Do you use Vim? Do you write? Would you like to combine the two to use Vim for writing prose?
Have you accepted an invitation to sit on a community board or participate on a committee? Do you and your friends want to crank up a new society? Is a valued organization or project failing and you feel the need to contribute to its revival. Here are the tools you and your associates will need to be effective.
Peer-to-peer (P2P) networks are fascinating! P2P networks can scale to thousands, millions or even billions of individual peers collaborating without a central, coordinating entity. These peers can be run by individuals or organizations - each contributing resources towards the total capacity of the network. This book explores how!
Software Architecture is a wide and complex discipline, full of different perspectives, opinions and definitions. The main idea behind this work is that, as fundamental as software architecture is, it shouldn't be considered a separate activity on the whole software life cycle, either to be performed by an isolated group or individual, or to be conceived in a separate phase from the majority of the construction process.
Learn graph databases the hands-on way — no servers, no setup, no fluff.Hands-On LadybugDB Cypher takes you from your first MATCH to complex recursive queries, shortest-path algorithms, and real-world AI agent graphs — all running locally in under a minute. 27 chapters. One evolving project. Every Cypher concept you need. Start querying graphs today.
Wave goodbye to slow exceptions and embrace clean, efficient error handling by encapsulating operations that may succeed or fail in a type-safe way.
Build TypeScript applications that are easier to reason about, safer to change, and simpler to test. Learn Effect v3 through practical examples that turn async chaos into composable, reliable code
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Modern Deep Learning models can be extremely large, often exceeding the memory capacity of a single GPU or CPU. In these cases, training must be distributed across multiple processors. This introduces the need for high-speed communication between GPUs—both within a single server and across multiple servers. Intra-node GPU communication typically relies on high-speed interconnects like NVLink, with Direct Memory Access operations enabling efficient data transfers between GPUs. Inter-node communication, however, depends on the backend network, either InfiniBand or Ethernet-based. Synchronization of model parameters across GPUs places strict requirements on the network: high throughput, ultra-low latency, and zero packet loss. Achieving this in an Ethernet fabric is challenging but possible. This is where datacenter networking meets Deep Learning. Understanding how GPUs communicate and what the network must deliver is essential for designing effective AI data center infrastructures.