Stintly – Offline-first app for freelancers with on-device AI

Hacker News

I built Stintly, an all-in-one business management app for freelancers, contractors, and self-employed professionals. It runs entirely offline with all data stored locally on your device. What it does: - Invoicing & estimates with on-site signature capture - Expense tracking with receipt scanning and voice input - Time tracking with one-tap timers - Job/project management with photo documentation - Schedule C tax tracking with quarterly estimates - Client management with revenue history - Reports & analytics with PDF export What makes it different: Privacy-first: All data lives on your device. No account required. No tracking, no ads, no data selling. Optional iCloud backup if you want it. Offline-first: Works 100% without internet — SQLite on device. Most competitors (FreshBooks, Wave, QuickBooks) require a connection. On-device AI: 20+ AI features (receipt OCR, voice expense entry, cash flow forecasting, tax optimization, client insights) run locally via Llama on Metal GPU. No data leaves your phone. Tech stack: React Native/Expo, SQLite, NativeWind, on-device LLM via react-native-llama.cpp with Metal acceleration, custom OTA updates via Cloudflare Workers + R2. Pricing: Free tier (5 clients, 5 invoices, unlimited use) forever. Pro at $12.99/mo, Premium with AI at $19.99/mo. Currently on iOS (iPhone/iPad/Mac). Would love feedback from other freelancers and indie devs on what's missing or what you'd want from a tool like this. App Store: https://apps.apple.com/us/app/stintly-invoice-expenses/id675... Website: https://stintly.app

Visit product View on Hacker NewsFeb 25, 2026

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

90%90% 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: mac, apple, apps · Missing: agents, macos, agent

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: llama, 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.

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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

19%19% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: time tracking · Missing: web3, chat, crypto

0%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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