Today's LLM & Language Models: Fastest-Growing Projects — April 29, 2026
Today's the LLM & Language Models space, we're seeing a surge in tools focused on personal knowledge bases and language model optimization. Many of these repositories are leveraging Karpathy's LLM Wiki pattern to create interlinked wikis from raw sources. Meanwhile, OpenAI is leading the charge with its Privacy Filter.
Openai/privacy-filter, with a growth score of 91.79 and over 1,753 stars, allows users to filter out sensitive information from their data using OpenAI's 1.5B model. Its high growth score indicates that developers are increasingly concerned about protecting user privacy in AI applications.
Hexiecs/talk-normal has gained significant traction with a growth score of 48.21 and over 1,527 stars. This tool makes any language model talk like a normal person by removing "AI slop" from its responses. Its popularity suggests that developers are looking for ways to make their models sound more human-like.
Sdyckjq-lab/llm-wiki-skill has amassed over 1,189 stars and boasts a growth score of 47.58. This repository provides a personal knowledge base skill built on Karpathy's llm-wiki method, supporting multiple platforms. Its growth can be attributed to the increasing demand for tools that help developers organize their knowledge.
Chiefautism/privacy-parser has reversed OpenAI's Privacy Filter approach, returning PII as structured spans instead of masking them. With a growth score of 39.67 and over 374 stars, this tool seems to be gaining attention from developers who want more control over how they handle sensitive information.
Lucasastorian/llmwiki is an open-source implementation of Karpathy's LLM Wiki that allows users to upload documents and connect their Claude account via MCP. Its growth score of 32.16 and over 714 stars suggest that developers are eager for tools that simplify knowledge management.
Amitshekhariitbhu/llm-internals has gained a following with its step-by-step guide on learning LLM internals, covering topics from tokenization to inference optimization. With a growth score of 31.24 and over 798 stars, this repository is likely popular among developers who want to deepen their understanding of language models.
Atomicmemory/llm-wiki-compiler has built a knowledge compiler that takes raw sources as input and produces an interlinked wiki as output. Its growth score of 28.35 and over 833 stars indicate that developers are looking for efficient ways to organize their knowledge bases.
VectifyAI/OpenKB is an open LLM knowledge base with a growth score of 28.34 and over 679 stars. This repository seems to be gaining traction among developers who want to build on top of existing knowledge bases.
Xoai/sage-wiki compiles papers, articles, and notes into a structured, interlinked wiki using language models. With a growth score of 26.50 and over 482 stars, this tool is likely popular among researchers and writers who need help organizing their sources.
Kessler/gemma-gem runs Google's Gemma 4 model entirely on-device via WebGPU, eliminating the need for API keys or cloud services. Its growth score of 26.35 and over 854 stars suggest that developers are interested in exploring alternative deployment options for language models.
Openai/privacy-filter, with a growth score of 91.79 and over 1,753 stars, allows users to filter out sensitive information from their data using OpenAI's 1.5B model. Its high growth score indicates that developers are increasingly concerned about protecting user privacy in AI applications.
Hexiecs/talk-normal has gained significant traction with a growth score of 48.21 and over 1,527 stars. This tool makes any language model talk like a normal person by removing "AI slop" from its responses. Its popularity suggests that developers are looking for ways to make their models sound more human-like.
Sdyckjq-lab/llm-wiki-skill has amassed over 1,189 stars and boasts a growth score of 47.58. This repository provides a personal knowledge base skill built on Karpathy's llm-wiki method, supporting multiple platforms. Its growth can be attributed to the increasing demand for tools that help developers organize their knowledge.
Chiefautism/privacy-parser has reversed OpenAI's Privacy Filter approach, returning PII as structured spans instead of masking them. With a growth score of 39.67 and over 374 stars, this tool seems to be gaining attention from developers who want more control over how they handle sensitive information.
Lucasastorian/llmwiki is an open-source implementation of Karpathy's LLM Wiki that allows users to upload documents and connect their Claude account via MCP. Its growth score of 32.16 and over 714 stars suggest that developers are eager for tools that simplify knowledge management.
Amitshekhariitbhu/llm-internals has gained a following with its step-by-step guide on learning LLM internals, covering topics from tokenization to inference optimization. With a growth score of 31.24 and over 798 stars, this repository is likely popular among developers who want to deepen their understanding of language models.
Atomicmemory/llm-wiki-compiler has built a knowledge compiler that takes raw sources as input and produces an interlinked wiki as output. Its growth score of 28.35 and over 833 stars indicate that developers are looking for efficient ways to organize their knowledge bases.
VectifyAI/OpenKB is an open LLM knowledge base with a growth score of 28.34 and over 679 stars. This repository seems to be gaining traction among developers who want to build on top of existing knowledge bases.
Xoai/sage-wiki compiles papers, articles, and notes into a structured, interlinked wiki using language models. With a growth score of 26.50 and over 482 stars, this tool is likely popular among researchers and writers who need help organizing their sources.
Kessler/gemma-gem runs Google's Gemma 4 model entirely on-device via WebGPU, eliminating the need for API keys or cloud services. Its growth score of 26.35 and over 854 stars suggest that developers are interested in exploring alternative deployment options for language models.