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Wagtail, our new Django CMS
Hacker NewsWagtail, our new Django CMS
Wagtail is a new, open-source Django CMS focused on flexibility and user experience. It was originally built for the Royal College of Art, but now we're able to share it with everyone. See https://github.com/torchbox/wagtail and http://wagtail.io (marketing site) and http://www.rca.ac.uk/ (first big site built on Wagtail) We're really proud of the user interface, and we hope that Wagtail will make it easy for Django developers to build beautiful, modern sites. Feedback very welcome! Tom Dyson, Technical Director at Torchbox (Wagtail developers)
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, open · Missing: mac, agents, macos80%80% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive37%37% 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, revenue15%15% 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, crypto0%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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