Rakesh Hadne Sreenath’s Post

A team came to us with a problem that did not look serious at first. “Everything works… until more users show up.” At low traffic, their product felt fine. It was fast, stable and reliable. But as usage grew, things started to crack: • APIs slowed down under load • Simple actions took seconds • New features started breaking existing ones Their first instinct? “Let’s optimize performance.” But that was not the real issue. Here’s what we found: The system was not built to scale. It was built to work for now. Tightly coupled components. No clear separation of responsibilities. Database queries were doing more work than they should. So instead of patching symptoms, we stepped back. What did we change? • Restructured the architecture for separation & scalability • Optimized data flow and reduced unnecessary load • Introduced async processing where it actually mattered • Simplified parts of the system that were over-engineered The result: The same product, but now it can handle growth without breaking. No panic fixes. No constant firefighting. No “we’ll deal with it later.” Scaling did not break their system. It revealed what was already fragile. At Coders Alley, we do not just build for launch day. We build for what happens after things start working. If your product works but feels fragile under growth, you do not need more patches. You need a better foundation. #SoftwareArchitecture #ScalableSystems #SystemDesign #BackendDevelopment #TechLeadership #StartupGrowth #Engineering #ProductDevelopment #BuildToScale #CodersAlley #DigitalTransformation

Four years ago at Edge Browser, I learned a hard truth: Writing code is only a small fraction of software engineering. As a junior dev, you think, "Why can't we just write logic and push?" But shipping enterprise software requires a massive lifecycle before a PR is merged: 1. Privacy and Security Reviews 2. Compliance and Design Approvals 3. Telemetry and Performance Analysis 4. Testing and Documentation Writing security docs before checking in a PR felt like a burden. But it teaches us how to build robust systems, forcing us to think about edge cases before touching your IDE. Today, that landscape is entirely different. With AI assisted development, the mental blockers that slowed us down are mostly gone. Need a privacy review? Explain your architecture and get a first draft in seconds. Use AI agents to handle repetitive manual checks. You don't have to be hesitant to build a quick POC. Instead of getting bogged down in boilerplate, your focus goes to what requires deep thought: system architecture, better design, and solving core engineering problems. We spend less time typing and more time engineering. But this speed brings a new bottleneck, which I will cover next. #SoftwareEngineering #SystemArchitecture #EnterpriseSoftware #AIAssistedDevelopment #Tech #Browser #EdgeBrowser

What was your favorite decade to be a software engineer—and why? Was it the early days of tight constraints and clever optimizations? The era of rapid enterprise growth? The open-source explosion? The mobile revolution? Or today’s world of AI-assisted development? Every decade seems to shape not just the tools we use, but how we think about building software: - The problems we solved - The constraints we worked within - The craftsmanship we took pride in Some of us miss the simplicity and focus of earlier environments. Others thrive in today’s abundance of frameworks, compute power, and intelligent tooling. For me, each era had its own personality—and tradeoffs. I’m curious: What decade would you go back to, if you could? And what made it special for you? #SoftwareEngineering #TechCareers #DeveloperLife #Programming #EngineeringCulture

“It works.” Probably the most dangerous sentence in tech. Because “working” doesn’t mean: ❌ Scalable ❌ Maintainable ❌ Efficient I’ve seen systems that: - Worked perfectly in development - Crashed in production Why? Because nobody asked: 👉 What happens under load? 👉 What happens if DB slows down? 👉 What happens if one service fails? We optimize for: “Does it work?” But real engineering is about: “How does it behave under stress?” That shift changes everything. Next time your code works… Don’t stop there. Ask: “What can break this?” That’s where real engineering begins. 💬 Have you faced a situation where “it worked” but still failed? #SoftwareEngineering #SystemDesign #BackendDevelopment #Scalability #TechLeadership #EngineeringMindset #DevLife #CleanCode #DistributedSystems #TechCareers #BuildInPublic #LearnToCode #DeveloperCommunity #CodingLife

Agentic swarm feature factories ultimately cannot succeed. This is because the idea of them is underpinned by one of two false assumptions: 1. Software engineers just churn out code and do not think critically about a problem 2. LLMs can think critically about a problem LLMs architecturally cannot think critically. Unless the fundamental architecture were to change, we're not seeing software engineers go anywhere into the future. The best teams will continue to need individuals who deeply understand the problem space.

