I vibe-coded an Invoicing Follow-up Platform"

Hacker News

Hi HN, I’ve been working on Invoicefy, an AI-powered invoicing follow-up tool that turns overdue bills into natural, context-aware conversations. It uses a GPT-4 Turbo core and time-series analysis (Python + pandas) to tailor reminders—via email or WhatsApp—based on how late an invoice is and each client’s payment habits. Under the hood, it’s webhook-first, so you can plug into Stripe, QuickBooks, Xero, or any custom stack. Every follow-up message, escalation path, and schedule is configurable via API or UI. The dashboard surfaces trends—like which clients are slowest to pay or which days see the most delays—so you can tweak terms or reallocate resources. Future plans include native Slack and Teams integrations, AI-driven dispute workflows, and predictive cash-flow forecasting to help you plan runway. Give it a spin: https://ninja-invoice-ai.lovable.app/ Lmk what you think

Visit product View on Hacker NewsApr 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, lovable, stripe · Missing: mac, agents, macos

89%89% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

26%26% 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

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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