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GitHub热门项目 | ❤️ Fredy - [F]ind [R]eal [E]state [D]amn Eas[y] - Fredy keeps searching for new apartments, houses, and flats in Germany on platforms like ImmoScout24, Immowelt, Immonet, eBay Kleinanzeigen, and WG-Gesucht and instantly delivers the results to you via Slack, Telegram, Email, Discord or ntfy, so you can focus on the more important things in life ;) | Stars: 1,038 | 41 stars today | 语言: JavaScript

GitHub热门项目 | Unlimited FREE AI coding. Connect Claude Code, Codex, Cursor, Cline, Copilot, Antigravity to FREE Claude/GPT/Gemini via 40+ providers. Auto-fallback, RTK -40% tokens, never hit limits. | Stars: 15,880 | 287 stars today | 语言: JavaScript

GitHub热门项目 | 🎬 seedance2接入 开源本地 AI 短剧 & 漫剧生成工具 —— 从故事到成片一站式完成，数据不出本机，短剧工作流管理平台，高灵活度，AI真人剧，AI漫剧本地搞定。 Open-source local AI short drama maker: story → storyboard → video, fully offline, your data stays yours. 纳米流水线 | Stars: 512 | 18 stars today | 语言: JavaScript

GitHub热门项目 | Stealth headless browser for AI agents — bypass Cloudflare, bot detection, and anti-scraping. Drop-in Puppeteer/Playwright replacement. | Stars: 6,199 | 103 stars today | 语言: JavaScript

GitHub热门项目 | Talk to any LLM with hands-free voice interaction, voice interruption, and Live2D taking face running locally across platforms | Stars: 8,047 | 65 stars today | 语言: Python

GitHub热门项目 | AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents | Stars: 2,641 | 46 stars today | 语言: Python

GitHub热门项目 | OCR, layout analysis, reading order, table recognition in 90+ languages | Stars: 20,463 | 93 stars today | 语言: Python

GitHub热门项目 | Code for Machine Learning for Algorithmic Trading, 2nd edition. | Stars: 18,163 | 570 stars today | 语言: Jupyter Notebook

GitHub热门项目 | | Stars: 6,231 | 31 stars today | 语言: Python

Thermacell has launched Liv 2.0, the next generation of its Wi-Fi-connected smart mosquito protection system. It features new hardware and can cover a larger area, and Thermacell says its formula can now deter no-see-ums. But it's also more expensive and requires professional installation. Liv 2.0 uses the same setup as the original Liv - a […]

Nintendo is trying to bring back Fox's mojo with a beautiful remake of the series' best game.

The CSS ::search-text pseudo-element selects the matching text from your browser's "find in page" feature. ::search-text originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Four days into a new supplier's first batch, my invoice extraction agent had filed 31 documents with amounts shifted by a decimal. Nothing raised an error. The downstream system accepted every record. The agent returned a 200 each time. The demo had run on five clean PDFs. Clear fonts, properly formatted dates, consistent layout. The extraction agent pulled vendor name, amount, due date, line items. Every field populated, every output valid. I ran it for the stakeholder meeting and it looked exactly like something you would ship. Three months in, the agent had processed around 800 invoices without complaint. Then a new supplier switched to scanned documents. Slightly rotated, thin fonts, OCR doing what it could on degraded source material. The model found text that resembled amounts and dates, and returned confident structured output. 1,247.50 read as 12,475.0. A due date resolved to a valid date three years in the future. The confidence was the problem. The model had no mechanism to say it was uncertain. It just answered. Nobody caught it for four days. What I built after The problem was not the model. The model did what it was designed to do. Find structure in text and return it. The straight pipeline from input to output had no gate in it. The fix was not more prompting or a better model. I added a validation layer between the agent output and the downstream system. It runs synchronously, takes about 80ms, and checks four things: Every required field is non-null. Amounts parse as positive numbers within a configured range for that supplier type. Dates fall within a 90-day future window. Extracted totals are consistent with line item sums, within a small tolerance. Anything failing a check routes to a review inbox instead of the queue. A human looks at it, corrects it if needed, marks it resolved. The system logs which check triggered and what the input looked like. In the first week after deployment, the layer caught 23 documents out of about 1,400. Eleven were b

