Code Splitting Interview Nuances: Boost FCP and TTI

100 Days of Growth Day 18 What Interviewers Are Really Looking For in Frontend Engineers Most candidates focus on one thing during interviews Getting the right answer. But that is only a small part of the evaluation. Because in reality, interviewers are not just hiring code. They are hiring how you think, how you communicate, and how you build. --- It is not just about correctness Writing code that works is important. But many candidates can do that. What separates you is everything around the code. --- 1. Problem-solving approach Interviewers pay attention to how you start. Do you: Jump straight into coding Or take time to understand the problem Strong candidates: Clarify requirements Break problems into steps Think before they write The approach often matters more than the final answer. --- 2. Communication Silence is one of the biggest mistakes. Interviewers want to follow your thinking. They are looking for: Clear explanations Structured reasoning Ability to explain decisions Even a good solution can be overlooked if it is not communicated well. --- 3. Code clarity It is not just about making it work. It is about how you write it. Is the code readable Are variables well named Is the logic easy to follow Clean code shows maturity. --- 4. UI and UX awareness For frontend roles, this is critical. Interviewers may look for: Understanding of user flows Handling of edge cases Attention to interaction details Awareness of accessibility This is where many candidates miss points. --- 5. Trade-off thinking There is rarely one perfect solution. Strong candidates: Consider alternatives Explain why they chose a particular approach Acknowledge limitations This shows real-world engineering thinking. --- 6. Handling feedback Interviews are not meant to be solo performances. You may get hints or corrections. What matters is how you respond. Do you get defensive Or adapt and improve your solution Collaboration is a key signal. --- A simple way to think about it Interviewers are asking “Can this person work effectively on our team?” Not just “Can this person solve this question?” --- Conclusion Frontend interviews are not just about coding. They are about: Thinking clearly Communicating effectively Building with users in mind That is what truly stands out. --- What do you think matters most in a frontend interview beyond coding? Would be interesting to hear different perspectives. --- #100DaysOfCode #FrontendEngineering #TechInterviews #SoftwareEngineering #CareerGrowth

𝗜 𝘁𝗵𝗼𝘂𝗴𝗵𝘁 𝗜 𝘄𝗮𝘀 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗺𝘆 𝗳𝗶𝗿𝘀𝘁 𝗯𝗶𝗴 𝘁𝗲𝗰𝗵 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄. 𝗜 𝘄𝗮𝘀 𝗻𝗼𝘁 𝗲𝘃𝗲𝗻 𝗰𝗹𝗼𝘀𝗲. I went through the full loop for 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝗗𝗘𝟭. Cleared early rounds. Felt confident. Then onsite hit me hard. 𝗜 𝘄𝗮𝘀 𝗻𝗲𝗿𝘃𝗼𝘂𝘀. 𝗦𝗸𝗶𝗽𝗽𝗲𝗱 𝗺𝗲𝗮𝗹𝘀. 𝗖𝗼𝘂𝗹𝗱 𝗻𝗼𝘁 𝘁𝗵𝗶𝗻𝗸 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗶𝗻 𝘀𝗼𝗺𝗲 𝗿𝗼𝘂𝗻𝗱𝘀. 𝗔𝗻𝗱 𝘁𝗵𝗮𝘁 𝘀𝗵𝗼𝘄𝗲𝗱. 𝗚𝗼𝘁 𝗿𝗲𝗷𝗲𝗰𝘁𝗲𝗱. But honestly, I learned more from this than any offer could have taught me. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗮 𝗳𝗲𝘄 𝘁𝗵𝗶𝗻𝗴𝘀 𝗜 𝘄𝗶𝘀𝗵 𝗜 𝗸𝗻𝗲𝘄 𝗲𝗮𝗿𝗹𝗶𝗲𝗿: 🧠 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗶𝘀 𝗵𝗮𝗹𝗳 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 I practiced a lot of coding questions. But most were short and clear. In the interview, the problem statements were long and messy. Real-world style. I struggled to even understand what was being asked. Takeaway: Practice reading complex problems. Slow down. Break them into parts. Clarify assumptions early. 🗣️ 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗰𝗮𝗻 𝘀𝗮𝘃𝗲 𝘆𝗼𝘂, 𝗼𝗿 𝘀𝗶𝗻𝗸 𝘆𝗼𝘂 In some rounds, I kept talking and explaining. That helped. In one round, the interviewer barely responded. I got stuck in my own thoughts. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Keep communicating even if it feels awkward. Structure your thoughts out loud. Interviewers look for clarity, not just answers. 😵 𝗬𝗼𝘂𝗿 𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝘁𝗮𝘁𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 I underestimated this. I was anxious the whole day. At one point I even felt dizzy. That impacted my thinking and performance. Takeaway: Treat interview day like a marathon. Eat. Hydrate. Take breaks. Staying calm is a real skill. 📖 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 This was actually my strongest area. I prepared stories, practiced them, and it showed. Takeaway: Do not wing behavioral questions. Prepare real stories. Know what signal each story is sending. 🎯 𝗔𝗽𝗽𝗹𝘆 𝘀𝗺𝗮𝗿𝘁, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗺𝗼𝗿𝗲 I mass applied and ended up interviewing for roles expecting more experience than I had. The gap showed. Takeaway: Target roles aligned with your experience. It increases your chances and confidence. 🧩 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 > 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 One question looked simple but had tricky rules hidden inside. I jumped to a familiar pattern and got confused. Takeaway: Spend time modeling the problem. The right structure makes the solution obvious. In the end, feedback was simple. Good communication. Not strong enough in core engineering. Fair enough. 𝗦𝘁𝗶𝗹𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗶𝘁, 𝘀𝘁𝗶𝗹𝗹 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴. If you are preparing for your first big interview, just know this. It is not just about coding. It is about clarity, calmness, and how you think under pressure. 𝗕𝗮𝗰𝗸 𝘁𝗼 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲.

