Ryan Fu

When you join a new org or switch teams in the same, it is quite an unsettling and borderline anxious experience. Here are a few things that I did to ramp up faster. 1. I consciously made an effort to remain unblocked 2. I read a ton of code and its history, sometimes even unrelated 3. I asked questions about the past, the present, and the future 4. I took up relatively mundane tasks - cross-team work, tests, and docs 5. I extended a helping hand in whatever capacity I could 6. I proactively met skip-level leaders and managers to understand the vision Some companies do have a culture of pairing up with someone existing in the team, but even if you do not get a mentor, it is important that you still navigate the situation and ramp up as quickly as you can. The above list is not exhaustive, by any means, and hence you can always add things that you find helpful in your context. but the lowest common denominator is to show extreme intent and interest to get output and drive outcomes. Hope this helps.

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Someone lost their job at Amazon when they were expecting their first child and looking forward to being a parent. Now their whole world is upside down. Someone got laid off in October, fought hard to find an internal role, and still got impacted in the next round. Someone who spent 10 years in the company, worked across three teams, and managed people with care, also lost their job today. It happens quickly. You get an email, the next thing you know you are locked out of the system, badge stopped working, chats gone. It feels like you were never part of the place to begin with. Layoffs are the worst part of this industry, and you can do almost nothing to prevent them once a list is made. A few things I want to say to anyone who got impacted today: 1. A spreadsheet decision is not a measure of your intelligence, your kindness, or the value you created for users and teammates. Please be kind to yourself. 2. Take time to process. Cry, vent, sleep. You are a human who just had their safety rug pulled, not a robot who must instantly “bounce back.” 3. The projects you shipped, the skills you built, the people you helped, all of that is real. A layoff cuts pay, not your track record. Do not feel like you’re not strong enough to find your way again, you are. 4. Tell friends, ex colleagues, mentors what happened. Ask for referrals, mock interviews, resume reviews, even just calls where you talk about anything except work. If you were not impacted, check on the ones who were. Nobody chooses to be on the receiving end of that email. The least we can do is remind them that the email is not the end of their story.

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At 3 AM on a Wednesday, Amazon needed me. I wasn’t even on that team anymore. Back when I was an SDE at Amazon, a Sev-2 had hit one of our services. Many customers were impacted. The team had been chasing it for hours. Still, they called me. I rolled out of bed, opened my laptop, and joined the bridge. The tricky part was that the fault started a week earlier. A code change had shipped behind a flag, traffic ramped slowly, and the failure signature was noisy. Logs were chatty in all the wrong places and silent where we needed them most.Dashboards showed errors, but no clear line to the root. We did unglamorous things. We wrote a minute-by-minute timeline. We diffed deployments, configs, and flags across regions. We checked queue backlogs and retry storms. We sampled logs with new filters. We bisected the traffic ramp. We rolled back the smallest safe thing first. Then we added one log, in one hot path, and the picture snapped into focus. By sunrise, we had the root cause and a fix. The impact was contained. Customers recovered. What stayed with me was not the fix. It was why they called. They did not call because I am smarter. They called because, over months, I had done the boring work. I wrote runbooks when no one asked. I paired with juniors on their first on-call. I shared context so others could make decisions without me. I showed up when it was not my ticket. Trust is not built in one night. It is built in all the small days that come before it. Ownership is simple. If it affects your customer, it is your problem. Org charts are for payroll, not for 3 AM. If you are early in your career, remember this: → Document the weird corners when you discover them. → Add one useful log where it hurts, not twenty where it is easy. → Keep a living checklist for rollback, not a static wiki page. → Teach one person the thing you just learned. → Read your dashboards when things are calm, not only when they are red. At 3 AM, you will not rise to the occasion. You will fall to the level of your habits. Build the right ones while the house is quiet. That’s how you earn trust that lasts beyond teams and ownership that outlives org charts. A few days later, this moment turned into an accolade from the team..  a reminder that trust is built long before it’s tested :)

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The first time I broke a feature at Amazon, I took it personally. My hands were shaking while rolling back. I replayed that mistake in my head for weeks. The first time my PR got picked apart, I took it personally. I started avoiding senior reviewers because I felt small around them. The first time someone rewrote my module without telling me, I took it personally. I felt invisible. Like my work didn’t matter. The first time a bug was found in a feature I shipped months ago, I took it personally. Even though it wasn’t my ticket, I felt responsible. It took me years to understand: > Your code is not your ego. > Your job is not your identity. Not every bug needs to be your burden. Not every decision is about you. Not every line of feedback is a personal attack. You’re part of a system. A team. A process. A business. The goal is to build, ship, learn, grow, together. You don’t need to prove your worth through over-owning. You don’t need to burn out to be seen as “dedicated.” Work with care. But work with boundaries. That's how you last. That’s how you grow. That’s how you build a career that doesn’t just look good but feels good.

