Blog
Guides, deep dives, and insights on AI-assisted development, prompt tracking, and building a verified developer portfolio.
The AI Coding Agent Landscape in 2026: What Changed and What Matters
From Cursor Composer to Claude Code to Kiro, AI coding agents evolved from autocomplete to autonomous builders. Here is what the shift means for developers and how to stay ahead.
Prompt Engineering for Code Generation: Patterns That Actually Work
Most developers write vague prompts and get vague code. Learn the 7 patterns that consistently produce production-quality output from Claude, GPT-4, and Gemini.
Measuring AI Developer Productivity: Beyond Lines of Code
Lines of code is a terrible metric. Token efficiency, prompt-to-commit ratio, and verification depth tell a better story. Here is how to measure what matters.
Why Local-First Architecture Matters for Developer Tools
Your prompts contain your thinking process, business logic, and sometimes secrets. Here is why local-first is not a feature but an architectural requirement.
AI Tool Adoption in Engineering Teams: A Data-Driven Approach
Most teams adopt AI tools without measuring impact. Track token spend, model performance, and tool drift to make informed decisions about your AI stack.
Kiro vs Cursor vs Windsurf: The New IDE War
Three AI-native IDEs competing for developer attention. We compare agent capabilities, context handling, pricing, and how each stores session data for tracking.
Building Verified Open Source Contributions with AI
Contributing to open source with AI tools raises questions about attribution. Here is how to contribute transparently and build a verified track record.
Token Economics: Understanding the True Cost of AI-Assisted Development
Claude Opus costs 10x more than Haiku per token. When should you use which model? A practical guide to optimizing AI spend without sacrificing output quality.
Git Hooks as Developer Workflow Automation: Beyond Linting
Git hooks can do more than run ESLint. Learn how post-commit and pre-push hooks enable automatic prompt capture, secret scanning, and profile syncing.
AI Skills in Hiring: What Recruiters Actually Look For in 2026
We interviewed 20 engineering managers about how they evaluate AI skills. The answer is not what most developers expect. Verified output beats claimed experience.
What Is AI Prompt Tracking and Why Every Developer Needs It
AI tools generate code, but there is no record of the prompts behind it. Prompt tracking changes that by linking every AI interaction to your git commits automatically.
Cursor vs Copilot vs Claude Code: Which AI Coding Tool Is Right for You?
A practical comparison of the three most popular AI coding assistants. We cover speed, accuracy, context handling, and how each stores session data locally.
How to Build an AI Developer Portfolio That Actually Gets You Hired
Recruiters want proof you can work with AI. Here is how to build a portfolio that shows verified AI contributions, not just self-reported claims.
Git Hooks for AI Prompt Capture: A Technical Deep Dive
How Qmmit uses post-commit and pre-push git hooks to automatically capture AI prompts, match them to commits, and sync to your profile without changing your workflow.
Why AI Attribution Matters for Open Source Projects
When 67% of job posts mention AI tools, proving your AI skills is no longer optional. AI attribution brings transparency to how code is actually written.
Verified AI Contributions: What They Are and How They Work
Every prompt linked to a real git commit SHA. Every contribution verifiable on GitHub, GitLab, or Bitbucket. Here is how verified AI contributions work under the hood.