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Ponzu.gg is a domain name that may be for sale. The website provides information about the domain and offers the option to inquire about purchasing it. It is a platform where users can explore the availability of the domain name and potentially acquire it for their own use. The site emphasizes copyright and privacy policy to protect intellectual property rights and user data.

Sttabot.io is a domain parking webpage generated by the domain owner using Sedo. The website provides resources and information related to the domain name. Visitors can find out if the domain name is for sale or contact the domain owner for inquiries. The webpage also includes a disclaimer stating that Sedo, the platform used for domain parking, does not have a relationship with third-party advertisers and does not control or endorse any specific service or trademark mentioned on the page.

Namy.ai is a domain generator that uses AI technology to help users quickly create website domain names. Users can describe their business or idea, and the tool generates domain suggestions in just 30 seconds. The tool also allows users to check domain availability and browse pre-generated domain options. With Namy.ai, users can easily find the perfect domain name for their business or project.

Namewizard is an AI-powered domain name generator that helps you find the perfect domain name for your business or project. With Namewizard, you can generate unlimited domain name suggestions, check domain availability, and use advanced search filters to narrow down your results. Namewizard also offers a history of your domain name suggestions so you can easily track your progress. Namewizard is the perfect tool for anyone who is looking for a creative and unique domain name.

jsontochatgpt.com is a domain available for purchase on GoDaddy Auctions. The website provides information about the domain and its availability for interested buyers. It is not an AI tool or application, but rather a platform for domain transactions and auctions.

Contentcale.com is a domain currently up for sale. The website serves as a platform for selling the domain and provides information about the domain. It is generated by Sedo Domain Parking, and the site disclaims any relationship with third-party advertisers. The contentcale.com domain may be purchased by interested parties.

Namify is an AI-powered business name generator that helps users create a memorable brand name effortlessly. It leverages the power of new domain extensions to curate meaningful domain name suggestions for various businesses. Namify also provides logo design options and checks domain name, social media username, and trademark availability to ensure a solid branding decision. With expert recommendations and a user-friendly interface, Namify streamlines the process of finding the perfect brand name for your business.

SmartyNames.com is a business name generator that uses AI to help entrepreneurs come up with creative and unique names for their businesses. The tool is easy to use and provides instant results. It also offers a variety of features to help users find the perfect name for their business, including a domain name checker and a reverse domain search. SmartyNames.com is a valuable tool for any entrepreneur who is looking for a unique and memorable name for their business.

Atai.world is a website domain that appears to be registered but available for purchase. The site does not provide any specific information or functionality, and it seems to be related to privacy settings and technical support. The website is available in English language.

NamePepper is a free AI business name generator tool that helps users unlock brandable names for their businesses. The tool generates hundreds of creative and iconic business names tailored to the user's preferences and industry. Users can quickly check domain availability, save favorite names, and get feedback from others. NamePepper aims to simplify the naming process and save users time and effort in creating a memorable brand identity.

Brandix is an AI-powered tool that helps users generate brand names in seconds. Users can choose from a variety of categories or input a message, and the tool will generate brand names accordingly. Additionally, Brandix instantly checks the domain availability for the generated names. With a user-friendly interface, Brandix has assisted thousands of people in finding the perfect brand name for their businesses.

Chainintel.info is a website that appears to be a domain for sale. The site simply states 'chainintel.info Buy this domain. 2024 Copyright. All Rights Reserved. Privacy Policy'. It seems to be a platform for purchasing the domain name 'chainintel.info'. The website does not provide any additional information or services beyond the domain sale.

Heissdocs.com is a domain selling platform that offers a secure and hassle-free process for buying and transferring domain names. The platform ensures buyer protection through a unique program, fast and easy transfers, and secure payment options. It provides information on Value Added Tax (VAT) for EU consumers and businesses. With a focus on simplicity and safety, Heissdocs.com aims to make domain name transactions straightforward and secure for users.

vnsplit.me is a domain for sale website that offers the domain 'vnsplit.me' for purchase. The website is copyright protected until 2024 and all rights are reserved. It also has a privacy policy in place to protect user data and information.

Emaildojo is an AI-driven email innovation hub that provides free and unique email tools to help users elevate their email game. With Emaildojo, users can generate catchy and innovative short-form content, prevent their emails from landing in spam, boost ROI by driving clicks from email inbox, detect, analyze, and fix email domain issues, and check, lookup, and validate their email identity instantly. Emaildojo is the perfect tool for email marketers who want to improve their email deliverability and engagement.

OpenFuture is the world's largest AI Tools Directory in 2024, offering a comprehensive collection of AI applications across various categories such as 3D generator, aggregators, AI detection, art generator, audio editing, and more. Users can explore a wide range of AI-powered tools to enhance productivity, streamline tasks, and improve efficiency in different domains. The platform serves as a valuable resource for individuals and businesses looking to leverage artificial intelligence technology for various purposes.

