Ranyodh Singh

In big tech, title inflation is real. So is title deflation. I’ve seen: • Staff engineers who only execute • Principals who avoid hard conversations • Seniors who operate like org-level architects Your title is a lagging indicator. Don’t wait for it to give you permission. Operate at the next level now. Make it obvious the system is behind. That’s exactly what I teach in the Top Engineer Method. Learn more ↓ https://lnkd.in/gMGfmVas

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If you want to become better at system design rounds, definitely read these 10 case studies by Neo Kim. I am 100% sure, they will make you smarter for your next interview.  1/ How Meta Achieves 99.99999999% Cache Consistency: → https://lnkd.in/e88kUZAm 2/ How Uber Computes ETA at Half a Million Requests per Second: → https://lnkd.in/eVKV2ePC 3/ How PayPal Was Able to Support a Billion Transactions per Day With Only 8 Virtual Machines: → https://lnkd.in/eqcb7MpP 4/ How YouTube Was Able to Support 2.49 Billion Users With MySQL: → https://lnkd.in/efmJw4Dx 5/ How WhatsApp Was Able to Support 50 Billion Messages a Day With Only 32 Engineers: → https://lnkd.in/gRqBgXfc 6/ How Uber Finds Nearby Drivers at 1 Million Requests per Second: → https://lnkd.in/eeqH9Hjh 7/ How Stripe Prevents Double Payment Using Idempotent API: → https://lnkd.in/erMkqwq4 8/ How Google Ads Was Able to Support 4.77 Billion Users With a SQL Database: → https://lnkd.in/efnSvwJp 9/ How Amazon S3 Achieves 99.999999999% Durability: → https://lnkd.in/eutGiK35 10/ How Slack Works: → https://lnkd.in/eATMDjrK

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The first SDE-2 absolutely nailed the system design round. The question was: Design a live streaming system. The second SDE-2 was asked to Design Google Drive. He did well, spoke about trade-offs, but missed one important detail: chunking the file, especially for large uploads. Yet, the second engineer got the job. These are from real interviews I took around 4 months ago. You might ask: Why? Only one reason, the second one nailed the behavioral questions like a boss. The question was about working on a project with tight timelines. The first SDE-2 (subtly) blamed the external team’s API for delays, which led to tight project deadlines. The second one, on the other hand, shared a story about how he helped his manager prioritize requirements to navigate the crunch. That’s how important behavioral interviews are. Yet, 90% of SDEs leave them to luck. If everything else is equal: - DSA (both solved the coding problems), - LLD (designed with solid object-oriented principles), - HLD (covered all the P0 functional and non-functional requirements) then your behavioral answers become the deciding factor. Yes, the second engineer missed the file chunking detail. But it wasn’t a critical red flag. He covered all other parts well and crushed the behavioral round. As long as you're working with humans (no matter how many), you’ll be judged on your behavioral skills. Call them soft skills, leadership skills, whatever you like, but you should prepare them well in advance. You can start preparing for behavioural interviews right away, with these 5 MUST read behavioural interview posts I wrote. (tip: save this post for later, as it may take some time to complete all of them) [1] How to answer any behavioural interview question https://lnkd.in/gH3N8b5q [2] 4 step guide for behavioural interview preparation in 3 hours https://lnkd.in/gccxG3MP [3] How to create 10+ stories for your past projects https://lnkd.in/g4nv_jqU [4] How to answer a failure story? https://lnkd.in/gtN-Dfuv [5] 10 behavioural interview mistakes to avoid at all cost https://lnkd.in/gpupvYVs If you’re preparing for mid-senior SDE or EM roles and need dedicated help with behavioral interview prep, DM me COACH.

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𝐔𝐧𝐩𝐨𝐩𝐮𝐥𝐚𝐫 𝐨𝐩𝐢𝐧𝐢𝐨𝐧: In 12 months, your dev team won’t be writing code. They’ll be managing agents that do. Sounds extreme, but let me explain. The way we build software is going through a fundamental shift. We’re moving from engineers-as-coders to engineers-as-architects. While agents handle every line of logic, engineers are designing how these agents work together in a unit. At SpotDraft, we already see this in action. Our teams are experimenting with cloud agents that handle: → Clause extraction → Risk classification → Suggesting redlines → Even summarising entire contracts Each one is like a junior dev with a very specific job. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐞𝐫𝐞 𝐈 𝐬𝐞𝐞 𝐨𝐮𝐫 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐡𝐞𝐚𝐝𝐢𝐧𝐠: In 12 months, a single engineer will manage 5 - 8 cloud agents like this. Their job won’t be to write low level code, it’ll be to review, quality-check, and fine-tune what these agents output. I think of it as a shift from builder to orchestrator. The question isn’t: “Will AI take my job?” It’s: “How good am I at managing a team of non-human contributors?” Are you training your engineers to think like quality checkers?

