#Amazon AI features

[themorningnewsinformer]

Amazon’s Ring Adds AI Video Descriptions to Boost Home Security

Amazon Ring, a leading smart security device brand, has announced the rollout of its first major AI-powered feature: Video Descriptions. This new capability allows users to receive real-time text notifications about motion activity captured by their Ring doorbells and cameras. The feature is currently available in beta for Ring Home Premium subscribers in the United States and Canada. Amazon…

[jcmarchi]

Starting reading the AI Snake Oil book online today

New Post has been published on https://thedigitalinsider.com/starting-reading-the-ai-snake-oil-book-online-today/

Starting reading the AI Snake Oil book online today

The first chapter of the AI snake oil book is now available online. It is 30 pages long and summarizes the book’s main arguments. If you start reading now, you won’t have to wait long for the rest of the book — it will be published on the 24th of September. If you haven’t pre-ordered it yet, we hope that reading the introductory chapter will convince you to get yourself a copy.

We were fortunate to receive positive early reviews by The New Yorker, Publishers’ Weekly (featured in the Top 10 science books for Fall 2024), and many other outlets. We’re hosting virtual book events (City Lights, Princeton Public Library, Princeton alumni events), and have appeared on many podcasts to talk about the book (including Machine Learning Street Talk, 20VC, Scaling Theory).

Our book is about demystifying AI, so right out of the gate we address what we think is the single most confusing thing about it: 

AI is an umbrella term for a set of loosely related technologies

Because AI is an umbrella term, we treat each type of AI differently. We have chapters on predictive AI, generative AI, as well as AI used for social media content moderation. We also have a chapter on whether AI is an existential risk. We conclude with a discussion of why AI snake oil persists and what the future might hold. By AI snake oil we mean AI applications that do not (and perhaps cannot) work. Our book is a guide to identifying AI snake oil and AI hype. We also look at AI that is harmful even if it works well — such as face recognition used for mass surveillance. 

While the book is meant for a broad audience, it does not simply rehash the arguments we have made in our papers or on this newsletter. We make scholarly contributions and we wrote the book to be suitable for adoption in courses. We will soon release exercises and class discussion questions to accompany the book.

Chapter 1: Introduction. We begin with a summary of our main arguments in the book. We discuss the definition of AI (and more importantly, why it is hard to come up with one), how AI is an umbrella term, what we mean by AI Snake Oil, and who the book is for. 

Generative AI has made huge strides in the last decade. On the other hand, predictive AI is used for predicting outcomes to make consequential decisions in hiring, banking, insurance, education, and more. While predictive AI can find broad statistical patterns in data, it is marketed as far more than that, leading to major real-world misfires. Finally, we discuss the benefits and limitations of AI for content moderation on social media.

We also tell the story of what led the two of us to write the book. The entire first chapter is now available online.

Chapter 2: How predictive AI goes wrong. Predictive AI is used to make predictions about people—will a defendant fail to show up for trial? Is a patient at high risk of negative health outcomes? Will a student drop out of college? These predictions are then used to make consequential decisions. Developers claim predictive AI is groundbreaking, but in reality it suffers from a number of shortcomings that are hard to fix. 

We have discussed the failures of predictive AI in this blog. But in the book, we go much deeper through case studies to show how predictive AI fails to live up to the promises made by its developers.

Chapter 3: Can AI predict the future? Are the shortcomings of predictive AI inherent, or can they be resolved? In this chapter, we look at why predicting the future is hard — with or without AI. While we have made consistent progress in some domains such as weather prediction, we argue that this progress cannot translate to other settings, such as individuals’ life outcomes, the success of cultural products like books and movies, or pandemics. 

Since much of our newsletter is focused on topics of current interest, this is a topic that we have never written about here. Yet, it is foundational knowledge that can help you build intuition around when we should expect predictions to be accurate.

