Best AI tools for< Identify Key Components >

Graphite Note is a no-code AI tool that helps businesses transform data into key drivers, predictions, and next-best actions. It is designed to be user-friendly and accessible to businesses of all sizes, even those without a data science team. With Graphite Note, businesses can quickly and easily generate AI models, understand patterns in their data, predict future outcomes, and get actionable insights. This can help businesses make better decisions, improve their operations, and grow their revenue.

Komodo Health is a healthcare technology company that provides software applications to enable users to deliver exceptional value to their customers, colleagues, and patients. The company's Healthcare Map is the industry's most precise view of the U.S. healthcare system, and it combines the world's most comprehensive view of patient-encounters with innovative algorithms and decades of clinical expertise. Komodo Health's software applications are used by life sciences companies, payers, providers, and consultancies to improve the certainty of pre-launch plans, calculate Rx-based ROI for digital marketing, find patients with complicated or rare conditions, and more.

Sybill is an AI-powered personal assistant designed specifically for sales teams. It uses advanced natural language processing (NLP) and machine learning algorithms to analyze sales conversations, identify key insights, and automate tasks. With Sybill, sales teams can save time, improve productivity, and close more deals.

Detangle is a legal document analysis tool that helps users understand complex legal documents without the need for an attorney. It provides a summary of the document, identifies key terms and concepts, and highlights potential issues. Detangle is designed to save users time and money by making legal documents more accessible and understandable.

ConvoZen.AI is a leading AI-driven conversational intelligence platform that provides businesses with insights and tools to improve their customer interactions. The platform offers a range of features, including AI-powered insights and key moment identification, conversation sentiment analysis, automated compliance audit, agent performance management, and custom reports and analytics. ConvoZen.AI integrates with enterprise CRM, emails, and other systems to provide real-time alerts and actionable insights. The platform is designed to help businesses improve sales, customer experience, compliance, and agent performance.

Diligen is a machine learning powered contract analysis tool that helps teams streamline their contract review process. It can identify key provisions, generate contract summaries, and help teams manage review with machine learning powered analysis. Diligen is used by law firms, legal service providers, and corporations around the world to make high quality contract review faster, more efficient, and more cost effective.

Workbounce is an AI-powered Voice-of-Customer insights platform that helps businesses understand the 'why' behind their key performance indicators. By integrating data from various sources like CRM, call recordings, emails, and messaging tools, Workbounce uses advanced AI to analyze sentiment, detect uncertainty, identify key topics, and uncover trends. The platform provides deep insights to help businesses improve win rates, shorten sales cycles, and make informed decisions.

Contify is a comprehensive market and competitive intelligence platform that helps businesses track information on competitors, customers, and industry segments. It enables users to collect, curate, and share actionable intelligence across their organization. Contify offers a range of solutions for different teams and industries, with features like customizable dashboards, AI-powered insights, noise-free news delivery, and seamless integration with various tools. The platform aims to help companies make strategic decisions, drive growth, and stay ahead in the dynamic market by providing critical insights and competitive advantages.

SummarizeYT is a YouTube summary tool that uses ChatGPT to generate summaries of YouTube videos. It is a free and easy-to-use tool that can help you save time and get the most out of your YouTube viewing experience.

Raijin.ai is an AI-powered Customer Discovery and Intelligence Hub designed to help teams aggregate and extract key insights from customer conversations. It accelerates product development by prioritizing features based on customer feedback. The platform offers features like AI Thematic Analysis, Report Writing, Segmentation, and Tags to streamline qualitative research and analysis processes. Raijin.ai is ideal for user researchers, product analysts, and teams looking to integrate AI seamlessly into their workflow to create customer-centric products and data-driven marketing strategies.

Qlik AutoML is an AI tool that offers automated machine learning for analytics teams. It allows users to create machine learning experiments, identify key drivers in data, train models, and make predictions. With a focus on no-code machine learning, Qlik AutoML simplifies the process of generating predictive models and understanding outcomes. The tool enables users to explore predictive data, test what-if scenarios, and leverage AI-powered connectors for seamless integration with other AI and machine learning tools.

VeedoAI is an advanced AI tool that supports large multimodal models to provide video insights that drive results. It helps make video content searchable, actionable, and intelligent to boost engagement, accelerate learning, and maximize revenue. VeedoAI offers features such as contextual search, flashcards, AI chat, short videos creation, video to blog conversion, frame explanation, transcription, smart scenes, and transcript summarization. The application is trusted by a community of 12,000+ creators and businesses for various use cases like telemedicine, insurance, e-learning, law, videography, sports, and sales. VeedoAI leverages cutting-edge language models and AI technologies to provide interactive learning experiences, engaging short videos, enhanced accessibility with captions, and conversational AI for Q&A experiences.

Insight7 is a powerful AI-powered tool that helps businesses extract insights from customer and employee interviews. It uses natural language processing and machine learning to analyze large volumes of unstructured data, such as transcripts, audio recordings, and videos. Insight7 can identify key themes, trends, and sentiment, which can then be used to improve products, services, and customer experiences.