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Stripe, Avoiding Double Payment to Customers💰 When collecting payments from customer via Stripe API, 3 error scenarios can happen, 1. The initial connection could fail as the client tries to connect to a Stripe server 2. The call could fail midway while the server is fulfilling the operation, leaving the work in limbo 3. The call could succeed, but the connection break before the server can tell its client about it To ensure successful payment, Client retries the request which can lead to double payment (as per scenario 3) To prevent double payment, Stripe accepts "idempotency key" into API header. Here is how it works, - Client generates a unique ID (as Idempotency-Key) to uniquely identify that operation and sends it up to the server along with the normal payload. - The server receives the ID and correlates it with the state of the request on its end. - If the client notices a failure, it retries the request with the same ID, and from there it’s up to the server to figure out what to do with it. If we consider our sample network failure cases from above: 1. On retrying a connection failure, on the second request the server will see the ID for the first time and process it normally. 2. On a failure midway through an operation, the exact behavior is heavily dependent on implementation, but if the previous operation was successfully rolled back by way of an ACID database, it’ll be safe to retry it wholesale. 3. On a response failure (i.e. the operation executed successfully, but the client couldn’t get the result), the server simply replies with a cached result of the successful operation. This is idempotence in Action, Guaranteeing “exactly once” semantics 🤔 over to you, for prolong outages retries will make it worse. How to prevent it? Let me know in the comments! 👇 --- You've got System Design skills but showcasing them in 60 minutes interview is a completely different challenge for e.g. where to start? How much time to spend on HLD vs LLD etc Let me share a FREE 7-day email course that provides a step-by-step framework for acing your 60-minute System Design interview 📣 Join 1700+ subscribers and sign up for the course to learn time allocation to demonstrate full range of design skills (link in comments 👇)

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Cracking a Senior SDE role at Google? → 100% impossible if you can’t design a real system with scale, trade-offs, and failure in mind. Getting through a backend round at Amazon for a Senior Role? → 100% impossible if your system design answer is just a bunch of boxes and buzzwords. Landing an L4/L5 offer at Microsoft or Meta? → 100% impossible if you can’t explain why your design works and what breaks under pressure. You don’t get rejected in system design rounds because you “don’t know enough.” You get rejected because you never practiced building, defending, and fixing real systems. Most engineers only ever: – Memorize a few diagrams, – Read a dozen blog posts, – Copy other people’s notes… But in the actual interview. You’re asked to map out a system, justify your trade-offs, predict failures, and handle scale live. If you haven’t built those muscles, you freeze, you bluff, or you get exposed. The result? Down-level offers, endless rejections, and “come back in 6 months.” Here’s what you should do instead: 1. Work on real, open-ended system design problems — not just reading blog posts or memorizing slides. 2. Sketch out the full data flow, not just high-level boxes — think through how data moves, where it bottlenecks, and what breaks at scale. 3. Practice breaking down requirements, choosing the right components, and justifying every architectural decision. 4. Simulate failures and edge cases — walk through what happens when a service crashes, when data spikes, or when latency jumps. 5. Time yourself — practice explaining your design, trade-offs, and reasoning in 45–60 minutes, just like the real interview. 6. Get instant, actionable feedback — don’t wait for a one-time mock; get feedback as you build, so you learn what actually works. 7.Fix and improve your designs after review — go back, iterate, and make your solution better with each attempt. 8. Create your own custom problems if you want to stretch further — test yourself on scenarios you haven’t seen before. 9. Discuss your solutions with real engineers — join a community, get challenged, and learn how others approach the same problem. And you know where you can all of this and more, at layrs.me.  In fact, that’s why we built Layrs, it’s Leetcode for system design. – Practice on an interactive canvas – Drag, drop, and explain your architecture – Get feedback after every problem – Try BYOP (Bring Your Own Problem) if you want – Join our Discord, discuss with real engineers Sign up today, DM me if you would like to know more, the platform is free to use!