𝗬𝗼𝘂𝗿 𝗔𝗻𝗱𝗿𝗼𝗶𝗱 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗶𝘀𝗻'𝘁 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄. 𝗜𝘁'𝘀 𝗮𝗹𝗹 𝗮𝗯𝗼𝘂𝘁 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸. 𝗔𝗻𝗱 𝗠𝗼𝘀𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗱𝗼𝗻'𝘁 𝗿𝗲𝗮𝗹𝗶𝘇𝗲 𝘁𝗵𝗶𝘀 𝘂𝗻𝘁𝗶𝗹 𝗶𝘁'𝘀 𝘁𝗼𝗼 𝗹𝗮𝘁𝗲. Here's the roadmap that changes everything 👇 The hard truth: Interviewers don't care if you memorized the lifecycle. They want to know — do you understand why it exists? That's the difference between knowing and understanding. 𝗝𝘂𝗻𝗶𝗼𝗿 𝗹𝗲𝘃𝗲𝗹 → Study what breaks, not what works. See, Anyone can describe onResume. Interviewers probe edges: → Memory leak from a lifecycle mistake → ViewModel during config change vs. process death → Why launchWhenStarted was deprecated Study the failure path, not the happy path. 𝗠𝗶𝗱-𝗹𝗲𝘃𝗲𝗹 → Stop naming patterns. Start defending them. When asked "why not put the API call in the ViewModel?" — don't say "separation of concerns." Say: "Because it would own retry logic, caching, error mapping — making it untestable and unreusable." That's the answer that gets you hired. 𝗖𝗼𝗿𝗼𝘂𝘁𝗶𝗻𝗲𝘀 — 𝘄𝗵𝗲𝗿𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝗱-𝗹𝗲𝘃𝗲𝗹𝘀 𝗹𝗼𝘀𝗲 𝗽𝗼𝗶𝗻𝘁𝘀. 𝗖𝗮𝗻 𝘆𝗼𝘂 𝗮𝗻𝘀𝘄𝗲𝗿 𝘁𝗵𝗲𝘀𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗴𝗼𝗼𝗴𝗹𝗶𝗻𝗴? → 𝗹𝗮𝘂𝗻𝗰𝗵 𝘃𝘀 𝗮𝘀𝘆𝗻𝗰 — what is the real difference? → 𝗦𝘁𝗮𝘁𝗲𝗙𝗹𝗼𝘄 𝘃𝘀 𝗦𝗵𝗮𝗿𝗲𝗱𝗙𝗹𝗼𝘄 - usecase? → 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗰𝗼𝗹𝗹𝗲𝗰𝘁 𝗮 𝗙𝗹𝗼𝘄 𝘀𝗮𝗳𝗲𝗹𝘆 𝗶𝗻𝘀𝗶𝗱𝗲 𝗮 𝗙𝗿𝗮𝗴𝗺𝗲𝗻𝘁? If you hesitated - that's your prep list. 𝗦𝗲𝗻𝗶𝗼𝗿 𝗹𝗲𝘃𝗲𝗹 → Numbers is more important than claims. Instead of saying this -> "I improved performance". Explain like this -> "ANRs dropped to zero after migrating to DataStore" beats 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗹𝗲𝘃𝗲𝗹 → They're testing judgment, not knowledge. For any design question, use this framework: Clarify → Constrain → Data flow → Failure modes → Tradeoffs They're not looking for the right answer. They're watching how you think under ambiguity. Answer every interview question at the level you're targeting — not the level you're at. The best prep isn't more flashcards. It's reading source code. Reviewing your old code. Explaining things out loud until you find where you lose clarity. If you can't explain it simply — you don't understand it yet. Do you want a structured path through all of this — 𝘐 𝘢𝘮 𝘥𝘰𝘪𝘯𝘨 𝘢 12-𝘸𝘦𝘦𝘬 𝘔𝘢𝘴𝘵𝘦𝘳 𝘈𝘯𝘥𝘳𝘰𝘪𝘥 𝘑𝘰𝘣-𝘙𝘦𝘢𝘥𝘺 𝘗𝘳𝘰𝘨𝘳𝘢𝘮 𝘧𝘰𝘳 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘴 𝘸𝘩𝘰 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘵𝘳𝘢𝘯𝘴𝘪𝘵𝘪𝘰𝘯 𝘪𝘯𝘵𝘰 𝘈𝘯𝘥𝘳𝘰𝘪𝘥 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘮𝘦𝘯𝘵 𝘰𝘳 𝘭𝘦𝘷𝘦𝘭 𝘶𝘱 𝘲𝘶𝘪𝘤𝘬𝘭𝘺. 𝘕𝘰 𝘧𝘭𝘶𝘧𝘧. 𝘕𝘰 𝘳𝘢𝘯𝘥𝘰𝘮 𝘵𝘶𝘵𝘰𝘳𝘪𝘢𝘭𝘴. 𝘑𝘶𝘴𝘵 𝘵𝘩𝘦 𝘴𝘬𝘪𝘭𝘭𝘴, 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦, 𝘢𝘯𝘥 𝘪𝘯𝘵𝘦𝘳𝘷𝘪𝘦𝘸 𝘤𝘰𝘯𝘧𝘪𝘥𝘦𝘯𝘤𝘦 𝘵𝘩𝘢𝘵 𝘨𝘦𝘵 𝘺𝘰𝘶 𝘩𝘪𝘳𝘦𝘥. Link in the comments 👇 Save this. Share it with someone preparing for Android interviews. #Android #AndroidDev #Kotlin #MobileDevelopment #TechInterviews #CareerGrowth #AndroidJobReadyProgram