Chapter 4: The long road to generative AI. Recent advances in generative AI can seem sudden, but they build on a series of improvements over seven decades. In this chapter, we retrace the history of computing advances that led to generative AI. While we have written a lot about current trends in generative AI, in the book, we look at its past. This is crucial for understanding what to expect in the future. 

Chapter 5: Is advanced AI an existential threat? Claims about AI wiping out humanity are common. Here, we critically evaluate claims about AI’s existential risk and find several shortcomings and fallacies in popular discussion of x-risk. We discuss approaches to defending against AI risks that improve societal resilience regardless of the threat of advanced AI.

Chapter 6: Why can’t AI fix social media? One area where AI is heavily used is content moderation on social media platforms. We discuss the current state of AI use on social media, and highlight seven reasons why improvements in AI alone are unlikely to solve platforms’ content moderation woes. We haven’t written about content moderation in this newsletter.

Chapter 7: Why do myths about AI persist? Companies, researchers, and journalists all contribute to AI hype. We discuss how myths about AI are created and how they persist. In the process, we hope to give you the tools to read AI news with the appropriate skepticism and identify attempts to sell you snake oil.

Chapter 8: Where do we go from here? While the previous chapter focuses on the supply of snake oil, in the last chapter, we look at where the demand for AI snake oil comes from. We also look at the impact of AI on the future of work, the role and limitations of regulation, and conclude with vignettes of the many possible futures ahead of us. We have the agency to determine which path we end up on, and each of us can play a role.

We hope you will find the book useful and look forward to hearing what you think. 

The New Yorker: “In AI Snake Oil, Arvind Narayanan and Sayash Kapoor urge skepticism and argue that the blanket term AI can serve as a smokescreen for underperforming technologies.”

Kirkus: “Highly useful advice for those who work with or are affected by AI—i.e., nearly everyone.”

Publishers’ Weekly: Featured in the Fall 2024 list of top science books.

Jean Gazis: “The authors admirably differentiate fact from opinion, draw from personal experience, give sensible reasons for their views (including copious references), and don’t hesitate to call for action. . . . If you’re curious about AI or deciding how to implement it, AI Snake Oil offers clear writing and level-headed thinking.”

Elizabeth Quill: “A worthwhile read whether you make policy decisions, use AI in the workplace or just spend time searching online. It’s a powerful reminder of how AI has already infiltrated our lives — and a convincing plea to take care in how we interact with it.”

We’ve been on many other podcasts that will air around the time of the book’s release, and we will keep this list updated.

The book is available to preorder internationally on Amazon.

[smallhatlogan]

[harshathusm]

How Much Does It Cost to Integrate AI and Personalization in an Amazon-Like App?

Read More: https://bit.ly/45EkPcY

Integrating AI and personalization in an Amazon-like app can cost depends on app complexity and feature depth. Key factors include recommendation engines, user behavior analysis, and real-time data processing. Advanced AI models may increase development time and cost. Partnering with an expert team ensures seamless integration and performance.

USM Business Systems

Services: Mobile app development Artificial Intelligence Machine Learning Android app development RPA Big data HR Management Workforce Management IoT IOS App Development Cloud Migration

Contact Us: https://usmsystems.com/contact-us/ Phone: 1-703-263-0855 Email: sales@usmsystems.com

[reazreviewzone]

Elixir App Review - World 1st Multiple Best System Creator By [Michaelmac]

Welcome to my Review Blog and Elixir Review. I hope you are well. I am ready for another review blog about Elixir AI. I think Elixir helps you with multiple systems because it is built with a lot of powerful tools. You can make a fully automated Amazon website in one click and build hosting traffic, & 99 premium als.

What is Elixir AI?

Introduction to Elixir AI Elixir AI is an advanced artificial intelligence platform designed for AI Amazon affiliate website creation suite, & to streamline complex tasks, enhance decision-making, and optimize workflows across industries. Built on cutting-edge machine learning and natural language processing technologies, Elixir AI empowers businesses to automate processes, analyze data, and generate actionable insights with unparalleled accuracy.