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LeetCode Knights who can't ship or collaborate as engineers are like Formula 1 drivers who've never driven in traffic. I say this as someone who has spent years on both sides. I have hired and interviewed at Amazon. I now work as a Senior Engineer at Nielsen. I have seen brilliant problem solvers who could not ship a stable feature. I have also seen engineers who were average on LeetCode but carried entire teams on their backs. LeetCode is useful. It sharpens your thinking, helps you speak the “interview language,” and gives you a structured way to practice. I used it myself when I was preparing for Amazon. I still recommend it to people who want to break into good companies. But here is the issue. Some engineers start treating LeetCode as the main measure of their worth. If they are hitting 200 problems a year, they feel “good.” If they are slow in a contest, they feel “bad.” Meanwhile, nobody is asking simple questions like: > Can you read a messy codebase and make sense of it? > Can you debug a production issue without panicking? > Can you explain trade-offs to a PM in plain language? > Can you design something that another engineer can safely change a year later? That is competence. Software engineering is less about solving a neat, isolated problem and more about dealing with half-baked requirements, legacy code, time pressure, and unclear constraints. It is about making progress even when the situation does not look like a clean LeetCode prompt. If you are serious about your career, you need to optimize for both. When you prepare for interviews: > Pick a fixed slot for DSA. For example, one or two hours a day. Do it with focus, then stop. > Map problems to concepts. “This is a two pointer idea.” “This is a graph traversal pattern.” That way you are building a toolkit, not just counting problems. When you grow as an engineer: > Own a feature end-to-end. Design it, build it, handle the bugs, and listen to user feedback. That pain teaches you more than ten contests. > Read code from seniors. See how they name things, structure modules, handle failure, and write tests. > Ask for feedback on your pull requests beyond “LGTM.” Ask, “How would you simplify this. What smells wrong here.” Good companies do not only look for who can solve the hardest LeetCode problem. They look for people who can: >Think clearly. >Communicate calmly. >Work with others without ego. >Handle ambiguity without freezing. >Take responsibility when things break. If you want a long career, build that person in yourself. By all means, grind LeetCode for a season when interviews are near. It is a tool. Use it well. Then close the tab and go back to building things that live in production. Your future manager will care far more about whether they can trust you with a problem than whether you once solved “Hard problem number 247” in 18 minutes.

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One thing your manager will never tell you Your promotion depends less on your performance and more on whether your team is actually growing. When I joined Amazon as an SDE-1, my team had 25 people. By the time I became Senior, it had 120+. Looking back, my “fast promotion” wasn’t magic. The org was expanding like crazy and growth creates opportunity. Here is the part people don’t like hearing: ❌ If your team hasn’t grown in years, your career won’t either. ❌ It doesn’t matter how good you are. There is nowhere to go. ❌ The only real path up becomes waiting for someone above you to leave. So if you want to grow faster: Choose teams that are growing. Not teams that are comfortable. Skills matter. Environment matters more.

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6 months into my job at Amazon, I broke production and thought this was the day my career ended because the company lost a lot of revenue...This is the story of one of the biggest learnings of my career. I was on an ad tech team. I was trying to learn the ad pipeline, went a bit too confident with a change, and triggered a failure. For 7–8 hours, ads were not being served. The moment I realised what had happened, my heart just dropped. I pinged my senior, called it out clearly, and stayed glued to the incident channel. Here is what surprised me. No one shouted. The first questions were: - “What changed?” - “How do we restore?” - “How do we stop this from spreading?” We rolled back. Checked the data. Verified that the pipeline was healthy again. Only after things were stable did the real work begin. That was the retrospective. We walked through the timeline, and I explained exactly what I did. We realised there was a big documentation gap. Some critical behavior of the pipeline lived only in people’s heads, not in any design doc or runbook. The conversation was not “Why did you mess this up.” It was “How did the system allow this to happen and how do we make sure the next person does not fall into the same trap?” I wrote the COE (Correction of Error) doc. My senior manager liked how honest and detailed it was. He told me, “You made a serious mistake. You also did the right things after. You reported it quickly, stayed on the fix, and left the system stronger than you found it.” That has stayed with me ever since. A healthy engineering culture does not pretend failures will never happen. It assumes they will. So it rewards people who: - Surface problems early instead of hiding them - Take ownership of their changes - Care enough to improve docs and processes after an incident - Focus on learning, not on finding a scapegoat Looking back, that outage was expensive for the company, but priceless for me. It taught me that good teams judge you not only by the bugs you ship, but by how you behave when those bugs show up in production. You will break something important at some point in your career. The question is not “How do I avoid mistakes forever?” It is “When it happens, will I have the courage to own it and the maturity to learn from it?”

