Henrik Jørgensen

AI summaries of customer feedback are boring and useless The problem with AI summaries is… that they summarize. Averaging everything you’re summarizing into a boring grey goo For example, feeding Spotify app store reviews to GPT gives you something like: Spotify app reviews reveal a mix of praise and criticism. Positive Feedback: • Music Discovery: Users appreciate Spotify's music discovery features and recommendation algorithms • User Interface: The interface is praised for its ease of use, making it simple to find songs and create playlists Negative Feedback: • Ads and Free Tier Limitations: Users on the free tier report frequent ads which can be frustrating  • Subscription Issues: Some users experience difficulties managing subscriptions • Content Recommendations: Some users feel the algorithm suggests unwanted genres This is boring/ useless… The purpose of a summary is to hit highlights, not to homogenize everything. A better version might look like: 𝐔𝐬𝐞𝐫𝐬 𝐚𝐫𝐞 𝐡𝐚𝐯𝐢𝐧𝐠 𝐢𝐬𝐬𝐮𝐞𝐬 𝐰𝐢𝐭𝐡 𝐬𝐡𝐮𝐟𝐟𝐥𝐞 𝐦𝐨𝐝𝐞 𝐚𝐧𝐝 𝐬𝐨𝐧𝐠 𝐪𝐮𝐞𝐮𝐞 (𝐫𝐞𝐩𝐨𝐫𝐭𝐞𝐝 𝐛𝐲 𝟒𝟎𝟎 𝐮𝐬𝐞𝐫𝐬 𝟒% 𝐨𝐟 𝐭𝐨𝐭𝐚𝐥) "𝑟𝑒𝑐𝑒𝑛𝑡𝑙𝑦 𝑖𝑛 𝑠ℎ𝑢𝑓𝑓𝑙𝑒 𝑚𝑦 𝑠𝑜𝑛𝑔𝑠 𝑤𝑖𝑙𝑙 𝑓𝑟𝑒𝑒𝑧𝑒 𝑎𝑓𝑡𝑒𝑟 𝑎 𝑓𝑒𝑤 𝑠𝑘𝑖𝑝𝑠"  "𝐼 𝑚𝑎𝑘𝑒 𝑝𝑙𝑎𝑦𝑙𝑖𝑠𝑡𝑠, ℎ𝑖𝑡 𝑡ℎ𝑒 𝑝𝑙𝑎𝑦 𝑏𝑢𝑡𝑡𝑜𝑛, 𝑎𝑛𝑑 𝑡ℎ𝑒 𝑠𝑎𝑚𝑒 4 𝑠𝑜𝑛𝑔𝑠 𝑝𝑙𝑎𝑦 𝑜𝑣𝑒𝑟 𝑎𝑛𝑑 𝑜𝑣𝑒𝑟 𝑖𝑓 𝐼 𝑑𝑜𝑛’𝑡 𝑟𝑒𝑠ℎ𝑢𝑓𝑓𝑙𝑒 𝑚𝑦𝑠𝑒𝑙𝑓" "𝑇ℎ𝑒 𝑞𝑢𝑒𝑢𝑒 𝑖𝑠 𝑏𝑟𝑜𝑘𝑒𝑛 𝑓𝑜𝑟 𝑚𝑒. 