Charles B. Jr

Software engineers: “I didn’t write any code today.” Also software engineers: constantly checking Slack, hopping between unrelated tickets, several quick syncs, browsing memes, and multiple coffee runs — all before lunch. Let’s be honest. The problem isn’t too much work. It’s work without focus. Every ping, every “quick sync,” every notification is a tax on your brain. You never enter “the zone”. You never solve deep problems. You spend your day context-switching and wonder why no real work got done. Here’s the truth: You don’t need a longer day. You need to guard your time and protect your focus. 5 ways to guard your time as an engineer: 1/ Block 2–3 hours of focus time on your calendar daily. Treat it like a meeting you cannot miss. 2/ Use that time for deep work: design docs, coding, code reviews, or anything that requires real concentration. 3/ Put your phone on Do Not Disturb, close Slack, silence notifications. 4/ Cluster meetings together to preserve larger focus blocks. 5/ Bring your team onboard and advocate for protected engineering time and fewer random interruptions so everyone can deliver results for customers. Deep work isn’t just about productivity. It’s therapy for engineers. It recharges you while you work. It reminds you why you started coding in the first place. And at the end of the day, you’ll leave the office feeling accomplished —not drained. Guard your time. Protect your focus. That’s how you make space to do the work that actually matters. #softwareengineering #focus #productivity

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Ever wondered how Spotify delivers music instantly and recommends the perfect playlist?   Behind the smooth experience is a scalable, distributed system architecture that balances performance, personalization, and real-time delivery.   This guide breaks down the core components of Spotify’s system design:   1. Clients connect through a Load Balancer that routes traffic to the App Service 2. Streaming Service handles real-time playback using caches for popular songs 3. Recommendations Service uses precomputed caches to serve personalized music suggestions 4. Search Service queries indexed databases for fast, relevant results 5. User Service manages user data, preferences, and playlists 6. Ad & Analytics Services ensure monetization and performance insights 7. CDNs and object stores optimize delivery speed and content availability across regions   Whether you are designing scalable apps or preparing for system design interviews, this is a great reference for building consumer-grade streaming platforms.   🔁Save this post and tag someone who is obsessed about learning system design. Everyone Who Codes (EWC) has a simple mission: Helping engineers get hired. 💼 Follow Lakshmi Marikumar for tech career & job search insights. DM me for 1:1 mentorship program to successfully land interviews and your dream tech job. Subscribe to our free newsletter on Substack: https://lnkd.in/gN83YA4H #FAANG #techinterviewprep #FAANGMentors #mentorship #softwareengineer #softwaredeveloper #jobsearch #joboffer #hiring #careercoach #machinelearning #AI #systemdesign #roadmap #careerguidance #DSA #ML #tier2 #tier3 #mockinterview #spotify

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It was 8:42 PM at the Amazon office when Priya, a senior engineer, noticed a Slack message from Raj, the newest backend SDE on her team. “Hi Priya, the orders API endpoint you own is suddenly returning 503s after my changes. Could you help me debug? Sorry for the late message.” Priya hesitated for a second. She’d spent months refining that endpoint, handling edge cases, scaling for Prime Day traffic, even documenting the entire flow for new team members. She remembered her own early days, when breaking something felt terrifying, and the seniors weren’t always patient. For a split second, Priya wanted to reply, “It was working fine earlier. Did you check the wiki?” But she didn’t. She joined the call. Raj was clearly stressed, walking through his recent commits. First month at Amazon, production issue, and it was late. Priya listened, asked focused questions, and together they tracked down a missing API key in the environment config. Raj sighed, “Thank you, Priya. Didn’t mean to break anything, just trying to add new promo logic.” Priya replied, “No worries. Bugs happen to all of us. What matters is catching them fast. I’d always rather have a teammate who asks for help than one who hides a problem.” The next morning, Priya shared a quick note in the team channel: > “Thanks to Raj for surfacing the issue quickly and collaborating on the fix. These things happen, what counts is how we respond.” And as she updated her notes, Priya reflected: → Being smart is good but being reliable, calm, and collaborative under pressure is what builds trust. → The most critical projects rarely go to the “lone genius.” They go to the person the team can depend on, day after day. Over time, more teammates started coming to Priya for tricky launches, because she was steady, approachable, and always showed up when it mattered. Because in the long run, that’s what makes you the engineer everyone trusts to deliver. And that’s the reputation that really opens doors.

