How AI helped me learn to code without a mentor

𝐓𝐨 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 𝐰𝐡𝐨 𝐰𝐨𝐫𝐫𝐲 𝐚𝐛𝐨𝐮𝐭 𝐀𝐈 𝐭𝐚𝐤𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐣𝐨𝐛: 𝐋𝐞𝐚𝐫𝐧 𝐭𝐨 𝐕𝐢𝐛𝐞 𝐂𝐨𝐝𝐞. When AI can write basic code, does it mean we need fewer developers? The answer is no, but we need different ones. The rise of Vibe Coding—where you prompt, guide, and test—is actually a massive opportunity to accelerate your learning and output. Learning Acceleration: Use AI to generate code for a concept you don't know (e.g., a new database connection). You can then read, debug, and learn from a functional example in minutes instead of days. The MVP Mindset: You can take an idea from 0% to 80% with a few prompts. This lets you focus on the user experience and business logic much faster than manual coding. The Future is Hybrid: Don't fear the machine; become the conductor. Your value is in your judgment, testing skills, and ability to give specific, high-leverage feedback. Vibe coding is not cheating; it's the new literacy. Start by using an AI assistant for your next complex function, then force yourself to review every line. #SoftwareDevelopment #Coding #Technology #Developer #VibeCoding #DevOps #VibeDeployment #EngineeringCulture #TechCulture #AI #Programming #JuniorDev #TechCareers #MachineLearning #CareerAdvice #CodingSkills #Mentorship

𝗬𝗔𝗣, 𝗩𝗜𝗕𝗘, 𝗢𝗥 𝗕𝗨𝗗𝗗𝗬? 𝗛𝗢𝗪 𝗬𝗢𝗨 𝗖𝗢𝗗𝗘 𝗪𝗜𝗧𝗛 𝗔𝗜 𝗠𝗔𝗧𝗧𝗘𝗥𝗦 Something strange happened when I started coding with LLMs. I stopped treating them like tools. They started feeling like teammates. But I noticed three distinct patterns in how people actually work with AI: 𝗬𝗮𝗽 𝗖𝗼𝗱𝗶𝗻𝗴 → You think out loud, the AI writes code. Great for exploring ideas when you're stuck at the start of a problem. 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 → You're jamming creatively, chasing flow over formality. Perfect for prototypes and demos when speed matters more than perfection. 𝗕𝘂𝗱𝗱𝘆 𝗖𝗼𝗱𝗶𝗻𝗴 → You're pair programming. You write code, AI reviews and tests it. This is for production systems that need to survive real users. Here's what I learned: good developers don't pick one style. They switch between all three. 𝗬𝗼𝘂 𝘆𝗮𝗽 𝘁𝗼 𝗲𝘅𝗽𝗹𝗼𝗿𝗲. 𝗬𝗼𝘂 𝘃𝗶𝗯𝗲 𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲. 𝗬𝗼𝘂 𝗯𝘂𝗱𝗱𝘆 𝘁𝗼 ���𝗵𝗶𝗽. The developers winning with AI aren't the ones writing the most prompts. They're the ones who know which mode to use when. Which style do you find yourself using most? #AI #LLM #DevOps #SRE #SoftwareDevelopment #Leadership

AI automation developer Sometimes, the hardest skill in tech is not code—it’s learning how to solve real problems that don’t come with solutions. Every new project challenges me to think deeper, not just type faster. Last week, debugging a complex frontend bug took me two days and plenty of patience. My AI tools like Copilot suggested code, but understanding *why* things broke—that part was all me. Working on my AI Design Assistant taught me that probes and experiments reveal more than tutorials can. All those hours wrestling with logic bugs? They were uncomfortable, but they made me sharper. Failing fast and iterating taught me practical problem solving—skills you won’t always find in textbooks. For anyone in tech: mastery is not knowing all the answers but being willing to ask the right questions. Takeaway: Real growth happens when you face problems head-on, stay patient, and enjoy the learning journey. #CareerGrowth #ProblemSolving #TechSkills #FrontEndDev #ManpreetSinghAI