Key Features of Elixir AI Elixir AI stands out for its scalability, adaptability, and user-friendly interface. It integrates seamlessly with existing systems, offering real-time analytics, predictive modeling, and personalized solutions. Whether for healthcare, finance, or retail, Elixir AI tailors its capabilities to meet specific industry needs, making it a versatile tool for innovation and growth.

Overview Of Elixir

Author/vendor – Michaelmac Product – Elixir Launce date – 24/02/25 Official website – VISITE HERE Front-end price - $13 Business – ok Watch/ Create Video – Any Language Social Media Marketing – Number One passive income – ok Recurring System – OK Payment – ONE Time Local Business – High Recommend Money-Back – 30 Days Money-Back Guarantee Funnel/Tool – Automated & Done-For-You Support – Effective Niche – Any Niche of your choice Amazon Affiliate – Ok Built Website – High Recommend That`s How It Works – Elixir?

Step-1:- Selicect your niche – no require domains, hosting, WordPress, or anything else.

Step 2:- Create an Amazon affiliate website with 99 premium als, & hosting in 60 seconds.

Step 3:- Generate unlimited targeted traffic on your site - SEO rankings, easy backlinks, easy Instagram, TikTok, and YouTube traffic and even create Hollywood-style movies.

Why This System Is A Game-Changer?

• Manage your social media sites • Build entire affiliate websites on Elixir • Write unlimited unique content • Create amazing HD videos and HOST • Never lose your precious data • Just choose your niche • 100% Newbie Friendly • Pay once, use Forever hosting • Create incredible pages • Dedicated server cluster with built-in DDOS protection • Unlimited Free end-to-end SSL

Why We Should Use Elixir Driving Innovation Elixir AI is a game-changer for businesses seeking to stay competitive & instantly approve Amazon products, write and build a website in a few seconds, and create content for both websites. Its predictive analytics and automation capabilities drive innovation, helping organizations anticipate trends and adapt to changing market dynamics.

Cost-Effective Solutions By automating repetitive tasks and optimizing operations, Elixir AI reduces operational costs. Its scalability ensures businesses only pay for what they need, making it a cost-effective solution for long-term growth and success. The agency provides its customers a massive opportunity, for example:- Unlimited Free end-to-end SSL, a Dedicated server cluster with built-in DDOS protection, and Use Our Platform For Stupid-Simple “Review” Affiliate Sites. Who Should Use Elixir

• Affiliate marketer • Web developer • Content Creator • Video maker & host • Beginner • Content writer • Replace manual effort • Freelancer • Graphic designer • Musician • Voiceover

Free Commercial License Of Elixir A free commercial license allows individuals or businesses to use, modify, and distribute software, content, or products without paying licensing fees, even for commercial purposes. Unlike personal-use-only licenses, it permits monetization, such as selling the product or incorporating it into commercial projects. Popular examples include open-source software under MIT or Apache 2.0 licenses. However, users must comply with specific terms, such as attributing the original creator or sharing modifications under the same license. Free commercial licenses foster innovation, collaboration, and accessibility, making them valuable for startups, developers, and creators seeking cost-effective solutions. Always review the license terms to ensure compliance and understand any restrictions.

Can Do For You – Elixir

Manage social media accounts.

Make HD video

Build website

Create unlimited unique content.

Autopilot system tools

99 premium als

Select targeted niche

Turn any text to human sound

No one Google rank

Website security protect

Drag and drop system

Money-Back Guarantee – Elixir A 30-day money-back guarantee is a customer-friendly policy that allows buyers to request a full refund within 30 days of purchase if they are unsatisfied with a product or service. This policy builds trust and confidence, encouraging potential customers to purchase with minimal risk. It demonstrates the seller's commitment to quality and customer satisfaction. To claim a refund, customers typically need to return the product in its original condition or cancel the service within the specified period. This guarantee is commonly used in industries like software, e-commerce, and subscription services. While it can increase sales, businesses must ensure clear terms to prevent abuse. Overall, it’s a win-win, offering customers peace of mind and businesses a competitive edge.

