Andre M

This is such a clever way to bridge the gap between AI models and real-world workflows, especially love the image-to-table flow for things like receipts and handwritten notes. It's impressive how accessible complex vision tasks become when wrapped in a spreadsheet interface.

I recently came across a post by OpenForge discussing how healthcare apps are leveraging AI for diagnostics, UX, and compliance. Made me wonder, do you see AI Sheets being adopted in regulated industries like healthcare or finance, where explainability and audit trails are critical?

Would love to hear how you're thinking about use cases that need both flexibility and traceability.

This is a fantastic deep dive into how Isaac’s Sim2Real pipeline is evolving medical robotics, the SO-ARM starter workflow is especially impressive for speeding up iteration in such a high-stakes domain.

Reading this alongside OpenForge’s Healthcare App Development: A Complete Guide -> recent piece on AI, UX, and compliance trends in healthcare app development really highlights how both hardware and software layers are converging around patient-centered intelligence.

Given that robotics workflows are increasingly handling critical procedures, how do you see design ethics and compliance considerations evolving when these AI-driven systems move from OR simulation to real surgical environments?

Really insightful breakdown, Gal, the level of depth and transparency RiskRubric brings is impressive, especially given how fast the open model ecosystem is growing. At Openforge.io we’ve worked with teams navigating healthcare and finance compliance, and the lack of clear security benchmarks for AI models is often a blocker for adoption.

The six-pillar framework feels like a solid step toward building that missing trust layer. Curious, do you see RiskRubric evolving into something that could be integrated into CI/CD pipelines for real-time scoring during development?

This is a fantastic primer, especially for engineers coming from outside the bio world. The framing around the challenge of separating true signal from population noise is so well articulated. It reminded me of how easily that complexity translates into real-world healthcare tools, where even basic outcome tracking can be skewed by heterogeneity in patient populations.

In this project, openforge.io/mobile-measures-case-study , a PT outcomes platform, we saw how embedding even simple real-time insights into mobile interfaces can transform provider decisions. So it’s exciting to imagine how something like this could evolve into usable tools for clinicians or researchers down the line. Curious if anyone here has explored clinical applications for models like STATE?

Really interesting direction for Claude + Hugging Face integration. One thing that could be explored more is how image generation tools perform under collaborative or production-level use.

Some companies, like OpenForge.com , have already built AI-based art generators with real-time features, it shows there’s a lot of potential beyond individual prompt → image use cases. Tools like this could really change how teams design, iterate, or even co-create content together.