Best AI tools for< Ap Government Teacher >

Stampli is a leading AP Automation & Invoice Management Software that streamlines financial processes by automating invoice processing, vendor engagement, and expense management. With advanced AI capabilities, Stampli offers fast deployment, easy integration with popular ERPs, and smart features like Billy the Bot for automating manual tasks. Stampli provides visibility and control over the entire invoice lifecycle, making AP automation efficient and accurate. The platform also offers integrated products for payments, vendor management, and insightful analytics for audit readiness.

Humanlike is an AI-powered AP/AR tool that helps businesses cut costs by 80% compared to outsourcing accounts payable and receivable. It uses human-like AI to process invoices efficiently and accurately. The tool is built by fintech veterans from Stripe and Modern Treasury, offering a risk-free trial period and SOC 2 compliance. Humanlike enables businesses to scale sub-linearly, grow without increasing headcount, and reduce reliance on outsourcing. It boasts 24/7 availability, a 4-week implementation time, and an average cost reduction of 80% by automating exception handling and shortening cycle time.

ExamFul.ai is a free online platform that provides access to a full range of AP, IB, and A-Level past papers along with AI tutoring services. The platform offers personalized assistance through AI-driven answers and 24/7 support to enhance study efficiency and exam success. With a vast collection of past papers and expert educator support, ExamFul.ai aims to empower students to excel in their exams by providing accurate solutions and detailed explanations for complex problems.

Quizbot.ai is an advanced AI question generator designed to revolutionize the process of question and exam development. It offers a cutting-edge artificial intelligence system that can generate various types of questions from different sources like PDFs, Word documents, videos, images, and more. Quizbot.ai is a versatile tool that caters to multiple languages and question types, providing a personalized and engaging learning experience for users across various industries. The platform ensures scalability, flexibility, and personalized assessments, along with detailed analytics and insights to track learner performance. Quizbot.ai is secure, user-friendly, and offers a range of subscription plans to suit different needs.

Veryfi is an OCR API tool for invoice and receipt data extraction. It offers fast, accurate, and secure document capture and data extraction on any type of document. Veryfi empowers users to process documents efficiently, automate manual data entry, and implement AI into various business processes. The tool is designed to streamline workflows, enhance accuracy, and unlock new levels of efficiency across industries such as finance, insurance, and more.

Glean.ai is an AI-powered software designed to enhance accounts payable (AP) processes, making them faster, easier, and smarter. It offers a range of features to streamline AP tasks, including automated data extraction, GL coding, bill approvals and payments, accruals, prepaid amortizations, and more. Glean.ai also provides valuable insights into spending patterns, helping businesses identify areas of overspending and uncover opportunities for cost savings. With its user-friendly interface and robust data benchmarking capabilities, Glean.ai empowers accounting and FP&A teams to collaborate seamlessly, plan effectively, and make informed decisions regarding vendor spend.

PrepGenius.ai is an AI-driven test preparation platform designed to revolutionize the way students prepare for AP courses, college admission tests, and more. The platform offers personalized study plans, real-time feedback, interactive learning tools, and comprehensive resources to help students understand their strengths and weaknesses. With PrepGenius.ai, students can study smarter, receive tailored feedback, and track their progress to improve their test scores effectively.

AppZen is an AI-powered application designed for modern finance teams to streamline accounts payable processes, automate invoice and expense auditing, and improve compliance. It offers features such as Autonomous AP for invoice automation, Expense Audit for T&E spend management, and Card Audit for analyzing card spend. AppZen's AI learns and understands business practices, ensures compliance, and integrates with existing systems easily. The application helps prevent duplicate spend, fraud, and FCPA violations, making it a valuable tool for finance professionals.

Ramp is a financial technology company offering Spend Management, Corporate Cards, and Accounts Payable Solutions. It provides an easy-to-use platform for corporate cards, bill payments, accounting, and more, all in one place. With features like Frictionless expenses, AP Automation, Smarter spend controls, and Procurement automation, Ramp aims to make finance teams faster and happier. The platform saves time and money for businesses by automating expense management, accounting processes, and compliance tasks. Ramp integrates seamlessly with accounting systems, offers global payment capabilities, and provides dedicated support to users.

