Posts

Prepending to a Vec in Rust: What Are Your Options?

Rust’s Vec is a powerful and flexible collection type. It’s fast at pushing and popping at the end , but sometimes you need to add an element to the front . That’s where things get tricky: Vec is backed by a contiguous array, so prepending requires shifting or reallocating elements. There’s no O(1) way to do this. In this post, we’ll walk through several ways to prepend to a Vec , compare their mechanics, and discuss when each is appropriate. The Problem You have a vector: let mut digits = vec![2, 3, 4]; You want to add 1 to the head , so that the result is [1, 2, 3, 4] . Option 1: Using insert(0, value) The simplest and most idiomatic solution: digits.insert(0, 1); Shifts all elements one place to the right. Uses ptr::copy under the hood (a single memmove ). Complexity: O(n) . This is usually the best choice unless you need to squeeze out every last cycle. Option 2: Build a New Vec With Capacity If you’re fine creating a new vector: let mut result = Vec::wi...

22 Advanced DynamoDB Gotchas You Don’t Want to Learn the Hard Way

Amazon DynamoDB promises near-infinite scalability, serverless operations, and low-latency performance. It’s a solid foundation for many cloud-native applications — from IoT to real-time analytics. But beneath the simplicity lies a jungle of design decisions, silent failures, and performance pitfalls. Whether you’re new to DynamoDB or an experienced AWS developer, these 22 advanced gotchas will help you avoid painful lessons in production. 1. Hot Partitions (Skewed Access) DynamoDB automatically partitions your data, but if too many requests hit the same partition key, it results in throttling. A common mistake is using low-cardinality partition keys like region = "US" or active user_id s, creating hot spots under load. Fix : Use high-cardinality partition keys or introduce artificial sharding (e.g., appending a random number to the key). 2. Exceeding the 400KB Item Size Limit Every DynamoDB item (including all attributes) must be under 400KB. This limit includes o...

Top 10 High-Paying Tech Skills to Learn in 2025

Technology is evolving at a speed that leaves no room for complacency. Skills that were cutting-edge just a few years ago are now table stakes, and employers are willing to pay a premium for professionals who stay ahead of the curve. If you want to secure a lucrative role in 2025, you need to invest in the right capabilities. After more than a decade working in high-scale engineering environments, I have seen which skills translate into real money and which are just resume decoration. This is not about following trends for their own sake. It is about mastering the skills that companies cannot scale without. 1. Cloud Architecture and Multi-Cloud Strategy The days when a company could afford to lock itself into a single cloud are ending. AWS, Azure, and Google Cloud all bring unique strengths, and enterprises want engineers who can design architectures that blend them efficiently. Knowing how to move workloads between providers, optimize for cost, and design for resilience across clou...

AWS vs Azure vs Google Cloud: Which One Should You Learn in 2025?

If you’re learning cloud in 2025, you’re standing at a fork in the road. You have three giants in front of you: Amazon Web Services (AWS) , Microsoft Azure , and Google Cloud Platform (GCP) . They’re not equal. They’re not interchangeable. Choosing the wrong one can waste months of your career and leave you with skills nobody values outside of a narrow niche. I’ve built and maintained global-scale systems across all three clouds: SaaS products with millions of users, IoT fleets with thousands of devices, data pipelines moving petabytes, and enterprise apps under constant compliance audits. Here’s the no-nonsense truth: Market Reality Check (2025 Edition) Before diving into tech, let’s talk market power. AWS: Still the king , with ~31–32% of global cloud market share. Every recruiter recognizes AWS. It’s the “default” cloud for startups, scale-ups, and many enterprises. Azure: Strong enterprise lock-in . Every Fortune 500 company using Microsoft 365 or Windows Server alread...

High-Level AI Landscape Overview (Mid-2025)

🔑 Core AI Terminology Term What it Means Why It Matters LLM (Large Language Model) Neural networks trained on massive text datasets to understand and generate human language Foundation of tools like ChatGPT, Claude, Gemini Embedding Dense vector representations of text, images, etc. Core for search, recommendations, semantic similarity Fine-tuning Training an existing model on a smaller, domain-specific dataset Needed when you want a custom model for your business RAG (Retrieval-Augmented Generation) Combines LLMs with external data sources (via search, vector DBs) Solves LLM limitations like outdated knowledge Agent Systems that use models + tools to autonomously achieve tasks Key for automation: AI workflows, coding agents Multimodal Model Models that process text, image, audio, video inputs Used in tools like GPT-4o, Gemini for richer applications ⚙️ Key Frameworks & Libraries Framework Purpose Popular Use LangChain Build apps with LLMs, chaining calls, tools, memory RAG, chatb...

In the Era of AI Dominance, Does It Still Make Sense to Write Prompts to Yourself?

 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...

Becoming an AI Developer Without the Math PhD: A Practical Journey into LLMs, Agents, and Real-World Tools

 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 ...