AI in My Code & Day: Lessons from Our Team Seminar

Introduction

At Glinteco, we’re constantly testing how AI can make developers faster, smarter, and more effective. In our latest seminar, the team explored a range of AI tools that we actually use day-to-day, from ChatGPT and Gemini to DeepSeek, GitHub Copilot, Kimi, and Grok.

We didn’t just demo the tools. We debated them. We questioned their limits. And we asked: What does a good developer look like in the AI era?

This post captures the highlights of that conversation, including some sharp arguments, lessons learned, and where we go next.

The Daily AI Lineup

Our workflow now spans multiple tools:

• ChatGPT: for instant debugging, TL;DR summaries, and quick insights
   

• Gemini: Google’s strength for research, Workspace integration, and CLI workflows
   

• DeepSeek: our mobile coding assistant, perfect for quick code snippets, PR reviews, and learning algorithms on the go  
   

• GitHub Copilot: the coding buddy for micro-tasks, refactoring, commit messages, and inline debugging
   

• Kimi: generates full slide decks, fixes failed rendering automatically, and supports English/Chinese equally well. Check our demo here.
   

• Grok: specialized in log file mining, JSON extraction, and large-file analysis

How We Use Them (and Where We Argue)

Writing vs. Research

• Pro-ChatGPT voices: “It’s unbeatable for clean explanations and summaries.”
   

• Gemini supporters: “But when you need research or Apps Script automation inside Google Drive and Calendar, Gemini is in a league of its own.”

Coding Assistance

• Copilot fans: “Assigning micro-tasks saves hours. Even commit messages become effortless.”
   

• Cautious voices: “But remember when Copilot injected a fake API that compiled but failed at runtime? Always test before merging.”

Mobile and Edge Use Cases

• DeepSeek advocates: “It’s a lifesaver for coding away from the desk, PR reviews, algorithm explanations, and quick snippets in seconds.”

File Ops and Data Handling

• Grok specialists: “For multi-megabyte logs, Grok delivers ready-to-paste Python snippets.”
   

• But also: “It once miscounted log lines after a timestamp format change. Lesson learned: double-check file formats.”

Best Practices We Agreed On

• Use Markdown prompts across platforms for clarity
   

• Confirm before execution in CLI workflows
   

• Reset context regularly to prevent overflow and hallucinations
   

• Vector databases are essential for scalable context memory
   

• agent.md files give AI a structured map of the workspace
   

• Always redact sensitive data before pasting into AI tools

BMX & BMAD: Structured AI for Projects

We also looked at structured AI frameworks:

• BMX Framework: lets AI agents act as personas (BA, PM, Dev, QA). Great for breaking down requirements into MVPs but expensive in tokens
   

• BRAD Method: another approach for structured AI-assisted development, encouraging step-by-step breakdowns and minimizing hallucinations.

Both methods reflect a trend: AI works best when guided by structure, not left to run free.

Developers in the AI Era

One of our strongest debates was philosophical:

• Is the best developer the fastest coder, or the deepest thinker?

  We concluded: AI shifts the balance.
   

• AI excels at boilerplate, refactoring, and structured tasks
   

• The new skill is mentorship - explaining tasks clearly to AI, like guiding a junior developer
   

• Efficiency can jump 60% or more, but only if humans remain the strategic thinkers

Where We Go Next

• Experiment with BMX and BRAD frameworks to improve structured AI workflows
   

• Create a shared vector database for team knowledge
  
   

• Push Gemini’s 2M token context to test full-repo analysis 
   

• Explore more AI services for the frontend tasks: Lovable and Replit

Conclusion

AI isn’t replacing developers as it’s reshaping what makes them valuable. For startups, SMEs, and even solo founders, mastering these tools can transform productivity. At Glinteco, we’ll keep experimenting, documenting mistakes, and sharing lessons because we believe the future belongs to teams that know how to argue with AI as much as they code with it.