The Blog
Essays, engineering deep-dives, and research notes on the intelligence that grows — verification, memory, knowledge graphs, and continuously learning AI.
Seedthink vs Microsoft GraphRAG — A Practical Comparison
How Seedthink compares to Microsoft GraphRAG for knowledge-graph RAG: extraction, verification, retrieval, and why a growing intelligence beats a static index.
Read essay →Anatomy of a Seed — How Seedthink Works, In Depth
An illustrated, end-to-end walkthrough of how Seedthink works: the Seed Core, the six-stage knowledge pipeline, the verified graph, four-layer memory, distillation, and the Tiny LLM behind every Seed.
Building a Knowledge Graph for RAG — A Step-by-Step Guide
The end-to-end pipeline for a knowledge-graph RAG system: extraction, entity resolution, edge typing, verification, and graph-traversal retrieval.
The Forget Principle
Why forgetting is as important as learning. How Seedthink separates knowledge from reasoning so intelligence stays current without full retraining — Learn, Verify, Remember, Distill, Forget, Improve.
Inside a Seed Dashboard — Grow, Operate, Ship, Earn
A walkthrough of every section inside a Seed: Overview, Knowledge, Memory, Intelligence, Reasoning, Verification, Learning, DNA, Genome, Evolution, Insights, Quality, Model, API, Deploy, Marketplace, Revenue — and how each step works.
10 Real-World Knowledge Graph Examples for AI
Practical knowledge graph use cases across healthcare, finance, supply chain, and more — and how Seedthink's growing graph differs from static implementations.
Seedthink — The Intelligence That Grows
An end-to-end look at how Seedthink works: Seeds, verified ingestion, the knowledge graph, the Intelligence Layer, and the feedback loop that makes it grow.
Seedthink, the Intelligence That Grows
Why Seed is architecturally constrained by verification while OpenAI's models are driven by probability — plus Seed DNA™ and the Evolution Timeline.
Measuring Verified Intelligence: VII, KRS, KCI, VIMM & CIB
How Seedthink replaces vibes-based model quality with five interlocking metrics — and what each one actually measures.
GraphRAG vs. Traditional RAG
Why knowledge-graph-driven RAG outperforms vector-only retrieval on multi-hop reasoning, provenance, and hallucination resistance.