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A harness gives a model tools, context and boundaries. As models learn more of the coordination once supplied by software, harness design increasingly turns toward permissions, decisions and human attention.
An AIE Europe debrief on agent infrastructure, model specialization, and what changes when software generation outruns human review.
From queued business-software requests to specialized software teammates, Part 1 explores agent composition, knowledge graphs, engineering tradeoffs, and the discovery infrastructure a network of agents would need.
Part 2 follows the practical problems of delegating to agents: generating interfaces, testing candidates, choosing models, pricing work, and sharing context—with a closing turn to domain markets and long-term conviction.
Part 3 of 3 moves from accessible video tools and controlled image edits to reusable expertise—and the personal and community priorities that keep useful work sustainable.
Canvas and Tasks connect interface design with model training: choosing when to act, understanding what the user wants, and building trust through collaboration.
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A conversation with Greg Brockman about OpenAI’s path to AGI, alongside our analysis of GPT-5, Codex, and the shift to agentic coding.
Thariq Shihipar on Claude Code’s next era: prompts, harnesses, plugins, and agents—and what it takes to keep pace with the frontier.
Marc Andreessen on the future of the browser, Pi and OpenClaw, and why this wave of AI feels different.
Priscilla Chan and Mark Zuckerberg on Biohub’s plans for frontier AI and virtual biology, and the ambition to cure or prevent disease.
Anima Anandkumar on modeling the physical world, from weather to fusion reactors, and why physics needs more than language models.
John Platt on automating science, tackling climate change, and how the next generation can contribute in the age of AI.
Anthropic brings interactive apps into Claude with an open MCP standard for richer AI interfaces.
What Sam Altman’s town hall reveals about building startups in the age of AI.
Moonshot’s multimodal model brings competitive coding, native image and video understanding, and parallel agents.
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