I spent ten hours preparing for one interview. Then I automated it. The panel invite had four interviewers. Three back to back. Okta and automation, engineering collaboration, automation mindset, then a fourth at end of day. Forty-five minutes each. One day. I had a week. I read the JD until I had it memorized. Researched the interviewers. Wrote out career stories in STAR format. Claude was in the mix from day one: surfacing likely questions, flagging things to emphasize, filling the role of a coach most people don't have access to. I did all the answering. But I wasn't coaching myself alone. I was prepared. I could feel it in the conversations. I didn't get the job. The feedback was specific: the team liked me but questioned my technical depth. I'd presented IT operations automation to a staff engineer who wanted Python and infrastructure-as-code. I'd directed most of the Okta work rather than writing the code myself. In that room, that distinction mattered. Ten hours of prep, and I still walked in with the wrong map. Here's what I didn't want to do: start from scratch for the next one. About 80% of interview prep never changes. Your career history, your stories, your achievements. Fixed. The only variable is the role-specific 20%: story selection, pitch framing, gap addressing. Write the 80% once. Use AI to handle the 20% fast. Claude didn't write my prep materials. It asked the right questions and I spent hours filling in the answers. The work was still long. It was just work I needed to do anyway, finally organized usefully. Three documents A Professional Background Master: Not a resume, the document behind the resume. Career narrative, quantified achievements, full tech stack, elevator pitches at multiple lengths. An Interview Stories Library: Fully written STAR stories by competency. Audience variation notes. Quantified results written somewhere you'll actually look. Feedback goes back in after every interview. The system compounds. A Quick Prep Template: Claude prefills this from the two reference docs. I review, edit, fill the gaps, complete what's mine to own. Reference docs live as markdown. Plain text is dramatically faster for AI to process than Word docs. Small decision, real difference. New posting: load the docs and the JD, run a quick analysis. Compatibility score, best-fit stories, gap assessment. Five minutes. Full prep for roles worth pursuing takes another hour. First interview after building this: ninety minutes instead of ten hours. The panel didn't end the way I wanted. But it's part of why I started treating AI as infrastructure to design workflows around. Including the workflow for getting the next job. I was too busy keeping the lights on to see where the lights were going. The job search gave me the time to actually build something. Claude was the resume and interview coach I didn't have to pay for. Part 2 is the blueprint. #jobsearch #interviewprep #artificialintelligence #careerdevelopment #itleadership

