Sonal Gupta

Why is India excluded from Cursor's 1-year free student plan? 🇮🇳🚫 Anysphere (Cursor’s parent company) offers a generous 1-year free plan for students — a fantastic initiative to support learning and innovation. 🎓💡 But surprisingly, India isn’t even on the list of eligible countries. Not an oversight. Not a bug. A conscious decision. 🤔 In a country that produces some of the world’s top engineers, open-source contributors, and AI researchers 🧠💻. And a bit ironic, considering how many Indian students are actively shaping the future of tech. 🌏🚀 So, the big question is: 👉 What’s the logic behind excluding one of the largest student developer communities in the world? Would love to hear thoughts from the dev community — does this feel like a missed opportunity for meaningful impact? 💭🌍 Michael T. Aman Sanger Sualeh A. #AI #CursorAI #StudentDevelopers #TechAccess #EdTech #DeveloperCommunity #IndiaInTech

54

13 Comments

🍀 6 months at Google.... Today I have completed 6 months at Google after having joined back in January 27th, 2025. These 6 months have been a rollercoaster of learning, exploration, meeting some exceptionally skilled people, and growth. Summarizing my journey at Google, here's the top 6 learnings for me over the period of the last 6 months... 1️⃣. Imposter Syndrome is real - Working at a place like Google, the talent density is the real deal. With being surrounded by some the smartest folks in the industry and working alongside them, its easy to feel lost and out of place. 2 things kept me going here - a. The support from the team in my rampup. b. The confidence that I've done harder things in the past - this too shall pass. 2️⃣. Ownership matters - At a place like Google, with highly skilled people all around, its kinda obvious that people like to own their work and expect you to do the same. How to own your work end to end, and to deliver it with the highest quality is something I've greatly learnt in the past few months. 3️⃣. Communication >>> Code - At a place, where any code change you make affects billions, communication and alignment regarding the same becomes something of utmost nessecity. Skillful communication within the team, cross teams, and with multiple stakeholders to effectively deliver work is something I've seen matter more than ever here. 4️⃣. Asking for help is a strength - In companies like Google and Microsoft, asking for help is looked on as a strength, not a weakness. The culture fosters that. However, to get help, you've to ask for it. Getting over the thought of judgment, to reach out for help, is a key learning which helped me a lot in the journey. 5️⃣. Make meaningful connections - Walking in any journey, what makes it worthwhile are the friends you make along the way. Making intentional connections, and going out of the way to help others move ahead in their journey, is the way to move ahead in the journey. This is something I also try to incorporate in my Linkedin journey by trying to give back to the community. 6️⃣. You're never too good at something, Keep learning - Tech is a fast moving space. The thought of "I already know enough" becomes obsolete as fast as the tech of today. Its a journey of constant of learning and upskilling. Parallely, make your basics stronger, that's what allows you to keep up the learning pace, with the industry. The last 6 months have been really exciting, fun, full of learnings and challenges all along the way. As its said in Google, being a Noogler is like "trying to drink from a firehose". Really excited for what the next 6 months unfolds. Stay tuned for more such content!!✌🏻🚀

907

85 Comments

Recently, I had the opportunity to interview at Google and Amazon for early-career SWE/SDE roles, encountering my first full DSA loops. The key takeaway was that success in such interviews relies more on intuition than mere memory recall. Here are some essential questions to consider: - Can you identify patterns in unfamiliar problems? - Are you comfortable making trade-offs? - Do you prioritize learned patterns over rote memorization? My preparation approach followed a focused trajectory, starting with core recognition and advancing towards comprehensive problem-solving: 1️⃣ Commence with Blind 75 to enhance pattern recognition and boost confidence. 2️⃣ Progress to NeetCode 150 → NeetCode 250 for improved problem-solving abilities and broader scope. 3️⃣ Explore Dynamic Programming and Graphs, the essence of intuition in DSA. ➡️ I suggest delving into Striver’s playlists for a thorough understanding. 4️⃣ Beginning early is crucial - mastering DSA demands time; an early start eliminates reliance on luck. Over the past three years, consistent problem-solving has honed my structured thinking, leading to a significant mindset shift vital for interviews. To all aspiring Big Tech interview candidates: initiate early, delve deep, cultivate your intuition, and trust the process. #Amazon #Google #FAANG #DSA #DynamicProgramming #GraphAlgorithms #TechInterviews #CodingInterviews #NeetCode #Blind75 #InterviewPrep #SoftwareEngineering #LeetCode #BigTech #CareerGrowth

