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CanvasMind is an AI-powered infinite whiteboard that lets users select any region of the canvas and ask AI to explain, simplify, find mistakes, expand ideas, or provide examples. Built with Next.js, TypeScript, Konva.js, FastAPI, and Gemini multimodal AI, with AI responses rendered directly onto the canvas.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Don't be fooled by the simplicity of this canvas - this one had me wanting to bang my head against the wall at times 😅 (I just got so mad I deleted all the crap!)
For this last submission, I chose the "Hobnail Celery vase", by Hobbs, Brockunier and Company.
This one, as you'll see in the video walkthrough in the comments, really struck me, not only for the color but for its translucence, light reflection, and the texture. I could just stare at it for hours 😍
and again, because I really enjoyed imagining a world where two pieces lived together in my previous submission, I also picked the "Bingata Panel with Tree Peonies" by Teruyo Shionara.
This one had the variety of warm tones - from the yellows to the pinks and reds and oranges in between - and was really calling my name. PLUS, seeing it together with the vase, it just clicked something in me 💡
As always, here's the public project link: https://app.flora.ai/view/js7aegs59at1hk640n5yc01m3d8dtjva
My canvas and final video are here - and the walkthrough can be found in the comments 👇
AI agents are most useful when they reduce uncertainty—not when they silently replace judgment.
The Next.js team just used a research agent to review a backlog of 2,244 GitHub issues. In roughly three weeks, 1,462 issues were closed and only 3 were reopened.
What made the workflow credible:
• every investigation ran in an isolated sandbox
• the agent checked code, versions, PRs, commits, releases, and docs
• it actively searched for evidence against its first conclusion
• risky actions stayed read-only until humans reviewed the evidence
• the clearest cases required a second-agent challenge before automation could close them
That is the pattern I trust for production AI automation: evidence first, structured outputs, bounded permissions, and reversible actions.
Where could an evidence-driven agent remove the most operational drag in your workflow?
Where it removed the most drag for me was real-estate contract deadlines - a chain of dates where one addendum moves a date and nothing throws an error. The agent is not there to judge, it is there to show that something moved.
The second-agent challenge is the bullet I would...
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