AutoPitch is an AI-powered sales research assistant that helps you sell technical products. It learns your product, automates research reports, and helps you write prospect-specific pitches. With AutoPitch, you can save time on research and write better emails that are more likely to get opened and clicked. AutoPitch integrates with 6+ data sources, including your website, the SEC, and Crunchbase, to provide you with the most up-to-date information on your prospects. It also uses AI to analyze this data and identify key insights that can help you close more deals.

OpusClip is an AI-powered video repurposing tool that allows users to easily transform long videos into engaging short clips optimized for various social media platforms. The tool leverages big data analysis and AI technology to identify key moments in videos, add dynamic captions, and seamlessly create viral-worthy content. With features like AI b-roll generation, auto reframing, customizable brand templates, and social post scheduling, OpusClip streamlines the video editing process and helps users grow their online presence. The platform supports multiple languages and offers a free trial of its Pro Plan, making it accessible to a wide range of content creators and marketers.

Munch is an AI-powered video repurposing platform that helps businesses and individuals extract the most engaging and impactful clips from their long-form videos. With its advanced machine learning capabilities, Munch analyzes video content to identify key moments, generate captions, and create social media posts. It supports multiple languages and provides insights into marketing trends to help users optimize their content for different platforms.

Eklipse is an AI-powered tool that helps streamers and content creators automatically generate highlights from their Twitch, YouTube, and Facebook streams and videos. It uses advanced AI technology to identify key moments in your streams, such as exciting gaming moments or funny in-game experiences, and then creates short, shareable clips that are perfect for TikTok, Reels, and YouTube Shorts. Eklipse also offers a range of editing tools that allow you to customize your clips and add your own branding. With Eklipse, you can save time and effort on editing, and focus on creating great content that will grow your channel.

Doclime is an AI-powered document analysis tool that helps users extract insights and automate tasks from their documents. It uses natural language processing and machine learning to analyze text, identify key information, and generate summaries, reports, and other insights. Doclime can be used for a variety of tasks, including contract review, due diligence, market research, and customer support.

Legalese Decoder is an AI-powered web application designed to translate legal documents into plain English. It utilizes artificial intelligence, natural language processing, and machine learning techniques to analyze legal documents, identify key terms and concepts, and provide definitions and explanations in simpler language. The tool aims to help users, especially non-legal professionals, understand complex legal jargon and agreements more easily, ultimately empowering them to make informed decisions regarding legal matters.

The website offers an AI tool that allows users to chat with any YouTube video, ask questions, analyze videos, discover insights, and identify key moments quickly. It is designed to enhance study and research efficiency by providing a powerful platform for users to interact with video content. Users can access a demo to experience the tool's capabilities and are encouraged to stop relying on the comments section for finding timestamps. The tool is free to use and aims to streamline the process of extracting valuable information from videos.

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

OpenRepoWiki is a tool designed to automatically generate a comprehensive wiki page for any GitHub repository. It simplifies the process of understanding the purpose, functionality, and core components of a repository by analyzing its code structure, identifying key files and functions, and providing explanations. The tool aims to assist individuals who want to learn how to build various projects by providing a summarized overview of the repository's contents. OpenRepoWiki requires certain dependencies such as Google AI Studio or Deepseek API Key, PostgreSQL for storing repository information, Github API Key for accessing repository data, and Amazon S3 for optional usage. Users can configure the tool by setting up environment variables, installing dependencies, building the server, and running the application. It is recommended to consider the token usage and opt for cost-effective options when utilizing the tool.

PatchWork is an open-source framework designed for automating development tasks using large language models. It enables users to automate workflows such as PR reviews, bug fixing, security patching, and more through a self-hosted CLI agent and preferred LLMs. The framework consists of reusable atomic actions called Steps, customizable LLM prompts known as Prompt Templates, and LLM-assisted automations called Patchflows. Users can run Patchflows locally in their CLI/IDE or as part of CI/CD pipelines. PatchWork offers predefined patchflows like AutoFix, PRReview, GenerateREADME, DependencyUpgrade, and ResolveIssue, with the flexibility to create custom patchflows. Prompt templates are used to pass queries to LLMs and can be customized. Contributions to new patchflows, steps, and the core framework are encouraged, with chat assistants available to aid in the process. The roadmap includes expanding the patchflow library, introducing a debugger and validation module, supporting large-scale code embeddings, parallelization, fine-tuned models, and an open-source GUI. PatchWork is licensed under AGPL-3.0 terms, while custom patchflows and steps can be shared using the Apache-2.0 licensed patchwork template repository.

crewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It provides a flexible and structured approach to AI collaboration, enabling users to define agents with specific roles, goals, and tools, and assign them tasks within a customizable process. crewAI supports integration with various LLMs, including OpenAI, and offers features such as autonomous task delegation, flexible task management, and output parsing. It is open-source and welcomes contributions, with a focus on improving the library based on usage data collected through anonymous telemetry.

