If you have built an estimate in the AWS Pricing Calculator by hand, you know the drill. Open calculator.aws, search a service, click in, stare at twenty fields half of which you do not need, guess at the ones the form does not explain, pick a region, repeat for every service. Then redo the whole thing next week when the customer asks what it looks like in Frankfurt. For presales that is not a small annoyance. It is the gap between giving a number on the call and saying "let me get back to you." I wired the AWS Pricing Calculator MCP into Claude, and the first real estimate I built took one sentence. What it is An MCP server - an AWS Samples project - that exposes the Pricing Calculator as tools an agent can call. You describe the workload, the agent assembles the estimate, the server saves it to the real calculator, and you get a shareable calculator.aws URL back. Same link you would have built by hand, minus the form. Three things make it usable in front of a customer: No AWS credentials. It hits the public, unauthenticated calculator.aws endpoints. You are not pointing it at an account or assuming a role. There is no blast radius. Live definitions. It pulls the calculator manifest at runtime - about 436 services - so it is current, not a snapshot from six months ago. Real, editable estimates. The URL it returns opens in the actual calculator. Tweak it, send it, whatever. The agent just did the boring part. It runs over stdio for local clients like Claude Desktop, Kiro, and Cursor, or over HTTP ( MCP_TRANSPORT=http ) if you want it hosted. It also handles the aws-iso and aws-eusc partitions, which matters for sovereign and regulated work. Context is the whole job The honest part: it is amazing when you feed it the right context . Ask for "an estimate for a web app" and you get back a web app someone else imagined. The calculator never knew your traffic - you did. The MCP does not change that. What it changes is the translation. Once you know the shape - two m5.lar

The cryptocurrency market is more competitive than ever. With thousands of new tokens, NFT collections, and Web3 projects launching every month, simply having a great product or utility isn't enough. You need eyeballs, community trust, and exchange visibility. However, traditional advertising platforms like Facebook and Google have strict restrictions on crypto ads, making it incredibly difficult for founders to reach their target audience. So, how do you get your project in front of the right investors and users? Enter FameSeller, your ultimate partner for crypto advertising and promotion. FameSeller offers a specialized suite of services designed to build credibility, boost social proof, and skyrocket your project's visibility across the most important platforms in the crypto ecosystem. The Trust Factor in Crypto Marketing In the Web3 space, trust is everything. Before an investor puts their money into your token or buys your NFT, they will check your social proof, community size, and verification badges. If your project looks empty or unverified, investors will move on to the next one. Strategic crypto advertising solves this problem. By leveraging FameSeller’s services, you can instantly establish your project as a legitimate, trending, and highly sought-after opportunity. Explore FameSeller's Top Crypto Advertising Services Let's dive into the powerful services available on FameSeller’s Crypto Advertising page that can take your project from unknown to trending. Dominate Crypto-Specific Social Platforms Unlike traditional social media, crypto communities live on specialized platforms. FameSeller helps you build a massive presence where active traders and degens hang out: Binance Square & Binance Moments: Binance is the largest crypto exchange globally. Growing your followers on Binance Square and listeners on Binance Moments puts your project directly in front of millions of active traders. Phantom Wallet Followers: If you are building on the Solana ecosystem,

You know the drill. You build a beautiful set of shadcn/ui components for Project A — a Button, a Card, a Dialog with custom animations. Then Project B kicks off. You copy the files over. Then Project C. Then a subtle bug is found in the Button. Now you're patching it in three places. This is the classic monorepo problem, and it's exactly what I set out to fix. What I Built turborepo-react-shadcn-starter is a production-ready monorepo template that wires up: Turborepo for workspace orchestration and intelligent build caching React + Vite for the fastest possible frontend dev experience shadcn/ui as a shared package — write once, use everywhere TypeScript across the entire workspace ESLint with shared config baked in The key insight: shadcn/ui lives in @repo/ui , a shared package, not inside any single app. Every app in your monorepo consumes the same components from the same source of truth. The Problem with Typical Setups Most teams drop shadcn/ui directly into a single app. That works fine until you need a second app. Then your choices are: Copy-paste the components → drift and duplication immediately Publish to npm → versioning overhead for internal code Monorepo with a shared package → ✅ This is the right answer Turborepo makes option 3 near-effortless, but setting it up from scratch (workspace configs, TypeScript path aliases, ESLint sharing, shadcn CLI pointing at the right package) takes a few hours of trial and error. This starter eliminates all of that. What's Inside turborepo-react-shadcn-starter/ ├── apps/ │ └── web/ # Vite + React app ├── packages/ │ ├── ui/ # @repo/ui — shared shadcn/ui components │ ├── eslint-config/ # @repo/eslint-config │ └── typescript-config/ # @repo/typescript-config ├── turbo.json └── package.json apps/web — The Main App A clean Vite + React app already wired to consume components from @repo/ui . No boilerplate to delete, no config to untangle. packages/ui — The Shared Component Library This is where all your shadcn/ui components

