Productivity Tools
Tools like Lucid, Notion, and Visual Studio Code to keep track of everything.
Overview
It became immediately obvious that I needed a structured way to track everything—from code decisions to architecture diagrams to ChatGPT conversations.
- Notion** (recommended by ChatGPT) became my go-to documentation hub. - Lucid** helped me visualize architecture components clearly. - VS Code & code analyzers** enabled me to track, measure, and document code at a granular level.
In a fast-moving AI-augmented development process, staying organized was just as important as writing the code.
Key Goals:
Maintain structured documentation of decisions, designs, and discussions.
Track the evolution of the codebase** and quantify auto-generated components.
Ensure consistency** in development tools and environments.
Complexity: Medium
Components
Lucid & Notion.so
Documentation and visualization tools** that kept me on track when distractions hit.
SOARL Summary
Managing code, ChatGPT transcripts, architecture diagrams, and design decisions needed a single source of truth.
No built-in tool** could handle both structured note-taking and visual diagrams.
{“Used Notion** to maintain”=>[“ChatGPT conversation logs”, “Feature roadmaps”, “Decision tracking**”]}
{“Used Lucid** for”=>[“High-level architecture diagrams”, “Workflow visualization”]}
A well-organized documentation system that let me quickly revisit past decisions.
Documentation isn’t just for later—it actively helps decision-making** in fast-moving projects.
Situation:
Obstacle:
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Learning:
Visual Studio Code (VS Code)
My primary IDE, optimized with extensions and virtual environment integrations.
SOARL Summary
Needed a fast, efficient development environment with Python, Django, and multi-service support.
Ensuring all necessary extensions were installed and working across multiple virtual environments.
{“Customized VS Code** with”=>[“Python virtual environment handling”, “Linting, auto-formatting, and debugging tools”, “Git and Docker integrations**”]}
A streamlined development workflow with efficient debugging and rapid iteration.
VS Code is as powerful as you configure it**—extensions make all the difference.
Situation:
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Code Analyzers
A custom-built Python tool that analyzes and categorizes the codebase.
SOARL Summary
{“Wanted quantifiable insights into”=>[“How much code was auto-generated** vs. manually written.”, “How many functions, classes, and modules existed**.”]}
No off-the-shelf tool gave detailed breakdowns of auto-generated vs. manual code.
{“Developed a Python-based analyzer** to”=>[“Tag and classify classes, functions, and generated files.”, “Feed structured insights into SQL for tracking.”]}
Integrated analysis into the SQL generator, making **code stats part of reporting.
Measuring what you build is key**—helps with optimization and tracking long-term evolution.
Situation:
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Key Learnings
- Documentation isn’t just a chore—it’s a powerful tool for faster iteration.** - Code analyzers help quantify progress** and reveal how much of the system is AI-generated vs. human-built. - Optimizing your IDE and workflow saves hours**—small tweaks compound over time.
Demos
Final Thoughts
The right productivity tools make AI-augmented development manageable.
By combining documentation, visualization, and code tracking, I was able to stay organized, iterate faster, and quantify progress. 🚀