hyperledger-labs/fabric-token-sdk
Feb 2026 – Feb 2026
1 Month active
Languages Used
GoPython
Technical Skills
Zero-Knowledge ProofsbenchmarkinggRPCperformance analysis
Effi-S
PROFILE
Effi-s
Developed a Zero-Knowledge Proof benchmarking suite for the hyperledger-labs/fabric-token-sdk repository, focusing on quantifying Fabric Smart Client node overhead on Transactions Per Second. The work involved implementing a benchmarking framework and creating scripts in Go and Python to parse benchmark logs and generate automated performance plots. This enabled data-driven analysis of throughput under varied workloads, supporting more informed capacity planning and performance governance for enterprise deployments. The approach emphasized observability and reproducibility, with clear commit practices and code quality standards. Skills applied included Zero-Knowledge Proofs, benchmarking, gRPC, and performance analysis, resulting in a robust tool for evaluating system scalability.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
4,047
Activity Months1
Your Network
32 peopleShared Repositories
32
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026: Delivered a Zero-Knowledge Proof (ZKP) Benchmarking Suite for FSC Throughput in hyperledger-labs/fabric-token-sdk. This feature introduces a ZKP benchmarking framework to quantify FSC node overhead on Transactions Per Second (TPS), with scripts to parse benchmark logs and generate performance plots, enabling data-driven performance analysis and capacity planning under varied workloads. The change improves observability, informs capacity decisions for enterprise deployments, and enhances performance governance.
1 Commits • 1 Features
Feb 1, 2026February 2026: Delivered a Zero-Knowledge Proof (ZKP) Benchmarking Suite for FSC Throughput in hyperledger-labs/fabric-token-sdk. This feature introduces a ZKP benchmarking framework to quantify FSC node overhead on Transactions Per Second (TPS), with scripts to parse benchmark logs and generate performance plots, enabling data-driven performance analysis and capacity planning under varied workloads. The change improves observability, informs capacity decisions for enterprise deployments, and enhances performance governance.
February 2026
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
GoPython
Technical Skills
Zero-Knowledge ProofsbenchmarkinggRPCperformance analysis
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