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I had a small but very interesting moment of tech evolution recently. After 5 years of hearing my name butchered in reminders, Alexa suddenly started pronouncing "Prashant Ramarao" perfectly. That's when it clicked - they must have integrated or upgraded an LLM to improve speech synthesis. So I decided to test other AI models: ChatGPT voice: Nailed it ✅ Siri: Still says "Pruh shaaawnt Romario" 😂 - seriously Apple Intelligence? It's fascinating how these subtle upgrades reveal the quiet integration of language models into everyday products. For those of us with non-English names, finally hearing our names pronounced correctly by technology feels surprisingly meaningful - a small but significant step toward more inclusive AI. The best innovations often aren't flashy new features but these simple nuanced improvements that make technology feel more human. #AI #MachineLearning #UserExperience #Inclusion #TechnologyTrends

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Last week, I spoke to someone who's an ex-Meta and ex-Google. She told me something about interviewing at these companies that people hardly talk about. She's a long-time friend and has cracked multiple FAANG interviews. She has also been on the other side - interviewing candidates. Her exact words were, "The interview is never about being correct. It’s about being convincing." That one line made a lot of sense, as I've cracked FAANG myself. → At Meta, most candidates can code → At Google, most candidates have great resumes → But only a few can sell their thought process She said, “If I had to reject someone, it usually wasn’t because their answer was wrong - it’s because I couldn’t follow their thinking.” Here are 3 non-obvious tactics she shared that can instantly improve your chances: 1. Don’t jump to the solution. Slow down. Before solving the problem, restate it. Clarify constraints.  Show that you're not a robot trying to optimize, and you're a human trying to understand. 2. Narrate trade-offs like you're explaining to a peer. Say things like: → “I could use X for speed, but it’ll cost memory.” → “Here’s why I’m leaning toward Y despite it being slower.” This builds trust way more than giving a perfect answer. 3. End with reflection, not just resolution. After finishing, don’t say “done.” Say: → “If we had more time or scale, I’d explore ABC as an optimization.” That’s how you show long-term thinking. The best candidates aren’t the ones who ace every question. They’re the ones who take the interviewer with them. So the next time you prepare, don’t just rehearse answers → Rehearse how you explain your thinking under pressure. Because in FAANG interviews, that’s the real test. P.S. Repost this if you want more insights like this. Follow me, if you are a job seeker in the U.S. I simplify job search strategy, salary negotiation, and interview prep.

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If I were having system design interviews at Uber, Google, Microsoft, Amazon, and Meta in the next 2 months...this is exactly how I would prepare (based on my experience of 5+ years in the Industry): 1. Get Clear on What System Design Actually Tests ⤻What you’re really being judged on:   → How you break down an ambiguous problem   → How you weigh trade-offs (performance, scalability, cost, maintainability)   → How you communicate complexity simply   → How well you handle constraints, edge cases, and real-world failures There’s no “perfect answer.” The goal is to show how you think, not just what you know. ⤻The main interview focus:   • Product Design: (e.g. “Design Uber’s backend”)   • Infrastructure Design: (e.g. “Design a rate-limiter”)   • Object-Oriented: (e.g. “Parking lot system,” less common for SDE2+)   • Frontend/App: (skip for most backend interviews)  In almost every company, you’re judged on:   → Problem Solving   → Solution Design   → Technical Depth   → Communication Clarity 2. Refresh The Fundamentals (Don’t Skip This) Before you solve big problems, nail the basics that every interviewer expects. Focus on depth over “checking boxes.” (Check the 1st Comment, I’ve put all the main fundamentals there) 3. Master The Building Blocks You don’t need to memorize every tech out there. Just deeply understand these 7 core components (know pros, cons, and failure modes): –Server/Compute: Monolith, microservice, serverless –Database: SQL, NoSQL (document, key-value, column) –Cache: Redis, Memcached (cache-aside, read/write-through) –Message Queue: Kafka, RabbitMQ, SQS (async, decouple, backpressure) –Load Balancer: Distributes traffic, handles failover –Blob Storage: S3, GCS (for unstructured, large objects) –CDN: Edge caching for speed, offloading origin, DDoS protection Bonus: Know how you’d monitor/log/alert for each piece. 4. Work Backwards From Common Problems (PRACTICE > THEORY) Passive learning is a trap. Pick up the most asked system design questions. Don’t only watch YouTube breakdowns - actually practice sketching, whiteboarding, and thinking aloud, and you can do it all on our platform - layrs.me. Join our discord: https://lnkd.in/g9wGRadq It’s like Leetcode for system design, which helps you give a proper, structured plan with problems so you understand what you need to learn to stand out. 1. Design a URL Shortener (Bitly) 2. Design Dropbox 3. Design Ticketmaster 4. Design a News Feed 5. Design WhatsApp 5. Mock Interviews & Feedback Loops Collect feedback, act on it. Find your blind spots (did you skip failure scenarios, did you not justify a choice, did you fumble API design?). You’ll see patterns repeat after 3–5 interviews. You’ll never “know it all.” But you can get dramatically better, fast, if you build on the fundamentals, master your building blocks, and drill through those 10 classic problems with practice and feedback. You got this.

