<?xml version="1.0" encoding="iso-8859-1"?>
<rss version="2.0"><channel><title>RasadaCrea rss feeds aggregator</title><link>http://www.rasadacrea.com</link><description>rss feed aggregated news on web services and technologies by RasadaCrea France : Category en_web training courses</description><lastBuildDate>Tue, 28 Apr 2026 15:31:18 GMT</lastBuildDate><generator>PyRSS2Gen-1.0.0</generator><docs>http://blogs.law.harvard.edu/tech/rss</docs><item><title>Adobe's Firefly AI Assistant Wants to Run Creative Cloud for You</title><link>https://www.hongkiat.com/blog/adobe-firefly-ai-assistant-creative-cloud/</link><description>Adobe is trying to turn Firefly into more than an image generator. 
 Firefly AI Assistant is Adobe's attempt to turn Creative Cloud into an agent-driven workflow layer. Instead of bouncing between Photoshop, Premiere Pro, Illustrator, Lightroom, and Express, users describe the result they want, and Firefly handles the app hopping in the background. 
 That is a much bigger move than adding another AI button inside one Adobe app. It extends the direction Adobe has already been pushing with features like Photoshop with AI , but at a broader workflow level. 
 
 What Adobe Announced 
 At the center of the announcement is a simple idea: one prompt should be able to trigger a multi-step workflow across several Creative Cloud apps while preserving context between sessions. Adobe laid that out in both its official blog post and its newsroom release . 
 The pitch is straightforward: spend less time figuring out which app, panel, or workflow to use, and more time describing the end result. 
 Adobe is positioning the assistant to work across apps including: 
 
 Photoshop 
 Premiere Pro 
 Express 
 Lightroom 
 Illustrator 
 additional Creative Cloud apps over time 
 
 It will also ship with prebuilt Creative Skills , reusable task flows for common .. cntd</description><pubDate>Fri, 17 Apr 2026 09:55:00 GMT</pubDate></item><item><title>YouTube Premium Raises US Prices Again</title><link>https://www.hongkiat.com/blog/youtube-premium-raises-us-prices/</link><description>YouTube Premium just got more expensive in the US again. 
 According to The Verge , the individual plan now costs $15.99 per month , up from $13.99 . The family plan is now $26.99 , up from $22.99 . Premium Lite, the ad-free plan without YouTube Music, goes from $7.99 to $8.99 . 
 
 This latest hike appears to apply to the US only, while many international users already saw YouTube Premium price increases in late 2024. 
 That is the headline. The bigger story is how often YouTube has changed the price and the product around it. 
 From Music Key to YouTube Red to YouTube Premium, the paid offering has been moving in one direction for years. 
 The 2026 Price Hike 
 The new monthly US prices are: 
 
 YouTube Premium Individual: $15.99, up from $13.99 
 YouTube Premium Family: $26.99, up from $22.99 
 Premium Lite: $8.99, up from $7.99 
 
 The increase applies immediately for new subscribers, while existing users are being notified ahead of their next billing cycle. 
 The standard individual plan is now $4 higher than it was at launch under the YouTube Premium name in 2018. 
 Where It Started 
 YouTube's paid subscription history is a little messy because the service has gone through three main eras. 
 1. YouTube Music Key, 2014 
 The .. cntd</description><pubDate>Mon, 13 Apr 2026 09:31:00 GMT</pubDate></item><item><title>Graham Dumpleton: Free Python decorator workshops</title><link>https://grahamdumpleton.me/posts/2026/04/free-python-decorator-workshops/</link><description>I've been working on a set of interactive workshops on Python decorators and they are now available for free on the labs page of this site. There are 22 workshops in total, covering everything from the fundamentals of how decorators work through to advanced topics like the descriptor protocol, async decorators and metaclasses. The workshops are hosted on the Educates training platform and accessed through the browser, so there is nothing to install. 
 An experiment in learning 
 We are well into the age of AI at this point. Need to know how to write a decorator in Python? Just ask ChatGPT or Claude and you will get an answer in seconds. Want to refactor some code to use decorators? Let an AI agent do it for you. The tools are genuinely impressive and I use them myself every day. 
 That said, as someone who spent years as a developer advocate helping people learn, the question that keeps coming up is whether there is still an appetite for actually learning how things work. Not just getting an answer, but understanding why the answer is what it is. Understanding the mechanics well enough that when the AI gives you something subtly wrong (and it will), you can spot it and fix it yourself. 
 These workshops are my experiment in finding .. cntd</description><pubDate>Thu, 02 Apr 2026 04:38:56 GMT</pubDate></item><item><title>PyBites: Why Building a Production RAG Pipeline is Easier Than You Think</title><link>https://pybit.es/articles/why-building-a-production-rag-pipeline-is-easier-than-you-think/</link><description>Adding AI to legacy code doesn't have to be a challenge. 
 Many devs are hearing this right now: &#8220;We need to add AI to the app.&#8221; 
 And for many of them, panic ensues. 
 The assumption is that you have to rip your existing architecture down to its foundation. You start having nightmares about standing up complex microservices, massive AWS bills, and spending six months learning the intricate math behind vector embeddings. 
 It feels like a monumental risk to your stable, production-ready codebase, right? 
 Here's the current reality though: adding AI to an existing application doesn't actually require a massive rewrite. 
 If you have solid software design fundamentals, integrating a Retrieval-Augmented Generation (RAG) pipeline is entirely within your reach. 
 Here's how you do it without breaking everything you've already built. 
 Get the Python Stack to do the Heavy Lifting 
 You don't need to build your AI pipeline from scratch. The Python ecosystem has matured to the point where the hardest parts of a RAG pipeline are already solved for you. 
 
 Need to parse massive PDFs? Libraries like docling handle it practically out of the box. 
 Need to convert text into embeddings and store them? You let the LLM provider handle the embedding .. cntd</description><pubDate>Tue, 03 Mar 2026 09:06:04 GMT</pubDate></item></channel></rss>