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AI 资讯

Siri AI is already changing how I use my iPhone

iOS 27 escaped the developer world today with the launch of the first public beta. I've been testing the new operating system since early June, looking for quirks and seeing if it can live up to the hype Apple promised in the keynote. This year's iOS upgrades are what one might call a Snow Leopard […]

2026-07-14 原文 →
AI 资讯

What Anthropic’s latest AI discovery does—and doesn’t—show

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Anthropic—currently the world’s most valuable AI company, with a nearly $1 trillion valuation—has a reputation for publishing strange and heady research. It’s looking into whether AI models can feel pain, for example,…

2026-07-14 原文 →
AI 资讯

The 6 wildest claims in Apple’s lawsuit against OpenAI

When Apple employees interviewed for jobs at OpenAI, the AI startup's hardware head allegedly asked them to show up with something unusual: components they were working on and unreleased product samples. That's according to a blockbuster lawsuit filed by Apple, which accuses OpenAI of stealing confidential documents, spying on hardware prototypes, and tricking one of […]

2026-07-14 原文 →
AI 资讯

How to Build More Resilient Local-First Applications With AT Protocol Infrastructure

Jake Lazaroff discussed the AT Protocol as a framework for distributed applications beyond social networking. He emphasised a local-first architecture where users maintain data in PDSs while leveraging shared infrastructure for synchronisation and updates. The presentation included experiments showcasing collaborative tools and highlighted the benefits of reduced reliance on app-specific backends. By Olimpiu Pop

2026-07-13 原文 →
AI 资讯

Extracting Invoices From WhatsApp Photos With AI Vision (Apps Script + Google Sheets)

Every logistics and field-sales team runs the same expensive process: a driver photographs a receipt into a WhatsApp group, and a back-office clerk manually types the invoice number, total, and date into a spreadsheet. Hundreds of receipts a week = transcription errors and thousands of wasted hours. AI vision models kill that bottleneck. Here's the pipeline that turns a blurry field photo into clean structured data in seconds. Why vision models beat traditional OCR OCR reads characters. Modern vision models (Claude Vision, Gemini Vision, GPT-4 Vision) read structure — they distinguish a tax ID from a total, and a date from an amount, even on crumpled, angled, or poorly lit receipts. No brittle per-vendor parsers. The pipeline (3–8 seconds end to end) WhatsApp image → Apps Script doPost → forward to vision model → model returns JSON { InvoiceNumber, TotalAmount, VendorName, Date, Category, confidence_score } → confidence routing: > 90 → auto-append to ledger 70–90 → flag for human review < 70 → ask driver to re-photo → write row to Google Sheet (+ link to original image) → auto WhatsApp confirmation to driver The confidence_score is the whole trick — it's what stops bad extractions from silently polluting your ledger. Model selection (this drives your bill) Gemini Vision — cost-efficient default, strong multilingual OCR, great on clean receipts. Claude Vision — highest accuracy on degraded receipts; use for high-stakes flows. GPT-4o Vision — competitive, strong structured extraction. Pattern: Gemini for the first pass, escalate only low-confidence cases to Claude / GPT-4o. The economics ~500 receipts/week: vision API $10–40 + WhatsApp API $30–60 + Apps Script free = ~$40–100/month . Versus a clerk at ~25 hrs/week = $2,000–4,000/month in loaded labor. Per-receipt cost: $0.005–0.02 (compress images to ~1024px to cut it further). Accuracy: 92–97% on legible receipts, 75–85% on handwritten/damaged — hence the confidence routing. Pitfalls to avoid Auto-appending with no c

2026-07-12 原文 →
AI 资讯

Turning WhatsApp Into a Mobile ERP for Field Logistics (Apps Script + Google Sheets)

Field-service software has an adoption problem: drivers won't use it. Heavy app, another login, crashes in low-signal areas. So the "real-time" data still shows up as end-of-shift phone calls. The fix that actually sticks: stop building an app and use the one drivers already live in — WhatsApp. With Apps Script and Google Sheets behind it, WhatsApp becomes a frictionless mobile ERP. Here's the build. WhatsApp as a data-entry terminal A driver texts Status ABC-1234 Delivered . An Apps Script doPost webhook receives it, parses it, and updates the Sheet in real time. Latency goes from hours to milliseconds — and there's nothing to install, so adoption hits 90%+ in a week (vs. 50–70% for custom apps). Two-stage parsing for messy input Real drivers type "done," not clean commands. So: Regex first pass — handles ~70% of messages (clean format) instantly and for free. LLM fallback — the remaining ~30% goes to a cheap model (GPT-4o-mini / Gemini Flash) with the known cargo IDs and valid statuses. It returns normalized JSON + a confidence score. Below-threshold messages surface to a dispatcher. The LLM normalizes correctly 95%+ of the time (~5% manual), and it handles multilingual input with zero extra code. Driver msg → Apps Script doPost → regex pass → (fail) LLM fallback w/ confidence score → Sheet update (timestamp + raw-message log) → optional outbound (route change, POD photo request) Why Google Sheets is the right backend Dependent formulas: time-to-delivery, SLA-breach flags Pivot tables for reporting Apps Script triggers for automatic client emails Conditional formatting dashboards Native Calendar / Maps / Drive integration (POD photos → Drive folder) It runs on free Google Workspace infrastructure with minimal API cost. Bidirectional by default The same integration pushes messages back to drivers: route changes, delivery instructions, shift reminders, exception alerts, proof-of-delivery photo requests — all in the same thread. Pitfalls that get your number banned T

2026-07-12 原文 →
AI 资讯

How to Develop a Mobile App? 📱 | A Step-by-Step Guide for Beginners

Hello DEV Community! 🚀 In my last post, I shared my passion for App Development. Today, I want to talk about the actual process of building an app. Whether you want to build an Android or iOS app, the core workflow remains the same. Here is a step-by-step roadmap for anyone starting out: 1. Planning and Research 💡 Before writing a single line of code, you need a clear idea. Identify the problem: What problem does your app solve? Target Audience: Who will use this app? Feature List: Write down the core features (e.g., login, dark mode, notifications). 2. UI/UX Design 🎨 Design is how your app looks and feels. Sketch your ideas on paper first. Use tools like Figma or Adobe XD to create wireframes and visual mockups. Keep the user interface clean and easy to navigate. 3. Choosing the Right Tech Stack 🛠️ You need to decide how you will build the app: Native Development: Use Kotlin/Java for Android, or Swift for iOS. Cross-Platform Development: Use Flutter (Dart) or React Native (JavaScript) to build for both Android and iOS with a single codebase. 4. Development (Coding) 💻 This is where the magic happens! Frontend: Building the screens and visual elements that users interact with. Backend: Setting up servers and databases (like Firebase or Node.js) to store user data, login details, etc. 5. Testing and Publishing 🚀 Before releasing it to the world, you must test it thoroughly. Test for bugs, crashes, and performance issues. Once everything is perfect, publish it on the Google Play Store or Apple App Store . Conclusion 🤔 App development takes time and patience, but seeing your app live on a smartphone is an amazing feeling! What framework are you using for your app development journey? Let me know in the comments below! 👇

2026-07-12 原文 →