- Authors
- Name
- Edwin Popham
As a software engineer living in this fast-evolving digital age, you’re likely surrounded by an ever-growing suite of AI tools. For me, this includes GitHub Copilot in the IDE and command line, Claude Code, Microsoft Copilot 365, Miro AI, Atlassian Rovo, and more. It’s like having a sprawling toolbox with shiny new gadgets — thrilling yet overwhelming. How do you avoid getting bogged down by the sheer number of options?
The answer is surprisingly simple: pick the right tool for the right job.
The AI Toolbox Analogy
Just like when you’re working with physical projects—whether it's fixing the family car, building a bookshelf, or tuning up a bike—you wouldn’t grab a hammer when a screwdriver is needed. The same logic holds for AI tools today.
You want to:
- Identify the task at hand. Are you coding, brainstorming ideas, managing projects, or automating workflows?
- Match the tool’s strengths. Each AI tool excels at certain jobs. For example:
- GitHub Copilot is a superb coding assistant, helping you write, refactor, and even explore new APIs inside your IDE.
- Claude Code offers insightful code analysis and explanation when you need a second set of eyes on tricky problems.
- Microsoft Copilot 365 seamlessly integrates AI into your office productivity suite, speeding up document creation and data analysis.
- Miro AI shines when you’re visualizing workflows or collaborating in remote brainstorming sessions.
- Atlassian Rovo excels at project tracking, linking AI-powered insights to your team’s Jira boards.
Guidance Is Key
Much like picking up a new power tool, at first you rely on manuals, recommendations from colleagues, and online tutorials. With practice and time, you develop intuition about which tool fits best where.
For me, it’s a mix of:
- Community tips: What are others finding useful? Open forums and peer conversations help a ton.
- Trial and error: Sometimes you try a tool on a problem just to see if it clicks.
- Experience: Over time, you build a mental map of the AI landscape and know when to reach for what.
Avoiding Tool Overload
The danger is spreading yourself too thin, jumping between AI tools without a clear purpose, which can waste time and scattered focus.
Here are some personal principles that keep me steady:
- Consistency: Stick with my main coding AI (like GitHub Copilot) for daily development. Supplement it with another tool only if it adds clear value.
- Context switching discipline: Be mindful of context switching cost. Batch tasks when possible to stay focused.
- Keep learning but stay selective: Always explore new AI capabilities, but critically evaluate whether they improve your workflow or just add noise.
Ultimately, AI tools are just that—tools. They amplify your skills, reduce grunt work, and ignite creativity. Armed with the right mindset, a well-matched AI toolbox is a software engineer’s best ally.
And as a Kiwi Aussie with a big whanau and a passion for sport and gaming, balancing the load—on and off the screen—is what keeps me thriving in this exciting AI-infused world.
What’s your AI toolbox look like? How do you pick the right tools to get the job done? Feel free to share your tips and tricks in the comments!