How to Actually Finish That Side Project Collecting Dust on GitHub

We need to talk about that repo. You know the one. It's sitting in your GitHub profile with its last commit from 2022, a README that says "TODO: add documentation," and half-implemented features that made total sense at 1 AM on a Saturday.

I have about a dozen of these. A CLI tool for managing dotfiles. A budget tracker with a React frontend I abandoned mid-migration from class components to hooks. A Rust rewrite of a Python script that I got 30% through before life happened.

The problem isn't that these projects were bad ideas. The problem is that picking them back up feels worse than starting from scratch.

Why Abandoned Projects Are So Hard to Resume

The core issue is context loss. When you step away from a codebase for months (or years), you lose the mental model you had while building it. You forget why you structured things a certain way, what that half-written function was supposed to do, and which approach you'd already tried and rejected.

Here's what typically happens when you try to resume:

1. You open the project and stare at it
2. You try to run it — it doesn't build because dependencies are outdated
3. You fix the build, then realize you don't remember the architecture
4. You read your own code like it was written by a stranger
5. You close the laptop and open Netflix

The friction isn't just technical. It's cognitive. The activation energy required to rebuild that mental model is enormous, and for a side project with zero deadline pressure, it's rarely worth the effort.

Until now, anyway.

How AI Coding Assistants Change the Equation

AI-powered coding tools — the ones that can read and reason about your entire codebase — fundamentally change the economics of resuming abandoned work. They can rebuild the context you lost, almost instantly.

Here's my actual workflow for reviving a dead project. I've used this on three repos in the last couple months, and two of them are now actually shipped.

Step 1: Let the AI Read Everything First

Before writing a single line of code, point your AI assistant at the entire project and ask it to explain the codebase back to you.

# If you're using a terminal-based AI coding tool, start in the project root
cd ~/projects/abandoned-budget-tracker

# Ask it to map out the project structure and explain the architecture
# Most AI coding assistants can read your files and give you a summary
# Example prompt: "Explain the architecture of this project,
# what's implemented, and what appears unfinished."

This is the step that changes everything. Instead of spending an hour reading your own code trying to figure out what Past You was thinking, you get a structured summary in about 30 seconds. The AI will typically identify:

• The overall architecture and tech stack
• Which features are complete vs. partially implemented
• Dead code and abandoned experiments
• Dependency issues and outdated packages

Step 2: Fix the Build Before Anything Else

Stale projects almost never build cleanly. Dependencies have moved on, APIs have changed, and that Node.js version you were using has been EOL'd for a year.

// package.json from 2022 — spot the problems
{
  "dependencies": {
    "react": "^17.0.2",
    "react-scripts": "4.0.3",
    "node-sass": "^6.0.0",  // notoriously breaks between Node versions
    "moment": "^2.29.1"      // still works, but you know...
  }
}

Ask the AI assistant to help you update dependencies and fix breaking changes. This is where these tools genuinely shine — they can read the migration guides mentally and apply the changes across your codebase in one pass. What used to be a full afternoon of googling "react-scripts 4 to 5 migration" becomes a 15-minute conversation.

# Get the AI to identify what's broken and propose fixes
# Example prompt: "Update all dependencies to current versions.
# Fix any breaking changes from the upgrades.
# Prioritize getting the dev server to start successfully."

# Then verify it actually works
npm install
npm run dev

Step 3: Triage Ruthlessly

This is where most people go wrong. They try to pick up exactly where they left off, implementing that half-built feature with the same approach from two years ago.

Don't do that.

Instead, ask the AI to help you audit the codebase and make hard cuts:

• Delete dead code aggressively. That experimental branch you merged halfway? Gone. The utility functions you wrote but never called? Gone. Less code means less to understand.
• Simplify the scope. Past You had grand ambitions. Present You just wants something that works. Cut features that aren't essential to the core idea.
• Rewrite the messy parts. If a module is confusing even with AI-assisted explanation, it's probably over-engineered. Have the AI help you rewrite it more simply.

# Before: what I wrote at 1 AM in 2022
class TransactionProcessor:
    def __init__(self, strategy=None, validators=None, 
                 hooks=None, middleware=None):
        self._strategy = strategy or DefaultStrategy()
        self._validators = validators or []
        self._hooks = hooks or HookChain()
        self._middleware = middleware or MiddlewarePipeline()
        # ... 200 more lines of enterprise architecture
        # for an app that processes maybe 50 transactions

# After: what the project actually needs
class TransactionProcessor:
    def __init__(self, db):
        self.db = db

    def add(self, amount: float, category: str, note: str = ""):
        """Add a transaction. That's it. That's the feature."""
        self.db.insert({
            "amount": amount,
            "category": category,
            "note": note,
            "date": datetime.now()
        })

Step 4: Use the AI for the Boring Parts

Every project has grunt work that killed your momentum the first time around. Writing tests. Setting up CI. Building that settings page. Handling edge cases in form validation.

This is the highest-leverage use of AI coding assistants for revival projects. The creative, architectural decisions? You should still make those. But the tedious implementation work that made you abandon the project in the first place? Delegate it.

• "Write tests for the transaction module"
• "Add proper error handling to the API routes"
• "Create a Dockerfile for this project"
• "Set up a GitHub Actions workflow for running tests on PR"

These tasks are well-defined, repetitive, and exactly the kind of thing that AI assistants handle reliably.

The Mindset Shift That Makes This Work

Here's the thing I had to get over: it felt like cheating. If I use AI to help finish a side project, is it really my project anymore?

Yes. Obviously yes.

The idea was yours. The architecture decisions are yours. The product vision is yours. The AI is doing what Stack Overflow and copy-pasting from documentation always did — it's just faster and more context-aware.

Think of it this way: you wouldn't feel guilty about using a power drill instead of a manual screwdriver to build a bookshelf. The bookshelf is still yours. You still designed it, measured the wood, and decided where it goes. The tool just made the assembly less painful.

Prevention: How to Make Future Projects Easier to Resume

Now that you know the pain of reviving a stale project, here are a few habits that make it dramatically easier:

• Write a DEVLOG.md file. Before you close your laptop, spend 60 seconds jotting down what you were working on, what's next, and any decisions you made. Future You will be incredibly grateful.
• Keep a working Makefile or justfile. One command to build, one to test, one to run. If resuming requires remembering three CLI flags and an environment variable, you won't resume.
• Pin your dependencies. Use lock files. Pin your runtime version in .node-version or .python-version. Don't let the ecosystem move out from under you.
• Commit small, commit often, write real messages. "fix stuff" tells you nothing in six months. "switch to SQLite because PostgreSQL was overkill for local-only storage" tells you everything.

Just Ship the Thing

That project you abandoned isn't dead. It's dormant. And the tools we have now make reviving it genuinely practical in a way it wasn't even two years ago.

Pick one repo this weekend. Point an AI assistant at it. Get it building. Cut the scope. Ship something.

The best side project isn't the most ambitious one — it's the one that actually exists.