I coded a Bento Grid tool to easily showcase like top brands

Hacker News

Hi HN, I’m Joan, developer of Gridwow. I created this web app to easily build Bento Grid Layouts, the same style top brands like Apple, Google, and Vercel use to showcase product features on their websites and keynotes. Rich-text, images, videos, and content from X can be added to build the layouts. Before Gridwow, I used tools like Figma and GIMP to create feature showcase images for my side projects. But whenever I needed to update content, the whole grid would break, and I couldn’t easily re-sort the elements. So, I built an app that allows me to easily add content into a grid and then drag, drop, and resize each element to fit your needs. As a bonus, since I sometimes stream on Twitch, I made it possible to pull content from a URL into the grid, which can be used in OBS and other streaming platforms to display Bento designs live. Technical Details: Built with Next.js (frontend & backend), using Cloudinary for media storage, TailwindCSS and Shadcn UI for styling, NextAuth for authentication, Resend for emails, and MongoDB as the database. 'react-grid-layout' handles drag-and-drop and resizing. What’s next? I want to add more features like pulling content from other social platforms (TikTok, Instagram, YouTube, Product Hunt, Reddit, Hacker News) and incorporating real-time elements like live YouTube subscriber counts or Twitch subscribers. I also plan to add even more customization options. Let me know your thoughts and feedback! It’s been a blast working on this so far, and I think it’s just neat :)

Visit product View on Hacker NewsSep 16, 2024

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

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, google · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscribers · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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