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AI Engineer summaries
Every long episode from AI Engineer, read in minutes: the key takeaways, the chapters, and a digest of the whole conversation. Talks, workshops, events, and training for AI Engineers.
Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs
In this hands-on workshop, Angelos Perivolaropoulos from ElevenLabs guides participants through the process of training an LLM from scratch using basic libraries like Torch, emphasizing the importance of tokenization and model architecture in building effective language models.
Skill Issue: How We Used AI to Make Agents Actually Good at Supabase — Pedro Rodrigues, Supabase
In this workshop, AI Engineer Pedro Rodrigues discusses how to enhance agent performance at Supabase using skills—structured workflows that improve agent interaction and efficiency. He shares insights on creating, testing, and implementing these skills, highlighting their significance in the development of agent-friendly applications.
Ralph Loops: Build Dumb AI Loops That Ship — Chris Parsons, Cherrypick
In this workshop, AI Engineer Chris Parsons introduces Ralph Loops, a method for building simple AI workflows that enhance productivity by iteratively refining tasks, using real coding examples like a Pomodoro timer. Participants learn to implement these loops practically, emphasizing the importance of context and feedback in AI-assisted coding.
Mergeable by default: Building the context engine to save time and tokens — Peter Werry, Unblocked
In this video, Peter Werry from Unblocked discusses the importance of a context engine for AI agents, emphasizing how it optimizes task execution by providing relevant context and reducing cognitive load for users. He debunks common myths surrounding context engines and illustrates their effectiveness in enhancing productivity and understanding within organizations.
Context Is the New Code — Patrick Debois, Tessl
In this talk, Patrick Debois presents the concept that context is the new code, emphasizing the importance of creating, testing, and distributing context in the software development lifecycle, particularly when using AI coding agents. He advocates for a systematic approach to context development that involves generating reusable prompts, evaluating their effectiveness, and leveraging feedback to improve overall coding practices.
Mastering AI Pricing: Flexible & Agile Monetization — Mayank Pant, Stripe
In this video, Mayank Pant from Stripe discusses the challenges and strategies of AI pricing, emphasizing the need for hybrid pricing models that adapt rapidly to changing features and customer needs, allowing companies to maintain competitive margins while fostering customer trust and engagement.
Shipping complex AI applications — Braintrust & Trainline
In this workshop by AI Engineer, participants learn how to ship complex AI applications using tools from Braintrust and Trainline, focusing on operationalizing AI systems and ensuring quality through observability and systematic workflows. The session emphasizes the importance of transitioning from prototypes to production-ready solutions while addressing challenges in AI deployment.
Building Conversational Agents — Thor Schaeff and Philipp Schmid, Google DeepMind
In this workshop, Thor Schaeff and Philipp Schmid from Google DeepMind guide participants through building conversational agents using the Gemini API, emphasizing hands-on experience with API key creation, tool integration, and the development of coding agents capable of executing commands and generating outputs.
Building your own software factory — Eric Zakariasson, Cursor
In this video, Eric Zakariasson from Cursor discusses the process of building your own software factory by leveraging AI tools to enhance autonomy in software development, emphasizing the need for structured systems, guardrails, and the shift from a worker to a manager mindset in overseeing AI-driven processes.
Why building eval platforms is hard — Phil Hetzel, Braintrust
In this video, Phil Hetzel from Braintrust discusses the complexities involved in building effective eval platforms for AI agents, emphasizing the importance of integrating observability and experimentation to ensure agents perform reliably in production. He outlines various stages of maturity in eval platform development, highlighting the need for collaboration across technical and non-technical teams.
One Login to Rule Them All: Cross-App Access for MCP — Garrett Galow, WorkOS
In this talk, Garrett Galow from WorkOS discusses the challenges of cross-app access in the MCP environment, highlighting the inefficiencies of repetitive consent screens and the need for a more streamlined authentication process. He introduces cross app access (XAA) as a solution that allows identity providers to facilitate seamless connections between applications, enhancing security and user experience.
[FULL WORKSHOP] AI Coding For Real Engineers - Matt Pocock, AI Hero
In this workshop, Matt Pocock discusses how to effectively utilize AI coding by leveraging software engineering fundamentals, emphasizing the importance of managing the smart zone and dumb zone of language models to enhance productivity and alignment in coding tasks. He introduces practical exercises to help developers navigate these concepts while coding with AI.
What Do Models Still Suck At? - Peter Gostev, Arena.ai, BullshitBench
In this video, AI Engineer Peter Gostev discusses the limitations of current AI models, emphasizing that despite impressive benchmark improvements, many models still struggle with nonsense questions and complex tasks, revealing a significant dissatisfaction rate among users. He advocates for a broader understanding of model performance beyond just top-tier advancements, highlighting the need to improve the overall distribution of capabilities.
The End of Apps — Kitze, Sizzy.co
The future of productivity is shifting from fragmented apps to integrated AI agents that proactively manage tasks, reversing the traditional prompting dynamic.
What goes into building generative image & video models at scale - Sander Dieleman, Google DeepMind
Building generative image/video models involves data curation, latent space diffusion, and guidance for quality. Key is iterative refinement and spectral analysis.
How AI is changing Software Engineering: A Conversation with Gergely Orosz, @pragmaticengineer
AI is reshaping software engineering, driving token maxing due to performance metrics and pushing companies to build custom AI infra, impacting team structures and engineer roles.
Full Workshop: Build Your Own Deep Research Agents - Louis-François Bouchard, Paul Iusztin, Samridhi
Build deep research agents and writing workflows to automate content creation, focusing on structured prompts, evaluator-optimizer loops, and observability for production-ready AI systems.
Frontier AI and the Future of Intelligence — Raia Hadsell, VP of Research, Google DeepMind
DeepMind's Raia Hadsell discusses frontier AI beyond language models, focusing on embedding models, advanced weather prediction (GraphCast, Gencast), and interactive world models (Genie 3) for new forms of entertainment and education.
State of the Claw — Peter Steinberger
Peter Steinberger discusses the explosive growth and security challenges of Open Claw, emphasizing the need for user education and a robust foundation for its future.
Harness Engineering: How to Build Software When Humans Steer, Agents Execute — Ryan Lopopolo, OpenAI
Learn Harness Engineering: shift from implementation to system design and delegation with AI agents as your primary workforce, making code abundant and human time the scarce resource.
One Registry to Rule them All - Sonny Merla, Mauro Luchetti, & Mattia Redaelli, Quantyca
Amplifon built an enterprise-grade AI registry system comprising an AI gateway, MCP registry, A2A registry, and use case registry to manage chaos and enable scalable, governed AI development across teams and continents.
Judge the Judge: Building LLM Evaluators That Actually Work with GEPA — Mahmoud Mabrouk, Agenta AI
Optimize LLM evaluators using the GEPA algorithm for calibrated judgments, moving beyond basic prompts to achieve higher accuracy and reliability in AI agents.
AI Didn’t Kill the Web, It Moved in! — Olivier Leplus (AWS) & Yohan Lasorsa (Microsoft)
AI is transforming the web development lifecycle, from coding and debugging to new agentic browser capabilities, requiring developers to adapt.
VoiceOps-fying Low-Latency Intelligence Extraction from Messy Audio Streams — Dippu Kumar Singh
AI transforms messy audio into actionable business intelligence by optimizing contact center workflows, reducing after-call work (ACW) by 50%.