252

10 Comments

"A completed syllabus is a false sense of security. ❌ I see students celebrate finishing a book while their scores remain stagnant. Why? Because 'completion' is qualitative. 'Mastery' is quantitative. We’ve built AdaptLearn to kill the guesswork. We’ve quantified knowledge so that your prep is no longer based on a 'feeling' but on mathematical coordinates. Check out our latest update on the company page below. 👇" https://lnkd.in/greAktJq #MasteryLearning #DeepWork #DataDrivenEducation

17

I cracked my Visa interview – Off Campus, New Grad SWE Role! (Experience) Sharing my experience to help others preparing for tech interviews — hope it helps! Round 1: Tech Fundamentals • DSA: Classic Linked List Reversal • DBMS + CN: Mostly conceptual — don’t skip the theory! • also a sql ques which tested my JOIN and GROUP BY understanding Round 2: System Design Deep Dive • Parking Lot Design — tested my ability to think at scale • Elevator System — a fun back-and-forth problem-solving session Round 3: Tech + Behavioral with Engineering Manager • Discussed my past projects + a deep dive into modern payment systems • Behavioral: Focused on teamwork + problem-solving mindset • Bonus: A casual chat about team dynamics and ongoing projects — asking thoughtful questions really helped! My Top Takeaways: • Strong fundamentals > memorized solutions • Think out loud — make it a conversation, not a monologue • Be genuinely curious about the company, team, and product If you’re preparing for New Grad roles — drop your questions below. Happy to help! #VisaInterview #SoftwareEngineer #OffCampusPlacement #NewGradSWE #TechInterviewTips #DSA #SystemDesign #BehavioralInterview #CodingInterview #CareerGrowth #LinkedInTech #InterviewExperience #ProgrammingLife #CrackTheInterview #JobSearchTips

811

51 Comments

Just heard Amazon Prime laid off so many employees, Because they shipped faster and automated things with AI. And this just scares me but at the same time I feel there’s huge gap between how managerial side is thinking about AI v/s the Reality of AI: Because: 1. AI writes the code but fails at quality, I need to spend time to get good final version 2. Doing LLDs with AI is too hectic, it skips what it doesn’t understand or comes up with solution which isn’t optimised 3. HLDs aren’t even AIs cup of tea yet This just about coding part, there’s lot of more that we do as engineers. So, is it managers illusion which is taking away job ? But whatever it is someone needs to wake these companies to reality and show how important engineers are for the company. Done with listening AI can replace engineers.