We’ve normalized whiteboard puzzles, long take-home assignments, and high-pressure tests—but do they actually reflect how developers work? I wrote an article that breaks this down clearly—and more importantly, shows what actually works instead. If you're hiring or preparing for interviews, this is worth a read 👇 https://lnkd.in/drHTdH-h Curious—what’s the worst (or best) interview experience you’ve had?

I've been a big proponent of doing regular mock interviews, so much so, that at InterviewNode you have to mandatorily do 1 every week with one of our 50+ FAANG coaches. It build muscle memory about performing under pressure, communicating clearly, and handling the psychological stress that no textbook can prepare you for. The reality is this: 🔹 Mock interviews bridge the gap between knowledge and real performance 🔹 But most engineers either skip them entirely or treat them like casual practice 🔹 Candidates doing structured, feedback-driven mocks are landing offers at 3x the rate What's working in 2025: ✅ Recording sessions and iterating based on structured feedback ✅ Treating mock interviews like flight simulators, not casual conversations ✅ Using AI tools for volume while leveraging peer practice for realistic pressure ✅ Combining technical problem-solving with behavioral storytelling using STAR method The engineers getting offers aren't the ones with perfect theoretical knowledge. They're the ones who've practiced performing under real interview conditions. Mock interviews aren't just practice - they're your competitive advantage. Every session transforms anxiety into confidence and preparation into actual performance. If you're interviewing this year, stop treating mocks as optional. Start treating them as the systematic training they should be. You could learn more here https://lnkd.in/dcjEzcDS

**From Interviewee to Interviewer: My Key Takeaways on Technical Hiring** After years on both sides of the technical interview table, I’ve seen what works—and what doesn’t. 🔍 **Beyond the Code** Technical skills matter, but so does problem-solving approach. The best interviews assess *how* you think, not just what you know. ⏱️ **Timed Tests Aren’t Everything** While timed quizzes have their place, real-world coding rarely happens under a stopwatch. Collaborative discussions often reveal more about a candidate’s fit. 🤝 **Team Interviews Shine** Group sessions with dev teams provide diverse perspectives and simulate real teamwork. They help gauge communication and cultural alignment. 💡 **What I Look For Now** - Clarity in explaining thought processes - Willingness to ask questions - Adaptability when faced with unknowns The goal? Finding engineers who thrive in your environment—not just ace a test. *What’s your take on technical interviews? Share your experiences below!* 👇 #TechInterviews #SoftwareEngineering #Hiring #CareerAdvice

