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Source channel @githubtrending · Post #14717 · May 18

#jupyter_notebook Learning about Large Language Models (LLMs) can be very beneficial. You can build exciting projects over eight weeks, starting with simple tasks and moving to more complex ones. This journey helps you develop deep expertise in AI and LLMs. You'll learn by doing hands-on projects, which is a fun and effective way to understand how these models work. By the end, you'll have skills that can be used in real-world applications, making it a valuable learning experience. https://github.com/ed-donner/llm_engineering

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@githubtrending · Post #14772 · 06/01/2025, 12:00 AM

#cplusplus#cache#cpp#database#fibers#in_memory#in_memory_database#key_value#keydb#memcached#message_broker#multi_threading#nosql#redis#valkey#vector_search Dragonfly is a modern in-memory data store compatible with Redis and Memcached, offering up to 25 times higher throughput and better cache efficiency while using up to 80% fewer resources. It scales well with larger servers, supports many Redis commands, and features a unique, memory-efficient cache and fast snapshotting. Dragonfly provides low latency, high performance, and is easy to configure with familiar Redis options. Its design ensures atomic operations and efficient resource use, making it ideal for fast, cost-effective cloud applications needing real-time data access and high scalability. This means you get faster, more efficient caching and data handling with minimal changes to your existing setup[5][2][4]. https://github.com/dragonflydb/dragonfly