The Rise of MCP Servers: Why Every Developer Will Have a Personal AI Toolchain
From AWS to GitHub to Podman, MCP servers are quietly becoming the new plug-in ecosystem for developers—and it’s changing how we work.
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Insights on engineering leadership, technology, and building high-performing teams.
17 posts
From AWS to GitHub to Podman, MCP servers are quietly becoming the new plug-in ecosystem for developers—and it’s changing how we work.
After building two open-source task managers to battle procrastination, I took it further — secure cloud sync, end-to-end encryption, and now an MCP server that connects your tasks to AI.
Software gets slower faster than hardware gets faster. Exploring Wirth's Law and why real progress might not be about adding more, but mastering the art of enough.
After testing every productivity app under the sun, I did what any reasonable engineer would do—I built my own. Two open-source, privacy-first task managers to help procrastinators like me get things done.
Even championship teams can lose the narrative when leadership loses clarity. McLaren’s latest Formula 1 victory is proof that success without alignment can still feel like failure.
The resistance to GenAI tools isn't simply about developers being stubborn or afraid of change—it's a rational response to tools that haven't yet proven their value universally, in an environment where people are already managing substantial change fatigue, and where the quality bar for production code remains high.
By coaching LLMs with timeless software design principles like SOLID, DRY, and YAGNI, you can transform raw code generation into consistently clean, maintainable, and production-ready software.
Exploring the evolution from AI to AI Agents with a hands-on FinOps example. Learn how AWS Strands enables autonomous agents to monitor cloud spend, notify on overages, and free humans to focus on higher-judgment work.
A humorous confession about subscribing to nine different AI development tools in my quest to find the best tool(s).
Just like Cloud before, today's AI transformation demands companies rebuild their operating models, leadership structures, and developer experience instead of retrofitting AI onto existing workflows.