TL;DR : Crypto arbitrage windows on liquid pairs now close in under 100 ms. A REST polling loop typically takes 1–1.5 seconds round-trip. WebSocket delivers the same data in 20–100 ms. If you're still polling REST endpoints for orderbook data in 2026, you're missing the majority of opportunities — not because your strategy is wrong, but because your data plane is fundamentally too slow. This post walks through the math, shows a benchmark I ran on a handful of major exchanges, and provides production-grade Python code for a WebSocket client that handles reconnects, heartbeats, and orderbook reconstruction. 1. The numbers that broke REST polling When I started writing crypto arbitrage bots a few years ago, polling Binance's REST API every 500 ms was perfectly acceptable. Spreads were wide, arbitrage windows lasted multiple seconds, and the orderbook for BTCUSDT moved slowly enough that a half-second-old snapshot was still tradeable. In 2026, the same approach doesn't work. Here are the numbers as they stand today: Metric Value Median crypto arbitrage window on liquid pairs 30–80 ms Window closes in under 100 ms ~90% of cases REST round-trip latency (request → response → JSON parse) 1.0–1.5 seconds WebSocket update delivery latency (push from exchange to client) 20–100 ms The math is brutal. A 100 ms window cannot be caught by a 1500 ms poll. By the time your REST response arrives, the orderbook you're reading is 15 cycles stale. You're not "slow" — you're not even in the same temporal universe as the event you're trying to react to. 2. Why REST is fundamentally slow REST APIs over HTTPS carry overhead that adds up: TCP handshake — three packets to establish, typically 50–150 ms on intercontinental hops. TLS handshake — another full round-trip, 30–100 ms. HTTP request/response — the actual data exchange. JSON parse — depending on payload size, 5–50 ms. Rate-limit budget — most exchanges cap REST to 10–20 requests per second per IP. Polling faster gets you banned. Yes,

Kyle Lexmond explains how to handle the high-pressure environment of severe production outages. He discusses the critical distinction between mitigation and root-cause resolution, sharing personal experiences from harrowing incident rooms. He shares valuable operational strategies on overcoming cognitive overload, establishing blameless cultures, and optimizing systems for faster recovery. By Kyle Lexmond

Meta-Optimized Continual Adaptation for coastal climate resilience planning with zero-trust governance guarantees It started with a nagging feeling of inadequacy. I was deep into a research project on adaptive AI for infrastructure planning, studying how reinforcement learning agents could optimize sea-wall placements and evacuation routes. The models worked—beautifully, in fact—on static datasets. But the moment I fed them real-time satellite imagery of a rapidly eroding coastline or a sudden storm surge, they stumbled. They forgot previous strategies, overfit to the new event, or, worse, made decisions that violated basic safety constraints. I realized then that the problem wasn't just about better AI; it was about trust and adaptation in the face of chaos. My exploration of this challenge led me down a rabbit hole of meta-learning, continual learning, and cryptographic governance. What emerged was a framework I now call Meta-Optimized Continual Adaptation (MOCA) with zero-trust governance guarantees—a system designed not just to learn, but to learn how to learn in dynamic, high-stakes coastal environments, all while ensuring that every decision is auditable and tamper-proof. This article shares that journey, the technical breakthroughs, and the hard-won lessons from my experiments. Technical Background: The Three Pillars of MOCA The core insight behind MOCA is that coastal climate resilience planning requires three seemingly contradictory properties: Continual adaptation – The system must update its models as new data streams in (e.g., sea-level rise, storm frequency, erosion patterns) without catastrophic forgetting. Meta-optimization – It must learn the learning algorithm itself, so that adaptation becomes faster and more sample-efficient over time. Zero-trust governance – Every model update and decision must be cryptographically verifiable, with no single point of failure or authority. In my research, I found that existing approaches tackled these individually

