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Vidéo tirée du stream du 02/04/2026

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Un regard impertinent et libre, orchestré par Patrick Menais et son équipe, sur le monde de l’image.

En avant-première du lundi au samedi à 17h00 sur Facebook, Twitter et YouTube.

Du lundi au samedi à 20h00 sur France 5.

https://www.facebook.com/vufrancetv

"Processing real-time voice data is an engineering minefield of latency, accents, and interruptions. This session explores the architecture of a Real-Time Voice Intelligence Pipeline deployed in a high-volume contact center.

We reduced post-call work by 50% by shifting compute from ""batch"" to ""stream.""

Speaker: Dippu Kumar Singh - Leader Of Emerging Technologies (Apps), Fujitsu North America Inc.

Dippu Kumar Singh has over 16 years of experience at the intersection of industry innovation and advanced research. He is a recognized authority in building scalable, trustworthy, and commercially viable AI systems. Being a Leader for Emerging Data & Analytics at Fujitsu North America, Dippu specializes in bridging the gap between theoretical AI concepts and enterprise-grade implementation. His strategic leadership has spearheaded multi-million in sales pipelines and delivered remarkable savings through AI-driven optimizations in transportation, manufacturing, utilities, and supply chain logistics.

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Nouvelle émission "La dernière" avec Guillaume Meurice, Aymeric Lompret, Juliette Arnaud et Pierre-Emmanuel Barré…Mais aussi le chercheur en sciences cognitives Mehdi Moussaïd, Mamari, Florence Mendez et Samah Karaki pour une nouvelle Dernière à vivre en intégralité en vidéo.

L'intégrale de "La dernière" du 5 avril est aussi en podcast ici : podcasts.nova.fr/radio-nova-la-derniere

🔔 Abonnez-vous à notre chaîne : http://j.mp/1cT7DvA

There are many variables in building RAG applications, from document parsing to the language model you pick for generation and everything in between. Combining Docling for document parsing, OpenSearch for retrieval, and Langflow for orchestration, plus local and remote models, OpenRAG is an opinionated, agentic, open-source stack for building the RAG application of your dreams.

Just because it has opinions doesn't make it inflexible though. In this talk we'll look at how OpenRAG gives you a great baseline for RAG and how you can tune it and evaluate the outcomes to create RAG applications that work well with your data. You'll learn how to get the best out of your documents with Docling, how OpenSearch provides more than just vector search, and how Langflow makes it easy to customise your pipeline to interact with your data the way you want to. You’ll leave with a playbook of options to improve your RAG app and a stack you can extend without reinventing everything.

Phil Nash - Developer relations engineer, IBM

Phil is a developer relations engineer for DataStax and Google Developer Expert living in Melbourne, Australia. He's been working in developer relations for a decade, speaking at conferences since 2012, and writing JavaScript since before jQuery. Away from the keyboard, Phil enjoys travel, live music, and hanging out with his mini sausage dog, Ruby.

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One AI agent is a feature. Fifty agents is a distributed systems problem nobody's discussing. I've seen this pattern: teams build one agent, then five, then drown in coordination problems unrelated to LLMs. Agent handoffs fail silently. Data goes stale. Decisions become untraceable. Drawing from Databricks production deployments, I'll expose orchestration anti-patterns killing multi-agent systems and show agent handoff protocols that work—state management, data contracts, failure modes. You'll see when to choreograph versus orchestrate and live multi-agent workflow with proper observability. This applies distributed systems engineering to agents: the infrastructure layer everyone needs but nobody's building.

Sandipan Bhaumik - Data & AI Tech Lead, Databricks

Sandipan Bhaumik has spent 18 years building data and AI systems inside environments that can't afford them to fail - NHS, Tier 1 banks, and large enterprises across EMEA. At AWS and now Databricks, he's seen firsthand where multi-agent systems break down between architecture and production. He is a regular speaker on data and AI system architecutr ebest practices, runs a community of AI practitioners, and he's here to talk about what actually holds together when you scale agentic AI systems in production.

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Every other personal AI demo has agents sending emails and managing calendars. I built the opposite: a read-only system that queries my data sources (email, journal, tasks, CRM, browser sessions, notes) but can't modify any of them. This is an intentional limitation. I'll cover why trust asymmetry matters (read is safe, write is dangerous), how cross-source pattern detection beats task automation, and why ""exhaust fume analysis"" of one's cognition is more valuable than yet another AI assistant trying to act on your behalf.

Šimon Podhajský - Head of AI, Waypoint AI

I'm Head of AI at Waypoint and a full-stack builder with a background in data science and data engineering. I built this personal AI system to scratch my own itch -- and discovered that the ""read-only"" constraint led to better architecture than the agent-first approaches I see everywhere.

I made a Github repo with a template for people to try out the read-only AI / personal intelligence system: https://github.com/shippy/personal-intelligence-kit

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As AI coding agents become first-class users of internal developer platforms, the practices that make platforms accessible to humans turn out to be the same ones that enable AI to thrive.

Self-service interfaces, well-defined APIs with schemas and documentation, local-first workflows, and rich observability have always been important elements of a good platform. Now they are prerequisites for agents that can autonomously build, debug, and ship software.

This talk explores what it means to design platforms where both humans and AI can collaborate effectively. We'll cover:

We'll also discuss the flip side: AI is making it easier than ever to contribute to platform code, but that comes with new responsibilities around quality gates, context files like CLAUDE.md, and maintainability.

Walk away with concrete practices to ensure your platform is ready for a future where agents are not just tools, but users of it.

Juan Herreros Elorza - Team Lead, Banking Circle

I'm Juan, a Platform Engineering enthusiast.

I am working for Banking Circle, as the Team Lead in our Cloud Native Technology team.

When I'm not working, I'm most likely rehearsing or performing improv comedy.

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We are using AI to write code. Moreover, we are using it to be more productive. However, giving AI access to our machine and let them run on their own is dangerous. Imagine, giving AI access to the server where you run your application! You want your users to interact with your application through a chat interface, and maybe build their own apps or customize the UI. If not supervised carefully, AI can break your application or worse leak private data.

So how do you run AI generated code within your application and allow users to build their own apps?

Harshil Agrawal - Sr. Developer Educator, Cloudflare

Working in the Developer Relations team at Cloudflare, Harshil enjoys sharing his learnings with the community. A JavaScript developer, open-source contributor, and a low-code enthusiast, Harshil loves experimenting with tech and building small projects.

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