𝐴𝑑𝑑𝑒𝑑 𝑠𝑜𝑛𝑔𝑠 𝑎𝑟𝑒 𝑟𝑒𝑚𝑜𝑣𝑒𝑑" 𝐈𝐬𝐬𝐮𝐞𝐬 𝐰𝐢𝐭𝐡 𝐜𝐚𝐫 𝐦𝐨𝐝𝐞 𝐦𝐚𝐲 𝐛𝐞 𝐜𝐚𝐮𝐬𝐢𝐧𝐠 𝐬𝐚𝐟𝐞𝐭𝐲 𝐢𝐬𝐬𝐮𝐞𝐬 (𝐫𝐞𝐩𝐨𝐫𝐭𝐞𝐝 𝐛𝐲 𝟖𝟎 𝐮𝐬𝐞𝐫𝐬 𝟎.𝟖% 𝐨𝐟 𝐭𝐨𝐭𝐚𝐥) “𝑁𝑜𝑤 𝑒𝑣𝑒𝑟𝑦 𝑡𝑖𝑚𝑒 𝐼 𝑛𝑒𝑒𝑑 𝑡𝑜 𝑓𝑖𝑥 𝑚𝑦 𝑒𝑞𝑢𝑎𝑙𝑖𝑧𝑒𝑟 𝑐𝑢𝑧 𝐼’𝑚 𝑜𝑛 𝑡ℎ𝑒 ℎ𝑖𝑔ℎ𝑤𝑎𝑦 𝑎𝑛𝑑 𝑤𝑎𝑛𝑛𝑎 𝑡𝑢𝑟𝑛 𝑢𝑝 𝑡ℎ𝑒 𝑣𝑜𝑙𝑢𝑚𝑒 𝐼 ℎ𝑎𝑣𝑒 𝑡𝑜 𝑑𝑖𝑠𝑎𝑏𝑙𝑒 𝑐𝑎𝑟 𝑚𝑜𝑑𝑒 𝑓𝑖𝑟𝑠𝑡? 𝐻𝑜𝑤’𝑠 𝑡ℎ𝑎𝑡 𝑠𝑎𝑓𝑒𝑟" “𝐼 𝑤𝑖𝑠ℎ 𝑡ℎ𝑒 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 𝑤𝑜𝑢𝑙𝑑 𝑠𝑡𝑎𝑦 𝑖𝑛 𝑡ℎ𝑒 𝑠𝑎𝑚𝑒 𝑜𝑟𝑖𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑟𝑒𝑔𝑢𝑙𝑎𝑟 𝑎𝑛𝑑 𝑐𝑎𝑟 𝑚𝑜𝑑𝑒” 𝐓𝐡𝐞𝐫𝐞 𝐢𝐬 𝐚 𝐬𝐩𝐢𝐤𝐞 𝐢𝐧 𝐢𝐬𝐬𝐮𝐞𝐬 𝐰𝐢𝐭𝐡 𝐨𝐟𝐟𝐥𝐢𝐧𝐞 𝐦𝐨𝐝𝐞 (𝟒𝟎𝟎 𝐮𝐬𝐞𝐫𝐬, 𝟒% 𝐨𝐟 𝐭𝐨𝐭𝐚𝐥 𝐮𝐩 𝟏𝟎𝐱 𝐌𝐨𝐌) "𝐷𝑜𝑤𝑛𝑙𝑜𝑎𝑑𝑒𝑑 𝑠𝑜𝑛𝑔𝑠 𝑎𝑟𝑒 𝑢𝑛𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑤ℎ𝑒𝑛 𝑜𝑓𝑓𝑙𝑖𝑛𝑒, 𝑒𝑣𝑒𝑛 𝑡ℎ𝑜𝑢𝑔ℎ 𝑡ℎ𝑒𝑦 𝑠ℎ𝑜𝑤 𝑎𝑠 𝑑𝑜𝑤𝑛𝑙𝑜𝑎𝑑𝑒𝑑."  "𝐴𝑝𝑝 𝑘𝑒𝑒𝑝𝑠 𝑑𝑒𝑓𝑎𝑢𝑙𝑡𝑖𝑛𝑔 𝑡𝑜 𝑜𝑓𝑓𝑙𝑖𝑛𝑒 𝑚𝑜𝑑𝑒 𝑒𝑣𝑒𝑛 𝑤ℎ𝑒𝑛 𝐼 ℎ𝑎𝑣𝑒 𝑖𝑛𝑡𝑒𝑟𝑛𝑒𝑡 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛." This is a sampling, not a summary of the most interesting specific comments made by users, including specific facts and statistics. Moreover it is actionable and gives a starting point as to where we might dig further. This is not something AI is good at out of the box. It requires the ability to quantify what is being reported by users, decide on the most important points and present an output that is suitable to the use case Others see the same?

