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🚀 Meet jamwithai/production-agentic-rag-course: a gem from today's GitHub trending list.
🔗 https://github.com/jamwithai/production-agentic-rag-course
📝 No description.
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The jamwithai/production-agentic-rag-course is a hands-on project where you'll build a complete research assistant system that automatically fetches academic papers, understands their content, and answers your research questions using advanced RAG techniques.
This course is designed for learners who want to master AI engineering skills, particularly in building production-grade RAG systems. The system, called The arXiv Paper Curator, uses a
Key features include:
- Automated data pipeline fetching and parsing academic papers from arXiv
- Production BM25 keyword search with filtering and relevance scoring
- Intelligent chunking and hybrid search combining keywords with semantic understanding
- Complete RAG pipeline with local LLM, streaming responses, and Gradio interface
- Production monitoring with Langfuse tracing and Redis caching for optimized performance
- Agentic RAG with LangGraph and Telegram Bot for mobile access
Technical highlights include:
The course is structured into 7 weeks, each focusing on a different aspect of building a production RAG system.
In summary, this course is perfect for those who want to build modern AI systems from the ground up and master in-demand AI engineering skills.
Takeaway: Building a production RAG system is not just about AI, it's about creating a robust search foundation first.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/jamwithai/production-agentic-rag-course
📝 No description.
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The jamwithai/production-agentic-rag-course is a hands-on project where you'll build a complete research assistant system that automatically fetches academic papers, understands their content, and answers your research questions using advanced RAG techniques.
This course is designed for learners who want to master AI engineering skills, particularly in building production-grade RAG systems. The system, called The arXiv Paper Curator, uses a
foundation-first approach, starting with keyword search foundations and then enhancing with vector search for hybrid retrieval. Key features include:
- Automated data pipeline fetching and parsing academic papers from arXiv
- Production BM25 keyword search with filtering and relevance scoring
- Intelligent chunking and hybrid search combining keywords with semantic understanding
- Complete RAG pipeline with local LLM, streaming responses, and Gradio interface
- Production monitoring with Langfuse tracing and Redis caching for optimized performance
- Agentic RAG with LangGraph and Telegram Bot for mobile access
Technical highlights include:
Docker, FastAPI, PostgreSQL, OpenSearch, and Airflow
The course is structured into 7 weeks, each focusing on a different aspect of building a production RAG system.
In summary, this course is perfect for those who want to build modern AI systems from the ground up and master in-demand AI engineering skills.
Takeaway: Building a production RAG system is not just about AI, it's about creating a robust search foundation first.
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🧠 Channel: https://t.me/GithubRe
Github Top Repositories
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🎯 supermemoryai/supermemory landed on trending. Worth a proper look.
🔗 https://github.com/supermemoryai/supermemory
📝 Memory engine and app that is extremely fast, scalable. The Memory API for the AI era.
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Supermemory is a state-of-the-art memory and context engine for AI that automatically learns from conversations, extracts facts, builds user profiles, and handles knowledge updates. It's designed to give AI tools a persistent memory graph across every conversation, making them smarter over time. With
The key features include:
- Memory: extracts facts from conversations and handles temporal changes, contradictions, and automatic forgetting
- User Profiles: auto-maintained user context with stable facts and recent activity
- Hybrid Search: combines RAG and memory in a single query
- Connectors: auto-sync with real-time webhooks from Google Drive, Gmail, Notion, and more
To get started, you can use the Supermemory app, browser extension, or plugins for various AI tools. For developers, it's easy to integrate with a
In short, Supermemory gives your AI the power of human-like memory - it remembers, so you don't have to.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/supermemoryai/supermemory
📝 Memory engine and app that is extremely fast, scalable. The Memory API for the AI era.
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Supermemory is a state-of-the-art memory and context engine for AI that automatically learns from conversations, extracts facts, builds user profiles, and handles knowledge updates. It's designed to give AI tools a persistent memory graph across every conversation, making them smarter over time. With
Supermemory, you can use it as a company or personal brain, and it's available as a single API for developers to add memory, RAG, user profiles, and connectors to their agents and apps. The key features include:
- Memory: extracts facts from conversations and handles temporal changes, contradictions, and automatic forgetting
- User Profiles: auto-maintained user context with stable facts and recent activity
- Hybrid Search: combines RAG and memory in a single query
- Connectors: auto-sync with real-time webhooks from Google Drive, Gmail, Notion, and more
To get started, you can use the Supermemory app, browser extension, or plugins for various AI tools. For developers, it's easy to integrate with a
single API and drop-in wrappers for major AI frameworks. Supermemory is also state of the art across major AI memory benchmarks, including LongMemEval, LoCoMo, and ConvoMem. In short, Supermemory gives your AI the power of human-like memory - it remembers, so you don't have to.
