Software Process and Measurement Cast · Thomas M. Cagley Jr

Open Source, Learning, Careers, and Leadership with Gorkem Ercan

·35 min·3 clips

Gorkem's open source code was recognized by a Red Hat interviewer who had used it as a foundation.

Tom Cagley starts with a career claim. Gorkem Ercan comes in as a founding distinguished engineer and CTO of Jozu, with a record of leading teams across different technologies. The episode treats open source as work that can move a career, not as weekend virtue signaling. Contributions to Eclipse, Apache Cordova, and developer tooling become the receipts. The resume list is useful, but the pattern is the part that sticks: public work teaches, exposes judgment, and gives other people something to build on. Cagley points to the Eclipse JDT language server, the VS Code Java extension, the YAML language server, the VS Code YAML extension, and Eclipse work as examples. The learning thread has a nice edge to it. Older programmers once argued about getting closer to the iron, even with assembler experience already in the room. That keeps the conversation from floating away. Every era seems to redraw the line between knowing the machine and just using the next layer up. Then AI enters the picture. Ercan says DevOps has a place in AI and ML, but the flow is messier than regular application delivery. Regular software gives teams a shot at repeatability: same source, same compiler parameters, same binary. AI does not behave that neatly. Change the data and the results can change, so teams need controls that ordinary delivery practices may not fully cover. Separate operations can help, but only up to a point. ML ops, LLM ops, and AI ops may be needed, yet each one can become another boundary inside the organization. The adoption problem is the sharper one. If AI and ML stay off to the side, teams will have a harder time getting them into existing applications and solutions. The close turns back to process. Cagley sends listeners to a Reread Saturday installment on a chapter about death and suicide in How to Be a Stoic, then tees up a future essay on work intake problems. It is a practical episode about careers, learning, tools, leadership, and AI delivery without trying to make any of it sound magical.

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A grounded talk on open source, learning, and why AI belongs in the workflow, not off to the side.

Gorkham Erjan frames open source leadership as daily work rather than branding. He talks through learning, contribution, and the pressure of bringing new tools into real software teams, then argues that AI and ML should sit inside the workflow if they are going to matter in…

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Listen if you want open source advice that still sounds like real engineering work.

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