200 summarised stories about Gemini, each linking back to the original source. Browse all topics →
TechCrunch 1 month ago 17
Barret Zoph, co-founder of Thinking Machines and former OpenAI executive, has joined Google as vice president of research. He spent five months at OpenAI, leaving in June. His move shifts his RL and post-training work to Google’s Gemini team.
Google 1 month ago 49 ● 3 sources
Google introduced Gemini Omni 1.1 Flash, a developer-focused generative video suite with studio-quality controls such as scene extension, frame targeting, faster 360p drafting, and 4K upscaling via the Gemini API. Scene extension can use up to 10 seconds of prior context and extend in 10-second increments for up to 40 seconds total cumulative length. Developers can produce longer, more consistent and smoother videos with quicker iteration and lower-cost previews before generating higher-resolution outputs.
The Verge 1 month ago 44
Google’s Gemini Notebook now pulls information from books you’ve purchased and lets you interact with their content.
Product Hunt 1 month ago 48 ● 3 sources
Gemini Omni 1.1 Flash adds a suite of creative controls and generative video capabilities for developers. The model release is named “Gemini Omni 1.1 Flash.” Developers can now build video-generating features with these added controls.
Google 1 month ago 17 ● 2 sources
Google introduced the world’s first double-blind evaluation of a proprietary frontier AI model, using a cryptographic “box” so external evaluators’ benchmarks can’t be used for later optimization. On August 27, 2026, Google said it would test a Gemini Flash Lite model against confidential benchmarks with partners including the Singapore AI Safety Institute, OpenMined, AVERI, and MLCommons. This adds cryptographic safeguards to reduce benchmark contamination and improve trust in the measured model results.
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17 October 2026
Microsoft’s MAI-Base-2 announcement sets the pace for the day, shifting attention from flashy model demos to the plumbing that makes large language models practical. The 2T-parameter base model is designed to become a full LLM after additional training, with 80B parameters activated per prompt. Microsoft says it trained the model for 38 days on 16,000 Nvidia GB200 chips using 50T tokens, and that MAI-Base-2 is up to 12× more hardware-efficient. The company’s licensing plan—availability through “trusted partners” on Azure—signals a platform strategy: build a reusable foundation that can spawn multiple task-specific LLM lines, rather than betting everything on a single downstream model.
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