Dagster is an open source, cloud-native data pipeline orchestrator for the data development lifecycle, with integrated lineage and observability. Users declare data assets as Python functions, and Dagster runs them and keeps assets up to date. It is designed for developing and maintaining data assets such as tables, data sets, machine learning models, and reports.
QSMM is a framework for learning finite automatons that perform goal-directed interaction with entities exhibiting deterministic or stochastic behavior. It implements the rudiments of a toolchain for working with probabilistic assembler programs, and development focus shifted to adaptive parsing and learning context-free grammars by template. It includes atd-parser and abu-parser tools.
ToolList.ai is a community-ranked directory for discovering, launching and comparing AI tools across many categories. It lists new AI tool launches daily and includes forum discussions. Paid plans offer public, searchable product listings with promotion across the site.
uithub provides GitHub repository context for use with LLMs. Changing one letter in a GitHub URL, from G to U, returns full repository context. The site also offers an API for obtaining LLM context and lists example uses such as code verification, code search and changelog tracking.
Credyt is real-time monetization infrastructure for AI. It is intended to let builders observe spend per API call, model and customer, enforce budget limits before usage is authorized, and bill customers across usage-based, subscription, hybrid, credit and token pricing models. The team cites backgrounds at Visa, Checkout.com, TrueLayer and SumUp.
MMDeploy is documented at version 1.3.1 and covers getting started, converting, running, evaluating and quantizing models, building from source or Docker image, and SDK usage. Its backend support pages describe ncnn, ONNX Runtime, OpenVINO, PPLNN, SNPE, TensorRT, TorchScript, RKNN, TVM and Core ML, and its codebase support covers several OpenMMLab projects.
Rebyte is an execution layer for enterprise agents. It runs agents in the cloud with durable execution, tools such as browsers, files, and compute, and policy controls over models, skills, MCP servers, and network access. Execution traces record model steps, tool calls, errors, tokens, and cost. The Agent API is compatible with the official OpenAI client and is described as model agnostic.
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QSMM is a framework for learning finite automatons that perform goal-directed interaction with entities exhibiting deterministic or stochastic behavior. It implements the rudiments of a toolchain for working with probabilistic assembler programs, and development focus shifted to adaptive parsing and learning context-free grammars by template. It includes atd-parser and abu-parser tools.
ToolList.ai is a community-ranked directory for discovering, launching and comparing AI tools across many categories. It lists new AI tool launches daily and includes forum discussions. Paid plans offer public, searchable product listings with promotion across the site.
uithub provides GitHub repository context for use with LLMs. Changing one letter in a GitHub URL, from G to U, returns full repository context. The site also offers an API for obtaining LLM context and lists example uses such as code verification, code search and changelog tracking.
Credyt is real-time monetization infrastructure for AI. It is intended to let builders observe spend per API call, model and customer, enforce budget limits before usage is authorized, and bill customers across usage-based, subscription, hybrid, credit and token pricing models. The team cites backgrounds at Visa, Checkout.com, TrueLayer and SumUp.
MMDeploy is documented at version 1.3.1 and covers getting started, converting, running, evaluating and quantizing models, building from source or Docker image, and SDK usage. Its backend support pages describe ncnn, ONNX Runtime, OpenVINO, PPLNN, SNPE, TensorRT, TorchScript, RKNN, TVM and Core ML, and its codebase support covers several OpenMMLab projects.
Rebyte is an execution layer for enterprise agents. It runs agents in the cloud with durable execution, tools such as browsers, files, and compute, and policy controls over models, skills, MCP servers, and network access. Execution traces record model steps, tool calls, errors, tokens, and cost. The Agent API is compatible with the official OpenAI client and is described as model agnostic.
