Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
A real story of removing console-only changes, adding drift detection, and getting Terraform back in charge.
Concrete systemd unit patterns that reduced flakiness: restart policies, resource limits, and structured logs.
Learn proven strategies to reduce AI inference costs including model quantization, caching, batching, and efficient prompt design. Real-world cost savings examples.
Linux Performance Baseline Methodology. Practical guidance for reliable, scalable platform operations.
How a small team moved from single-region risk to a simple active/passive multi-region setup without doubling complexity.
Compare fine-tuning and few-shot learning for adapting LLMs. Learn when to use each approach and their trade-offs in terms of cost, performance, and complexity.
Practical game day scenarios for CI/CD: broken rollbacks, permission issues, and slow feedback loops—and how we fixed them.
Cloud Disaster Recovery Runbook Design. Practical guidance for reliable, scalable platform operations.
A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.
Learn how to monitor AI models in production. Track performance, detect drift, and ensure model reliability with comprehensive observability strategies.
Evolve CI/CD toward autonomous pipelines that detect issues and roll back safely.
