Update app/src/content/chapters/folding/01-hero.mdx

app/src/content/chapters/folding/01-hero.mdx

@@ -3,21 +3,21 @@ import Note from "../../../components/Note.astro";
 import Wide from "../../../components/Wide.astro";
 import Stack from "../../../components/Stack.astro";
 
-We went from 0% to 90% success rate on autonomous t-shirt folding, and the biggest lever wasn't the model. It was the data.
+We went from 0% to 90% success rate on autonomous t-shirt folding; our secret ingredient? High quality data.
 
 <Sidenote>
-  Read time: ~30 minutes. Each section stands on its own feel free to skip to what interests you most.
+  Read time: ~30 minutes. Each section stands on its own, feel free to skip to what interests you most.
 </Sidenote>
 
-This isn't a model release. It's the full behind-the-scenes of training an open-source bimanual robot to fold t-shirts. Published demos share insights and show polished results, but the reality is messier, and more iterative.
+We're gonna walk you through the behind-the-scenes of training an open-source bimanual robot to fold t-shirts, and release our model at the end. Published demos are usually limited to sharing insights and showing polished results, but the reality is messier, and more interesting.
 
-In this blog we will walk you through the complete journey, not just the final recipe that worked, but also the surprising lessons and the small details that turned out to matter more than we expected. You'll see why cheap 3D-printed leader arms helped more than the large ones, why early data collection is more wasteful than you'd think, and how a trained reward model helped us separate good demonstrations from bad ones.
+So for the sake of interest we're also gonna share the surprising lessons and the small details that turned out to matter more than we expected for this project's success. You'll see how cheap 3D-printed leader arms helped more than the large ones, why early data collection is more wasteful than you'd think, and how a trained reward model helped us separate good demonstrations from bad ones.
 
-By sharing this we hope to contribute to our bigger vision: **democratize robotics and robot learning**. By open-sourcing every piece tools, data, models, and knowledge we want to enable a community that pushes this technology further. We've tried to avoid just listing what we did in favor of telling the story of how we got here. We hope being this open will help close the gap between closed-lab demos and what the open-source community can achieve.
+By sharing this we hope to contribute to our bigger vision: **democratize robotics and robot learning**. We're open-sourcing tools, data, models, and knowledge in order to foster a community that pushes this technology further and help close the gap between closed-lab demos and what you can achieve.
 
 Everything we built for this project [SARM](https://huggingface.co/docs/lerobot/sarm), [RTC](https://huggingface.co/docs/lerobot/rtc), DAgger, [Open Arms](https://huggingface.co/docs/lerobot/openarm), and Open Arms Mini is now merged into [LeRobot](https://github.com/huggingface/lerobot) and ready for the community to use.
 
-Let's jump in does it actually work?
+So, how does it work?
 
 #### Links