ReSee
AI Expense Copilot with spending insights
ReSee launched on May 10, 2026.
What it does
ReSee is an iPhone app that turns paper receipts into searchable expense records and AI-powered spending insights. Scan a receipt, get item-level details extracted, keep the original image, understand your spending patterns, and export clean reports for reimbursements, tax prep, or personal tracking.
Download Apus: AI Receipt Scanner by MAI LI on the App Store. See screenshots, ratings and reviews, user tips, and more apps like Apus: AI Receipt Scanner.
Free · In‑App Purchases · Designed for iPad. Not verified for macOS. Business Developer MAI LI Language EN + 28 More Size 61.9 MB iPhone, iPad Apus is an ultra-premium, AI-first receipt scanner, expense manager, and mileage tracker that turns messy paper, photos, PDFs, and bills into clean, audit-proof digital records. Built for freelancers, small business owners, corporate travelers, and anyone ready to eliminate manual bookkeeping. By automating line-item extraction and tracking every business drive, Apus helps self-employed professionals reclaim $1,200 to $1,2000+ in annual tax write-offs while saving business owners over 5 hours every single month. ◆ ZERO TYPING, INSTANT PARSING Snap a…from apps.apple.com
Does a similar job
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ReceiptIQ Pro — Snap & Track Spending2025 · receiptiq.me · ▲13Hi HN! I’m a solo iOS dev who kept losing track of where my grocery and shopping money was going, so I built ReceiptIQ Pro. What it does: - Scan any receipt with on-device AI/OCR and instantly categorize the items. - Auto-build spending charts, budgets, and exportable CSVs—no manual typing. - Works offline; iCloud backup/restore optional. No data leaves your phone unless you choose to. - Small extras: warranty reminders, bill tracking, and income logging. Tech bits: - Swift/SwiftUI on iOS. - Apple’s Vision framework + my own ML post-processing. - A lightweight Python backend…
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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