Introducing Sora 2
A detailed overview of OpenAI's announcement of Sora 2, a flagship video and audio generation model, and the new Sora app, which introduces novel features like "Cameo" for personalized content creation and a new social experience.
Feature
How To Train An LLM with Anthropic's Head of Pretraining
Anthropic's Head of Pre-training, Nick Joseph, details the immense engineering and infrastructure challenges behind training frontier models like Claude. He covers the evolution from early-stage custom frameworks to debugging hardware at massive scale, balancing pre-training with RL, and the strategic importance of data quality and team composition.
The Blind Spots of Platform Engineering • Matt McLarty & Erik Wilde
Matt McLarty and Erik Wilde explore the blind spots in platform engineering, arguing that a narrow focus on developer velocity and toolchains overlooks the critical need for creating reusable, API-driven business capabilities that deliver tangible value and organizational optionality.
Zero-Click Attacks: AI Agents and the Next Cybersecurity Challenge
Explores the mechanics of zero-click attacks, which require no user interaction, and details how the integration of autonomous AI agents can amplify these threats. The summary covers historical examples like Pegasus and proposes a multi-layered defense strategy, including AI firewalls, the principle of least privilege, and a zero-trust architecture.
Quantized LLM Training at Scale with ZeRO++ // Guanhua Wang // AI in Production 2025
Guanhua Wang from Microsoft's DeepSpeed team explains ZeRO++, a system that tackles the communication bottleneck in large-scale LLM training. By quantizing weights and gradients, ZeRO++ reduces communication volume by 4x, leading to training speedups of over 2x, particularly in low-bandwidth and small-batch-size environments.
Beyond Chatbots: How to build Agentic AI systems with Google Gemini // Philipp Schmid
A deep dive into the evolution from static chatbots to dynamic, agentic AI systems. Philipp Schmid of Google DeepMind explores how to design, build, and evaluate AI agents that leverage structured outputs, function calling, and workflow orchestration with Google Gemini, covering key agentic patterns and the future of AI development.