12 Mistakes you should NEVER make when it comes to interviews: 1. Thinking “DSA is enough”   No, it’s not. Stop living in that delusion. Companies don’t hire LeetCode bots. If you can’t explain trade-offs, design decisions, or real-world systems, your “300 questions solved” is useless noise. 2. Mugging up patterns like a parrot   You memorized sliding window, great. Now what? The moment the problem is twisted even slightly, you freeze. Interviews are not pattern-recognition contests. They test thinking, not memory. 3. Not speaking while coding   Dead silence = dead candidate. If the interviewer has to guess what’s going on in your head, you’ve already lost. Communication is half the game. Stay mute and you look clueless. 4. Ignoring edge cases like an amateur   You wrote a perfect solution? Cool. Now break it. Empty input, null values, huge constraints. If you don’t think about these, you look like someone who’s never shipped real code. 5. Writing messy, ugly code   Indentation messed up, variable names like x1, temp2… what is this garbage? Clean code isn’t optional. It reflects your thinking. If your code looks like a*s, your thought process probably is too. 6. Jumping into coding without thinking   You start typing within 10 seconds of hearing the problem? That’s not confidence, that’s stupidity. Good engineers pause, plan, and THEN execute. Rushing shows immaturity. 7. Not asking clarifying questions   You assume things and then build on wrong assumptions. Brilliant. Clarify constraints, inputs, expectations. Interviews are collaborative. If you don’t ask, you look like a rookie. 8. Overengineering simple problems   Not every problem needs a fancy data structure. Sometimes a simple loop works. If you’re bringing a bazooka to kill a mosquito, you just look insecure, not smart. 9. Ignoring system design completely   “System design toh baad mein dekh lenge.” No, you won’t. This is where most people get destroyed. If you can’t think at scale, you’re not getting into serious companies. 10. Faking confidence instead of building it   Interviewers can smell fake confidence from miles away. If you don’t know something, say it. Bluffing makes you look worse than being honest. 11. Believing “luck matters more than preparation”   This is the biggest loser mindset. Luck might get you one question. Preparation gets you the offer. Stop blaming luck for your lack of depth. 12. Not doing mock interviews   You think solving alone prepares you? It doesn’t. Pressure, communication, time constraints — all missing. Mock interviews expose your weak spots brutally. Avoid them, and the real interview will destroy you. Most people don’t fail because they’re dumb. They fail because they’re careless, overconfident, and delusional. Fix that… and you’re already ahead of 90% of candidates. Cheers!

I just built my first LLM-powered app, and it is an AI Interview Coach. The idea is simple: job seekers often struggle with interview preparation because they do not have access to realistic, personalized mock interviews. So I built a Streamlit app that simulates a full HR interview experience using Groq's LLaMA 3.3 70B model, and gives you actionable feedback at the end. Here is how the app works: 1. You fill in your name, experience, skills, the position you are targeting, your seniority level, and the company you are interviewing at. 2. The AI becomes an HR executive from that specific company and conducts a real, context-aware interview tailored to your background. 3. You go through 5 interview rounds, answering questions just like a real interview. 4. Once the session ends, a second AI instance reviews the entire conversation and gives you a score out of 10 along with detailed feedback on your performance. What I used to build it: - Streamlit for the frontend and session management - Groq API with LLaMA 3.3 70B for fast, real-time streaming responses - Python for the backend logic - streamlit-js-eval for the session reset functionality The biggest challenge was handling Groq's streaming response correctly inside Streamlit. The chunks come back as raw objects, not plain text, so I had to build a custom generator to extract the content from each chunk before passing it to the UI. This is just the beginning. I am planning to add voice input, CV upload, and role-specific question banks next. If you are preparing for a data job interview, I would love to hear what features would make this more useful for you.

𝗨𝗻𝗱𝗲𝗿𝗿𝗮𝘁𝗲𝗱 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘁𝗶𝗽𝘀 𝗳𝗿𝗼𝗺 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗧𝗲𝗮𝗺 👇 𝟭. 𝗖𝗹𝗮𝗿𝗶𝗳𝘆 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗰𝗼𝗱𝗲 - Strong candidates don’t jump into coding. - They ask questions, remove ambiguity, and align on requirements. - A clear problem is already half solved. 𝟮. 𝗧𝗵𝗶𝗻𝗸 𝗼𝘂𝘁 𝗹𝗼𝘂𝗱 - Silence is your biggest enemy in interviews. - Interviewers aren’t just evaluating your solution—they’re evaluating your thinking. - When you verbalize your approach, you invite guidance, hints, and collaboration. 𝟯. 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝘀𝗽𝗲𝗲𝗱 𝘄𝗶𝘁𝗵 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 - It’s not just about solving fast. - It’s about solving well. - Clean structure, readable code, and clear trade-offs matter just as much as arriving at the solution. 𝟰. 𝗧𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘆𝗼𝘂 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗰𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 - Don’t default to the “latest” project. - Pick one you’re genuinely excited about. - Talk about:  - Why it was built  - Who it was for  - What impact it had  - What you- specifically owned That’s what makes your story memorable. 𝟱. 𝗗𝗲𝗯𝘂𝗴 𝗹𝗶𝗸𝗲 𝗮𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿, 𝗻𝗼𝘁 𝗮 𝗴𝘂𝗲𝘀𝘀𝗲𝗿 - Edge cases. - Test scenarios. - Systematic thinking. Master these, and you’re already ahead of most candidates. 🚀 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗹𝗶𝘀𝘁 𝗼𝗳 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻'𝘀 𝗳𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: https://lnkd.in/dDb8n7b9 Like. Comment. Repost. Save for later.