Reviving Broken Open Source: Debugging, Stabilizing & Scaling the KubeEdge Synergy AI Toolkit

In open-source ecosystems, many high-quality projects end up being unusable because of issues such as dependency drift, pipeline breakage, and a lack of maintainability. In my LFX mentorship program under the CNCF KubeEdge SIG AI community, I restored the Synergy AI distributed benchmarking toolkit, a project that had become hard to use and extend.

This presentation will take audience through a case study of an open-source restoration project, with emphasis on how we:

1.Solved Python environment & dependency conflicts

2.Restored broken examples and outdated execution pipelines

3.Implemented CI/CD pipelines with GitHub Actions

4.Enhanced developer onboarding and reproducibility

5.Mitigated long-term technical debt in a distributed AI system

Audience will also delve into the architecture of Synergy AI in the edge computing and distributed AI benchmarking space, and why such tools are essential for workload evaluation across cloud-edge stacks.

This presentation is for developers interested in contributing to open-source projects, managing existing FOSS projects, or enhancing reliability and reproducibility in distributed systems and AI tooling.

1.How to approach and revive a broken FOSS project
2.Debugging dependency and environment issues in Python systems
3.Setting up CI/CD pipelines for open-source reproducibility
4.Best practices for maintaining distributed AI/edge systems
4.How to reduce technical debt in community-driven projects
5.A clear pathway to start contributing to CNCF / LFX open source projects