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ProductDiv – A Website Editor for Developers

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ProductDiv – A Website Editor for Developers

I've been working on a library for developing websites visually. There are many tools for building websites, but none that fit my workflow as a front-end developer, so I made my own! It is an open source library called ProductDiv and it lets you: - Drag and drop any HTML template into your site. - Change elements with utility classes you can customize - Export clean HTML elements and copy into your source code. - Use in any framework! ProductDiv is developer-first, meaning the configuration for the editor lives in your source code. You can evolve the configuration as your project grows (as it inevitably will!). This lets you standardize design patterns and makes it easy to distribute high quality components to your team. I published a demo at https://awestbro.github.io/ that includes Bootstrap 5 utility classes and some basic templates to experiment with. I would love your feedback on UX and to answer the big question: Would you use this in your project? Why or why not?

Visit productView on Hacker NewsApr 6, 2022
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6points
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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: visual, code, open · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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.

Incorrect prediction on native model

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