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Which of Kuse’s strategies would you borrow?.– Clear “workspace” positioning?.– Multimodal product hook?.– Demo-first launch?.

6. Lessons for builders.– Don’t just build another chatbot → frame it as a workspace or system.– Show, don’t tell → launch with demo-first storytelling.– Lean into trends, but pick an angle (visual + collaborative AI in this case).

5. Why it worked.✅ Solves a real pain: turning scattered content into usable insights.✅ Unique differentiation: AI-native canvas, multimodal inputs.✅ Strong demo appeal: screenshots/videos make the value obvious.✅ Positioned as a platform, not just a feature.

4. Smart launch strategy.– Polished landing page with strong visuals.– Demo videos showcasing multimodal magic.– Early adopters amplified the “wow” factor by sharing their outputs.– Clear free trial + upgrade path.The launch wasn’t just “here’s a tool”—it was “see how much faster.

3. Trend timing + community fit.The AI productivity wave is hot, but crowded. Kuse leaned into:.– Visual AI (charts, mindmaps, infographics).– Collaboration (shared canvases, synced notes).It speaks to students, teams, and knowledge workers who outgrew plain chatbots.

2. Unique product experience.Most AI tools focus on single-modal input (just text or code). Kuse goes multimodal:.– Drag-and-drop docs, PDFs, images, YouTube links.– AI extracts insights and transforms them into visual canvases.– Users can rearrange, edit, and keep iterating.This.

1. Clear positioning.Kuse’s pitch is instantly understandable:.“Your all-in-one AI efficiency space.”.Instead of another chatbot, Kuse framed itself as a workspace where AI:.– Summarizes docs, videos, & images.– Generates charts, mind maps, HTML pages.– Enables collaboration in.

Kuse isn’t just another AI note app. It launched as a one-stop AI productivity hub—and quickly climbed to #1 Product of the Day. Here’s why the launch gained so much traction 👇.

Case Study: How @kuseHQ Hit #1 on Product Hunt with an “AI Workspace for Everything”

The garden is evolving.

This isn’t random. Some companies are far more vulnerable than others. The question leaders must ask: Is my product at risk of AI disruption?.That’s where the AI Disruption Risk Assessment comes in. (Part 2 👇.

AI disruption isn’t evenly distributed, but no product is fully insulated. Some can integrate AI slowly. Others face collapse if they wait. Full analysis by @ravi_mehta on Reforge 👇.🔗

The same logic applies across growth, defensibility, and monetization. AI reshapes acquisition channels, erodes weak moats, and compresses pricing power. The outcome: a clear AI Vulnerability Score — showing how urgently you need to adapt.

Take Use Case Risk:.•Is your product a primary workspace (Figma) or an adjacent tool (Jasper)?.•Do customers want “outlier” quality (Procreate) or “commodity” answers (Chegg)?.•Does human judgment matter, or can pattern recognition replace it?

In his framework, @ravi_mehta lays out 4 dimensions to evaluate AI disruption risk:.1️⃣ Use Case.2️⃣ Growth Model.3️⃣ Defensibility.4️⃣ Business Model.Together, they create an AI Vulnerability Score. Here’s the breakdown 👇.

Stack Overflow is the cautionary tale. Developers still had the same problem — but they found a faster, more integrated solution in GitHub Copilot + ChatGPT. The once-virtuous loop of Q&A → answers → more Q&A snapped, and traffic collapsed.

Why now?.Customer expectations used to rise gradually. But with AI, the PMF threshold spikes almost instantly. It took ChatGPT just 5 days to reach 1M users. The bar for “wow” resets overnight.

Ravi Mehta (@ravi_mehta), ex-CPO of Tinder and Reforge partner, just published a sharp analysis on AI Disruption Risk. He explains why products like Chegg and Stack Overflow collapsed almost overnight — and what that means for the rest of us. Here’s the summary 👇.

RT @alexcooldev: B2C App Build & Marketing Playbook. 1. Build fast → MVP in weeks, not months. Ugly but usable. 2. Pick 1 channel → Don’t….