# Postgres Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/postgres
> Markdown URL: https://aitinkerers.org/technologies/postgres.md
> Technology record last updated: 2026-09-18T15:13:56Z
> Generated: 2026-09-22T04:37:46Z

Postgres is the world's most advanced open-source object-relational database, engineered for enterprise-level reliability and data integrity.

PostgreSQL (Postgres) is a powerful, open-source object-relational database system: it extends the SQL standard and handles complex data workloads with proven architecture. Its origins trace back to 1986 at UC Berkeley. The platform is fully ACID-compliant and uses Multi-Version Concurrency Control (MVCC) to manage high transaction rates with minimal locking. Key features include robust extensibility, allowing users to define custom data types and functions, plus a vast ecosystem of add-ons like the PostGIS geospatial extender. This focus on reliability and feature depth makes it the top choice for critical applications across all major operating systems.

- Official technology site: https://www.postgresql.org/
- Public AI Tinkerers demos and talks: 12
- Result page: 1 of 1

## Recent Public Talks and Demos

### [mBoss - n8n for DBOS via a VS Code Extension](https://seattle.aitinkerers.org/talks/rsvp_aLz-n8_gv4o)

mBoss is an open-source agentic IDE for building durable applications with DBOS—available as a free VS Code extension. mBoss combines a visual workflow language, generated TypeScript, local Postgres-backed execution, and coding agents including Claude Code, Codex CLI, and Gemini through the Agent Client Protocol. Agents don’t blindly rewrite the workflow. They work through mBoss’s MCP tools and Agent Skills, with proposed workflow changes appearing visually on the canvas before the developer applies them. The resulting application remains real, inspectable TypeScript: mBoss generates the DBOS orchestration while developers own ordinary typed business logic. The development loop continues into runtime. Developers can run workflows locally, inspect every recorded step on the workflow graph, replay execution from a selected step, or hand the recorded evidence back to the coding agent to investigate a failure. The result is an experiment in what development environments could look like when coding agents understand not only source code, but the structure and execution history of the application they’re helping build. mBoss is MIT-licensed and available as a VS Code extension.

- Event context: AI Dev Tools Track - Seattle - September 8 — 2026-09-09 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_aLz-n8_gv4o

### [Making local elections more transparent with AI](https://la.aitinkerers.org/talks/rsvp__2-l-YlpnxM)

Local elections decide a lot and almost nobody covers them. California city races are nonpartisan by law, so the ballot doesn't share a candidate's party, and local coverage of city hall has thinned to almost nothing. The records are public but unusable: filings as PDFs, a county voter file behind a paywall, endorsements scattered across a dozen sites. Torrance Watch is the public reference that could not exist before LLMs. Every contested race, every candidate covered the same way: background, where they stand, who endorsed them, and who is funding them, split into direct contributions, independent PAC spending, and how many donors actually live in Torrance. Every claim is footnoted to a filing you can open, and equal treatment is a compile gate, so a race covered unevenly fails to build. About 26,000 people used it, and roughly a quarter million saw the findings across Facebook groups, Reddit and Nextdoor. The busiest day was June 8, mid-count, not election night. It forecast the count and called four races itself, for a city of 145,000, because no wire service covers a city election. And it put a number on the money: $148,000 from Orange County real estate PACs across the five races, seven times what Torrance public-safety unions spent. Claude Code built it: 423 merged pull requests, one person, April to August. Six pipelines work the review queues, matching 2,543 donors against 6.4 million voter registrations, checking endorsements against their sources, and drafting candidate profiles. A deterministic scorer runs first and settles what it can. What it cannot resolve goes to LLM judges working under rules written before the run, and every decision records who made it. For the hardest cases three models judge separately, blind to the matcher's own pick, and the label only counts if all three agree. Nothing about it is specific to Torrance. The same pipeline would work for any city.

