(pre-beta SaaS) "Get organized, get Headspace"

Hacker News

I am a software engineer working alone on a passion project, a website that helps you stay organized easily and efficiently. Headspace.io is a webmail, notepad, task list and calendar, all rolled into on. The basic premise is that any data in Headspace can be "tagged" and then all relevant information for a particular tag can be displayed in one screen. This is a pre-beta build and I'm hoping to get some early feedback from the wise and generous HN folks. Would you pay a small monthly fee ($5 maybe) for a service like this? http://app.headspace.io/ (This is pre-production so please do not use real data.)

Visit product View on Hacker NewsDec 30, 2013

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

75%75% 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 released

59%59% 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 · Strong signals: month, monthly · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting

36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%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, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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