# Database Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/database
> Markdown URL: https://aitinkerers.org/technologies/database.md
> Technology record last updated: 2026-02-22T16:48:56Z
> Generated: 2026-09-22T11:39:55Z

Databases are the structured, electronic backbone for all applications, managing data storage, retrieval, and updates via query languages like SQL and NoSQL.

A database is an organized, electronic collection of structured or unstructured data, managed by a Database Management System (DBMS) to ensure integrity and efficient access. These systems are foundational: they handle all CRUD operations (Create, Read, Update, Delete) for applications ranging from financial ledgers to social media feeds. Key types include Relational Databases (RDBMS), which use structured tables and SQL (e.g., PostgreSQL, Oracle), and NoSQL databases, which offer flexible schemas for massive scale and speed (e.g., MongoDB, Cassandra). Choosing the right architecture (relational for transactional integrity or NoSQL for high availability) is the critical first step in any data-driven project.

- Official technology site: https://www.tadabase.io/what-is-a-database-complete-overview-examples
- Public AI Tinkerers demos and talks: 8
- Result page: 1 of 1

## Recent Public Talks and Demos

### [From 1 Agent to 11: Patterns for Multi-Agent Systems That Actually Work](https://columbus.aitinkerers.org/talks/rsvp_nZaEJ2xzX0c)

I built an AI operating system (AIOS) that runs my technology community organization, Cloud Nirvana, using 11 specialized AI agents coordinated from a Mac Mini. Each agent owns a domain: email triage, speaker pipeline QA, partnership tracking, CRM operations, content, community engagement, financial operations, and strategic planning. They share an encrypted CRM, communicate through file-based memory, and operate under a graduated trust model where each agent earns autonomy based on demonstrated reliability. Along the way, everything broke at least once. I documented those failures as reusable engineering patterns (inspired by the Gang of Four) and open-sourced them. The demo walks through the live system, shows agents handling real work in real time, and demonstrates how any builder can discover and apply these patterns against their own codebase.

- Event context: AI Tinkerers - Columbus April Meetup — 2026-04-06 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_nZaEJ2xzX0c

### [Get Shit Done: De idea a SaaS en producción en 7 días con Claude Code](https://bogota.aitinkerers.org/talks/rsvp_Ibf98jRs5c4)

Voy a mostrar cómo construí KeepItX — una plataforma SaaS para captura colaborativa de fotos en eventos — en exactamente una semana usando el framework "Get Shit Done" y Claude Code como mi copiloto de desarrollo. Voy a hacer un walkthrough del código real: la arquitectura Next.js + Supabase, cómo estructuré los prompts y sesiones con Claude Code para maximizar velocidad, las decisiones técnicas que tomé (y las que delegué al AI), y el flujo completo desde el primer commit hasta tener usuarios reales pagando $99 por evento. Sin slides, puro código y terminal.

- Event context: AI Tinkerers Bogotá: El Primer Meetup de 2026 — 2026-02-26 — Bogotá
- Public talk page: https://bogota.aitinkerers.org/talks/rsvp_Ibf98jRs5c4

### [Grok like system but home made](https://montreal.aitinkerers.org/talks/rsvp_4jmt53Nli5M)

Scrappy implementation of how you can use tool base model to fetch from database / web url or even other to enable end user into a SQL like language for your own workflow in simple

- Event context: AI Tinkerers Montreal - February 2026 Meetup — 2026-02-24 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_4jmt53Nli5M

### [Agent Eval and Scores at the Thread Level](https://seattle.aitinkerers.org/talks/rsvp_f-J46kYFOcA)

I've noticed a lot of "log and display" pipelines for LLM observability. A few of these even offer LLM-as-judge performance eval, but this doesn't really help with agent eval unless there's some sort of parser with tests both at the individual-event and conversation-thread level. If you had that, you could quickly find and aggregate what actually went wrong with an agent, and why it happened. So I built one! And it looks pretty good - in this demo, I'll show off how it works and demonstrate finding particular human-relevant problems in agents, like wandering off track or failing to resolve the user's questions. More specifically, I'm planning to show code for a chatbot and simulated user, that I've instrumented to send all the LLM I/O to a server and DB, plus a mechanism to declare the start of the chatbot "thread". Then I'll show some server code that does scoring and how scoring at the thread level is different and complementary to scoring at the event level. I'm hoping to have some plots that show the results, and how agent behaviors can be aggregated and identified quickly this way.

- Event context: AI Tinkerers Seattle - December 2024 Meetup — 2024-12-12 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_f-J46kYFOcA

### [Living Museum](https://toronto.aitinkerers.org/talks/rsvp_EFLikAFW2Z4)

The Living Museum is a museum exhibit from the future, powered by AI. Visitors can search over a museum's collection semantically using natural language, with queries like "pottery from ancient rome", "aboriginal australian tools", "modern art in the style of andy warhol", etc. They can also have conversations with individual artifacts, which have complete knowledge about themselves and their origins. Visitors can ask them about the time period when they're from, what life was like back then, how they came to be, and how they ended up in the museum. I originally wanted to build an AI-powered search engine over the British Museum's collection, so I scraped 2.5 million artifacts from them. But then I experimented with the personification angle, and found it very compelling as a completely novel modality for educating the public about history and culture. Editor's note: It's about 95% done, just working out some glitches in the UI, esp. on mobile. If you're testing this, and run into issues with searching, just try rerunning or waiting -- there's a cold start bug with the db, it can take time to warm up right now, but I'm working on it!

- Event context: AI Tinkerers - Toronto - September 2024 Meetup — 2024-09-20 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_EFLikAFW2Z4

### [YT shorts finder](https://austin.aitinkerers.org/talks/rsvp_TpbSnDWuX0s)

A way to extract meaning from youtube shorts and search over your personalized database of them, using a mix of vision models, speech transcription models, and general purpose LLMs

- Event context: Community AI Demos - September Edition — 2024-09-12 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_TpbSnDWuX0s

### [Infrahub - AI enabled road maintenance](https://nyc.aitinkerers.org/talks/rsvp_egM2CDBK8EI)

Our software is used by department of transportations (DOT) to better manage their road infrastructure. The foundation of this software is a database of a DOT’s road infrastructure - for example, the location of all their road signs. We create this database by processing images with a variety of AI vision models.

- Event context: AI Tinkerers NYC May 2024 Meetup — 2024-05-22 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_egM2CDBK8EI

### [Enhancing Application Data and Logic with OpenAI Functions](https://chicago.aitinkerers.org/talks/rsvp_uu5a4Xh3nks)

OpenAI Functions provides a means of translating generative AI responses into application processable JSON, bridging the gap of generative AI from text to data. Let’s see some ways OpenAI Functions can be leveraged to enhance a simple database web application!

- Event context: Chicago AI Tinkerers 2024 Kickoff — 2024-01-23 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_uu5a4Xh3nks

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