language:
- en
license: apache-2.0
task_categories:
- text-generation
- question-answering
tags:
- coding
- english
- kicaulah-ai
- natural-language
- coding
- programming
- python
- javascript
- typescript
- react
- fastapi
- docker
- sql
- debugging
- software-engineering
pretty_name: Kicaulah AI - Dataset Coding (Generation, Debugging & Architecture)
size_categories:
- 1K<n<10K
Kicaulah AI — Dataset Coding (Generation, Debugging & Architecture)
📖 Description
A production-grade Programming, Code Generation, Debugging, and Software Architecture dataset in natural English. Communicated through the voice of experienced senior software engineers—pragmatic, focused on root causes, and strictly free of generic AI boilerplate. Covers 10 essential software engineering domains: Python Debugging & Internal Gotchas (Mutable defaults, pandas, GIL), Frontend React & Next.js (Infinite loops, stale closures, Server Components), Backend FastAPI & Express (CORS, Connection Pools, JWT), Relational Database SQL (B-Tree Indexes, N+1 Queries, Deadlocks, Zero-Downtime Migrations), Git Version Control (Merge Conflicts, Rebase, Reflog), DevOps & Docker Containers (OOMKilled exit 137, Multi-stage builds, Non-root security), JavaScript & TypeScript Essentials (Coercion, Event Loop, Generics), Algorithms & Data Structures (Hash Tables, Two Pointers, Floyd's Cycle, Big O), RESTful API Design (Status codes, Idempotency, Cursor Pagination), and Application Security in Code (Bcrypt vs MD5, RCE Deserialization, Timing Attacks, NoSniff).
This dataset is strictly curated under the Anti-AI-Speak Standard:
- Built using authentic, everyday conversational language.
- Free from generic robotic clichés such as "As an AI language model..." or "That's a great question!".
- Prioritizes genuine empathy, real-world context, practical analogies, and natural variations in tone (casual, formal, emotional, concise, and in-depth).
🎯 Use Cases
- Fine-tuning conversational coding assistants and AI pair programmers in natural, idiomatic English.
- Training debugging models to identify root causes and deliver production-ready code fixes.
- Benchmarking code comprehension, system architecture evaluation, and code review capabilities.
📊 Dataset Structure
| Column | Type | Description | Example |
|---|---|---|---|
| instruction | string | User inquiry or realistic scenario | "My 2-year-old has had a 102F fever for 2 days, what should I do?" |
| response | string | Natural, human, solution-oriented response | "Take a deep breath first. A 102 fever on day 2 is scary..." |
| category | string | Specific domain subcategory | "coding" |
📈 Statistics
- Total Examples: 1,100
- Train (80%): 880
- Validation (10%): 110
- Test (10%): 110
🚀 How to Use
from datasets import load_dataset
dataset = load_dataset("Kicaulah/dataset-coding")
print(dataset["train"][0])
📑 Citation
@misc{kicaulah_coding,
title={Kicaulah AI - Dataset Coding (Generation, Debugging & Architecture)},
author={Kicaulah AI Team},
year={2025},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/datasets/Kicaulah/dataset-coding}}
}