Using AI Workflows and PGL to Achieve Financial Freedom

Building in public 27 January 2026 Updated: 27 Jan 2026

I've spent 15 years writing code.

Thousands of commits. Dozens of production systems. Every stack you can name React, Angular, Node.js, C#, Python.

I was confident in my craft. Comfortable in my full-time role. Building projects on the side like most senior developers do.

But in late 2024, something shifted that made me question everything I thought I knew about building software.

The shift wasn't about learning a new framework or mastering another language.

It was about realizing that my most valuable skill is no longer writing code it's orchestrating AI workflows that write better code than I ever could manually.

That realization led me to build ProjXplorer.

Not just another AI tools directory. But a systematic approach to discovering, testing, and implementing the AI stack that transforms solo developers into one-person development armies.

Here's what 15 years of experience taught me about AI-augmented development in 2026 and why everything you learned about "best practices" before 2024 is already obsolete.

The Shift: Why Traditional Software Development Is Dead

Let me be direct: If you're still coding the way you did in 2020, you're competing with one hand tied behind your back.

I realized this while building my third side project in 2024.

I was doing everything "right":

Writing clean, maintainable code
Following SOLID principles
Implementing proper test coverage
Documenting every decision

And I was slow as hell.

While I spent three weeks building authentication and user management, developers using Claude Code and Cursor were shipping complete MVPs in days.

What Actually Changed in 2025-2026

Three fundamental shifts happened that changed how we build software:

1. AI Coding Tools Surpassed Junior Developer Capabilities

The benchmarks don't lie:

Claude Code scores 76.2% on SWE-bench Verified (fixing real GitHub issues)
Gemini 3 Pro achieves 1487 Elo on WebDev Arena
These tools now handle 70-80% of routine coding tasks better than most human developers

I tested this extensively on ProjXplorer's backend infrastructure running on Google Cloud.

Tasks that used to take me 4-6 hours — building API endpoints, setting up authentication, implementing CRUD operations — now take 20-30 minutes with proper AI workflow setup.

2. Context Engineering Became More Valuable Than Syntax Knowledge

Here's what separates amateur AI users from pros:

Amateurs ask: "Build me a login system"

Pros provide: Complete architectural context, security requirements, integration patterns, error handling conventions, and testing strategies — then let AI handle implementation.

I spent 200+ hours testing different prompting strategies and context management approaches. The results were shocking:

Well-contexted AI requests: 85% success rate on first try
Generic requests: 23% success rate, requiring 3-5 iterations

The skill that matters now is architecting the problem space, not implementing the solution.

3. Cloud Infrastructure Simplified Deployment

Google Cloud's infrastructure removed the complexity barrier.

Before: Managing servers, configuring environments, wrestling with deployment scripts.

After: Cloud Run for containerized deployments, Firestore for database management, Cloud Functions for serverless operations.

ProjXplorer runs entirely on Google Cloud with minimal DevOps overhead. AI tools handle the code, Google Cloud handles the infrastructure, I focus on architecture and strategy.

The Three Workflows That Actually Matter

After testing every major AI coding tool for 12 months, I've identified three workflows that separate productive developers from those still stuck in 2020:

Workflow #1: Spec-Driven Development

Stop coding first, documenting later.

The new pattern:

Write comprehensive specs in natural language
AI generates implementation plan
Review and refine architecture
AI writes code, tests, and documentation
You validate the output

I use this workflow for every major feature in ProjXplorer.

Example: When building the AI tools directory search functionality, I spent 2 hours writing detailed specs covering:

Search algorithm requirements
Filter mechanisms
Performance constraints
UI/UX patterns
SEO considerations

AI generated 1,800 lines of production-ready code in 15 minutes.

My role? Architectural decisions and quality validation.

Workflow #2: Parallel Agent Execution

One AI assistant per project is amateur hour.

Pros orchestrate teams of AI agents working simultaneously.

My typical ProjXplorer development session:

Agent 1: Frontend component development
Agent 2: Backend API implementation
Agent 3: Database schema optimization
Agent 4: Documentation updates

Each agent works in isolated Git worktrees. I review completed work and merge when ready.

