Today's LLM & Language Models: Fastest-Growing Projects — September 22, 2026
This week, the landscape of Large Language Models (LLMs) and language models continues to expand rapidly with a variety of innovative projects focusing on areas such as narrative repair for fiction writing, comprehensive learning paths for mastering LLMs, and multimodal embedding models. Among these, one project stands out with an impressive growth score, reflecting its growing popularity and the community's interest in its unique approach.
Nanako0129/sepia is a tool designed to enhance narrative architecture repair for fiction writing and professional prose by addressing AI-generated text through techniques like de-AI writing skills. With over 2,751 stars on GitHub, it has seen significant growth, likely due to its unique approach in repairing narrative structures that are often distorted when generated by AI.
youngyangyang04/llm-master offers a comprehensive learning route and Chinese tutorials for mastering LLMs, covering various aspects such as prompt engineering, retrieval-augmented generation (RAG), model deployment, and more. This project has garnered over 871 stars, indicating its wide appeal among developers and professionals looking to deepen their understanding of large language models.
Tencent/WeMM-Embedding, developed by the WeChat Vision Team at Tencent, is a suite of universal multimodal embedding models designed for applications in multimodal understanding and retrieval. With over 1,644 stars, its popularity reflects the growing interest in multimodal AI solutions that integrate diverse data types such as text, images, and videos.
baojian/llm-26-fall is a repository connected to Fudan University's curriculum for natural language processing (NLP) and large language models. Despite having only 51 stars, it has seen steady growth with numerous commits over the past month, suggesting active development and community engagement around educational content.
tomnio/rubric provides schema-first structured extraction from LLMs, allowing users to efficiently extract information based on predefined schemas. With 86 stars, its growing popularity might stem from its utility in handling complex data extraction tasks with ease and precision.
555cute/r20-quantum-trader is an ambitious project aiming to develop a self-evolving quantitative trading terminal powered by LLMs, integrating AI-driven decision-making for financial markets. Its 223 stars indicate growing interest among developers looking to leverage advanced AI capabilities in the finance domain.
ops120/wechat-local-viewer offers a local chat record viewer for WeChat that includes features such as session browsing and full-text search, with an optional LLM-powered smart summary feature. With 247 stars, it highlights the demand for privacy-focused tools that operate entirely offline without external dependencies.
aimeoa/hanshuang-codex is a project focused on breaking down the defenses of advanced AI models like GPT and V4flash through experimental techniques. Its 339 stars suggest significant interest in reverse-engineering or testing large-scale language models for research purposes.
inin-zou/codex-privacy-hud, with 108 stars, is an open-source project that won a privacy hackathon held in Paris, aiming to enhance the security and privacy of AI tools like Codex. Its growth score indicates its relevance in addressing critical issues around data privacy and security within AI applications.
rokbenko/quackd allows users to interact with small robots using plain language commands, leveraging LLMs to interpret and execute these commands. With 225 stars, it reflects the growing interest in developing more intuitive interfaces for AI-driven devices, making complex tasks accessible through simple conversational inputs.
These projects showcase a diverse range of applications and innovations within the realm of LLMs and language models, from educational resources and privacy-focused tools to advanced trading platforms and experimental research projects.
Nanako0129/sepia is a tool designed to enhance narrative architecture repair for fiction writing and professional prose by addressing AI-generated text through techniques like de-AI writing skills. With over 2,751 stars on GitHub, it has seen significant growth, likely due to its unique approach in repairing narrative structures that are often distorted when generated by AI.
youngyangyang04/llm-master offers a comprehensive learning route and Chinese tutorials for mastering LLMs, covering various aspects such as prompt engineering, retrieval-augmented generation (RAG), model deployment, and more. This project has garnered over 871 stars, indicating its wide appeal among developers and professionals looking to deepen their understanding of large language models.
Tencent/WeMM-Embedding, developed by the WeChat Vision Team at Tencent, is a suite of universal multimodal embedding models designed for applications in multimodal understanding and retrieval. With over 1,644 stars, its popularity reflects the growing interest in multimodal AI solutions that integrate diverse data types such as text, images, and videos.
baojian/llm-26-fall is a repository connected to Fudan University's curriculum for natural language processing (NLP) and large language models. Despite having only 51 stars, it has seen steady growth with numerous commits over the past month, suggesting active development and community engagement around educational content.
tomnio/rubric provides schema-first structured extraction from LLMs, allowing users to efficiently extract information based on predefined schemas. With 86 stars, its growing popularity might stem from its utility in handling complex data extraction tasks with ease and precision.
555cute/r20-quantum-trader is an ambitious project aiming to develop a self-evolving quantitative trading terminal powered by LLMs, integrating AI-driven decision-making for financial markets. Its 223 stars indicate growing interest among developers looking to leverage advanced AI capabilities in the finance domain.
ops120/wechat-local-viewer offers a local chat record viewer for WeChat that includes features such as session browsing and full-text search, with an optional LLM-powered smart summary feature. With 247 stars, it highlights the demand for privacy-focused tools that operate entirely offline without external dependencies.
aimeoa/hanshuang-codex is a project focused on breaking down the defenses of advanced AI models like GPT and V4flash through experimental techniques. Its 339 stars suggest significant interest in reverse-engineering or testing large-scale language models for research purposes.
inin-zou/codex-privacy-hud, with 108 stars, is an open-source project that won a privacy hackathon held in Paris, aiming to enhance the security and privacy of AI tools like Codex. Its growth score indicates its relevance in addressing critical issues around data privacy and security within AI applications.
rokbenko/quackd allows users to interact with small robots using plain language commands, leveraging LLMs to interpret and execute these commands. With 225 stars, it reflects the growing interest in developing more intuitive interfaces for AI-driven devices, making complex tasks accessible through simple conversational inputs.
These projects showcase a diverse range of applications and innovations within the realm of LLMs and language models, from educational resources and privacy-focused tools to advanced trading platforms and experimental research projects.