A developer's challenge with "AI spaghetti code" when orchestrating multiple AI agents using tools like Superpower, where sub-agents lose context from parent rules and produce messy, over-complex code…
The author published a popular Dev.to article about building a local RAG system using Docker and Llama 3, which generated significant engagement and algorithm-friendly buzz. However, the $59 Dockerize…
Mirabile is an AI-powered career roadmap platform designed to help users land jobs at top tech companies like Google. It generates personalized, company-specific learning journeys for engineering role…
Google AI Studio now supports native Kotlin development for Android apps, integrating with Firebase services to create full-stack applications using the Gemini 3.5 model. The author used this new capa…
Based solely on the provided text, the author describes their internship at Mantis Software Company, where they learned about AI-assisted development and prompt engineering while creating an AI-powere…
Engineering behind TitleTrust, an AI-powered land fraud detection system built for Kenya to combat systematic land theft, including "air supply" scams where non-existent plots are sold. The system use…
The Model Context Protocol (MCP) is a standardized communication framework that acts as a universal bridge, enabling AI models to seamlessly connect with external tools, databases, APIs, and software …
This article reviews two technical approaches from GeekyAnts: AI-driven end-to-end testing using Playwright 1.56’s agentic loop, and custom mobile image processing in React Native using the Expo image…
Embedding and processing CJK (Chinese, Japanese, Korean) text in Rust is significantly more complex than handling Latin scripts due to the enormous size of CJK fonts, which require font subsetting to …
The author, after discovering that AI answer engines could not accurately recommend their own services due to a lack of machine-readable information, created a seven-step checklist to make their agenc…
New blog series aimed at tracking the frequent renaming and rebranding of technologies, services, and companies in the fields of Cloud, AI, and Web Development. The author explains that the series wil…
Using AI for coding does not compensate for a developer's lack of fundamental knowledge; instead, it amplifies weaknesses by executing flawed instructions with confidence. The author illustrates this …
Phase 2 of the .NET AI Architect Laboratory project, where an autonomous AI agent was built using .NET 10, Google Vertex AI, and Microsoft Semantic Kernel to analyze local project files for security v…
Author's personal .NET AI Architect Laboratory project, which aims to deeply understand the engineering principles of integrating Large Language Models (LLMs) into applications rather than just using …
AI coding agents fail in real-world projects not because the models are unintelligent, but because they lack the necessary infrastructure—such as conventions, memory, and project architecture—to opera…
To build an AI agent using Claude Code and Zernio that automatically analyzes video or image content, generates platform-optimized captions and hashtags, and publishes to nine social media platforms s…
Google's recent I/O announcements, which reveal a strategic shift toward integrating AI deeply into its productivity tools like Workspace and Search, often through new subscription tiers. The author w…
A structured solution (the "PITFALL Rules") for organizing error-related knowledge in AI agent skill files, addressing the common problem of agents repeatedly making the same mistakes. The author argu…
`dolphindb-agent-skills`, a tool that can be installed via pip in under 30 seconds to provide AI coding assistants like Claude Code and Cursor with an offline knowledge base for the DolphinDB database…
The article distinguishes between two types of AI integrations in operational software: a basic support chatbot that queries documentation and a more advanced AI agent that uses purpose-built query to…