Latest 10 Trending Repositories - June 18, 2025

📅 Date: June 18, 2025

Welcome to this week's collection of the latest Github REPOS! Below you'll find the top 10 repos for each category.

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Exploring the Latest Trending Repositories on GitHub - June 18, 2025

In this dynamic landscape of software development, keeping an eye on trending repositories is crucial for developers, researchers, and tech enthusiasts. This article highlights the top 10 trending repositories as of June 18, 2025, offering a snapshot of the most exciting and innovative projects currently capturing the attention of the GitHub community. These repositories span various domains, from AI agents and 3D asset generation to cybersecurity exploits and Telegram scraping tools, showcasing the breadth and depth of ongoing development efforts. Each project has its unique features and contributions, making them worthy of exploration.

1. agents-towards-production: Building Production-Grade GenAI Agents

One of the standout repositories is agents-towards-production, a project focused on delivering end-to-end, code-first tutorials for building production-grade GenAI agents. This repository is invaluable for developers looking to delve into the world of Generative AI (GenAI) and implement AI agents in real-world applications. The project provides comprehensive guidance, covering every layer of production-ready agents, ensuring developers can move from initial concepts to scalable solutions. The use of proven patterns and reusable blueprints facilitates a smooth transition from experimentation to actual deployment. With 1240 stars, this Jupyter Notebook-based project has garnered significant interest, reflecting the growing demand for practical resources in the field of AI agents. The tags associated with this repository, including agent, agent-framework, ai-agents, genai, llm, and mlops, highlight its core focus areas and the technologies it encompasses. The structured approach and emphasis on real-world launches make it an essential resource for those serious about integrating GenAI into their workflows.

This repository serves as a comprehensive guide for developers looking to implement GenAI agents in production environments. It offers practical, code-first tutorials that cover all layers of agent development, from initial spark to scalable solutions. The value of this repository lies in its ability to bridge the gap between theoretical knowledge and real-world application, providing reusable blueprints and proven patterns for successful GenAI agent deployments. The project's structured approach ensures developers can navigate the complexities of GenAI, making it an invaluable resource for both beginners and experienced practitioners.

2. agent-rules: Enhancing Agent Interactions with Rules and Knowledge

The agent-rules repository, with 1231 stars, addresses the critical aspect of improving interactions with AI agents like Claude Code and Cursor. This repository emphasizes the importance of rules and knowledge in enhancing the performance and reliability of these agents. By providing a set of predefined rules, developers can ensure that AI agents behave predictably and effectively in various scenarios. This is particularly crucial in complex applications where agents need to follow specific guidelines or adhere to certain constraints. The repository, primarily written in Shell script, offers a lightweight yet powerful way to manage agent behavior. The tags agent, claudecode, cursor, and llms indicate its focus on popular AI tools and large language models, making it relevant for developers working with these technologies. The emphasis on rules and knowledge underscores the need for a structured approach to agent interaction, ensuring that AI agents can operate safely and efficiently.

This repository fills a crucial need in the AI agent development space by focusing on the practical application of rules and knowledge. It helps developers ensure that AI agents, such as Claude Code and Cursor, behave predictably and effectively. The use of Shell script makes it a lightweight and accessible solution for managing agent behavior, particularly beneficial in complex applications. The project’s emphasis on structured agent interaction ensures that AI agents can operate safely and efficiently, making it an invaluable resource for developers aiming to build reliable AI systems.

3. Hunyuan3D-2.1: Generating High-Fidelity 3D Assets

Hunyuan3D-2.1, a Python-based project with 925 stars, showcases the cutting-edge capabilities in 3D asset generation. This repository focuses on creating high-fidelity 3D assets from images, incorporating production-ready Physically Based Rendering (PBR) materials. The ability to generate realistic 3D models is highly valuable in various industries, including gaming, animation, and virtual reality. The tags 3d, 3d-aigc, 3d-generation, hunyuan3d, and image-to-3d highlight its core functionalities. The project’s ability to convert images into 3D assets with realistic textures represents a significant advancement in the field of 3D content creation. This repository is an essential resource for developers and artists looking to leverage AI in their 3D workflows, streamlining the asset creation process and opening up new possibilities for interactive and visual experiences.