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What I thought Software Engineering was in 2021 vs What I think as a Senior SWE at Walmart in 2025: => In 2021 as a junior developer: - Seniors have all the answers - Impact = number of tickets closed - The best engineers are the smartest - Asking questions makes you look junior - You should always say yes to show initiative - Writing clean code is the most important part of the job - If you’re not shipping code every day, you’re not growing - Promotions happen when your manager notices your effort - You need to fix everything yourself to be seen as reliable - Whoever solves the ticket fastest is the best engineer - Hard work will automatically get recognized - Learning more frameworks = growth - Documentation is a “nice-to-have” - If code works, it’s good enough - Meetings are distractions => In 2025 as a Senior SWE: - Asking good questions is a senior-level skill - Visibility + clarity = recognition (not effort alone) - Impact = clarity, velocity, and outcome, not volume - The right meetings build alignment and unblock progress - Depth in fundamentals beats hopping between frameworks - The best engineers make the team faster, not just themselves - Thinking, designing, mentoring, debugging, that’s growth too - Writing maintainable code is more valuable than just clean code - Knowing who to loop in is as important as knowing what to fix - Promotions happen when you start operating at the next level - Strategic “no” is sometimes more powerful than blind “yes” - Seniors ask better questions not give perfect answers - The best engineers are calm, curious, and consistent - If your code confuses someone else, it’s broken - Documentation is a force multiplier In 2021, I thought software engineering was about building things. In 2025, I’ve learned it’s about enabling others to build better, faster, and safer. Follow me Akash Sinha for more content on SW Engineering, How to grow as a SWE and AI

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Interviewing software engineers is easier than most people think. Describe a real business problem. Then ask: How would you solve it? A CTO will ask about alignment with company strategy, trade-offs between speed and cost, risks, and how the decision impacts hiring and future roadmaps. A senior engineer will focus on architecture, reliability, testability, edge cases, and how to deliver value quickly while leaving room to evolve. A junior engineer will ask where the requirements are, which framework to use, or for an example to follow. Same question. Radically different answers. At Full Scale, this is how we interview engineers. Because in the era of AI, coding tests don’t prove much anymore. The only thing that matters is how someone thinks and solves problems.

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Hiring senior engineers at early stage is one of the biggest challenges we face. You need people who can go from zero to one, wear five hats, and still ship clean code. Meanwhile, your company isn’t a household name yet, and you’re competing with FAANG comp packages. That’s where founders burn out — spending nights sourcing, chasing intros, interviewing, only to have a candidate decline your offer. Allen Osgood (founder of Eisen) found that listening to candidates' needs and building a plan to support them worked best! “The biggest thing I came to appreciate during closing was finding out what’s important to this person… Some candidates have family situations that require higher cash, lower equity. Others really care about growth, learning budgets, opportunity. It’s about tailoring your entire closing process to their incentive set, life situation, and preferences.”

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Your cache isn’t broken because of LRU. It’s broken because you forgot TTL. Why LRU Alone Fails 1. Stale data: Hot keys never expire. Yesterday’s price lives forever. 2. Idle memory: If the cache never fills, nothing gets evicted. Dead keys stay. 3. Broken correctness: Wrong data is worse than a miss. Why TTL Alone Fails 1. Thundering herd: Expiration hits, and every client stampedes back to source. 2. Wasted space: Long TTLs keep cold keys alive even if nobody needs them. 3. Poor efficiency: Old data hogs memory until timers run out. Eviction and TTL solve two very different problems: -> Eviction (LRU/LFU): Kicks in when memory is full. Optimizes space. -> TTL: Kicks in when data gets old. Optimizes correctness. Think of a cache like a fridge: - Eviction = throwing out items when it’s too full. - TTL = expiration dates so you don’t drink spoiled milk. - Together = fresh food and free space. Which one burned you first: stale data or memory pressure?

