Watch how Spotify turned a "lessons learned" moment into one of the fastest-growing projects in the cloud native landscape in our full documentary: Backstage: The Open Portal to Happy Developers. With Backstage, Spotify chose a different path than its predecessor, Helios: * Open Governance: Donating the project to the CNCF to foster neutral growth. * Extensibility: Building a plugin architecture that thrives on global contribution. * Sustainability: Ensuring the tool evolves far beyond the walls of a single company. Go behind the scenes to see how a shift in mindset created a new standard for developer experience. 🚀 https://lnkd.in/gJzJmZw5
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Speed and stability aren’t trade-offs. The real lesson from Spotify isn’t to "test more" but to "design better release systems." Layered rollouts, feature flags, and automation make fast delivery safer, not riskier. https://lnkd.in/dkk_RQU2
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The company that burned 100M tokens last month might be losing to the one that burned 10M. Anthropic now shows you how many tokens you’ve used. It’s satisfying the way Spotify Wrapped is satisfying. Big numbers feel like progress. But measuring token spend is like measuring employee company card spend. Higher isn’t always better. Most of the time it’s not. But sometimes the spend is justified and you want it. The number alone tells you nothing. What matters is what you got for it. The real metrics for AI adoption aren’t about consumption: 👍 How many tasks run without a human babysitting them. 👍 Whether context flows between people and agents without breaking. 👍 Whether your prompts are getting better over time or just longer. We don’t have established practice for any of this yet. Nobody does. Everyone’s experimenting in the dark, and the orgs that figure out their feedback loops first will compound while others just burn tokens.
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Inspired by Spotify’s “Wrapped” 🎧, this April we launch EU Programmes Wrapped2025 – a digital campaign showcasing how EU investments create real impact in Bulgaria. From education and social support to infrastructure, innovation, and green solutions 🌍 - we turn results into clear, visual stories 📊 For More: https://linkly.link/2fMsu #EUFunds #CohesionPolicy #EUinBulgaria #Wrapped2025 #Storytelling
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Rive is the new engagement booster lately And Spotify realized this shift early. Spotify reported that they used Rive for the Spotify wrapped of 2025. Which resulted in engagement going up by 20% as 300 million users engaged and the shares going up by 42% as 630 million shared. According to the Co CEO Alex Norström: “We turned up the dial this year, and the response was redeeming.” This was possible because Spotify turned the wrap into an interactive experience. Makes you wonder, how many products are still just showing information instead of trying out Rive to impress their users?
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Honestly? I feel like feature flags are SO underrated. Barely anyone talks about them. I didn't give them much thought either. Assumed it was just a simple on/off toggle. Then I saw how we actually use them at work. Our app runs for millions of users and it changed how I think about shipping software entirely. A flag isn't just "enable or disable." It's a full config. It controls who sees what, how much, and when. 🎛️ → Roll out to 1% of users first → A/B test without touching code → Kill a broken feature instantly ⚡ → Run the same feature differently per region or user type - same code, zero extra deployments 🌍 Came across Spotify doing this recently and had to share: Their home screen flag controls font size, shortcuts, layout, all from one config object. No redeployment. No risk. Just flip. 🎵 The quiet superpower? You separate CODE DEPLOYMENT from FEATURE RELEASE. Ship whenever. Release on your terms. Simple idea. Massive impact. Wildly used. 🔥 I'll keep dropping things I find interesting at work, the small stuff that doesn't get enough attention but actually matters. 🔗 Spotify's take on feature flags: https://lnkd.in/gPXVyHGB #Engineering #FeatureFlags #ProductDevelopment #SoftwareEngineering #TechLearnings
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Most product teams leverage data to enhance their offerings. Spotify exemplified this by making users feel acknowledged, bridging the gap between a feature and a cultural moment. In 2015, Spotify faced a retention issue hidden within a growth narrative. While users were streaming and numbers appeared strong, the experience felt transactional users came, listened, and left. The team posed a pivotal question: "Why should a user feel that Spotify is theirs?" This inquiry led to the creation of Wrapped. Rather than introducing a new feature, Wrapped emerged from the data Spotify already possessed; 365 days of plays, skips, saves, and late-night guilty pleasures, repackaged as a gift for users. Key decisions that transformed raw data into a viral marketing phenomenon included: - Designing for sharing rather than mere views - Utilizing specificity to create the "how did they know?" moment - Transforming statistics into identity labels that users wanted to adopt - Engineering a sense of belonging alongside individuality The outcome was significant: - A 21% spike in app downloads in December 2020 - 90 million users engaged that year - 227 million monthly active users engaged in 2023 A crucial lesson learned was that when Wrapped ceased to feel personal in 2024, users quickly noticed. The product that cultivates loyalty is one that continually earns it. This is what Product Sense looks like in practice not just a framework on a slide deck. It starts with human psychology and works backward to the feature. What product do you believe demonstrates the best Product Sense currently? Share your thoughts in the comments. Save this post if you're involved in product development or management; this framework is applicable to any product utilizing user data. #ProductManagement #ProductSense #Spotify #ProductThinking #GrowthStrategy #ProductLeadership #UXDesign #StartupLessons #BuildInPublic