Why I Recommend Using Elixir AI Efficiency and Precision I recommend Elixir AI for its ability to deliver precise results while saving time and resources & Approved Cloud App Builds INSTANT 1-CLICK SEO-Optimized Affiliate Sites. Its advanced algorithms ensure high accuracy in data analysis, enabling businesses to make informed decisions quickly.

User-Centric Design This is a completely done-for-you High Converting Affiliate Sites in 60 Seconds with built-in Cloud Hosting, 99-in-1 Premium AIs, And Stunning Website Designs. Elixir AI prioritizes user experience, offering intuitive tools that require minimal technical expertise. Its adaptability makes it accessible to small and large businesses, fostering productivity and innovation.

Pros & Cons Of Elixir Pros Of Elixir • Unlimited content create • 24/7 support • Make HD video • One-time payment • No require experience and skill • Done-for-you all tools • Autopilot system • Faster loading • Live review site Cons Of Elixir • Depending on the internet connection Free Bonus & Pricing – Elixir Review Free Bonus & Pricing - Profitable Niche Finder Free Bonus & Pricing - Pack Of 50+ Premium Design Templates For Your Sites Total Value $19,973!

Frequently Asked Question – Elixir Review

Do I get the support here? Hmm, There is an effective support team.

Is There A Money-Back Guarantee? The agency provides you with 30 days money-back guarantee.

How can I Get Started This? You can start this only by following 3 steps.

Do I need any skills to drive the app? NO. No need for any tech skill drive to the app.

Affiliate disclaimer Thank you for reading my honest review. My honest opinion is shared in the review. An affiliate disclaimer is a statement to inform audiences that a company or individual may earn a commission or other compensation if they purchase products or services through links on their website, blog, social media, or other platforms. This disclaimer is essential for maintaining transparency and complying with legal requirements, such as those set by the Federal Trade Commission (FTC) in the United States. It ensures readers or viewers know of any potential bias or financial incentive behind recommendations. Typically, the disclaimer is placed prominently, either at the beginning or end of content and clearly states the nature of the affiliate relationship. For example, "This post may contain affiliate links, meaning I earn a commission if you purchase through my links at no extra cost." This builds trust with the audience while protecting the content creator from legal issues.

[neosciencehub]

Educational Reforms Prepare Youth for AI

Educational Reforms Prepare Youth for AI @neosciencehu #AI #healthcare #NVIDIA #AmazonWebServices #AItechnology #cloudcomputing #cybersecurity

With the establishment of initiatives like the AI curriculum in government schools and strategic partnerships with tech giants, how is Telangana preparing its younger generation for the AI-driven future? Telangana is taking significant strides to prepare its younger generation for an AI-driven future by integrating AI education into the mainstream academic curriculum and forming strategic…

[virtualrealitynewstoday]

Ray-Ban Meta智能眼鏡新增Amazon音樂、Calm應用功能，並支援Instagram簡易分享

自從Ray-Ban Meta智能眼鏡最近的更新後不到一個月，該產品已經在美國和加拿大推出了Meta AI with Vision的測試版，並增添了透過WhatsApp和Messenger分享視頻通話畫面的能力。如今，該公司再次帶來新的免提功能，使Ray-Ban Meta智能眼鏡變得更實用、有趣及具有社交性。此外，全球市場也已推出一些新款式供消費者購買。 為了使用戶在忙碌中也能保持冷靜與專注，Ray-Ban Meta與Calm合作，提供了一系列導向冥想和正念練習的內容，用戶僅需簡單的語音指令即可輕鬆使用。此外，所有Ray-Ban Meta智能眼鏡用戶還能獲得三個月的Calm訂閱服務，增加了這款眼鏡的附加價值。 隨著科技的進步，Ray-Ban…

