DreamASMR – Generate Veo3 Binaural ASMR Videos from One Prompt

Hacker News

Hi HN, I recently pivoted a generic video-generation side-project into DreamASMR : a web app that turns a text prompt into a fully-downloadable, 16:9 ASMR video with true binaural audio in ~30 seconds. Why niche down to ASMR? 1. Slicing / pouring / tapping videos dominate “oddly satisfying” niches; audio quality is crucial yet underserved. 2. Veo3’s macro rendering + high FPS beats SD/Sora for tiny textures (glass, sand, honey). 3. Most existing tools lack a licensing path for commercial ASMR creators. Key features • Prompt enhancer – backend auto-adds acoustic & visual context so beginners get good results. • Binaural pipeline – converts mono Kie.ai output to left-right stereo; best with headphones. • One-click download – videos stored on Cloudflare R2; server proxy sidesteps CORS. • Credit billing – only charge 1 credit on success, zero on failure. Tech stack • Next.js (App Router, mostly server components) + Tailwind + Shadcn • Veo3 API via Kie.ai proxy • PostgreSQL + drizzle-orm • Cloudflare R2 for storage • Next-Auth (Google / Email) • Cloudflare Turnstile to stop bots • 100 % TypeScript; minimal client-side JS Demo https://dreamasmr.org/ Looking for feedback on 1. Generation quality – which objects/sounds still feel off? 2. Prompt UX – text box vs. template presets? 3. Pricing – would you pay for commercially licensed ASMR loops? 4. Perf / architecture – any bottlenecks in large file downloads or concurrency? Roadmap • Perfect-loop mode • Pure audio ASMR generator • WebM + alpha channel for compositing • Public API / SDK Thanks for reading & ship your thoughts!

Visit product View on Hacker NewsJun 19, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, google, email · Missing: agents, macos, agent

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

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nativeThis product was originally launched on this platform.

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1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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