Lead
Google unveiled nine new demo videos of its Gemini Omni and Gemini 3.5 models on May 29, 2026, and simultaneously announced a fix for a quota‑draining bug in the Gemini app.
Context
At Google I/O on May 19, 2026 the company introduced Gemini 3.5, a series of models that blend “frontier intelligence with action,” and hinted at a broader “agentic Gemini era.”Source The same keynote released a dedicated blog post for Gemini 3.5, describing it as the latest step toward more capable, action‑oriented AI.Source A follow‑up post on May 29 shared nine short videos that demonstrate the models in real‑world tasks, ranging from multimodal image analysis to voice‑driven workflows.Source
Impact
The videos give developers a concrete look at how Gemini Omni can process video, audio, and text together, while Gemini 3.5 shows tighter integration with external tools and APIs. Early adopters can now see the models’ limits and strengths before building production services.
However, the same day the videos were posted, The Decoder reported a bug in the Gemini app that let a single or double‑video request consume an entire usage quota. The flaw affected both regular and Ultra members, prompting complaints about rapid quota exhaustion.Source Google responded by fixing the bug, ensuring that failed requests no longer count against users and doubling the number of video generations available to Ultra subscribers. The company also pledged greater transparency around usage metrics.
What’s Next
Google’s roadmap indicates more granular usage dashboards and clearer billing rules for Gemini services in the coming weeks. With the bug resolved, developers can experiment with the nine demo scenarios without fearing unexpected quota loss. The company’s I/O messaging suggests that future Gemini releases will expand the “agentic” capabilities, potentially adding tighter integration with Google Workspace and third‑party platforms.
For now, the nine videos serve as the most detailed public look at Gemini Omni and Gemini 3.5’s practical output, and the quota fix restores confidence for early testers eager to embed the models in their products.
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