Lettria is a no-code AI platform for text that helps users turn unstructured text data into structured knowledge. It combines the best of Large Language Models (LLMs) and symbolic AI to overcome current limitations in knowledge extraction. Lettria offers a suite of APIs for text cleaning, text mining, text classification, and prompt engineering. It also provides a Knowledge Studio for building knowledge graphs and private GPT models. Lettria is trusted by large organizations such as AP-HP and Leroy Merlin to improve their data analysis and decision-making processes.

A curated list of Generative AI projects, tools, artworks, and models

This repository aims to promote global equality for individuals pursuing a PhD in NLP by providing resources and information on various aspects of the academic journey. It covers topics such as applying for a PhD, getting research opportunities, preparing for the job market, and succeeding in academia. The repository is actively updated and includes contributions from experts in the field.

AP-PLUGIN is an AI drawing plugin for the Yunzai series robot framework, allowing you to have a convenient AI drawing experience in the input box. It uses the open source Stable Diffusion web UI as the backend, deploys it for free, and generates a variety of images with richer functions.

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

LiveBench is a benchmark tool designed for Language Model Models (LLMs) with a focus on limiting contamination through monthly new questions based on recent datasets, arXiv papers, news articles, and IMDb movie synopses. It provides verifiable, objective ground-truth answers for accurate scoring without an LLM judge. The tool offers 18 diverse tasks across 6 categories and promises to release more challenging tasks over time. LiveBench is built on FastChat's llm_judge module and incorporates code from LiveCodeBench and IFEval.

Pipeline is a Python library designed for constructing computational flows for AI/ML models. It supports both development and production environments, offering capabilities for inference, training, and finetuning. The library serves as an interface to Mystic, enabling the execution of pipelines at scale and on enterprise GPUs. Users can also utilize this SDK with Pipeline Core on a private hosted cluster. The syntax for defining AI/ML pipelines is reminiscent of sessions in Tensorflow v1 and Flows in Prefect.

NNStreamer is a set of Gstreamer plugins that allow Gstreamer developers to adopt neural network models easily and efficiently and neural network developers to manage neural network pipelines and their filters easily and efficiently.

This project is a smart chatbot based on a large model, supporting WeChat, WeChat Official Account, Feishu, and DingTalk access. You can choose from GPT3.5/GPT4.0/Claude/Wenxin Yanyi/Xunfei Xinghuo/Tongyi Qianwen/Gemini/LinkAI/ZhipuAI, which can process text, voice, and images, and access external resources such as operating systems and the Internet through plugins, supporting the development of enterprise AI applications based on proprietary knowledge bases.

K2 (GeoLLaMA) is a large language model for geoscience, trained on geoscience literature and fine-tuned with knowledge-intensive instruction data. It outperforms baseline models on objective and subjective tasks. The repository provides K2 weights, core data of GeoSignal, GeoBench benchmark, and code for further pretraining and instruction tuning. The model is available on Hugging Face for use. The project aims to create larger and more powerful geoscience language models in the future.

MoneyPrinterPlus is a project designed to help users easily make money in the era of short videos. It leverages AI big model technology to batch generate various short videos, perform video editing, and automatically publish videos to popular platforms like Douyin, Kuaishou, Xiaohongshu, and Video Number. The tool covers a wide range of functionalities including integrating with major AI big model tools, supporting various voice types, offering video transition effects, enabling customization of subtitles, and more. It aims to simplify the process of creating and sharing videos to monetize traffic.

Generative BI using RAG on AWS is a comprehensive framework designed to enable Generative BI capabilities on customized data sources hosted on AWS. It offers features such as Text-to-SQL functionality for querying data sources using natural language, user-friendly interface for managing data sources, performance enhancement through historical question-answer ranking, and entity recognition. It also allows customization of business information, handling complex attribution analysis problems, and provides an intuitive question-answering UI with a conversational approach for complex queries.

Yi-Ai is a project based on the development of nineai 2.4.2. It is for learning and reference purposes only, not for commercial use. The project includes updates to popular models like gpt-4o and claude3.5, as well as new features such as model image recognition. It also supports various functionalities like model sorting, file type extensions, and bug fixes. The project provides deployment tutorials for both integrated and compiled packages, with instructions for environment setup, configuration, dependency installation, and project startup. Additionally, it offers a management platform with different access levels and emphasizes the importance of following the steps for proper system operation.

