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.
In this episode, Ben Lorica and Evangelos Simoudis discuss how AI is fundamentally reshaping the modern workplace. They explore the necessary evolution of knowledge work, from a focus on routine execution to problem definition and spec-driven development, and outline the critical skills professionals must cultivate—including rapid experimentation, AI agent orchestration, and systems thinking—to remain valuable and navigate a more volatile labor market.
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.
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.
This talk introduces 'structured dissent,' a multi-agent orchestration pattern where believer, skeptic, and neutral agents debate decisions to overcome the 'confidently wrong' failure mode of single-agent LLM systems, improving reliability for high-stakes tasks like cybersecurity analysis.
A deep dive into the security vulnerabilities of Multi-Context Protocol (MCP) for AI agents. The talk explores how identity loss, "all-or-nothing" permissions, and disappearing audit trails create significant attack surfaces, and presents solutions like identity chain tracking, context-aware permissions, and intelligent auditing to secure agent-to-tool communication.
A detailed overview of a modular, five-agent system designed to combat the entire lifecycle of digital misinformation. Based on an ICWSM research paper, this practitioner's guide details the roles of the Classifier, Indexer, Extractor, Corrector, and Verifier agents. The system emphasizes scalability, explainability, and high precision, moving beyond the limitations of single-LLM solutions. The talk covers the complete blueprint, from agent coordination and MLOps to holistic evaluation and optimization strategies for production environments.
Panos Stravopodis, CTO of Elyos AI, shares the infrastructure and orchestration challenges of building production-ready voice AI agents. He details the four pillars for success—latency, consistency, context, and recovery—and provides engineering patterns for error handling, context management, and achieving conversational coherence in real-time systems.
Aishwarya Naresh Reganti and Kiriti Badam, with experience from OpenAI, Google, and Amazon, share a framework for building successful enterprise AI products. They detail why AI development differs from traditional software, emphasizing the challenges of non-determinism and the agency-control trade-off, and introduce their 'Continuous Calibration, Continuous Development' (CC/CD) lifecycle to build reliable, value-driven AI systems.
A deep dive into the key takeaways from CES 2026, covering the surge in humanoid robotics and the evolution of software-defined vehicles, followed by a nuanced analysis of the shifting US-China export controls on advanced AI chips.
Get updates straight to your inbox. No spam, just useful content.