About us
About TorontoAI
TorontoAI is a vibrant, inclusive community of engineers, builders, founders, and curious minds passionate about making AI infrastructure more accessible, human-centered, and scalable.
We host bi-weekly in-person socials, tech meetups, and hands-on webinars to connect people across disciplines — from DevOps to Data Science, from students to senior architects. Whether you're deploying LLMs in production or just exploring what Databricks does, you're welcome here.
🤝 We’re Building More Than a Meetup
In a world dominated by virtual everything, we believe in real, human-to-human connection.
TorontoAI is a space to:
- Share ideas over coffee
- Spark collaborations face-to-face
- Meet people who understand your stack and your journey
- Build your network beyond LinkedIn likes
💬 What We Talk About:
- Scalable AI & LLM infrastructure (Kubernetes, GPUs, vLLM, Ollama, LangChain)
- Databricks, Snowflake, Fivetran, dbt — building the modern data stack
- MLOps, LLMOps, DevOps — the operational glue of AI systems
- Real-world engineering stories, founder spotlights, and tool breakdowns
🌈 Who We Welcome:
- DevOps, SREs & Platform Engineers moving into data/AI
- Data Engineers, Analysts & ML practitioners
- Founders, freelancers, and technologists in transition
- Students and early-career professionals seeking real-world exposure
We’re committed to creating a welcoming, diverse, and equity-focused space where all voices matter — no gatekeeping, no rockstars, just good humans building cool stuff.
📍 Based in Toronto, open to the world
📅 Join an event — and be part of something human, helpful, and hands-on.
Upcoming events
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Panel Discussion: Intellectual Property in the Era of Vibe Coding
401 Bay Street, Meet at Bay Street Entrance, Toronto, ON, CAWhen code becomes commoditized, what actually gets protected?
This is an in-person panel discussion hosted at a Dipchand Law office, bringing together experts from legal, AI, and strategy domains to explore how intellectual property is evolving in the age of AI-assisted development and “vibe coding.”
Why this matters:
AI tools are rapidly commoditizing software development. The barrier to building products is dropping, shifting the focus from writing code to owning ideas, data, and systems. This creates new challenges around ownership, licensing, and long-term defensibility.
Key discussion areas:
- Whether code still holds value as intellectual property
- Ownership of AI-generated code and outputs
- What developers and companies should protect beyond code (data, workflows, architecture)
- Enterprise risks including compliance, governance, and data exposure
- How organizations build defensibility when building becomes easy
Speakers:
Stephano Salani
Intellectual Property Lawyer, Dipchand LLPYulia Pavlova, PhD
Applied AI and Governance Leader, RBC Borealis AIMohit Rajhans
AI Consultant, ThinkStart.caEvent details:
Date: May 20, 2026
Time: 5:30 PM to 7:30 PM EDT
Location: Dipchand LLP Office, Toronto, ONWhat to expect:
Panel discussion, networking, and Q&A sessionRSVP - https://luma.com/0ktn86g3
2 attendees
Genie, Agent Bricks, or Build Your Own on Databricks Lakebase
·OnlineOnlineData Engineering leaders deciding whether to adopt Genie, build a custom text-to-SQL stack, or wire something in between.
90 minutes. Live build, not slides. Real workspace, real data, a real LLM call across an HTTP boundary you control.What you will see
I'll go from an empty Databricks workspace to a working text-to-SQL agent that:
- Joins live OLTP rows in Lakebase (managed Postgres) with pre-aggregated gold tables in Unity Catalog Delta — through Lakehouse Federation, in a single query.
- Generates SQL via a pluggable LLM endpoint — Databricks Model Serving, OpenAI-compatible APIs, or a self-hosted vLLM on a neo-cloud GPU — switched with one environment variable.
- Validates every SQL string before execution with a SELECT-only safety guardrail that catches the Databricks-specific destructive ops generic validators miss (`OPTIMIZE`, `VACUUM`, `ZORDER`, `COPY`).
- Is auditable end-to-end: one question = one LLM call, one SQL statement, one execution. No autonomous loops, no surprise bills.
What you will leave with
- A decision framework for db-agent vs Genie vs Agent Bricks for your specific use case — including when not to build.
- The companion open-source db-agent repo (presented at AAAI-25, ships a Databricks Apps deployment variant) and a quick-lab repo with a step-by-step build.
- A reference architecture diagram and the actual code — pipeline orchestrator is ~60 lines of Python, safety validator is ~30.
- Specific gotchas that cost me a half-day each: federation database options, Lakebase token rotation, Streamlit/Apps reverse-proxy traps, context-window blowouts on real catalogs.
Who is this for
- Heads of Data, Data Engineering Managers, Staff and Principal Data Engineers.
- Teams already on Databricks (or evaluating) who are being asked: "Can we put an AI agent on top of this?"
- Anyone making a build-vs-buy call between Genie, Agent Bricks, and a custom text-to-SQL stack — and wants to make it with their eyes open.
- Demo of the Reference Architecture explained here - https://becloudready.com/blog/text-to-sql-databricks-lakebase-db-agent
This is a technical session. We'll read code. Bring your senior engineers.
Agenda
- The architecture in one slide (5 min)
- Lakebase + Unity Catalog + Lakehouse Federation — why both data planes, and what breaks (15 min)
- The agent pipeline — schema → prompt → LLM → validate → execute (20 min)
- The SQL safety guardrail — what generic SELECT-only validators miss on Databricks (10 min)
- The pluggable LLM layer — live swap from a hosted API to a self-hosted vLLM on a neo-cloud GPU (15 min)
- db-agent vs Genie vs Agent Bricks — when to use which, and why (10 min)
- Q&A (15 min)
About the Speaker
Chandan Kumar — founder of BeCloudReady, organizer of the TorontoAI community (10K+ members), and a Databricks Partner. Maintainer of the open-source db-agent text-to-SQL agent, presented at AAAI-25. Runs the Databricks Lakehouse Bootcamp and works with engineering teams on getting AI agents into production against real data11 attendees
Past events
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