Best AI tools for< Build Following >

Medium is a popular online publishing platform where writers can share their thoughts and stories with a wide audience. It offers a diverse range of articles on various topics, written by both professionals and enthusiasts. Users can explore different categories, follow their favorite writers, and engage with the community through comments and claps.

The AI Lean Canvas Generator is an AI-powered tool designed to help businesses create Lean Canvases quickly and efficiently. It uses artificial intelligence to analyze company descriptions and generate Lean Canvases that summarize key aspects of a business model. The tool aims to streamline the process of creating and validating business models, following the Lean Startup methodology to reduce risk and uncertainty in the early stages of a business. It provides a user-friendly interface for users to input their company information and receive a comprehensive Lean Canvas that includes target market, value proposition, revenue streams, cost structure, and key metrics.

The website provides valuable information for individuals considering building a home, specifically focusing on custom-built homes. It addresses common questions and concerns related to custom-built homes, offering insights and solutions. The content covers topics such as maximizing room space, neighborhood etiquette, secrets to successful custom-built homes, considerations for building in a distant location, family-friendly home layouts, advantages of custom-built homes over buying pre-built houses, incorporating creativity in home design, flexibility in construction projects, creating disaster-resistant homes, selecting suitable land for building, and designing homes suitable for smaller families. The site also offers tips on creating stylish and natural custom-built homes, considerations for individuals with a second car, assessing safety in the neighborhood before building a custom home, following exterior wall trends, and more. The site is authored by Takayoshi Shirasaki, providing expert insights and recommendations based on experience in the construction industry.

UPTO3 is a decentralized event knowledge graph protocol that aims to provide consensus verification for Web3 events by turning them into NFTs. Users can mint and verify events on the platform, with rewards based on the outcomes. The platform promotes transparency, open data access, and unbiased analysis through economic incentives. UPTO3 will be built on Blast(L2) and offers features such as event minting as NFTs, decentralized verification tasks, and gas rewards for validators. The project launch is scheduled to coincide with Blast's mainnet launch in February, following the testnet launch in January.

Tala is an AI-powered language tutor designed for hands-on learners. It encourages free-flowing conversation early in the learning journey, focusing on natural language acquisition rather than rote memorization. With advanced speech recognition technology, Tala helps users build confidence in speaking and offers a flexible learning experience with adjustable listening speeds and easy access to look-up tools. The platform aims to make language learning engaging and immersive, allowing users to practice without fear of embarrassment and improve their pronunciation through interactive conversations.

Tutor AI is an AI English-speaking application designed to assist individuals in practicing their spoken English skills with the aid of an artificial intelligence chatbot. The app offers a safe and judgment-free environment for users to engage in free-flowing, natural conversations with diverse AI characters. It provides real-time feedback, suggests better ways to express oneself, and offers adjustable features to enhance the learning experience. Tutor AI aims to improve users' spoken English skills confidently and effectively through personalized lessons and interactive learning.

This is a unified DevOps platform to build AI applications. It provides a comprehensive set of tools and services to help developers build, deploy, and manage AI applications. The platform includes a variety of features such as a code editor, a debugger, a profiler, and a deployment manager. It also provides access to a variety of AI services, such as natural language processing, machine learning, and computer vision.

Build Chatbot is a no-code chatbot builder designed to simplify the process of creating chatbots. It enables users to build their chatbot without any coding knowledge, auto-train it with personalized content, and get the chatbot ready with an engaging UI. The platform offers various features to enhance user engagement, provide personalized responses, and streamline communication with website visitors. Build Chatbot aims to save time for both businesses and customers by making information easily accessible and transforming visitors into satisfied customers.

The website 'What should I build next?' is a platform designed to help developers generate random development project ideas. It serves as an ultimate resource for developers seeking inspiration for their next project. Users can pick components or randomize to create unique project ideas. The platform offers free credits to active users daily and encourages user engagement. With a user-friendly interface, the website aims to support developers in overcoming creative blocks and kickstarting new projects.

Google Cloud is a suite of cloud computing services that runs on the same infrastructure as Google. Its services include computing, storage, networking, databases, machine learning, and more. Google Cloud is designed to make it easy for businesses to develop and deploy applications in the cloud. It offers a variety of tools and services to help businesses with everything from building and deploying applications to managing their infrastructure. Google Cloud is also committed to sustainability, and it has a number of programs in place to reduce its environmental impact.

Bubble is a no-code application development platform that allows users to build and deploy web and mobile applications without writing any code. It provides a visual interface for designing and developing applications, and it includes a library of pre-built components and templates that can be used to accelerate development. Bubble is suitable for a wide range of users, from beginners with no coding experience to experienced developers who want to build applications quickly and easily.

Gemini is a large and powerful AI model developed by Google. It is designed to handle a wide variety of text and image reasoning tasks, and it can be used to build a variety of AI-powered applications. Gemini is available in three sizes: Ultra, Pro, and Nano. Ultra is the most capable model, but it is also the most expensive. Pro is the best performing model for a wide variety of tasks, and it is a good value for the price. Nano is the most efficient model, and it is designed for on-device use cases.