I’ve always been fascinated by PDFs. They look simple on the surface. Just a document you can open anywhere but underneath they’re a full layout engine, object graph, drawing model, and archival format all at once. I enjoy that mix of precision and complexity and that is exactly what led me to build PDF::Make (and yes I had some help from Claude LLM). I wanted a fully featured toolkit that could both generate PDFs and let me inspect/edit them programmatically. At the low level, PDF::Make exposes the raw building blocks of the format: PDF objects, pages, the drawing canvas, a parser/reader, and import/merge primitives. This is the layer you reach for when you need fine grained control or want to work with the structure of a document directly. For everyday document creation, PDF::Make::Builder sits on top of that foundation and provides a higher level API. It handles the boilerplate of page setup, fonts, text flow, and layout so you can produce a polished PDF in just a few lines of Perl. The same toolkit is also designed for post-processing. You can open an existing PDF, extract structured text along with its coordinates, and then draw annotations or overlays back onto the page, making it straightforward to build review, QA, or markup workflows on top of documents you didn’t originally generate. This post shows a practical two-step flow: Create a PDF Re-open it, extract text coordinates, and draw border highlights around matched words 1) Create a PDF with PDF::Make::Builder Script: #!/usr/bin/perl use strict ; use warnings ; use PDF::Make:: Builder ; my $pdf = PDF::Make:: Builder -> new ( file_name => ' source_demo.pdf ', configure => { text => { font => { family => ' Helvetica ', size => 12 , colour => ' #222222 ' }, }, }, ); $pdf -> add_page ( page_size => ' Letter ') -> add_h1 ( text => ' PDF::Make blog demo ') -> add_text ( text => ' PDF::Make builds and edits PDF files directly from Perl. ') -> add_text ( text => ' In the next step we extract text coordinates and

The Euclid space telescope's stunning photo of our galaxy's “crowded heart” captures more than 60 million stars.

An unadorned ebike is a blank canvas. Here, get tips for maximizing its cargo-hauling and person-carrying capabilities.

The Chinese supercomputer LineShine was ranked as the fastest in the world, despite not using any GPUs.

Currently available as a beta in Xcode 27, Swift 6.4 introduces a range of enhancements: better C interoperability, simplified OS availability check, fine-grained warning control, async support in defer, efficient iteration for non-noncopyable types, up to 4x faster URL parsing, and improved interoperability between Swift Testing and XCTest. By Sergio De Simone

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I've seen lots of so-called "smart" bike locks over the years, but none so far could justify the added cost. A newcomer that got its start securing ATMs for banks is trying to change that. There's nothing wholly unique about the TMD Chain Lock, but the combination of materials, performance, and insurance-friendly ART-2 certification makes […]

I gave GLM-4.5-Air (106B, open weights) 12 coding tasks through opencode on my RTX 3090. It scored 0% — never edited a single file. Same model, same GPU, same tasks, but driven by a ~150-line LangGraph agent instead: 93% . The model was never the problem. The orchestrator was. Here's the benchmark — including the part nobody else measures, the electricity cost per correct task . Setup RTX 3090 (24 GB) + 128 GB RAM , models via ollama , Q4 quants, temp 0.2 5 recent open models × 2 orchestrators (opencode vs custom LangGraph ReAct with ollama-native tool-calling) 17 graded tasks (12 coding in Python/JS/C++ + 5 general-agent) with hidden unit tests Every run priced in GPU watts via my open-source homelab-monitor Results Model tok/s opencode adh. LangGraph adh. LangGraph coding LangGraph general Qwen3-Coder 30B-A3B 130 92% 100% 100% 100% GLM-4.5-Air 106B 5.7 0% 100% 89% 100% Devstral Small 24B 49 8% 53% 8% 40% Seed-OSS 36B 9.5 0% 7% 0% 20% DeepSeek-R1-Distill 32B 6.7 0% 0% 0% 0% Tool-adherence = % of tasks where the model actually called a tool instead of just printing code in chat. It was the master variable. (GLM's headline "93%" is its blended score across all 17 tasks: 89% coding + 100% general.) Three takeaways The framework can matter more than the model. opencode sends a frontier-shaped system prompt + 12 tools over its OpenAI-compat path; most local models fall back to chatting. Native tool-calling through a lean agent fixes that — GLM went 0% → 93%. (Qwen3-Coder is the exception: it's tuned for agentic tool use and aces opencode out of the box.) Acting ≠ solving. LangGraph made Devstral act (8% → 53% adherence) but not solve (coding stayed 8%). The framework decides whether a model acts; the model decides whether it's right. The wattmeter ranks honestly. Qwen solved tasks at ~0.0005 BGN each; the models that scored zero still burned 10–30× more energy for nothing. On a home rig, the cheapest model is the fast, correct one — and MoE (Qwen activates ~3B of 30B pe

Ever planted seeds at the wrong time and watched them die? Me too. That's why I built PlantingCalendar.net - a free tool that tells you exactly what to plant every single day based on your climate zone. Built with AI coding tools in about 4 hours. 365 pages, each with unique planting instructions. Static site on Cloudflare Pages, zero server cost. Free, no sign-up.

I've been working on Churo, an open-source voice assistant built entirely in Python. It features high-quality speech-to-text and text-to-speech, web search, image understanding, and agentic capabilities. It runs with Ollama models and is designed to be easy to modify and extend. The goal is to provide a capable, local-first voice assistant that developers can actually inspect, customize, and build on. Repository: https://github.com/MathObsession/Churo-assistant or run it with(You need Ollama): pip install churovoice churovoice --voice male Feedback, issues, and contributions are welcome.