enhancements to the AccelerationConsortium/ac-training-lab streaming pipeline, focusing on reliability, configurability, and automation. Implemented authenticated FFmpeg URL handling, RTMP timestamp stabilization, Pi Zero stability improvements, input stabilization, and YouTube live transition automation. Introduced robust stream lifecycle controls (force_new, ingestion-aware delays, pre-transition status polling) and environment-driven configuration (stream quality, input codec, OS-based overrides). Restored silent audio input and stabilized audio timestamps with resample fixes. Increased default quality targets (854x480 @ 12fps, with a subsequent move toward 15fps) while applying conservative quality improvements. Note: some MJPEG/H.264 camera output experiments were rolled back to preserve production stability.
April 2026
March 2026
19 Commits • 4 Features
Mar 1, 2026March 2026 monthly summary for AccelerationConsortium/ac-training-lab highlighting delivered features, fixed issues, and overall impact. Focused on security, reliability, streaming quality, and developer velocity with a clear mapping to business value and end-user experience.
March 2026
19 Commits • 4 Features
Mar 1, 2026March 2026 monthly summary for AccelerationConsortium/ac-training-lab highlighting delivered features, fixed issues, and overall impact. Focused on security, reliability, streaming quality, and developer velocity with a clear mapping to business value and end-user experience.
Activity
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Quality Metrics
Correctness93.8%
Maintainability88.0%
Architecture88.0%
Performance87.2%
AI Usage20.8%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
API integrationAWSAWS LambdaContinuous IntegrationError HandlingGitHub integrationPythonPython developmentPython programmingPython scriptingRaspberry PiSoftware Developmentaudio processingauthenticationauthentication flow
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