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Here are some thoughts from Anton Osika on AI for programming from an event and presentation he did yesterday!

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☕ The Coffee-Driven Code Review Manifesto: A Sleep-Deprived Guide to Excellence [Everyone talks about clean code, but let's be honest - most of our best reviews happen after that third espresso shot] 🌅 6 AM: First Commit Code looking pristine Tests all green Documentation spotless Reality: Haven't written a single line yet ☀️ 9 AM: The Morning Stand-up Revelation "Wait, did I just promise to deliver this by EOD?" Frantically opens IDE Types while drinking coffee Spills coffee on keyboard Git commits anyway 🌮 12 PM: The Lunchtime Code Review PR Comment: "Interesting choice of variable names. Is 'pizzaTime' really the best name for a payment processing function?"  Me: "I was hungry when I wrote that..." 🌙 5 PM: The Final Push git commit -m "Fixed everything (hopefully)"  git push origin please-work  git prayer --recursive 📊 Our Revolutionary Metrics: Bugs Found vs Coffees Consumed (1:1 ratio) Time Spent Reviewing vs Time Spent Making Memes About Code Number of "LGTM" Comments That Actually Meant "I Trust You” 🎯 Pro Tips for Sleep-Deprived Reviews: If the code works on the fifth try, it's probably production-ready Comments like "Magic. Don't Touch." are perfectly acceptable documentation If you can't explain your code, blame it on the keyboard 🤔 The Real Code Review Checklist: Does it work? No one knows why it works But it works Ship it! 💭 Question for fellow night owls: What's your record for most consecutive hours staring at a PR before realizing you were looking at the wrong branch? #CodingLife #TechHumor #SoftwareEngineering #DeveloperLife #CodeReview #CoffeeDriver Development #TechMemes

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𝟴𝟱% 𝗼𝗳 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗙𝗮𝗶𝗹: 𝗛𝗲𝗿𝗲'𝘀 𝗛𝗼𝘄 𝗜 𝗖𝗮𝗻 𝗛𝗲𝗹𝗽 𝗬𝗼𝘂 𝗕𝗲 𝘁𝗵𝗲 𝟭𝟱% 𝗧𝗵𝗮𝘁 𝗦𝘂𝗰𝗰𝗲𝗲𝗱 Did you know a staggering 85% of AI projects fall flat on their face? That's a sobering statistic, but don't despair! The good news is, by understanding the pitfalls, you can navigate them and join the elite 15% who triumph with AI. So, what do those 85% have in common? It often boils down to a few key missteps: -𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗜𝗻, 𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗢𝘂𝘁: AI is all about data. If your data is messy, incomplete, or biased, your AI will learn those biases and deliver flawed results. -𝗘𝘅𝗽𝗲𝗰𝘁𝗶𝗻𝗴 𝗠𝗮𝗴𝗶𝗰: AI is a powerful tool, but it's not a silver bullet. Trying to solve the wrong problem with AI, or expecting superhuman performance, is a recipe for disappointment. -𝗧𝗵𝗲 𝗦𝗸𝗶𝗹𝗹 𝗚𝗮𝗽: Developing and deploying AI solutions requires specialized knowledge. Without the right expertise on your team, you might struggle to get your project off the ground. Here's where I can help you become part of the successful 15%. My expertise in AI allows me to: -𝗦𝗰𝗿𝘂𝘁𝗶𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮: I can analyze your data to ensure it's clean, unbiased, and sufficient to train an accurate AI model. -𝗔𝗹𝗶𝗴𝗻 𝗬𝗼𝘂𝗿 𝗚𝗼𝗮𝗹𝘀: We'll work together to identify the problems AI can genuinely solve within your organization, setting your project up for success. -𝗕𝗿𝗶𝗱𝗴𝗲 𝘁𝗵𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗮𝗽: Whether you need help developing your AI model or integrating it seamlessly into your workflows, I'm here to guide you. Don't let AI become another failed experiment. Let's work together to make your project one of the 15% that thrives. 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 𝗺𝗲 𝘁𝗼𝗱𝗮𝘆 𝗮𝗻𝗱 𝗹𝗲𝘁'𝘀 𝘁𝘂𝗿𝗻 𝘆𝗼𝘂𝗿 𝗔𝗜 𝘃𝗶𝘀𝗶𝗼𝗻 𝗶𝗻𝘁𝗼 𝗿𝗲𝗮𝗹𝗶𝘁𝘆! . . . #artificalintelligence #aiprojects #naveedsarwar #webdevelopment #appdevelopment

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Thank you Sigurd Meldgaard for the boost 🙌🏻 3 weeks ago Ive raised a suggestion request for pub.dev on LinkedIn and on GitHub too, luckily some of the Flutter Dev and Google community members liked my idea and within a week they implemented the idea on https://pub.dev website ✨ ⭐ What's new on pub.dev: Now you can copy the latest version of any of your package from https://pub.dev website *directly from the listing* 😋 You dont have to dive into the detail page 'just to copy' the latest version ⚡ Also thanks for the approval István Soós 🌟 and thanks Lukas Klingsbo for the Github issue reminder 🦺 🔗 Heres the reference of my previous post: https://lnkd.in/em9vHCaW 🔗 Github Issue reference: https://lnkd.in/eUcj5grc #flutter #google #dart #flutterdev #flutterdeveloper #opensource #pubdev #linkedin #github #flutterdeveloper #fluttercommunity #support #suggestion #enhancement #developer #hybriddeveloper #userexperience

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Get Shit Done - Bootstrapping edition There is nothing better then the bootstrapping phase. Use what you have. Create more with less. I’ll bootstrap our venture to 10M EUR ARR. It will take time. I’ll invest time.