193

9 Comments

It has now been a little more than a month that I joined Meta and I wanted to share the long interview experiences that I had faced in the last 6-9 months. Apart from Meta, I have interviewed with Apple, Google, Nvidia, Qualcomm, Amazon, Waymo and Samsung. It was an enlightening experience, sometimes frustrating and at other times quite enriching. Common themes across companies for SWE hiring are an initial phone screen and then 5 or 6 rounds of interview loops but the similarities end there and differences abound. Most of the interviews are drawn out over months, sometimes more than 3 months mostly because it takes a long time to schedule interviews and then to get decisions. Some companies insist on having all interview rounds back-to-back in 1 day where the next interviewer joins the call even before you are done with the previous interviewer while others spread the final loop over days, often weeks. Some notify their decisions within days of completion of interviews while others may ghost you for 2-3weeks without any interim updates. Technically, almost all companies test the coding rounds quite similarly. The differences mostly lie in the system design round and that too, because ML compiler design is quite a small niche field, compared to general SW system design. Some interviewers stage the questions as conversational problem solving discussions to collect signals whereas some others have a belittling mindset that appears more of a grilling than an interview. Some companies test for concepts from actual ML compiler domain ranging from scheduling and utilization optimizations to LLM specific mapping on accelerator architecture while others test out general compiler concepts such as memory planning, register allocation and instruction scheduling, which made preparation tricky given that you don't know what to expect, unlike traditional SW system design questions. Overall, I personally felt that Meta and Google did a great job in testing the right themes within a fair framework. Let me know if you can relate to any of these in your interview experiences or disagree with the sentiments.

190

13 Comments

My Amazon 2025 SDE New Grad Interview Experience . . . Thrilled to share my journey interviewing for the Software Development Engineer (SDE) New Grad role at Amazon — a process that tested not only my coding skills but also my ability to think structurally and stay calm under interview pressure. From designing scalable systems to solving algorithmic problems, every round pushed me to prepare smarter. Many have asked about how I approached each phase. So, I’ve written a detailed Medium post that covers the following: 1. How I applied and prepped 2. Breakdown of each round (including the Object Oriented Design and DSA-heavy rounds) 3. My strategies, key resources, and what I’d do differently if I were starting today 👉 Checkout the full blog here: https://lnkd.in/eAXSaTAQ 📌 Learn more about me at: tharunpolu.com If you're currently navigating the SDE hiring process at Amazon or any other big tech/MAANG company, I hope this helps guide your preparation. Feel free to reach out if you need any help or prep tips! #Amazon #SDE #NewGrad #InterviewExperience #SoftwareEngineering #Medium

320

17 Comments

Hi there 👋 As we grow in our careers, our work starts revolving more around design decisions, not just writing code.   And interviews reflect that shift too. From my recent SDE-2 and SSE interviews, here’s the usual pattern  🔹 1–2 rounds on DSA / problem-solving 🔹 1 round on Low-Level Design (LLD)  🔹 1 round on High-Level Design (HLD) Many hiring managers even ask HLD questions based on your real-world projects, not just textbook systems. When it comes to HLD prep, the amount of content can be overwhelming.   If you’re starting out, I’d recommend:   HelloInterview , Alex Xu’s System Design series , YouTube mock interviews — super helpful for learning how to explain clearly under time pressure One of my juniors recently asked - “Why do companies still ask DSA?” Simple - DSA tests your problem-solving mindset.   It’s not about memorizing patterns but showing how you think.   Even 30 - 45 minutes of daily consistency goes a long way. 🔹 In short:  DSA → builds problem solving skills  LLD → reflects code level thinking  HLD → shows system level design     All three matter, just at different depths as you grow. #CareerGrowth #SoftwareEngineering #InterviewPreparation #SystemDesign #DSA

122

5 Comments

Mays Turabi wrote a beautiful piece about two apps I built, Noto and Aura. It was a great conversation and I’m grateful for the feature. But the part that stuck with me? She asked how I built them. The answer: vibe coding. I built both apps as a single person. No team. No agency. No months of runway burned on design sprints and dev handoffs. Not long ago, taking an idea from concept to design to development to launch required a team of 20+ people. Designers, engineers, PMs, QA. Now? One person with the right AI tools and product instincts can ship something production-ready and start making money. Vibe coding isn’t just a trend. It’s becoming a core skill in software engineering, the same way learning Git or knowing SQL was a decade ago. And what worries me was our education system was built for a different world. A world where job descriptions were stable, where assignments mapped neatly to careers, where a degree was a reliable bridge to employment. That world is changing faster than curriculum can catch up. The old assignments don’t reflect the new skills. The old JDs don’t capture what actually gets work done today. Students are being trained for roles that are already evolving or disappearing. I’m not saying traditional roles go away. I’m saying they evolve, and the people who adapt will be the ones who treat AI as a creative partner, not a threat. The shift isn’t coming. It’s here. Aura and Noto are small examples of what’s possible when one person and AI work together. Imagine what happens when an entire generation learns to build this way. https://lnkd.in/d6pTNqUZ

