A compliance team at a mid-size bank in Abu Dhabi processes new customer applications every week....

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Earlier this year, the “Because I Got High” rapper went viral for winning a case against the cops. Now he’s crypto’s free-speech hero, even though he isn’t quite sure how the digital currency works.

I ran a 4-bit medical-triage model on a laptop GPU and on a CPU. For one patient, the GPU said urgent and the CPU said emergency. Same model file, same prompt, same input. Here's the mechanism and why "validated on hardware X" doesn't mean what you'd hope. I've been building Aegis-MD , a local-first emergency-department triage console. You hand it a structured clinical picture: chief complaint, vitals, age, pain score, a few risk modifiers, and it returns an urgency category on the Australasian Triage Scale (ATS 1–5), where ATS-1 means resuscitate now and ATS-5 means this can wait two hours . The whole thing runs on-device: a quantized MedGemma 4B served through Ollama, a small RAG layer over open guidelines, and a deterministic rule-based floor underneath the model. I never set out to write about floating-point arithmetic. But while running my evaluation set across two machines, I hit a result that stopped me, and the explanation turned out to be more interesting and more current than the textbook answer most people reach for. The setup, and why a 4-bit model Two things about Aegis-MD's design matter for this story. First, it's local by design. Triage data is about as sensitive as data gets, so nothing leaves the machine. The trade-off is that I'm running a small, heavily quantized model: MedGemma 1.5 4B at Q4_K_XL , about 3.4 GB rather than a frontier API. Four-bit weights are the price of running offline on consumer hardware. Second, I tested on two configurations on purpose. The intended deployment is local GPU inference (an RTX 5070 Ti Mobile, 12 GB). But the public demo runs CPU-only on Cloud Run, because GPU instances need a paid quota I don't have. So I ran the same evaluation against both: the GPU build and the CPU build, same model, same code, same prompts. The eval is 17 hand-written cases spanning all five ATS levels, cardiac arrest down to a medical-certificate request. (Seventeen is a smoke test, not a validation; I won't quote a percentage off a sampl

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Key takeaways Give an AI agent live web data by connecting it to Crawlora's hosted MCP endpoint — it calls documented tools (search, maps, commerce, social, finance) and gets normalized JSON back, with no scraping code or proxies to run. MCP (Model Context Protocol) is an open standard: agents discover and call tools through one interface instead of a bespoke integration per data source. Connect over Streamable HTTP at https://mcp.crawlora.net/mcp with your API key — about three minutes in Claude, Cursor, Cline, Windsurf, or any MCP client. One connection exposes 319 tools across 33 platforms (393 REST endpoints underneath): Google/Bing/Brave search, Google Maps, Amazon, YouTube, TikTok, Yahoo Finance, CoinGecko, and more. You pay only on a successful (2xx) response — failed calls are free — and the free tier includes 2,000 credits a month with no card. Versus writing your own scrapers: no per-source glue code, normalized JSON instead of HTML, and proxy routing, rendering, and retries handled behind the endpoint. You can give an AI agent live web data by connecting it to a hosted MCP endpoint : your agent calls documented tools — search, maps, e-commerce, app stores, social, finance, and more — and gets back normalized JSON, with no scraping code to write or proxies to run. This guide explains what MCP is, what data you can pull, how to connect in about three minutes, and what a real tool call and its response look like. Most LLMs are frozen at their training cutoff and can't see the live web. The usual fix — writing a scraper per source, then maintaining proxies, headless browsers, and parsers — is exactly the work teams don't want to own. MCP plus a hosted data server removes it: the model gets a stable set of tools, and the fetching lives behind an endpoint. What is MCP, and why does it matter for agents? The Model Context Protocol (MCP) is an open standard that lets an AI agent call external tools through one consistent interface. Instead of wiring a bespoke int

