AI-assisted debugging and edge case discovery

🚀 I built this project using Claude Code but not casually.

I applied real workflow engineering principles behind it.

After going deep into how top teams use Claude internally, I realized most AI frustration is not about capability.

It’s about workflow.

So while building my upcoming project, I followed these principles:

1️⃣ Plan Mode First

Before writing a single line of code, I:
• Broke tasks into clear steps
• Wrote specs
• Reduced ambiguity
• Designed verification before implementation

No rushing into coding.

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2️⃣ Subagent Strategy

For complex problems:
• Used multiple parallel explorations
• Offloaded research and structure analysis
• Kept main context clean and focused

Think of it like running a small AI engineering team instead of a single assistant.

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3️⃣ Verification Before Done

Nothing was marked complete unless:
• Logs were checked
• Edge cases reviewed
• Behavior diffed between versions
• Production state verified

No “it works locally” mindset.

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4️⃣ Autonomous Bug Fixing

Instead of micromanaging fixes:
• Pointed AI at logs
• Let it trace distributed flows
• Forced root cause analysis

Real debugging. Not patching.

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5️⃣ Skill Reuse & System Thinking

Turn repeated tasks into reusable skills.
Reduce context switching.
Design process once, reuse forever.

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6️⃣ Continuous Self-Improvement Loop

After every correction:
• Document the lesson
• Update rules
• Reduce future mistake rate

AI improves when your workflow improves

Code review has always been my least favorite part of being a software engineer. Ever since we’ve started using AI at work though, I’ve noticed that most of my day has become reviewing code.

I genuinely don’t understand how some people are enjoying this more than coding by hand. Sure, debugging has gotten WAY easier but building things is just not as fun anymore. It’s like the difference between doing a puzzle yourself vs telling someone to do it and checking their work.

My theory: maybe I’m stuck in a loop of reviewing and correcting because my prompts are not precise enough. Maybe if I spent a lot more upfront time thinking about design and tradeoffs, that’ll get my creative juices flowing again.

Has anyone managed to get that “craftsmanship” feeling back in the age of AI?

I’ve been using the Copilot CLI on a daily basis for coding, review, planning, design, and for debugging production systems. It’s awesome! Glad to see it reach GA. 👏

I expected to slow down. I didn’t expect to realize how much I’d already lost.

Let me be upfront: I’m not anti-AI. I use it every day. I’ve written about it. I’ve defended it in code reviews when teammates were skeptical.

But three weeks ago, I closed Copilot, stopped opening Claude for code, and went cold turkey for two weeks.

Not because I hate the tools. Because I started noticing something that scared me more than any production incident I’d ever worked through.

I was getting faster. And dumber.

I've been using Claude Code daily for 6+ months. It's replaced most of my routine work:
• To understand code base - yes true this is game changing for me. its creates nice tech documents.
• Boilerplate generation
• Refactoring large codebases
• Debugging edge cases
• Writing tests & automation scripts - top notch in UT's
It outperforms Cursor and GitHub Copilot for deep reasoning and architectural changes. Period.
⚠️ BUT — always validate. Review the code. Run your tests. You're still the final gatekeeper. AI hallucinates less here, but "less" ≠ "never."

In my previous post I listed the top 5 things I use AI for apart from writing code.
Reena Garg asked which AI tools I use for these tasks 🙏 — sharing my list👇

⭐️ Thinking partner for ideas Tool: Cursor (Plan/Ask mode) I use Plan mode to discuss a feature/design: “what am I missing?”, “what can go wrong?”, “what’s the simplest version?”, “how should I break this into steps?”, “How can we implement this?”,

⭐️ Finding edge cases Tool: Claude (Opus-4.6) I paste the context (API, flow, PR description) and ask it to list edge cases: retries, timeouts, caching, permissions, weird inputs, etc. Opus works great for any code related tasks.

⭐️ Code review before the code review Tool: CodeRabbit, GitHub Copilot, Cursor Review These tools are really great to get a review before asking for a human review. I use them on all of my PRs.

⭐️ Turning manual processes into clean steps Tool: ChatGPT / Claude This is usually a back-and-forth conversation until it becomes a clean checklist/runbook and sometimes an automation script.

⭐️ Writing clearer communication Tool: Any AI works, but I mostly use ChatGPT PR descriptions, Slack updates, incident notes — it helps turn messy thoughts into clear writing.

If you’re using any AI tools in a different way, please share — I’d love to learn and try them too 🙌
Cheers, Princi 👩🏻💻

I’m a software engineer by training, but let’s be honest: my days of memorising specific syntax and wrestling with JavaScript frameworks are mostly behind me.

Last Friday night, a message popped up in our Slack: a customer was struggling with a UI bug. A hover state was being blocked, making a feature frustrating to use.

In the "old" world, I’d have flagged it for the team to handle on Monday. In the Claude Code world, I decided to get my hands dirty.

The result? From customer feedback → Slack → Me making the fix → Engineering review → Merged into Trunk. All within the hour.

Here’s what I’ve realised about leading in the AI era:

Syntax is no longer the gatekeeper. I didn’t need to worry about the specific boilerplate. I focused on the logic.

Context is the new currency. Because I understand our business and our customers, I knew exactly what needed to change. The AI just handled the how.

It’s not "Vibe Coding." My foundational engineering knowledge meant I wasn't just guessing. I could review the output, understand the architecture, and respect the guardrails set by our senior devs.

The future doesn't belong to the people who can memorise the most libraries. It belongs to the leaders who have the curiosity to learn new tools, the context of the customer, and the foundations to know when the code is right.

I’m spending less time on syntax and more time on the business. And honestly? Getting back into the code feels like freedom.

To my fellow CEOs & Leaders: Don’t just "oversee" AI transformation. Use the tools. Make a small PR. You’ll see the future a lot more clearly when you’re building it.