Best AI tools for< Check Schedules >

Dola is an AI calendar assistant that helps users schedule their lives efficiently and save time. It allows users to set reminders, make calendar events, and manage tasks through natural language communication. Dola works with voice messages, text messages, and images, making it a versatile and user-friendly tool. With features like smarter scheduling, daily weather reports, faster search, and seamless integration with popular calendar apps, Dola aims to simplify task and time management for its users. The application has received positive feedback for its accuracy, ease of use, and ability to sync across multiple devices.

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HowsThisGoing is an AI-powered application designed to streamline team communication and productivity by enabling users to set up standups in Slack within seconds. The platform offers features such as automatic standups, AI summaries, custom tests, analytics & reporting, and workflow scheduling. Users can easily create workflows, generate AI reports, and track team performance efficiently. HowsThisGoing provides unlimited benefits at a flat price, making it a cost-effective solution for teams of all sizes.

Alice is a desktop application that provides access to advanced AI models like GPT-4, Perplexity, Claude 3, and others. It offers a user-friendly interface with features such as keyboard shortcuts, pre-built prompts (Snippets), and the ability to run automations within other applications. Alice is designed to enhance productivity and streamline tasks by providing quick access to AI-powered assistance.

Felix is a Slack AI assistant that helps you get work done faster and more efficiently. With Felix, you can: * Schedule meetings * Set reminders * Create tasks * Get news and weather updates * And much more!

Narrato is an AI-powered content creation and project management platform that offers a wide range of features to assist users in creating high-quality content efficiently. It provides tools such as AI writing assistant, topic generator, grammar and readability checker, plagiarism detector, content calendar, task assignment, custom content templates, style guides, freelancer management, user roles management, content collaboration, WordPress publishing, image search, and more. Narrato aims to streamline the content creation process for individuals and teams, making it easier to manage projects and collaborate effectively.

Harver is a talent assessment platform that helps businesses make better hiring decisions faster. It offers a suite of solutions, including assessments, video interviews, scheduling, and reference checking, that can be used to optimize the hiring process and reduce time to hire. Harver's assessments are based on data and scientific insights, and they help businesses identify the right people for the right roles. Harver also offers support for the full talent lifecycle, including talent management, mobility, and development.

Cognitive Calls is an AI-powered platform that enables users to automate incoming and outgoing phone and web calls. It offers solutions for various industries such as customer support, appointment scheduling, technical support, real estate, hospitality, insurance, surveys, sales follow-up, recruiting, debt collection, telehealth check-ins, reminders, alerts, voice assistants, learning apps, role-playing scenarios, ecommerce, drive-through systems, automotive systems, and robotic controls. The platform aims to enhance customer interactions by providing personalized support and efficient call handling through voice AI technology.

Botonomous is an AI-powered platform that helps businesses automate their workflows. With Botonomous, you can create advanced automations for any domain, check your flows for potential errors before running them, run multiple nodes concurrently without waiting for the completion of the previous step, create complex, non-linear flows with no-code, and design human interactions to participate in your automations. Botonomous also offers a variety of other features, such as webhooks, scheduled triggers, secure secret management, and a developer community.

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Freeday AI is an AI application that revolutionizes workflows with generative AI technology. It offers digital employees as specialized AI assistants to optimize customer service, finance, and KYC departments. These digital employees seamlessly integrate with existing infrastructure, providing cost-efficient services automation and data-driven decision-making. Freeday AI aims to redefine the integration of artificial intelligence in the workplace by freeing teams from routine tasks and empowering them with AI automation.

VanillaHR is an AI-powered hiring platform that helps businesses find the best candidates for their open positions. The platform uses AI to automate tasks such as screening resumes, scheduling interviews, and conducting background checks. This helps businesses save time and money while also improving the quality of their hires. VanillaHR is trusted by some of the world's leading companies, including Google, Amazon, and Microsoft.

SmileDial is a natural dental AI receptionist designed for Canadian dental practices. It offers a 24/7 AI receptionist system to help dentists save time, reduce costs, and enhance patient satisfaction. The AI-driven receptionist, named Susan, assists in real-time scheduling, automated reminders, insurance checks, and PHIPA compliance. SmileDial aims to maximize bookings, decrease no-shows, and offload time-consuming tasks, ultimately improving the efficiency and patient experience in dental offices.

Screenloop is an applicant tracking system (ATS) that goes beyond traditional tracking systems by offering interview intelligence, AI notes, and more at a fraction of the cost. It is designed to help businesses win top candidates, scale seamlessly, and use powerful AI to streamline operations and reduce costs. Screenloop's Talent Operations Platform provides access to a full suite of tools, including applicant tracking, interview intelligence, pulse surveys, background checks, referencing, interviewer training, data & analytics, and more.