The AI landscape is evolving faster than ever. Keeping track of the right tools can feel like trying to drink from a firehose. I recently dug through my extensive bookmarks folders and compiled every single AI tool and Autonomous Agent I've saved. Whether you're looking for an autonomous coding agent, a rapid app builder, an LLM benchmark, or a creative suite, you need the right tool for the job. Bookmark this page, because you're going to want to refer back to it. Superdesign Maskara.ai Google Labs: Google's home for AI experiments - Google Labs Kilo Code - Open source AI agent VS Code extension hunyuan bolt.new Rocket.new | Build Web & Mobile Apps 10x Faster Without Code AI Web Scraping Extension | Chat4Data Sarvam AI Lovable Starc- film ShumerPrompt aipai.app Flowe MiniMax Official Website - Intelligence with everyone new.website | Build Websites with AI Higgsfield HeyBoss.ai Mitte Trickle AI - Turn your ideas into live apps and websites with AI. Dora: Start with AI, ship 3D animated websites without code Kimi AI – Think Bigger. Search Smarter. Write Better. a0.dev - Create Mobile Apps with AI sesame Vogent - Create AI Voice Agents Orchids - Make something beautiful Same PromptBase | Prompt Marketplace: Midjourney, ChatGPT, Sora, FLUX & more. LM Studio Mindstone Chat with Z.ai - Free AI for Presentations, Writing & Coding AI Model & API Providers Analysis | Artificial Analysis T3 Chat - Advanced AI Assistant & ChatGPT Alternative | $8/month Poe Freepik | All-in-One AI Creative Suite Replit – Build apps and sites with AI unwind ai Magic Patterns Soapbox - Build Your Decentralized Platform Shakespeare - AI Website Builder AI recruitment engine to hire top global talent | micro1 Ponder AI | New Way to Work with Knowledge Using AI Ask AI Questions · Question AI Search Engine · iAsk is a Free Answer Engine - Ask AI for Homework Help and Question AI for Research Assistance Firecrawl Kiro: The AI IDE for prototype to production Le Chat CodeArena – Which LLM codes best?

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Every five minutes, Polymarket lists a new question: Will Bitcoin be higher at the end of this window than at the start? You can buy Up or Down . One side pays $1. The other pays $0. The market resolves using Chainlink BTC/USD — not Coinbase, not Binance. That last detail matters more than most people expect. This post is a rough, developer-friendly overview of a spike bot we built to trade those windows. It explains the shape of the system — feeds, decision loop, risk, logging — without publishing the parts that actually make it work: momentum math, tuned parameters, hedge mechanics, or when we choose to run it. Think of this as the architecture blog post, not the strategy leak. Why these markets are interesting (and annoying) Five-minute binary markets are a strange hybrid: They behave like options (probability priced between $0 and $1). They settle like oracles (Chainlink, on a schedule). They trade like retail sports books (fast repricing, thin books, emotional flow). The friction creates opportunity — but only if your bot understands which price is authoritative and which price is merely early. We run two price feeds in parallel: Feed Role Chainlink (via Polymarket RTDS) Resolution price. Slow but official. A fast exchange lead feed Early warning. Moves before the oracle updates. The bot's thesis is simple to state and hard to execute: When spot moves sharply, the prediction market sometimes lags. If you can detect the move early and buy the underpriced side before the book catches up, you have edge. We call that a spike bot — not because it only trades violent moves, but because entries are triggered by short-term momentum events rather than a fixed countdown window. System architecture (the boring, important part) Most of the complexity lives in plumbing. The strategy is one module; the infrastructure is the product. ┌─────────────────┐ ┌──────────────────┐ │ Chainlink RTDS │ │ Lead exchange WS │ │ (oracle) │ │ (fast spot) │ └────────┬────────┘ └────────┬────

I Built a GDPR Compliance Scanner Using the Claude API - Here's How It Works A few months ago I noticed something that kept bugging me. I was building and handing off websites for clients and every single time, GDPR compliance was either an afterthought or a panic right before launch. Privacy policies copied from templates, cookie banners slapped on at the last minute, no one really sure if the contact form was actually compliant. The bigger problem: there was no quick, affordable way to check . Enterprise compliance tools cost hundreds per month. Legal consultants cost more. Most small businesses just crossed their fingers. So I built ClearlyCompliant - an automated GDPR compliance scanner that analyses a website and delivers a detailed PDF report for a one-off fee. No subscription, no jargon, just a clear picture of where a site stands. Here's how it actually works under the hood. The Stack Django (Python) - backend and web app BeautifulSoup + requests - crawling and HTML parsing Python threading - async scanning without the overhead of Celery/Redis Anthropic Claude API (Haiku) - AI-powered policy analysis ReportLab - PDF report generation Stripe - payments IONOS SMTP - email delivery Gunicorn + Nginx on an IONOS VPS The Scanning Pipeline When a user submits a domain and completes payment, the scan kicks off immediately. Rather than making them wait on a loading screen, the scan runs asynchronously in a background thread and the report gets emailed when it's done. I deliberately avoided Celery and Redis here. For the scale I needed, Python's built-in threading module was more than sufficient and kept the infrastructure simple. One less thing to maintain, one less thing to break. import threading def run_scan_async ( domain , order_id , customer_email ): thread = threading . Thread ( target = run_full_scan , args = ( domain , order_id , customer_email ) ) thread . daemon = True thread . start () The scan itself runs 23 individual GDPR checks across several categori