[thegardengrows-infrozenrain]

dear amazon:

this information is fucking worthless to me. if you're going to ai-generate a summary of reviews, the least you could do is scrape the reviews to tell me what flowers the bees have been fed with (as that's what flavours the honey, is the nectar they're regurgitating to store in their hives, and the listing itself actually doesn't make mention? i suppose they forgot or something - however the reviews do say what it is if you scroll down), not tell me that the wax is crumbly and makes it hard to use. (?????? it's a fucking 7 oz honeycomb????? not a candle?????? you're supposed to eat it?????? and beeswax isn't crumbly anyways it's actually rather firm and chewy??????? it's almost like gum????)

seriously the least it could do mentioning the fact it's a "uNiQuE aNd DeLiCiOuS tReAt" is mention why that is.

also, how is crystallization a bad thing? it happens to honey sometimes and honestly the sugar crystals are really tasty because, you know, it's a little chunk of sugar, separating off from the rest of the honey.

by the way the honeycomb i was looking at was this lmao

[exampurnaukariadda]

Moto G04 With AI-Powered Quad Pixel Camera Launched In India: Check Price & Specifications

Moto G04, one of the most budget-friendly 5G smartphones by Motorola, has been launched in India. The Moto g04 offers a refresh rate of 90Hz, IPS LCD punch-hole display which runs on the latest Android 14 operating system. It comes with an acrylic glass finish (PMMA) design and you can get it in four colour variants. In this article, you will find all the details about the newly launched…

[jcmarchi]

Why Bad Product Data Is Costing Fashion More Than Ever and Where AI Fits In

New Post has been published on https://thedigitalinsider.com/why-bad-product-data-is-costing-fashion-more-than-ever-and-where-ai-fits-in/

Why Bad Product Data Is Costing Fashion More Than Ever and Where AI Fits In

In fashion, visuals are everything. But behind every product description page is data. From the cut of a hem to the color name in a dropdown, product data dictates how items are discovered, displayed, purchased, and returned. When it’s accurate, it quietly powers the entire system. When it’s not, the consequences hit everything from logistics to customer trust.

A 2024 Forrester Consulting study found that 83% of e-commerce leaders admit their product data is incomplete, inconsistent, inaccurate, unstructured, or outdated. And the effects aren’t just limited to the backend. Poor product data delays launches, limits visibility, frustrates customers, and drives up returns. In fashion, where precision drives sales and margins are tight, that becomes a serious liability.

As brands scale across more retail channels, the problem multiplies. Managing dozens of formatting requirements, image standards, and taxonomies at once adds layers of complexity. But multimodal AI–models that can process both images and text–is emerging as a tool that can finally address these challenges at scale.

When Product Data Undercuts the Sale

Every product page in digital retail is a customer touchpoint, and in fashion, that interaction demands accuracy. Mislabeling a color, omitting a material, or mismatching an image with its description doesn’t just look unprofessional, it disturbs the buying experience.

And it matters to shoppers. According to industry research:

42% of shoppers abandon their carts when product information is incomplete.

70% exit a product page entirely if the description feels unhelpful or vague.

87% say they’re unlikely to buy again after receiving an item that doesn’t match its online listing.

And when products are purchased based on inaccurate product descriptions, brands are being hit hard by returns. In 2024 alone, 42% of returns in the fashion sector were attributed to misrepresented or incomplete product information. For an industry already burdened by return costs and waste, the impact is hard to ignore.

And that’s only if the shopper ever sees the product—error-ridden data can tank visibility, burying items before they even have a chance to convert, leading to lower sales overall.

Why Fashion’s Data Problem Isn’t Going Away

If the issue is this widespread, why hasn’t the industry solved it? Because fashion product data is complicated, inconsistent, and often unstructured. And as more marketplaces emerge, the expectations keep shifting.

