Mnemcore

The description states that Mnemcore is a semantic memory system for video. It transforms human-authored timestamped notes and video into a searchable knowledge base. Users can ask natural-language questions and receive answers grounded in the original video moments.

It uses:

• Semantic vector search
• Lexical search
• Timestamp constraints
• Reciprocal rank fusion
• OpenAI models for answer synthesis

The system is built with Vue, TypeScript, Tailwind, FastAPI, PostgreSQL, Supabase, pgvector, and cloud video infrastructure.

Evidence The author’s own write-up.

Inference Mnemcore appears to be a hybrid search and retrieval system that combines vector embeddings with traditional text search and temporal constraints to surface relevant evidence from video content.

The description states that Mnemcore is designed to help teams preserve the meaning they discover while watching video, turning disconnected notes into shared organizational knowledge. It positions itself as a tool for storing, organizing, and extending human experience through AI.

It began with sports but has a broader vision: “anywhere important knowledge is trapped inside video.”

Evidence The author’s own write-up.

Inference Mnemcore's positioning evolved from a niche solution for sports analysis to a general-purpose semantic memory system for any domain where video contains valuable, scattered insights.

The description states that Mnemcore was inspired by the need to preserve organizational knowledge in sports teams. It targets organizations that have accumulated knowledge inside video but lack systems to retrieve it effectively.

It also mentions potential applications in education, research, training, operations, interviews, and creative work.

Evidence The author’s own write-up.

Inference The primary ICP appears to be teams or individuals who consume video content for learning or analysis and want to make that knowledge searchable and reusable. It is not clear if there are specific verticals prioritized beyond sports.

Not evidenced.

The description does not mention any pricing, monetization strategy, or business model.

Evidence The author’s own write-up.

Mnemcore uses:

• Semantic vector search (pgvector, text-embedding-3-large)
• Lexical search (PostgreSQL full-text search)
• Hybrid retrieval (reciprocal rank fusion)
• Timestamped evidence linking
• Retrieval-augmented generation (RAG) pipeline with OpenAI models
• Video ingestion and note capture workflows

It is built using Vue.js, TypeScript, FastAPI, PostgreSQL, Supabase, Docker, Cloudflare Pages, and other technologies.

Evidence The author’s own write-up.

Inference The system is designed to be modular and scalable, with a focus on retrieval accuracy over generative complexity. It emphasizes grounding answers in evidence rather than hallucination.

Not evidenced.

There is no mention of users, customers, revenue, or adoption beyond the author’s own development work.

Evidence The author’s own write-up.

Not evidenced.

The description does not reference any competitors or market positioning relative to existing tools for video analysis, semantic search, or knowledge management.

Evidence The author’s own write-up.

1. No evidence of real-world use: The system appears to be a prototype built by one person for a hackathon.
2. Unproven scalability: The description mentions challenges with scale and stability, especially around clustering and signal detection.
3. Lack of commercial traction: No data on users, revenue, or customer feedback.
4. Overreliance on author's own evaluation: The system’s performance is based on internal testing rather than external validation.

Evidence The author’s own write-up.

1. What specific use cases have you tested Mnemcore with beyond your own development?
2. How do you plan to validate the accuracy and utility of retrieved evidence in real-world settings?
3. Are there any early adopters or pilot users who are providing feedback?
4. What is the current architecture for handling large-scale video ingestion and embedding?
5. How do you intend to monetize this product, if at all?

Evidence The author’s own write-up.