Ap

App based on idea from Y Combinator blog

Hacker News

App based on idea from Y Combinator blog

Chasing late and overdue invoices takes time and for my business I've used Google spreadsheet with a list of few overdue and late invoices. It is not convenient to switch into email, send email, then manually update spreadsheet, manually write follow up emails but it was OK for a few invoices per month. Then I saw a blog post on Y Combinator blog: http://blog.ycombinator.com/13-startup-ideas/ where the idea about Account Receivables as a service was mentioned and decided to give it a try. After creating a few mockups I found Ruby on Rails developer and in in more than a month we created a simple version of the online app that is available for public now. Yes, there are some features still missing but as a programmer I know that polishing the app and adding more and more features may take a long time and in the end you may discover that you've created a monster. So, in this app you may add invoices, see which ones are late and send email follow ups and calls. This is a side project for me to help me in my remote work (where I need to deal with overdue invoices) and it works good especially when used from iPhone browser. Programming with Ruby on Rails still rocks and almost every issue can be googled in 5 minutes. The only negative experience was spending 2 days on trying to get a naked domain to work on Route 53 and Heroku. Route 53 still missing the option to set A record as alias and official tutorial is misleading! Finally I found a solution on Stackoverflow luckily and fixed it all in just 20 minutes (tl;dr: you need to setup s3 bucket that redirects to www. version, then setup cloudfront distribution that uses s3 website address and set this cloud front as A record in Route 53). If I could travel myself back in time then instead of programming an app I would try to collect feedback before the actual programming! Any feedback, comments, suggestions are very welcome! The app is here: https://unpaid.io

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Actual performance

2points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
91%91% 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: google, email · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
64%64% 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, google · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
30%30% 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
16%16% 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
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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