FlutterFlow is a low-code development platform that enables users to build cross-platform mobile and web applications without writing code. It provides a visual interface for designing user interfaces, connecting data, and implementing complex logic. FlutterFlow is trusted by users at leading companies around the world and has been used to build a wide range of applications, from simple prototypes to complex enterprise solutions.

Whimsical is an iterative workspace designed for product teams to collaborate effectively. It offers a range of tools such as flowcharts, wireframes, mind maps, and documentation features to help teams visualize ideas, streamline processes, and create a shared understanding. With Whimsical, users can generate diagrams quickly, brainstorm and organize ideas visually, and build wireframes with ease. The platform promotes clarity, collaboration, and efficiency in product development projects.

Zyro is a website builder that allows users to create professional websites and online stores without any coding knowledge. It offers a range of features, including customizable templates, drag-and-drop editing, and AI-powered tools to help users brand and grow their businesses.

Apify is a full-stack web scraping and data extraction platform that allows developers to build, deploy, and publish web scraping, data extraction, and web automation tools. The platform offers ready-made web scraping tools, serverless programs, integrations with various apps and services, storage for results, anti-blocking proxies, and an open-source web scraping and crawling library. Apify simplifies web scraping challenges by providing a full-stack platform that includes tools for developing, deploying, and running web scrapers efficiently.

Durable is an AI-powered website builder and small business software platform that offers tools for building business websites, managing customers, creating blogs, and establishing brand identity. With Durable, users can effortlessly create professional websites in seconds without the need for coding skills. The platform also provides resources, advice, and industry-specific guides to help businesses succeed. Durable's AI tools aim to simplify the process of starting, growing, and managing a business, offering features like SEO optimization, marketing tools, and review automation.

Pinecone is a vector database that helps power AI for the world's best companies. It is a serverless database that lets you deliver remarkable GenAI applications faster, at up to 50x lower cost. Pinecone is easy to use and can be integrated with your favorite cloud provider, data sources, models, frameworks, and more.

Cursor is an AI-first code editor that helps developers build software faster. It provides a variety of features to help developers, including code completion, code generation, and error detection. Cursor is also designed to be easy to use and integrates with popular development tools like VSCode.

Abacus.AI is the world's first AI platform where AI, not humans, build Applied AI agents and systems at scale. Using generative AI and other novel neural net techniques, AI can build LLM apps, gen AI agents, and predictive applied AI systems at scale.

Awesome-Papers-Autonomous-Agent is a curated collection of recent papers focusing on autonomous agents, specifically interested in RL-based agents and LLM-based agents. The repository aims to provide a comprehensive resource for researchers and practitioners interested in intelligent agents that can achieve goals, acquire knowledge, and continually improve. The collection includes papers on various topics such as instruction following, building agents based on world models, using language as knowledge, leveraging LLMs as a tool, generalization across tasks, continual learning, combining RL and LLM, transformer-based policies, trajectory to language, trajectory prediction, multimodal agents, training LLMs for generalization and adaptation, task-specific designing, multi-agent systems, experimental analysis, benchmarking, applications, algorithm design, and combining with RL.

llm-vscode is an extension designed for all things LLM, utilizing llm-ls as its backend. It offers features such as code completion with 'ghost-text' suggestions, the ability to choose models for code generation via HTTP requests, ensuring prompt size fits within the context window, and code attribution checks. Users can configure the backend, suggestion behavior, keybindings, llm-ls settings, and tokenization options. Additionally, the extension supports testing models like Code Llama 13B, Phind/Phind-CodeLlama-34B-v2, and WizardLM/WizardCoder-Python-34B-V1.0. Development involves cloning llm-ls, building it, and setting up the llm-vscode extension for use.

The Artificial Intelligence Incident Database (AIID) is a collection of incidents involving the development and use of artificial intelligence (AI). The database is designed to help researchers, policymakers, and the public understand the potential risks and benefits of AI, and to inform the development of policies and practices to mitigate the risks and promote the benefits of AI. The AIID is a collaborative project involving researchers from the University of California, Berkeley, the University of Washington, and the University of Toronto.

Aiwnios is a HolyC Compiler/Runtime designed for 64-bit ARM, RISCV, and x86 machines, including Apple M1 Macs, with plans for supporting other architectures in the future. The project is currently a work in progress, with regular updates and improvements planned. Aiwnios includes a sockets API (currently tested on FreeBSD) and a HolyC assembler accessible through AARCH64. The heart of Aiwnios lies in `arm_backend.c`, where the compiler is located, and a powerful AARCH64 assembler in `arm64_asm.c`. The compiler uses reverse Polish notation and statements are reversed. The developer manual is intended for developers working on the C side, providing detailed explanations of the source code.

