An AI productivity studio that provides smart chat functionality, autonomous agent capabilities, and access to more than 300 pre-built assistants. It offers unified access to a range of frontier large language models for user workflows.
Keras is a deep learning API that focuses on debugging speed, code conciseness and maintainability. It uses a multi-backend approach that allows work with JAX, TensorFlow and PyTorch, and the KerasHub library provides Keras 3 implementations of popular model architectures with pretrained checkpoints.
Maige is open-source infrastructure for running natural language workflows on a codebase. After connecting a repository, users write rules describing what should happen when issues and pull requests are opened, and the tool can label, assign, comment, review code, and run simple code snippets. It works with the GitHub API. A standard plan is offered after free use, with usage-based payment.
A collaboration platform where the machine learning community shares models, datasets and applications. It hosts public models, datasets and Spaces, and offers an open source stack including libraries such as Transformers, Datasets and Accelerate. It also provides paid Compute and Enterprise solutions, with an Inference Providers API giving access to models from multiple AI providers.
Taplio is described as an all-in-one AI tool for LinkedIn growth, offering idea finding, writing assistance, scheduling at suggested times, and converting an audience into leads. Pricing information lists Starter, Growth, and Pro plans, with AI content, scheduling, and analytics included in all plans, and a 7-day free trial.
Fiber is a Python-based distributed computing library for modern computer clusters. It provides the same API as Python's standard multiprocessing library, uses Nanomsg for communication, and allows dynamic allocation of CPU, memory, and GPU resources. Installation uses pip install fiber.
A free downloadable guide from Kolena Inc. on testing large language models. It identifies principles for creating reliable testing protocols and details approaches for measuring model effectiveness in NLP tasks such as text summarization and prompt engineering, and discusses tracking performance differences over time.
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Keras is a deep learning API that focuses on debugging speed, code conciseness and maintainability. It uses a multi-backend approach that allows work with JAX, TensorFlow and PyTorch, and the KerasHub library provides Keras 3 implementations of popular model architectures with pretrained checkpoints.
Maige is open-source infrastructure for running natural language workflows on a codebase. After connecting a repository, users write rules describing what should happen when issues and pull requests are opened, and the tool can label, assign, comment, review code, and run simple code snippets. It works with the GitHub API. A standard plan is offered after free use, with usage-based payment.
A collaboration platform where the machine learning community shares models, datasets and applications. It hosts public models, datasets and Spaces, and offers an open source stack including libraries such as Transformers, Datasets and Accelerate. It also provides paid Compute and Enterprise solutions, with an Inference Providers API giving access to models from multiple AI providers.
Taplio is described as an all-in-one AI tool for LinkedIn growth, offering idea finding, writing assistance, scheduling at suggested times, and converting an audience into leads. Pricing information lists Starter, Growth, and Pro plans, with AI content, scheduling, and analytics included in all plans, and a 7-day free trial.
Fiber is a Python-based distributed computing library for modern computer clusters. It provides the same API as Python's standard multiprocessing library, uses Nanomsg for communication, and allows dynamic allocation of CPU, memory, and GPU resources. Installation uses pip install fiber.
A free downloadable guide from Kolena Inc. on testing large language models. It identifies principles for creating reliable testing protocols and details approaches for measuring model effectiveness in NLP tasks such as text summarization and prompt engineering, and discusses tracking performance differences over time.
