Good morning. It’s Wednesday, November 20th.

Did you know: On this day in 1985, Microsoft released Windows 1.0.

In today’s email:

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Promise, a new AI-driven production studio backed by Andreessen Horowitz and Peter Chernin, plans to use generative AI tools to produce films and series. The startup, which emerged following a successful funding round, aims to create a multi-year lineup of content in collaboration with Hollywood stakeholders.

A key part of Promise’s strategy is the development of Muse, a production software that integrates AI throughout the filmmaking process. Promise joins other major players, such as OpenAI’s Sora and Adobe’s AI-driven text-to-video tools, as part of the wider shift toward AI-assisted media production. Read more.

Perplexity has launched "Buy With Pro," an AI shopping assistant for U.S.-based Pro users. It combines product research with one-click purchasing and integrates a visual search tool, "Snap to Shop," for item identification via images.

The platform offers AI-curated recommendations and handles over 100 million queries weekly. Read more.

ElevenLabs has expanded its platform to support conversational AI agents, building on its voice cloning and text-to-speech capabilities. Developers can fine-tune language, tone, and creativity, while integrating top-tier LLMs like Gemini, GPT, and Claude. The platform also offers custom LLM integration through its SDK, with Python, JavaScript, and other languages supported.

Harnessing its expertise in speech synthesis, ElevenLabs is now venturing into speech-to-text, aiming to take on heavyweights like Google and OpenAI. Its modular approach to model selection and deep customization pits it squarely against competitors like Vapi and Retell. Read more.

📄 Generative World Explorer

📄 BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices ​​

📄 Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement

📄 Continuous Speculative Decoding for Autoregressive Image Generation

📄 Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering

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