If you’ve been trying AI-assisted coding (or “vibe coding”) and complaining that the code it generates isn’t great, you’re missing the point. AI is not here to replace how you code. It’s here to redefine why you code. Your role as a software engineer is shifting… from writing perfect code → to framing the right problems. When you start by understanding the problem deeply, AI becomes your collaborator, not your crutch. I recently heard of a company that completed 6 months of work in just 3 months using white coding. That’s not hype; it’s happening right now. So, instead of blaming the AI, Embrace the new coding culture. Explore tools like Cursor and Lovable. Experiment, build, and learn how AI fits your workflow. Because soon, “AI-first coding” won’t be an advantage; it’ll be the norm. What challenges are you facing while adapting to it, let me know in the comments so that I can help you out! #GenAI #GenAIPeople #VibeCoding #Lovable #Cursor

🤯 Are You a Victim of #Productive_Distraction? It happens to all of us. You sit down to master a simple concept, and an hour later, your strategic brain—fueled by powerful AI tools—has dragged you elbows-deep into a complex, future-state feature. This isn't laziness. It's the "Easy Button" trap: ⚡️ The Excitement Factor: You see bigger possibilities and jump ahead. 🤖 The #GitHub_Copilot Effect: AI makes it incredibly easy to skip the foundational "grind" and jump straight to impressive results. 🧠 The Systems Thinker: Your mind is wired for features and architecture, not line-by-line syntax. While exhilarating, this distraction hijacks your learning phase, leaving critical gaps in the deeper "how" and "why." You get the output, but miss the fluency. 💡 The Solution: Two Modes for Mastery Don't stifle your strategic brain—structure it. Learn to split your work into distinct sessions: 🧪 Learning Mode (Focus: Coding Fluency) Rules: No Copilot. Write code line-by-line. Debug manually, and reflect deeply on the logic. 🚀 Building Mode (Focus: Orchestration) Rules: Use Copilot Freely. Focus on architecture, feature planning, and integration. You can alternate days or split a single session (e.g., 30 mins learning, 1 hour building). 3 Actionable Lessons to Build Confidence: 🔍 Use Copilot as a Reviewer, Not a Coder: First, write your own code. Then, ask Copilot: “How would you improve this?” Compare the versions and learn from the differences. 📝 Log Your Learning Separately: Keep a simple log: What concept did I learn today? What did I code myself? What did I delegate? This keeps you honest and focused. 👑 Reframe Your Mindset: Instead of feeling like you're "not coding," reframe it: "I’m learning how to lead AI-powered development while building coding fluency alongside." That’s a powerful combo. Let's build smart and learn deep with #AI, and truly master our craft. How do you manage the lure of "Productive Distraction" in your own workflow? Share your strategies below! #LearningAndDevelopment #SoftwareDevelopment #Productivity #AItools #CareerGrowth #Focus #StrategicThinking #GitHub Infosys Simplilearn Unacademy UpgradeSkills Agilemania GitHub OpenAI Microsoft Copilot Google

