标签：#p

I build algorithmic trading bots as a side project. Nothing fancy — just small strategies that trade US equity options automatically. The problem I kept running into wasn't the strategy logic. It was the data. Every time I wanted to pull real-time options chains, Greeks, or IV, I had two options: Pay $99+/mo to a data provider Scrape something I probably shouldn't be scraping Neither felt right for a hobbyist project. So I built Market-Options — a simple REST API for US equity options data at $20/mo. What It Does It's a plain REST API. No SDK, no special client library — just HTTP requests and JSON responses. It covers four endpoints: chain — full options chain for a given underlying contract — data for a single contract contracts — batch lookup across multiple contracts contract-overview — Greeks, IV, expiration details Coverage is the top 100 US equity underlyings, which accounts for roughly 95% of actual US options volume. If you're building a bot that trades SPY, QQQ, AAPL, TSLA, or anything in that tier — it's covered. Why Only 100 Underlyings? Because that's what most people actually trade. When I looked at my own bots, and at what most retail algo traders focus on, the top 100 covers everything practical. Exotic underlyings with low volume are also harder to get real data on reliably — so rather than promise coverage I can't deliver, I focused on doing the core well. A Simple Example curl "https://api.market-option.com/chain?symbol=SPY&expiration=2025-01-17" \ -H "Authorization: Bearer YOUR_API_KEY" Response is clean JSON: { "symbol" : "SPY" , "expiration" : "2025-01-17" , "options" : [ { "strike" : 480 , "type" : "call" , "bid" : 3.45 , "ask" : 3.50 , "iv" : 0.182 , "delta" : 0.42 , "gamma" : 0.031 , "theta" : -0.18 , "vega" : 0.29 } ] } No parsing headaches, no weird date formats. Pricing Free tier : 1,000 credits/day — enough to test and build Pro : $20/mo, unlimited within fair use Trial : New accounts get 7 days of Pro, no credit card required Who It's F

On the new episode of Equity, we discussed what actually prompted the administration's latest moves against Anthropic, and what this might mean for the AI ecosystem.

We all know the grind of working with data, even with AI tools: every experiment starts with re-explaining everything, every iteration needs you to prompt, wait, review, correct, and repeat. And the moment you close the session, everything learned is gone. It makes us the bottleneck, and this hinders human-AI collaboration... So I built 𝐎𝐩𝐞𝐧𝐃𝐚𝐭𝐚𝐒𝐜𝐢, an autonomous agent purpose-built for DS/ML, and tested it on Kaggle. I enrolled in a recent competition, ran the agent with no hints, no guidance, while ironing my shirts. In one shot, it landed AUC 0.95, a top-30% finish out of 3K+ teams and 36K+ submissions using hashtag#Anthropic's Claude Sonnet 4.6. (More on this in README) The top-1 outperformed this agent by merely 0.004, but at the cost of massive manual effort even while using popular AI tools. The needed a dozen model families, deep learning, 400-feature notebooks, AutoML sweeps across many libraries, and 186 models ensembled carefully. Essentially a few weeks worth of effort and time!! OpenDataSci abstracts away all the complexity and has so much to offer for DS/ML automation: → Owns the entire development lifecycle from EDA to final evaluation → Plans, codes, and executes autonomously in a secure local sandbox → Self-reviews and corrects before anything reaches you → Remembers your data across sessions, gets smarter each run → Runs parallel experiments and ensembles → Has advanced context management for token efficiency and quality → Ships with predefined skills for DS/ML, so it knows how to do things right → Bring your own knowledge: out-of-the-box support for custom skills → Works with any major LLM provider (hashtag#Anthropic, hashtag#OpenAI, hashtag#Bedrock, hashtag#VertexAI, hashtag#Ollama, hashtag#vLLM, and any OpenAI-compatible server). This and so much more!! You set the goal. It does the work. No data science knowledge required. 🔗 https://github.com/f4roukb/open-data-sci 📦 pip install open-data-sci Spin it up on your data and see what it achieves!