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🧠 Channel: https://t.me/GithubRe
Github Top Repositories
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📌 Spotted on GitHub Trending: Open-LLM-VTuber/Open-LLM-VTuber — let's break it down.
🔗 https://github.com/Open-LLM-VTuber/Open-LLM-VTuber
📝 Talk to any LLM with hands-free voice interaction, voice interruption, and Live2D taking face running locally across platforms
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Open-LLM-VTuber is a voice-interactive AI companion that supports real-time voice conversations and visual perception. It features a lively Live2D avatar and can run completely offline on your computer. The project offers
The desktop client has a
Key technical highlights include
The project is suitable for users looking for a personalized AI companion and developers interested in contributing to or customizing the project.
Get your own AI companion today - it's like having a
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/Open-LLM-VTuber/Open-LLM-VTuber
📝 Talk to any LLM with hands-free voice interaction, voice interruption, and Live2D taking face running locally across platforms
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Open-LLM-VTuber is a voice-interactive AI companion that supports real-time voice conversations and visual perception. It features a lively Live2D avatar and can run completely offline on your computer. The project offers
cross-platform support for Windows, macOS, and Linux, and has two usage modes: web version and desktop client. The desktop client has a
transparent background desktop pet mode, allowing the AI companion to accompany you anywhere on your screen. It also supports advanced interaction features like visual perception, voice interruption, touch feedback, and Live2D expressions.Key technical highlights include
extensive model support for Large Language Models, Automatic Speech Recognition, and Text-to-Speech, as well as high customizability through simple module configuration, character customization, and flexible Agent implementation.The project is suitable for users looking for a personalized AI companion and developers interested in contributing to or customizing the project.
Get your own AI companion today - it's like having a
virtual friend by your side!──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🎯 chopratejas/headroom landed on trending. Worth a proper look.
🔗 https://github.com/chopratejas/headroom
📝 Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.
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The Headroom project is a context compression layer designed for AI agents, aiming to reduce the number of tokens used in communication between agents and language models. This library provides a range of features, including a
The technical highlights of Headroom include its ability to compress JSON, AST, and prose using various algorithms, as well as its CacheAligner and IntelligentContext features to optimize compression. The project also supports cross-agent memory and reversible compression, ensuring that originals are always retrievable.
Headroom is suitable for users who run AI coding agents daily, work across multiple agents, and need reversible compression. It is compatible with various agents, including Claude Code, Codex, and Cursor, and can be integrated into any stack using its API and CLI tools.
Overall, Headroom offers a powerful solution for reducing token usage in AI agent communication, with a range of features and technical highlights that make it an attractive choice for developers and users alike.
The key takeaway is: Headroom helps you do more with less, compressing up to 95% of tokens without sacrificing accuracy.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/chopratejas/headroom
📝 Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.
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The Headroom project is a context compression layer designed for AI agents, aiming to reduce the number of tokens used in communication between agents and language models. This library provides a range of features, including a
compress function for Python and TypeScript, a proxy mode for zero-code changes, and a wrap mode for coding agents. It also includes a headroom learn feature to mine failed sessions and write corrections to agent documentation.The technical highlights of Headroom include its ability to compress JSON, AST, and prose using various algorithms, as well as its CacheAligner and IntelligentContext features to optimize compression. The project also supports cross-agent memory and reversible compression, ensuring that originals are always retrievable.
Headroom is suitable for users who run AI coding agents daily, work across multiple agents, and need reversible compression. It is compatible with various agents, including Claude Code, Codex, and Cursor, and can be integrated into any stack using its API and CLI tools.
Overall, Headroom offers a powerful solution for reducing token usage in AI agent communication, with a range of features and technical highlights that make it an attractive choice for developers and users alike.
The key takeaway is: Headroom helps you do more with less, compressing up to 95% of tokens without sacrificing accuracy.
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🧠 Channel: https://t.me/GithubRe
🔥 NousResearch/hermes-agent is trending — and it deserves your attention.
🔗 https://github.com/NousResearch/hermes-agent
📝 The agent that grows with you
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Hermes Agent is a self-improving AI agent built by Nous Research. It has a built-in learning loop, allowing it to create skills from experience, improve them during use, and search its own past conversations. You can use
Key features include a real terminal interface, a closed learning loop, scheduled automations, and the ability to delegate and parallelize tasks. Hermes Agent is also research-ready, with batch trajectory generation and trajectory compression for training the next generation of tool-calling models.
To get started, you can install Hermes Agent using a one-liner command, and then configure it to your liking. The agent is designed to be flexible and adaptable, with a range of tools and features at your disposal.
