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.
Onshore founder Dominic Vitucci discusses how AI is causing a tectonic shift in the accounting industry, moving from a model based on billable hours to one of technology-driven outcomes, and what this means for the future of the profession and the legacy firms that dominate it.
A detailed breakdown of the updated OWASP Top 10 vulnerabilities for Large Language Models (LLMs), explaining threats like prompt injection, data poisoning, and supply chain risks, along with practical defense strategies.
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.
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.
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.
Robert from OpenAI discusses the critical role of structured evaluations (evals) and graders for developing advanced collaborative agents. He explores the limitations of 'vibe-based' assessments, introduces a maturity model for evals, and presents a comprehensive rubric for measuring agent performance beyond simple accuracy, connecting these concepts to the power of Reinforcement Fine-Tuning (RFT).
Karan Goel, CEO of Cartesia, discusses the fundamental limitations of Transformer architectures, arguing they behave more like retrieval systems than learning systems. He explains how State Space Models (SSMs) enable compression and abstraction, and why Cartesia is tackling multimodal intelligence by first solving for voice AI, aiming to develop a transferable 'recipe' for end-to-end representation learning.
Spec-Driven Development offers a structured, reproducible, and reliable alternative to 'vibe coding' in the AI era. Al Harris from the Kiro team explains how to leverage specs as living documentation, integrate external tools via MCPs, and use property-based testing to create a tight feedback loop from natural language requirements to verified code.
This episode explores the strategic implications of the Disney-OpenAI licensing deal, critiques Time Magazine's "Architects of AI" focus on business over research, analyzes NVIDIA's full-stack ambitions with the Neotron 3 model release, and delves into Anthropic's unique approach to AI safety with the "Claude Soul Document".
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