Tutorial: Antigravity & AI Studio Using Gemini APIs | Future of Data and AI | Agentic AI Conference
The video showcases Google DeepMind’s latest AI Studio platform and the suite of Gemini APIs released over the past six months, highlighting models such as Gemini 3.1 Flash Live, Flashlight, Nanobanana 2, Embeddings 2.0, LIA 3, Genie 3, and the open‑source Gemma 4 family. Paige Bailey walks through how developers can access AI Studio via ai.dev, generate free API keys, monitor usage, and experiment with multimodal inputs—text, audio, video, and images. Key insights include the unprecedented multimodal capability of Gemini models, which can ingest and output across text, code, images, audio, and video. Cost efficiency is emphasized: the tiny Gemini 3.1 Flashlight processes a 2‑hour YouTube lecture for roughly 33 cents, while token pricing remains a fraction of a dollar. The platform also integrates database and OAuth services, enabling rapid app creation without extensive infrastructure. Demonstrations feature real‑time video segmentation, multilingual transcription, and the LIIA music‑generation model that produces songs with lyrics in Hindi, Arabic, and other languages, even supporting karaoke‑style lyric highlighting. The new “Build” tool lets users describe an app—such as a bookshelf image recognizer that enriches data via Google Search and stores results in Firebase—and the model generates the full codebase automatically. These capabilities lower barriers for engineers, data scientists, and creators, allowing them to prototype sophisticated AI‑driven products at near‑zero cost. Enterprises can embed multimodal intelligence into workflows, while independent developers gain a turnkey stack for building, deploying, and scaling AI applications.
Tutorial: Google ADK & Cloud Run: AI Agents at Scale | Future of Data and AI | Agentic AI Conference
The session walks participants through building a distributed, multi‑agent AI system on Google Cloud using the ADK and Cloud Run. Attendees first claim a $5 credit, create a new project, attach billing, and enable required APIs before cloning a starter...
Tutorial: Powering Agentic Inference with @SambaNovaSystems | Agentic AI Conference
The tutorial highlighted SambaNova’s strategy for solving the "agentic inference" infrastructure crisis by showcasing its end‑to‑end stack—from custom SM50 silicon to cloud‑run deployments. Quasian Koma, director of AI solutions engineering, explained how the company’s full‑stack platform combines low‑power racks, high‑throughput...
Tutorial: GitHub Copilot Across CLI VS Code Cloud | Future of Data and AI | Agentic AI Conference
The session, led by Microsoft developer advocate Kale Cinnamon, demonstrates how GitHub Copilot has expanded beyond a simple pair‑programming assistant to a ubiquitous AI companion available in VS Code, other IDEs, the terminal, Azure DevOps, and even the GitHub mobile app. Cinnamon...
What Does AI Cost Management Look Like As Models Mature? | João Moura X Data Science Dojo
In a Future of Data and AI podcast, João Moura, CEO of CrewAI, argues that AI cost management is becoming a central concern as models mature. While newer models are cheaper and more capable, premium offerings like Claude Opus 4.5 remain...
What Convinces An Investor To Back A First Time Founder | João Moura X Data Science Dojo
The video features João Moura discussing what convinces investors to back first‑time AI founders. He frames the current AI boom as a rare, high‑stakes opportunity, but warns that the surge in tools and applications creates fierce competition and an uphill...
João Moura on Multi-Agent Systems, Autonomous Workflows & AI Entrepreneurship | Ep 09
In this episode, João Moura (Joe Mora), founder and CEO of Crew AI, discusses the company’s mission to deliver end‑to‑end multi‑agent workflows for large enterprises. He explains how the startup grew from an open‑source library into a platform that handles everything...
João Moura on Multi-Agent Systems, Autonomous Workflows & AI Entrepreneurship | Teaser
João Moura, reflecting on building CREI, argued that startups often must build complete systems in-house because partial solutions fail to reach production—teams expose the wrong data, lack deployment and monitoring, and ultimately don’t trust their models. He advised first-time AI...
Understanding Basic Vector Search With KNN | Vector Databases for Beginners | Part 12
The video introduces the K‑Nearest Neighbors (KNN) algorithm as the core of vector search, explaining how each query and document is transformed into a numeric embedding that lives in a multi‑dimensional space. By measuring the distance between these vectors, the...
Search Systems & Why Keyword Search Falls Short | Vector Databases for Beginners | Part 11
The video introduces vector search as a modern alternative to traditional keyword‑based search systems, explaining why the latter often fails to capture user intent. It outlines how keyword search relies on exact token matching, requiring precise terms and extensive synonym...
How Fortune 100 Companies Adopt Knowledge Graphs in Practice | Emil Eifrem X Data Science Dojo
The discussion centers on how Fortune 100 enterprises are actually implementing knowledge graphs, contrasting idealized, organization‑wide visions with the pragmatic routes companies are taking today. Two adoption patterns emerge. Large firms often build an “enterprise knowledge graph” that mirrors portions of their...
Scaling AI Beyond Single Agents: Multi-Agent Architectures with LangChain
The webinar hosted by Nabeha and Isma discussed scaling AI beyond single agents, focusing on multi‑agent architectures using LangChain. It outlined fundamentals of AI agents—LLM brain, tools, memory—and why monolithic agents struggle as tasks grow. The presenters highlighted token‑bloat, context‑window exhaustion,...
Beyond Diffusion: Flow Matching for Generative AI
Yuri Zilai’s webinar introduced flow matching as a next‑generation alternative to diffusion‑based generative AI. He outlined the agenda—reviewing fundamental generative models, dissecting diffusion, explaining flow‑matching mechanics, showcasing real‑world deployments, and a live 2‑D notebook demo. All generative models map Gaussian noise...
Creating & Ingesting Your Own Embeddings in Weaviate | Vector Databases for Beginners | Part 7
The video walks viewers through building custom text embeddings with a SentenceTransformers model from HuggingFace and loading them into a Weaviate vector database. The presenter demonstrates the workflow in a Google Colab notebook, pulling a subset of 100 arXiv paper...
How To Explain A Concept Without Dumbing It Down | Joshua Starmer X Data Science Dojo
The video features Joshua Starmer discussing how to explain complex data‑science concepts without "dumbing them down." He emphasizes a constant self‑check: can the idea be presented more simply while staying true to the original algorithm and its intent? This mindset...