Somewhere right now a guy at a bar is making a stranger sign an NDA on a napkin. For an app idea. Just sit with that. That napkin is going in a drawer. The drawer is a graveyard. Quick tour. Exhibit A: bro we should open a bar Two beers in, you're suddenly a hospitality mogul. Picking a name. Arguing about taco night. By the time the check comes, the bar is already dead. It died of "let's talk about this again soon," which never happens. Exhibit B: bro we should start a band A guitar shows up at a party. Someone says "we should actually start something." One rehearsal happens. In a garage. A neighbor complains. The band dies before it has a name or a single original song. RIP. Exhibit C: the app idea, may it rest in your Notes app The big one. Open your Notes app, it's a cemetery. "App that reminds you to text people back." Dead. "Tinder but for gym buddies." Dead. These ideas didn't fail. They never even got born. Why none of these make it out alive The ideas aren't even bad. Some are genuinely good. The problem is ideas are free and easy to say out loud. Building one makes it real, and real things can fail in public with your name on them. Saying "we should open a bar" costs nothing. Actually opening one costs $400,000, a liquor license, and every Saturday for five years. Guess which one people actually do. Building also got way easier, which makes this worse. You don't need a technical cofounder anymore. You can describe an app to a chat box and watch a prototype show up before your coffee's cold. The wall that used to stop people is mostly gone. People are still standing where it used to be, out of habit. A small ceremony for the ones we lost HERE LIES: "the app idea I had in the shower" born: tuesday died: tuesday, when I got out of the shower HERE LIES: "our band" born: one guitar, one party died: one noise complaint HERE LIES: "the bar we were gonna open" born: 1:47am died: 1:48am, checked the bill Two graves that still have a heartbeat Here's the part nobody

An App Store rating looks like a verdict. It behaves more like a monument, built over years and slow to move. It says very little about how this month's users feel. I took the 12 most-rated Productivity apps on the US App Store, 32 million ratings between them, and split the headline star into the two numbers it hides: how far recent sentiment has fallen below the lifetime average, and whether the developer replies when users complain. How it is measured Population truth. Lifetime ratings and the star histogram come from Apple's full ratings data, every rating an app has ever received. Recent sentiment. A fixed window of the most recent reviews by date, so an app captured to a depth of thousands is not compared on a multi-year average against an app with a few hundred. Same window for everyone. Developer response. Reply share and median latency over that recent window. Complaints are bucketed with a rule-based taxonomy. It is a heuristic, not a trained classifier, and I treat it as one. What turned up The AI assistants now own this chart, and they reply to almost no one. App Lifetime Recent Reply share ChatGPT 4.8 4.18 0% Claude 4.7 3.06 0% Grok 4.9 3.77 0% Perplexity 4.8 3.60 0% Google Gemini 4.7 3.65 13% Dropbox 4.8 2.75 58% Gmail 4.7 2.40 26% Google Drive 4.8 3.90 23% Microsoft Authenticator 4.7 2.18 1% The older tools are the ones still in the trenches: Dropbox answers 58% of recent reviewers, Gmail 26%, Drive 23%. The steepest recent drops belong to Microsoft Authenticator (4.7 to 2.18), Gmail (4.7 to 2.40) and Dropbox (4.8 to 2.75). Plotted on two axes, backlash against response, every app falls into one of four archetypes: Firefighters, Ghost Ships, Complacent Giants and Resilient Leaders. Eight of the twelve are Ghost Ships, taking a recent hit in near silence. The honest limits Recent reviewers self-select toward the dissatisfied. A person who hits a bug is far more likely to leave a review than a contented one, so a low recent average blends genuine declin

Why Bohmian Mechanics, Go Programs, AI, and EBP 2.1 Could Help Reopen the Deepest Questions in Physics There is a quiet crisis in theoretical physics, and it has nothing to do with the equations. The equations are fine. Quantum mechanics predicts with staggering accuracy. General relativity bends light exactly as calculated. The Standard Model matches experiment after experiment. The mathematics is not the problem. The problem is what happens between the equations and the claims. A researcher writes a beautiful paper. The math is correct. The toy model works. A suggestive ratio appears. An analogy crystallizes. And then, in the discussion section, a modest result becomes a bold narrative: classical cosmology is recovered , the problem of time is resolved , spacetime emerges from the quantum . This is not fraud. It is not even intentional. It is the natural gravity of theoretical work — ideas fall toward overclaiming the way matter falls toward mass. Two small codebases, written in Go and governed by an epistemic protocol called Elephant Bridge Protocol v2.1 , are trying to build a tool against that gravity. They are not trying to solve quantum gravity. They are trying to make it harder to pretend you have solved quantum gravity when you haven't. One is called Bell–MIPT . It builds toy models connecting Bohmian mechanics to measurement-induced phase transitions in many-body quantum systems. The other is called BMC — Bohmian Minisuperspace Cosmology. It builds toy models of quantum cosmology using Bohmian guidance in Wheeler–DeWitt minisuperspace. They share the same philosophy. They share the same protocol. And they share the same radical commitment: no claim may be promoted until its debts are paid . What Is BMC? BMC stands for Bohmian Minisuperspace Cosmology . The name is deliberately modest. It is not "Bohmian Quantum Gravity." It is not "The Theory of Everything in Go." It is a cosmology toy model — a wind tunnel, not an airplane. The physics idea behind it is o

