# Edge computing Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/edge-computing
> Markdown URL: https://aitinkerers.org/technologies/edge-computing.md
> Technology record last updated: 2026-02-23T23:07:03Z
> Generated: 2026-09-22T14:51:13Z

Edge computing moves processing and storage closer to the data source (IoT devices, factory floors), slashing latency for real-time operations.

This distributed IT architecture processes data at the network's periphery, bypassing the distant cloud to deliver sub-10-millisecond response times for critical applications. It is essential for time-sensitive operations: autonomous vehicles require instant sensor analysis for emergency braking, and industrial IoT (IIoT) uses it for predictive maintenance on factory floors. Processing data locally (on-site servers, gateways) reduces backhaul bandwidth, cuts network costs, and enhances data sovereignty compliance (GDPR) by limiting transmission of sensitive raw data.

- Official technology site: https://www.intel.com/content/www/us/en/what-is/edge-computing.html
- Public AI Tinkerers demos and talks: 6
- Result page: 1 of 1

## Recent Public Talks and Demos

### [From Image to Structured Data: Building a Local AI Document OCR Platform for Administrative Workflows](https://tokyo.aitinkerers.org/talks/rsvp_Vs-o12h3f_U)

Administrative and compliance-heavy professions still rely heavily on paper and scanned documents. However, sending sensitive documents to cloud OCR or AI services is often not acceptable due to privacy, regulatory, or client confidentiality requirements. In this talk, I will present a professional web-based OCR processing platform designed for secure, local-first document handling — with a focus on real-world administrative document workflows such as those handled by 行政書士 professionals.

- Event context: AI Tinkerers Tokyo - Toranomon Meetup - February 19, 2026 — 2026-02-19 — Tokyo
- Public talk page: https://tokyo.aitinkerers.org/talks/rsvp_Vs-o12h3f_U

### [AI for RNA](https://miami.aitinkerers.org/talks/rsvp_Jw6Bfgq5fd0)

Demonstration of a browser based cell labeling algorithm that uses onnx and wasm

- Event context: AI Tinkerers - Miami: Building the Future of AI in Miami Tech — 2025-04-22 — Miami
- Public talk page: https://miami.aitinkerers.org/talks/rsvp_Jw6Bfgq5fd0

### [Build AI Agents using No Code / Low Code Builder](https://miami.aitinkerers.org/talks/rsvp_gDuUq0Jg0tU)

Lamatic is A managed PaaS with a low-code visual builder, VectorDB, and integrations to apps and models for building, testing, and deploying high-performance GenAI apps on edge.

- Event context: AI Tinkerers - Miami Inaugural Meetup (January) — 2025-01-22 — Miami
- Public talk page: https://miami.aitinkerers.org/talks/rsvp_gDuUq0Jg0tU

### [Transforming large scale data into vector embeddings](https://munich.aitinkerers.org/talks/rsvp_Ioie7Lci91g)

Quasara is a platform for your information retrieval focused on turning large volumes of complex visual data into high quality domain specific vector embeddings.

- Event context: AI Tinkerers Munich - November 21 — 2024-11-21 — Munich
- Public talk page: https://munich.aitinkerers.org/talks/rsvp_Ioie7Lci91g

### [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

### [Multiplayer AI on the Edge](https://london.aitinkerers.org/talks/rsvp_AY8-6GI3p7k)

There are two kinds of AI assistants: - Ones that you have to ask to do things. - Ones that watch everything you do in the background and act on your behalf for you I implemented Maggie Appleton's "AI Daemons" design concept, with her permission, from https://maggieappleton.com/lm-sketchbook#daemons. This demo shows how I built it, and deployed it to the edge using Cloudflare Workers platform.

- Event context: AI Tinkerers London July - RSVP REQUIRED — 2023-07-18 — London
- Public talk page: https://london.aitinkerers.org/talks/rsvp_AY8-6GI3p7k

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