In a landmark shift for both Hollywood and generative AI, The Walt Disney Company is investing $1 billion in OpenAI and licensing more than 200 of its iconic characters—spanning Disney, Marvel, Pixar and Star Wars—to appear in Sora, OpenAI’s AI-driven short-video generator.

Under a three-year agreement, fans will soon be able to generate and share AI-created clips featuring these characters based on text prompts, with select content even slated for distribution on Disney+. The deal explicitly excludes real actors’ likenesses or voices, aiming to strike a balance between creative expansion and IP protections. 

This move marks Disney’s most significant embrace of AI to date, transitioning from past legal challenges over unauthorized use of its properties to a strategic partnership that embeds generative models at the heart of its storytelling ecosystem.

Disney will also adopt OpenAI’s technology internally—incorporating tools like ChatGPT across its operations and products—while both companies stress “responsible” use aimed at protecting creators’ rights. The collaboration signals a broader trend of legacy content owners forging deeper alliances with AI platforms, even as the industry continues wrestling with questions around creative labor, copyright and the cultural impact of automated media.

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Former Google design ethicist Tristan Harris delivers an unflinching forecast of how artificial intelligence is poised to transform society far faster than most people realize. Drawing on his years inside Google and more than a decade studying technology’s social impact, Harris argues that 2027 marks a tipping point—one where AI systems begin reshaping jobs, economies, politics, and daily life at a speed that overwhelms existing institutions.

In this deeply personal and urgent warning, Harris connects the dots between AI and issues as wide-ranging as student debt, immigration, national security, and democratic stability. He illustrates how AI will quietly redefine the assumptions underpinning modern society, often before governments or the public can understand what is happening. Beneath the analysis is the story of his own shift from Silicon Valley insider to one of the world’s most vocal advocates for responsible technology. Driven, he says, by a belief that the window to prepare is closing fast.

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GitHub just dropped a practical deep-dive into what makes a custom agents.md file succeed or fail, based on analysis of more than 2,500 public repositories. The upshot: vague, generic AI-agent descriptions rarely deliver. Instead, the most effective agent files treat the agent as a carefully scoped specialist. That means specifying exactly what the agent does (its role and skills), the tech stack (with version numbers), the file structure it should see, the commands it can run, and even the things it should never touch. Including real code snippets that demonstrate desired output and enforcing strict boundaries like “never commit secrets” or “don’t modify vendor files”, drastically improves consistency and reliability.

The article argues that a good agents.md is less like a broad-purpose instruction manual and more like a job description + operations handbook tailored to each agent’s responsibilities. It outlines six core dimensions: commands, testing, project structure, code style, git workflow, and boundaries, that together create a robust, predictable agentic workflow. For developers who’ve struggled with flaky AI-agent behavior, this serves as a reminder: designing thoughtful “agent config” is as important as writing code.

For someone building automation-heavy projects (like you), the implications are clear: if you want an AI-driven workflow to remain stable and trustworthy, investing time in a carefully structured agents.md isn’t optional — it’s foundational.

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