Weaviate
Open-source vector database
Episodes
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#4832: Your AI Memory Should Be Markdown FilesWhy your AI project memory should live in Git-tracked Markdown files, not a proprietary platform. -
#4778: SSH MCP and Tailscale: Docker Migration GuideHow to configure Tailscale for Docker containers when migrating from public IPs to tailnet addresses. -
#4092: How AI Remembers What You Never Told ItHow ChatGPT connected "wall anchors" to a power tool you bought days ago — without being asked. -
#3673: Knowledge Graphs vs SQL: How Custom Relationships Change RetrievalWhy naming relationships (not just connecting data) transforms how you retrieve information. -
#3553: Can AI Review Your Lease in Israel?Can AI actually understand Israeli tenant law? We explore the tools, the gaps, and how to build your own. -
#2755: How to Build AI Memory That Actually WorksStop jumping to conflict resolution. The real challenge is getting data in and out cleanly. -
#2673: The Embedding Coupling Problem: Editing Vector StoresCan you edit or delete individual chunks in Pinecone? And can you actually back up a vector index? Yes—but with critical caveats. -
#2466: The Hidden Trap of Embedding Model Lock-InWhat happens when your vector database works great — until your embedding model gets deprecated and your vectors become useless. -
#2271: Vector Search in a Single FileWhat if you could do vector search with just SQLite? We explore sqlite-vec, the extension that adds embeddings to the world's simplest database, an... -
#1794: RAG Is Cheaper Than You Think (Until It’s Not)From a $1 embedding bill to a $10k/month vector database bill, here’s the real math behind RAG in 2026. -
#1792: Google's Native Multimodal Embedding Kills the Fusion LayerGoogle’s new embedding model maps text, images, audio, and video into a single vector space—cutting latency by 70%. -
#1784: Context1: The Retrieval CoprocessorChroma's new 20B model acts as a specialized "scout" for your LLM, replacing slow, static RAG with multi-step, agentic search. -
#1778: Audio Is the New "Read Later" GraveyardWhy listening to AI conversations beats reading dense PDFs, and how serverless GPUs make it cheap. -
#1483: The Recall-Per-Dollar Era: Mastering Vector Database TuningStop burning money on unoptimized vector searches. We dive into HNSW tuning, distance metrics, and the vital "recall-per-dollar" metric. -
#1235: Beyond "No Training": Securing the New Agentic AI StackThink your data is safe because of a "no training" clause? We deconstruct the hidden security risks within the modern agentic AI stack. -
#1215: The Vector DB Hangover: Scaling Without Going BrokeStop overpaying for your AI's memory. We break down the math of self-hosting vectors and the rise of serverless search. -
#1213: Is Your JSON Store Just a Postgres Feature Now?Explore why the NoSQL revolution didn't kill SQL and how document databases are finding a new second life in the era of AI and RAG pipelines. -
#1212: The Postgres Vector Revolution: Killing the SprawlIs your tech stack a sprawling suburb of microservices? Discover why a 40-year-old database is winning the AI infrastructure war. -
#1113: The Ghost Company: The High Cost of AI Agent BureaucracyCan a company run entirely on AI? Explore the hidden costs and "agentic bureaucracy" of building autonomous agent hierarchies. -
#810: The Agentic Interview: How AI Learns to Know YouStop dumping data. Discover how agentic interviews are transforming AI from a passive listener into a proactive, structured partner. -
#30: Which AI Tools Will You Still Use Next Year?RAG vs. Memory: Are you building resilient AI? Discover the crucial difference between these two foundational pillars.