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𝗪𝗵𝘆 𝘄𝗼𝘂𝗹𝗱 𝘄𝗲 𝗹𝗲𝘁 𝗔𝗜 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗽𝗮𝗶𝗿 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗲𝗻𝘁𝗶𝗿𝗲𝗹𝘆? The idea is floating around: “With tools like GitHub Copilot, why not just skip human pair programming and let AI handle it?” Sure, AI can speed up repetitive tasks, help us get unstuck, and suggest code in an instant. But is that enough to replace pair programming? Not really - and here’s why: • 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗿𝗲𝗮𝗹 𝘁𝗲𝗮𝗺 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝘀. Pair programming isn’t just about one person’s productivity - it’s about leveling up the entire team. When two minds collaborate, each coder brings unique insights that grow everyone’s skills, enhancing the team as a whole. AI’s focused on helping the individual; human pairing benefits everyone. • 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. AI can be fast and clever, but it doesn’t grasp the big picture - the project’s goals, potential challenges, or the larger context that can impact decisions. A human partner intuitively adjusts to these layers, helping ensure that the code aligns with the long-term vision. • 𝗙𝗼𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗮𝗻𝗱 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆. There’s a big difference between code “suggestions” and true co-ownership. When we pair with another person, we share accountability for the final product. That sense of ownership builds commitment, quality, and trust within a team - something AI can’t replicate. AI is an incredible tool for productivity, but treating it as a “replacement” for pair programming risks missing out on the deeper benefits of human collaboration. What’s your take - could AI ever fully replace pair programming, or are we risking too much by letting go of human-to-human connection? #SoftwareDevelopment #Collaboration #PairProgramming #Copilot #AI

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Knowledge Sharing: Day 22 Breaking change if you are planning to upgrade your compileSDK to Android 15 or higher 📢 If you use Kotlin's list.removeFirst() or list.removeLast() extensions in your Android app, then, after bumping compileSdk to 35, your app will crash at runtime on older Android versions. #android15 #android #behaviour #development Kaara

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Its late for now. While im suft facebook i saw one question that make me confuse. Its something like this “ when client have 100.000 input and user in that time only fill in 50.000 input and they closing their browser, in next time they open that application its still have 50.000 input that that they filled out previously. How can we do that ? The condition given to us is : if the input not equal to 100.000 when client send request to server its will not save in database and do not using any db of client browser like localstorage. So 🧐-— How we can make that ? ——🧐” I think we should using cloud or store draft data in csv file if the input form is less than 100.000 while send request to server. What is you guy solution about this case 💻💻💻

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A year ago, my toolbox looked completely different. I had been testing tools, swapping them in and out, trying to find the perfect fit. Some stuck; others didn’t. Take OpenAI—it’s great, and now with its integration into Visual Studio Code, I love how seamlessly it fits into my workflow. The Pro version? It’s powerful, no doubt, but it’s still not there yet. I catch myself wishing for more speed, better organization—something to match the pace of my ideas. I know it’s coming, but I’m still waiting. Then there’s Galileo I stumbled on it while brainstorming one day. Imagine rapid prototyping without the Figma grind. It’s not about perfection; it’s about ideas flowing faster than you can sketch them And Notion. Ah, Notion. My everything-organizer. A year ago, it felt irreplaceable. Now? Still useful, but the sparkle’s dimmed. The AI isn’t what I hoped for, but it’s my rock for structure. Some tools, like Glide, aren’t just tools—they’re partners. I’ve been using Glide for years, and watching it evolve has been a joy. It’s flexible, intuitive, and the AI features? Game-changers. Others, like Botpress, are harder to categorize. Is it love? Maybe. It’s a solid tool for conversational prototypes, but it’s pricey, and I’m always wondering if there’s a replacement around the corner. And then there’s Zapier—not flashy, not perfect, but oh, the things it lets me do. Testing, automating, moving faster. It’s like duct tape for ideas. On the voice side, VAPI has been a standout. I’ve seen it grow, and it’s been rewarding to watch it improve over time. While competitors are making moves, VAPI consistently delivers. Speaking of voice, 11labs is unmatched in quality. It’s pricier than some alternatives, but you can’t beat the results. Whether it’s for experimenting or creating polished work, this tool always impresses me. On the creative side, I discovered Suno. I don’t know the first thing about music, but this tool makes me feel like I do. I’m creating tracks that sound professional, and I can’t stop imagining the fun projects I’ll build with it. Visuals? That’s been a journey. I’ve started exploring Sora for images—imperfect but promising. Runway has pulled me into video editing, and while I’ve read great things about Veo 2, I’m still waiting to get my hands on it. As for MidJourney, it was fun, but my focus has shifted. Then there’s Tavus. I’ve been testing it, and while it doesn’t feel completely natural yet, I see so much potential. The way it integrates with VAPI is promising—I’m excited to see where it goes next. Of course, not everything’s sunshine. Dialogflow and Meta environments? A daily battle. Twilio and Vonage? Necessary evils. And then there’s Airtable. Great tool, but somehow, it just didn’t find its place in my workflow. A year ago, I couldn’t have predicted where I’d land. Now, I’m excited to see what sticks, what evolves, and what’s just around the corner. What tools are surprising you lately?