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Heard about the Amazon layoffs and couldn’t help but look back at my 6+ years there. My team grew from a cozy 6-7 people to a solid 25, and I saw closely how this giant moved fast, made bold moves, and pushed innovation like no other. What stood out back then was how individual growth wasn’t just encouraged, it was part of the culture. Along with customer obsession and high standards, Amazon invested heavily in people’s learning and development. It genuinely felt like you were growing as fast as the company. But around the time I left, things started to shift. The buzz changed from “grow and learn” to “cut costs and hit revenue targets.” Talent and individual growth took a backseat, and suddenly, the culture felt different. This isn’t just an Amazon story. Big companies everywhere seem to be hitting the brakes on people and stepping on the gas for profits. Quick decisions still happen, but the heart? That seems to be missing sometimes. If there’s one lesson? Adapt. Keep learning. Stay ready. The game is changing fast, and only those who roll with it will win. #Amazon #Layoffs #BigTech #CareerRealTalk #StayAdaptable #WorkCulture #Innovation

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Evergreen advice to those in SF: don't let the rat race kill you. From time to time, a friend of mine moves back to SF for their startup or fund. They put their head down for 2-3 years, work very hard to invest in their future, neglecting their friends and family and mental health. They pick up their head from the sand, realize that in a power-law, non-Normal Distribution sector (venture-backed startups) they introduced much more risk to their career and personal life than they thought. To be clear, I have been this person before. I don't regret it, made lasting connections and success, but I learned a lot the hard way. The better approach is to have a 7-10 year horizon on your entrepreneurial career. Over that time-scale, if you stay in the game, you will be successful. Your fund would have returned, your startup would have had some sort of exit. Otherwise, you are very very likely to crash and burn, on the professional and personal front. Don't take my word for it - just find anyone that's been in this game longer than five years and they will relate these same stories of personal ruin. This does not mean you shouldn't pursue your dreams in SF / SV and the startup ecosystem, it just means you need to be a bit more shrewd, understand your are in a non-Normal Distribution space. There is no 50th percentile outcome like if you chose a traditional profession. Power-law dynamics. It requires thinking a bit more long-term, prioritizing yourself and family along with your business. If you don't, the meat-grinder will get you. SF/SV culture eats everything for breakfast, including you.

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re:Invent Day 1 is done and it was packed! My day began with a roundtable with some of our best partners talking about how we can better help customers migrate and modernize their AI workloads and use cases. Then Priya Arora and I presented on-stage on how Agentic AI is opening opportunities for partners, from ISVs and SIs, to startups. We shared data from our survey we did with BCG showing the trends in customer adoption of AI, focusing on the industries and use cases with the highest momentum. We also uncovered areas where customers need the most help and support as they roll out their agentic use cases. After that I sat down with 3 key partners, grabbed lunch with a few AWS peers I don't get to see often enough, and had a lot of ad hoc hallway meetings. (It's hard to walk 10 feet at re:Invent without seeing someone you know) But my highlight was launching the new AI Competency and Agentic AI categories for partners. I had a chance to join the AWS OnAir team to talk about it. My team was the driving force behind the launch and I'm proud of what this means for our partner community. We spent months listening to partners tell us they needed a way to stand out in customer conversations around agentic AI. The new AI Competency creates three distinct specialization paths. Agentic AI Applications recognizes partners delivering production-ready autonomous systems. Agentic AI Tools validates partners providing the infrastructure and tooling that makes agent development possible. Agentic AI Consulting Services distinguishes partners with proven expertise helping enterprises design, build, and scale agentic deployments. Each path requires demonstrated technical depth and validated customer outcomes, not just certifications or marketing claims. We launched with 60 partners who achieved the AI specialization. That's the highest number of launch partners in any AWS Competency program so far. Partners like Loka, Anthropic, LangChain, and Mission are already proving their ability to deploy AI systems that handle real business processes autonomously. That validation matters when enterprises are making critical technology decisions. Partners achieving these specializations gain access to funding, co-marketing resources, and priority placement in customer engagements. But what matters most is the market differentiation. When customers are evaluating dozens of partners claiming agentic AI expertise, this competency provides a clear signal about who has actually done the work. For enterprises evaluating partners, this competency provides the differentiation signal you need. These partners have solved the hard problems of moving agents from demo to production, from single use case to enterprise platform. The timing matters. Enterprises are moving past whether to deploy agentic AI and into how to do it at scale. Partners with proven expertise become increasingly valuable as deployment complexity increases. That's what makes this competency so important.

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