Large language models

GPT-OSS vs. Qwen vs. Deepseek: Comparing Open Source LLM Architectures

GPT-OSS vs. Qwen vs. Deepseek: Comparing Open Source LLM Architectures

A technical breakdown and comparison of the architectures, training methodologies, and post-training techniques of three leading open-source models: OpenAI's GPT-OSS, Alibaba's Qwen-3, and DeepSeek V3. The summary explores their different approaches to Mixture-of-Experts, long-context, and attention mechanisms.

How 80,000 companies build with AI: Products as organisms and the death of org charts | Asha Sharma

How 80,000 companies build with AI: Products as organisms and the death of org charts | Asha Sharma

Asha Sharma, CVP of Product for Microsoft's AI Platform, shares insights from working with over 15,000 companies building AI. She discusses the shift from "product as artifact" to "product as organism," the rise of post-training as the new competitive moat, and how agents are transforming organizational structures from hierarchies ("org charts") into task-based networks ("work charts").

How This 25-Year-Old Built A $675M Legal AI Startup (With No Legal Experience)

How This 25-Year-Old Built A $675M Legal AI Startup (With No Legal Experience)

Max Junestrand, co-founder and CEO of Legora, shares insights on building a successful vertical AI company for the legal industry. He discusses their product strategy, the technical stack designed for a multi-model future, the go-to-market motion for conservative industries, and the challenges of scaling from 10 to 100 people in 13 months.

Conext Engineering for Engineers

Conext Engineering for Engineers

Jeff Huber of Chroma argues that building reliable AI systems hinges on 'Context Engineering'—the deliberate curation of information within the context window. He challenges the efficacy of long-context models, presenting a 'Gather and Glean' framework to maximize recall and precision, and discusses specific challenges and techniques for AI agents, such as intelligent compaction.

Aaron Levie and Steven Sinofsky on the AI-Worker Future

Aaron Levie and Steven Sinofsky on the AI-Worker Future

Experts from a16z, Box, and Microsoft debate the definition and future of AI agents. They explore the shift from monolithic AGI to specialized agent networks, the technical challenges of autonomous systems, and how this new platform will reshape enterprise software, workflows, and the very nature of work.

The Moonshot Podcast Deep Dive: Jeff Dean on Google Brain’s Early Days

The Moonshot Podcast Deep Dive: Jeff Dean on Google Brain’s Early Days

Google DeepMind’s Chief Scientist Jeff Dean discusses the origins of his work on scaling neural networks, the founding of the Google Brain team, the technical breakthroughs that enabled training massive models, the development of TensorFlow and TPUs, and his perspective on the evolution and future of artificial intelligence.