A lot of devs wait for that one “big moment” that changes everything. But honestly, most real growth comes from the small stuff you do every day. This picture says it better than anything: - Do nothing: (1.00)³⁶⁵ = 1   - Get 1% better daily: (1.01)³⁶⁵ ≈ 37.7 I’ve seen this again and again in my own software engineering journey. It’s not about trying to learn everything overnight. It’s more like: - Writing code that’s just a bit cleaner than yesterday   - Picking up one solid idea each day (performance, scalability, architecture, etc.)   - Getting better at debugging, not only building   - Making small improvements that stack up over time  The real difference between an okay developer and a really strong one isn’t “grinding hard for 3 days” — it’s showing up consistently for months. After being in this field for a while, one thing’s obvious: Tiny daily improvements turn into huge results long-term. #SoftwareEngineering #CleanCode #Scalability #GrowthMindset #Developers #Consistency

Experience in software engineering is really just a collection of: “Ohhh… I’ve seen this nonsense before.” 😄 At some point, problems stop feeling completely new. They start looking like variations of bugs you’ve already chased 🐛, fixed 🔧, and maybe even caused yourself 😅 Real growth isn’t just about writing cleaner code 💻 — it’s about recognizing patterns faster ⚡, asking sharper questions ❓, and shortening the gap between confusion 😵💫 and clarity 💡 That’s when things start to click. #SoftwareEngineering #DevLife #Engineering #CareerGrowth

It's both funny and astonishing to see folks who have never coded themselves are predicting the future of software engineering.  #artificialintelligence #tech #software

𝟗 𝐘𝐞𝐚𝐫𝐬 𝐢𝐧 𝐓𝐞𝐜𝐡 — 𝐋𝐞𝐬𝐬𝐨𝐧 #𝟑 Don’t be too rigid in tech. The industry moves fast. New tools appear. New ideas emerge. Better approaches replace old ones. Exploring is part of staying relevant. #tech #code #softwareengineer #seniordeveloper

How our brain manipulates us !!! We like to think of software engineering as a purely logical pursuit—a world of if/else statements and mathematical certainty. But the hardware we run on (our brains) comes pre-installed with "mental shortcuts" that can lead to catastrophic bugs in our judgment. This roundtable is an open post-mortem on the human element of engineering. We’ll discuss how cognitive biases—like clinging to a failing architecture because we’ve already spent months on it, or trusting a buggy PR just because it was submitted by a "rockstar" dev—distort our technical decisions. Come prepared to share your own "logical" lapses and discuss how we can build better team processes to counteract our own biological programming. Topics for the Circle: The "Rockstar" Halo: How our perception of coworkers colors our code reviews. The Sunk Cost Trap: Recognizing when to pivot vs. when to keep polishing a "lemon" solution. Reframing the Problem: How the way we describe a bug or a feature dictates the technical path we choose. Ego vs. Evidence: Building a team culture where "being right" is less important than "getting it right." Kick-off Questions: Can anyone share a time you championed a specific tool or architecture, realized halfway through it was the wrong choice, but kept going anyway? What was the moment you finally let go? Do you find yourself skimming code reviews more quickly when they come from a senior dev you admire versus a junior dev you don't know? How do we fix that bias without slowing down the pipeline? We often judge a new codebase based on the 'mess' we just came from (The Contrast Effect). How do we set objective standards for 'good code' that don't just depend on how much we hated our last project? How does this look to you? If you’re happy with this direction or want to tweak the abstract/questions, please let me know by March 6th. We’re really looking forward to seeing the insights your session will spark! #DroidconParis #AndroidMakers #Cognitive_Biases #Computer_Science #Paris #i_DesignHub droidCon