Articles about software development, artificial intelligence and technology.
It is not a bubble. AI's individual gain is real and measured, but it leaks on the way to the company's bottom line. With data from WRITER, MIT, Gartner, Goldman, Forrester and Google DORA, I show where the money disappears and what separates the 29% who capture value.
A real reward hacking case in mcp-graph: how feature-depth became an optimization target and why static analysis needs behavioral validation in agentic loops.
AISE is the emerging discipline of 2026. I prove with the repo and the DORA Report 2025 that mcp-graph v12 is the runtime layer where Specification-Driven Development meets Context-Driven Engineering.
Using Obsidian as AI context is a tell. It reveals you don't understand how context actually works. A no-diplomacy critique of the 'second brain'.
I ran a factorial program with 18 cells, 14 models, and N=50 per cell to test whether adding harness at inference time improves agent quality. In 17 of 18 cases: no effect. What matters is the training objective.
In teams using autonomous agents, the biggest hallucination driver isn't the LLM. It's the absence of contracts, gates, and traceability in code.
A classic behavioral psychology analogy reveals the most overlooked principle in building AI agents: without walls, there is no path.
I built a $900 setup with an RTX 3060 Ti and ran models from 8B to 70B parameters. The data revealed a phenomenon nobody documented: the GPU Offloading Cliff.
I read The Lean Startup in 2026, 15 years after publication. What I found was a framework that fits perfectly with AI agents and mcp-graph as an execution guardrail.
Not every problem needs AI. Many have deterministic solutions that are faster, cheaper and more reliable. A practical framework for choosing between AI, no-AI and hybrid approaches.
AI does everything. But if you don't understand what's underneath, you're replaceable. The technical fundamentals that separate those who use AI from those used by it.
Claude Code just quietly shipped one of the smartest agent features I've ever seen. It's called Auto Dream, and it works like the human brain during sleep.
Why chasing frameworks and certifications has become chasing the wind. A philosophical thesis on what truly matters for developers in the age of artificial intelligence.
Advanced prompt engineering techniques that go far beyond 'be specific'. Chain-of-thought, few-shot patterns, structured output, system prompts, and context management strategies I rely on in production.
A hands-on guide to building Retrieval-Augmented Generation systems that go beyond toy demos. Covering architecture, chunking strategies, embedding models, vector databases, and the real challenges nobody talks about.
A real study on how one person and an AI built a complete software product in just 9 days. Understand the process, the numbers, and the lessons.
How execution graphs, local RAG, and multi-agent coordination transform AI-augmented development - and why discipline matters more than prompts.
An AI agent that doesn't understand your code architecture will break things. GitNexus solves this by turning repositories into navigable graphs with impact analysis and hybrid search.
The transition from assistants that suggest code to agents that navigate, understand and modify entire projects. What changed, what works and what still doesn't.
How Model Context Protocol went from an Anthropic project to the industry standard in 5 months. Architecture, why it works and how it changes AI-powered development.
Most developers spend 30-50% more tokens than needed. Understand why this happens, what it actually costs and which techniques reduce waste without losing quality.