20+ years in tech. Amazon. JPMorgan. And one lesson has stayed with me: Senior engineering is never just about writing better code. Over the years, I’ve designed systems, led engineering teams, worked on large-scale technology programmes, interviewed engineers, mentored professionals, and made decisions where architecture was only one part of the challenge. The other part was always human. How do you explain a difficult trade-off? How do you influence stakeholders? How do you handle ambiguity, conflict, ownership, and risk? How do you communicate like a senior engineer when there is no perfect answer? That is exactly why I wrote “System Design and Behavioral Intelligence.” We wanted to bring technical thinking and behavioral intelligence together in one practical guide. Inside, we cover distributed systems, scalability, databases, security, observability, architecture patterns, and complete designs including Slack-like messaging, real-time bidding, distributed key-value stores, and Agentic AI systems. But we also go deeper into STAR+R, leadership storytelling, DORA, RACI, stakeholder management, risk thinking, and how senior engineers are evaluated beyond technical correctness. Because the next level of your engineering career requires more than knowing how systems work. It requires showing how you think, decide, communicate, and lead. 📘 System Design and Behavioral Intelligence If you are preparing for senior engineering, architecture, or Big Tech roles, this is the book we wished we had earlier in our careers. P.S. Preparing for a software engineering role? Check out my book, System Design and Behavioral Intelligence. It will help you: •⁠  ⁠Build strong System Design foundations and approach design problems with confidence •⁠  ⁠Develop practical frameworks to navigate complex behavioral and leadership challenges Get Your Copy anywhere internationally: US: amazon.com/System-Design-Beh… UK: amazon.co.uk/System-Design-B… India : amazon.com/System-Design-Beh… If you got the book already, dont forget to share your review with me.

An AI gateway is becoming one of the most important control layers in a production AI stack. Once multiple models, users, agents, and applications start sharing the same AI infrastructure, direct model access becomes difficult to govern. That is where the gateway earns its place. A strong AI gateway should provide eight core capabilities: 1. Authentication Verify who is calling the gateway and enforce access before any model or tool is reached. 2. Model Routing Send each request to the right model based on cost, latency, quality, or task complexity. 3. Rate Limiting Control request volume and token usage to protect capacity and prevent runaway spend. 4. Prompt Security Inspect prompts for injection attempts, jailbreaks, unsafe instructions, and policy violations. 5. PII Protection Detect, mask, or redact sensitive data before it crosses security boundaries. 6. Token Tracking Measure input and output token usage to understand cost, performance, and consumption patterns. 7. Caching Reuse repeated responses or prompt context to reduce latency and unnecessary model calls. 8. Observability Capture logs, metrics, traces, and alerts so teams can debug failures and monitor reliability. The bigger point is simple: An AI gateway is not just a routing layer. It becomes the control plane between your applications and the models they depend on. As AI systems scale, teams need a consistent place to enforce security, manage cost, route intelligently, protect sensitive data, and understand what is happening across every model call. That is what turns model access into production infrastructure. P.S. Preparing for a software engineering role? Check out my book, System Design and Behavioral Intelligence. It will help you: •⁠ ⁠Build strong System Design foundations and approach design problems with confidence •⁠ ⁠Develop practical frameworks to navigate complex behavioral and leadership challenges Get Your Copy anywhere internationally: US: lnkd.in/eVy2ACxQ UK: lnkd.in/evzmHKm4 India : lnkd.in/e25PthqU

Networking is everywhere. Every API call, page load, database request, and real-time message depends on a few core networking concepts working together. Here are 8 concepts every developer should understand: → 𝗗𝗡𝗦 𝗥𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 Converts domain names into IP addresses so clients can find the correct server. → 𝗛𝗧𝗧𝗣 / 𝗛𝗧𝗧𝗣𝗦 Handles web communication through methods, headers, status codes, and secure TLS transport. → 𝗧𝗖𝗣 𝘃𝘀 𝗨𝗗𝗣 TCP focuses on reliable, ordered delivery. UDP prioritizes speed, low latency, and minimal overhead. → 𝗥𝗘𝗦𝗧, 𝗚𝗿𝗮𝗽𝗵𝗤𝗟 & 𝗴𝗥𝗣𝗖 Each API style offers different trade-offs around simplicity, flexibility, typing, and performance. → 𝗪𝗲𝗯𝗦𝗼𝗰𝗸𝗲𝘁𝘀 Create persistent two-way connections for chat, live dashboards, collaboration, and real-time applications. → 𝗧𝗟𝗦 & 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 Protect data in transit through authentication, encryption, session keys, and integrity checks. → 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗣𝗿𝗼𝘅𝗶𝗲𝘀 Handle routing, caching, TLS termination, rate limiting, and load balancing before traffic reaches backend services. → 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Uses pooling, keep-alive, timeouts, and backpressure to reduce latency and improve throughput. You do not need to become a network engineer to write better software. But understanding how requests actually move across a network makes APIs, backend systems, distributed applications, and cloud architecture much easier to design and debug. Which networking concept took you the longest to understand? P.S. Preparing for a software engineering role? Check out my book, System Design and Behavioral Intelligence. It will help you: •⁠ ⁠Build strong System Design foundations and approach design problems with confidence •⁠ ⁠Develop practical frameworks to navigate complex behavioral and leadership challenges Get Your Copy anywhere internationally: US: lnkd.in/eVy2ACxQ UK: lnkd.in/evzmHKm4 India : lnkd.in/e25PthqU