What used to take me 8 hours sequentially now happens in 2 hours with parallel execution.

Workflow #3: Context-Aware Code Documentation

Documentation isn't an afterthought anymore.

With AI, documentation becomes a strategic asset that improves with every commit.

I use Claude Code to:

Generate comprehensive system architecture docs
Create decision records for major technical choices
Build troubleshooting guides from production issues
Maintain up-to-date API documentation

The documentation quality rivals what tech writing teams produce — because AI understands the entire codebase context, not just individual functions.

The Stack: My Complete AI Tool Architecture

Most developers treat AI tools like random productivity hacks.

They install Cursor, maybe try Claude Code, watch YouTube tutorials, and wonder why they're not seeing the productivity gains everyone talks about.

Here's the truth: You need a stack, not a single tool.

After 12 months of systematic testing (documented across 150+ experiments), here's the complete AI architecture I use to build ProjXplorer and other projects.

Core Development Environment

Primary: Claude Code

Why Claude Code won:

Best context management of any terminal-based AI tool
Native Git integration that actually works
Subagent workflows for complex tasks
Works seamlessly with Google Cloud deployments

I've tested Claude Code against Cursor, Windsurf, GitHub Copilot, and a dozen others.

Claude Code wins on three critical metrics:

Architectural thinking (planning before coding)
Multi-file refactoring consistency
Context retention across long sessions

Real test case: Refactoring ProjXplorer's database schema across 47 files. Claude Code handled it in one session with zero manual fixes needed.

Secondary: Cursor (for visual work)

I use Cursor for:

Frontend development with live preview
Complex UI debugging
CSS/styling work where visual feedback matters

The new Visual Editor in Cursor is impressive for dragging elements and seeing changes instantly.

But for backend logic, API development, and system architecture? Claude Code dominates.

Specialized AI Models

Gemini 3 Pro (via Gemini CLI)

Best for:

Research and analysis tasks
Multi-step reasoning problems
When I need second opinion on architecture decisions

I run Gemini CLI alongside Claude Code for complex projects.

Example workflow: Claude Code builds the implementation, Gemini CLI reviews for security vulnerabilities and performance bottlenecks.

GitHub Copilot

Still valuable for:

Autocomplete during manual coding sessions
Generating test cases
Quick code snippets

Copilot isn't my primary tool anymore, but it's excellent for micro-optimizations during code review.

Content and Visual Creation

Nano Banana (Gemini 2.5 Flash Image)

This is Google's secret weapon for image generation.

I use it for all ProjXplorer visuals:

Tool preview images
Blog post banners
Social media graphics
UI mockups

Why Nano Banana beats Midjourney and DALL-E:

1-2 second generation time
Consistent character rendering across edits
Text-based editing that actually works
Free tier is generous

Real example: Created 50+ tool preview images for ProjXplorer's directory in one afternoon.

NotebookLM

Research and content synthesis tool.

I feed NotebookLM:

Competitor analysis documents
Market research reports
User feedback compilations

It generates:

Audio overviews (podcast-style summaries)
Study guides for technical topics
Quick reference materials

This tool saved me 40 hours during ProjXplorer's initial market research phase.

Infrastructure: Google Cloud

Why Google Cloud for ProjXplorer:

Cloud Run for containerized backend services
Firestore for NoSQL database (scales automatically)
Cloud Functions for event-driven operations
Cloud Storage for asset management
Firebase for authentication and real-time features

The beauty of Google Cloud? It handles scaling automatically.

When ProjXplorer gets traffic spikes, Cloud Run scales up. When traffic drops, it scales down. I pay only for what I use.

AI tools build the features. Google Cloud runs them reliably.

How ProjXplorer Fits Into This Stack

Here's the critical insight: Tool discovery is the bottleneck.

You can have the perfect workflow architecture, but if you don't know which tools exist or how they compare, you're building on quicksand.

ProjXplorer solves this by:

1. Curated Discovery

I personally test every tool before adding it to the directory.

Not just "it exists" — but "here's what it actually does well" and "here's where it falls short."

Current directory: 92 tools, 0 fluff.

