Raiders of the Lost Code: Unearthing Abandonware with AI Agents

How AI Agents Turn Your Forgotten Projects into Gold with Minimal Effort

Ever opened a folder and discovered dozens of half-finished projects? 💻 That game you started coding, the productivity app that's 60% done, or that brilliant algorithm you abandoned mid-implementation?

You're not alone. Our creative brains are wired to chase the next shiny idea—starting is exciting, continuing is hard, and finishing? Well, that rarely happens. One idea sparks five more, and before you know it, you've got a digital graveyard of brilliant-but-incomplete code.

🤯 The revelation? These abandoned projects aren't dead ends—they're untapped gold mines. With today's AI coding agents, you can transform this abandonware into finished products with minimal effort. This isn't science fiction; it's happening right now.

🧠 Why We Abandon Projects: The Psychology

The cycle is familiar: initial excitement → coding sprint → obstacle → new idea → abandonment. Rinse and repeat.

Why? Dopamine. Our brains flood with this reward chemical when we start something new and exciting. But the steady work of finishing? That's where dopamine fades and motivation crashes.

The irony? Completion also triggers a massive dopamine reward—one that's often stronger and more satisfying than starting something new. But we rarely experience it because we jump ship too early.

🚀 TL;DR: The Revolution

• Those half-built projects sitting on your hard drive contain immense potential value
• AI coding agents can now collaborate with you to finish what you started
• You maintain creative control while AI handles the implementation grind
• Every finished project delivers a psychological win, retraining your brain to value completion
• Your abandonware could be worth actual money once completed

🛠️ The AI Coding Landscape (April 2025)

Before diving in, let's map the current tools revolutionizing project completion:

⭐ My top pick (for today): Claude Desktop with MCP offers the best balance of power, cost, and ease of use for resurrecting abandoned projects.

📂 The Magic File Structure

Want to revive your abandoned project? First, let's set up the perfect environment for AI collaboration. Create these crucial files:

project-folder/
├── 📄 pitch.md                # Elevator pitch (automatically updated by the AI)
├── 📄 init.md                 # Project introduction, context, and agent/human instructions
├── 📄 agent-tasks.md          # AI agent's task list (detailed plan and progress, keeper of ALL requirements, preserving app specifications)
├── 📄 human-tasks.md          # Manual tasks for human intervention (e.g., API keys, DB paths)
├── 📄 agent-bugs.md           # List of temporary issues and bugs (removed when fixed)
└── 📄 tech-requirements.md    # Technical preferences and constraints (tech stack, coding styles, etc.)

What Each File Does

📄 pitch.md:

Your project's elevator pitch—a quick abstract that explains what it does and why it matters. This living document gets automatically updated by the AI to provide a snapshot of the project's key aspects at a glance.

📄 init.md:

The introduction and context file. It explains your vision, what has been done so far, and any background information that the AI should consider. Think of it like onboarding a new team member into the project's history and context.

📄 agent-tasks.md:

This file is not just a task list — it's a living, evolving specification of your entire app. It captures everything from high-level goals like "finish the app" to detailed features like "implement OAuth login." Both the AI and you, the human, will continuously add new tasks during development, after testing, or when inspiration strikes. Completed items are preserved as part of the app’s history — a self-updating blueprint that enables future super agents to instantly understand and enhance the project.

📄 human-tasks.md (Optional):

This file is specifically designed for tasks that require human intervention. It’s where the AI agent instructs you on manual actions that cannot be automated—such as obtaining an API key, setting the path to your database, or updating environment variables. Use this file as your to-do list for items that need your direct input or hands-on configuration.

📄 agent-bugs.md:

A temporary record of issues and bugs that need fixing. Items here are removed once resolved to maintain a clean context and efficient token usage.

📄 tech-requirements.md:

Your technical playbook. This file lists your technology choices (npm vs. yarn, React vs. Vue, TypeScript vs. JavaScript, etc.), coding style guidelines, and commands (for example, always use npm run dev to test). This ensures the AI consistently follows your established conventions.

Pro Tip:

Instruct the AI Agent to keep each code file concise and focused on a single responsibility. This minimizes token usage and prevents context overflow, especially when restarting sessions with the AI as well as when focusing only on one single feature.

