Ambient-AI-VCP-System dfeen87 · PARTIAL
Ambient AI VCP is a production‑ready system for running distributed AI workloads across diverse machines. It uses secure WASM sandboxing, supports federated learning, and verifies results with zero‑knowledge proofs to ensure correctness without exposing raw data.
github.com/dfeen87/Ambient-AI-VCP-System · ★ 1 · Forks 0 · Size 5.2 MB
SUMMARY
Technologies 25
Scored 21
Observed 21
Practices 6
Evidence 34
Skips 4
COVERAGE
Analyzed 170 files · 720 commits · 0 API calls
TECHNOLOGIES & DEPTH
Dockerfile LANGUAGE Depth 70
1 files · PRODUCTION
C++ LANGUAGE Depth 70
1 files · PRODUCTION
Shell LANGUAGE Depth 70
2 files · PRODUCTION
Rust LANGUAGE Depth 70
69 files · PRODUCTION
JavaScript LANGUAGE Depth 70
5 files · PRODUCTION
JSON LANGUAGE Depth 70
2 files · PRODUCTION
TOML LANGUAGE Depth 70
9 files · PRODUCTION
Markdown LANGUAGE Depth 70
41 files · PRODUCTION
YAML LANGUAGE Depth 70
3 files · PRODUCTION
Python LANGUAGE Depth 70
2 files · PRODUCTION
SQL LANGUAGE Depth 70
18 files · PRODUCTION
Cargo BUILD_TOOL Depth 80
1 files · CONFIGURATION
npm BUILD_TOOL Depth 80
1 files · CONFIGURATION
Axum FRAMEWORK Depth 74
10 files · PRODUCTION
Bytes LIBRARY Depth —
0 files · config only
Clap LIBRARY Depth —
0 files · config only
Flask FRAMEWORK Depth 58
1 files · PRODUCTION
NumPy LIBRARY Depth 58
1 files · PRODUCTION
PostgreSQL DATABASE Depth 73
3 files · CONFIGURATION, TOOLING
Redis CACHE Depth 69
2 files · CONFIGURATION
Reqwest LIBRARY Depth —
0 files · config only
SQLx DATABASE Depth 74
6 files · PRODUCTION, TEST
Serde LIBRARY Depth 74
39 files · PRODUCTION
Tokio LIBRARY Depth 74
26 files · PRODUCTION, TEST
Tracing OBSERVABILITY Depth —
0 files · config only
PRACTICES
documentation · observedautomated_tests · observedcontinuous_integration · observedcontainerization · observedlinting · absentformatting · absent
ACTIVITY & OWNERSHIP
First commit 2026-02-14
Last commit 2026-09-27
Active months 6
Commits 720