- Event context: AI Tinkerers LA — August 6: Builder Meetup & Live Demos — 2026-08-07 — Los Angeles
- Public talk page: https://la.aitinkerers.org/talks/rsvp__2-l-YlpnxM

### [Building a production GTM agent: what broke and how we fixed it](https://boston.aitinkerers.org/talks/rsvp_KyMpqyZSw7M)

We built a GTM agent that runs outbound end-to-end. The agent finds prospects in your ICP, comments on their LinkedIn posts, sends connection requests and messages, and books meetings for you. I can share how the agent works and walk through the architecture, including things like: - how we decide which parts of the workflow should be handled by an agent versus deterministic code - how we store structured state in Postgres so the agent can keep track of prior actions and workflow progress - how we built human-in-the-loop review so users can approve actions before the agent proceeds - how the workflow pauses and resumes across long-running tasks I can also show a few examples of where the system initially broke and what we changed to make it more reliable in production.

- Event context: AI Tinkerers Boston: GTM Agentic AI Launch — 2026-06-29 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_KyMpqyZSw7M

### [Enhancing AI Agents with Durability](https://boston.aitinkerers.org/talks/rsvp_feJAeO6zZ-M)

We’ve combined AI agents with Durable Execution techniques to create agents that are durable and observable. The result is a pattern that can be used to continue execution in the event of outages, conserve LLM calls, record past run history, catch rare production failures and reproduce them in house. I’ll demo using a basic research agent and show what the code looks like.

- Event context: AI Tinkerers Boston: GTM Agentic AI Launch — 2026-06-29 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_feJAeO6zZ-M

### [Vibe-Coded Plug-Ins](https://columbus.aitinkerers.org/talks/rsvp_6qoJy3Zx-eM)

I’ll live-demo a prompt-generated dashboard plug-in system: a user asks for a custom dashboard tile, the backend generates a vue component plus a typed props schema, live props are resolved through an agent with tenant-scoped tools, and the browser renders the result inside a sandboxed iframe

- Event context: AI Tinkerers - Columbus June Meetup — 2026-06-01 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_6qoJy3Zx-eM

### [Building an Enterprise Digital Brain](https://valencia.aitinkerers.org/talks/rsvp_a3Hziwgp_h8)

A Digital Brain is your company's private Wikipedia leveraged with AI. Document every SOP and team member know-how (structured, semi-structured and unstructured) and make it "legible". Then leverage AI to find precise answers quickly, turn them into SKILLS that AI agents can execute. All in one place. In the demo we will look at: - General content storage in a virtual file system - Find precise answers with agentic search - Write natural language skills the agent can execute - Customize chat UX with dynamic tools via skills (work-in-progress)

- Event context: AI Tinkerers Valencia May Meetup — 2026-05-26 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_a3Hziwgp_h8

### [ibaAgent: Agentic time-series analysis.](https://nurnberg.aitinkerers.org/talks/rsvp_DcXjk1ibxVU)

I developed an agentic system using LangGraph to analyze high-resolution time-series data via natural language. Since high-resolution data is too token-intensive for LLMs, the agent operates "blind" by dynamically writing and executing Python analysis scripts to process data locally. This is supported by a specialized memory architecture that allows the agent to iteratively build an internal map of undocumented, messy data stores, and a rigorous Langfuse experiment pipeline used to tune hyperparameters and prompts for predictable reasoning and precise tool use. Currently transitioning from proof-of-concept to product at iba AG, my demo will showcase LangGraph traces of the agent’s execution and its self-correction within the Python environment. I will also highlight our Langfuse evaluation workflow and how the memory layer enables the agent to navigate complex data structures over time. I’m looking to exchange ideas with fellow tinkerers specifically on memory persistence, planning strategies, and robust evaluation for agentic workflows.

