19+ min ago (619+ words) When the Kiroween Hackathon was announced, I wanted to try something different " something playful, something nostalgic, and something that would push the boundaries of what an AI-powered IDE like Kiro could help me build. Most hackathon projects either look forward into the future or try to solve an immediate problem. I wanted to look backward. That's how I came up with MCP Time-Traveler " an app that reconstructs historical tech stacks from 2015 to 2025 across Node, Python, and Ruby, using real version data from npm, PyPI, and RubyGems. What surprised me most wasn't the idea. It was how quickly Kiro turned it into a real, working project. This is the story of how it came to life. " Where the Idea Started Developers often discuss "what stack should I use?" But rarely do we ask, "what did this stack look like five or…...

32+ min ago (345+ words) Token-Oriented Object Notation offers 30-60% token reduction and 4.8x faster parsing. Here's why that matters and how to use it. JSON is everywhere. It's the backbone of modern web development, API communication, and data storage. But here's the thing: JSON is verbose. Every quote, comma, and brace adds up. In AI/LLM applications, this verbosity translates directly to higher costs and limited context windows. When you're paying per token, those extra characters matter. Enter TOON (Token-Oriented Object Notation) " a format that promises to solve these problems while maintaining (or improving) human readability. TOON achieves 30-60% fewer tokens compared to JSON. Let's see why: Notice the difference? No quotes around keys. No commas. No braces. Just clean, indentation-based structure. The TOON version uses approximately 40% fewer tokens. TOON isn't just more compact " it's also up to 4.8x faster to parse. This matters for real-time applications, high-throughput…...

43+ min ago (369+ words) Most IoT projects today lean heavily on Python, C, or Node.js, and that's fine. But during my recent academic paper selection process, I came across "The Benefits of Tierless Elixir/Potato for Engineering IoT Systems, and it completely shifted how I think about building IoT architectures. The paper raised a question that stuck with me: why do we keep separating the logic, runtime and UI layers in IoT systems if functional, tierless architectures can unify everything? That curiosity, combined with the influence of a professor(Adolfo Neto) who strongly advocates for functional programming and the BEAM, pushed me to test this idea in practice. So I built a complete IoT prototype using Elixir, Circuits, Raspberry Pi, and Phoenix LiveView. And honestly? It felt like IoT the way it should be: supervised, fault-tolerant, reactive, and consistent from the edge to…...

1+ hour, 3+ min ago (349+ words) Building a real-world client application is more than just writing code'it's about creating smooth, clean, reliable user experiences that scale in production. In this post, I'm sharing selected UI/UX previews from a recently delivered mobile app project, built completely from scratch with Flutter and Supabase. I recently completed a client project where I designed and developed a production-ready mobile application tailored for job placement and talent management workflows. To respect confidentiality and protect core business logic, I'm sharing only selected UI/UX mockups and visual screens from the project. These previews highlight the design system, visual hierarchy, and real-world usability that guided the development process. " Note: To maintain client confidentiality and protect the core product idea, I'm only sharing selected screens and mockups rather than the entire application flow. The application was designed for a company working in job…...

1+ hour, 5+ min ago (211+ words) The task is to create a function to return the n-th number string in a sequence. n starts from 1. The boilerplate code function getNthNum(n) { // your code here } Start with the first number if(n === 1) return "1"; To get the next number, describe the digits of the previous ones. Count how many times the same digits appear one after the other. When it changes to a new digit, write the count down and the digit being counted. let result = "1"; for (let i = 2; i The task is to create a function to return the n-th number string in a sequence. n starts from 1. Start with the first number To get the next number, describe the digits of the previous ones. Count how many times the same digits appear one after the other. When it changes to a new digit, write the count down…...

1+ hour, 9+ min ago (76+ words) For this project, I wanted to practice working with external APIs that require an API key. The OMDb API is perfect for this " free, fast, and provides detailed movie information. This project includes two versions: By building this app, I learned how to work with authenticated APIs, handle search results, manage missing data, and build a better UI using Streamlit. The Movie Search App allows you to: OMDb requires a free API key. Get one here:...

1+ hour, 14+ min ago (236+ words) Part 6 of The Hidden Failure Point of ML Models Series If overfitting and underfitting are the symptoms, the Bias'Variance Tradeoff is the underlying physics driving them. Most explanations of bias and variance are abstract and mathematical. But in real ML engineering, this tradeoff is practical, measurable, and essential for building resilient models that survive production. This article will finally make it intuitive. Bias is how wrong your model is on average because it failed to learn the true pattern. High bias happens when: High Bias " Underfitting Variance is how sensitive your model is to small variations in the training data. High variance happens when: High Variance " Overfitting You can think of bias and variance as opposite forces: Your goal isn't to minimize both. Your goal is to find the sweet spot where total error is minimized. Imagine shooting arrows at…...

1+ hour, 16+ min ago (426+ words) AI is reshaping SaaS marketing more in the past twelve months than in the last decade. Search behavior is changing. Discovery patterns are shifting. And SaaS companies that depend on organic traffic can no longer rely on outdated keyword tactics or conventional on-page optimization. One surprising insight from agencies like MADX is that AI does not replace SEO. It amplifies it. The winners are those combining human strategic thinking with AI-powered execution. Here are seven AI-driven SEO strategies that will define SaaS growth in 2025. 1. AI-Powered Content Clustering Instead of generating content randomly, AI tools now map out semantic topic clusters based on search intent relationships. SaaS brands use these maps to build authoritative content ecosystems. Clustering strengthens topical authority and improves ranking stability despite algorithm volatility. 2. Predictive Search Trend Modeling AI forecasts emerging topics before they explode in search volume....

1+ hour, 17+ min ago (737+ words) Most developers spend weeks or months building something, then rush through the launch in a single afternoon. They tweet about it, post on a few forums, and then wonder why nobody shows up. The product is solid, but everything around it is an afterthought. I've done this myself. I've also watched dozens of other developers make the same mistakes. Here's what gets forgotten most often. You launch. Something breaks. A user hits the bug, gets frustrated, and leaves. You have no idea this happened because there's no error tracking, no feedback widget, nothing. You find out three weeks later when someone finally emails you. Set up error monitoring before you launch. Sentry takes maybe 20 minutes to integrate and will save you from silent failures. But error tracking only catches crashes. It doesn't catch confusing UI, missing features, or workflows that…...

1+ hour, 26+ min ago (250+ words) You know the feeling. You push a PR, excited about the complex logic you just solved. You wait for a review. Ping! A comment. "Indentation is off here." Ping! Another one. "Missing trailing comma." It's draining. You spend more time fixing style issues than discussing architecture. We've all been there'staring at 50+ files just to find formatting errors that a machine should have caught. I built CodeProt to handle the noise so I can focus on the logic. When your team is growing, code reviews become a bottleneck. Senior engineers spend hours reviewing basic syntax instead of looking for security flaws or architectural inconsistencies. This leads to "LGTM" fatigue, where critical bugs slip through because the reviewer was too tired from pointing out missing semicolons. CodeProt isn't just another linter. It uses AI to understand the intent of your changes. It…...