on Google Gemini AI is quietly elevating the AI ​​landscape, reaching a milestone few thought possible: The simultaneous processing of multiple visual streams in real time.

This achievement – which allows Gemini not only to watch live video feeds but also to analyze static images simultaneously – is not disclosed through Google’s flagship platforms. Instead, it came out of an experimental application called “Any Chat.”

This unexpected leap highlights the untapped potential of The architecture of Geminipushing the boundaries of AI’s ability to handle complex, multi-modal interactions. For years, AI platforms have been limited to handling either live video streams or static photos, but not all at once. With AnyChat, that barrier is completely broken.

“Even Gemini’s paid service can’t do this yet,” Ahsen Khaliq, Gradio’s machine learning (ML) lead and the creator of AnyChat, said in an exclusive interview with VentureBeat. “You can now have a real conversation with the AI ​​as it processes your live video feed and any images you want to share.”

How Google’s Gemini is quietly changing the vision of AI

The technical breakthrough behind Gemini’s multi-stream capability is at its forefront neural architecture — an infrastructure skillfully exploited by AnyChat to process large amounts of visual input without sacrificing performance. This capability is already available in Gemini’s APIbut it is not available in official Google applications for end users.

In contrast, the computational demands of many AI platforms, incl ChatGPTlimit them to single-stream processing. For example, ChatGPT currently disables live video streaming when an image is uploaded. Even managing a video feed can be a drain on resources, especially when it is combined with static image analysis.

The potential applications of this development are as revolutionary as they are immediate. Students can now point their camera at a calculus problem while shows Gemini a book for step-by-step guidance. Artists can share works-in-progress with reference images, receiving nuanced, real-time feedback on composition and technique.

The technology behind Gemini’s multi-stream AI breakthrough

What makes AnyChat unique is not only the technology itself but the way it circumvents the limitations of Gemini’s official deployment. This achievement was made possible by special allowances from Google Gemini APIwhich enables AnyChat to access features that remain outside of Google’s own platforms.

Using these expanded permissions, AnyChat optimizes Gemini’s attention mechanisms to track and analyze multiple visual inputs simultaneously — all while maintaining a conversation. Developers can easily replicate this capability with a few lines of code, as demonstrated by AnyChat’s use of ORGANIZEDan open-source platform for building ML interfaces.

For example, developers can launch their own Gemini-powered video chat platform with image upload support using the following code snippet:

This simplicity highlights how AnyChat is not only a demonstration of Gemini’s potential, but a toolkit for developers looking to build custom visionary AI-enabled applications.

What makes AnyChat unique is not only the technology itself, but the way it circumvents the limitations of Gemini’s official deployment. This achievement was made possible by special allowances from Google’s Gemini team, which enabled AnyChat to access features that remained out of Google’s own platforms.

“The real-time video feature of the Google AI Studio cannot handle uploaded images during streaming,” Khaliq told VentureBeat. “There is no other platform that implements this kind of concurrent processing right now.”

The experimental app that unlocks Gemini’s hidden capabilities

AnyChat’s success was not a simple accident. The platform’s developers have worked hard on Gemini’s technical architecture to expand its limits. In doing so, they revealed a feature of Gemini that even Google’s official tools have yet to explore.

This experimental approach allows AnyChat to handle simultaneous streams of live video and static images, essentially breaking the “single-stream barrier.” The result is a platform that feels more dynamic, intuitive and able to handle real-world use cases more effectively than its competitors.

Why concurrent visual processing is a game changer

The implications of Gemini’s new capabilities go far beyond creative tools and casual AI interactions. Imagine a medical professional showing an AI both live patient symptoms and historical diagnostic scans at the same time. Engineers can compare real-time equipment performance against technical schematics, receiving immediate feedback. Quality control teams can match production line output against reference standards with unprecedented accuracy and efficiency.

In education, the potential is transformative. Students can use Gemini in real time to analyze textbooks while working on practice problems, receiving context-aware support that bridges the gap between static and dynamic learning environments. For artists and designers, the ability to display multiple visual inputs simultaneously opens up new avenues for creative collaboration and feedback.

What AnyChat’s success means for the future of AI innovation

Currently, AnyChat remains an experimental developer platform, operating with extended rate limits provided by Gemini’s developers. However, its success proves that the simultaneous, multi-stream AI vision is no longer a distant dream – it is a present reality, ready for large-scale adoption.

AnyChat’s emergence raises some vexing questions. Why didn’t Gemini’s official rollout include this capability? Is this an oversight, a deliberate choice in resource allocation, or a sign that smaller, more nimble developers are driving the next wave of innovation?

As the AI ​​race accelerates, AnyChat’s lesson is clear: The most important advances may not always come from the tech giants’ vast research labs. Rather, they may come from independent developers who see the potential of existing technologies – and dare to push them further.

With Gemini’s groundbreaking architecture now proven capable of multi-stream processing, the stage is set for a new era of AI applications. Whether Google will fold this capability into its official platforms remains uncertain. One thing is clear, though: The gap between what AI can do and what it officially does is very interesting.