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I built something this weekend that I’m genuinely proud of! Wuhoo! As a Software Engineer who also loves working out, one thing that always bothered me was how messy my Spotify playlists were — slow songs, hype tracks, and cooldown vibes all mixed together. Not exactly ideal when you’re mid-squat fighting for your life or doing interval runs and Araw Gabi by Regine Velasquez suddenly plays. Lol. So I built (and honestly vibe-coded) a Workout Playlist Builder that automatically sorts any Spotify playlist by BPM into Warmup, Peak, and Cooldown zones. I went into this with only a bit of knowledge in Next.js and just a rough idea in my head. Claude helped me structure my thoughts and turn that idea into actual steps I could build on. The rest was a lot of experimenting, Googling, and learning things properly by doing them. Building this helped a lot of concepts finally click for me — especially authentication flows, API orchestration, and how data actually moves across a full-stack app. Tech stack: → Next.js 14 + TypeScript → Spotify Web API for fetching & saving playlists → Spotify Web Playback SDK → Deezer API + Essentia.js for BPM detection → NextAuth.js for OAuth → Deployed on Vercel The hardest part was Spotify recently tightened their Developer Mode rules where apps are now limited to only 5 authorized users unless granted Extended Access, which is mostly reserved for larger organizations. So this project currently runs as a small beta with a few testers only. Shoutout to Jonalyn Benliro, Jomer Cabrera, and three other friends who I forced to test this (just kidding!) and get some feedback. But the most satisfying part was I could now listen to tracks that would help me finish my sets! ;) Building tools that solve your own problems while learning a new stack along the way really was something! Try it here: https://lnkd.in/gVKiyc4K (Note: access is limited due to Spotify developer mode restrictions.)
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Zaid builds with Soundcheck. Soundcheck is Spotify’s scorecard plugin for Backstage. It promotes tech standards in a way that isn’t annoying to your developers. As it turns out, developers *want* to keep up with migrations, security protocols, testing requirements, and other standards — they just don’t always know what those standards are, where to find them, or what changed since the last time they looked. Soundcheck lets you define standards and makes them visible and actionable in the same place you manage your components — right inside Backstage. That way owners — and their AI helpers — can improve the health of components one satisfying green check mark at a time. Suddenly, keeping up with standards isn’t confusing or boring. Built for developers. Build like Spotify. See more ways we build with Backstage: https://hubs.li/Q047qhG60 🔈✅ #BuildLikeSpotify
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Spotify's largest cost line doesn't behave like normal operating expenses — it's locked in by contract, not driven by market forces. While most companies see their biggest costs fluctuate with input prices, labour markets, or operational efficiency gains, Spotify's content licensing payments are contractually fixed as revenue percentages. This means the company's primary cost structure moves in lockstep with its top line, regardless of underlying economic conditions or technological improvements that might otherwise drive deflation. The mechanism is straightforward: when Spotify negotiates with major labels, the deals typically specify royalty rates as percentages of revenue rather than per-stream payments or fixed amounts. As streaming revenue grows, content costs grow proportionally. When revenue falls, content costs fall by the same percentage. This creates a fundamentally different cost dynamic than most technology companies experience. This contractual structure insulates Spotify from certain types of cost inflation but also prevents it from capturing the full benefit of technological efficiency gains that normally flow to company margins. The question becomes whether this revenue-linked cost base creates competitive advantages or disadvantages when AI-driven deflation reshapes content creation, discovery, and distribution economics across the broader media landscape. Link in comments 👇
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Spotify's largest cost line doesn't behave like normal operating expenses — it's locked in by contract, not driven by market forces. While most companies see their biggest costs fluctuate with input prices, labour markets, or operational efficiency gains, Spotify's content licensing payments are contractually fixed as revenue percentages. This means the company's primary cost structure moves in lockstep with its top line, regardless of underlying economic conditions or technological improvements that might otherwise drive deflation. The mechanism is straightforward: when Spotify negotiates with major labels, the deals typically specify royalty rates as percentages of revenue rather than per-stream payments or fixed amounts. As streaming revenue grows, content costs grow proportionally. When revenue falls, content costs fall by the same percentage. This creates a fundamentally different cost dynamic than most technology companies experience. This contractual structure insulates Spotify from certain types of cost inflation but also prevents it from capturing the full benefit of technological efficiency gains that normally flow to company margins. The question becomes whether this revenue-linked cost base creates competitive advantages or disadvantages when AI-driven deflation reshapes content creation, discovery, and distribution economics across the broader media landscape. Link in comments 👇
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