Ever heard of *“AI Pair Programming”*? If not, you’re in for a treat. The rise of AI-powered coding assistants—think GitHub Copilot, Amazon CodeWhisperer, and others—is already reshaping how we write software. But beyond the hype, what’s really fascinating is how AI is evolving from just autocomplete on steroids to becoming an active *collaborator* in your coding workflow. Here’s why AI Pair Programming is a game-changer: 1. **Contextual Code Suggestions** Unlike traditional autocomplete, modern AI models understand your entire project context—variable names, function definitions, even comments. This means you get suggestions that are often more relevant, cohesive, and tailored to your style. 2. **Faster Prototyping** Need to sketch out a new feature quickly? AI can whip up boilerplate, draft APIs, or propose algorithms to test ideas faster. It’s like having a super knowledgeable teammate who never gets tired. 3. **Learning and Exploration Buddy** Struggling with an unfamiliar language or library? AI pair programmers can generate code snippets, explain complex parts, or even suggest better patterns. This makes learning on the job smoother and less intimidating. 4. **Automating the Mundane** Repetitive tasks like writing getters/setters, boilerplate tests, or data transformations become less draining when an AI helps handle the grunt work, freeing you to focus on higher-level design and problem-solving. But (and there’s always a but), it’s not about blindly accepting AI’s output. Good engineers keep the reins: reviewing suggestions critically, tailoring generated code to fit nuanced requirements, and maintaining the creative spark only humans bring. So, if you haven’t yet, give AI coding assistants a spin during your next development sprint. Think of it less as a tool and more as a teammate—one who’s always learning, never tires, and is ready to dive into messy code right alongside you. Curious how AI will redefine your workflow? Drop your thoughts or experiences in the comments! Let’s explore together. #AI #PairProgramming #DeveloperTools #CodingLife #SoftwareEngineering #AIProductivity #TechTrends #ProgrammingTips

When I started learning to code… I felt like I had to always have a project in progress. With AI assisted coding, this feeling only grew stronger. In the age of AI-assisted coding, shipping fast is easier than ever — but that’s also the trap. AI gives you momentum, but if you never pause to realign with what actually excites you, you end up building faster and faster… toward things you don’t even care about. But here's the truth: taking regular breaks can actually enhance our productivity and creativity. I’ve seen this pattern over and over: ✅ Finished a project ❌ Immediately asks “What’s the next thing I should build?” ⚠️ Momentum without meaning → motivation flatlines within weeks The truth nobody tells new devs: The real unlock isn’t speed — it’s direction. And you only find direction when you step away and ask: “What am I genuinely curious about right now?” “What’s a problem I’d obsess over even if nobody paid me?” “What kind of problem would I love to keep improving for years?” AI will help you build anything. But only curiosity will keep you building when the AI suggestions run out. So if you just wrapped a project — do not immediately start another one. Take a few days. Learn. Explore. Follow a weird obsession. Build something because you want to, not just because you can. That’s how you avoid burnout. That’s how you build portfolio pieces that matter. That’s how you become unstoppable — not just productive. Curious — do you give yourself a “creative reset” between projects, or do you feel guilty when you’re not immediately building something new? 👇 #softwareengineering #newdevelopers #careeradvice #aiassistedcoding #buildinpublic

As a developer in 2025, I've been reflecting on a question that divides our community: Should we embrace AI as our coding partner, or are we compromising our craft? Here's my take on both sides... ✅ Why Developers SHOULD Work With AI 1. Accelerated Productivity - Automate repetitive tasks (boilerplate code, documentation, unit tests) - Focus on complex problem-solving and architecture - Reduce time spent on routine debugging 2. Learning Amplifier - Instant access to best practices and patterns - Explore unfamiliar languages/frameworks faster - Get explanations for complex codebases 3. Code Quality Enhancement - Catch potential bugs early - Suggest optimizations and security improvements - Maintain consistent coding standards 4. Competitive Advantage - Stay relevant in evolving tech landscape - Deliver projects faster without sacrificing quality - Adapt to market demands efficiently 5. Creative Problem Solving - AI handles routine tasks → more time for innovation - Explore multiple solutions quickly - Break through mental blocks with AI-assisted brainstorming --- ⚠️ Why Developers Should Be CAUTIOUS With AI 1. Skill Degradation Risk - Over-reliance can weaken fundamental programming skills - Copy-paste culture without understanding - Loss of deep problem-solving abilities 2. Code Quality Concerns - AI-generated code may contain subtle bugs - Security vulnerabilities in suggestions - Technical debt from non-optimized solutions 3. Intellectual Property Issues - Unclear licensing of AI-generated code - Potential copyright violations - Company policy conflicts 4. Loss of Critical Thinking - Accepting solutions without questioning - Missing edge cases and context-specific requirements - Reduced code ownership and understanding 5. Professional Growth Limitations - Shortcuts may prevent mastery - Difficulty in interviews without AI assistance - Dependency on tools that may not always be available --- The most professional and high-quality approach is: AI-Augmented Development with Strong Fundamentals The developers who will thrive are those who: - Build solid programming foundations - Embrace AI as a productivity multiplier - Maintain critical thinking and code ownership - Continuously adapt and learn - Balance efficiency with understanding AI is not replacing developers; it's raising the bar for what exceptional developers can achieve. The highest professional quality comes from developers who combine: ✅ Strong fundamentals ✅ AI-powered efficiency ✅ Critical thinking ✅ Continuous learning #SoftwareDevelopment #AI #CodingWithAI #DeveloperProductivity #TechDebate #ProgrammingBestPractices #ProfessionalGrowth"