If you've worked with Angular Material, you know theming can be one of the trickiest parts of the library — especially after the move to Material 3. Token-based theming, custom palettes, dark mode, component-level overrides... there's a lot going on under the hood. I built a full course to break it all down, and I'm excited to announce it's now completely free . What's in the course Angular Material Theming System is a deep, practical walkthrough of Angular Material's theming API for Material 3. By the end, you'll be able to: Build and customize themes from scratch Apply themes at the application level Override and extend themes for individual components Work confidently with Angular Material's theming tokens and APIs It's 46 lessons and roughly 4.5 hours of content, all hands-on and example-driven. Where to find it 🎥 Watch on YouTube: https://www.youtube.com/playlist?list=PLOjtJUnDeEIyaeUs_jrxylnD2IxSb3Ku7 📝 Read the article version: https://angular-ui.com/courses/angular-material-theming/ 💻 Full source code on GitHub: https://github.com/Angular-UI-com/angular-material-theming If you're building with Angular Material and theming has ever felt like a black box, give it a watch. I'd love to hear your feedback in the comments. If this helped you, consider checking out Angular Material Blocks — a library of pre-built Angular Material + Tailwind components, available via a simple CLI.

submitted by /u/yusufaytas [link] [留言]

Choosing a learning path is one of the most critical decisions you make as a beginner. When I set out to learn web development, I knew exactly what I needed: resources that were thorough, accessible 24/7, and completely free—both online and in print. Before settling on my 2026 learning path, I experimented with a few popular options. Here is a look at what I tried, why they didn’t quite stick, and where I ultimately landed. What Didn’t Work for Me Scrimba Scrimba offers highly interactive introductory courses in web development. However, I realized a bit too late that the platform isn't entirely free; a paywall kicks in shortly after you begin the core HTML and CSS sections. Because I was looking for fully open resources, I had to move on. Frontend Mentor Frontend Mentor is an excellent platform for practicing UI design, but it operates on a freemium model. While the basic learning paths are free, accessing project solutions, starter files, and advanced challenges requires a paid upgrade. 100Devs 100Devs is a free, self-paced, community-taught bootcamp led by Leon Noel. Originally broadcast live on Twitch, the full 30-week course repository is now available on YouTube and communitytaught.org. I actually joined the inaugural cohort in 2020 and attempted subsequent restarts. Leon is an engaging instructor, but the live-stream format presents some hurdles for self-paced learners. The videos are several hours long and include significant time spent interacting with the live chat, managing stream tangents, and breaking away from the core curriculum. Ultimately, the pacing made it difficult for me to stay focused and build momentum. (Note: I also found myself misaligned with some of the community’s culture and leadership choices over time, which made it easier to look for a fresh start elsewhere.) What did I choose? Ultimately, I chose The Odin Project (TOP) as my primary framework, and I couldn't be happier with the decision. The Odin Project checks every single box for

Siri’s AI overhaul may have grabbed the headlines at WWDC, but some of Apple’s most useful AI features are arriving elsewhere in iOS 27.

submitted by /u/TrustSig [link] [留言]

GitHub热门项目 | Grab your football API data for FIFA World Cup 2026 competition! | Stars: 301 | 103 stars this week | 语言: JavaScript

GitHub热门项目 | Open Video Downloader - A cross-platform GUI for youtube-dl made in Rust with Tauri and Vue + Typescript. | Stars: 8,609 | 66 stars today | 语言: Rust

GitHub热门项目 | All components used to run Base | Stars: 703 | 10 stars today | 语言: Rust

GitHub热门项目 | Build AI Agents, Visually | Stars: 53,863 | 107 stars today | 语言: TypeScript

GitHub热门项目 | Lexical is an extensible text editor framework that provides excellent reliability, accessibility and performance. | Stars: 23,545 | 3 stars today | 语言: TypeScript

GitHub热门项目 | The API to search, scrape, and interact with the web at scale. 🔥 | Stars: 136,071 | 505 stars today | 语言: TypeScript

GitHub热门项目 | Review-first terminal diff viewer for agentic coders | Stars: 5,267 | 142 stars today | 语言: TypeScript

GitHub热门项目 | The classic Sillytavern, now has been rewritten in Tauri/Rust. | Stars: 741 | 13 stars today | 语言: JavaScript

GitHub热门项目 | OK影视、tvbox配置文件，如果喜欢，请Fork自用。使用前请仔细阅读仓库说明，一旦使用将被视为你已了解。 | Stars: 9,952 | 31 stars today | 语言: JavaScript