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Web2API — Turning Websites into REST APIs (and MCP Tools)
Fresh AI, programming and Big Tech news. Scrollable decks, neon accents — maximum focus with minimal scrolling.
Useful Websites
# https://same.new/ # https://stackoverflow.com/ # https://21st.dev/community/components # https://chathub.gg/ # https://www.enki.com/ # https://www.n2yo.com/api/ # https://www.data.com/ # https://devdocs.io/ # https://roadmap.sh/ # https://regex101.com/ # https://jsoncrack.com/ # https://www.postman.com/explore # https://replit.com/ # https://codesandbox.io/ # https://github1s.com/ # https://freecodecamp.org/ # https://overapi.com/ # https://cssbattle.dev/ # https://ui.shadcn.com/ # https://phind.com/ # https://omni.studio/ # https://rapidapi.com/ # https://apipost.com/ # https://metatags.io/ # https://tinywow.com/ # https://jwt.io/ # https://http.cat/ # https://httpstatus.io/ # https://uxdesign.cc/ # https://systemdesign.one/ # https://excalidraw.com/ # https://app.diagrams.net/ # https://deepai.org/
AI — Latest Models & Research
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Web2API — Turning Websites into REST APIs (and MCP Tools)
I needed data from websites that don't have APIs. Not once, not as a quick scrape, but as persistent,...
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AI
Stop Sending Health Data to the Cloud! Build a Privacy-First Symptom Checker with WebGPU
Privacy is the ultimate luxury in the age of AI. When it comes to health data, the stakes are even...
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AI
How to Detect Immigration Sentiment Shifts with the Pulsebit API (Python)
How to Detect Immigration Sentiment Shifts with the Pulsebit API (Python) The...
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AI
I Automated My Morning Routine Into a Single Email
How I replaced checking 6 apps every morning with a $0.003/day AI-generated email brief for me and my wife.
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AI
Can Statcast Data Improve MLB Player Performance Predictions? — Beating Marcel with LightGBM
I tried to beat the Marcel projection system using MLB Statcast tracking data (exit velocity, barrel rate, Whiff%, Stuff+). Here's what happened: +12.1% improvement for batters, +4.0% for pitchers.
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AI
Command Line Habit Tracker - a Tiny Useful Project
After several weeks of following courses and tutorials, I dove into making something of my own. ...
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AI
We Scanned 100,000 AI Agent Files for EU AI Act Compliance. 90% Failed.
We Scanned 100,000 AI Agent Files for EU AI Act Compliance. 90% Failed. Over the past few...
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AI
What “Production-Ready LLM Feature” Really Means
When people talk about LLM features, they usually talk about prompts, models, and demos. But in real...
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AI
I got tired of writing 30 lines of LangChain boilerplate every time. So I published a fix.
Every time I started a new project that needed RAG, I wrote the same 30 lines. Load documents. Split...
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AI
How I Built a Rental Property Deal Analyzer with FastAPI and AI
I spent months evaluating rental properties with spreadsheets and paid calculator subscriptions...
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AI
I Found an Open-Source AI Agent That Runs on Your Laptop and Talks Through Telegram, Discord, and WhatsApp
What if your AI assistant lived on YOUR machine, not someone else's cloud? That question...
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AI
Show HN: Market Trade Simulator for AI Agent with No Strategy Restrictions
I built a backtesting platform where the market simulator runs in the browser using WebAssembly, while trading strategies run locally on your machine.<p>Running the simulator in the browser keeps the SaaS setup simple (no installation), while still allowing strategies to run in a fully unrestricted local environment.<p>I started working on this while experimenting with AI coding agents writing trading strategies. Most backtesting platforms execute strategies inside their own infrastructure, which usually means restrictions on compute, libraries, GPUs, or external APIs.<p>This system separates the simulator and the strategy runtime.<p>Architecture roughly looks like this:<p>Browser
↓
WebAssembly market simulator
↓
WebSocket
↓
Local Python strategy<p>Because strategies run locally, the strategy environment is effectively unrestricted. You can use GPUs, external APIs, local datasets, or basically any Python library.<p>This setup also works well with AI coding agents (like Claude Code). An agent can write strategies, run backtests, analyze results, and iterate.<p>The platform also exposes APIs designed for autonomous agent workflows where an AI agent can run strategies, evaluate results, and improve them in a loop.<p>On the analysis side, positions are traced at the ticket level rather than immediately aggregated, so the lifecycle of each ticket can be inspected in the UI. There are also diagnostics like exit analysis, MFE/MAE statistics, equity curves, and drawdown analysis.<p>The backend is a lightweight Django service running on AWS.<p>It's still early and probably rough in places, but curious what people think.<p>It's currently free during preview while I gather feedback.<p>App:
<a href="https://app.hawk-backtester.com" rel="nofollow">https://app.hawk-backtester.com</a><p>Python client library:
<a href="https://github.com/nima555/hawk-bt" rel="nofollow">https://github.com/nima555/hawk-bt</a>
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