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2+ min ago (913+ words) JavaScript runs synchronously. The Event Loop enables efficient management of asynchronous operations, providing non-blocking code execution. Understanding how the Event Loop, the call stack, the task queue, and the microtask queue work helps developers write more efficient and responsive web applications. The Event Loop is a mechanism in JavaScript that allows asynchronous processing of events and tasks, ensuring non-blocking code execution. This is especially important for web applications, where the user interface must remain responsive even when long-running operations are performed. Main components of the Event Loop: Call Stack: This is a data structure that keeps information about the currently executing function calls. When a function is called, it is pushed onto the stack, and when it finishes execution, it is popped off the stack. If the call stack becomes empty, it means all synchronous tasks have completed. Web APIs:…...

6+ min ago (467+ words) As AI systems like ChatGPT, Gemini, Perplexity, and Copilot continue to reshape how users find information, brands must adapt to a world where visibility depends on being included in AI-generated answers, not just traditional search results. AI engines give single, definitive responses " meaning only a handful of brands appear at the top of every query. To stay competitive, businesses rely on AI ranking analyzer tools. These platforms evaluate how often your brand appears inside answers generated by leading AI models, identify the phrases associated with your business, and highlight gaps where competitors perform better. Below are three powerful AI ranking analyzer tools " starting with the most complete solution on the market. AI Rank Checker stands out as the most comprehensive tool for analyzing, measuring, and optimizing your visibility inside AI search engines. While most tools focus on either tracking or…...

13+ min ago (492+ words) Large Language Models (LLMs) like Claude possess incredible reasoning capabilities, but they suffer from a critical flaw: They have no object permanence. Once you close a chat session, the "mind" is wiped. While features like "Projects" or huge context windows (200k+ tokens) help, they are temporary buffers, not true memory. They are expensive, slow to re-process, and don't persist across different interfaces (e.g., moving from VS Code to the web interface). To move from Chatbots to true Agents, we need to solve the state problem. The Model Context Protocol (MCP) is often described as a way to "connect AI to tools." But theoretically, it allows us to decouple the reasoning engine (the LLM) from the state (the data). Instead of trying to cram everything into the prompt (Context Stuffing), we can use an MCP Server as a Sidecar Attachment. By building a server…...

13+ min ago (63+ words) [neeleshwark17] LLM and AI for Full-Stack Developers: A Practical Guide to Modern Development Neel " Jun 11 #ai #llm #chatgpt #githubcopilot Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well...

18+ min ago (411+ words) The final chapter of the Serverless FastAPI app tetralogy has arrived, we started with developing our app locally, then we wrote tests and in the last chapter we used native tooling and services within AWS to secure our app from bad actors. We've reached a fork in the road, we can continue to manually deploy our app by running commands locally or we can incorporate a more traditional approach to automatically test and deploy our app using a CI/CD pipeline. I initially wanted to use Azure Pipelines, that's what I have been using at work daily for the past 6 years. I appreciate Azure DevOps, lovely platform. - Cristiano Ronaldo voice (Infamous rant about nothing changing at Man Utd) To change things up, I then thought why not use GitHub Actions ? The infrastructure and application code already exists in GitHub. GitHub…...

18+ min ago (175+ words) There's no question that the fans and all of our supporters, what this means -- means a lot to us," Day said of the Michigan game. "That's what hurt the last couple of years, more than anything. You could see it in my face. ... You just feel like you're letting everybody down -- that's just not a good feeling. "So you work like hell just to make sure that you do everything you can to get your guys prepared. ... And our guys really captured the moment and played great After a shaky start, which gave Michigan an early 6-0 lead, the Buckeyes dominated in every facet the rest of the way. After a shaky start, which gave Michigan an early 6-0 lead, the Buckeyes dominated in every facet the rest of the way. Templates let you quickly answer FAQs or store snippets for re-use....

28+ min ago (211+ words) A complete, repeatable lifecycle for keeping your application secure Modern applications face a constant wave of threats " supply-chain attacks, browser exploits, token theft, package vulnerabilities, misconfigurations, and more. Most teams treat security updates as "catch-up work," but real protection requires a systematic, end-to-end security intelligence framework. Below is a practical E2E framework " a cycle your team can follow to continuously stay updated on security risks and keep your app safe. This framework has 6 stages: Each stage includes tools, examples, and best practices. Your first job is staying aware. This means actively monitoring reliable, reputable sources of security information. Goal: Never miss a vulnerability relevant to your ecosystem. Not all vulnerabilities matter. Detection identifies which ones apply to your codebase, dependencies, or infrastructure. Goal: Quickly spot vulnerabilities that could impact your app. This is where senior engineers add value. This is where…...

37+ min ago (449+ words) While traditional sports require physical infrastructure, esports only needs a device and an internet connection. This accessibility opens the door for millions of players globally, especially in regions where traditional sports facilities are limited. Today's esports landscape resembles traditional sports in structure: " Dedicated training facilities " Coaches " Tactical analysts " Nutrition and fitness programs " Sponsorships and branded collaborations This professional ecosystem legitimizes esports as a serious career path for talented players. AI is reshaping the way pros train: " Data-driven performance reviews " Automated replay analysis " Predictive opponent modeling " Personalized improvement plans These tools provide insights that go far beyond human analysis. Esports has cultivated global communities united by shared interests. Forums, Discord servers, Reddit groups, and social media discussion spaces thrive with strategy talk, content sharing, and fan engagement. These communities are highly active and play a crucial role in shaping game balance,…...

38+ min ago (145+ words) This week was an important step forward for Secrin. Until now, we could ingest repositories, commits, and file diffs, but only for languages we explicitly supported. TypeScript was always on the horizon, but now it is officially part of the system. I implemented TypeScript language support directly into Secrin's ingestion pipeline. That means: It may look like a small feature, but it unlocks a huge segment of real world projects, including frontend applications, backend services, libraries, and modern JavaScript tooling. TypeScript is used widely across engineering teams and ecosystems. Supporting it brings Secrin closer to becoming a universal commit aware knowledge engine. More language support leads to: Each step pushes Secrin closer toward a system that remembers and understands code the way developers do. If you want to try Secrin, explore the codebase, or contribute, here are the links: Building…...

44+ min ago (345+ words) Supervisor pattern is the only one that survived a production spike without a hot-fix. Single agents are great for a sprint demo " and terrible for anything that hits the internet. I drew this on a whiteboard for our CFO after she saw the second invoice: Rule we now write into every SoW: PoC = managed. Day-1 prod = Runtime. The moment you need a custom MCP tool or a side-car Lambda, the console becomes a drag. Our first Kindo chatbot went live with 37 manually-written examples. Two weeks later a student asked "What grade do I need to pass?" and the agent calmly invented a 42 % cutoff (it's 50 %). Cue 4 a.m. rollback. We fixed it the boring way: Accuracy jumped from 67 % " 92 % and the support ticket queue dropped by half. Payroll bot rewrite: one supervisor + three specialised subs (policy, leave-balances, tickets). 60 % less copy-paste Lambda code, and we…...