🔄 The One-Prompt Revolution

The most incredible part? You can skip all the files and start with just ONE simple prompt:

"Finish my half-made todo list application and make sure to ask me questions along the way if you need."

That's it. No complex instructions needed. The AI will:

1. Read your codebase to understand the current state
2. Check your init.md for context
3. Follow tech-requirements.md for technical decisions
4. Populate agent-tasks.md with a plan (or follow your existing plan)
5. Start implementing, asking questions only when necessary

You're still in control, but the grunt work happens almost magically.

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Here's the cleaned version with all unusual characters removed:

Detailed Setup: Claude Desktop + MCP

Since Claude Desktop with MCP is my recommended approach, here's a step-by-step guide to get it running (refer to the official documentation for the latest approach):

1. Sign up for Claude Pro

Subscribe for $20/month at claude.ai or download the Claude Desktop app directly from Anthropic's website

2. Install Claude Desktop

Download and install the appropriate version for your operating system (Windows/Mac supported)

3. Install Node.js

If you haven't already, install Node.js (v18.x or later) and verify the installation:

node --version
npm --version

4. Install MCP Servers Globally

Open Command Prompt or Terminal with administrative privileges and run:

npm install -g @modelcontextprotocol/server-filesystem @modelcontextprotocol/server-brave-search @modelcontextprotocol/server-everything @modelcontextprotocol/server-sequentialthinking

These servers enable Claude to interact with your filesystem, perform web searches, and utilize additional capabilities

5. Configure MCP for File Access

• On Windows:

%AppData%\Claude\claude_desktop_config.json

• On Mac:

~/Library/Application Support/Claude Desktop/claude_desktop_config.json

• Add configuration (adjust paths as needed):

{
    "mcpServers": {
      "filesystem": {
        "command": "node",
        "args": [
          "C:\\Users\\YourUsername\\AppData\\Roaming\\npm\\node_modules\\@modelcontextprotocol\\server-filesystem\\dist\\index.js",
          "C:\\path\\to\\your\\projects\\folder",
          "C:\\path\\to\\another\\directory"
        ]
      },
      "brave_search": {
        "command": "npx",
        "args": [
          "-y",
          "@modelcontextprotocol/server-brave-search"
        ],
        "env": {
          "BRAVE_API_KEY": "your_brave_api_key_here"
        }
      },
      "everything": {
        "command": "npx",
        "args": [
          "-y",
          "@modelcontextprotocol/server-everything"
        ]
      },
      "sequential_thinking": {
        "command": "npx",
        "args": [
          "-y",
          "@modelcontextprotocol/server-sequentialthinking"
        ]
      }
    }
  }

Note: The Brave Search MCP server requires an API key:

• Visit Brave Search API
• Create account and obtain API key
• Replace placeholder with your actual key

You get up to 2,000 free searches/month with Brave Search API. Ensure directory paths match your actual folders.

6. Restart Claude Desktop

After saving configuration, restart Claude Desktop. Look for "MCP" indicator when active.

7. Verify Access

Test by asking:

• "Can you list files in my projects directory?"
• "Search the web for latest AI developments news" Claude should request permission and show results

8. Ready to Go!

Claude can now read/write files in specified directories. For advanced setups, explore mcp.so for additional plugins (terminal access, tests, etc).

This setup allows Claude to work with project files while maintaining security through permission requests.

👥 AI-Human Collaboration Patterns

Developers exhibit diverse preferences in collaborating with AI. Here are four effective collaboration patterns:

🤖 The Observer

You set clear goals and observe as the AI implements changes, intervening only when necessary. This approach suits those who trust the AI's capabilities and prefer to oversee the development process without direct involvement.

Example prompt: "Implement user authentication using Firebase. Reference the tech requirements and follow best practices for security."

🧩 The Pair-Programmer

In this collaborative approach, you and the AI work together, brainstorming and implementing solutions. The AI not only generates code but also explains its rationale, fostering a deeper understanding of the development process.

Example prompt: "Let's implement user authentication together. What approach would you recommend, and what files would we need to modify?"

