Insights for Problem-Solving from Technology Blogs

Support teams are outpacing engineers at building 'software'?! But is this happening in India? I asked Twitter. The responses from founders building at decent scale were eye-opening. Ankit Sawant at OnArrival told me about a colleague who isn't an engineer but identified a critical workflow problem. This person built a tool that connects multiple Freshdesk accounts through APIs, centralizing customer queries that were previously scattered across different systems. The solution was so effective that it's now being polished by the tech team for company-wide deployment. What's remarkable isn't the technical achievement—it's that someone who understood the daily pain of juggling multiple support dashboards could articulate the exact solution needed. Abhimanyu Saxena at Scaler confirmed a similar pattern: their IT support and HR teams are directly using Cursor to build internal tools. Automated candidate screening and the likes. These aren't engineers moonlighting in other departments—these are domain experts who got tired of waiting for technical solutions and decided to build them themselves. The implications are profound: the people who understand business processes most intimately are becoming the ones who optimize them. Three lessons for staying relevant: --> Your domain expertise is your competitive advantage. Support teams understand support workflows better than any engineer ever will. Sales teams know sales friction better than any product manager. HR teams know people processes better than any developer. --> Stop saying "that's a technical request" and start saying "let me figure this out." The barrier between identifying problems and solving them is disappearing. --> Learn to think in solutions, not syntax. AI handles the implementation; you handle the business logic and user experience. Trust me, tools like Replit have made it real easy. The future belongs to problem-solvers who can articulate what needs to happen, not just those who know how to make it happen. Drop a comment if your non-technical team has built something that solved a real business problem? Want to collect more such real-world stories.

“What Do We Know To Be Absolutely True?”  This simple yet profound question is at the heart of first principles thinking—a problem-solving approach that challenges assumptions and rebuilds solutions from the ground up. 💡  In a world full of best practices, templates, and "this is how we've always done it," thinking from first principles forces us to strip problems down to their fundamental truths and reimagine possibilities.  Here’s how it works:   1️⃣ Identify the fundamental truths: What do we know for sure, without relying on assumptions?   2️⃣ Rebuild from scratch: Starting from those truths, what’s the most effective solution?   3️⃣ Challenge the norm: Let go of the status quo if it doesn’t align with these core truths.  Examples of First Principles Thinking: 🔹 Elon Musk and SpaceX   - Problem: Reducing the cost of space travel.   - First Principles Thinking: Musk analyzed the components of a rocket (materials, fuel, etc.) rather than accepting the high prices set by existing manufacturers. He researched the cost of raw materials and concluded that building rockets from scratch could be significantly cheaper than purchasing them. This approach led to breakthroughs in reusable rockets and affordable space exploration.  🔹 Software Development  - Problem: Building a new software application.   - First Principles Thinking: Rather than copying existing applications, a developer could identify the core functionalities needed by users, understand the underlying technologies (like databases and programming languages), and create a solution that meets those needs in the most efficient way.  Why does this matter? It’s a mindset that fosters creativity, innovation, and game-changing insights. Whether in technology, design, strategy, or personal growth, questioning assumptions opens doors to better solutions.  Have you ever applied first principles thinking in your work or life? Share your experiences below—I’d love to hear how you’ve challenged assumptions and innovated! 👇  #Leadership #Innovation #FirstPrinciples #ProblemSolving #GrowthMindset #Creativity #Strategy #Entrepreneurship #Technology #CriticalThinking #PersonalGrowth #BusinessTransformation

I spent 200 hours reading AI company blogs. These 7 taught me what $120K courses couldn't. After leading AI teams for years, I've seen too many engineers get stuck in academic theory. So I dove deep into engineering blogs from OpenAI, Anthropic, Google, Meta, Cohere, DeepMind, and Hugging Face to find what really matters in production. Here's what these companies understand that most don't: 🔸 OpenAI's Scaling Laws Blog https://lnkd.in/gm8UU3j5 The real insight: Model size isn't everything ↳ Compute, data, and parameters must scale together ↳ There's a sweet spot for efficiency ↳ Bigger isn't always better for your use case 🔸 Anthropic's Constitutional AI Series https://lnkd.in/giMBdmdc The real insight: Alignment isn't an afterthought ↳ Build safety into the training process ↳ Let AI critique and improve itself ↳ Values must be baked in, not bolted on 🔸 Google's Attention Is All You Need https://lnkd.in/gMGHJGAC The real insight: Simplicity beats complexity ↳ Transformers replaced complex architectures ↳ Parallel processing changed everything ↳ Sometimes removing parts makes things better 🔸 DeepMind's Chinchilla Paper https://lnkd.in/gpaSVi5A The real insight: We've been training models wrong ↳ Most models are undertrained on data ↳ Optimal ratios exist between parameters and tokens ↳ Smaller models + more data = better results 🔸 Meta's LLaMA Blog Posts https://lnkd.in/g_MFCRyi The real insight: Open source changes the game ↳ Efficiency matters more than raw performance ↳ Community innovation beats closed development ↳ Accessibility drives real-world impact 🔸 Cohere's RAG vs Fine-tuning Guide https://lnkd.in/gGb6_gEX The real insight: Choose your weapon wisely ↳ RAG for real-time, dynamic information ↳ Fine-tuning for deep domain expertise ↳ Combine both for maximum impact 🔸 Hugging Face's Model Training Insights https://lnkd.in/gX_zGg6c The real insight: Democratization drives innovation ↳ Tools matter as much as models ↳ Community knowledge compounds faster ↳ Making AI accessible creates exponential value The pattern across all these? Every breakthrough came from questioning assumptions. Think like engineers, not just researchers: • Question everything • Test at scale • Share what works • Build for impact The best part? All this knowledge is free. Most engineers just don't know where to look. What company blogs have changed how you build AI? ♻️ Repost to help other engineering leaders ➕ Follow me, Sairam, for more production AI insights

