Eugene Cheah, CEO of Featherless AI, presents a four-step framework for cutting AI inference costs at scale: measure token spend by team and workload, right-size models through evals, classifiers, and fine-tuning, buy GPU capacity with an understanding of the speed-versus-cost tradeoff, and check whether organizational processes can absorb the extra output.
Nacho Martinez argues that the model is fixed while the harness (agent loop, memory, context engineering, tools and skills, continual learning) is where engineers can make real gains. He walks through the agent stack and applies it to an autonomous AML agent that flags suspicious bank transactions, then demonstrates the workshop environment and answers questions on memory design, skill retrieval, caching, and hybrid search.
A roundtable of AI power users reviews OpenAI DevDay and asks what actually works in daily use. Topics include personal assistants and their trust, permission, and safety trade-offs; durable agent state and remote execution; the Jev decision model and calibrated classification; model preferences across Anthropic, OpenAI, xAI, Meta, and open models; subagents and context management; and the gap between impressive capabilities and products that hold up.
Jack McDonald of Apollo's Redding Ridge and Michael Manapat of Rowspace discuss where AI becomes mission-critical in structured credit and CLO management. They cover compressing portfolio construction from days to minutes, automating the weekly trade reconciliation, and why embedding engineers with front-office teams, rather than relying on generic LLMs, is what earns trust in financial data.
Grepr CEO Jad Naous talks with a16z's Martin Casado about how coding agents have changed a 10-person engineering team, with projects that once took six months now finishing in about ten days. They examine whether AI can reason about and troubleshoot production distributed systems, why operations is a harder problem than code generation, and where AI adoption is likely to go next.
Drawing on a wave of new studies, this piece examines whether AI stops children from learning and argues the answer hinges on how it is used. AI as a shortcut that outsources thinking undermines learning, while AI as a tutor that explains concepts and gives feedback can improve it. It covers a large natural experiment in China, a Turkish tutoring study, and practical takeaways that apply equally to adults using AI at work.