# IPFS Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/ipfs
> Markdown URL: https://aitinkerers.org/technologies/ipfs.md
> Technology record last updated: 2026-02-27T15:12:04Z
> Generated: 2026-09-23T00:36:08Z

The InterPlanetary File System (IPFS): a peer-to-peer hypermedia protocol for content-addressed data storage and transfer.

IPFS (InterPlanetary File System) is a core Web3 protocol, designed in 2014 by Juan Benet and developed by Protocol Labs: it replaces the traditional location-based addressing of HTTP with content-based addressing. Instead of requesting data by *where* it is stored (a server IP), the network requests it by *what* it is (a unique Content Identifier, or CID, which is a cryptographic hash). This peer-to-peer system, which leverages a Distributed Hash Table (DHT) and a protocol derived from BitTorrent, ensures data is distributed across thousands of nodes, boosting resilience, lowering latency, and providing censorship resistance.

- Official technology site: https://ipfs.tech
- Public AI Tinkerers demos and talks: 3
- Result page: 1 of 1

## Recent Public Talks and Demos

### [PinMe: Teaching AI Agents to Deploy Frontends in One Command](https://orange-county.aitinkerers.org/talks/rsvp_lTngvw_LXU8)

PinMe is a deployment CLI that lets developers and AI coding agents deploy websites with a single command. In this demo, I'll show how PinMe turns a local project into a live website, how AI agents use it, and how we built a deployment workflow that works reliably without configuration. I'll walk through the CLI architecture, agent workflow, deployment pipeline, and a live deployment in 5 minutes.

- Event context: AI Tinkerers Orange County: Tuesday, July 21, 2026 at Centercode — 2026-07-22 — Orange County
- Public talk page: https://orange-county.aitinkerers.org/talks/rsvp_lTngvw_LXU8

### [Beyond Public Data: Unlocking AI's Next Frontier with Secure Private Data](https://waterloo.aitinkerers.org/talks/rsvp_cXgF867zQU8)

This session will provide a technical deep dive into building secure data access infrastructure that enables AI systems to train on private data without compromising privacy or security. We'll walk through the complete architecture of a decentralized data access system that uses Trusted Execution Environments (TEEs), encryption, and blockchain technology to create a trustless environment for data access. You'll leave with practical knowledge of the technical challenges in private data aggregation and working code examples you can adapt for your own projects.

- Event context: AI Tinkerers - Waterloo April Meetup — 2025-04-21 — Waterloo
- Public talk page: https://waterloo.aitinkerers.org/talks/rsvp_cXgF867zQU8

### [Federated Learning with Flame](https://seattle.aitinkerers.org/talks/rsvp_HniMKJNpKf8)

Traditional machine learning depends on the centralization of data, but that comes with privacy and computational concerns. A reality with billions of edge devices diminish those issues, especially with the advent of federated machine learning. Training may be performed on edge devices directly, keeping datasets decentralized and private. Additionally, offloading work to different nodes means less computation per device. Projects like GBoard, Siri, and even the medical and military fields already use federated learning. One current open-source framework for federated machine learning is Project Flame (maintained by Cisco Systems). Flame uses object-oriented programming to implement different graphs between edge devices for a federated learning network. Flame can be extended to different kinds of topologies and executed across multiple devices that run Python code using P2P communication.

- Event context: AI Tinkerers Seattle - February 2024 Meetup — 2024-02-29 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_HniMKJNpKf8

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