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AI 资讯

Escudo

Privacy-First Personal Finance for iOS Your finances. On your phone. Nowhere else. A privacy-first personal finance app that connects your banks, brokerage, and investment accounts into one unified dashboard — entirely on-device, no backend, no account required, no subscription. View on GitHub At a Glance 🏦 Multi-source 🔒 100% On-device 📊 Full picture Banks, Revolut, Trading 212 and more No server. No account. Your data stays in your Keychain. Net worth, spending, investments — all in one place Screenshots Log Insights Budget Transaction Entry Settings About Escudo Built out of two frustrations: every decent finance app costs a monthly subscription, and none of them support Trading 212. Escudo connects your banks, brokerage, and investment accounts and gives you a single view of your net worth, spending, and investments — without your data ever leaving your phone. Key Features Net worth dashboard — aggregated balance across all accounts and investment portfolio P&L Unified transaction log — every account in one feed, auto-categorised Spending insights — breakdown by category and trends over time Budget tracking — per-category budgets with visual progress dials Multi-currency — EUR, GBP, USD with stored exchange rates Recurring transactions — template-based recurring transaction engine Shortcuts support — deep linking via escudo:// URL scheme Integrations Source Method Trading 212 REST API — portfolio, orders, dividends Revolut Enable Banking OAuth 2.0 Bankinter PT Enable Banking OAuth 2.0 SIBS SIBS Open Banking (PT market) CSV import Revolut & Bankinter statements All credentials live in the iOS Keychain — never in UserDefaults, never in iCloud, never on a server. Known Limitations Enable Banking does not expose credit card accounts — only bank accounts and transactions are available through the PSD2 API; credit card balances and transactions are not accessible No token auto-refresh for Enable Banking — manual re-auth when tokens expire Categorisation rules are hard

2026-06-03 原文 →
AI 资讯

I built an open-source AirDrop alternative that works in any browser — no app, no account, no cloud

AirDrop only works between Apple devices. Most alternatives require an app install, a cloud account, or route files through a third-party server. I wanted something simpler: Open a URL → discover nearby devices → send files. So I built LocalDrop — a peer-to-peer file transfer app that works entirely in the browser over local Wi-Fi. GitHub: https://github.com/akshaykdadheech/localdrop Live Demo: https://localdrop-4fddd39fb6ad.herokuapp.com How It Works Devices connected to the same Wi-Fi network automatically discover each other through a lightweight signaling server. Once discovered, WebRTC establishes a direct peer-to-peer connection: Browser A ──► Signaling Server ◄── Browser B └──────── WebRTC P2P ────────┘ The signaling server only helps devices find each other. File transfers happen directly between browsers via WebRTC and are DTLS encrypted, so the server never sees your files. Interesting Challenges Backpressure Handling WebRTC DataChannels on Chromium have a ~16 MB buffer limit. Sending data too aggressively can crash the tab. I solved this using: bufferedAmountLowThreshold Flow control based on drain events Cross-Platform Compatibility Different browsers expose different capabilities. Android Chrome supports the File System Access API iOS Safari does not This required separate file-receiving flows for each platform. Large File Transfers Keeping multi-gigabyte files in memory isn't practical. On Chrome, showSaveFilePicker() is triggered after the transfer completes, allowing transfer progress to remain visible throughout the process without buffering everything in RAM. Tech Stack Svelte 5 + Vite TypeScript WebRTC DataChannel Node.js + ws Docker Self-Hosting git clone https://github.com/akshaykdadheech/localdrop cd localdrop docker compose up -d Then open: http://your-ip:3001 from any device connected to the same Wi-Fi network. I'd love feedback from anyone who's worked with WebRTC DataChannels, especially on mobile browsers. If you find the project useful, a

2026-06-03 原文 →
AI 资讯

How to Integrate the OpenAI API into a Production Express App

Last year I helped a startup integrate the OpenAI API into their product. It was a chat feature — users could ask questions about their data and get natural language answers. The integration took about a day. Three days after launch, the founder messaged me: "Hey, something's wrong. Our AWS bill just showed an unexpected charge." It was $340. For three days. They had 60 users. The issue wasn't a bug — it was that production API usage looks nothing like a tutorial. The tutorial shows you openai.chat.completions.create() and returns a response. The tutorial doesn't show you what happens when users send 500-token messages, when they open 15 browser tabs each maintaining their own chat context, or when one user fires requests 30 times per minute because they think it's broken. This guide covers what the tutorials skip: rate limiting, token counting, cost guards, streaming, error handling with retries, and model selection. These aren't optional additions — they're what separates a demo from a production feature. Why Production Is Different Here's the gap between tutorial code and production code, stated plainly: Concern Tutorial Code Production Code Cost control Not mentioned Token counting, spending limits, model selection by task Rate limiting Not mentioned Per-user and per-IP limits to prevent abuse Error handling try/catch that logs to console Typed errors, retries with backoff, user-facing messages Response delivery Wait for full completion, return at once Streaming via SSE — response appears as it generates Context management Each request is independent Conversation history managed, truncated at token limit Secrets management API key hardcoded or in .env (no rotation) Rotation strategy, usage monitoring, per-feature keys Let's build a production-grade Express API that addresses all of this. We'll go layer by layer. The Architecture ┌─────────────────────────────────────────────────────────┐ │ CLIENT (Browser / Mobile) │ │ POST /api/chat { messages: [...] } │ │ GET

