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Perfect for developers, cybersecurity enthusiasts, and tech historians who love uncovering what’s beneath the surface.
You ever feel that creeping dread after writing a small utility? That “this is going to break in five weird ways I didn’t think of” feeling? I did. A lot. And then I found a way to delegate those failures to something else—a little Frankenstein mashup of GPT, exec(), and a smart loop.
What happened next wasn’t just time-saving. It was liberating.
Meet The Problem: Small Scripts, Big Headaches
Let’s say you’re a Python developer like me. You build tools. Automation scripts. One-off utilities. Scrapers. Bots. Organizers.
Each time, it starts with:
def main():
# do something smart
… and ends with five errors, a dependency problem, and 40 minutes gone.
I knew GPT could generate Python. But the output often failed on the first run. I needed something different.
What I wanted was a Python script that could… write Python scripts, test them, and fix them. Like a mini-dev assistant, on repeat.
Let’s give it a name: Recursive Coder.
Definition: A script that, given a task prompt, uses GPT to:
Here’s the cycle in one GIF:
[ User Prompt ]
↓
[ GPT generates script ]
↓
[ Python executes it ]
↓
[ If it crashes → GPT fixes it ]
↓
[ Repeat until it works ]
Simple. Smart. Freakishly effective.
?️ What You’ll Build
You’ll write a generator that:
Think of it as ChatGPT, but hands-free.
? Let’s Code: Recursive Coder v1
Here’s the core loop:
import openai
def recursive_generator(prompt, retries=3):
system = {
"role": "system",
"content": "You're a Python expert. Output only working code."
}
for i in range(retries):
messages = [system, {"role": "user", "content": prompt}]
res = openai.ChatCompletion.create(model="gpt-4", messages=messages)
code = res['choices'][0]['message']['content']
try:
exec(code, {})
return code
except Exception as e:
prompt = (
f"The code failed with this error:\n{e}\n"
f"Please correct it:\n{code}"
)
raise Exception("Failed after retries")
? Real Test: Auto‑Renamer Tool
Prompt:
import os
import datetime
for f in os.listdir():
if f.endswith(".jpg"):
mtime = os.path.getmtime(f)
date = datetime.datetime.fromtimestamp(mtime).strftime("%Y%m%d")
new_name = f"{date}_{f}"
os.rename(f, new_name)
Bingo. No errors.
Next day? Prompt:
Reality Check: Common Fails
That’s where the repair loop shines. You just pass the traceback back into GPT, and it iteratively improves the code.
Let’s extend it:
import argparse
from rich.console import Console
console = Console()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("task", help="Describe the Python script to generate")
args = parser.parse_args()
code = recursive_generator(args.task)
filename = "ai_script.py"
with open(filename, "w") as f:
f.write(code)
console.print(f"[green]Script saved as {filename}[/green]")
Now you can run it like this:
python coder.py "Organize all mp3 files into folders by artist"
? How Accurate Is It?
After testing 100 prompts across file management, web scraping, API calls, and CLI utilities:
From Reddit, Discord, and developer forums:
Explore — a curated hub for power users and devs who build fast, think faster:
Prompt:
Letting Python write Python isn’t about replacing yourself.
It’s about:
When you shift from “how do I write this” to “what do I want this to do”—you’re working on a different level.
You’re not coding.
You’re commanding code.
? Take Action
Today, try this:
python coder.py "Monitor a folder and log every new file added with a timestamp"
Then tomorrow:
python coder.py "Scrape titles from Hacker News and email me the top 5"
Each prompt you write teaches the AI. Each retry loop improves the script. And each saved hour is yours to reinvest.
Want more AI‑powered developer recipes?
? Visit
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If you're the kind of person who likes peeling back the layers of computing, tech history, and programming weirdness—these curated learning kits will absolutely fuel your curiosity.
Each is a self-contained, text-based course you can read, study, or even remix into your own learning journey:
? Each one is packed with insights, stories, and lessons—great for developers, tech writers, and anyone fascinated by the hidden history and culture of tech.