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90% of system design interviews are based on these 10 areas, so master these and you’ll be unstoppable. I’ve added a detailed list of topics under each area: ♦ Part 1: Scalable Data Storage - Partitioning: Vertical vs. horizontal sharding. - SQL vs. NoSQL: When to pick which and why. - Indexing: Primary, secondary, and covering indexes. - Consistency Models: Strong, eventual, and causal consistency. - Replication: Primary-Secondary, leader-follower configurations. - Data Modeling: Schema design for relational and NoSQL databases. - Storage Optimization: Compression and data deduplication techniques. ♦ Part 2: Caching - Cache Invalidation: Consistency between cache and source data. - Client-Side vs. Server-Side: Choosing the right caching layer. - Application-Specific Cache: Query and result-level caching. - Strategies: Write-through, write-back, write-around. - Distributed Cache: Redis, Memcached, and beyond. - Cache Metrics: Hit ratios, latency monitoring. - Eviction Policies: LRU, LFU, random eviction. ♦ Part 3: Load Balancing - Sticky Sessions: When and why to use them. - Reverse Proxies: Tools like Nginx and HAProxy. - Horizontal Scaling: Expanding services dynamically. - Failover Mechanisms: Handling downtime gracefully. - Global Load Balancing: Multi-region traffic distribution. - Health Monitoring: Ensuring backend servers are always up. - Techniques: Round-robin, consistent hashing, least connections. ♦ Part 4: Asynchronous Processing - Event-Driven Architecture: Benefits and implementation patterns. - Pub/Sub Models: Decoupling producers and consumers. - Dead Letter Queues: Handling undeliverable messages. - Scalability: Partitioning and scaling message brokers. - Message Brokers: Kafka, RabbitMQ, and SQS. - Task Queues: Managing retries and timeouts. - Stream Processing: Real-time data handling. Continue reading here: https://lnkd.in/eJ2T966N

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10 GitHub repositories every aspiring SOFTWARE ENGINEER should bookmark to land jobs in 2025. (With interview prep, hands-on projects, and real-world applications) If you're an international student or early-career dev trying to stand out in this brutal market you need projects, patterns, and proof. These GitHub repos will help you with exactly that. 1. System Design Primer One of the most-starred repos on GitHub. Breaks down complex system design topics in plain English. Topics: Scalability, caching, load balancing, databases 📍 GitHub: https://lnkd.in/gmTP7kwc 2. Developer Roadmap Visual roadmaps for every major tech role (frontend, backend, DevOps, etc). Great to know what to learn next. Covers: Tech stacks, tools, paths to mastery. 📍 GitHub: https://lnkd.in/g_PGUvVX 3. Build Your Own X Want to really understand how things work? Build your own database, OS, blockchain, etc. from scratch. Great for: Learning by doing. 📍 GitHub: https://lnkd.in/g4cWqgjA 4. Tech Interview Handbook Everything you need for interviews: DSA patterns, resume tips, system design, and behavioral prep. Built by ex-Facebook engineers. 📍 GitHub: https://lnkd.in/gbE2x95p 5. JavaScript Algorithms & DSA Clean and beginner-friendly repo to practice data structures and algorithms in JS. Includes: Visuals, code samples, and explanations. 📍 GitHub: https://lnkd.in/gu324JHD 6. OSSU: CS Self-Taught Degree A full computer science curriculum using open-source materials. No $100K loans required. Topics: From intro CS to AI, compilers, DBs, and more. 📍 GitHub: https://lnkd.in/guuhFE_8 7. Project-Based Learning A huge collection of programming projects across languages. Perfect for resumes and GitHub proof. Build apps while learning React, Python, Go, and more. 📍 GitHub: https://lnkd.in/gFFuHsRs 8. Awesome-Prompt-Engineering With AI becoming the new interface, this repo helps you learn the actual skill of prompting effectively. Build smarter tools with ChatGPT, Claude, and others. 📍 GitHub: https://lnkd.in/gcNV4EHm 9. DevOps Exercises Hands-on practice questions to master CI/CD, containers, cloud infrastructure, and Linux. Bonus: Realistic scenarios you’ll face on the job. 📍 GitHub: https://lnkd.in/gxpt4Nh9 10. Free Programming Books Yes, literally. Thousands of books (and tutorials) across 30+ languages. Open-source goldmine. Languages: Python, Java, C++, Rust, and more. 📍 GitHub: https://lnkd.in/gPK9RNPT … If you're trying to land a job in tech, especially as an international student, focus on building more proof of work than the degree (though it's equally important.) Start with one project, push it to GitHub, document what you learned, and then repeat.

