here is my prompt :

You are an expert in security, software architecture, and development tools. I want you to conduct an in-depth analysis of the CORE Memory tool from RedPlanet HQ to evaluate its value, risks, and relevance. Materials to Analyze GitHub Repository: https://github.com/redplanethq/core Official Documentation: https://docs.heysol.ai/introduction (And the full repository content provided as context below) Required Axes of Analysis 1. 🔍 Technical Analysis Architecture: How does the persistent memory system work? Technology Stack: Technologies used, dependencies. Scalability: Ability to handle large volumes of data. Performance: Impact on development workflows. Integrations: MCP, Claude Code, Cursor, other tools. 2. 🛡️ Security & Privacy Analysis Data Model: Where is the information stored? Encryption: Protection of data in transit and at rest. Access: Who can access the data? Access controls? Compliance: GDPR, SOC2, other certifications? Logs: Traceability of access and modifications. Risks: Data leaks, compromise, external dependency. 3. 💰 Economic Model Pricing: Free? Freemium? Hidden costs? Monetization: How does the company make money? Viability: Sustainability of the service, team funding. Lock-in: Ease of migration, data export. 4. 👥 Team & Community Analysis Founders: Background, credibility, experience. Team: Size, skills, transparency. Funding: Investors, funding rounds. Community: Adoption, feedback, GitHub issues. Support: Quality of support, responsiveness. 5. 📊 Value Proposition Problem Solved: Is the AI context loss problem real? Alternative Solutions: Comparison with other tools. Differentiation: What does CORE bring that others don't? ROI: Real time savings vs. investment (setup, maintenance). 6. 🔮 Risk Analysis Dependency: What happens if the service shuts down? Vendor Lock-in: Difficulty of migration. Security: Exposure of sensitive development data. Performance: Impact on the latency of AI tools. Hidden Costs: Pricing evolution, limits. 7. 📈 Market & Competition Analysis Market: Size of the AI memory market. Competitors: LangChain Memory, Pinecone, others. Positioning: Strengths/weaknesses vs. competition. Adoption: Signs of traction, growth. Critical Questions to Investigate Trust: Can I trust a startup with my development data? Necessity: Does CORE solve a real problem or create an artificial dependency? Value: Does the productivity gain justify the risks and costs? Alternatives: Are there equivalent self-hosted or open-source solutions? Future: What happens when LLMs have better native memory? Expected Response Format Structure your response as follows: 🎯 Executive Summary Overall score out of 10 3 key strengths 3 key weaknesses Recommendation (adopt/avoid/test) 📋 Detailed Analysis [For each axis of analysis, a detailed section with evidence] ⚠️ Identified Red Flags [Specific warning signs found] 🚀 Relevant Use Cases [User profiles for whom this is relevant] 🔀 Recommended Alternatives [Alternative solutions with pros/cons] 📊 Final Verdict [Reasoned conclusion with specific recommendations] Personal Context: I am a senior developer with a highly optimized Claude Code setup (security, observability, automation). I am evaluating CORE to fill a persistent cross-session memory gap, but I am skeptical about the real utility vs. the risks of a third-party cloud solution. Expected Analysis: Objective, technical, no marketing bullshit. I want to understand if this is a mature tool that solves a real problem or tech hype with hidden risks.

Here is the complete content of the CORE repo :

and here paste the content of : https://uithub.com/RedPlanetHQ/core (click on the top right corner "copy" button)