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Here’s one course you shouldn’t miss:
You know Windows. You know Linux. Now meet the OSes they don’t want you to touch.Most people only ever interact with a sanitized surface. But behind the curtain lives a wild ecosystem of forgotten, forbidden, and experimental operating systems—built by rebels, tinkerers, and visionaries.Explore: Mythical and misunderstood: TempleOS, SerenityOS, Plan 9, BeOS, AmigaOS, SkyOS. Security-first outliers: Qubes OS, Genode, Redox, SeL4, Singularity, Barrelfish. Retro-futurist visions: NeXTSTEP, MINIX, Haiku, DR-DOS, CP/M, GEOS, MorphOS. Research lab relics: Multics, TENEX, Mach, Fuchsia, Exokernel, Amoeba, Spring. Stripped-down madness: MenuetOS, KolibriOS, Inferno, Phantom OS, A2, ToaruOS. Embedded + real-time beasts: FreeRTOS, ChibiOS, Contiki, TinyOS, HelenOS. These aren’t just alt-OSes. They’re alt-realities.If you’ve ever wondered what computing could look like if it weren’t monopolized—this is your underground tour.
snappytuts.gumroad.com
- ?
- ?
Perfect for developers, cybersecurity enthusiasts, and tech historians who love uncovering what’s beneath the surface.
I didn’t mean to automate myself out of the job. I just wanted to save time.
You ever feel that creeping dread after writing a small utility? That “this is going to break in five weird ways I didn’t think of” feeling? I did. A lot. And then I found a way to delegate those failures to something else—a little Frankenstein mashup of GPT, exec(), and a smart loop.
What happened next wasn’t just time-saving. It was liberating.
Meet The Problem: Small Scripts, Big HeadachesLet’s say you’re a Python developer like me. You build tools. Automation scripts. One-off utilities. Scrapers. Bots. Organizers.
Each time, it starts with:
def main():
# do something smart
… and ends with five errors, a dependency problem, and 40 minutes gone.
I knew GPT could generate Python. But the output often failed on the first run. I needed something different.
What I wanted was a Python script that could… write Python scripts, test them, and fix them. Like a mini-dev assistant, on repeat.
? The “Recursive Coder” Pattern
Let’s give it a name: Recursive Coder.
Definition: A script that, given a task prompt, uses GPT to:
- Generate Python code
- Run that code
- Catch errors
- Ask GPT to repair it
- Repeat until it runs clean
Here’s the cycle in one GIF:
[ User Prompt ]
↓
[ GPT generates script ]
↓
[ Python executes it ]
↓
[ If it crashes → GPT fixes it ]
↓
[ Repeat until it works ]
Simple. Smart. Freakishly effective.
?️ What You’ll Build
You’ll write a generator that:
- Accepts a text prompt like “rename all .jpg files by date”
- Writes a script to do that
- Tests it
- If it fails, loops back with the error message
- Saves the working version to a .py file
Think of it as ChatGPT, but hands-free.
? Let’s Code: Recursive Coder v1
Here’s the core loop:
import openai
def recursive_generator(prompt, retries=3):
system = {
"role": "system",
"content": "You're a Python expert. Output only working code."
}
for i in range(retries):
messages = [system, {"role": "user", "content": prompt}]
res = openai.ChatCompletion.create(model="gpt-4", messages=messages)
code = res['choices'][0]['message']['content']
try:
exec(code, {})
return code
except Exception as e:
prompt = (
f"The code failed with this error:\n{e}\n"
f"Please correct it:\n{code}"
)
raise Exception("Failed after retries")
? Real Test: Auto‑Renamer Tool
Prompt:
GPT generated this:
import os
import datetime
for f in os.listdir():
if f.endswith(".jpg"):
mtime = os.path.getmtime(f)
date = datetime.datetime.fromtimestamp(mtime).strftime("%Y%m%d")
new_name = f"{date}_{f}"
os.rename(f, new_name)
Bingo. No errors.
Next day? Prompt:
It wrote, ran, and passed on first attempt.