TL;DR: I built PackagePal — paste in any package from any language, pick your target language, and AI instantly finds the equivalent. No more Googling "what's the Node.js version of Python's requests ?" The Problem That Drove Me Crazy You know that moment when you're migrating a project — or just jumping between ecosystems — and you hit a wall trying to find the right package? I do. Every time. # You're used to this in Python import requests response = requests . get ( " https://api.example.com/data " ) And you move to Node.js and think: "Okay, what do I use here? axios? node-fetch? got? undici?" So you Google it. You find a Stack Overflow thread from 2019. Half the answers recommend packages that are now deprecated. You open 6 tabs. 20 minutes later you're still not sure which one is the current best choice. This wasn't a once-in-a-while thing for me. It happened constantly — switching between Python, JavaScript, Go, and Ruby on different projects. I was wasting real hours on a problem that felt completely solvable. So I built PackagePal . What PackagePal Does PackagePal uses AI to understand what a package actually does — its purpose, not just its name — and finds the best equivalent in whatever language you're moving to. The key insight: this isn't a lookup table. A simple mapping of requests → axios misses context. What if you're using requests for its session management? Or its retry logic? PackagePal surfaces options and explains why each one is a good match. Example searches people use it for: Python's pandas → JavaScript Ruby's devise → Node.js Go's cobra → Python JavaScript's lodash → Go Just type the package, pick the target language, and get results in seconds. 👉 Try it: packagepal.dev How I Built It Tech Stack 🤖 AI: Gemini Pro — handles the semantic understanding of what a package does and why an alternative matches ⚛️ Frontend: React + TypeScript ⚙️ Backend: Node.js + TypeScript on Google Cloud ⚡ Caching: Redis — so repeat searches (e.g., "requests → No

Transit.c is an addition to the set of libraries to support transit data interchange format written in C11. It supports full 0.8 specification of cognitect's transit-format: JSON, JSON-Verbose and MessagePack encodings, all ground and extension types, compression via keys caching, extensibility via custom tag handlers.

Every time a course batch completes, you have a list of students who need certificates. The manual way: open Canva, duplicate the template, change the name, export, repeat — for every single student. If you have 10 students, that's annoying. If you have 200, that's a full afternoon. The better way A single n8n workflow that: Reads student names from Google Sheets Calls the RenderPix batch API Gets back 200 certificate images Emails each student their certificate Total time: ~2.5 minutes. Total manual work: zero. What you'll need A RenderPix account (free tier works for testing, Starter plan for production) n8n (self-hosted or cloud) n8n-nodes-renderpix community node A Google Sheet with student data Install the n8n node: npm install n8n-nodes-renderpix Or search "RenderPix" in n8n's community node panel. Step 1 — Design your certificate template Write your certificate in plain HTML. Here's a clean starting point: <div style= "width:1200px;height:850px;background:white; display:flex;flex-direction:column;align-items:center; justify-content:center;border:20px solid #0f172a; font-family:Georgia,serif;padding:60px;box-sizing:border-box" > <div style= "font-size:16px;letter-spacing:5px;color:#64748b; text-transform:uppercase;margin-bottom:24px" > Certificate of Completion </div> <div style= "width:80px;height:2px;background:#22d3ee;margin-bottom:32px" ></div> <div style= "font-size:52px;font-weight:700;color:#0f172a;margin-bottom:16px" > {{name}} </div> <div style= "font-size:18px;color:#475569;text-align:center;max-width:600px" > has successfully completed </div> <div style= "font-size:28px;font-weight:600;color:#1e293b;margin:16px 0 40px" > {{course}} </div> <div style= "font-size:14px;color:#94a3b8" > {{date}} </div> </div> Notice the {{name}} , {{course}} , {{date}} placeholders — RenderPix replaces these at render time. Step 2 — Set up Google Sheets Create a sheet with these columns: name course date Jane Smith Advanced n8n Automation June 2026 John Doe Advanced n8n

I run a web design agency and there is already way too much stuff to deal with every day. Hosting client websites, maintaining them, building new sites, replying to clients, fixing random issues, handling support, doing outreach. Once you start managing a lot of company websites it quickly becomes overwhelming. That’s why I never wanted cold calling to become my main way of getting clients. I know cold calling can work, but I personally hate doing it. It drains my energy and takes up so much time. Sitting there making calls all day was never the kind of business I wanted to build. So instead I focused on email automation. The reason it works so well for me is because I can set everything up once and let interested businesses reply instead of spending my whole day chasing people. But I also don’t do the typical outreach where agencies send generic messages saying “your website is outdated” or “you need a redesign.” I use a tool called Swokei where I upload lists of company websites and it analyzes them for actual problems like speed, SEO, mobile responsiveness, layout issues, and design problems. Then it automatically creates personalized outreach emails based on those issues. That’s what helped me stand out because the emails actually feel relevant to the business instead of sounding copied and pasted. The reply rates became way better once I stopped sending generic outreach. Now I spend most of my time building websites, working with clients, and scaling the agency instead of letting outreach take over my entire day. submitted by /u/Murky_Explanation_73 [link] [留言]