As a software engineer, I’ve woven AI into my workflow to boost productivity and sharpen my thinking—without letting it run the show. Here’s how it fits into my daily routine:
I use tools like Cursor for coding, but I don’t just accept its suggestions outright—I verify and refine them. I also upload code files to query their logic or ask high-level questions, like “What’s this module doing?” It’s a sounding board with answers. I do the same for code reviews—both my colleagues’ and my own—asking AI to spot bugs or gaps in logic. It’s like an extra pair of eyes that never gets tired.
Documentation? I upload files and fire off questions to cut through the fluff. For communication, I draft internal posts or rephrase messages with AI’s help. Native English speaker or not, I’ve got bad habits—like overwriting—that it trims down for clarity and brevity. I also lean on AI to build small utility tools that increase my productivity, like this script that converts CSVs to GitHub issues. When brainstorming technical solutions, I keep it high-level, asking for ideas without tying it to specific frameworks or code.
Finally, I export long threads or GitHub comments and have AI summarize points or flag patterns—like recurring bugs or buried consensus. I don’t necessarily think AI is here to replace me; I see it as a teammate that saves time and sharpens my focus for the real challenges.
I’m always on the lookout for new ways to use AI to make me more productive and effective. It’s an evolving toolset, and I’m excited to keep pushing its limits in my work.
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