Handover

Handover

TrustMRR

Handover is a headless CMS and client handover platform for freelance web designers and agencies. Ship editable client sites with hosted content, media, Markdown docs, blog posts, contact forms, and a

Handover is a headless CMS and client handover platform for freelance web designers and agencies. Ship editable client sites with hosted content, media, Markdown docs, blog posts, contact forms, and a white-labeled /admin, without managing a CMS or database for every project. Scaffold with the CLI, connect an API key, and let clients edit safely on their own domain. A payment-protection lock switch helps protect unpaid work professionally.

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating7

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
74%74% 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 NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
42%42% 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, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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