OK, really excited about this one because it takes a huge step forward in visual context.
Tested it by asking it to find all the red dots in an image. Instead of trying to "eyeball" it (which models usually fail at), Gemini 3 Flash realized that "counting by eye" is imprecise. So it decided to act like an engineer and write a professional OpenCV script to solve it accurately.
The logic flow was fascinating:
Task: Precision counting.
Reasoning: Visual models have error margins -> I should use Python tools.
Action: Filter pixels via HSV color space -> Use findContours to locate them.
This actually blew my mind. Natively realizing the "Perception - Reasoning - Action" loop in vision is critical for real-world apps.
The demos in Google AI Studio are also worth checking out. Definitely some of the most interesting and inspiring visual use cases I've seen.
Impressive direction. The real value here isn’t just “bigger model,” but how naturally Gemini works across modalities. If the text–image–code handoff feels truly seamless in real-world workflows, this could change how people actually use AI day to day — not just experiment with it.
with the 90% cost reduction mentioned, does this apply to multimodal inputs like huge image datasets used as part of a system prompt?
About Agentic Vision in Gemini on Product Hunt
“Agentic visual reasoning with code execution”
Agentic Vision in Gemini launched on Product Hunt on January 29th, 2026 and earned 188 upvotes and 2 comments, placing #7 on the daily leaderboard. Agentic Vision, a new capability introduced in Gemini 3 Flash, converts image understanding from a static act into an agentic process
Agentic Vision in Gemini was featured in Artificial Intelligence (466.5k followers) and Development (5.8k followers) on Product Hunt. Together, these topics include over 90.5k products, making this a competitive space to launch in.
Who hunted Agentic Vision in Gemini?
Agentic Vision in Gemini was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Reviews
Agentic Vision in Gemini has received 144 reviews on Product Hunt with an average rating of 5.00/5. Read all reviews on Product Hunt.
Want to see how Agentic Vision in Gemini stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Zac Zuo
Hi everyone!
OK, really excited about this one because it takes a huge step forward in visual context.
Tested it by asking it to find all the red dots in an image. Instead of trying to "eyeball" it (which models usually fail at), Gemini 3 Flash realized that "counting by eye" is imprecise. So it decided to act like an engineer and write a professional OpenCV script to solve it accurately.
The logic flow was fascinating:
Task: Precision counting.
Reasoning: Visual models have error margins -> I should use Python tools.
Action: Filter pixels via HSV color space -> Use findContours to locate them.
This actually blew my mind. Natively realizing the "Perception - Reasoning - Action" loop in vision is critical for real-world apps.
The demos in Google AI Studio are also worth checking out. Definitely some of the most interesting and inspiring visual use cases I've seen.