BluePond GenAI PaaS is an automation and insights powerhouse tailored for Property and Casualty Insurance. It offers end-to-end execution support from GenAI data scientists, engineers & human-in-the-loop processing. The platform provides automated intake extraction, classification enrichment, validation, complex document analysis, workflow automation, and decisioning. Users benefit from rapid deployment, complete control of data & IP, and pre-trained P&C domain library. BluePond GenAI PaaS aims to energize and expedite GenAI initiatives throughout the insurance value chain.

second.dev is a domain that is currently parked for free, courtesy of GoDaddy.com. The website does not offer any specific services or products but rather serves as a placeholder for potential future use. It is important to note that any references to companies, products, or services on the site are not controlled by GoDaddy.com LLC and do not imply any association or endorsement by GoDaddy.

Maximus.guru is a domain that has expired and is currently parked for free by GoDaddy.com. The website does not offer any specific services or products but rather displays a message indicating that the domain is no longer active. It is important to note that any references to companies, products, or services on this site are not endorsed or associated with GoDaddy.com LLC.

Sttabot.io is a website that appears to be for sale. It seems to be a domain name that is not currently in use. The site provides limited information, with a copyright notice for the year 2024 and a privacy policy. It is unclear what the original purpose of the website was or what services it offered.

ail-typo-squatting is a Python library designed to generate a list of potential typo squatting domains using a domain name permutation engine. It can be used as a standalone tool or to feed other systems. The tool provides various algorithms to create typos by adding, changing, or omitting characters in domain names. It also offers DNS resolving capabilities to check the availability of generated variations. The project has been co-funded by CEF-TC-2020-2 - 2020-EU-IA-0260 - JTAN - Joint Threat Analysis Network.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. It provides a common API to deliver inference solutions on various platforms, including CPU, GPU, NPU, and heterogeneous devices. OpenVINO™ supports pre-trained models from Open Model Zoo and popular frameworks like TensorFlow, PyTorch, and ONNX. Key components of OpenVINO™ include the OpenVINO™ Runtime, plugins for different hardware devices, frontends for reading models from native framework formats, and the OpenVINO Model Converter (OVC) for adjusting models for optimal execution on target devices.

**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.

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

The vscode-pddl extension provides comprehensive support for Planning Domain Description Language (PDDL) in Visual Studio Code. It enables users to model planning domains, validate them, industrialize planning solutions, and run planners. The extension offers features like syntax highlighting, auto-completion, plan visualization, plan validation, plan happenings evaluation, search debugging, and integration with Planning.Domains. Users can create PDDL files, run planners, visualize plans, and debug search algorithms efficiently within VS Code.

The llama-recipes repository provides a scalable library for fine-tuning Llama 2, along with example scripts and notebooks to quickly get started with using the Llama 2 models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama 2 and other tools in the LLM ecosystem. The examples here showcase how to run Llama 2 locally, in the cloud, and on-prem.

Nightshade Antidote is an image forensics tool used to analyze digital images for signs of manipulation or forgery. It implements several common techniques used in image forensics including metadata analysis, copy-move forgery detection, frequency domain analysis, and JPEG compression artifacts analysis. The tool takes an input image, performs analysis using the above techniques, and outputs a report summarizing the findings.

Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency. It helps users improve AI output quality through data quality, take control of their data and models, and improve efficiency by quickly iterating on the right data and models. Argilla is an open-source community-driven project that provides tools for achieving and maintaining high-quality data standards, with a focus on NLP and LLMs. It is used by AI teams from companies like the Red Cross, Loris.ai, and Prolific to improve the quality and efficiency of AI projects.

aiwechat-vercel is a tool that integrates AI capabilities into WeChat public accounts using Vercel functions. It requires minimal server setup, low entry barriers, and only needs a domain name that can be bound to Vercel, with almost zero cost. The tool supports various AI models, continuous Q&A sessions, chat functionality, system prompts, and custom commands. It aims to provide a platform for learning and experimentation with AI integration in WeChat public accounts.

Yet Another Applied LLM Benchmark is a collection of diverse tests designed to evaluate the capabilities of language models in performing real-world tasks. The benchmark includes tests such as converting code, decompiling bytecode, explaining minified JavaScript, identifying encoding formats, writing parsers, and generating SQL queries. It features a dataflow domain-specific language for easily adding new tests and has nearly 100 tests based on actual scenarios encountered when working with language models. The benchmark aims to assess whether models can effectively handle tasks that users genuinely care about.

