How AI Has Transformed My Coding Experience
Embracing AI for Accelerated Development
Over the past years, I’ve leveraged AI-assisted development (with ChatGPT) to supercharge my software engineering process, especially in building complex systems like DUFT Server. AI has helped me write and refine code at an unprecedented speed, allowing me to develop the core of DUFT Server in under three weeks, a timeline that would have otherwise stretched much longer.
Beyond just speed, AI has enabled me to push the limits of Django/Python as well as React, implementing advanced constructs and solutions that would have taken me weeks to research and perfect manually.
Innovative AI-Assisted Solutions
Bringing Guard Statements to Python
Inspired by Swift’s guard statement, I wanted a similar mechanism in Python. While Python lacks native support, with AI’s assistance, I designed a custom decorator that provides equivalent functionality:
from functools import wraps
from typing import Any, Callable
def guard(condition_func, exception_func=lambda: ValueError("Guard condition failed"), raiseExceptionOnException=False):
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
if raiseExceptionOnException:
try:
if not condition_func(*args, **kwargs):
raise exception_func()
except Exception:
raise exception_func()
else:
if not condition_func(*args, **kwargs):
raise exception_func()
return func(*args, **kwargs)
return wrapper
return decorator
@guard(lambda value: value is not None, lambda: ValueError("Value is None"))
def example_function(value):
print(f"Value is {value}")
This decorator significantly improves code readability and safety, ensuring that functions fail fast when invalid conditions are met.
Building the Data Task Engine for DUFT
DUFT’s Data Task Engine required complex engineering logic to handle ETL pipelines efficiently. By using AI, I was able to design robust unit tests quickly:
def test_run_data_task_notebook_with_task():
while dte_state["is_running"]:
time.sleep(1)
with unittest.mock.patch(
"services.data_task_engine.service.data_task_engine._run_process_in_the_background"
):
DataTaskPreparer(
"SAMPLE-NOTEBOOK", {"from_unit_test": "true"}
).load_task().prepare_script().set_parameters().execute()
With AI-assisted coding, I was able to rapidly iterate, debug, and optimise the system.
The Power of AI in Parsing Complex Data Structures
One of the most exciting challenges AI helped me tackle was building the 3DL Parser for DUFT. This parser dynamically converts XML-based configurations into fully functional React components, eliminating the need for hardcoded layouts. Here’s an example:
XML Input
<Dashboard>
<Row>
<Tile title="New Cases">
<QueryData>SELECT COUNT(*) FROM cases WHERE year = '$year'</QueryData>
</Tile>
</Row>
</Dashboard>
React Component Output
<Dashboard>
<Row>
<DuftTile title="New Cases">
<QueryData useQuery={useQueryData} client={client}>
SELECT COUNT(*) FROM cases WHERE year = '$year'
</QueryData>
</DuftTile>
</Row>
</Dashboard>
With AI, I was able to generate the JSXParser-driven solution that dynamically renders these components, making DUFT incredibly flexible and adaptable to new requirements without modifying the frontend code.
AI-Enhanced Development: A Competitive Advantage
Using AI has really changed my software development workflow, allowing me to:
- Write complex, scalable systems faster
- Push programming languages beyond their perceived limits
- Leverage automation to reduce debugging and testing time
- Solve problems creatively with AI-assisted logic
AI is no longer just a tool–it’s an essential collaborator when I code. My experience with AI-powered development proves that when used effectively, AI doesn’t actually replace developers; rather, it’s like coding on steroids.