A technical guide on migrating ML experiments from Neptune to Weights & Biases, covering the migration script, API-level code changes, and best practices for organizing projects and analyzing results in the W&B platform before the Neptune sunset.
W&B Models is the system of record for the entire model development lifecycle. This guide explores how to monitor training, tune hyperparameters, track artifacts and lineage for reproducibility, and automate MLOps workflows like evaluation and deployment using a central platform.
An expert overview of post-training techniques for language models, covering the entire workflow from data generation and curation to advanced algorithms like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning (RL), along with practical advice on evaluation and iteration.
Kwangjun Ahn from Microsoft Research provides a technical overview of orthonormal optimizers (like Muon and Dion2), a new class of algorithms for large-scale AI model training that are emerging as powerful successors to AdamW. The talk covers their theoretical foundations, empirical benefits, distributed implementation strategies, and practical guidelines for integration into modern training pipelines.
Experts on the Mixture of Experts podcast analyze Perplexity Computer's pivot to agent orchestration and debate its closed-system approach versus open alternatives like OpenClaw. They also discuss Anthropic's new memory import feature for Claude, questioning if memory is still a competitive moat, and explore NullClaw, a minimalist agent framework that sparks a conversation about the future of edge-based agent swarms. Finally, they tackle the controversial debut of Tilly Norwood, the world's first AI actor, and debate the implications for the entertainment industry and the personification of AI.
Cursor's team discusses their latest Cloud Agents launch, which gives agents full cloud VMs to test changes, record demo videos, and provide remote access. We explore parallel model swarms, bug reproduction workflows, and the future of agentic coding where throughput and new bottlenecks in review and CI/CD take center stage.
Ajay Chankramath and Nic Cheneweth discuss the critical elements of effective platform engineering, emphasizing a product mindset, the foundational role of control planes and API-first design, the common pitfalls of implementing Backstage, and the emerging impact of AI and agents on the platform landscape.
Experts Sam Newman, Jacqui Read, and Simon Rohrer explore the nuances of software design, its intersection with architecture, and the critical role of communication in scaling technical clarity. The discussion covers practical advice on implementing Architectural Decision Records (ADRs), the evolving role of the architect as a facilitator, and strategies for creating agile enterprise architectures.
Docker educators Bret Fisher and Elton Stoneman discuss the second edition of Stoneman's book, "Learn Docker in a Month of Lunches". They explore why Docker fundamentals remain crucial in a Kubernetes-dominated world, the evolution of the container ecosystem over the past five years, and the key skills that differentiate a Docker expert from a beginner, such as multi-platform builds, security, and configuration management.
Jacqueline Cheong and Robin Tang, founders of real-time data streaming platform Artie, discuss their journey from identifying the critical need for fresh data at companies like OpenDoor to building a production-ready solution, acquiring their first major customer Substack via a cold email, and navigating the complex technical challenges of real-time data processing at scale.
David Jayatillake, VP of AI at Cube.dev, discusses the critical role of a headless, open-source semantic layer in the modern data stack. He argues against proprietary, BI-tool-specific semantic layers that create vendor lock-in and advocates for a decoupled approach. The conversation explores how AI agents can automate the entire data pipeline—from ingestion and transformation to generating and querying the semantic layer—and compares the functionalities of semantic layers and feature stores, highlighting the crucial difference of temporality.
Félix GV, an architect of LinkedIn's Venice database, discusses its unbundled, planetary-scale architecture. He covers how components like Kafka and RocksDB form independent distributed systems, details their rigorous chaos engineering practices, explains CAP theorem trade-offs in multi-region deployments, and explores the experimental integration of DuckDB for SQL-based analytics.
![If You Can't See Inside, How Do You Know It's THINKING? [Dr. Jeff Beck]](https://img.youtube.com/vi/Ucqfb33GJJ4/maxresdefault.jpg)
Dr. Jeff Beck explores the philosophical and technical definitions of agency, arguing that the distinction between an agent and an object lies in computational sophistication, particularly the capacity for planning and counterfactual reasoning. The conversation provides a deep dive into Energy-Based Models (EBMs), Yann LeCun's JEPA for learning in latent space, and a pragmatic approach to AI safety centered on inverse reinforcement learning rather than fears of rogue superintelligence.
Explore the shift from reactive to predictive DevSecOps with Akshay Mittal. This discussion covers how AI-Augmented DevSecOps and Agentic Workflows are creating self-healing systems, the critical role of Explainable AI (XAI), and a four-layer architecture for building scalable, enterprise-grade AI solutions.
Professor Ellen Zhong discusses the shift from viewing proteins as static objects to dynamic molecular machines. She explores how cryo-electron microscopy (cryo-EM) combined with machine learning creates complex inverse problems to reveal protein motion, moving beyond the "solved" problem of static structure prediction and toward a future of AI-driven scientific discovery.
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