AI Engineer

Building pi in a World of Slop — Mario Zechner

$1 AI Guardrails: The Unreasonable Effectiveness of Finetuned ModernBERTs – Diego Carpentero

Paperclip: Open Source Human Control Plane for AI Labor — Dotta Bippa

Agents need more than a chat - Jacob Lauritzen, CTO Legora

AIE Europe Keynotes & Coding Agents ft. Pi, Google Deepmind, Anthropic, Cursor, Linear, & more

AI Engineer Europe Day 2 highlights the shift from simple AI assistance to complex agentic workflows and the resulting engineering challenges. Google’s Gemma 4 demonstrates the power of open, on-device models, while Anthropic’s Model Context Protocol (MCP) establishes a standard for agent connectivity across diverse ap...

One Registry to Rule them All - Sonny Merla, Mauro Luchetti, & Mattia Redaelli, Quantyca

Amplifon's "Amplify" program addresses the challenges of scaling AI across a global organization. The program, launched in January 2025, establishes rules for AI adoption through an operating model with a control tower for setting guidelines and a committee for strategy execution. Key focuses include governance, ensuri...

Judge the Judge: Building LLM Evaluators That Actually Work with GEPA — Mahmoud Mabrouk, Agenta AI

AI Didn’t Kill the Web, It Moved in! — Olivier Leplus (AWS) & Yohan Lasorsa (Microsoft)

Running LLMs locally: Practical LLM Performance on DGX Spark — Mozhgan Kabiri chimeh, NVIDIA

AIE Europe Keynotes & OpenClaw ft Deepmind, OpenAI, Vercel, @pragmaticengineer , @mattpocockuk

The AI Engineer Europe 2026 conference highlights the rapid evolution of software engineering, where AI agents are transitioning from simple coding assistants to autonomous builders and users of software. The industry is shifting toward "harness engineering," where human developers act as system architects and delegato...

Contact Center Voice AI: Low-Latency Intelligence Extraction from Messy Audio Streams — Dippu Singh

OpenRAG: An open-source stack for RAG — Phil Nash

From Chaos to Choreography: Multi-Agent Orchestration Patterns That Actually Work — Sandipan Bhaumik

Scaling multi-agent AI systems requires transitioning from simple model-based development to rigorous distributed systems engineering. Moving beyond a single agent introduces exponential growth in coordination complexity, leading to critical issues like race conditions, stale cache reads, and cascading failures. Reliab...

Cognitive Exhaust Fumes, or: Read-Only AI Is Underrated — Šimon Podhajský, Head of AI, Waypoint

Platforms for Humans and Machines: Engineering for the Age of Agents — Juan Herreros Elorza

Juan Herreros Elorza discusses strategies for optimizing internal developer platforms for AI agents, drawing from his experience at Banking Circle. He argues that while many software development best practices remain crucial, the integration of AI agents amplifies the need for self-service platforms. He advocates for A...

Why, and how you need to sandbox AI-Generated Code? — Harshil Agrawal, Cloudflare

Your Insecure MCP Server Won't Survive Production — Tun Shwe, Lenses

Let LLMs Wander: Engineering RL Environments — Stefano Fiorucci

Bending a Public MCP Server Without Breaking It — Nimrod Hauser, Baz

Agentic Engineering: Working With AI, Not Just Using It — Brendan O'Leary