Hey everyone!
I m excited to share something I ve been building AnnotateAI: a platform that helps you turn raw image datasets into high-quality, model-ready annotations faster, using AI + human-in-the-loop.
While working on computer vision projects, I kept hitting the same bottleneck annotation was either:
Coderrr
Burner
The real-time intervention piece is what makes this worth paying attention to — to add to the point about stop signs, edge cases in annotation are where models quietly learn the wrong thing, and catching that mid-job rather than post-training is a meaningful workflow difference. Curious how AnnotateAI handles disagreement between the AI's labels and the human reviewer — does that signal get surfaced anywhere, or is it just a correction and move on?
Coderrr
@wcrtr Keeping fast prototyping in mind, I kept it pretty straight forward. User corrects and moves on.
auto-annotation cool
til your model learn nonsense
human in the loop ain’t weakness
that’s control
speed without discipline?
just faster mistakes
data clean > hype loud