Practical guides,built on real work
Refactix is written by Tharindu Perera, a software engineer and tech lead. The site publishes hands-on guides on AWS architecture and AI engineering.
The content is grounded in production experience designing and building systems on AWS and integrating AI into real workflows. No syndicated content, no rewrites of vendor blogs.
What I cover
Two areas. I go deep rather than broad. Each guide is written against a real problem, with code and the trade-offs that usually get skipped over.
AWS
Architecting new systems on AWS and getting more out of the one you already run, serverless, containers, cost, and the deployment and reliability calls that matter in production. I dig into trade-offs and operational reality rather than marketing claims.
AI
Building with Claude Code, coding agents, and modern agentic frameworks, and putting AI to work on everyday operations. I write about the patterns that hold up in real projects and the ones that mostly waste tokens.
How I write
Every article starts from a concrete problem, the kind a team might actually hit in production. I write with an opinion, call out trade-offs clearly, and skip the feature-list filler.
When I test something myself, I say so. When I'm summarizing documentation or well-established community patterns, I cite sources. I don't claim benchmarks I didn't run, and if I'm less certain about something, I flag it rather than paper over it.
Code examples are reference implementations. They're written to be clear, not production-hardened. Review and adapt anything here in your own environment before using it in production.
About the author
Tharindu Perera is a software engineer and tech lead. He writes Refactix to share patterns from production work across AWS and AI engineering. Everything on the site is written by him directly, not generated in bulk or republished from elsewhere.
Connect with Tharindu on LinkedIn, or follow Refactix for new articles and commentary.
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Refactix is built for people who want to go beyond surface-level explanations and understand how systems actually behave in real-world environments.
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