2. Category Architecture

Tools organized by actual use case:

Custom GPTs for specific tasks
AI Applications for complete solutions
Development Tools for coding workflows
Asset Library for templates and frameworks

3. Implementation Context

Each tool listing includes:

What it actually does (not marketing copy)
Where it fits in your stack
Alternatives and comparisons
Real pricing (not just "free trial" bait)

I built ProjXplorer because I got tired of:

Testing 50 "AI coding assistants" that are all forks of the same thing
Reading marketing pages that don't explain actual capabilities
Wasting time on tools that don't integrate with my workflow

Now when I need a new capability, I check ProjXplorer first.

If it's not there, I test it, document it, and add it.

The directory grows with my actual needs, not random submissions.

From Earnings to Wealth: Why Saving Isn't Enough

Here's where most technical content stops.

Tools and workflows — but no discussion of why you're building the business in the first place.

Most developers make a critical mistake: They optimize for earning, but forget about money working for them.

The Saving Trap I Fell Into

For the first 10 years of my career, I followed conventional wisdom:

Maximize salary through job changes
Save 20-30% of income
Keep it in savings accounts or index funds
Hope compound interest does its magic

The problem? Time is the only variable you're leveraging.

You trade time for money. Then you store that money. Your purchasing power barely keeps up with inflation.

I was "financially responsible" but still decades away from any real freedom.

The Shift: From Passive Saving to Active Compounding

In 2024, I realized something fundamental:

Saving is defense. Investing systematically is offense.

Most developers understand this conceptually. But they make two mistakes:

Mistake #1: They invest randomly

Buy crypto when it's hyped
Sell stocks when markets drop
Chase "hot" opportunities
Let emotions drive decisions

Mistake #2: They optimize the wrong variable

Focus on picking "the best" asset
Time the market
Obsess over individual positions

Both approaches fail because they're fighting human psychology.

What Actually Works: Systematic Rebalancing

I spent 12 months back-testing different investment strategies.

The winner? A simple, emotionless system that forces profitable behavior.

Enter the Perpetual Growth Loop (PGL).

The Strategy: Building Wealth Through PGL

Financial freedom isn't about accumulating wealth.

It's about generating $3K-5K monthly passive income that covers your essentials. That's the number that lets you say "no" to jobs you don't want.

What Is PGL?

PGL is a systematic rebalancing strategy across four assets:

30% GLD (Gold) — Stability anchor
30% ETH (Ethereum) — Growth catalyst
30% QQQ (Nasdaq ETF) — Tech exposure
10% Cash — Rebalancing fuel

The system forces "buy low, sell high" behavior through mathematical triggers, not emotional decisions.

Core Rule: Rebalance when any asset drifts more than 5% from target allocation.

When one asset crashes, you automatically buy it (it's below 30%). When one asset surges, you automatically sell it (it's above 30%).

No predictions. No timing. No emotions.

Real Performance Data

I started testing PGL in January 2024 with $10,000.

12 months later: $15,850 (58.5% return)

The S&P 500 returned 24% during the same period.

PGL outperformed because:

Systematic rebalancing captured volatility
Emotional decision-making removed completely
Compound growth across uncorrelated assets

I've documented every rebalancing trigger, every decision, every outcome.

22 articles covering the complete methodology, available on Medium.

Why This Matters for Developers

Most developers fall into one of two traps:

Trap #1: All-in on startup equity

You build for 3 years. The company fails. You have nothing.

Trap #2: All-in on salary

You save diligently. But you're trading time for money forever.

PGL creates a third path:

Build multiple small income streams while systematically investing returns.

Your startup might fail. But your investment system keeps compounding.

The Half-Salary Rule

Here's my operating principle:

Stay in your full-time job until side projects generate at least half your salary.

Why half?

Financial safety net remains intact
No desperate decisions from cash flow pressure
Time to build sustainable systems, not quick hacks
Psychological freedom to experiment

I'm still employed full-time. ProjXplorer and other projects generate 40% of my salary.

When they hit 50%, I'll reassess. Not before.

This is survivability over optimization.