🚦 The Gatekeeper

You maintain strict control over the development process, with the AI suggesting changes but not implementing them without your approval. This method ensures that you review and understand each modification before it is integrated.

Example prompt: "Suggest how we should implement user authentication. Show me the code you'd write, but don't make changes yet."

🎵 The Vibe Coder

Embracing the "vibe coding" philosophy, you rely heavily on AI to generate and implement code based on high-level prompts, with minimal intervention or understanding of the underlying code. This approach is ideal for rapid prototyping or when the focus is on the end result rather than the coding process. However, it may lead to challenges in debugging and maintaining the code due to a lack of deep understanding.

Example prompt: "Create a responsive e-commerce website with user authentication, product catalog, and payment integration."

These patterns offer flexible frameworks for integrating AI into software development, allowing developers to choose the level of AI involvement that best aligns with their project needs and personal working styles.

Choose the style that matches your comfort level with AI. You might start as a Gatekeeper and gradually shift to Observer as trust builds.

⚡ Getting Started in 10 Minutes

1. Pick a project from your abandonware collection
2. Create a Git repo for it (crucial for backups—things can break!)
3. Add at least the four key files (pitch.md, init.md, agent-tasks.md, tech-requirements.md)
4. Install Claude Desktop and enable MCP (for file access)
5. Paste your one-line prompt and watch your project come alive

💰 Hidden Value in Your Old Code

That app you never finished? It might be more valuable than you think:

• Passive income: A completed app could generate revenue
• Portfolio showcase: Finished projects boost your credibility
• Productivity tool: Something you build for yourself could save hours
• Mental freedom: Completing projects releases cognitive burden
• Learning catalyst: Watch how AI approaches problems in your codebase

Keep in mind, any one can probably clone your project in a few minutes, but they can never take away the great feeling of completing something!

🎉 The Dopamine of Completion

When you finally finish a project—even with AI help—your brain rewards you with dopamine. This builds a positive feedback loop:

✅ Completion → 🧠 Dopamine reward → 💪 Motivation → 🚀 Next project

Over time, this rewires your brain to value finishing over endless starting. Imagine the satisfaction of converting your project graveyard into a showcase of completed work!

🧠 Breaking the Endless Starting Cycle

Why do we keep starting new projects without finishing old ones? Psychology offers some insights:

🧪 The Novelty Bias

Our brains naturally prefer novelty—it's how we've evolved to learn and discover. New ideas trigger dopamine without requiring the hard work of completion. Breaking this cycle requires conscious effort.

🏆 Small Wins Strategy

Studies show that breaking projects into tiny milestones—each with its own "completion moment"—can retrain your brain's reward system. Instead of seeking the big dopamine hit from a new project, you get micro-doses of satisfaction from small completions.

Try this approach:

1. Identify the smallest possible feature you could implement (even just a UI button that does nothing)
2. Implement it with the AI's help
3. Celebrate the mini-completion (seriously—do a little dance or reward yourself)
4. Repeat with slightly larger features

This gradually retrains your brain to associate project progress with reward, rather than just starting new things.

📱 The Public Commitment Hack

Tell someone (or tweet) that you're reviving an old project with AI. Social accountability dramatically increases follow-through rates. When we publicly commit to finishing something, we're much more likely to do it.

🚫 The Project Limit Rule

Set a hard cap on unfinished projects—say, three. Anytime you want to start something new, you must first finish an existing project with AI help. This creates a healthy pressure to complete rather than endlessly begin.

🔒 Important: Back Up Your Work!

Always keep your project under version control. AI can move fast and make mistakes:

• Create a Git repo (GitHub private repos are free)
• Commit before turning the AI loose
• Use branches for major changes
• Push regularly to remote

This lets you revert any changes that don't work out.

🚧 Common AI Coding Challenges (And Solutions)

Even with advanced AI agents, you'll encounter some challenges. Here's how to overcome them:

❓ The AI Misunderstands Your Project

Solution: Go back to your init.md file and make it more explicit. Add a "Common Misunderstandings" section that clearly states what the project is NOT, along with what it IS.

