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Source channel @githubtrending · Post #14720 · May 19

#cplusplus#firefox#firefox_based#firefox_browser#zen_browser Zen Browser is designed to help you browse the internet with balance between speed, privacy, and productivity. It offers features like **workspaces** to organize your tabs, **compact mode** to reduce distractions, and **split views** to multitask. You can customize almost everything to fit your needs. Zen is built on Firefox, ensuring you get the latest security updates. It's available for Windows, macOS, and Linux, and is open-source, allowing community contributions. This means you can enjoy a fast, secure, and highly personalized browsing experience. https://github.com/zen-browser/desktop

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djangoproject

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

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 · 08/13/2017, 11:53 AM

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.

GitHub Trends

@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