Integration with AI Workflows

Here's where this becomes powerful:

AI tools let you build multiple income-generating projects simultaneously.

My current revenue streams:

ProjXplorer (directory + future products)
Medium writing (partner program)
Digital products (investment tools and templates)
Consulting (AI implementation for dev teams)

Each stream generates $500-$2,000/month.

Total: $3K-6K monthly, invested into PGL system.

The wealth compounds while I sleep.

And because AI handles 70% of the implementation work, I maintain all streams without burning out.

This is the model: Build smart, invest systematically, achieve freedom.

The Invitation: What We're Building Together

ProjXplorer isn't just a directory.

It's the foundation for a community of developers who understand that the future belongs to AI-augmented builders.

Here's what I'm building:

Phase 1: The Directory (Current)

Curated AI tools tested and documented by someone who actually uses them in production.

No affiliate spam. No SEO garbage. Just useful tools with real context.

Current count: 92 tools across Custom GPTs, AI Applications, Development Tools, and Asset Library.

Goal: 500+ tools by end of 2026, all personally tested.

Phase 2: The Knowledge Base (Q2 2026)

Comprehensive guides covering:

AI workflow implementation
Context engineering techniques
Parallel agent orchestration
Production deployment patterns on Google Cloud
Systematic investment strategies (PGL implementation)

Not theory. Not generic "10 tips" articles.

Deep technical guides with code examples, architecture diagrams, and real-world case studies from ProjXplorer's development.

Phase 3: The Community (Q3 2026)

Private community for AI-augmented developers:

Weekly workshops on emerging tools
Collaborative testing of new AI capabilities
Shared workflow templates
Investment strategy discussions

This isn't a Discord where questions go unanswered.

This is a curated group of developers actively building with AI, sharing what actually works.

Phase 4: The Products (Q4 2026)

Based on what I build for myself:

AI Workflow Starter Pack: Complete setup guide for Claude Code + parallel agent workflows + Google Cloud deployment automation

PGL Investment Toolkit: Google Sheets templates, rebalancing calculators, portfolio trackers (the exact system I use)

Context Engineering Course: How to architect problems for AI, with before/after examples from real projects

Each product solves a problem I've personally struggled with and systematically solved.

No generic "learn AI" courses. Just battle-tested systems that work.

Final Thoughts: The AI-Augmented Developer

Traditional software development is dead.

Not because coding is obsolete — but because the skillset that matters has fundamentally changed.

The developers who thrive in 2026 aren't the ones who write the most code.

They're the ones who:

Architect complex systems through natural language specifications
Orchestrate teams of AI agents working in parallel
Build context engineering strategies that extract maximum value from AI tools
Systematically invest their earnings to achieve financial freedom

This is what ProjXplorer teaches.

Not through generic tutorials. But through systematic documentation of what actually works in production environments.

I'm not building this alone.

I'm building it with developers who understand that AI isn't replacing us — it's amplifying what we can accomplish when we work systematically.

If you want to transform from developer to AI-augmented builder, start with three steps:

Step 1: Explore the ProjXplorer directory and identify 3 tools that fit your current workflow gaps

Step 2: Implement one systematic process this week (spec-driven development is the easiest starting point)

Step 3: Set up your PGL investment system — even with $1,000, the compound growth starts immediately

The shift from traditional to AI-augmented development doesn't happen overnight.

But it starts with one decision: Stop coding like it's 2020.

Have you made the shift to AI-augmented development yet? What's been your biggest challenge? Let me know in the comments.

Let's Connect!

If you are new to my content, my name is Ozkan.

I'm a 15-year full-stack developer who has evolved from being fully dedicated to my day job to building a comprehensive entrepreneurial journey focused on financial freedom through systematic approaches.

What I do:

Build and document AI-augmented workflows at ProjXplorer
Share investment strategies through the Perpetual Growth Loop system
Test and review AI tools that actually work in production
Write about the intersection of development, AI, and financial freedom

Follow ProjXplorer:

Explore AI Tools Directory
Subscribe to Newsletter (coming soon)
Follow on Medium (coming soon)

Using AI Workflows and PGL to Achieve Financial Freedom