LinkedIn Caption Template

Building something new this week! I’m participating in the AI Hackathon organized by Product Space in collaboration with Wispr Flow, and I’m excited to be building alongside some of the sharpest product and tech minds out there. The problem I picked: [One-line description of the problem statement, e.g., “Helping small clinics cut down the time doctors spend on patient notes.”] Why this one? [Your personal connection or insight, e.g., “I’ve watched a family member who’s a doctor spend hours every evening catching up on documentation. That’s time taken away from patients and family. When I saw this problem statement, it felt personal.”] The direction I’m taking: [Your approach in 2–3 lines, e.g., “I’m building a voice-first assistant that listens to the consultation, structures it into clean notes, and flags anything that needs a follow-up. The goal is zero extra typing for the doctor.”] What I’m hoping to learn: [e.g., “How to scope an AI product tightly enough to ship in days, and how to design for trust when AI is handling sensitive information.”] What excites me most about hackathons like this is the constraint. Limited time forces you to focus on what actually matters to the user and cut everything else. I’ll be sharing updates, wins, and probably a few failures along the way. If you’ve worked on something similar or have thoughts on this problem, I’d love to hear from you in the comments! #AIHackathon #ProductSpace #WisprFlow #BuildInPublic #ProductManagement #AI

We shipped it! Over the past [X days/hours], I built [Product Name] as part of the AI Hackathon by Product Space in collaboration with Wispr Flow. Here’s what came out of it. The problem: [One line recap, e.g., “Doctors at small clinics spend hours every evening writing patient notes.”] What I built: [2–3 lines on the product, e.g., “A voice-first assistant that listens to consultations, turns them into structured notes, and flags follow-ups automatically. The doctor just talks; the paperwork handles itself.”] [Demo video / link / screenshots below] How it works: [Brief walkthrough or tech stack, e.g., “Voice input via Wispr Flow → transcription → an LLM that structures the notes into a standard format → a simple dashboard for review and edits.”] What went right: [e.g., “Keeping the scope tiny. I cut three features on day one, and that’s the only reason I finished.”] What didn’t: [e.g., “My first approach to structuring notes was way too rigid. Real conversations are messy, so I had to rethink the prompt design halfway through.”] Biggest takeaway: [e.g., “Users don’t care how clever the AI is. They care whether it saves them time without making them double-check everything.”] What’s next: [e.g., “I’m planning to test this with two clinics over the next month and see if it holds up in the real world.”] Huge thanks to Product Space and Wispr Flow for putting this together, and to [teammates/mentors, if any] for the support and feedback along the way. I’d love your honest feedback on this. Would you use it? What would you change? Drop your thoughts in the comments #AIHackathon #ProductSpace #WisprFlow #BuildInPublic #ProductManagement #AI