- Event context: AI Tinkerers Nürnberg: April Meetup — 2026-04-22 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_DcXjk1ibxVU

### [Who Shipped What: Maintaining Codebase Context](https://singapore.aitinkerers.org/talks/rsvp_7b-BmRva9Ls)

Trajan is an always-on agent that maintains the real-time state of a codebase by continuously ingesting commits, pull requests, and repository structure, and turning them into up-to-date, queryable context. I’ll connect Trajan to a live repository, show how it reconstructs what shipped across contributors, generates and updates documentation automatically, and answers questions about the system using current state, without requiring manual prompts or periodic re-indexing.

- Event context: AI Tinkerers Singapore: The Agentic Future & Dev/Eng Workflows — 2026-04-21 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_7b-BmRva9Ls

### [Using AI to make the science behind Kabuki Syndrome more accessible](https://boston.aitinkerers.org/talks/rsvp_JYH_omkmLYc)

Kabuki Syndrome is a rare disease affecting 1 in 32,000 births. There is currently no cure, but there is a fair clip of research being conducted and published every month. As a parent of a child with Kabuki, I have long struggled to read and comprehend the dense, jargon-filled academic papers that report on this research, despite my great curiosity. Due to the rarity of the condition, there is little to no journalistic reporting on these kinds of academic papers. I will share a project I'm working on that is intended to bridge that gap with AI, using the latest LLMs to craft stories more accessible to the broader Kabuki community. In the demo I will show how I use AI to break down the papers into their individual claims, then how I transform those claims, using multiple prompts and workflows, into easy-to-read plain english versions. These plain-english versions live in a UI that has two goals. One is to be polished and reader-friendly, up to the standard of a national magazine, with subheaders, pull quotes, etc. Another is to provide an inline interactive lens onto the original text of the article, with individual sentences that can morph to show the claims and sentences in the original paper they are based on.

- Event context: {Cancelled due to Blizzard Warning } AI Tinkerers Boston: February 2026 Meetup — 2026-02-23 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_JYH_omkmLYc

### [Rapid MCP - A Security First MCP Proxy Server](https://sf.aitinkerers.org/talks/rsvp_BBddpDQOFDM)

RapidMCP is designed to be a security first MCP proxy server. It allows users to collect and connect MCP servers of all kinds under one URL for use with any MCP client. RapidMCP gives users additional security screening and recommendations per server, including code scanning and dependency audits. Tools can be selectively turned on or off for greater control over what is being exposed to your LLM. The proxy also gives users additional schema constraints that can be applied to the schemas sent by the upstream MCP servers, providing a way to layer additional security when using Tools. RapidMCP also provides a monitoring area, where Tools (and in the future resources/prompts) can be audited for usage and content. Input and Output guardrails are also available as first and last lines of defense against prompt injection or other types of harmful content.

- Event context: AI Tinkerers - Advanced MCP Showcase — 2025-06-19 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_BBddpDQOFDM

### [Spacedust 3D: Fast 3D Character Animation with AI](https://seattle.aitinkerers.org/talks/rsvp_fHjDZ89xdl4)

Spacedust 3D is a web-based system that leverages AI to make it easy to generate and animate 3D character models.I will talk about the current state of the art for prompt to 3D and image to 3D neural systems, describe the technical architecture of Spacedust 3D, and talk about of several of the AI models used. Finally, I will do a live demo where I make someone in the audience into an animated 3D avatar.

- Event context: Friday - AI Tinkerers Seattle - May Meetup — 2025-05-31 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_fHjDZ89xdl4

### [Learn with Lumi - How AI is Transforming Education](https://atlanta.aitinkerers.org/talks/rsvp_LvygltH7E8k)

Dustin Carr and Evan Marie Carr will be presenting the technical implementation of Learn With Lumi, an AI-driven personalized education platform designed to adapt to individual learning styles. This demo will break down how we built Lumi, our interactive AI learning companion, and the underlying AI models that power adaptive learning experiences.

- Event context: AI Tinkerers Atlanta February Meetup — 2025-02-27 — Atlanta
- Public talk page: https://atlanta.aitinkerers.org/talks/rsvp_LvygltH7E8k

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