Mahim Kumar’s Post

The hardest bugs 🪲 aren’t in the code. They’re in how we think about the problem. Sometimes we rush to fix things, add more logic, try another tool, when the real solution is stepping back and asking, “Am I even solving the right problem?” Slowing down, simplifying, and rethinking has saved me more time than any framework ever could. Still unlearning. Still improving! #SoftwareEngineering #ProblemSolving #DeveloperMindset #TechLife #CodingJourney #ContinuousLearning

𝐌𝐨𝐧𝐝𝐚𝐲 𝐃𝐞𝐛𝐮𝐠 𝐓𝐡𝐨𝐮𝐠𝐡𝐭 𝐌𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐭𝐡𝐢𝐧𝐤 𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐜𝐨𝐝𝐞 𝐢𝐬 𝐡𝐚𝐫𝐝. 𝐈𝐭’𝐬 𝐧𝐨𝐭.  Understanding why it breaks —> that’s the real skill. Bugs don’t waste time. Unstructured debugging does. When something fails, I don’t guess anymore. I follow a system: • Reproduce consistently • Narrow down the scope • Eliminate assumptions • Verify each layer (UI → Logic → API → Data) Because debugging isn’t about luck. . . . It’s about controlled thinking under uncertainty. Growth in tech doesn’t come from just building features… It comes from how you handle what’s not working. . . This week, don’t just code. Observe how you debug. That’s where your real edge is. #iOSDevelopment #Debugging #SoftwareEngineering #MobileDevelopment #TechMindset #ProblemSolving #DevelopersLife #EngineeringExcellence #CleanCode #LearnToCode

The real risk of 100% vibe coding is not the first demo or the initial delivery. It is the next 12 months of bug fixes, handovers, edge cases, and support tickets as the project grows bigger and more complex. AI-generated code can absolutely accelerate delivery, and the speed can be incredible. But if teams do not fully understand, review, test, and structure that code, they may simply be creating technical debt at high speed. #SoftwareEngineering #VibeCoding #AICoding #TechLeadership #SoftwareMaintenance #RandomThoughts

Currently I'm building an internal knowledge base for Kissflow — a system that turns our code into facts and synthesizes those facts into knowledge our team can actually use. One of my engineers (GOWTHAM E V) pushed back hard: "Why do you built this KB system, claude code with code repo can answer better than your KB system. What's differentiator?" I love this kind of debate from my team. It forces clarity, not slogans. So I sat with it. He's right on the easy answer: on pure code Q&A, a frontier model with direct repo access often does win. We've measured it. But the real question I wasn't asking was: who is this for? The KB was not only built for engineers. It's for our non product teams — teammates who need to understand the product without reading Python or opening a terminal. People closing tickets, handling escalations, talking to customers in real time. That clarified everything. Code tells you the facts. The KB tells you the consequences. Claude Code reads code. Our KB reasons about code. For a support team handling a live escalation, the question isn't "what does this function do?" It's "if this fails, what does the customer experience? what's the workaround? who else is impacted? what should I tell them right now?" That answer doesn't live in the code. It lives in the synthesis — across code, docs, PR history, support tickets, and community threads. Built once. Compounded with every PR ($0.05–$0.50) - Periodic delta sync. Queryable by anyone, regardless of whether they can read a stack trace. Claude Code is a brilliant reader of code. Our KB is a reasoning layer about code — for the people who'll never read code. Both matter. Conflating them is the mistake. The hardest questions on your roadmap usually come from your own team. Don't dodge them. The clarity they force is the differentiator. When your AI reads the code, who reads the reasoning — for the people who can't? #RAG #AIPlatforms  #KnowledgeBase  #EngineeringLeadership

If you have not started using Claude Code yet, you may be leaving a lot of your creative and technical horsepower on the table. I’m adding my voice to the people calling it a paradigm shifter and a real personal productivity boost. You do not need to be a lifelong coder to get value from it. If you can clearly explain what you want to build, explore, automate, or improve, you can do meaningful work with it. That is the shift. The distance between idea and execution just got a lot shorter. #claude

One small trigger. Massive consequences. That’s true for global events— and for a single line of code in production. Think deeper. Code smarter. 🚀 #DeveloperLife #SoftwareEngineering #SystemDesign #CodingMindset

Readable code scales better than clever code. Clarity beats complexity when systems grow and teams expand. Write for humans first—machines will follow. Maintainable code is what truly stands the test of time. #CleanCode #SoftwareEngineering #CodingBestPractices #DeveloperMindset #CodeQuality

From monoliths to microservices, we’ve spent years optimising systems for scalability and performance, but now the biggest gains are coming from how we write code itself. #AI #GenerativeAI #ClaudeAI #SoftwareEngineering #DeveloperProductivity #DevTools #Programming #Automation #AICoding #FutureOfWork

Gemma 4 26B Reasoning Block Suppressed in Opencode During Tool Execution 📌 Gemma 4’s powerful reasoning engine is silently suppressed in Opencode during tool execution - a critical flaw that breaks agentic workflows. While the model freely outputs CoT logic in llama.cpp, Opencode strips away the <|channel>thought> blocks when tools are defined, hiding vital decision trails. This inference gap forces developers to revert to native interfaces or manual prompt engineering to preserve structured thinking. 🔗 Read more: https://lnkd.in/dpmFCPm4 #Gemma4 #Opencode #Toolexecution #Chainofthought #Reasoningcontent

In the era of AI-native development, communication is your most valuable technical skill. To effectively orchestrate autonomous agents like Claude Code, you must be able to speak the precise language of software engineering. The Jargon Vault by Terminal Velocity AI is a curated roadmap of 100 essential terms designed to help you move from basic prompting to high-level system architecture. 🏛️ Mastering the Language of Engineering To give an AI agent the context it needs to build reliable systems, you need to master the technical vocabulary across four critical domains: System Architecture: Learn to define the "skeleton" of your project using terms like Microservices vs. Monoliths, Middleware, and Schema Design. Technical Requirements: Speak clearly about the performance and functionality of your app with concepts like CRUD operations, Latency, Scalability, and Technical Debt. AI Mechanics: Optimize your CLI results by understanding the internal logic of Context Windows, Tokens, and Few-Shot Prompting. Professional Workflow: Maintain control over AI-generated code by mastering Git essentials, including Cherry-picking, Pull Requests, and Merge Conflicts. 🚀 Shift Your Cognitive Load The goal of the Jargon Vault isn't just memorization—it's about evolving your workflow. When you speak the language of engineering, you stop worrying about "how to write a loop" and start focusing on how to manage the architecture and context of an entire system. By mastering these 100 terms, you transition from a coder to a System Architect, spending less time on syntax and more time solving high-level business problems. 🔗 Get Started for Free Ready to master the lexicon of the AI-native era? Access the full resource here: https://lnkd.in/dBDuAQhQ #SoftwareEngineering #ClaudeCode #AINative #TerminalVelocityAI #TechJargon #PromptEngineering #SoftwareArchitecture #DevLife #FutureOfCoding #SystemArchitect ✈️