200 summarised stories about Gemini, each linking back to the original source. Browse all topics →
TechCrunch 2 months ago 36 ● 2 sources
Google will allow users to turn off the visible watermark on Gemini AI generations, including images, videos, and songs. The setting will be available for the Nano Banana, Omni, and Lyria models. The visible watermark becomes optional while invisible SynthID watermarking and C2PA metadata remain for content labeling and transparency.
The Verge 2 months ago 8 ● 2 sources
Google is adding a toggle in Gemini and Flow to let users turn off visible watermarks on AI-generated images, videos, and music. The option removes the bottom-right “sparkle” watermark that appears when using the Nano Banana and Omni models. Invisible SynthID watermarks and C2PA metadata will still be embedded even when visible watermarks are disabled.
Trending Topics 2 months ago 41 ● 8 sources
Google released Gemini 3.7 Flash on August 13, a Flash-line model aimed at enterprise coding, agent workflows, and document processing. Until December 31, 2026 it is priced at $0.75 per million input tokens and $3.75 per million output tokens, with a planned doubling of list prices to $1.50 and $7.50 on January 1, 2027 and retroactive discounts for Gemini 3.6 Flash. As a result, API costs for Flash usage drop through year-end, while evaluations continue to show mixed benchmark performance versus rivals.
Latent Space 2 months ago 35 ● 2 sources
Gemini 3.7 Flash was updated, with Latent.Space saying the focus moves back to GDM after a dip in earlier Flash versions. The update was posted on Aug 14, 2026, and it highlights how 3.5 and 3.6 Flash fell behind Claude 4.8+ and GPT 5.5+. As a result, the article reframes Gemini 3.7 Flash as regaining competitive attention versus the newer rival series, though the full details are behind a paywall.
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17 October 2026
Microsoft used today’s update to put hard numbers behind the next wave of “base model” ambition. Its MAI-Base-2 is a 2T-parameter foundation model that activates 80B parameters per prompt, trained for 38 days on 16,000 Nvidia GB200 GPUs across 50T tokens. The company says the result is up to 12× more hardware-efficient, and it intends to license the model to “trusted partners” on Azure as a starting point for a new line of LLMs that can be shaped with additional training.
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