Dust apps rely on model providers to interact with large language models. You can setup your first model provider by clicking on the Providers pane and setting up the OpenAI provider. You’ll need to create an account at OpenAI and retrieve your API key.
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Learn 25 VS Code tips and tricks that will help you write code faster. Try out awesome new features and extensions that turn your editor into a full-blown IDE
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This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale and secure your production machine learning
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Sketch is an AI code-writing assistant for pandas users that understands the context of your data, greatly improving the relevance of suggestions. Sketch is usable in seconds and doesn’t require adding a plugin to your IDE.
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Aqueduct gives you a simple Python-native API to define machine learning pipelines, the ability to deploy those pipelines on your existing infrastructure (e.g., Spark, Kubernetes, Lambda), and visibility into the code, data, and metadata associated with your workflows. Aqueduct is fully open-source and runs securely in your cloud.
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This is a Telegram bot that lets you chat with the chatGPT language model using your local browser. The bot uses Playwright to run chatGPT in Chromium, and can parse code and text, as well as send messages. It also includes a /draw command that allows you to generate pictures using stable diffusion. More features are coming soon.
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Diagrams lets you draw the cloud system architecture in Python code.
It was born for prototyping a new system architecture without any design tools. You can also describe or visualize the existing system architecture as well.
Diagram as Code allows you to track the architecture diagram changes in any version control system.
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A well-established rule of thumb that applies to most machine learning projects is that the larger and cleaner the dataset, the better the performance.
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For data scientists and data engineers, d6tflow is a python library which makes building complex data science workflows easy, fast and intuitive. It is primarily designed for data scientists to build better models faster. For data engineers, it can also be a lightweight alternative and help productionize data science models faster. Unlike other data pipeline/workflow solutions, d6tflow focuses on managing data science research workflows instead of managing production data pipelines.