LABO is a time series forecasting and analysis framework that integrates pre-trained and fine-tuned LLMs with multi-domain agent-based systems. It allows users to create and tune agents easily for various scenarios, such as stock market trend prediction and web public opinion analysis. LABO requires a specific runtime environment setup, including system requirements, Python environment, dependency installations, and configurations. Users can fine-tune their own models using LABO's Low-Rank Adaptation (LoRA) for computational efficiency and continuous model updates. Additionally, LABO provides a Python library for building model training pipelines and customizing agents for specific tasks.

LF AI & Data Landscape is a map to explore open source projects in the AI & Data domains, highlighting companies that are members of LF AI & Data. It showcases members of the Foundation and is modelled after the Cloud Native Computing Foundation landscape. The landscape includes current version, interactive version, new entries, logos, proper SVGs, corrections, external data, best practices badge, non-updated items, license, formats, installation, vulnerability reporting, and adjusting the landscape view.

MiniCheck is an efficient fact-checking tool designed to verify claims against grounding documents using large language models. It provides a sentence-level fact-checking model that can be used to evaluate the consistency of claims with the provided documents. MiniCheck offers different models, including Bespoke-MiniCheck-7B, which is the state-of-the-art and commercially usable. The tool enables users to fact-check multi-sentence claims by breaking them down into individual sentences for optimal performance. It also supports automatic prefix caching for faster inference when repeatedly fact-checking the same document with different claims.

BehaviorTree.CPP is a C++ 17 library that provides a framework to create BehaviorTrees. It was designed to be flexible, easy to use, reactive and fast. Even if our main use-case is robotics, you can use this library to build AI for games, or to replace Finite State Machines. There are few features which make BehaviorTree.CPP unique, when compared to other implementations: It makes asynchronous Actions, i.e. non-blocking, a first-class citizen. You can build reactive behaviors that execute multiple Actions concurrently (orthogonality). Trees are defined using a Domain Specific scripting language (based on XML), and can be loaded at run-time; in other words, even if written in C++, the morphology of the Trees is not hard-coded. You can statically link your custom TreeNodes or convert them into plugins and load them at run-time. It provides a type-safe and flexible mechanism to do Dataflow between Nodes of the Tree. It includes a logging/profiling infrastructure that allows the user to visualize, record, replay and analyze state transitions.

GPTSwarm is a graph-based framework for LLM-based agents that enables the creation of LLM-based agents from graphs and facilitates the customized and automatic self-organization of agent swarms with self-improvement capabilities. The library includes components for domain-specific operations, graph-related functions, LLM backend selection, memory management, and optimization algorithms to enhance agent performance and swarm efficiency. Users can quickly run predefined swarms or utilize tools like the file analyzer. GPTSwarm supports local LM inference via LM Studio, allowing users to run with a local LLM model. The framework has been accepted by ICML2024 and offers advanced features for experimentation and customization.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

JAX AI Stack is a suite of libraries built around the JAX Python package for array-oriented computation and program transformation. It provides a growing ecosystem of packages for specialized numerical computing across various domains, encouraging modularity and innovation in domain-specific libraries. The stack includes core packages like JAX, flax for building neural networks, ml_dtypes for NumPy dtype extensions, optax for gradient processing and optimization, and orbax for checkpointing and persistence utilities. Optional packages like grain data loader and tensorflow are also available for installation.

Open Life Science AI: Medical Reasoning Tasks is a collaborative hub for developing cutting-edge reasoning tasks for Large Language Models (LLMs) in the medical, healthcare, and clinical domains. The repository aims to advance AI capabilities in healthcare by fostering accurate diagnoses, personalized treatments, and improved patient outcomes. It offers a diverse range of medical reasoning challenges such as Diagnostic Reasoning, Treatment Planning, Medical Image Analysis, Clinical Data Interpretation, Patient History Analysis, Ethical Decision Making, Medical Literature Comprehension, and Drug Interaction Assessment. Contributors can join the community of healthcare professionals, AI researchers, and enthusiasts to contribute to the repository by creating new tasks or improvements following the provided guidelines. The repository also provides resources including a task list, evaluation metrics, medical AI papers, and healthcare datasets for training and evaluation.

A branding expert for all business types, specializing in creative names and domain checks.

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Website Value Appraisal Tool - Value My Website. I'm a checker and calculator tool for appraising the value and worth of websites, estimating prices based on domain & website's daily, monthly, annual revenue.

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Calculate website worth by analyzing monthly revenue, using industry-standard valuation methods to provide approximate, informative value estimates.

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GPT expert on CISA's KEV Catalog and CVE existence check

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A mental health self-check tool assessing weather dependency. Powered by WeatherMind

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ChatGPT Essay Writer helps you to write essays with OpenAI. Generate Professional Essays with Plagiarism Check, Formatting, Cost Estimation & More.