LongRoPE is a method to extend the context window of large language models (LLMs) beyond 2 million tokens. It identifies and exploits non-uniformities in positional embeddings to enable 8x context extension without fine-tuning. The method utilizes a progressive extension strategy with 256k fine-tuning to reach a 2048k context. It adjusts embeddings for shorter contexts to maintain performance within the original window size. LongRoPE has been shown to be effective in maintaining performance across various tasks from 4k to 2048k context lengths.

ViGenAiR is a tool that harnesses the power of Generative AI models on Google Cloud Platform to automatically transform long-form Video Ads into shorter variants, targeting different audiences. It generates video, image, and text assets for Demand Gen and YouTube video campaigns. Users can steer the model towards generating desired videos, conduct A/B testing, and benefit from various creative features. The tool offers benefits like diverse inventory, compelling video ads, creative excellence, user control, and performance insights. ViGenAiR works by analyzing video content, splitting it into coherent segments, and generating variants following Google's best practices for effective ads.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

Ant Design X is a tool for crafting AI-driven interfaces effortlessly. It is built on the best practices of enterprise-level AI products, offering flexible and diverse atomic components for various AI dialogue scenarios. The tool provides out-of-the-box model integration with inference services compatible with OpenAI standards. It also enables efficient management of conversation data flows, supports rich template options, complete TypeScript support, and advanced theme customization. Ant Design X is designed to enhance development efficiency and deliver exceptional AI interaction experiences.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

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 Radicalbit AI Monitoring Platform provides a comprehensive solution for monitoring Machine Learning and Large Language models in production. It helps proactively identify and address potential performance issues by analyzing data quality, model quality, and model drift. The repository contains files and projects for running the platform, including UI, API, SDK, and Spark components. Installation using Docker compose is provided, allowing deployment with a K3s cluster and interaction with a k9s container. The platform documentation includes a step-by-step guide for installation and creating dashboards. Community engagement is encouraged through a Discord server. The roadmap includes adding functionalities for batch and real-time workloads, covering various model types and tasks.

The AI-Exchange Protocol (AIXP) is a communication standard designed to facilitate information and result exchange between artificial intelligence agents. It aims to enhance interoperability and collaboration among various AI systems by establishing a common framework for communication. AIXP includes components for communication, loop prevention, and task finalization, ensuring secure and efficient collaboration while avoiding infinite communication loops. The protocol defines access points, data formats, authentication, authorization, versioning, loop detection, status codes, error messages, and task completion verification. AIXP enables AI agents to collaborate seamlessly and complete tasks effectively, contributing to the overall efficiency and reliability of AI systems.

This solution accelerator is built on Azure Cognitive Search Service and Azure OpenAI Service to synthesize post-contact center transcripts for intelligent contact center scenarios. It converts raw transcripts into customer call summaries to extract insights around product and service performance. Key features include conversation summarization, key phrase extraction, speech-to-text transcription, sensitive information extraction, sentiment analysis, and opinion mining. The tool enables data professionals to quickly analyze call logs for improvement in contact center operations.

帮你自动拆解并解决复杂问题，这只仓鼠超有用！

Design Transformer delivers a concise, expert analysis of key design system components, blending global trends and professional insights for a comprehensive overview.

Identify key claims and sniff past the BS with your personal AI Logic Checker and Fallacy Expert.

使用GPTS快速识别关键因素，提高决策效率和工作效率，找到关键的20%，Use GPTS to quickly identify key factors, improve decision-making efficiency and work efficiency, and find the key 20%.

Efficient in identifying key customer outcomes, related KPIs, and thoughts-provoking, outcomes-deciphering questions.

Summarizes events into key takeaways, listing speakers.

This prompt is structured to provide an effective methodology in generating progressively more detailed and specific summaries, focused on key entities.

Decodes AI papers: Summarizes, clarifies topics, and lists key ideas.

Provide a Topic and an Audience and we'll help you create 1. Course description 2. Outline 3. Learning Outcomes 5. Skills-Knowledge-Attitude objectives 5. Key points per lesson

Step 1: Upload RFP Step 2: Prompt: I need a comprehensive summary of the RFP. Split the summary in multiple blocks / section. After giving me one section wait for my command to move to the next section. Step 3: Prompt: Move to the next section, please :)

A GPT specializing in historical primary source analysis

I'm EmailThreadAnalyzer, here to help you with your email thread analysis.

Expert in Salesforce release notes analysis and advice.

Legal Expert in reviewing Employment Agreement (Full-Time) (Powered by LegalNow ai.legalnow.xyz)）

Upload an image of a statistical analysis and we'll interpret the results: linear regression, logistic regression, ANOVA, cluster analysis, MDS, factor analysis, and many more

Medical image analysis for better diagnostic insights and preventive health assessments.

I refine complex knowledge into granular, integrated solutions.

This GPT assist you in analyzing ecosystems based on the Ecosystem Value Blueprint of Ron Adner

Summarizes Indian companies, excluding financial performance in news.

专注于从剧本文本提取高光片段