⚡ AI-Powered Coding Assistants — Friend or Foe for Developers? Picture this 👇 You’re deep into coding. Your coffee’s half cold ☕, bugs everywhere 🐛, and deadlines closing in. Then you open GitHub Copilot, type a few lines… and it finishes the rest. Feels like magic, right? ✨ But here’s the real question — Is AI making coders smarter… or softer? 👀 💻 The Rise of AI Coding Companions From GitHub Copilot to Cody, Tabnine, and Replit Ghostwriter, AI tools now write functions, fix bugs, and even explain logic. They’ve become the “pair programmers” we never had. Copilot suggests entire code blocks before you blink. Cody can debug errors and explain why your logic fails. Tabnine predicts your next few lines based on your style. It’s like having a 24/7 mentor — minus the attitude. 😅 ⚙️ The Bright Side — Productivity on Steroids ✅ Write code faster. ✅ Spend less time debugging. ✅ Learn new syntax effortlessly. AI assistants are helping developers focus on logic, not boilerplate. Instead of reinventing the wheel, you’re building faster cars. 🚗💨 ⚠️ The Dark Side — Dependency Danger But here’s where it gets tricky: If AI writes 80% of your code, are you still learning? Over-reliance can slowly dull your problem-solving edge. It’s like using GPS for every trip — one day, you forget the way to your own house. 🧭 We’re at a point where AI can code, but it can’t think like a human developer. It doesn’t understand why something works — it just knows what usually works. 💬 The Balance — Smart Developer, Smarter Tools The key is not to replace yourself with AI — but to amplify yourself through it. Let AI do the repetitive stuff — but you still own the creativity, logic, and architecture. 🧠💡 Use AI to accelerate, not substitute. 💭 So tell me: all my LinkedIn family and Jeyaram B , Raghavendra Reddy Tatiparthi Do you think AI tools are making developers more productive… or more dependent? 👇 #AI #Coding #GitHubCopilot #Tabnine #Cody #Developers #Programming #AICommunity #TechDebate #MachineLearning #Productivity #SoftwareEngineering #FutureOfWork #AIinTech

Some real talk from a developer’s perspective: Lately, I’ve felt worn out by AI coding tools. As much as they promise productivity, the reality is too often the opposite. • The same input never results in the same output twice. • Features that work in one commit break in the next, sometimes without clear reason. • Meticulously crafted project rules and planning files still get ignored by the models. After countless unpredictable results, I’ve started ruthlessly checking in every working version to GitHub or DevOps the minute it works—treating every ‘green’ state as precious, because I have no guarantee it will be repeatable. I’m not quitting AI completely, but I’m now leaning more on my own skills for planning and implementation, and using AI only for very targeted tasks. Has anyone else hit this same wall? Would love to hear if others have found a balance—or if unpredictability is just baked into the current generation of tools. #AICoding #SoftwareEngineering #DevOps #Productivity