#AI Engineer Europe
31 articles with this tag
Remoby: Remote Terminal Control for AI Agents
Connor Adams unveiled Remoby, an open-source progressive web app that enables remote control of tmux terminal sessions and AI agents from mobile devices.
TNG AI Chess: AI Explains Chess Like a Human Trainer
Stephan Steinfurt from TNG Technology Consulting discusses their AI agent that explains chess games like a human trainer, combining LLMs with specialized tools to create automated video analyses.
Teaching AI Agents to Master Spreadsheets
Nuno Campos of Witan Labs discusses teaching AI agents to master spreadsheets using a REPL approach, improving accuracy and efficiency.
AI Agents Don't Always Follow Rules
Talha Sheikh from Checkout.com discusses the unreliability of AI coding agents and the critical need for verification layers and guardrails to ensure dependable AI outputs.
Together AI Pushes LLM Context Limits to 5 Million Tokens
Max Ryabinin from Together AI discusses breaking barriers in LLM training, detailing techniques to achieve 5 million token context lengths and their impact on memory and performance.
OpenClaw's Vincent Koc on 'Dark Factories' and AI Speed
Vincent Koc of OpenClaw discusses the rapid acceleration of AI development, comparing it to the industrial revolution and highlighting OpenClaw's efficient "dark factory" approach.
Tailscale's Remy Guercio on Network as a Sandbox
Remy Guercio from Tailscale discusses how the network can serve as a secure sandbox for AI agents, enabling granular control and simplified management of AI tools and infrastructure.
Why Enterprise Agentic AI Projects Often Fail
Accenture's Jess Grogan-Avignon & Jack Wang explain why most enterprise agentic AI projects fail and how to succeed by embracing iterative delivery and building trust.
Neo4j: Context Graphs for AI Agents
Neo4j experts Andreas Kollegger and Zaid Zaim discuss how context graphs enhance AI agents for explainable and decision-aware operations.
Angus McLean on Bounded Autonomy in AI
Angus J. McLean of Oliver discusses 'Bounded Autonomy' in AI, exploring the shift to agentic processes in advertising and offering practical advice for building AI agents.
Mardu Swanepoel on AI Agent Best Practices
Mardu Swanepoel of Flinn AI outlines the four core traits of top AI agents: focused modes, transparent execution, personalization, and reversibility.
Stop Babysitting AI Agents: Build a Context Engine
Brandon Walsenuk from Unblocked discusses the critical need for context engines to empower AI agents, moving beyond simple data access to true understanding and autonomous operation.
Does GenAI Belong to Data Scientists?
Phil Hetzel of Braintrust discusses the evolving role of data scientists in Generative AI agent development, arguing for a collaborative, multidisciplinary approach.
Michael Richman on FOMAT: Fear of Missing Agent Time
Michael Richman of Cmd+Ctrl discusses FOMAT (Fear of Missing Agent Time) and how his platform helps manage AI agent workflows.
Rachel Nabors: The Infinite Canvas of the Web Agent
Rachel Nabors discusses how AI agents can leverage the web's 'infinite canvas' using MCP tools and WebMCP, transforming browser interactions.
Sarah Chieng: Fast Models Need "Slow" Developers
Cerebras' Sarah Chieng discusses how fast AI coding models like Codex Spark necessitate new developer habits and workflows for optimal results.
4 Levels of AI Agent Maturity: Don't Build Slop
Ara Khan outlines a 4-level framework for building mature AI agents, emphasizing state machines, visualization, and cloud-native deployment to avoid "slop" and ensure scalability.
Lawrence Jones on Fighting AI with AI
Lawrence Jones of incident.io discusses how AI can be used to debug and manage complex AI systems, highlighting the importance of structured data and automated analysis pipelines.
AI UX is Broken, Not the Model
Mike Christensen from Ably explains why AI UX is broken due to flawed infrastructure, not models, and how to fix it with durable sessions and channels.
Chris Lovejoy on Building Domain-Native AI Organizations
Chris Lovejoy of Notius Labs discusses the critical role of domain experts in AI product development, outlining three key organizational models: Oracle, Evaluator, and Architect.
Mike Spitz on Post-Engineer Engineering Org
Mike Spitz discusses how AI agents are transforming engineering by boosting productivity and changing workflows, advocating for a phased approach to adoption.
Building an AI Chess Coach: Take Take Take
Anant Dole and Asbjorn Steinskog discuss building an AI chess coach, the limitations of LLMs in chess, and their eval framework.
Vincent Koc on Adaptive AI Evaluation
Vincent Koc of Comet ML discusses the limitations of static AI evaluation and the shift towards adaptive, intent-based methods for measuring AI agents.
Sally-Ann Delucia on AI Agent Context Management
Sally-Ann Delucia of Arize discusses the challenges and strategies for context management in AI agents, highlighting the importance of memory and sub-agents.
AI Agent: From Simple Setup to Life OS
Radek Sienkiewicz of VelvetShark details the evolution of his personal AI agent, from a simple tool to a life-managing infrastructure, highlighting key principles for builders.
Mastering AI Pricing: Stripe's Blueprint for Agile Monetization
Stripe's Mayank Pant reveals how AI companies are rapidly shifting to hybrid pricing models to manage costs and deliver value, emphasizing iteration and guardrails.
AI Agents are Taking Over More Than Just Coding
AI agents are increasingly handling tasks beyond coding, from design to research and data management, boosting productivity and enabling 'tiny teams'.
AI Agents Failures & How To Stop Them
Danilo Campagna from Posthog discusses common LLM code generation failures and strategies for improvement, focusing on context, architecture, and human error.
AgentCraft: Gaming the AI Agent Workflow
Ido Salomon unveils AgentCraft, a platform that visualizes AI agent workflows using game-like interfaces, fostering human-AI collaboration and task management.
Kitze's AI Agent Journey: From "Life OS" to "Wolf"
AI creator Kitze reflects on his decade-long quest for a 'Life OS' powered by AI agents, detailing his journey from early to-do apps to self-hosted solutions and his vision for the future of human-computer interaction.
Google DeepMind's Gemma 4 Models Shine at AI Engineer Europe
Google DeepMind's Omar Sanseviero shared insights into the Gemma 4 family of open AI models at AI Engineer Europe, highlighting their performance, on-device capabilities, and community adoption.