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Have you tried the FEDn API Client to interact with your Studio project? Learn about it from Niklas Holmström in this mini-tutorial: https://lnkd.in/d6fEUM5Q #federaredlearning

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Bubble .io is like a giant box of building blocks. It's awesome, but just like any tool, it works best for certain types of projects. So, when is Bubble the perfect playdate for your idea, and when might you need a different kind of tool? Bubble Shines When: - The Idea Box is Overflowing: Got a brilliant idea but coding seems like a mountain to climb? Bubble is the place for you! - Speed is Key: Need to develop your app fast? Bubble lets you build quickly, so you can get valuable feedback and iterate on your app in a flash. - Complexity Stays Manageable: Bubble excels at building user-friendly apps with clear functionality. For example to-do lists, social media calendars, video conference apps, dashboards, appointment schedulers, or internal team tools. Maybe Consider Another Tool When: - Going Hyper-Complex: Think apps with tons of complicated calculations. While Bubble is powerful, for these highly technical projects, traditional coding might be a better fit. - Gaming Galaxy: Building the next Fortnite? Bubble is great for many things, but a complex game with advanced graphics will require a different approach. Remember, Bubble is a fantastic tool, but it's not a one-size-fits-all solution. The key is understanding your project's needs.

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In this compelling episode of the Mob Mentality Show, we dive deep into the art of Sticky Mob Programming with Lennart Fridén, who brings a wealth of knowledge from his decade-long journey coaching mob programming in Sweden. Whether you're a seasoned mob programmer or new to the concept, this episode is packed with actionable insights and real-world examples that can help you make mobbing a lasting fixture in your team's workflow. https://lnkd.in/g8k_WAjF https://lnkd.in/gv_GWxCy ### Key Discussion Points: **Sticky Mob Programming:** - Lennart’s insights on mob stickiness - Beyond just trying mobbing - Persistent mob habits even without the nudging of a coach  - Mob stickiness challenges and triumphs - Roles: outsider, insider, first follower (AKA eager first dance partner),  - Lessons from "Power of Habit" - Mob as keystone habit leading to a cascade of other beneficial practices - Community and belonging - Dealing with stagnation - Vulnerability and growth **Technical Coaching and Redundancy:** - Embedding coaching and making the coach's role redundant (times two) - Teaching for sustainability - Gradual coach transition examples - Handling eager vs. apathetic first follower (potential future coach) **P.S.** Reach out to Lennart Fridén for guidance on introducing mobbing and ensuring it sticks in your organization. Tune in to discover how to make mob programming a sticky and sustainable practice in your organization! #MobProgramming #EnsembleProgramming #PairProgramming #Coaching #SoftwareDevelopment #Habit #OrganizationChange #ContinuousImprovement #DiffusionOfInnovation #TDD #Learning #Change #ExtremeProgramming #Mentoring #SystemsThinking #StartWithWhy

