Practical Insights on Agents, AI Applications, and HMC
Practical lessons, tested workflows, and best practices for teams that want to adopt AI in a responsible and effective way.
What You Will Find Here
The focus is actionable knowledge over hype: concise guides, deep dives, honest retrospectives, and templates you can apply directly in real projects.
-
Agents in Production
Real-world notes on planning, prompting, tool usage, and quality controls in agentic workflows.
-
Building AI Applications
From concept to operations: architecture, guardrails, data quality, and observable runtime processes.
-
HMC and Best Practices
Human-machine collaboration with clear roles, practical governance, and repeatable outcomes.
Latest Posts
All 9 posts →-
One Person, a Whole Team: How aSPARK Turns Claude Code into an Agile Delivery Process
Why I built an agile AI team as a Claude Code plugin, how the SPARK loop works, and what happened when I ran a real feature through it.
- agents
- agentic-workflows
- claude-code
-
AI Beyond Language Models: World Models, Embodied Systems, and Physical AI
A practical way to categorize modern AI systems beyond LLMs, from perception and simulation to action in the physical world.
- ai-models
- world-models
- physical-ai
-
Qwen 3.6 vs Current Anthropic Models Performance, Cost, and Takeaways
A practical comparison of Qwen 3.6 and Claude 4.x on benchmark performance, token economics, and model selection strategy.
- llm
- model-comparison
- cost-optimization