numpy-ml vs stable-hash — Trust Score Comparison

Name: numpy-ml vs stable-hash Trust Comparison
Creator: Nerq

Side-by-side trust comparison of numpy-ml and stable-hash. Scores based on security, compliance, maintenance, popularity, and ecosystem signals.

numpy-ml scores 65.8/100 (B-) while stable-hash scores 56.8/100 (D) on the Nerq Trust Score. numpy-ml leads by 9.0 points. numpy-ml is a AI tool tool with 16,274 stars. stable-hash is a uncategorized tool with 0 stars.

numpy-ml

65.8

B-

CategoryAI tool

Stars16,274

Sourcegithub

Security0

Compliance92

Maintenance0

Documentation0

stable-hash

56.8

Categoryuncategorized

Stars0

Sourcenpm_full

Compliance100

Detailed Metric Comparison

Metric	numpy-ml	stable-hash
Trust Score	65.8/100	56.8/100
Grade	B-	D
Stars	16,274	0
Category	AI tool	uncategorized
Security	0	N/A
Compliance	92	100
Maintenance	0	N/A
Documentation	0	N/A
EU AI Act Risk	N/A	N/A
Verified	No	No

Verdict

numpy-ml leads with a trust score of 65.8/100 compared to stable-hash's 56.8/100 (a 9.0-point difference). Both agents should be evaluated based on your specific requirements.

Detailed Analysis

Security

Security scores measure dependency vulnerabilities, CVE exposure, and security practices. numpy-ml scores 0 and stable-hash scores N/A on this dimension.

Maintenance & Activity

Activity scores reflect how actively each project is maintained. numpy-ml: 0, stable-hash: N/A.

Documentation

Documentation quality is evaluated based on README, API docs, and example coverage. numpy-ml: 0, stable-hash: N/A.

Community & Adoption

numpy-ml has 16,274 GitHub stars while stable-hash has 0. numpy-ml has significantly broader community adoption, which typically means more Stack Overflow answers, more third-party tutorials, and faster ecosystem development.

When to Choose Each Tool

Choose numpy-ml if you need:

Higher overall trust score — more reliable for production use
Larger community (16,274 vs 0 stars)

Choose stable-hash if you need:

Consider if it better fits your specific use case

Switching from numpy-ml to stable-hash (or vice versa)

When migrating between numpy-ml and stable-hash, consider these factors:

API Compatibility: numpy-ml (AI tool) and stable-hash (uncategorized) serve different categories, so migration may require significant refactoring.
Security Review: Run a security audit after migration. Check the numpy-ml safety report and stable-hash safety report for known issues.
Testing: Ensure your test suite covers all integration points before switching in production.
Community Support: numpy-ml has 16,274 stars and stable-hash has 0. Larger communities typically mean better Stack Overflow answers and migration guides.

numpy-ml Safety Report stable-hash Safety Report numpy-ml Alternatives stable-hash Alternatives

Frequently Asked Questions

Which is safer, numpy-ml or stable-hash?

Based on Nerq's independent trust assessment, numpy-ml has a trust score of 65.8/100 (B-) while stable-hash scores 56.8/100 (D). The 9.0-point difference suggests numpy-ml has a stronger trust profile. Trust scores are based on security, compliance, maintenance, documentation, and community adoption.

How do numpy-ml and stable-hash compare on security?

numpy-ml has a security score of 0/100 and stable-hash scores N/A/100. There is a notable difference in their security assessments. numpy-ml's compliance score is 92/100 (EU risk: N/A), while stable-hash's is 100/100 (EU risk: N/A).

Should I use numpy-ml or stable-hash?

The choice depends on your requirements. numpy-ml (AI tool, 16,274 stars) and stable-hash (uncategorized, 0 stars) serve different use cases. On trust, numpy-ml scores 65.8/100 and stable-hash scores 56.8/100. Review the full KYA reports for each agent before making a decision. Consider factors like integration requirements, documentation quality (0 vs N/A), and maintenance activity (0 vs N/A).

Related Comparisons

Last updated: 2026-06-15 | Data refreshed weekly
Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.