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AI 资讯

How I built an E2EE chat in Go + React (with AI agent support)

🚀 Try it now: Open the Arthas web app — create a room, share the code, chat with E2EE. No signup needed. TL;DR — Try It in 2 Minutes No signup required. A free public server is running at wss://arthas100-arthas-server.hf.space/ws . 1. Create an encrypted room (CLI) # Linux/macOS — download and make executable curl -L -o arthas-cli https://github.com/michaelwang123/arthas/releases/latest/download/arthas-cli chmod +x arthas-cli # Windows (PowerShell) — download the .exe # curl.exe -L -o arthas-cli.exe https://github.com/michaelwang123/arthas/releases/latest/download/arthas-cli-windows-amd64.exe # Create a room — generates AES-256 key locally, outputs share code ./arthas-cli create --server wss://arthas100-arthas-server.hf.space/ws --name "Alice" # Windows: .\arthas-cli.exe create --server wss://arthas100-arthas-server.hf.space/ws --name "Alice" # Output: # ✓ Room created! Share code: # QYEq9uxfKP9h-KCUsPUay:NlZezXoUErYr92grhif3Y-Hy3FOOK1ocb3WocCJJrQM # # The encryption key never leaves your device. ⚠️ Keep this terminal open — the room exists only while at least one participant is connected. 2. Join from another terminal (or send the code to a friend) # Linux/macOS ./arthas-cli join QYEq9uxfKP9h-KCUsPUay:NlZezXoUErYr92grhif3Y-Hy3FOOK1ocb3WocCJJrQM \ --server wss://arthas100-arthas-server.hf.space/ws \ --name "Bob" # Windows # .\arthas-cli.exe join QYEq9uxfKP9h-KCUsPUay:NlZezXoUErYr92grhif3Y-Hy3FOOK1ocb3WocCJJrQM --server wss://arthas100-arthas-server.hf.space/ws --name "Bob" That's it — you're chatting end-to-end encrypted. The server only sees ciphertext blobs; it cannot read, store, or parse anything. 💡 Prefer a web UI? Open the Arthas web app , create a room, and share the code. Bonus: Connect an AI Agent to the Same Room Every AI agent channel today (Telegram bots, Slack apps, Discord) transmits prompts in plaintext. With Arthas, your AI joins the encrypted room as a regular participant — the server can't tell human from bot (both are encrypted binary blobs). npm

2026-06-03 原文 →
AI 资讯

Why Your LLM Agent Gives a Different P-Value Every Time (And What to Build Instead)

Hand the same paired before/after dataset (n = 25) to ChatGPT five times. Same prompt: "These are the same subjects measured before and after an intervention. Did their scores change significantly?" Four of the five runs return p = 0.009 from a paired t-test. The fifth run does a Shapiro–Wilk normality check on the differences first, decides they're non-normal, switches to a Wilcoxon signed-rank test, and reports p = 0.000018 . All five reach the same conclusion (significant). But notice what happened: only one run out of five thought to check an assumption you'd want it to check. The other four skipped it. The choice of method — and the test statistic, and the p-value — depended on whether the LLM happened to run an assumption check that time. On borderline data, this is the difference between reject and don't reject. If you're using LLMs for exploratory data analysis on a weekend project, you might shrug. If you're using them for anything that gets cited, gets submitted to a regulator, or gets handed to a clinician, this is a problem. It's a known problem — Cui & Alexander (2026) documented exactly this kind of method-divergence empirically; AIRepr (Zeng et al., 2025) shows the same thing across reproducibility metrics. The current answer in the literature is to constrain the agent so its execution is replayable. But replayability fixes "did we run the same code." It doesn't fix "did we run the right analysis." I've spent the last two months building a different fix. The more interesting half is the architecture. Let me walk through it. The real problem isn't temperature The first reflex is "set temperature=0 ." It's not enough. temperature=0 doesn't make a tool-using agent deterministic across runs. Three reasons: Inference isn't bitwise deterministic, even at temperature=0. Production LLM serving batches requests dynamically, and the attention kernels aren't batch-invariant — so the same input produces different output tokens depending on what other requests it

2026-06-03 原文 →
AI 资讯

Under the Hood: Redis Enterprise Cluster

Welcome to another post in the "Under the Hood" series. The power of Redis lies in its simplicity. One thread, one event loop, zero locks . Single-threaded execution eliminates the "lock contention" that slows down traditional databases. Limitation : A single process can only utilise one CPU core. On a 64-core server, 98% of your hardware sits idle. Redis Core Design To scale, Redis Enterprise doesn't make the engine "bigger"; it makes the fleet smarter. Key Design Decisions One Core to Many (Multi-Tenancy) Instead of one massive process, Enterprise runs multiple Redis Cores (shards) on a single node. From Gossip to Proxy Standard Redis Clusters use a Gossip Protocol. The client must "know" the cluster topology and handle redirections. Solution : The Zero-Latency Proxy acts as the "Front Desk". The client talks to one endpoint; the proxy handles the complexity. It is multi-threaded and uses cut-through routing to ensure the "hop" is sub-millisecond. Separation of Concerns (Control Plane) Distributed Cluster Watchdogs oversees failovers and promotions. By separating the Data Path (Redis shards) from the Control Plane (watchdogs), the database can heal itself without interrupting traffic. Note : In the diagram, it may seem the watchdogs are coupled with the Redis shards, but in reality, they just share the hardware space for resource efficiency. Redis Cluster Architecture

2026-06-03 原文 →
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 →
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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