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Source channel @githubtrending · Post #15018 · Aug 1

#typescript#ai_bot#bot#http_api#python_bot#whatsapp#whatsapp_api#whatsapp_automation#whatsapp_bot#whatsapp_chat#whatsapp_web#whatsapp_web_api You can quickly set up WAHA, a WhatsApp HTTP API, on your own server using Docker in just a few minutes. It lets you send and receive WhatsApp messages (text, images, videos, voice) through simple HTTP requests, automating WhatsApp communication without limits on message volume or time. You start by running the API, creating a session by scanning a QR code with your phone, and then sending messages via API calls. This gives you full control, privacy, and flexibility to integrate WhatsApp messaging into your apps or services easily, without relying on third-party SaaS platforms[1]. https://github.com/devlikeapro/waha

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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