AI Engineer Paris 2025 (Day 2)

The transcript captures the AI Engineer Paris event, featuring multiple speakers and discussions on AI engineering topics. The event includes presentations from industry leaders on subjects such as managing state in AI, the evolution of AI infrastructure, and advancements in generative AI models. Speakers share insights, demo new technologies, and engage in Q&A sessions, providing a comprehensive overview of the current state and future directions of AI engineering. The discussions cover a range of topics, from the challenges of building AI agents to the potential of open-source models and the importance of ethical considerations in AI development.

Outlines

Sign in to continue reading, translating and more.

Continue

AI Engineer

13:33Introduction to AI Engineering and Managing State

Introduction to AI Engineering and Managing State

22:31A Brief History of Application Architecture and the Data Layer

A Brief History of Application Architecture and the Data Layer

28:12Four Properties of a Kick-Ass Data Layer for AI Applications

Four Properties of a Kick-Ass Data Layer for AI Applications

37:16Disambiguating Data and Neo4j's Vision

Disambiguating Data and Neo4j's Vision

42:25Neo4j Demo and Q&A: Agentic Memory and Innovation

Neo4j Demo and Q&A: Agentic Memory and Innovation

52:59Docker and the Future of Agents

Docker and the Future of Agents

1:00:30MCP Protocol and Lessons Learned

MCP Protocol and Lessons Learned

1:11:11Challenges and Solutions in MCP Deployment

Challenges and Solutions in MCP Deployment

1:21:44The Future of MCP and Advice for Students

The Future of MCP and Advice for Students

1:28:25Introduction to Coeb and the Agentic AI Stack

Introduction to Coeb and the Agentic AI Stack

1:37:41Defining AI Agents and Infrastructure Requirements

Defining AI Agents and Infrastructure Requirements

1:45:00Addressing Bottlenecks and Scaling Agentic Workloads

Addressing Bottlenecks and Scaling Agentic Workloads

1:51:21Q&A: Evolution of Infrastructure and Future Predictions

Q&A: Evolution of Infrastructure and Future Predictions

2:00:16Unifying Text-to-Image Generation and Image Editing

Unifying Text-to-Image Generation and Image Editing

2:06:15Latent Flow Matching and Model Architecture

Latent Flow Matching and Model Architecture

2:11:11Model Speed and Community Engagement

Model Speed and Community Engagement

2:21:28MCPs and Remote MCP Servers

MCPs and Remote MCP Servers

2:27:23Remote MCP Servers and Authentication

Remote MCP Servers and Authentication

2:33:34MCP Registries and Community Involvement

MCP Registries and Community Involvement

2:40:06Coeb and the Agentic AI Stack

Coeb and the Agentic AI Stack

2:45:00Defining AI Agents and Infrastructure Requirements

Defining AI Agents and Infrastructure Requirements

2:50:22Black Forest Labs and Flux Models

Black Forest Labs and Flux Models

2:57:10Latent Flow Matching and Model Architecture

Latent Flow Matching and Model Architecture

3:03:31Model Speed and Community Engagement

Model Speed and Community Engagement

3:11:14The H Company and Computer User Agents

The H Company and Computer User Agents

3:17:17Architecture and Efficiency of Computer User Agents

Architecture and Efficiency of Computer User Agents

3:22:31Training and Open-Source Philosophy

Training and Open-Source Philosophy

3:28:21Future Directions and Community Engagement

Future Directions and Community Engagement

3:34:07Generating Data and Building Knowledge Graphs

Generating Data and Building Knowledge Graphs

3:44:04Building and Deploying the Knowledge Graph

Building and Deploying the Knowledge Graph

3:53:56The State of Open LLMs in 2025

The State of Open LLMs in 2025

4:04:09Deployment Options and Lessons Learned

Deployment Options and Lessons Learned

4:11:11Trends in Open LLMs and Future Directions

Trends in Open LLMs and Future Directions

4:22:31Introduction to Prompt Learning

Introduction to Prompt Learning

4:34:07Prompt Learning on Coding Agents

Prompt Learning on Coding Agents

4:41:14Benefits of Online Evaluation and Prompt Iteration

Benefits of Online Evaluation and Prompt Iteration

4:49:50ZML and High-Performance AI Inference

ZML and High-Performance AI Inference

4:57:27AttentionD and Kernel Bypass

AttentionD and Kernel Bypass

5:03:31Live Demo and Community Engagement

Live Demo and Community Engagement