Prompt engineering

Jul 14, 2026

Anthropic's Katelyn Lesse & Angela Jiang: Building an Ecosystem, not a Walled Garden

Katelyn Lesse and Angela Jiang, leaders of Anthropic's developer platform, outline their strategy built on a "three-layer cake": knowledge, execution, and coordination. They emphasize moving towards advanced "strategies" or meta-harnesses that assign distinct jobs to tokens, fostering a robust and open AI ecosystem. The discussion covers empowering builders, setting industry standards, and Anthropic's nuanced approach to an open platform versus a walled garden, focusing on architectural soundness over infrastructure ownership.

Jul 06, 2026

Field Guide to Fable — Thariq Shihipar, Anthropic

Thariq Shihipar from Anthropic introduces Fable, their newest model, as a major leap forward, emphasizing "capability overhang" where tool use unlocks unprecedented intelligence. He provides a "field guide" on working with Fable, discussing evolving prompt engineering, advanced tool interactions (like HTML reports), and strategies for identifying project unknowns. He concludes by challenging the necessity of traditional tradeoffs, advocating for ambitious development that is both good, fast, and cheap, and urging AI engineers to prove the transformative power of AI.

Jul 02, 2026

The Prompt Is Still a Punch Card - Ted Johnson, JoinIn AI

Ted Johnson argues that current AI interfaces, particularly prompting, operate on an outdated "batch processing" protocol akin to punch cards. Despite advanced LLM capabilities, this interface design forces humans to adapt to machines, hindering natural interaction. He advocates for a shift towards human-compatible interfaces where AI actively participates in real-time conversation, leveraging its intelligence to remove user burdens and amplify human potential.

Jun 29, 2026

Frontier results, on device - RL Nabors, Arize

RL Nabors discusses the significant costs associated with using frontier AI models, covering security, latency, and financial implications. She introduces a framework for right-sizing AI solutions by leveraging smaller, task-specific models and Small Language Models (SLMs). The framework details how to prove task feasibility, establish success criteria with golden datasets, conduct capability evaluations (using tools like Phoenix), and select the most appropriate "Small And Good Enough" (SAGE) model. Nabors further demonstrates how prompt engineering, particularly few-shot prompting, and post-processing can close performance gaps with larger models, while advocating for continuous regression evaluations to maintain performance integrity. The overarching message is to "prototype big, deploy small" to optimize AI deployments.

Jun 16, 2026

He's Building an AI That Can't Lie | Dan Klein, Scaled Cognition

Dan Klein discusses the critical shift in AI from a 'nothing works' to an 'everything works' problem, where fluent LLM outputs often mask deep unreliability. He explores the nature of hallucinations, how reinforcement learning can inadvertently teach deception, and the necessity of building AI systems with inherent metacognition and verifiability. Klein's company, Scaled Cognition, is architecting models where truth and action semantics are first-order design principles, aiming to provide guarantees in a field increasingly dominated by end-to-end optimization.

Jun 15, 2026

Context Engineering for Coding Agents

A deep dive into advanced engineering techniques for coding agents, focusing on effective context management in LLMs like Claude. The talk introduces a practical framework using a brain-inspired analogy, proposing a Markdown-based 'wiki' as a long-term memory system to augment the agent's limited context window. This approach is demonstrated through a real-world challenge of extracting structured data from technical drawings.