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Sorry, no generic quotes written on the side of a coffee cup today (maybe later though!). Another pattern.... The Chain of Responsibility pattern allows you to structure code with independent handlers that each take on a single responsibility. Not only does this keep your code modular, but it also makes it far easier to test, modify, or even swap out specific tasks. Why use Chain of Responsibility? Consider an endpoint that requires a request to be authenticated, authorised and valid. You could set up a chain of handlers: - An Auth handler checks if the user is logged in. - A Permissions handler checks if the user has the correct permissions. - A Validation handler ascertains if the request itself is valid. This chain allows each handler to focus on its specific task, making it easy to update or improve individual parts of the moderation process without overhauling the entire system. Here's some advantages: - Single Responsibility: Each handler in the chain has a well-defined job, making it easy to pinpoint where to make changes or fix issues. - Flexible and Extensible: Need to add or remove a validation step? No problem! Handlers can be reconfigured without disrupting the entire chain. - Scales with Complexity: As the application grows, this pattern eliminates the need for an oversized, monolithic processing class. Instead of piling logic into one place, we break it down and maintain control. But what about an alternative? For smaller projects, a single class might seem appealing—it’s straightforward and reduces file management.... ... But as your project grows, so does the headache of maintaining one bloated class i.e - a monolithic class. More information here: https://lnkd.in/empV6q72 I'll keep adding more. 🤠

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I recently had a conversation with Mariia Vain, a Staff Software Engineer at AgencyAnalytics, about one of the most critical skills for Staff+ engineers: prioritization. As Mariia pointed out, there will always be countless things you could be fixing, improving, or building. The challenge isn’t in finding what to work on but in narrowing it down to the 2-3 things that will have the most impact over the year. This can involve engaging in deep conversations and interviews with engineers across different areas, identifying opportunities where solving one issue could have rippling effects across teams. Using data like performance metrics, security reports, and reliability stats to back up these decisions can also make a stronger case for why a particular direction is chosen. Setting up recurring meetings with focused teams—like a backend council or guild—can help align on priorities and gather insights from those closest to the work. At the end of the day, it’s up to the Staff+ engineer to make the final call and communicate this clearly to engineering leadership. While ensuring that all voices are heard, sometimes you need to disagree and commit in order to move forward. How do you navigate prioritization in your role? I’d love to hear your strategies and experiences! If you're a Staff+ Engineer I'd love to connect: https://lnkd.in/gKT7unhC

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"Stop creating architecture diagrams no one understands." In this interview with Björn Helgeson, VP of Engineering at Mindler, he discusses why shared vocabulary should come before technical documentation, and how it enables better business-engineering alignment. Is your architecture documentation building bridges or barriers between teams? Read Björn's interview, featured in the latest edition of Exec Engineering: https://lnkd.in/eh9SYcJh

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I built this quiz in ~60 minutes yesterday - and it would be an insult to call me a developer. It is fully functional with scoring, a progress bar, and a name/email form at the end (check it out in the first comment if you want). You've probably seen a lot of cool stuff being done with AI, but maybe not when it comes to development (both front and backend). Here are some cool platforms you can use to 1) Prototype, 2) Build front-end and 3) Build back-end - but do note that the current state is mostly comparable to a junior developer. 𝐕𝟎: This NextJS/Tailwind tool lives up to its name: Version 0. It's a great way to compose a design simply by having a conversation with the bot—and it works really well. Try it out. 𝐂𝐮𝐫𝐬𝐨𝐫: A code editor that can help you code - the cool thing is that it can include your codebase, external docs and more. Try it out if you're a bit more technical. 𝐂𝐥𝐚𝐮𝐝𝐞: Claude is similar to ChatGPT, but it's better for development (I only used Claude to create the quiz - it even helped me implement it in WordPress with Formidable Forms). 𝐒𝐨𝐦𝐞 𝐭𝐢𝐩𝐬: 1. If it's not working, tell the AI it's not working. You can upload a screenshot, error message or just describe what's going on, and it will come up with a solution. You may have to do this multiple times, and it's a skill in itself. 2. You can try all these tools for free! 3. You can prime the AI by giving it more context on what you want to achieve, rather than just a simple prompt. Find links to each platform in the comments 👇

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A few weeks ago @wearepixelau forked and started maintaining a cart package for #laravel. The original one we used on 20+ custom eCommerce builds was no longer actively maintained. Since then, we've doubled the tests, and published 6 new features. What's next? A small 🧵 We love building custom eCommerce shops on Laravel. It offers unparalleled customisation for our clients and means we can do some incredibly complex discounting with the use of conditions in this package. We can literally do anything we want. We want to make this THE BEST cart package for Laravel. I have been looking through other options to look through open issues, PRs, etc, all in an attempt to find out what people are requesting and what people want to see in a cart package for Laravel. My question for everyone who sees this, is: What would you love to see in a cart package? Let me know or open a discussion/issue on the repository: 🔗 https://lnkd.in/g9NvbSjT

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