ManipVQA is a framework that enhances Multimodal Large Language Models (MLLMs) with manipulation-centric knowledge through a Visual Question-Answering (VQA) format. It addresses the deficiency of conventional MLLMs in understanding affordances and physical concepts crucial for manipulation tasks. By infusing robotics-specific knowledge, including tool detection, affordance recognition, and physical concept comprehension, ManipVQA improves the performance of robots in manipulation tasks. The framework involves fine-tuning MLLMs with a curated dataset of interactive objects, enabling robots to understand and execute natural language instructions more effectively.

The AWS Lex Web UI is a sample Amazon Lex web interface that provides a chatbot UI component for integration into websites. It supports voice and text interactions, Lex response cards, and programmable configuration using JavaScript. The interface can be used as a full-page chatbot UI or embedded as a widget. It offers mobile-ready responsive UI, seamless voice-text switching, and interactive messaging support. The project includes CloudFormation templates for easy deployment and customization. Users can modify configurations, integrate the UI into existing sites, and deploy using various methods like CloudFormation, pre-built libraries, or npm installation.

The AV-Deepfake1M repository is the official repository for the paper AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset. It addresses the challenge of detecting and localizing deepfake audio-visual content by proposing a dataset containing video manipulations, audio manipulations, and audio-visual manipulations for over 2K subjects resulting in more than 1M videos. The dataset is crucial for developing next-generation deepfake localization methods.

LxgwZhenKai is a Chinese font derived from LXGW WenKai, manually adjusted for boldness and supplemented with AI assistance for character additions. The font aims to provide a comfortable reading experience on screens while also serving as a bold version of LXGW WenKai for temporary use. It contains over 13,000 characters, including common simplified and traditional Chinese characters, and is licensed under SIL Open Font License 1.1. Users are allowed to freely use, distribute, modify, and create derivative fonts based on LxgwZhenKai.

AgentPoison is a repository that provides the official PyTorch implementation of the paper 'AgentPoison: Red-teaming LLM Agents via Memory or Knowledge Base Backdoor Poisoning'. It offers tools for red-teaming LLM agents by poisoning memory or knowledge bases. The repository includes trigger optimization algorithms, agent experiments, and evaluation scripts for Agent-Driver, ReAct-StrategyQA, and EHRAgent. Users can fine-tune motion planners, inject queries with triggers, and evaluate red-teaming performance. The codebase supports multiple RAG embedders and provides a unified dataset access for all three agents.

AnyParser provides an API to accurately extract unstructured data (e.g., PDFs, images, charts) into a structured format. Users can set up their API key, run synchronous and asynchronous extractions, and perform batch extraction. The tool is useful for extracting text, numbers, and symbols from various sources like PDFs and images. It offers flexibility in processing data and provides immediate results for synchronous extraction while allowing users to fetch results later for asynchronous and batch extraction. AnyParser is designed to simplify data extraction tasks and enhance data processing efficiency.

The HDU Computer Science Lecture Notes is a comprehensive guide designed to help students navigate through various challenges in the field of computer science. It covers topics such as programming languages, artificial intelligence, software development, and more. The notes provide insights on how to effectively utilize university time, balance grades with project experience, and make informed decisions regarding career paths. Created by a collaborative effort involving students, teachers, and industry experts, the lecture notes aim to serve as a guiding tool for individuals seeking guidance in the computer science domain.

A repository sharing literatures and resources about Large Language Models (LLMs) and beyond. It includes tutorials, notebooks, course assignments, development stages, modeling, inference, training, applications, study, and basics related to LLMs. The repository covers various topics such as language models, transformers, state space models, multi-modal language models, training recipes, applications in autonomous driving, code, math, embodied intelligence, and more. The content is organized by different categories and provides comprehensive information on LLMs and related topics.

Expert in AP Gov crafting content strictly aligned with Collegeboard materials.

Offers detailed explanations of economic theories, guidance on microeconomic principles, example problems and solutions, and insights into real-world economic scenarios.

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