NexTechTide

 It started innocently. A complex refactoring task loomed ahead—one of those multifile, semantically delicate, potentially hair-pulling situations that could ripple across three services and two time zones worth of code ownership. Naturally, like any respectable developer in 2025, I turned to my trusty assistant: ChatGPT. But before I could hit Enter, I found myself writing out the prompt… in excruciating detail. The context, the goal, the edge cases, the internal trade-offs, the modules involved, the caveats, the naming conventions, the rollback strategy. Twenty minutes in, I stopped. I looked at the prompt. I looked at my terminal. And then I just did it myself. The Accidental Clarity of Prompting Here lies the strange new ritual of modern software development: articulating a problem so clearly that it becomes obvious how to solve it before the AI has a chance to reply. Is this a failure of AI? Absolutely not. Is it a failure of you? Also no. (Unless you count being c...

 For the past year, the world has been obsessed with what artificial intelligence can do for us. From ChatGPT writing emails to MidJourney generating fantastical images, the dominant narrative has been "how to use AI." But what if you're not satisfied just prompting models? What if you want to build them, customize them, run them offline, and deploy them securely in the cloud? This is the journey I'm starting now: learning to build with AI, not just use it. And in this post, I’ll lay out the core principles, motivations, and roadmap that will guide my exploration into becoming an AI developer—with a specific focus on LLMs (Large Language Models), agents, training workflows, and cloud/offline deployment . Let me be clear: I’m not here to write a research paper, derive equations, or become a machine learning theorist. I don’t need to build a transformer from scratch in NumPy. My goal is pragmatic: I want to learn how to train, run, integrate, and deploy powerful ...

 In the not-so-distant past, software developer interviews focused on whiteboard problems, live coding, and brainteasers designed to test raw problem-solving ability under pressure. The reasoning was clear: these were proxies for technical aptitude and performance on the job. However, the rise of generative AI tools like GitHub Copilot, ChatGPT, and Sourcegraph Cody has completely reshaped how developers write, test, and reason about code in real-world settings. Yet ironically, the interview process has become more resistant to AI assistance than ever before. This contradiction has created a new paradox in technical hiring: companies are screening candidates against the tools they expect them to use for the job. This post explores how the AI revolution is transforming the goals, dynamics, and shortcomings of the software developer hiring process. We'll examine both sides of the hiring equation—candidates and employers—as well as the history behind the current system, what the fu...

  In recent years, Rust has gone from a niche systems programming language to a silent powerhouse reshaping how modern cloud infrastructure is built. With a focus on safety, performance, and concurrency, Rust offers exactly what cloud-native platforms need: speed without sacrificing reliability. Despite its steep learning curve, more and more teams are betting on Rust for building the foundational blocks of scalable, secure, and efficient cloud services. This post explores how Rust is increasingly being adopted in production by major players, and what makes it uniquely suited to cloud infrastructure. We'll also examine real-world examples and explain why Rust's influence is likely to grow even stronger in the coming years. Why Rust for Cloud Infrastructure? Rust brings a rare combination of features that directly address the pain points of modern cloud systems: Memory safety without garbage collection : Rust’s ownership model ensures memory safety at compile time, elimin...

There are cities that impress you. And then there’s New York—bold enough to shake you, embrace you, and leave you a little breathless, all in a single day. From the moment I stepped onto its busy streets, I felt something different. It’s not just the skyline or the lights or the sheer size of it all. It’s the rhythm—the unapologetic, nonstop, beautiful chaos that pulses through every borough and alley. New York isn’t a city you just visit. You feel it. You live it. You walk faster, you think quicker, you speak louder. It doesn’t matter where you’re from—once you're here, you’re part of the energy. It’s intimidating, inspiring, and oddly addictive. I came to New York hoping to see the sights, maybe eat a bagel, take some photos. What I didn’t expect was how deeply the city would get under my skin. This blog post is my way of capturing that feeling—not through a checklist, but through moments. Moments that made me pause, laugh, reflect, and occasionally just stop and stare. So if...

 Diagramming is an essential tool in software development and system design. Whether you are modeling relationships, visualizing processes, or illustrating architectural components, diagrams enhance communication and clarity. Unlike graphical tools such as Visio or Draw.io, PlantUML allows developers to generate diagrams from plain text, providing a code-first approach that is reproducible, version-controllable, and easy to maintain. This guide will walk you through everything you need to start using PlantUML, from installation to creating your first diagrams. What is PlantUML? PlantUML is an open-source tool that uses a concise, human-readable language to describe diagrams. Rather than dragging shapes and connecting them manually, you write text files that are rendered into images such as PNG, SVG, or ASCII art. PlantUML supports a variety of diagrams, including: Class Diagrams Sequence Diagrams Activity Diagrams Component Diagrams State Diagrams Object Diagrams ...