16

1 Comment

Hiring International Students is simpler than you think. A company once told me, “We don’t sponsor visas.” I wasn’t asking for sponsorship. I was on OPT. This happens more than people realize. There’s this invisible tension the moment someone says: “I’m on an F-1 visa.” The room changes. What many teams don’t realize: Hiring an F-1 student during OPT is often the simplest hiring decision you can make. For up to 3 years in many STEM cases, there is: -- No sponsorship paperwork -- No immigration legal fees -- No special visa petition It’s just employment. But confusion creates friction. Recruiters hear “visa” and think: Lawyers. Costs. Risk. Complexity. So resumes get filtered quietly. Not because of skill. Because of uncertainty. And here’s where I’ll say something uncomfortable: International students cannot assume recruiters understand the nuance. You have to lead the clarity. If you’re on OPT, say it clearly: “I’m authorized to work for X months without sponsorship.” That one sentence removes doubt. Doubt kills momentum. Clarity creates interviews. This isn’t about policy. It’s about awareness. When companies understand this, they widen their talent pool overnight. When students communicate confidently, doors open faster. Most hiring friction isn’t about capability. It’s about misunderstanding. Fix that, and the system works better for everyone. 🔁 Share this with someone who still thinks “F-1” automatically means “sponsorship.” Follow me Hari Prasad for more International Student tips. Thanks to Madhan for spreading the awareness. His post made me to share this.

79

8 Comments

One of the hardest parts of being an international student in the U.S. is the constant feeling of being behind. Not behind in effort — behind on timelines. You might be 24, 28, or 32 and still figuring out: your first full-time job, your visa path, your finances, or even whether staying here long-term makes sense. Meanwhile: * Some people already have H1Bs. * Some are buying homes. * Some are switching companies for higher pay. And even if you are doing fine, it rarely feels that way. Because back home, progress was visible and shared. You passed exams, got into good schools, and everyone understood what that meant. Here, progress is fragmented. Different visas, different salaries, different cities, different timelines. So your reference point keeps shifting. And social media makes sure you see the best version of everyone else’s life — every single day. That does something subtle to you. You stop feeling curious about others and start feeling measured against them. Community quietly turns into comparison. That’s where things break. People stop sharing what they know. They hold back referrals. They don’t mention the lawyer who helped them or the tax setup that worked. They treat information like leverage instead of something to pass forward. But none of that actually improves your position. It doesn’t increase your odds. It doesn’t shorten your timeline. It doesn’t make the process fairer. It just makes it lonelier. What does help is boring, unglamorous, long-term behavior: * Sharing what you learn about visas and deadlines. * Helping someone think through their resume or job strategy. * Talking openly about money, mistakes, and what didn’t work. * Investing in relationships before you “need” them. Not because you’re being nice. But because you’re being rational. Immigration is not a single event. It’s a multi-year system with uncertainty, delays, and randomness. And systems are navigated better with networks than alone. What actually increases your long-term odds: * Understanding your timelines early. * Building a financial buffer so one bad month doesn’t derail you. * Having people who would help if your job, visa, or health situation changed suddenly. * Becoming someone others trust and want to support. That’s not softness. That’s strategy. Think in decades, not in comparison snapshots. Don’t let short-term envy distort long-term thinking. Your journey is slower than some. It is also deeper than it looks. #InternationalStudents #Careers #Immigration #Tech #USA #Canada #FAANG

