# Celery Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/celery
> Markdown URL: https://aitinkerers.org/technologies/celery.md
> Technology record last updated: 2026-02-25T06:34:18Z
> Generated: 2026-09-23T08:56:30Z

Celery is a simple, highly reliable distributed task queue: it processes vast amounts of messages in real-time, offloading work from your main application.

Celery is an open-source, Python-based distributed task queue, engineered for asynchronous operation and real-time processing. It separates long-running, resource-intensive tasks (like email delivery or video transcoding) from your main application's request-response cycle: this ensures a fast user experience. The system relies on a message broker (RabbitMQ or Redis are the feature-complete transports) to mediate between clients and dedicated worker processes. Tasks are executed concurrently across one or more worker nodes, providing high availability and horizontal scaling. For example, systems like Instagram use Celery to process millions of tasks daily, leveraging its reliability and built-in scheduling features (Celery Beat) for periodic jobs.

- Official technology site: https://docs.celeryq.dev
- Public AI Tinkerers demos and talks: 4
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Building a Narrative Intelligence Graph and a narrative App from 200+ News Sources Using LLM Agents](https://austin.aitinkerers.org/talks/rsvp_vxPl4WEYVfg)

I built a narrative intelligence system that continuously ingests articles from 200+ news sources and automatically extracts events, actors, narratives, affiliations, and stance changes over time. The system uses multiple LLM-powered agents to transform raw news into a structured intelligence graph that can answer questions such as: • Who is pushing a specific narrative? • Which actors changed their position? • Which organizations consistently align on issues? • How did a narrative evolve over time? During the demo I'll show the live pipeline, extraction workflow, graph construction process, and how the system generates narrative intelligence from real-world news events.

- Event context: AI Tinkerers Austin: GTM Engineering & Agentic Sales Systems — 2026-06-26 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_vxPl4WEYVfg

### [Talking to Your Infrastructure: Building a Conversational DevOps Agent with Rasa](https://paris.aitinkerers.org/talks/rsvp_IjdWIc5gQv4)

In this talk, I’ll share how I built a conversational AI agent using Rasa and the Scalingo API to manage real applications through natural language. Create apps, scale services, check logs, or update environment variables all with a single sentence. If infrastructure became conversational, what you will told her?

- Event context: AI Tinkerers Paris feat Scalingo: Conversational DevOps & AI Infrastructure — 2026-05-21 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_IjdWIc5gQv4

### [Building a Prompt-Driven Voice Agent: Scenarios to Live Calls](https://dallas-fort-worth.aitinkerers.org/talks/rsvp_Z6I4OQmFXPM)

I’ll demo how we built Ranom’s internal voice agent system that generates live, tunable agents from structured prompts. Using a template editor, we can define negotiation or logistics scenarios, attach variables (like customer name, VIN, or route info), and spin up a voice agent instantly. I’ll walk through the pipeline - prompt creation → agent config → call execution → logs - and show a live call to demonstrate how the system works in practice.

- Event context: AI Tinkerers October Meetup – Dallas-Fort Worth — 2025-10-09 — Dallas Fort Worth
- Public talk page: https://dallas-fort-worth.aitinkerers.org/talks/rsvp_Z6I4OQmFXPM

### [AI-first Clinical Information System](https://singapore.aitinkerers.org/talks/rsvp_pem0gXbq6u0)

We are building an AI-first Unified Healthcare Information System designed to fix the core inefficiencies plaguing modern healthcare. Today’s medical institutions rely on fragmented, outdated software that traps critical information in disconnected silos. This leads to doctor burnout, lost revenue, and missed opportunities for better care. Our system solves this by unifying medical databases and modules into a seamless, all-in-one platform—the way Palantir unified battlefield intelligence for the U.S. Army, we’re doing the same for hospitals and clinics. No more system-switching or duplicated workflows—just clean, integrated operations. In addition, we’re deploying AI automation across clinical documentation (e.g., referral letters), reducing paperwork and freeing up doctors to see more patients. Trials have already shown increased satisfaction for 85% of patients, lower burnout, and up to 80% ROI for clinics. But we’re going even further: we’re preparing for AI-first clinical care, where AI drafts diagnoses and treatment plans, and doctors act as validators. This model, already live in regulatory sandboxes like Saudi Arabia (e.g., Synyi.ai), promises more accurate care and scalable efficiency.

- Event context: AI Tinkerers Singapore: May Meetup - May 21st, 2025 — 2025-05-21 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_pem0gXbq6u0

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