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Decoding Data Science

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Build real connections, discover opportunities, and learn from builders who ship. This community brings together engineers, data scientists, product leaders, founders, and investors for high-signal conversations, lightning demos, and collaborations that turn ideas into shipped products.

What to expect

Curated networking: Meet peers by domain (AI, product, growth, data) and by stage (MVP → scale).

Lightning talks & demos: Practical, 5-minute sessions on what’s working now in AI, product, and growth.

Founder × Investor huddles: Problem–solution matchmaking, warm intros, and rapid feedback.

Jobs & collabs board: Hire faster or join a team where your skills matter.

Special spotlight: A quick overview of our AI Residency (hands-on, production-grade LLM/RAG/agents) and the launch of an 8-day AI App Challenge with mentorship and community showcases.

Who should attend

Developers/Data Scientists: Ship LLM apps, tools, and automations.

Founders/Operators: Validate ideas, find talent, and accelerate GTM.

Investors/Advisors: Spot early signals and connect with high-intent builders.

Bring your current challenge (tech, traction, or team). Leave with three actionable next steps, two warm intros, and a clear path to progress.

Everyone is talking about AI Agents. Very few can explain how Agentic AI actually works end-to-end.We’re hosting an open community discussion on Agentic AI: From LLM to Multi-Agent Systems—practical, builder-focused, and clear enough to apply immediately.What we’ll cover (with real examples):<ul style="list-style-type: disc;text-align:left;margin-top: 0px; padding-left: 16px; font-size: 16px; line-height: 24px;font-family:Helvetica, Arial, sans-serif;color: #191c1e;"><li style="">Tools + function calling: how agents take actions safely</li><li style="">Vector stores: why retrieval is the backbone of reliable agents</li><li style="">MCP (Model Context Protocol): connecting models to external systems cleanly</li><li style="">Multi-agent patterns: planner/executor, researcher/writer, critic loops, routing</li><li style="">Where agents fail: reliability, evaluation, observability, cost</li></ul>

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