Unblocked AI Code Review — High-signal comments based on your team's context
High-signal comments based on your team's context
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Hey Product Hunt 👋
I'm one of the makers of APEX API — a tool we built after realizing how difficult it is to evaluate visual AI models in the messy, unpredictable environments they’re meant to operate in.
🎯 What it does: APEX is an API that helps you automatically red team your object detection models. You define the operational context (e.g. lighting conditions, object angles, occlusions), and we generate data and simulate those conditions to probe for weaknesses and failure modes — all via API.
🧠 Why we built it: Visual AI is being deployed in high-stakes scenarios, but most testing still happens in overly controlled, idealized environments. We wanted a better way to uncover how models behave in context, before they fail in the wild.
💡 Who it’s for: We’ve seen early interest from teams working across a variety of use cases, such as:
Defense: Evaluating aerial object detection systems for spotting vehicles in mountainous terrain — across varying times of day, weather conditions, and camera angles.
Autonomous systems: Stress-testing warehouse robot vision with low-light environments, cluttered shelves, shiny surfaces, and moving obstacles.
AgTech: Testing fruit detection and ripeness classification under inconsistent lighting, natural occlusions (branches, leaves), and different growth stages.
If you’ve ever asked “but will this model actually work in the real world?” — APEX is for you.
Congrats on the launch! Context-aware testing is such a critical piece in building reliable AI systems, glad to see you making it accessible via API. This is going to be a game changer for many teams.
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With the abundance of both supply and demand in off the shelf vision models, it's crucial for the users to stress test such models and to build confidence before deployment in their target applications. Thanks to the YRIKKA team for filling this void and looking forward to some hands on experience with the API!
Hey Product Hunt 👋
I'm one of the makers of APEX API — a tool we built after realizing how difficult it is to evaluate visual AI models in the messy, unpredictable environments they’re meant to operate in.
🎯 What it does:
APEX is an API that helps you automatically red team your object detection models. You define the operational context (e.g. lighting conditions, object angles, occlusions), and we generate data and simulate those conditions to probe for weaknesses and failure modes — all via API.
🧠 Why we built it:
Visual AI is being deployed in high-stakes scenarios, but most testing still happens in overly controlled, idealized environments. We wanted a better way to uncover how models behave in context, before they fail in the wild.
💡 Who it’s for:
We’ve seen early interest from teams working across a variety of use cases, such as:
Defense: Evaluating aerial object detection systems for spotting vehicles in mountainous terrain — across varying times of day, weather conditions, and camera angles.
Autonomous systems: Stress-testing warehouse robot vision with low-light environments, cluttered shelves, shiny surfaces, and moving obstacles.
AgTech: Testing fruit detection and ripeness classification under inconsistent lighting, natural occlusions (branches, leaves), and different growth stages.
If you’ve ever asked “but will this model actually work in the real world?” — APEX is for you.
🔓 Today we’re opening API access for object detection models.
No waitlist. Just sign up, integrate, and start testing.
→ https://github.com/YRIKKA/apex-quickstart
We’re excited to hear what you think! Feedback, questions, use cases — drop them below 👇
That’s amazing, congrats on the launch 🚀 So excited to start testing the API!
@ali_afshar3 Thank you! Please reach out to help@yrikka.com if you have any questions.
Checkout this tutorial for an AgTech use case here: https://github.com/YRIKKA/apex-quickstart/blob/main/notebooks/agtech_example.ipynb
Congrats on the launch! Context-aware testing is such a critical piece in building reliable AI systems, glad to see you making it accessible via API. This is going to be a game changer for many teams.
With the abundance of both supply and demand in off the shelf vision models, it's crucial for the users to stress test such models and to build confidence before deployment in their target applications. Thanks to the YRIKKA team for filling this void and looking forward to some hands on experience with the API!