karthikreddy-7/ai-engineering-playbook
A zero-to-100 learning path for applied AI engineering — RAG, embeddings, vector search, agents, MCP, and the production engineering around them. 56 pages, built as a searchable site.
63
Stars
17
Forks
37.3
Radar Score
Rising
Momentum
Scoring Breakdown
Impact22.6
Velocity46.2
Health48.1
Growth Metrics
7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
MDX
Why it ranks
Steady growth — consistently collecting stars and contributors.
RAG · vector databaseTop 66% globally
Popular Alternatives & Competitors
Top projects in the same category (RAG) — compare side by side.
🛡️ Enterprise Readiness & Commercial Safety
Organic Growth
95%
High Confidence
Growth pattern looks organic.
License
Permissive (Commercial Friendly)
Safe for commercial use, modification, and distribution.
Health Grade
B
Based on maintenance, issue response, and release cadence.
Radar Score
37.3
Composite of Impact, Velocity, and Health.
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.
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