Essay Check is a free AI-powered tool that helps students, teachers, content creators, SEO specialists, and legal experts refine their writing, detect plagiarism, and identify AI-generated content. With its user-friendly interface and advanced algorithms, Essay Check analyzes text to identify grammatical errors, spelling mistakes, instances of plagiarism, and the likelihood that content was written using AI. The tool provides detailed feedback and suggestions to help users improve their writing and ensure its originality and authenticity.

Check Typo is an AI-powered spell-checker tool designed to assist users in eliminating typos and grammatical errors from their writing. It seamlessly integrates within various websites, supports multiple languages, and preserves the original text's style and tone. Ideal for students, professionals, and writers, Check Typo enhances the writing experience with AI-driven precision, making it perfect for error-free emails, professional networking on platforms like LinkedIn, and enhancing social media posts across different platforms.

Copyright Check AI is a service that helps protect brands from legal disputes related to copyright violations on social media. The software automatically detects copyright infringements on social profiles, reducing the risk of costly legal action. It is used by Heads of Marketing and In-House Counsel at top brands to avoid lawsuits and potential damages. The service offers a done-for-you audit to highlight violations, deliver reports, and provide ongoing monitoring to ensure brand protection.

Fact Check Anything (FCA) is a browser extension that allows users to fact-check information on the internet. It uses AI to verify statements and provide users with reliable sources. FCA is available for all browsers using the Chromium engine on Windows or MacOS. It is easy to use and can be used on any website. FCA is a valuable tool for anyone who wants to stay informed and fight against misinformation.

Rizz Check is a swipe game where users can befriend AI celebrities and ask them on dates. The game is built with Rizz, a library created by boredhead00.

LLM Price Check is an AI tool designed to compare and calculate the latest prices for Large Language Models (LLM) APIs from leading providers such as OpenAI, Anthropic, Google, and more. Users can use the streamlined tool to optimize their AI budget efficiently by comparing pricing, sorting by various parameters, and searching for specific models. The tool provides a comprehensive overview of pricing information to help users make informed decisions when selecting an LLM API provider.

Neuromatch Academy NeuroAI Course Syllabus is a repository that contains the schedule and licensing information for the NeuroAI course. The course is designed to provide participants with a comprehensive understanding of artificial intelligence in neuroscience. It covers various topics related to AI applications in neuroscience, including machine learning, data analysis, and computational modeling. The content is primarily accessed from the ebook provided in the repository, and the course is scheduled for July 15-26, 2024. The repository is shared under a Creative Commons Attribution 4.0 International License and software elements are additionally licensed under the BSD (3-Clause) License. Contributors to the project are acknowledged and welcomed to contribute further.

The AWS Generative AI Constructs Library is an open-source extension of the AWS Cloud Development Kit (AWS CDK) that provides multi-service, well-architected patterns for quickly defining solutions in code to create predictable and repeatable infrastructure, called constructs. The goal of AWS Generative AI CDK Constructs is to help developers build generative AI solutions using pattern-based definitions for their architecture. The patterns defined in AWS Generative AI CDK Constructs are high level, multi-service abstractions of AWS CDK constructs that have default configurations based on well-architected best practices. The library is organized into logical modules using object-oriented techniques to create each architectural pattern model.

OptScale is an open-source FinOps and MLOps platform that provides cloud cost optimization for all types of organizations and MLOps capabilities like experiment tracking, model versioning, ML leaderboards.

RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly compute the hidden state for the "RNN" mode. So it's combining the best of RNN and transformer - **great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding** (using the final hidden state).

Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models.

aiorun is a Python package that provides a `run()` function as the starting point of your `asyncio`-based application. The `run()` function handles everything needed during the shutdown sequence of the application, such as creating a `Task` for the given coroutine, running the event loop, adding signal handlers for `SIGINT` and `SIGTERM`, cancelling tasks, waiting for the executor to complete shutdown, and closing the loop. It automates standard actions for asyncio apps, eliminating the need to write boilerplate code. The package also offers error handling options and tools for specific scenarios like TCP server startup and smart shield for shutdown.

A fast, easy-to-use, open source AI constraint solver for software developers. OptaPlanner is a powerful tool that helps developers solve complex optimization problems by providing a constraint satisfaction solver. It allows users to model and solve planning and scheduling problems efficiently, improving decision-making processes and resource allocation. With OptaPlanner, developers can easily integrate optimization capabilities into their applications, leading to better performance and cost-effectiveness.

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

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

Ragna is a RAG orchestration framework designed for managing workflows and orchestrating tasks. It provides a comprehensive set of features for users to streamline their processes and automate repetitive tasks. With Ragna, users can easily create, schedule, and monitor workflows, making it an ideal tool for teams and individuals looking to improve their productivity and efficiency. The framework offers extensive documentation, community support, and a user-friendly interface, making it accessible to users of all skill levels. Whether you are a developer, data scientist, or project manager, Ragna can help you simplify your workflow management and boost your overall performance.

Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

This repository contains an MLIR-based toolchain for AI Engine-enabled devices, such as AMD Ryzen™ AI and Versal™. This repository can be used to generate low-level configurations for the AI Engine portion of these devices. AI Engines are organized as a spatial array of tiles, where each tile contains AI Engine cores and/or memories. The spatial array is connected by stream switches that can be configured to route data between AI Engine tiles scheduled by their programmable Data Movement Accelerators (DMAs). This repository contains MLIR representations, with multiple levels of abstraction, to target AI Engine devices. This enables compilers and developers to program AI Engine cores, as well as describe data movements and array connectivity. A Python API is made available as a convenient interface for generating MLIR design descriptions. Backend code generation is also included, targeting the aie-rt library. This toolchain uses the AI Engine compiler tool which is part of the AMD Vitis™ software installation: these tools require a free license for use from the Product Licensing Site.

Azure HPC On-Demand Platform (az-hop) provides an end-to-end deployment mechanism for a base HPC infrastructure on Azure. It delivers a complete HPC cluster solution ready for users to run applications, which is easy to deploy and manage for HPC administrators. az-hop leverages various Azure building blocks and can be used as-is or easily customized and extended to meet any uncovered requirements. Industry-standard tools like Terraform, Ansible, and Packer are used to provision and configure this environment, which contains: - An HPC OnDemand Portal for all user access, remote shell access, remote visualization access, job submission, file access, and more - An Active Directory for user authentication and domain control - Open PBS or SLURM as a Job Scheduler - Dynamic resources provisioning and autoscaling is done by Azure CycleCloud pre-configured job queues and integrated health-checks to quickly avoid non-optimal nodes - A Jumpbox to provide admin access - A common shared file system for home directory and applications is delivered by Azure Netapp Files - Grafana dashboards to monitor your cluster - Remote Visualization with noVNC and GPU acceleration with VirtualGL

Olah is a self-hosted lightweight Huggingface mirror service that implements mirroring feature for Huggingface resources at file block level, enhancing download speeds and saving bandwidth. It offers cache control policies and allows administrators to configure accessible repositories. Users can install Olah with pip or from source, set up the mirror site, and download models and datasets using huggingface-cli. Olah provides additional configurations through a configuration file for basic setup and accessibility restrictions. Future work includes implementing an administrator and user system, OOS backend support, and mirror update schedule task. Olah is released under the MIT License.

NanoFlow is a throughput-oriented high-performance serving framework for Large Language Models (LLMs) that consistently delivers superior throughput compared to other frameworks by utilizing key techniques such as intra-device parallelism, asynchronous CPU scheduling, and SSD offloading. The framework proposes nano-batching to schedule compute-, memory-, and network-bound operations for simultaneous execution, leading to increased resource utilization. NanoFlow also adopts an asynchronous control flow to optimize CPU overhead and eagerly offloads KV-Cache to SSDs for multi-round conversations. The open-source codebase integrates state-of-the-art kernel libraries and provides necessary scripts for environment setup and experiment reproduction.

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

HAMi is a Heterogeneous AI Computing Virtualization Middleware designed to manage Heterogeneous AI Computing Devices in a Kubernetes cluster. It allows for device sharing, device memory control, device type specification, and device UUID specification. The tool is easy to use and does not require modifying task YAML files. It includes features like hard limits on device memory, partial device allocation, streaming multiprocessor limits, and core usage specification. HAMi consists of components like a mutating webhook, scheduler extender, device plugins, and in-container virtualization techniques. It is suitable for scenarios requiring device sharing, specific device memory allocation, GPU balancing, low utilization optimization, and scenarios needing multiple small GPUs. The tool requires prerequisites like NVIDIA drivers, CUDA version, nvidia-docker, Kubernetes version, glibc version, and helm. Users can install, upgrade, and uninstall HAMi, submit tasks, and monitor cluster information. The tool's roadmap includes supporting additional AI computing devices, video codec processing, and Multi-Instance GPUs (MIG).

Obsei is an open-source, low-code, AI powered automation tool that consists of an Observer to collect unstructured data from various sources, an Analyzer to analyze the collected data with various AI tasks, and an Informer to send analyzed data to various destinations. The tool is suitable for scheduled jobs or serverless applications as all Observers can store their state in databases. Obsei is still in alpha stage, so caution is advised when using it in production. The tool can be used for social listening, alerting/notification, automatic customer issue creation, extraction of deeper insights from feedbacks, market research, dataset creation for various AI tasks, and more based on creativity.

Synmetrix (prev. MLCraft) is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube (Cube.js) for flexible data models that consolidate metrics from various sources, enabling downstream distribution via a SQL API for integration into BI tools, reporting, dashboards, and data science. Use cases include data democratization, business intelligence, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

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