Oxen is a data version control library, written in Rust. It's designed to be fast, reliable, and easy to use. Oxen can be used in a variety of ways, from a simple command line tool to a remote server to sync to, to integrations into other ecosystems such as python.

Curated Transformers is a transformer library for PyTorch that provides state-of-the-art models composed of reusable components. It supports various transformer architectures, including encoders like ALBERT, BERT, and RoBERTa, and decoders like Falcon, Llama, and MPT. The library emphasizes consistent type annotations, minimal dependencies, and ease of use for education and research. It has been production-tested by Explosion and will be the default transformer implementation in spaCy 3.7.

YGO Agent is a project focused on using deep learning to master the Yu-Gi-Oh! trading card game. It utilizes reinforcement learning and large language models to develop advanced AI agents that aim to surpass human expert play. The project provides a platform for researchers and players to explore AI in complex, strategic game environments.

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Unity Catalog is an open and interoperable catalog for data and AI, supporting multi-format tables, unstructured data, and AI assets. It offers plugin support for extensibility and interoperates with Delta Sharing protocol. The catalog is fully open with OpenAPI spec and OSS implementation, providing unified governance for data and AI with asset-level access control enforced through REST APIs.

LARS is an application that enables users to run Large Language Models (LLMs) locally on their devices, upload their own documents, and engage in conversations where the LLM grounds its responses with the uploaded content. The application focuses on Retrieval Augmented Generation (RAG) to increase accuracy and reduce AI-generated inaccuracies. LARS provides advanced citations, supports various file formats, allows follow-up questions, provides full chat history, and offers customization options for LLM settings. Users can force enable or disable RAG, change system prompts, and tweak advanced LLM settings. The application also supports GPU-accelerated inferencing, multiple embedding models, and text extraction methods. LARS is open-source and aims to be the ultimate RAG-centric LLM application.

Void is an open-source Cursor alternative, providing a full source code for users to build and develop. It is a fork of the vscode repository, offering a waitlist for the official release. Users can contribute by checking the Project board and following the guidelines in CONTRIBUTING.md. Support is available through Discord or email.

Build-your-own-AI-Assistant-Solution-Accelerator is a pre-release and preview solution that helps users create their own AI assistants. It leverages Azure Open AI Service, Azure AI Search, and Microsoft Fabric to identify, summarize, and categorize unstructured information. Users can easily find relevant articles and grants, generate grant applications, and export them as PDF or Word documents. The solution accelerator provides reusable architecture and code snippets for building AI assistants with enterprise data. It is designed for researchers looking to explore flu vaccine studies and grants to accelerate grant proposal submissions.

The Octopus-v4 project aims to build the world's largest graph of language models, integrating specialized models and training Octopus models to connect nodes efficiently. The project focuses on identifying, training, and connecting specialized models. The repository includes scripts for running the Octopus v4 model, methods for managing the graph, training code for specialized models, and inference code. Environment setup instructions are provided for Linux with NVIDIA GPU. The Octopus v4 model helps users find suitable models for tasks and reformats queries for effective processing. The project leverages Language Large Models for various domains and provides benchmark results. Users are encouraged to train and add specialized models following recommended procedures.

Activepieces is an open source replacement for Zapier, designed to be extensible through a type-safe pieces framework written in Typescript. It features a user-friendly Workflow Builder with support for Branches, Loops, and Drag and Drop. Activepieces integrates with Google Sheets, OpenAI, Discord, and RSS, along with 80+ other integrations. The list of supported integrations continues to grow rapidly, thanks to valuable contributions from the community. Activepieces is an open ecosystem; all piece source code is available in the repository, and they are versioned and published directly to npmjs.com upon contributions. If you cannot find a specific piece on the pieces roadmap, please submit a request by visiting the following link: Request Piece Alternatively, if you are a developer, you can quickly build your own piece using our TypeScript framework. For guidance, please refer to the following guide: Contributor's Guide

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

The TensorRT-LLM Backend is a Triton backend designed to serve TensorRT-LLM models with Triton Inference Server. It supports features like inflight batching, paged attention, and more. Users can access the backend through pre-built Docker containers or build it using scripts provided in the repository. The backend can be used to create models for tasks like tokenizing, inferencing, de-tokenizing, ensemble modeling, and more. Users can interact with the backend using provided client scripts and query the server for metrics related to request handling, memory usage, KV cache blocks, and more. Testing for the backend can be done following the instructions in the 'ci/README.md' file.

This project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used

DataEngineeringPilipinas is a repository dedicated to data engineering resources in the Philippines. It serves as a platform for data engineering professionals to contribute and access high-quality content related to data engineering. The repository provides guidelines for contributing, including forking the repository, making changes, and submitting contributions. It emphasizes the importance of quality, relevance, and respect in the contributions made to the project. By following the guidelines and contributing to the repository, users can help build a valuable resource for the data engineering community in the Philippines and beyond.

MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

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