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Kanal tas-sors @linuxgram · Post #17821 · Fra 18

📰Linus T tells The Reg how Linux solo act became a global jam session Ts'o, Hohndel and the man himself spill beans on how checks in the mail and GPL made it all possible If you know anything about Linux's history, you'll remember it all started with Linus Torvalds posting to the Minix Usenet group on August 25, 1991, that he was working on "a (free) operating system (just a hobby, won't be big and professional like gnu) for 386(486) AT clones. 🔗 Source: https://go.theregister.com/feed/www.theregister.com/2026/02/18/linus_torvalds_and_friends/ #linux#gnu

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Tfittxija: #memcached

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djangoproject

@djangoproject · Post #411 · 13/08/2017 12:08

http://sendapatch.se/projects/pylibmc/ #pylibmc is a client in Python for #memcached. It is a wrapper around TangentOrg‘s libmemcached library. The interface is intentionally made as close to python-memcached as possible, so that applications can drop-in replace it. pylibmc leverages among other things configurable behaviors, data pickling, data compression, battle-tested GIL retention, consistent distribution, and the binary memcached protocol.

djangoproject

@djangoproject · Post #410 · 13/08/2017 11:53

https://pypi.python.org/pypi/python-memcached This software is a 100% Python interface to the #memcached#memory#cache daemon. It is the #client side software which allows storing values in one or more, possibly remote, memcached servers. Search google for memcached for more information.

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

#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