Every brand manages catalogs differently. Some rely on manual spreadsheets, others wrestle with rigid in-house systems, and many are tangled up in complex PIMs or ERPs. Meanwhile, retailers impose their own rules: one requires cropped torso shots, another insists on white backgrounds. Even the wrong color name–”orange” instead of “carrot”–can get a listing rejected.

These inconsistencies translate into a tremendous amount of manual work. A single SKU might need several different formatting passes to meet partner requirements. Multiply that by thousands of products and dozens of retail channels, and it’s no surprise that teams spend as much as half of their time just correcting data issues.

And while they’re doing that, priorities like seasonal launches and growth strategy fall behind. Listings go live missing key attributes, or are blocked entirely. Customers scroll past or purchase with incorrect expectations. The process meant to support growth becomes a recurring source of drag.

The Case for Multimodal AI

This is exactly the kind of problem multimodal AI is built to address. Unlike traditional automation tools, which rely on structured inputs, multimodal systems can analyze and make sense of both text and images, similar to how a human merchandiser would.

It can scan a photo and a product title, recognize design features like flutter sleeves or a V-neckline, and assign the correct category and tags required by a retailer. It can standardize inconsistent labels, mapping “navy,” “midnight,” and “indigo” to the same core value, while filling in missing attributes like material or fit.

At the technical level, this is made possible by vision-language models (VLMs) — advanced AI systems that jointly analyze product images and text (titles, descriptions) to understand each item holistically. These transformer-based models are trained on platform requirements, real-world listing performance, and historical catalog data. Over time, they get smarter, learning retailer taxonomies and fine-tuning predictions based on feedback and outcomes.

Tasks that used to take weeks can now be completed in hours, without sacrificing accuracy.

Why Clean Data Speeds Everything Up

When product data is complete, consistent, and well-organized, everything else runs much more smoothly. Items surface in the right searches, launch without delays, and appear in the filters customers actually use. The product shoppers see online is the one that arrives at their door.

That kind of clarity leads to tangible results across the entire retail operation. Retailers can onboard SKUs without lengthy back-and-forths. Marketplaces prioritize listings that meet their standards, improving visibility and placement. When information is clear and consistent, shoppers are more likely to convert and less likely to return what they bought. Even support teams benefit, with fewer complaints to resolve and less confusion to manage.

Scaling Without the Burnout

Brands aren’t just selling through their own sites anymore. They’re going live across Amazon, Nordstrom, Farfetch, Bloomingdale’s, and a long list of marketplaces, each with its own evolving requirements. Keeping up manually is exhausting, and over time, unrealistic and unsustainable.

Multimodal AI changes that by helping brands build adaptive infrastructure. These systems don’t just tag attributes, they learn over time. As new marketplace-specific rules are introduced or product photography evolves, listings can be updated and reformatted quickly, without starting from scratch.

Some tools go further, automatically generating compliant image sets, identifying gaps in attribute coverage, and even tailoring descriptions for specific regional markets. The goal isn’t to replace human teams. It’s to free them up to focus on what makes the brand unique, while letting AI handle the repetitive, rule-based tasks that slow them down.

Let Brands Be Creative and Let AI Handle the Rest

Fashion thrives on originality, not manual data entry. Messy product data can quietly derail even the strongest brands. When the basics aren’t right, everything else–from visibility to conversion to retention–starts to slip.

Multimodal AI offers a realistic, scalable path forward. It helps brands move faster without losing control, and brings order to a part of the business that’s long been defined by chaos.

Fashion moves fast. The brands that succeed will be the ones with systems built to keep up.

[malcolmschmitz]

So, there's a dirty little secret in indie publishing a lot of people won't tell you, and if you aren't aware of it, self-publishing feels even scarier than it actually is.

There's a subset of self-published indie authors who write a ludicrous number of books a year, we're talking double digit releases of full novels, and these folks make a lot of money telling you how you can do the same thing. A lot of them feature in breathless puff pieces about how "competitive" self-publishing is as an industry now.