The Hunyuan3D-2.1 repository stands out for its focus on generating high-fidelity 3D assets, a critical need in industries such as gaming, animation, and VR. By creating realistic 3D models from images and incorporating PBR materials, this project showcases significant advancements in 3D content creation. This Python-based repository is invaluable for developers and artists looking to integrate AI into their 3D workflows, offering a streamlined approach to asset creation and unlocking new opportunities for visual experiences.

4. MiniMax-M1: A Large-Scale Hybrid-Attention Reasoning Model

The MiniMax-M1 repository, with 646 stars, introduces the world's first open-weight, large-scale hybrid-attention reasoning model. This Python-based project represents a significant step forward in the field of large language models (LLMs). The hybrid-attention mechanism allows the model to process and reason about complex information more effectively, making it suitable for a wide range of applications. The tags large-language-models, llm, and minimax-m1 underscore its focus on advanced AI models. The open-weight nature of the model encourages community collaboration and further development, making it a valuable resource for researchers and practitioners in the AI domain. This repository highlights the ongoing advancements in LLMs and their potential to revolutionize various industries by enabling more sophisticated reasoning and decision-making capabilities.

MiniMax-M1 marks a significant advancement in the realm of large language models (LLMs) by introducing the first open-weight, large-scale hybrid-attention reasoning model. The project’s hybrid-attention mechanism allows for more effective processing and reasoning of complex information, making it suitable for various applications. This Python-based repository is a valuable resource for researchers and practitioners, fostering collaboration and further development in the AI domain. The model’s sophisticated reasoning capabilities underscore the potential of LLMs to revolutionize industries requiring advanced decision-making.

5. apple-on-device-openai: OpenAI-Compatible API for Apple Devices

The apple-on-device-openai repository addresses the need for running OpenAI-compatible APIs on Apple devices. This Swift-based project enables developers to leverage the power of OpenAI models directly on Apple hardware, opening up new possibilities for on-device AI processing. With 377 stars, this repository demonstrates the growing interest in edge computing and the ability to run AI models locally. By providing an API server compatible with OpenAI, developers can seamlessly integrate AI functionalities into their iOS and macOS applications, enhancing user experiences and enabling new features. This repository is particularly relevant for applications requiring low latency and data privacy, as it eliminates the need to send data to external servers for processing.

This repository addresses the increasing demand for edge computing by enabling developers to run OpenAI-compatible APIs directly on Apple devices. By providing a seamless integration of AI functionalities into iOS and macOS applications, the project enhances user experiences and supports new features. The apple-on-device-openai repository is especially valuable for applications requiring low latency and data privacy, as it eliminates the need for external server processing.

6. SEAL: Self-Adapting Language Models

The SEAL repository, a Python-based project, focuses on Self-Adapting Language Models. With 330 stars, this repository highlights the importance of language models that can dynamically adapt to changing data and contexts. Self-adapting models are crucial for maintaining accuracy and relevance in real-world applications, where data distributions can shift over time. This project contributes to the ongoing research and development of more robust and flexible language models. The ability of language models to adapt autonomously to new information makes them more effective in various tasks, from natural language processing to content generation. This repository is a valuable resource for researchers and practitioners interested in the cutting-edge advancements in language model technology.

The SEAL repository is dedicated to Self-Adapting Language Models, an essential area in natural language processing. These models dynamically adapt to changing data and contexts, crucial for maintaining accuracy in real-world applications. This Python-based project contributes to the advancement of robust and flexible language models, making it a valuable resource for researchers and practitioners focused on cutting-edge language model technology.

7. CVE-2025-33073: PoC Exploit for NTLM Reflection SMB Flaw

In the realm of cybersecurity, the CVE-2025-33073 repository provides a Proof-of-Concept (PoC) exploit for the NTLM reflection SMB flaw. This Python-based project, with 296 stars, serves as a critical resource for security professionals and researchers. Understanding and mitigating security vulnerabilities is paramount in protecting systems and data from cyber threats. This repository enables security experts to analyze the NTLM reflection SMB flaw and develop effective countermeasures. By providing a practical exploit, the project facilitates a deeper understanding of the vulnerability and its potential impact. This repository underscores the importance of continuous security research and the proactive identification of vulnerabilities to maintain robust cybersecurity defenses.

