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Showing prices in a user's local currency increases trust, reduces bounce rates, and improves conversions — especially for international audiences. Here's how to do it automatically in React. Detect the user's currency from their IP address (no user input needed) Fetch the live exchange rate for that currency Format the price according to their locale // hooks/useCurrency.ts import { useEffect, us
IP geolocation is one of the most accessible fraud signals available. It won't stop sophisticated attackers, but it catches a significant amount of low-effort fraud with very little implementation overhead. Impossible travel: A user logs in from Germany, then from Brazil 10 minutes later. Country mismatch: Billing address is in the US, but the IP is in a different region. High-risk region matching
A walkthrough of prompt injection attacks against OopsSec Store's AI assistant, bypassing its input filters to extract a flag from the system prompt. OopsSec Store has an AI support assistant with a secret embedded in its system prompt. The only thing standing between us and the flag is a regex blocklist. Spoiler: four regexes are not enough. Initialize the OopsSec Store application: npx create-os
I have a confession. For years, when a developer proudly showed me their Python app — gray square buttons, a Listbox straight out of 1998 — I would politely nod. I've stopped doing that. Not because I turned mean. Because PyQt6 exists, and there's no excuse anymore. This article is my attempt to convince you — yes, you, the one still typing import tkinter out of habit — that something radically be
TL;DR — One API call subscribes a customer endpoint. Centrali signs each delivery with HMAC-SHA256, retries 5 times over ~40 minutes on failure, logs every attempt, and exposes a one-line replay endpoint. No queue. No retry logic. No Svix. The whole subscribe call is right below — scroll to it if you just want the shape. Your customers want webhooks. You know the checklist: A queue so user request
The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.
Most text analysis solutions fall into one of two problems: Too expensive — OpenAI API costs money for every call Too complex — Hosting your own Hugging Face model requires infra, GPU, maintenance I built TextAI Pro — a lightweight REST API that does the job without the overhead. Two endpoints: POST /analyze Sentiment: positive / negative / neutral Confidence score (0–1) Top keywords Word count PO