2026-06-03 原文 →
开发者

I Built 10 Developer Tools in One Day - Here They Are (Free, Open Source)

Last week I had a single tool: a JSON-to-TypeScript converter. Today I have 10. I spent a day building a full suite of developer tools - all free, all client-side (nothing leaves your browser), and all open source on GitHub. The Suite DevForge is a collection of tools I built because I kept opening 15 different tabs every day: 1. JSON ? TypeScript Paste JSON, get TypeScript interfaces or types. Supports nested objects, arrays, custom root names. 2. CSV ? JSON Bidirectional conversion with header detection and delimiter support (comma, semicolon, tab). 3. Regex Tester Live regex testing with flags (g, i, m, s, u, y), match highlighting, and replace functionality. 4. Base64 Encoder/Decoder Encode text to Base64 and decode Base64 back to text. Unicode-safe. 5. JWT Decoder Decode JWT tokens into header, payload, and signature components. Read-only - no verification, no security risk. 6. SQL Formatter Uppercase keywords, indentation control. Handles SELECT, JOIN, INSERT, CREATE - the common stuff. 7. Diff Checker Side-by-side text comparison with LCS-based diff algorithm. Color-coded additions and removals. 8. UUID Generator Generate UUID v4 (or v1) in bulk. Copy individual or all at once. 9. Timestamp Converter Convert Unix timestamps to readable dates and back. Shows seconds, milliseconds, ISO 8601, UTC, and relative time. 10. Color Converter Convert between HEX, RGB, HSL, and CMYK. Color picker, presets, named color support. The Tech Stack Zero frameworks. Zero dependencies. Zero backend. Every tool is a single HTML file with embedded CSS and JavaScript. They all run entirely in the browser - no data is sent to any server. Hosted on GitHub Pages. Free forever. Why I Built This I'm a solo developer based in Nepal. Building things that help other developers is how I learn, grow, and (hopefully) earn. The suite is free, but there's a Pro tier ( one-time) that unlocks: File exports Batch operations Custom naming conventions Priority support I also do freelance development

2026-06-03 原文 →
AI 资讯

chroma-vs-qdrant-vs-weaviate-2026

This article was originally published on aifoss.dev --- title: 'Chroma vs Qdrant vs Weaviate 2026: RAG Database Compared' description: 'Compare Chroma, Qdrant, and Weaviate for local RAG in 2026: version snapshots, filtering tradeoffs, hybrid search, quantization, and a clear pick by use case.' pubDate: 'May 27 2026' tags: ["vectordb", "ai", "rag", "python", "opensource"] The three most commonly recommended open-source vector databases for RAG — Chroma, Qdrant, and Weaviate — are not interchangeable. Chroma is a prototyping tool that grew into a real product. Qdrant is a production workhorse written in Rust with the best filtering performance of the three. Weaviate is an enterprise-grade platform with hybrid search and the most built-in integrations. Using Weaviate when you need Chroma adds unnecessary ops overhead. Using Chroma when you need Qdrant means migrating under pressure when your collection outgrows it. Versions covered: ChromaDB v1.5.9 (May 2026), Qdrant v1.17.1 (March 2026), Weaviate v1.37 (May 2026). The quick answer Situation Best choice Local prototyping, notebooks, under 100K vectors Chroma Embedded in a Python process — no separate service Chroma Production RAG with filtering-heavy queries Qdrant Multi-user deployment, concurrent queries Qdrant Memory-constrained deployment at millions of vectors Qdrant Hybrid search (BM25 + vector in one query) Weaviate Multi-modal retrieval (text + images + audio) Weaviate Built-in re-ranking or generative AI modules Weaviate Kubernetes, team-operated, agentic MCP workflows Weaviate Getting from zero to working RAG in 10 minutes Chroma What each tool actually is ChromaDB (Apache 2.0, chroma-core/chroma ) started as a pure-Python embedded database and was rebuilt in Rust for the v1.0 release. The Rust core eliminates Python's GIL bottlenecks and delivers roughly 4× faster writes and queries compared to the pre-1.0 implementation — write throughput went from ~10K to ~40K+ vectors/second in server mode. Chroma's des

2026-06-02 原文 →
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