A lot of these authors aren't being completely honest with you, though. They'll give you secrets for time management and plotting and outlining and marketing and what have you. But the way they're able to write, edit, and publish 10+ books a year, by and large, is that they're hiring ghostwriters.

They're using upwork or fiverr to find people to outline, draft, edit, and market their books. Most of them, presumably, do write some of their own stuff! But many "prolific" indie writers are absolutely using ghostwriters to speed up their process, get higher Amazon best-seller ratings, and, bluntly, make more money faster.

When you see some godawful puff piece floating around about how some indie writer is thinking about having to start using AI to "stay competitive in self-publishing", the part the journalist isn't telling you is that the 'indie writer' in question is planning to use AI instead of paying some guy on Upwork to do the drafting.

If you are writing your books the old fashioned way and are trying to build a readerbase who cares about your work, you don't need to use AI to 'stay competitive', because you're not competing with these people. You're playing an entirely different game.

[wolfliving]

Amazon Alexa+ AI surveillance warning signs

[realbigpodcastslut]

As much as I like Spotify Wrapped, I have to say this year was awful and the nail in the coffin for me.

Firstly, it was later than usual and was all AI. That isn't 100% a bad thing, but it was missing a ton of stats in favor for what AI generated genre description we were listening to through out the year. I wanted to know what my top genres were this year! What my tarot or energy was! But there was none of that, instead an AI podcast that reguritated the wrapped story and got it wrong for me. (Last time I checked by biggest listneing day couldn't have been 12k minutes as there's not even that many minutes in a week).

Spotify isn't even the best music streaming platform by far. It sucks with music quality where it is very noticible even if you aren't an audiophile. Let's compare individual plans. For 11.99 USD the highest streaming is 320kbps and doesn't have high-res. Amazon, Tidal, and Apple Music all have the ability to stream CD quality (for example Tidal is 1,411kbps) for their 10.99 USD each. And they have the same if not bigger catelogs. Oh and Tidal actually pays their artists and is clear about it (Spotify pays $0.00348 per stream, Amazon does $0.00426, Apple $0.00675, and Tidal $0.00876).

The biggest draw to Spotify for me was the statistics. I really enjoyed being able to see what I'm listening to and compare but now every other streaming service has that. I liked that. Except, each service is doing that now. And Every Noise At Once, a website I use to find great new music, is no longer updating cause Spotify laid off a ton of their staff. While it wasn't a Spotify "offical" thing, the person that ran it worked at Spotify and was a part of the daylist creation along with other things.

Another thing is the music recommendations. They suck. Spotify used to introduce me to a lot of new music but I feel like it just gives me the same five same songs. Shuffle is also rigged and anything generated has gone downhill since the layoff. There's no new layout or UI changes that have been asked for years. Like covers for playlist folders or album collections.

Spotify might have podcast integration but it SUCKS. There's so many better free options. I'm a big podcast listener and their podcatcher is the worst I've used. By far.

The only good thing about Spotify right now is the audiobooks for premium users. I liked that. Except the selection is limited you get 15 hours a month. There's other free options like Libby which is integrated with local libraries.

If you were disapointed with Spotify Wrapped this year, maybe look into some other options. Spotify has some nice features but I'm finding it less and less worth it.

[shamnadt]

Amazon Alexa to Get ChatGPT-like Capabilities; Plans for India, and Next Big Thing for Alexa

Big tech is going big on generative AI. Almost every tech product is set to add generative AI features in some form or the other. Amazon Alexa, the popular virtual assistant, is also getting its dose of conversational AI tools that can help accelerate the company’s vision to create a great AI-powered personal assistant. “It’s now a question of just how do we implement it,” said Dave Limp, senior…

[theslimeologist]

Like im not even kidding here I just had to tap two share buttons (one was just an image pointing to the interactive meatball menu) to copy something. to copy a link.

Copy? I think you mean share.