Is Betterneural Safe?
Betterneural — Nerq Trust Score 49.5/100 (D grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Betterneural is a Python package with a Nerq Trust Score of 49.5/100 (D), based on 3 independent data dimensions. Last analyzed: 2026-03-20 Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Betterneural safe?
Trust Score Breakdown — Betterneural has a Nerq Trust Score of 49.5/100 (D). Measured across 2 independent trust signals (as of 2026-03-20).
What is Betterneural's trust score?
Betterneural has a Nerq Trust Score of 49.5/100, earning a D grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Betterneural?
Betterneural's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Betterneural and who maintains it?
| Author | riioze |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Betterneural
What is Betterneural?
Betterneural is a Python package — A package with tools for Neural networks.
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=betterNeural
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Betterneural has a Nerq Trust Score of 50/100 (D). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Betterneural has a Trust Score of 50/100 (D).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.