# Weaviate Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/weaviate
> Markdown URL: https://aitinkerers.org/technologies/weaviate.md
> Technology record last updated: 2026-03-03T19:17:31Z
> Generated: 2026-09-22T14:42:04Z

Weaviate: The open-source, AI-native vector database for high-performance hybrid search, scaling to billions of objects for RAG and semantic applications.

Weaviate is the open-source, cloud-native vector database built for AI-first applications. It stores both objects and vectors, enabling high-performance hybrid searches (vector similarity plus structured filtering) across billions of data points. Engineered in Go for speed and reliability, it seamlessly integrates with major vectorizers (like OpenAI and HuggingFace) and supports critical use cases: RAG (Retrieval-Augmented Generation), recommendation engines, and high-scale semantic search. With over 20 million downloads and SDKs for Python, Go, and TypeScript, Weaviate is the production-ready core for your generative AI stack.

- Official technology site: https://weaviate.io
- Public AI Tinkerers demos and talks: 5
- Result page: 1 of 1

## Recent Public Talks and Demos

### [MCP vs Vector Database](https://valencia.aitinkerers.org/talks/rsvp_Fo1y5UD6Px0)

In this talk, I’ll explore the difference between Model Context Protocol (MCP) and Vector Databases, and explain how they work together to power the next generation of intelligent AI systems. We’ll look at how Vector Databases enable semantic search and long-term memory for large language models, while MCP allows models to securely access live, dynamic data and perform real-time actions through APIs or databases. Through practical examples, like using AI to search and report on enterprise SharePoint data. I’ll show when to use each approach, their strengths and trade-offs, and why combining them creates a more powerful, context-aware AI architecture. By the end of this session, you’ll understand how memory (Vector DB) and action (MCP) complement each other to move beyond static RAG pipelines toward adaptive, real-time AI systems.

- Event context: Opening AI Tinkerers Valencia x VDS - 22 October — 2025-10-22 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_Fo1y5UD6Px0

### [Teaching AI to read ancient Maya Glyphs](https://montreal.aitinkerers.org/talks/rsvp_Z7tI1x8ww7I)

The ancient Maya developed one of the most beautiful, complex, and intriguing writing systems in human history. For centuries, these glyphs recorded stories, rituals, dynasties, and the deep relationship between people, time, and the cosmos. Yet this knowledge was silenced — burned, buried, and forgotten for nearly a thousand years. Even today, only a few thousand people in the world can read Maya writing. I will the development of an AI agent designed to help bridge that gap. Using computer vision and deep learning, the system can detect glyph blocks, identify individual signs, and suggest possible readings by comparing them to a vast epigraphic corpus. It is not perfect — but it is a powerful first step toward using artificial intelligence to preserve and reactivate knowledge that was nearly lost forever. In doing so, we demonstrate how AI can be more than just code — it can be a force for memory, preservation, and cultural resilience. I will be presenting a live demo.

- Event context: AI Tinkerers Montreal – Spooky October 2025 Meetup — 2025-10-21 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_Z7tI1x8ww7I

### [Airweave: Make Any App Searchable for AI Agents](https://amsterdam.aitinkerers.org/talks/rsvp_53OZ-WKXwj0)

In this talk, we'll show how we built Airweave, an open-source tool that makes source app, API, and database searchable for AI agents. Airweave automates ingestion, transformation, and indexing, while handling authentication, mapping, interweaving, and syncing to vector stores and graph databases. Originally built to solve retrieval challenges in our own AI agent stack, we decided to open-source it. We’ll walk through its architecture, the challenges re ran into when building it (as well as the ones we're facing right now), and structured data retrieval for AI agents.

- Event context: AI Tinkerers Amsterdam - February Edition — 2025-02-26 — Amsterdam
- Public talk page: https://amsterdam.aitinkerers.org/talks/rsvp_53OZ-WKXwj0

### [From Haikus to Helper - Wrangling Agentic LLM's](https://montreal.aitinkerers.org/talks/rsvp_ykR1yUDQJ08)

We built a product that uses LLM's for data transformations + enrichment ( think spreadsheet) but also supports Agentic responses/actions on these datasets. I'll do a brief demo of the product and describe the architecture and challenges productizing augmentation and agentic behaviours. We'll walk through adding an AI enrichment column, and specifically cover: * The path of data through the system: Ingestion => SQL / Vectorization => Retrieval, * LLM touch points - importance estimation, schema prediction, extraction/augmentation. Use the agent to analyze results and support the user: * Prepare a report, have it posted to slack

- Event context: AI Tinkerers - Montreal Inaugural Meetup (October) — 2024-10-29 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_ykR1yUDQJ08

### [Vector + Graph Friends](https://sf.aitinkerers.org/talks/rsvp_Uuh1JCTOe1k)

This project writes and sends emails connecting individuals from an event (like Summer Social) based on interests and skill sets. It pairs both Vector and Graph Retrieval-Augmented Generation (RAG) for long-term memory and for recommending matches.

- Event context: AI Tinkerers - San Francisco - August 2024 Meetup — 2024-08-22 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_Uuh1JCTOe1k

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