Today's AI Research: Fastest-Growing Projects — June 26, 2026
Today's AI Research, we see a continued surge in repositories that support researchers across their entire project lifecycle, from initial literature review to publication. These tools are gaining traction due to their comprehensive approach and practical application in real-world research scenarios. Leading the pack is Light-skills by Light0305, which offers a suite of skills covering every stage of scientific research.
Light-skills provides 28 skills that facilitate the entire research process from literature review to submission, complete with nine verified knowledge bases. Its growth score of 26.13 and 273 stars reflect its strong appeal among researchers looking for an all-in-one solution to streamline their work. Another repository worth noting is Awesome-Vibe-Research by modelscope, which collects and curates resources for AI-assisted scientific research.
Awesome-Vibe-Research aims to build a comprehensive resource hub for agents, skills, workflows, tools, and best practices throughout the entire research lifecycle. With a growth score of 18.14 and 250 stars, this repository is growing quickly as it provides valuable resources for researchers utilizing AI across various stages of their projects.
Stunspot's guide to AI systems offers an operational doctrine designed for practical AI system design. The project has seen significant activity with 84 commits in the last month despite having fewer stars at 33. This indicates a focused and active community contributing to its development. Meanwhile, Deep-Research-Agent by CYC2002tommy is an autonomous pipeline for academic research that includes strict DOI verification and multi-agent retrieval systems.
This repository has earned 14.41 in growth score and 259 stars due to its robust features aimed at rigorous academic research processes. MaineCoon, developed by catnip-ai-tech, aims to create a real-time audio-visual social world model, with a technical report and project links available on the website.
With a growth score of 13.50 and 87 stars, this tool is gaining traction for its innovative approach to integrating multiple sensory inputs in AI research. Keyuchen21's agentic-engineering-handbook provides an extensive learning roadmap covering OpenAI, Claude, MCP, Harness, Evals, and production agent systems.
This repository has garnered 9.76 growth points and 116 stars due to its detailed documentation aimed at developers interested in building sophisticated AI agents for various applications. Mexregkan's claude-for-researchers is a practical guide and toolkit designed specifically for physicists and mathematicians using Claude Code, built from real-world research experience.
With a growth score of 9.10 and 37 stars, this project demonstrates the growing need for specialized tools in niche scientific communities. Benchflow-ai's awesome-evals compiles resources for building and evaluating AI agents, including papers, blogs, talks, tools, and benchmarks.
This repository has accumulated 4.27 growth points and 273 stars due to its curated content that serves as a reliable resource hub for researchers working on AI agent evaluation. InternLM's RNGBench is the official implementation of an evaluation framework for multimodal large language models in non-Markov games, with 39 stars.
Its growth score of 3.15 highlights the increasing interest in benchmarking and evaluating advanced AI systems that go beyond traditional observation frameworks. Lastly, llmsresearch's llm-flashcards offers hand-drawn flashcards to explain how LLMs work, providing 19 free samples from a larger deck of 180 cards.
This project has achieved a growth score of 2.15 and 59 stars due to its unique educational approach in making complex concepts accessible through visual aids. Each of these projects contributes uniquely to the evolving landscape of AI research, offering tools that range from comprehensive guides to specialized resources for specific scientific communities.
Light-skills provides 28 skills that facilitate the entire research process from literature review to submission, complete with nine verified knowledge bases. Its growth score of 26.13 and 273 stars reflect its strong appeal among researchers looking for an all-in-one solution to streamline their work. Another repository worth noting is Awesome-Vibe-Research by modelscope, which collects and curates resources for AI-assisted scientific research.
Awesome-Vibe-Research aims to build a comprehensive resource hub for agents, skills, workflows, tools, and best practices throughout the entire research lifecycle. With a growth score of 18.14 and 250 stars, this repository is growing quickly as it provides valuable resources for researchers utilizing AI across various stages of their projects.
Stunspot's guide to AI systems offers an operational doctrine designed for practical AI system design. The project has seen significant activity with 84 commits in the last month despite having fewer stars at 33. This indicates a focused and active community contributing to its development. Meanwhile, Deep-Research-Agent by CYC2002tommy is an autonomous pipeline for academic research that includes strict DOI verification and multi-agent retrieval systems.
This repository has earned 14.41 in growth score and 259 stars due to its robust features aimed at rigorous academic research processes. MaineCoon, developed by catnip-ai-tech, aims to create a real-time audio-visual social world model, with a technical report and project links available on the website.
With a growth score of 13.50 and 87 stars, this tool is gaining traction for its innovative approach to integrating multiple sensory inputs in AI research. Keyuchen21's agentic-engineering-handbook provides an extensive learning roadmap covering OpenAI, Claude, MCP, Harness, Evals, and production agent systems.
This repository has garnered 9.76 growth points and 116 stars due to its detailed documentation aimed at developers interested in building sophisticated AI agents for various applications. Mexregkan's claude-for-researchers is a practical guide and toolkit designed specifically for physicists and mathematicians using Claude Code, built from real-world research experience.
With a growth score of 9.10 and 37 stars, this project demonstrates the growing need for specialized tools in niche scientific communities. Benchflow-ai's awesome-evals compiles resources for building and evaluating AI agents, including papers, blogs, talks, tools, and benchmarks.
This repository has accumulated 4.27 growth points and 273 stars due to its curated content that serves as a reliable resource hub for researchers working on AI agent evaluation. InternLM's RNGBench is the official implementation of an evaluation framework for multimodal large language models in non-Markov games, with 39 stars.
Its growth score of 3.15 highlights the increasing interest in benchmarking and evaluating advanced AI systems that go beyond traditional observation frameworks. Lastly, llmsresearch's llm-flashcards offers hand-drawn flashcards to explain how LLMs work, providing 19 free samples from a larger deck of 180 cards.
This project has achieved a growth score of 2.15 and 59 stars due to its unique educational approach in making complex concepts accessible through visual aids. Each of these projects contributes uniquely to the evolving landscape of AI research, offering tools that range from comprehensive guides to specialized resources for specific scientific communities.