44+ min ago (105+ words) A dev resume isn't just a list - it's a context + impact sales doc. After reviewing more than 25 developer resumes (mobile, backend, fullstack, career changers, etc.), the same 3 mistakes kept showing up. The most common error: the whole resume is written like this: That says nothing about: Recruiters read this all day. It turns into noise. Fix: turn each bullet into technical action + what + how + metric/scale Lots of people fell into these: Fix: One column only Saw this a lot: Fix: Only list tech you can defend in an interview and tie each one to a role or project....

4+ hour, 48+ min ago (830+ words) If you're new to AWS, one of the first questions you'll probably ask is: "Where should I start learning?" It's a good question " because AWS is huge. There are hundreds of services, dozens of certifications, and countless tutorials everywhere. But the good news is that AWS itself provides several official learning programmes, trusted worldwide and kept up to date as technologies evolve. Whether you're new to cloud computing, switching careers, or just curious about AWS " there are several official AWS programmes that help you learn, practice, and build real cloud skills. In this blog post, I cover three major AWS learning programmes: what they are, what they offer, and how you can register today. Before we begin, here's a quick note. Before we dive into these learning programmes, here's something important. Some people who land on this article might still…...

6+ hour, 5+ min ago (306+ words) Ragas is an open-source evaluation framework designed specifically for RAG pipelines. Instead of evaluating the LLM output in isolation, Ragas assesses the full loop: user question " Retrieved context " Generated answer from LLM " Final Answer. Below are the core metrics used for evaluating the full loop. In 2025, the four metrics are widely used to evaluate the output: Note: Older tutorials sometimes mention metrics like answer_correctness or context_relevance, but they are rarely used in modern pipelines . Below are 4 Core RAGAS Metrics : "Out of everything that should have been retrieved to answer the question correctly, how much did we actually manage to retrieve?" An LLM extracts relevant statements from the ground-truth answer and checks if they appear in the retrieved context (no need for full corpus labels). "Of all the chunks we brought back, how many are truly useful vs. how many are just noise…...

10+ hour, 26+ min ago (128+ words) I'm glad to share that we've opened CODEON " Recruitment Phase 1.0, and applications are now live. The goal of CODEON is to reduce the complexity of traditional programming syntax and make code creation more intuitive, readable, and accessible'without compromising real output quality. What this recruitment phase is for We are now forming a small, focused team to help build the ecosystem around CODEON (not the core compiler). Open roles include: The compiler core will remain private, but contributors will work on real, production-level components of the platform. This is a strong opportunity for students and early developers to contribute to a live programming-language project, gain portfolio-worthy experience, and become part of CODEON's foundational team. Thank you for your interest and support....

10+ hour, 51+ min ago (1492+ words) The concept of literacy has evolved dramatically since the industrial age. What began as simply reading and writing has expanded to encompass digital literacy, media literacy, and now, increasingly, AI literacy. This progression reflects society's recognition that true participation in modern life requires understanding the systems that shape our world. The emerging AI education landscape reveals a troubling pattern that mirrors historical educational inequalities whilst introducing new dimensions of disadvantage. Elite institutions are not merely adding AI tools to their existing curricula; they are fundamentally reimagining education for an AI-integrated world. This training disparity has cascading effects on classroom practice. Teachers who understand AI systems can guide students in using them effectively whilst maintaining focus on human skill development. Teachers without such understanding may either ban AI tools entirely or allow their use without proper pedagogical framework, both of which…...

11+ hour, 22+ min ago (279+ words) When I started evaluating LLMs, I noticed a disturbing pattern: most evaluation frameworks relied on simulated metrics. Dashboard showing "98% accuracy", "95% coherence", and "92% consistency" - all completely fabricated numbers with zero scientific basis. This is worse than having no metrics at all. It gives users false confidence in model performance. I decided to build a framework with one rule: No fake data. Period. " Uses only real academic benchmarks " Shows honest performance metrics The Tradeoff: Smaller model = faster but less accurate. No surprises, no fake "98% quality". Users assume dashboards show real data. When they discover metrics are simulated, trust is destroyed. Solution: I deleted 171 lines of fake data generation and replaced with real benchmark integration. Lightweight models score 20-60% on real benchmarks. That's honest. That's reality. Don't inflate scores. Show the truth and let users make informed decisions. Comparing 288 tok/s (lightweight) vs 47 tok…...

11+ hour, 53+ min ago (150+ words) Machine Learning engineers often celebrate higher accuracy, better architectures, newer models " but there's another equally powerful lever that rarely gets attention: Training Efficiency " how fast you can experiment, iterate, and improve. In real engineering environments, speed = productivity. Faster model training means: So instead of upgrading to bigger GPUs or renting expensive cloud servers, I ran an experiment to explore how far we can optimize training using software-level techniques. Caching + Prefetching alone nearly cut training time in half. In smaller datasets, data loading " GPU idle time is often the bottleneck. Fix the pipeline, not the model. There is no perfect technique. There are only informed trade-offs. The best engineers choose based on the actual bottleneck. You don't always need a bigger GPU. You need smarter training. Efficiency engineering matters " especially at scale. What's the biggest training speed improvement you've ever achieved,…...

12+ hour, 18+ min ago (312+ words) Sunday, November 30th 2025, marks the 18th annual Blue Beanie Day. Okaaaaay, why is this relevant for inclusion? Blue Beanie Day reminds us to pay attention to web standards in order to create sites that load faster, reach more users, and cost less to maintain. Accessibility is part of these standards. The web was designed with accessibility in mind. All of the above still stands true today. Web design and development have become popular career paths, with many emerging self-taught talents. Being self-taught myself, I know the pros and cons. Pro: With no degree or anything to show, you really have to prove your skills through your skills. Con: You only learn what you choose to learn. Many things can slip in between the grooves of your keyboard like crumbs, so close yet always eluding your fingertips. You don't learn what you don't…...

13+ hour, 30+ min ago (171+ words) If you are learning Java or preparing for interviews, one thing is clear: MCQs help you learn faster. They sharpen your logic, test your understanding, and expose the small details you often miss while reading long tutorials. That is why we created the Quipoin MCQs Section " a growing collection of beginner-friendly, detailed, and practical multiple-choice questions for developers. Practice MCQ's: https://www.quipoin.com/practice-mcqs/testseries If you are learning Java or preparing for interviews, one thing is clear: MCQs help you learn faster. They sharpen your logic, test your understanding, and expose the small details you often miss while reading long tutorials. That is why we created the Quipoin MCQs Section " a growing collection of beginner-friendly, detailed, and practical multiple-choice questions for developers. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It…...

16+ hour, 12+ min ago (324+ words) TL;DR: I created a free, privacy-first interview prep app that uses AI to generate questions and provide feedback'all running locally in your browser. No servers, no API keys, no data collection. Let's be honest: interview prep is expensive and stressful. Professional coaches charge $100+ per hour, and practicing alone doesn't give you the feedback you need to improve. I wanted to change that. I built an AI-powered interview coach that: The magic happens thanks to Transformers.js, a library that runs Hugging Face AI models directly in your browser using WebAssembly. Here's the flow: The first time you use it, the AI model (~250MB) downloads and caches in your browser. After that, everything is instant and works offline. Your interview responses are personal. With this app, they stay on your device. No cloud servers, no data collection, no privacy concerns. No…...