#jupyter_notebook
Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
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📰 FRANK OS 1.0 Launches With a Retro Windows 95-Like Desktop
FRANK OS 1.0 debuts with a windowed desktop inspired by Windows 95, running on RP2350 microcontrollers using the FreeRTOS kernel.
🔗 Source: https://linuxiac.com/frank-os-launches-with-a-retro-windows-95-like-desktop/
#kernel
📰 FreeBSD's Rust Kernel Support Could Be Stable Enough To Try This Year
The FreeBSD Project has published their Q4'2025 status report to outline progress made on their software, infrastructure, and other initiatives over the past quarter. Meanwhile among the work to look forward to this year in FreeBSD is getting their Rust kernel driver support up to scratch...
🔗 Source: https://www.phoronix.com/news/FreeBSD-Q4-2025-Status-Report
#kernel