NexTechTide

 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...