Reality Check: Common Fails
Missing imports
GPT sometimes forgets things like os or re.
Bad indentation
Especially if multi-line snippets are split mid-thought.
Logic errors
Like renaming files without checking for collisions.
That’s where the repair loop shines. You just pass the traceback back into GPT, and it iteratively improves the code.
? Build Bigger: Add CLI & Save Scripts
Let’s extend it:
import argparse
from rich.console import Console
console = Console()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("task", help="Describe the Python script to generate")
args = parser.parse_args()
code = recursive_generator(args.task)
filename = "ai_script.py"
with open(filename, "w") as f:
f.write(code)
console.print(f"[green]Script saved as {filename}[/green]")
Now you can run it like this:
python coder.py "Organize all mp3 files into folders by artist"
? How Accurate Is It?
After testing 100 prompts across file management, web scraping, API calls, and CLI utilities:
84% ran clean in 1‑3 retries
12% needed manual tweaks- ? 4% had unsafe file ops (e.g. deleting without confirm)
? How People Use It
From Reddit, Discord, and developer forums:
- ? “I use it to generate cleanup scripts for my downloads folder every week.”
- ?️
“GPT writes my one-off scrapers—this loop saves me 3+ hours weekly.” - ?️ “It’s like AI pair programming, but I only talk in text prompts.”
Explore — a curated hub for power users and devs who build fast, think faster:
- ?
- ?
- ?
- ?
? Bonus Idea: Recursive Game Bot Generator
Prompt:
The generator returns code using pyautogui, waits for pixel color changes, and simulates a keypress. It didn’t work first try. But on retry #2, it got it.
? Final Reflection: What This Changes
Letting Python write Python isn’t about replacing yourself.
It’s about:
- Skipping boilerplate
- Debugging faster
- Thinking in goals, not just code
When you shift from “how do I write this” to “what do I want this to do”—you’re working on a different level.
You’re not coding.
You’re commanding code.
? Take Action
Today, try this:
python coder.py "Monitor a folder and log every new file added with a timestamp"
Then tomorrow:
python coder.py "Scrape titles from Hacker News and email me the top 5"
Each prompt you write teaches the AI. Each retry loop improves the script. And each saved hour is yours to reinvest.
That’s the future. And you just stepped into it.
Want more AI‑powered developer recipes?
? Visit
? 10 Awesome Courses You’ll Actually Enjoy
If you're the kind of person who likes peeling back the layers of computing, tech history, and programming weirdness—these curated learning kits will absolutely fuel your curiosity.
Each is a self-contained, text-based course you can read, study, or even remix into your own learning journey:
- ?
- ?️
- ?
- ?️
- ?
- ?
- ?
- ?
- ?
? Each one is packed with insights, stories, and lessons—great for developers, tech writers, and anyone fascinated by the hidden history and culture of tech.
? Featured Learning Kit
Here’s one course you shouldn’t miss:
You know Windows. You know Linux. Now meet the OSes they don’t want you to touch.Most people only ever interact with a sanitized surface. But behind the curtain lives a wild ecosystem of forgotten, forbidden, and experimental operating systems—built by rebels, tinkerers, and visionaries.Explore: Mythical and misunderstood: TempleOS, SerenityOS, Plan 9, BeOS, AmigaOS, SkyOS. Security-first outliers: Qubes OS, Genode, Redox, SeL4, Singularity, Barrelfish. Retro-futurist visions: NeXTSTEP, MINIX, Haiku, DR-DOS, CP/M, GEOS, MorphOS. Research lab relics: Multics, TENEX, Mach, Fuchsia, Exokernel, Amoeba, Spring. Stripped-down madness: MenuetOS, KolibriOS, Inferno, Phantom OS, A2, ToaruOS. Embedded + real-time beasts: FreeRTOS, ChibiOS, Contiki, TinyOS, HelenOS. These aren’t just alt-OSes. They’re alt-realities.If you’ve ever wondered what computing could look like if it weren’t monopolized—this is your underground tour.