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Source channel @githubtrending · Post #14826 · Jun 12

#jupyter_notebook#ai#llm#llms#multi_modal#openai#python#rag Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data. https://github.com/FareedKhan-dev/all-rag-techniques

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Libreware

@libreware · Post #1554 · 02/22/2026, 03:26 PM

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The Internet, Reinvented. In this video, I build a #Reticulum#RNode and prove that completely different radios — #LoRa and Wi-Fi — can communicate through a hardware-agnostic networking stack. Reticulum routes traffic above the radio layer, automatically bridging dissimilar frequencies, interfaces, and modulation types. I then run it over Wi-Fi HaLow Haven nodes to create a long-range, encrypted IP #mesh with no traditional infrastructure. Finally, I push it further by running #ATAK across the network, demonstrating a fully open-source, decentralized communication stack in action. Checkout https://rmap.world/ You can install rnode software on your esp32/nrf52 based meshtastic/meshcore hardware

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#reticulum #rnode #lora #mesh #atak