Theoretical physicist Alex Lupsaska shares his journey from AI skeptic to collaborator, detailing how advanced GPT models solved a year-long problem in quantum field theory concerning gluon scattering amplitudes, and then generalized the solution to gravitons, signaling a new era of AI-accelerated scientific discovery.
Filip Makraduli from Superlinked discusses the common infrastructure gaps and profiling mistakes encountered when deploying small embedding and transformer models. He introduces the Superlinked Inference Engine (SIE), an open-source solution designed for dynamic model loading, hot-swapping, and memory-aware eviction to maximize GPU utilization and streamline the path from development to production.
A deep dive into building production-ready, stateless, and scalable LLM agents by leveraging the Model Context Protocol (MCP) and Strands SDK on AWS Lambda. The session demonstrates how to expose real-world functionality to language models, moving beyond mere reasoning to tangible action.
A practical guide to the engineering principles and trade-offs involved in training a small language model from scratch on a local machine, based on a workshop by Angelos Perivolaropoulos from ElevenLabs.
Boris Cherny, creator of Claude Code, discusses the future of software development at AI Ascent 2026. He argues that coding is effectively a solved problem, detailing his personal workflow of shipping dozens of PRs daily from his phone. Cherny explores the shift from typeahead to autonomous agents, the rise of cross-disciplinary generalist teams, and uses the printing press as an analogy for the coming democratization of software creation for all.
A deep dive into building, testing, and iterating on Agent Skills to improve AI agent performance. This workshop covers the core concepts of progressive disclosure, eval-driven development, and practical application using a real-world Supabase and PostgreSQL security scenario.