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If you want to perform better in system design interviews, spend time understanding these trade-offs, and your interviews will go 10x better. Because when you're building systems, every decision is a trade-off. This is where most candidates fail, they can’t explain why they made a choice. Don’t let that be you. 1. Core Concepts The fundamentals you can’t skip. – Scalability vs Performance: Handle more work or make the system faster — rarely both at the same time. – Latency vs Throughput: Quick responses vs high volume processing; real-time vs batch-focused needs. – SQL vs NoSQL Databases: Structured, reliable queries vs flexible, scalable, schema-less data storage. – Stateful vs Stateless Architecture: Systems that remember session context vs fully independent requests. 2. Foundational Trade-offs Principles that shape every architecture decision. – Vertical vs Horizontal Scaling: Upgrade one server or add more servers for distributed growth. – Consistency vs Availability (CAP Theorem): Always accurate data vs systems that never go down. – Strong vs Eventual Consistency: Immediate accuracy vs short delays for better performance and uptime. – Normalization vs Denormalization: Clean, duplicate-free storage vs faster reads with some redundancy. 3. System Optimization Patterns for performance and reliability. – Read-Through vs Write-Through Cache: Cache when requested vs write to cache and database simultaneously. – Synchronous vs Asynchronous Processing: Wait for each task vs process tasks in the background. – Batch vs Stream Processing: Process data in bulk vs handle it in real time as it arrives. – Long Polling vs WebSockets: Periodically request updates vs keep a live, two-way connection. 4. Advanced Architecture High-level decisions that define modern systems. – Monolithic vs Microservices Architecture: One unified codebase vs many smaller, independent services. – TCP vs UDP: Reliable, ordered delivery vs faster, less reliable transmission. – REST vs GraphQL: Fixed endpoints with standard responses vs flexible, efficient queries for exactly what you need. If you can talk through these trade-offs with clarity, you’ll stand out in system design interviews. It’s not just about what you build, t’s about why you built it that way. Which of these trade-offs do you still struggle with? — P.S: If you’re trying to master system design, check out my platform, layrs.me. I quit my job at Google to change the way we learn system design. Layrs will help you build, understand, and learn how system design looks in real life. From understanding these trade-offs, you can see how constraints, components, and architecture all come together to power the biggest systems in the world. 

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AWS Lambda is one of those technologies that looks perfect for almost everything. Take a notification service: "Event-driven by nature" "Scales effortlessly" "Pay only when used" On paper, Lambda feels like the obvious choice. But here’s the trap: if the Lambda is cold, the alert goes out seconds late. - A payment failure notification delayed by 3 seconds frustrates customers. - A security alert delayed by 3 seconds undermines trust. That’s the cold start problem. The good news: AWS has solutions today: 1. Provisioned Concurrency keeps functions pre-warmed. 2. SnapStart snapshots initialized runtimes for instant startup. Don’t just ask “Can I put this on Lambda?” Ask “What happens when it’s cold?” Wisdom in engineering often comes from knowing not just what a tool can do, but when it silently breaks your guarantees.

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