Introducing the github-codereview dataset: A compilation of 200k+ human-written code reviews from top OSS projects (React, Tensorflow, VSCode...).
I finetuned a Qwen2.5-Coder-32B-Instruct model with this dataset and saw significant improvements in generating better code fixes and review comments (4x improved BLEU-4, ROUGE-L, SBERT scores compared to base model).
Introducing the WebUI dataset: a compilation of screenshot to code pairs of modern websites detailing the styling, framework used, and box bounds for all viewports (Desktop, mobile, tablet).
This dataset showed signs of improved performance in web design LLM benchmarks for a finetuned QWEN 2.5 VL-7B!
Introducing the github-top-code dataset: A curated dataset of 1.3M+ source code files from GitHub's top ranked developers.
I collected the best source code files from Github's highest trending developers of all time, and compiled a dataset to train LLMs to write well-structured, production-grade code.
Introducing the LeetCode Assembly Dataset: a dataset of 400+ LeetCode problem solutions in assembly across x86-64, ARM64, MIPS64, and RISC-V using GCC & Clang at -O0/-O1/-O2/-O3 optimizations.
This dataset is perfect for teaching LLMs complex compiler behavior!
Introducing the github-top-developers dataset: A comprehensive dataset of the top 8000 developers on GitHub (2020-2025). This dataset captures the evolution of GitHub's trending developers repositories over time and the projects they work on.
Introducing the github-top-projects dataset: A comprehensive dataset of 423,098 GitHub trending repository entries spanning 12+ years (August 2013 - November 2025).
This dataset captures the evolution of GitHub's trending repositories over time, providing insights into software development trends across programming languages and domains, popular open-source projects and their trending patterns, and community interests and shifts in developer focus over 12 years.
Introducing the twitter-trending-hashtags dataset, a compilation of 12,000+ unique trending hashtags on Twitter / X from 2020 to 2025. This dataset captures viral and cultural moments on Twitter / X and is perfect for researchers studying viral content patterns on social media.
Introducing the tiktok-trending-hashtags dataset: a compilation of 1,830 unique trending hashtags on TikTok from 2022 to 2025. This dataset captures viral one-time and seasonal viral moments on TikTok and is perfect for researchers, marketers, and content creators studying viral content patterns on social media.
Introducing the india-trending-words dataset: a compilation of 900 trending Google searches from 2006-2024 based on https://trends.withgoogle.com. This dataset captures search trends in 80 categories, and is perfect for analyzing cultural shifts and predicting future trends in India.
Introducing the japanese-trending-words dataset: a dataset consisting 593 words from Japan’s annual trending word rankings (流行語大賞) from 2006-2025. This dataset provides the top 30 words from each year and its meaning in Japanese and english. This resource is awesome for NLP tasks understanding recent Japanese culture and history.
Introducing the google-trending-words dataset: a compilation of 2784 trending Google searches from 2001-2024 based on https://trends.withgoogle.com. This dataset captures search trends in 93 categories, and is perfect for analyzing cultural shifts, predicting future trends, and understanding how global events shape online behavior.
Introducing the Japanese Character Difficulty Dataset: a collection of 3,003 Japanese characters (Kanji) labeled with official educational difficulty grades. It includes elementary (grades 1–6), secondary (grade 8), and advanced (grade 9) characters, making it useful for language learning, text difficulty analysis, and educational tool development 🎉
CAG preloads document content into an LLM’s context as a precomputed key-value (KV) cache. This caching eliminates the need for real-time retrieval during inference, reducing token usage by up to 76% while maintaining answer quality.
CAG is particularly effective for constrained knowledge bases like internal documentation, FAQs, and customer support systems, where all relevant information can fit within the model's extended context window.