24

1 Comment

ayuRAG: My UCSC chatbot experiment Been deep in RAG research while building a University of California, Santa Cruz -specific chatbot, experimenting with Agentic RAG workflows like Adaptive RAG, Corrective RAG, Self-RAG, RAT-RAG, RARE-RAG, multi-agent systems,ReAct frameworks etc. Each has unique strengths that taught me something valuable. The result? Our hybrid, confidence-driven approach : ayuRAG (pun intended), hit 85.4% on RAGAS’s faithfulness + answer relevancy (vs. 81.2% for the next best), delivered strong BLEU and ROUGE-L scores, and ranked top 4 on the remaining RAGAS metrics(Context Precision and Context Recall). Remarkably, even our local-only variant still secured second place; outperforming methods that rely on web search to augment their knowledge. More importantly, I learned that the best workflow is usually a custom blend, not a copy-paste solution. ayuRAG workflow has 2 main components: 1. Retrieval tool with web search enabled - it can rewrite queries for efficient vector search retrieval with k tries (k = 3 in our case) and a boolean flag for web search. When enabled, it performs web search k times while rewriting the query (but we need the original query to write every new query or else information can be lost). This tool also has document reranking and document relevancy guardrails. 2. Agent - First calls the retrieval tool, then gets the answer. If it can answer with confidence, it returns the result. Else, we use Chain-of-Thought (CoT) to split the question into atomic steps and then call the retriever tool for each sub-thought before answering. Simple questions get direct answers without web search, complex ones get decomposed into manageable sub-problems. The workflow is simple yet powerful - the real magic happens in prompt engineering in these components! Key insights from building this: 1. Domain trumps architecture - Study your dataset and design your retriever based on the specific use case. Tune chunk size (e.g., 200–500 tokens), chunk overlap (20–50 tokens), and similarity scoring (e.g., cosine with dense embeddings). 2. Prompt engineering is undervalued - Better prompts often outperform complex agent orchestration by significant margins. 3. Reranking prevents hallucination - Too many documents in context overwhelms models. 4. Human queries NOT EQUAL Vector queries - "How do I apply?" won't match "university enrollment application procedures." Rewrite for embedding similarity and even for web search. 5. Cot doesn’t always mean it will make things better. If the query is simple, doing COT is an overkill. Would love to hear your thoughts! Which workflow patterns have worked best in your domain? #RAG #PromptEngineering #AgenticWorkflows #FAISS #LangChain #LangGraph #AdaptiveRAG #SelfRAG #VectorDatabases #DocumentRetrieval #LLMApplications #AIEngineering #MLEngineering #RAGSystems #ProductionAI #MachineLearning #OpenAI #ReAct #ChainOfThought #AIOptimization #NLP #Agents #AIAgents

54

4 Comments

We talk a lot about AI/ML development, cutting-edge research, and fancy tech jargon. But the ground reality in many low-tier engineering colleges in India is very different. There is no real coding culture. No ecosystem that encourages curiosity, experimentation, or problem-solving. In many places: - Teachers are not only weak in their subjects, they also don’t care to upskill. - Faculty time goes into FDPs, paperwork, and managerial formalities, not teaching. - Colleges operate like coaching centres. One building. Minimal infra. No labs worth the name. - Universities chase NAAC accreditation optics, not academic depth. During inspections, everything suddenly looks perfect. Buildings are cleaned overnight. A few obedient students are selected to give “ideal” feedback. And once the inspection is over, things go back to normal. This is not about naming and shaming a single institute. It’s about acknowledging a systemic failure. We cannot keep dreaming about producing world-class AI engineers when: - Foundational CS is broken - Teaching has become transactional - Learning is reduced to attendance, notes, and exams Real change will not come from better brochures or higher NAAC scores. It will come when teachers learn again, when students are encouraged to think, and when education stops pretending and starts building. If we don’t fix the foundation, all this AI/ML hype is just noise. P.S. The class that I’m seen teaching in this photo is from a different, much better university.

79

1 Comment