📱Apple Explores Google's Gemini to Fix Siri
Apple is reportedly in early talks with Google about using Gemini to power a completely rebuilt Siri —following setbacks that pushed the voice assistant's major upgrade to 2026.
- —Apple had Google build a custom Gemini model that would run on Apple's private servers, with Google already training a version for testing
- —The company is simultaneously developing two Siri versions internally: Linwood using Apple's own models and Glenwood running on external tech
- —Apple has also explored similar partnerships with Anthropic and OpenAI (with ChatGPT already helping power Siri's answering capabilities)
- —Bloomberg reported that Apple is still "several weeks away" from a decision on both using internal vs. external models and who the partner would be
Why it matters: For all the negativity surrounding Apple's AI issues, moving externally to bring on one of the frontier labs could be the best possible outcome for iPhone users. The alternative is hoping Apple can develop its own — but with talent fleeing to rivals and already facing setbacks, it seems like a long and arduous path.
🎨Meta Partners with Midjourney for 'Aesthetic' AI
Meta just announced a new partnership with Midjourney to integrate the startup's 'aesthetic technology' into future AI models and products, a major shift from the company's in-house creative model development.
- —Meta's Chief AI Officer Alexandr Wang said the 'technical collaboration' will combine teams to upgrade visual capabilities across Meta's product lineup
- —Meta currently has a series of visual generation tools, including Imagine, Movie Gen, and research-focused models like Dino V3
- —Founder David Holz emphasized that Midjourney is still an "independent, community-backed research lab with no investors" despite the partnership
- —Midjourney launched its first video generation capabilities in June with its V1 model, giving users the ability to turn images into five-second extendable clips
Why it matters: Meta bringing Midjourney aesthetics to its billions of users would be a big change from the quality seen in its previous in-house models, with MJ having a special vibe that is just hard to match. Meta is also showing a new willingness to look externally (not just poach talent) to help push its own AI development forward.
🤖Build an AI Email Agent That Auto-Schedules Meetings
In this tutorial, you'll learn how to build an AI agent for your inbox that you can CC into any email to automatically find meeting times and book directly in your Google Calendar.
- —Sign up for Lindy AI and duplicate our pre-built template
- —Configure triggers: set your AI's email address (e.g., [email protected]) and whitelist your personal email as the filtered sender
- —The agent works in two modes: Direct Booking (you specify "Tuesday 2PM") or Time Finding (agent checks calendar and suggests options)
- —Adjust availability blocks in "Find Available Times" - set your workday start/end hours and available days
- —Test both modes, then simply CC your AI assistant on any scheduling email
Why it matters: Add team members' emails as "from" conditions and connect their calendars — this lets anyone CC the agent to coordinate meetings across everyone's availability.
🧬OpenAI, Retro Biosciences Make Old Cells Young Again
OpenAI just published a case study with Retro Biosciences, using a custom AI model to redesign proteins that turn cells into stem cells, achieving 50x better efficiency than the original Nobel-Prize winning versions discovered in 2012.
- —Researchers built GPT-4b micro, an AI trained on biological data rather than internet text, to redesign 'Yamanaka' proteins that reprogram aging cells
- —The AI-designed proteins converted the cells into stem cells 50x more efficiently, showing dramatically better DNA repair abilities
- —The results essentially reversed one of the key signatures of aging at the cellular level, with multiple labs validating the results across testing methods
Why it matters: While public models are leveling up users in their own work, custom models trained by domain experts could unlock discoveries that general-purpose AI would never find — turning biology, chemistry, and materials science into computational playgrounds where decades of lab work compresses into weeks.