🔄 The AI Gets Stuck in a Loop

Solution: Break the current task into smaller, more specific subtasks. Sometimes a vague instruction like "add user authentication" is too broad. Try "Create a login form with email and password fields" instead.

🔀 The AI Modifies Too Many Files at Once

Solution: Explicitly ask it to tackle one file or component at a time. For example: "First, let's just focus on the login UI component before touching the API endpoints."

🐞 Introducing Bugs or Breaking Changes

Solution: Ask the AI to write tests before implementing features. Claude is particularly good at TDD (Test-Driven Development) and will write tests that validate its own code.

📦 Dependency Hell

Solution: In your tech-requirements.md, specify exact version numbers for critical dependencies. For example: "React 18.2.0, not newer" to prevent compatibility issues.

🥴 The AI Doesn't "Get" Your Code Style

Solution: Add an "examples" section to tech-requirements.md showing your preferred patterns for common tasks (e.g., how you structure components, name variables, etc.)

📈 Token Usage Grows Too Large

Solution: Follow the single responsibility principle for components. Keep files small and focused, and consider using agent-finish.md for instructions that should be followed just before completion. This prevents token bloat and keeps sessions efficient..

💡 Success Story: From Abandonware to Revenue

Well!! Follow my upcoming posts and we will see : ) At least I finished this article, didn't I?

🚀 The Future of Your Abandonware

The fate of your abandoned projects is no longer a slow death on your hard drive. With AI as your coding partner, these half-finished ideas can transform into completed software.

Better yet, as models improve, the agent-tasks.md file you create today becomes a valuable specification for future AI. Your work now becomes investment for later upgrades.

📈 Beyond 2025: What's Coming Next

The trajectory of AI coding agents is clear—they're getting more capable every quarter. Here's what we'll likely see in the next wave:

• Local AI Agents: Full-power coding assistants running on your local GPU, eliminating API costs and privacy concerns
• Multi-Agent Teams: Specialized AI agents collaborating on your project (one for UI, one for backend, one for testing)
• Full-Project Generation: Giving the AI a sketch and getting back a complete, functional app
• Continual Learning: AI that remembers your preferences across multiple projects without explicit instructions
• Unlimited context: AI that with no effort, keep track of all context you can think of, maybe add all your old projects in to one big monolithic super AI Agent session! Todays agent struggle a bit on this topic...

But you don't need to wait for these advancements. Today's tools are already powerful enough to turn your abandonware into finished products.

So dust off that project folder, create your guide files, and let AI help you finish what you started. Your future self—and your portfolio—will thank you.

The revolution isn't coming. It's here. the window is definitely open! Your abandonware is just waiting to be turned into gold.

🔥 Coming Soon!

I just discovered something game-changing that might revolutionize abandonware resurrection even further. Follow me to learn about this exciting new tool in my next post—I'll be testing it thoroughly and sharing my results!

By Jesper Wilfing - Coder and Startup procrastinator since 1995, running Conzeon AB - "Innovation Executed, Not Just Imagined" since 2012.

The file templates for a quick start

init.md

# Project Introduction

[Provide a brief description of your project, its purpose, current status, and background.]

## Agent Instructions

- You are referred to as the **agent** and I am the **human**.
- Read and follow the instructions in **agent-tasks.md** at all times.
- For any temporary issues or bugs, refer to **agent-bugs.md**.
- Keep **pitch.md** updated automatically as the project evolves.
- Maintain concise file sizes and component isolation for token efficiency.
- Always refer to **tech-requirements.md** for tech stack and style guidelines.
- Use "npm run dev" as the primary testing command unless otherwise specified.

## Initial Task

Finish the partially completed [app name] application. Ask questions whenever clarity is needed.

Optional reading - Free to use templates for your project revival!

Folder Structure

project-folder/
├── 📄 pitch.md                # Elevator pitch (automatically updated by the AI)
├── 📄 init.md                 # Project introduction, context, and agent/human instructions
├── 📄 agent-tasks.md          # AI agent's task list (detailed plan and progress, keeper of ALL requirements, preserving app specifications)
├── 📄 human-tasks.md          # Manual tasks for human intervention (e.g., API keys, DB paths)
├── 📄 agent-bugs.md           # List of temporary issues and bugs (removed when fixed)
└── 📄 tech-requirements.md    # Technical preferences and constraints (tech stack, coding styles, etc.)