We Scanned 100 AI Repos on GitHub. Here's What We Found. A drone firmware project with 3× more stars than the real one. A crypto protocol that turned GitHub into a points farm. A README with 6,289 stars and 2 commits. As a developer turned architect, I used to treat GitHub stars as a proxy for trust. More stars meant more legitimate, fewer reasons to question before cloning. That instinct got me thinking. So I built TrustStar , audited hundreds of repos, and found that some people had figured out that instinct before me. Here's what the data showed. Case 1: The Airdrop Farm (QuipNetwork) 🔴 DANGEROUS Repository Stars Forks Fork/Star ratio hashsigs-py 11,200 9 0.0008 hashsigs-rs 11,300 42 0.0037 hashsigs-ts 11,300 31 0.0027 hashsigs-solidity 11,300 33 0.003 quip-protocol 11,645 159 0.014 ethereum-sdk ~11,400 72 0.006 cpp-sdk ~11,300 44 0.004 Six repos in completely different languages (Python, Rust, TypeScript, Solidity, C++) all converging on exactly ~11,300 stars. Projects with genuinely different audiences don't do that. The mechanism was on their own website: "Each GitHub repo star earns 5 QUIP points." QuipNetwork launched a crypto airdrop in early February 2026. Users who wanted QUIP tokens starred every repo in the organization. 11,000 stars in 48 hours, after five months of zero activity. The tell: dashboard.quip.network has 2 stars. nodes.quip.network has 2 stars. The repos they forgot to include in the airdrop show the real numbers. This is the first documented instance of a crypto airdrop using GitHub as a gamification layer. These aren't bots. They're real users who just wanted tokens. Case 2: The Typosquat (ShlkOfTheRa/scarab-osd) 🔴 DANGEROUS. The most dangerous case in this dataset. ShikOfTheRa/scarab-osd is a legitimate drone flight controller firmware project. 468 stars, built over 10 years. ShlkOfTheRa/scarab-osd , one character different, was created March 3, 2026. Byte-for-byte identical code. Twelve days later, 1,485 stars purchased in a 90-minute

Installs your API into a customer's codebase in one command Discussion | Link

Turn photos into 3D Gaussian Splats on your Mac Discussion | Link

There are only 3 LLM API protocols, but unlimited providers running the same protocol. Separate protocol from identity — protocol is code, provider is data — and complexity drops from N×M to N+M. 300 lines of TypeScript. Zero dependencies. The problem isn't "it doesn't work." It's "it won't tell you it broke." In May, I built a Claude Code skill called unblind . I use DeepSeek as my daily driver, but it can't see images. So unblind forwards images to Mimo and OpenAI's vision APIs. The MVP had two providers. A few dozen lines of if-else. It worked. Then I noticed something more unsettling: an expired API key — no warning. A network hiccup — no retry. A missing permission — silently skipped. This tool didn't fail. It quietly stopped working without telling you. I added Phase 0 self-healing, circuit breakers, persistent caching, and a security sandbox. Now unblind wouldn't fail silently. But then I noticed something else. The circuit breaker doesn't care if you're calling a vision API or a translation API. The cache doesn't care if the response is an image description or OCR text. The error normalization doesn't care whether the other end is Mimo or OpenAI. A universal provider infrastructure, trapped inside a vision skill. First attempt: follow the ecosystem, hit the ceiling The largest similar project in the ecosystem is vision-support, with 19 providers. The pattern is standard—base class + subclasses, GoF Template Method. I followed it for v2.0. class BaseProvider { async analyzeImage ({ image , prompt , options }) { const { url , body , headers } = this . _buildRequest ( image , prompt , options ); const res = await apiRequest ( url , { body , headers }); return { content : await this . _parseResponse ( res ), model : this . _model }; } } class MimoProvider extends BaseProvider { ... } // 54 lines class OpenAIProvider extends BaseProvider { ... } // 45 lines class GeminiProvider extends BaseProvider { ... } // ~50 lines One subclass per provider. I expanded unblin