Jul 30, 2026

Let's integrate AI Agents in Event-Sourced Systems — Divakar Kumar, FlyersSoft

This presentation explores integrating AI agents into existing event-sourced architectures to resolve ambiguous cases in real-time fraud detection. By leveraging a semantic layer built from various bounded contexts (transaction, device, account), specialized agents like Risk Analyzer and Behavior Analyzer use tools and short-term memory to reach a verdict, addressing the "gray zone" where traditional rule-based and ML systems fall short. The approach emphasizes layering agents without replacing existing infrastructure, enhancing judgment in production systems.

Jul 29, 2026

Alexandr Wang: From Los Alamos to Superintelligence

Alexandr Wang discusses his journey from Scale AI to Meta's superintelligence lab, emphasizing the importance of conviction, systems thinking, and identifying exponential growth opportunities in AI. He highlights Meta's vision for 'personal superintelligence,' the strategic role of open-source and affordable models, and the immense potential of agentic looping for driving innovation and outcompeting incumbents. His core advice for young entrepreneurs is to develop an unshakeable internal compass for the future, embracing vision and ambition as the new scarce resources.

Jul 28, 2026

OpenAI’s Plan to Make ChatGPT the Everything App — Akshay Nathan, OpenAI

Akshay Nathan, head of Core Product Engineering at OpenAI, discusses the journey and rationale behind ChatGPT Work. He explains how Codex's unexpected adoption by non-developers led to a unified agent harness, blurring the lines between developer and knowledge worker tools. The conversation delves into model capabilities, the role of artifacts and interactive 'Sites' in replacing traditional documents, and how AI fosters a new era of 'T-shaped' generalists. Nathan emphasizes the shift in productivity bottlenecks to 'ideas and taste' and the importance of 'quality at-bats' over mere 'motion' in an AI-powered development landscape.

Jul 28, 2026

Llama.cpp vs vLLM: Which Local LLM Engine Actually Scales?

Explore the strengths and optimal use cases of `llama.cpp` and `vLLM` for local LLM inference. `llama.cpp` excels on consumer hardware with optimizations like quantization and CPU support, while `vLLM` is designed for production-scale efficiency with features like continuous batching and speculative decoding on high-performance accelerators.

Jul 25, 2026

Why Large? Tiny LMs & Agents on Edge/Robotics — Cormac Brick, Google

Cormac Brick from Google AI Edge discusses how the increasing constraint of DRAM cost on edge devices necessitates the development and deployment of increasingly smaller AI models. He outlines the work of his team in optimizing models like Gemma, achieving 2.9 bits per weight for a 2 billion parameter model capable of running on a Raspberry Pi at 7.6 tokens/second, or on an NPU at 31 tokens/second decode for vision tasks. The talk delves into 'tiny models' (50M-500M parameters) that extend AI to older devices and enable features like robust voice-to-function calling via fine-tuning with synthetic data, exemplified by an offline voice dictation app.

Jul 25, 2026

What Big Tech Missed And How Startups Can Still Win

Alexandre LeBrun, CEO of AMI Labs, discusses his career building and selling AI companies, emphasizing his strategy of tackling problems "20 years too early." He delves into AMI Labs' contrarian bet on "world models" over traditional LLMs, highlighting their ability to learn directly from real-world sensory data, unlike LLMs which learn from human-written text. LeBrun explains how this approach is critical for developing intelligent robots and avoiding the pitfalls of Vision-Language Assistants (VLAs). He also touches upon the challenges of securing talent, data, and compute for such an ambitious project, the strategic choice of location, and the importance of holding an extremely large vision while solving a narrow problem for early founders.