Search Results for "sandbox:/mnt/data/project_plan.pod"

aeson is a high-performance Haskell library for JSON parsing and encoding, optimized for speed and ease of use. It serves as a foundational tool in the Haskell ecosystem for handling JSON data efficiently. High-performance optimized for real-world workloads. Widely used and well-maintained community library. Compatible with popular frameworks and the Haskell web ecosystem. Easy-to-use API (e.g., FromJSON, ToJSON typeclasses). Fast JSON parsing and serialization.

Hasktorch is a powerful Haskell library for tensor computation and neural network modeling, built on top of libtorch (the backend of PyTorch). It brings differentiable programming, automatic differentiation, and efficient tensor operations into Haskell’s strongly typed functional paradigm. This project is in active development, so expect changes to the library API as it evolves. We would like to invite new users to join our Hasktorch discord space for questions and discussions....

...To reflect this, Beam maintains a modular design. While the core package provides standard functionality, Beam is split up into a variety of backends which provide a means to interface Beam's data query and update DSLs with particular RDBMS backends. Backends can be written and maintained independently of this repository. For example, the beam-MySQL and beam-firebird backends are packaged independently.

Polysemy is a high-performance, zero-boilerplate effect system for Haskell, designed to simplify the handling of side effects in functional programs. Unlike traditional monad transformer stacks, Polysemy uses a modern approach based on freer monads and interpreters, allowing developers to define, compose, and interpret effects in a more modular and testable way. It aims to offer both flexibility and performance without sacrificing type safety or expressiveness.

Haxl is a Haskell library that simplifies access to remote data, such as databases or web-based services. Haxl can automatically batch multiple requests to the same data source, request data from multiple data sources concurrently, cache previous requests, and memoize computations. Having all this handled for you behind the scenes means that your data-fetching code can be much cleaner and clearer than it would otherwise be if it had to worry about optimizing data-fetching. ...