Hermes Agent is perfect for anyone looking for a powerful and flexible AI agent that can learn and improve over time. With its unique combination of features and capabilities, it's an ideal choice for researchers, developers, and anyone looking to push the boundaries of what's possible with AI.
One-liner takeaway: Hermes Agent is the ultimate AI sidekick that learns, adapts, and evolves with you.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/NousResearch/hermes-agent
📝 The agent that grows with you
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Hermes Agent is a self-improving AI agent built by Nous Research. It has a built-in learning loop, allowing it to create skills from experience, improve them during use, and search its own past conversations. You can use
hermes on a variety of platforms, including Telegram, Discord, and CLI, and switch between different models with the hermes model command.Key features include a real terminal interface, a closed learning loop, scheduled automations, and the ability to delegate and parallelize tasks. Hermes Agent is also research-ready, with batch trajectory generation and trajectory compression for training the next generation of tool-calling models.
To get started, you can install Hermes Agent using a one-liner command, and then configure it to your liking. The agent is designed to be flexible and adaptable, with a range of tools and features at your disposal.
Hermes Agent is perfect for anyone looking for a powerful and flexible AI agent that can learn and improve over time. With its unique combination of features and capabilities, it's an ideal choice for researchers, developers, and anyone looking to push the boundaries of what's possible with AI.
One-liner takeaway: Hermes Agent is the ultimate AI sidekick that learns, adapts, and evolves with you.
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🧠 Channel: https://t.me/GithubRe
Github Top Repositories
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📌 Spotted on GitHub Trending: affaan-m/ECC — let's break it down.
🔗 https://github.com/affaan-m/ECC
📝 The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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The ECC (Engine for Cross-Harness) GitHub repository offers a harness-native operator system for agentic work, built from real-world multi-harness engineering workflows. This system is designed to work across various AI agent harnesses, including Codex, Claude Code, Cursor, and OpenCode. The ECC system provides a complete set of features, including
The ECC repository includes guides that explain everything, from setup and foundations to philosophy and advanced topics. These guides are available in multiple languages and cover topics such as token optimization, memory persistence, and security.
The ECC system is designed for production-ready agents, with features such as skills, hooks, rules, and legacy command shims. It also supports cross-harness workflows and includes tools for operator workflows and outbound workflows.
Technical highlights include support for multiple programming languages, such as TypeScript, Python, Go, and Java, as well as a
The ECC repository is free and open-source, with a MIT license, and is suitable for developers and operators who want to build and deploy agentic workflows. With over 182K stars and 28K forks, the ECC repository is a popular and widely-used platform for agentic work.
The ECC system is constantly evolving, with new features and updates being added regularly. Recent releases include v2.0.0-rc.1, which adds a
In summary, the ECC repository offers a powerful and flexible platform for building and deploying agentic workflows, with a wide range of features and tools to support developers and operators. The key takeaway is that ECC is the ultimate tool for building and deploying agentic workflows, with a strong focus on production readiness, security, and ease of use.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/affaan-m/ECC
📝 The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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The ECC (Engine for Cross-Harness) GitHub repository offers a harness-native operator system for agentic work, built from real-world multi-harness engineering workflows. This system is designed to work across various AI agent harnesses, including Codex, Claude Code, Cursor, and OpenCode. The ECC system provides a complete set of features, including
skills, instincts, memory optimization, continuous learning, and security scanning. The ECC repository includes guides that explain everything, from setup and foundations to philosophy and advanced topics. These guides are available in multiple languages and cover topics such as token optimization, memory persistence, and security.
The ECC system is designed for production-ready agents, with features such as skills, hooks, rules, and legacy command shims. It also supports cross-harness workflows and includes tools for operator workflows and outbound workflows.
Technical highlights include support for multiple programming languages, such as TypeScript, Python, Go, and Java, as well as a
Shell interface and Markdown documentation. The ECC system also includes a dashboard GUI and supports GitHub App installation.The ECC repository is free and open-source, with a MIT license, and is suitable for developers and operators who want to build and deploy agentic workflows. With over 182K stars and 28K forks, the ECC repository is a popular and widely-used platform for agentic work.
The ECC system is constantly evolving, with new features and updates being added regularly. Recent releases include v2.0.0-rc.1, which adds a
dashboard GUI and operator workflows, and v1.9.0, which includes selective install architecture and language expansion.In summary, the ECC repository offers a powerful and flexible platform for building and deploying agentic workflows, with a wide range of features and tools to support developers and operators. The key takeaway is that ECC is the ultimate tool for building and deploying agentic workflows, with a strong focus on production readiness, security, and ease of use.
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🧠 Channel: https://t.me/GithubRe
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