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Launching: No-BS Engineering Metrics Most engineering dashboards today are built on noise. Only showing no. of Commits. PRs. Comments. Vanity metrics with zero context, and zero connection to real impact. They don’t: - Scan your code. - Know what your developers are actually building. - See the security or quality risks being introduced. That’s why “developer productivity” metrics are BS. We’re changing that. At CodeAnt AI, we’ve rethought engineering metrics from the code up. - Our Quality & Security Platform scans every branch and every commit, catching risks across SAST, IaC, secrets, SCA and more. - Our AI Code Reviews checks every PR for bugs, issues in quality, security, and compliance. Our new Productivity Platform connects the dots ✅ From IDE → PR → CI/CD ✅ So you understand the true impact of every single developer / agent This is the future of engineering metrics [STAY TUNED] → We’re launching the Risk Graph next. A live map of every developer and AI agent, showing exactly where risks are born.

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Progress Update: Accelerating Onboarding with Coding Agents Over the past two months, our team of 20+ engineers has been exploring how a coding agent can improve the way we work. The results so far have been promising—new hires are onboarding into complex codebases faster, knowledge is spreading more quickly across the team, and repetitive tasks are being handled by the agent, freeing engineers to focus on higher-value work. We’ve learned that quality improves significantly when the agent is given strong example code or best practices to work from, especially for complex enterprise level design (such as mono repo) that aren’t typically represented in model training data. Just as importantly, we’ve found that the most effective way to work with an agent is to treat it like a teammate: ask curiosity-driven questions, explore “what if” scenarios, and brainstorm together toward alternative solutions. As our coding speed has improved, a new question has emerged: how do we ensure that system design quality and technical documentation keep pace? To tackle this, I’ve proposed exploring a “document agent” that can help capture design ideas, record decision points, and integrate relevant industry research into our dev process. By pairing a coding agent for execution with a document agent for design and knowledge sharing, we aim to not only work faster but also build smarter, more resilient systems. Stay tuned as we continue to push the boundaries of intelligent collaboration.

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PMs in SF make $280-$380K with 5 YOE. Here's how to get one of those roles: I researched the data and processes at top firms. 1. PM pays right under SWE When you compare pay by roles for 5 YOE in SF: • SWE: $320-430K • PM: $280-380K • Data Scientist: $240-320K • UX Designer: $200-280K • Marketing Manager: $180-250K • Technical Writer: $160-220K (This is the 10th-90th percentile range I estimate based on lots of experience in market, excluding outliers) Companies like Meta, OpenAI, Airbnb, Microsoft, Amazon, Netflix, Apple, Google, Stripe and Uber are constantly hiring PMs towards the top end of that band. 2. There are 5 Categories of Interviews: a. Behavioral Interviews (40%) STAR is your friend here. Don't overcomplicate. Build a story bank of 5 stories that you can apply to different questions. Choose stories that exemplify your advantages in experience related to the specific role. More guidance: https://lnkd.in/gS-CKH9P b. Case Interviews (30%) These fall into many buckets: • Product sense • Product design • Product strategy • Product execution Bring your own set of favorite frameworks for each. My guide: https://lnkd.in/eAYkV7S3 c. Leadership (15%) They want to see you exemplify predictable traits: • Vision setting • Team leadership • Stakeholder management More detail: https://lnkd.in/eGhgVNyh d. Presentation + Homework (8%) These are big time investments: • Structure your story • Show exec presence • Build a base for common assignments Guidance: https://lnkd.in/eiP9MBQH e. Technical Fluency (7%) The more technical the PM role, the more likely these questions. They're often just 1 round out of a 6-7 round process. And just 1/2 that interview. Topics like: • APIs & integrations • Database basics • Cloud infra • Mobile v web Breakdown: https://lnkd.in/dG9EFhiR Remember: practice is the most important thing! Practice with ChatGPT, friends, YouTube... Practice! P.S. I'm coaching a group of 30 PMs to dream their jobs. Apply before all seats sell: https://www.landpmjob.com/ 📌 Want to prep for a PM interview? Comment 'Interview' + DM me for my 150 PM question bank. This is the latest state of the PM interview. Follow Aakash Gupta for daily PM tips.

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I got 122 replies to my question: "If you had to teach a busy person to become technical from scratch asap, how would you do it today?" Here's the best answer: 1. Pick a domain you want to build something in 2. Pick a dead simple tech stack 3. Start building using Claude Code learning mode 4. Find a friend who's technical you can talk to

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