SynapCores Blog

News, updates, and deep dives into AI-native database technology.

![Traditional five-system AI stack plus glue versus one AI-native engine](/diagrams/traditional-vs-ai-native.svg)...

![One engine: vector, graph, SQL, AutoML and LLM in a single SynapCores binary](/diagrams/architecture.svg)...

![One engine: vector, graph, SQL, AutoML and LLM in a single SynapCores binary](/diagrams/architecture.svg)...

![RAG workflow: retrieve relevant context, augment the prompt, generate the answer](/diagrams/rag-workflow.svg)...

![RAG workflow: retrieve relevant context, augment the prompt, generate the answer](/diagrams/rag-workflow.svg)...

![One engine: vector, graph, SQL, AutoML and LLM in a single SynapCores binary](/diagrams/architecture.svg)...

![One engine: vector, graph, SQL, AutoML and LLM in a single SynapCores binary](/diagrams/architecture.svg)...

![Traditional five-system AI stack plus glue versus one AI-native engine](/diagrams/traditional-vs-ai-native.svg)...

SynapCores v1.8.6.1 — Docker EMBED works out of the box again...

RAG and GraphRAG on LlamaIndex with one database — SynapCores...

![Traditional five-system AI stack plus glue versus one AI-native engine](/diagrams/traditional-vs-ai-native.svg)...

SynapCores v1.8.5 — Agent memory as three SQL functions...

![One engine: vector, graph, SQL, AutoML and LLM in a single SynapCores binary](/diagrams/architecture.svg)...

SynapCores SQL Reference...

SynapCores v1.8 — One binary. No daemon. Faster than CPU Ollama....

A recurring piece of feedback on my chatbot-memory posts goes something like this: "The two-table pattern is fine for a tutorial, but is that really ...

A few weeks ago I wrote about [conversation memory with a rolling summary](/blog/conversation-memory-rolling-summary) — two tables, store every turn,...

How to Swap the GGUF Model in SynapCores (Native + Docker)...

![Conversation memory with rolling summary — full history, tiny context](/blog-images/conversation-memory-rolling-summary/cover.png)...

![Vector search finds nearby points; graphs encode why points belong together](/blog-images/why-vector-search-needs-graph-relationships/cover.png)...

![Vector search returns proximity, not paths](/blog-images/why-vector-search-fails-complex-reasoning/cover.png)...

![Traditional databases push data out to ML services; AI-native engines bring AI in](/blog-images/why-traditional-databases-fail-modern-ai/cover.png)...

![SQLv2: AI functions as first-class SQL primitives](/blog-images/what-is-sqlv2/cover.png)...

![In-database ML: model artifacts and training data share the same engine](/blog-images/what-is-in-database-machine-learning/cover.png)...

![GraphRAG: vector retrieval plus graph traversal in one engine](/blog-images/what-is-graphrag/cover.png)...

![HNSW: a hierarchical small-world graph for approximate nearest neighbor search](/blog-images/hnsw-explained/cover.png)...

![GraphRAG vs traditional RAG: when vector similarity stops being enough](/blog-images/graphrag-vs-traditional-rag/cover.png)...

![GraphRAG is a retrieval pattern; Neo4j is a graph database. Different layers.](/blog-images/graphrag-vs-neo4j/cover.png)...

![Building AI chat memory: the four-stage retrieval loop](/blog-images/building-ai-chat-memory-systems/cover.png)...

![One engine: vector, graph, SQL, AutoML and LLM in a single SynapCores binary](/diagrams/architecture.svg)...

December 25, 2025

SynapCores AutoML Guide...

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SynapCores SQLv2 vs PostgreSQL: The Evolution of Database Systems...

Tutorial: Build a Smart Product Catalog...

SynapCores Query Optimizer...

Payment Anomaly Detection: Real-Time Fraud Prevention...

Healthcare & Medical AI Applications...

RAG Functions - SQL Reference...

SynapCores AIDB - Complete Features For Marketing and Sales Use...