The CVE-2025-33073 repository is a crucial resource for cybersecurity professionals, offering a Proof-of-Concept (PoC) exploit for the NTLM reflection SMB flaw. Understanding and mitigating such vulnerabilities is essential for protecting systems and data. This Python-based project enables security experts to analyze the flaw and develop effective countermeasures, emphasizing the importance of proactive vulnerability identification and robust cybersecurity defenses.

8. generative_ai_project: A Production-Ready Template for Generative AI

The generative_ai_project repository, with 280 stars, offers a production-ready template for kickstarting Generative AI projects. This project provides a structured and scalable foundation for developing AI applications, making it easier for developers to bring their ideas to life. The tags ai-projects, ai-template, generative-ai, llm, and prompt-engineering highlight its focus areas. By offering a well-defined template, this repository streamlines the development process, allowing developers to focus on the unique aspects of their projects. The template includes best practices and architectural patterns that ensure scalability and maintainability, making it a valuable resource for both beginners and experienced developers in the field of Generative AI. This repository promotes efficient development workflows and accelerates the deployment of AI-powered applications.

This repository provides a production-ready template for Generative AI projects, offering a structured and scalable foundation for developers. The generative_ai_project repository streamlines the development process, allowing developers to focus on unique project aspects. By including best practices and architectural patterns, the template ensures scalability and maintainability, making it a valuable resource for both novice and experienced developers in Generative AI.

9. Telegram-Scraper: Scraping Messages and Media from Telegram Channels

The Telegram-Scraper repository, a Python-based project with 265 stars, is a powerful tool for scraping messages and media from Telegram channels. This repository utilizes the Telethon library to enable real-time continuous scraping, media downloading, and data export capabilities. The extensive list of tags, including telegram, telegram-api, telegram-scraper-2025, and telegram-search-bot, indicates its comprehensive functionality. This tool is valuable for researchers, analysts, and marketers looking to extract data from Telegram channels for various purposes, such as market research, sentiment analysis, and information gathering. The ability to scrape messages and media in real-time makes it a versatile tool for monitoring and analyzing trends on the Telegram platform. However, users must ensure compliance with Telegram's terms of service and respect user privacy when using this tool.

The Telegram-Scraper repository offers a powerful Python script for scraping messages and media from Telegram channels. Utilizing the Telethon library, this tool provides real-time continuous scraping, media downloading, and data export capabilities. The repository is valuable for researchers, analysts, and marketers looking to extract data for market research, sentiment analysis, and information gathering. However, users must adhere to Telegram's terms of service and respect user privacy when using this tool.

10. Kimi-Dev: An Open-Source Coding LLM for Software Engineering Tasks

The Kimi-Dev repository, a Python-based project with 242 stars, introduces an open-source coding LLM (Large Language Model) designed for software engineering tasks. This repository contributes to the growing trend of using AI to assist in software development, making it easier for developers to write, test, and maintain code. The open-source nature of the project fosters collaboration and innovation within the developer community. By providing a coding-specific LLM, this repository addresses the unique challenges of software development and offers a valuable tool for automating various coding tasks. This project highlights the potential of AI to transform the software engineering landscape and improve developer productivity.

The Kimi-Dev repository introduces an open-source coding LLM designed to assist in software engineering tasks. This Python-based project contributes to the growing use of AI in software development, simplifying code writing, testing, and maintenance. The open-source nature fosters collaboration and innovation, making it a valuable tool for automating coding tasks and improving developer productivity. The project highlights the transformative potential of AI in software engineering.

Conclusion: The Cutting Edge of Software Development

The top 10 trending repositories on June 18, 2025, reflect the dynamic and innovative nature of the software development landscape. From AI agents and 3D asset generation to cybersecurity exploits and Telegram scraping tools, these projects showcase the diverse range of ongoing development efforts. Each repository offers unique contributions and insights, making them valuable resources for developers, researchers, and tech enthusiasts. By exploring these trending projects, individuals can stay abreast of the latest advancements and leverage them to drive innovation in their own endeavors. The collaborative and open-source nature of GitHub continues to foster a vibrant ecosystem where developers can share, learn, and build the future of technology.