Web Development

When I talk about layouts, I’m referring to how you place items on a page. The CSS properties that are widely used here include: display — often grid or flex nowadays margin padding width height position top, left, bottom, right I often include border-width as a minor item in this list as well. At this

Wrong CSS doesn’t exist — here’s why. From backwards-compatibility to platform responsibility, find out why CSS advice is context, not law. Continue reading There Is No “Wrong” in CSS on SitePoint.

Learn how to test your Express API using Jest and Supertest — covering routes, database mocking, authentication, and CI/CD integration in under 10 minutes. Continue reading Testing Node.js APIs with Jest: A Frontend Developer’s Guide to Backend Testing on SitePoint.

A look at the exploding category of ‘Generative UI’. Compares the market leader (v0) with open alternatives. Key Sections: 1. **The Promise:** Text to React components in seconds. 2. **Vercel v0:** The polished, proprietary experience. Pros/Cons. 3. **OpenClaw Canvas:** The open, hackable alternative. Pros/Cons. 4. **Code Quality:** Analyzing the output (Tailwind usage, accessibility). 5. **Workflow

An exploration of Anthropic’s new ‘Claude Code’ tool. How it fundamentally changes the dev loop from ‘write’ to ‘review’. Key Sections: 1. **What is Claude Code?** The shift to terminal-based agentic workflows. 2. **Installation & Auth:** Getting started. 3. **Core Workflow:** The ‘Ask -> Plan -> Execute -> Verify’ loop. 4. **Real-World Test:** Refactoring a

A data-driven article comparing the leading local models of 2026. Focuses on practical developer metrics rather than abstract scores. Key Sections: 1. **Methodology:** Hardware used, prompt set (coding, reasoning, creative). 2. **The Contenders:** MiniMax2.5, Llama 3, Mistral Large 2, Gemma 2. 3. **Results – Coding:** Python/JS generation accuracy. 4. **Results – Speed:** Tokens per second

A guide for DevOps engineers on orchestrating LLMs availability and scaling using Kubernetes. Key Sections: 1. **Prerequisites:** GPU Operator setup, Nvidia Container Toolkit. 2. **Serving Options:** KServe vs Ray Serve vs simple Deployment. 3. **Resource Management:** Requests/Limits for GPU, dealing with bin-packing. 4. **Scaling:** HPA based on custom metrics (queue depth). 5. **Example:** Full Helm

A guide for CTOs and DevSecOps engineers on hardening local AI deployments. Just because it’s local doesn’t mean it’s secure. Key Sections: 1. **Threat Vectors:** Prompt injection, model theft, training data poisoning. 2. **Network Security:** Air-gapping requirements, mTLS for inference usage. 3. **Access Control:** Implementing API keys and usage quotas for internal LLM APIs. 4.