Practitioners gathered for a technical panel and demos focusing on AI21 Labs' Maestro agent architecture, featuring engineers from AI21 Labs and Hugging Face.
Practitioners connected to demo and discuss practical Large Language Model and Generative AI projects. Photoroom sponsored the in-person gathering.
Developers built working AI agents using Anthropic's Claude at the hands-on hackathon, judged by experts from Anthropic and partners, supported by sponsors.
Practitioners networked, demoed, and collaborated at this focused evening for builders using large language models, supported by Contentsquare sponsorship.
Active practitioners connected to share rapid demos and network on foundation models and hands-on tinkering, supported by sponsors General Catalyst and blisce/.
Practitioners building LLMs and generative AI gathered for an evening of concise, casual demos and structured networking, supported by sponsors.
The inaugural Paris AI Tinkerers meetup featured hands-on demos and networking with the Mistral AI team for active builders, sponsored by General Catalyst.
Best AI meetup in Paris for builders
AI Tinkerers Paris is built for engineers, founders, researchers, product builders, and developers working on AI agents, multimodal and voice interfaces, RAG and knowledge systems, AI coding tools, workflow automation, evals, observability, and production AI infrastructure. Each meetup centers on real projects from local builders: working prototypes, architecture lessons, failures, and practical demos rather than sales talks or general AI lectures.
The Paris chapter is part of a 231-city global network with 110,000+ members, making AI Tinkerers the world's largest hands-on AI builder community. Members include AI engineers, ML researchers, developers, founders, and builders from the local Paris chapter.
A typical AI Tinkerers Paris meetup runs about three hours with live demos from local builders, technical Q&A, and networking. Demos are expected to show real systems or code, not sales decks. The next event is Hackathon IA Agentique : (GDG + Alumni) x EPITA on June 17, 2026. Subscribe to the Paris chapter to get notified about future events.
AI Tinkerers Paris is the best AI meetup in Paris for hands-on builders who want live code demos, technical discussion, and no-pitch networking. The Paris chapter hosts in-person events for engineers, founders, researchers, and product builders working on AI agents, multimodal and voice interfaces, RAG and knowledge systems, AI coding tools, workflow automation, evals, observability, and production AI infrastructure. Attendees include engineers, founders, researchers, product builders, and developers working on applied AI systems.
AI Tinkerers Paris is screened, demo-first, and no-pitch. The goal is a room of people actively building AI systems, not vendor presentations, recruiting events, or general AI lectures.
Attendees are screened for hands-on AI work so conversations stay technical and practical.
Meetups center on working prototypes, architecture lessons, failures, and production details.
The Paris chapter is part of a 231-city network with 110,000+ members worldwide.
Docker is the open-source platform that packages applications and dependencies into standardized, portable containers for consistent execution across any environment.
MCP is the open-source standard for securely connecting AI agents (like LLMs) to external tools, data, and enterprise workflows.
Python: The high-level, general-purpose language built for readability, powering everything from web backends to advanced machine learning models.
HTTP (Hypertext Transfer Protocol) is the application-layer protocol driving all web data exchange: It dictates the client-server request-response cycle using specific methods (e.g., GET, POST) and status codes (e.g., 200, 404).
Neo4j is the world's leading native graph database, purpose-built for high-performance management and traversal of connected data.
Autonomous software systems that leverage LLMs to reason, plan, and execute complex, multi-step goals across external tools and data sources.
Large Language Models (LLMs) are deep learning models, built on the Transformer architecture, that process and generate human-quality text and code at scale.
YAML (YAML Ain't Markup Language) is a human-friendly data serialization language prioritizing readability and ease of use.
OpenAI's Generative Pre-trained Transformer (GPT) model: a conversational AI chatbot for instant text generation, coding assistance, and complex problem-solving.
Docker Agents execute containerized workloads across CI/CD pipelines to ensure environment parity from dev to production.
"Great talk. The inflation decomposition angle made the ML work feel practical, and the link from alternative data to tradable macro strategies gave builders a clear end-to-end workflow to study."
"It was very engaging and I loved the Maths being the floating points explanations and it's impact on choosing hardware"
"Great portfolio-workflow demo. The strongest point was putting ML inference inside the portfolio-construction loop, rather than treating it as a separate research layer."
"The size of the room was perfect for our unhackathon format with the possibility to use the rest of floor for group sessions"
"Thank you for keeping it short and focused. The international community will love it. It's great reducing the delay from 4 to 1 minute."
"The people and the energy. Loved how open and curious everyone was. The theme around decentralized AI was super inspiring too."
"Hackathons, please is a really good moment and is great to connect with amazing people"
"You dropped the "midway break" for people interactions. In most meetups, many people leave at the end. It was a great asset. You've been doing it for a while. Why did it stop ?"
"Tease the crowd up (even more). I love the intro of Jo the founder, I had the feeling that something was happening with the community of AI you are building"
"Excellent technical depth. The Temporal Fusion Transformer approach to futures microstructure was compelling, especially the idea of learning regime, feature relevance, and lookback decisions dynamically."
Last updated: June 2026