File Templates

init.md

# Project Introduction

[Provide a brief description of your project, its purpose, current status, and background.]

## Agent & Human Instructions

- You are referred to as the **agent** and I am the **human**.
- **Agent instructions:**
  - Read and follow the instructions in **agent-tasks.md** at all times.
  - Keep **pitch.md** updated automatically as the project evolves.
  - Maintain small file sizes and component isolation for optimal token efficiency.
  - Always refer to **tech-requirements.md** for tech stack and coding style guidelines.
- **Human instructions:**
  - Use **human-tasks.md** to provide additional context, creative directions, or clarifications.
  - Ask questions when needed to ensure the agent understands the vision.

## Initial Task

Finish the partially completed [app name] application. Ask questions whenever clarity is needed.

pitch.md

# [App Name]

[App Name] is a [one-sentence description of your app's purpose, unique selling point, and importance].

[This document will be automatically updated by the AI agent to reflect the current state, key features, and vision of the project as it evolves.]

agent-tasks.md

# Agent Task List

- **Continuously update this file** with any new features or tasks identified during our sessions.
- **All task items must be retained as documentation/specification**; when a task is completed, mark it as completed (with a timestamp if desired) without removal.
- When instructions are given in the chat, add them to this document so that it always reflects the current requirements and features.

## Tasks

🔲 Finish initial review and understanding of the project based on init.md
🔲 Restructure project if needed to achieve best practices and clean code

## On-hold Tasks

⏸️ Upgrade react version

## Completed Tasks

✅ Read agent-tasks.md

## Notes

- New tasks or modifications shall be added here based on ongoing conversation and evolving project goals.

human-tasks.md (Optional)

# Human Tasks & Interventions

This file contains tasks that require your manual intervention—actions that the AI cannot automate. The AI agent will populate this file with instructions when it needs you to:

- Provide necessary API keys or tokens.
- Configure specific environment variables (e.g., database paths, third-party integrations).
- Perform actions that require human confirmation or decision-making.

## Pending Human Tasks

🔲 *[Example]*: Set up API access by obtaining the API key from [Service Name] and add it to your environment configuration.
🔲 *[Example]*: Configure the database path for your application as per the project requirements.

## Instructions

- Review the tasks listed here carefully.
- Complete these tasks as soon as possible, then mark them as done by checking the box and optionally adding a timestamp.
- If a clarification is needed, respond or update this file with your questions.

agent-bugs.md

# Temporary Issues and Bugs

**This document lists temporary issues and bugs.**  
When an issue is resolved, it should be removed to maintain a 100% bug-free solution.

Example:
🔲 Bug in the login form causing an incorrect redirect (temporary — remove once fixed)
🔲 Mobile menu responsiveness issue on Safari (temporary — remove once resolved)

[Note: Items here are meant for immediate tracking and will be removed after resolution to save token space.]

tech-requirements.md

# Tech Stack and Preferences

## General
- Package Manager: npm (not yarn)
- Default test/run command: `npm run dev`

## Frameworks and Libraries
- React 18+
- Next.js for routing
- Tailwind CSS for styling
- Firebase (for backend/authentication)

## Code Standards
- Use functional components with React hooks
- Strictly follow the provided ESLint configuration in the repository
- JavaScript: ES2021 (ES12) or newer
- TypeScript preferred (with strict type checking enabled)
- Avoid unnecessary comments except when absolutely essential for clarity

## Testing
- Prefer Playwright for end-to-end tests

## Version Control
- Use a private GitHub repository for backups
- Commit frequently and use branches for feature implementations

## Performance and Responsiveness
- Ensure a fully responsive, mobile-first design
- Optimize for speed and accessibility

## Additional Guidelines
- Maintain small, isolated components (adhering to the single responsibility principle) to optimize token usage
- Avoid large, combined files exceeding roughly 150 lines
- Follow Clean Code principles (Robert Martin)
- Use modern patterns and practices
- Implement the newest stable versions of libraries
- Do not use commonly occupied ports (3000, 8080)