A simple bug turned into a deep dive into algorithms. Who knew bioinformatics could fix a JavaScript UI bug? Curious how sequence alignment solved browser text selection chaos? Read on. Sometimes, the best solutions come from unexpected places. I was reminded of an algorithm I learned in my undergraduate bioinformatics days, and it turned out to be the key to solving a tricky web development bug. I applied the Smith-Waterman sequence alignment algorithm—typically used for DNA/protein analysis—to match messy browser text selections to clean HTML content. I recently faced a frustrating bug in my canvas-chat project: text highlighting broke when users selected content from markdown tables with KaTeX-rendered math. My initial normalization approach quickly became a tangle of edge cases and off-by-one errors. The breakthrough came when I reframed the problem as a sequence alignment challenge. By leveraging Smith-Waterman, I could robustly match user selections (with all their browser-induced quirks) to the source HTML, handling insertions, deletions, and mismatches gracefully. This experience reinforced the value of cross-domain knowledge. Algorithms from one field can unlock solutions in another—sometimes in ways you’d never expect. If you’re interested in the technical details or want to see the code, check out my full write-up here: https://lnkd.in/ev54Zjeg Have you ever solved a problem by borrowing an idea from a completely different field? I’d love to hear your stories! #webdevelopment #algorithms #bioinformatics #javascript #problemsolving

Starting with the Problem, not an AI solution matters! The approach we take with Generative AI tools like ChatGPT, Co-Pilot, Gemini, and Claude can make or break their impact on a business. Too often, we rush into solutions like chatbots, fancy AI features, or shiny tools without fully understanding the problems we’re trying to solve. This leads to disappointment and inefficiencies. Sound familiar? The real key lies in starting with the problem: 🎯 Understand the pain points. 🎯 Map the workflows (or analyze them if they’re not formalized). 🎯 Design solutions tailored to your unique needs. Take Toyota, for example. Their success with Lean principles wasn’t about adding tools—it was about improving processes. Generative AI needs the same approach. Whether it’s breaking tasks into smaller steps for specialized AI agents or automating repetitive processes, success comes when AI aligns with real business needs. Are we too focused on chatbot-first thinking, or are you seeing success starting with the problem? #GenerativeAI #AIinBusiness #ProblemSolving #ProcessImprovement #WorkflowAutomation #AILeadership #TechInnovation #DigitalTransformation #ThoughtLeadership #FutureOfWork #AI

I feel like I've started to sound like a broken record, continually telling folks to solve for the problem, not the discomfort.  What do I mean by that? Not everyone sits in a role or team that can see the "big picture" or the dependencies involved around an issue. It's our job to gather business requirements and conduct root cause analysis to determine what the right solution for the business problem is.  Remember, we barely have enough time to do it once, let alone have to go back and redo a second or third time because we solved for the discomfort instead of the problem. With that in mind, here are my 5 reasons you need to know the problem to solve it: 1) Avoid Ineffective Solutions: Jumping straight to a solution without fully understanding the root causes of the problem can lead to ineffective or incomplete solutions. The solution may address surface-level symptoms but fail to resolve the underlying issues. 2) Align Perspectives: Different stakeholders might have varying views on the core problem. By exploring the problem collaboratively first, you can get alignment on the true nature of the challenge before proposing solutions. 3) Encourage Creativity: Coming with a preconceived solution can limit your thinking and blind you to better alternatives. An open exploration of the problem fosters a creative environment where more innovative and effective ideas can emerge. 4) Avoid Overconfidence: Proposing a solution before understanding the problem comes across as presumptuous. It signals you may not fully appreciate the nuances and complexities involved from others' viewpoints, causing defensiveness and resistance. 5) Build Stakeholder Buy-In: Defining the problem collaboratively ensures stakeholders feel heard and involved. They are more likely to support solutions they helped shape from the outset. #ProcessImprovement #LearningAndDevelopment #BusinessRequirements #CriticalThinking

Most people ask AI one question and get a 500-word essay back. 𝗜 𝗮𝗺 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝘁𝗵𝗶𝗻𝗸𝘀 𝗶𝗻 𝗹𝗮𝘆𝗲𝗿𝘀 𝗶𝗻𝘀𝘁𝗲𝗮𝗱. Here's what I learned after watching teams struggle with AI outputs that looked impressive but were impossible to act on: The real problem isn't that AI can't solve complex problems. It's that it dumps everything into one massive response that nobody knows how to validate, break down, or actually use. So I created the Hierarchical Reasoning System - a framework that transforms messy problems into structured, actionable strategies. Instead of getting one giant wall of text, you get: → Strategic foundation layer (the big picture) → Component analysis layer (the building blocks) → Action execution layer (what to do tomorrow) Why this matters: Every executive I know has the same frustration: "AI gives me answers, but I need a roadmap." This isn't just another AI tool. It's a thinking partner that structures problems the way our brain actually needs to solve them. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? Teams can now take complex challenges - market entry strategies, legal case analysis, technical architecture decisions - and get outputs they can immediately present to stakeholders, iterate on, and execute. 𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗶𝘁 𝗹𝗶𝘃𝗲 𝗮𝘁: https://lnkd.in/eQ6dXt8Y What complex problem would you want an AI to break down into clear, actionable layers for you? #AI #ProblemSolving #Innovation #ArtificialIntelligence #BusinessStrategy #TechInnovation #Entrepreneurship #Leadership #MachineLearning #Startups #ProductivityHacks #TechCareers #AIRevolution #BuildInPublic #SoftwareDevelopment #DataScience #Founders #TechFounders