The Problem: When “Working Code” Isn’t Good Enough
Let’s be honest—we’ve all had that moment.
You write a clever Python function. It works. It’s elegant. You’re proud. Then you run it on real data… and you wait. And wait. And maybe make lunch while the progress bar crawls across your screen.
That was me last month. I had a painfully slow script. I’d profiled it, found the bottleneck, and still had no clear way to make it faster. It was a mess of nested loops and awkward logic.
Then the thought hit me:
“Why can’t an AI help me optimize this?”
We use AI to generate art, write poetry, and compose emails. Why not have it help refactor clunky Python code into faster versions?
The Big Idea: Not Just Another Linter
There are tons of tools out there that check your code for bugs or styling issues—linters, static analyzers, formatters. But these are like grammar checkers.
I wanted something else.
I wanted a creative partner—a tool that could read my logic, understand it, and suggest a better, faster alternative. Not just “you missed a semicolon,” but:
“Have you considered using a generator expression here instead of building a massive list?”
This wasn’t about finding bugs.
This was about unlocking performance.
Understanding the Language of Code with ASTs
To teach AI how to read and reason about code, I needed more than plain Python text.
Enter ASTs—Abstract Syntax Trees.
Think of an AST as diagramming a sentence in English class. Python’s ast module breaks code into a structured tree of logic. It doesn’t care about indentation or formatting—it sees the bones.
Each function, loop, assignment, and condition becomes a node.
Once I had that structured representation, it became data I could feed into an AI model.
That was my “aha!” moment.
Building the Brain with PyTorch
With structured ASTs in hand, I framed the optimization problem as a translation task:
Translate a “slow AST” → into → a “fast AST”
I used PyTorch to build a sequence-to-sequence model—the same kind of architecture that powers tools like Google Translate.
But now, my dataset wasn’t made of sentences—it was code transformations:
- For loop → List comprehension
- Manual counter → Built-in
sum() - Redundant memory allocation → Lazy evaluation
To train it, I created a custom dataset. I went deep into GitHub PRs, cherry-picking commit diffs where people had clearly optimized a function or refactored for performance. Each one was a lesson.
Debugging the AI (and My Sanity)
Let me be clear: it did not work smoothly at first.
Some of the early results were comedy gold.
One “optimized” version returned a totally broken syntax—looked like a cat walked across the keyboard.
But that’s the reality of building with AI—it’s messy.
I learned that my training data was biased. Too many list comprehensions, not enough advanced transformations. The AI didn’t yet know when not to change something.
So I tweaked the model, diversified the dataset, and retrained.
The First Real Win: When It Actually Worked
After a few weeks, I went back to my original slow function. I converted it to an AST, passed it through the trained model, and held my breath.
It returned a suggestion:
- Replace a large intermediate list with a generator expression
- Slightly restructured the main loop
- Preserved all original logic
I applied the suggestion, tested the output (identical), and reran the script.
It was nearly 10x faster.
I actually laughed out loud.
That was the moment it clicked:
AI can help us write better-performing code—not by replacing us, but by offering smart nudges we wouldn’t have thought of.
Why This Matters: The Future of Developer Tools
This project showed me something bigger than just one optimized function.
We’re heading toward a future where:
- AI understands code not just syntactically, but semantically
- Developers use AI-enhanced workflows to go from “it works” to “it screams”
- Performance optimization becomes collaborative, not just trial and error
AI won’t replace software engineers.
But it will become a co-pilot—one that sees patterns we miss, spots bottlenecks we ignore, and speeds up tasks we dread.
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What’s Next?
I’m thinking of open-sourcing a lightweight version of this tool. It would:
- Accept raw Python code
- Parse it into an AST
- Suggest optimized versions via a trained PyTorch model
- Highlight the “before” and “after” in a UI
If that sounds interesting—or if you’ve had your own experience using AI to work on code—let me know in the comments or connect with me at InternBoot.
Let’s make our code faster, together.
