Best AI tools for< Identify Coins >

Trend Hunter is an AI-powered platform that offers a vast database of ideas and innovations, trend reports, consumer insights, advisory services, and training programs. It combines human researchers with AI to provide data-driven insights for innovators. The platform helps businesses accelerate innovation, identify trends, and stay ahead of the competition. Trend Hunter's AI capabilities include natural language processing, machine learning, image recognition, and consumer insights analysis.

Pl@ntNet is a citizen science project available as an application that helps you identify plants from your photos. It is a collaborative project that brings together scientists, naturalists, and citizens from all over the world to collect and share data on plant diversity. The app uses artificial intelligence to identify plants from photos, and the data collected is used to create a global database of plant diversity. Pl@ntNet is free to use and is available in over 20 languages.

CvSorter is an AI-powered CV and resume screening tool that streamlines the hiring process by automating screening, improving accuracy, and saving time. It allows users to upload job descriptions and candidate CVs to identify top talent efficiently. With customizable criteria and detailed reporting, CvSorter enhances recruitment workflow by focusing on identifying the best candidates quickly and accurately.

ACHIV is an AI tool for ideas validation and market research. It helps businesses make informed decisions based on real market needs by providing data-driven insights. The tool streamlines the market validation process, allowing quick adaptation and refinement of product development strategies. ACHIV offers a revolutionary approach to data collection and preprocessing, along with proprietary AI models for smart analysis and predictive forecasting. It is designed to assist entrepreneurs in understanding market gaps, exploring competitors, and enhancing investment decisions with real-time data.

Nextatlas is an AI-powered trend forecasting service that helps businesses understand, innovate, launch, make, and win. It provides data-rich trend prediction built through analysis on the interests and behaviors from the consumers that drive change, experts, and innovators. Nextatlas' AI can quickly be tailored to your specific business challenges and uncover attractive business opportunities. It brings you to findings that represent what will happen in the future, that you cannot know when you begin searching.

Wildlife Insights is an AI application that brings cutting-edge technology to wildlife conservation. It streamlines decision-making by providing machine learning models and tools to manage, analyze, and share camera trap data. Users can easily upload, identify, analyze, and discover wildlife through the platform, enabling better decisions to help wildlife thrive globally.

Talkwalker is a leading consumer intelligence platform that provides social listening, media monitoring, and social benchmarking technology. It helps businesses navigate the dynamic world of social and consumer data to drive loyalty, win new customers, and outplay competitors. Talkwalker's platform is used by over 2,500 brands and is a leader in The Forrester New Wave™ AI-Enabled Consumer Intelligence Platforms Q3 2021.

Dezan AI is a DIY data collection and analysis platform powered by AI, designed to help users generate surveys in seconds. The platform allows users to set survey goals, craft surveys, and collect real-time data from interest-based respondents worldwide. Dezan AI offers various survey templates, question types, and data analysis features to streamline the survey creation process. With a focus on interest targeting, the platform ensures reaching the right audience for data collection campaigns. Users can enhance their surveys with AI suggestions and deploy campaigns through Google Ads for targeted audience engagement.

Medgic is an advanced AI tool designed to scan, analyze, and detect skin problems using powerful AI technology. Users can simply take a photo of their skin condition, and Medgic provides results along with friendly advice. The AI robot aims to reimagine medicine by continuously learning and evolving to contribute towards solving global healthcare challenges. It is available for free on all devices and offers general information for educational purposes only, not as a replacement for physician consultation.

TechTarget is a leading provider of purchase intent data and marketing services for the technology industry. Our data-driven solutions enable technology companies to identify and engage with their target audiences, and to measure the impact of their marketing campaigns. We offer a range of products and services, including:

Immunifai is a cybersecurity company that provides AI-powered threat detection and response solutions. The company's mission is to make the world a safer place by protecting organizations from cyberattacks. Immunifai's platform uses machine learning and artificial intelligence to identify and respond to threats in real time. The company's solutions are used by a variety of organizations, including Fortune 500 companies, government agencies, and financial institutions.

SWMS AI is an AI-powered safety risk assessment tool that helps businesses streamline compliance and improve safety. It leverages a vast knowledge base of occupational safety resources, codes of practice, risk assessments, and safety documents to generate risk assessments tailored specifically to a project, trade, and industry. SWMS AI can be customized to a company's policies to align its AI's document generation capabilities with proprietary safety standards and requirements.

The website is an AI tool that offers a wide range of AI, software, and tools for enterprise growth and automation. It provides solutions in areas such as AI hardware, automation, application security, CRM, cloud services, data management, generative AI, network monitoring, process intelligence, proxies, remote monitoring, surveys, sustainability, workload automation, and more. The platform aims to help businesses leverage AI technologies to enhance efficiency, security, and productivity across various industries.

LogRocket is a session replay, product analytics, and issue detection platform that helps software teams deliver the best web and mobile experiences. With LogRocket, you can see exactly what users experienced on your app, as well as DOM playback, console and network logs, errors, and performance data. You can also surface the most impactful user issues with JavaScript errors, network errors, stack traces, automatic triaging, and alerting. LogRocket also provides product analytics to help you understand how users are interacting with your app, and UX analytics to help you visualize how users experience your app at both the individual and aggregate level.

MapZot.AI is an advanced retail site selection and market analysis AI tool that leverages big data and unique algorithms to provide real-time insights for businesses. It monitors local and national chains, predicts their next locations with high confidence, and offers decision analytics to pinpoint the best real estate locations for various industries. With features like internal data utilization, store cannibalization models, and over 90% confidence in decision-making, MapZot.AI is a powerful platform for site selection and market planning.

Graphio is an AI-driven employee scoring and scenario builder tool that leverages continuous, real-time scoring with AI agents to assess potential, predict flight risks, and identify future leaders. It replaces subjective evaluations with AI-driven insights to ensure accurate, unbiased decisions in talent management. Graphio uses AI to remove bias in talent management, providing real-time, data-driven insights for fair decisions in promotions, layoffs, and succession planning. It offers compliance features and rules that users can control, ensuring accurate and secure assessments aligned with legal and regulatory requirements. The platform focuses on security, privacy, and personalized coaching to enhance employee engagement and reduce turnover.

Blue Dot is a leading AI tax compliance platform that offers solutions for global tax management and VAT recovery. The platform provides a comprehensive view of employee-driven transactions, ensuring tax compliance and reducing vulnerabilities. Blue Dot's technology leverages AI and ML to optimize VAT outcomes and automate the review process for taxable employee benefits. The platform is fully integrated with expense management systems, helping organizations streamline compliance efforts and improve data integrity.

Inkdrop is an AI-powered tool that helps users visualize their cloud infrastructure by automatically generating interactive diagrams of cloud resources and dependencies. It provides a comprehensive overview of the infrastructure to speed up onboarding and understand complex resource relationships for effective troubleshooting. With seamless integration, users can effortlessly update documentation via CI pipeline integration. Meet the founders Antoine Descamps, Cofounder and CEO, and Alberto Schillaci, Cofounder and CTO. Inkdrop is trusted by partners who believe in its mission.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::n894q-1726678978147-1c9e4ad82a70) for reference. Users are directed to check the documentation for further information and troubleshooting.

Audiense is a leading provider of audience intelligence and social media marketing solutions. Our mission is to democratize audience insights and empower marketers with the data and tools they need to make better decisions. We offer a suite of products that provide deep insights into social media audiences, digital consumer behavior, and demand intelligence. With Audiense, you can understand your target audience, create more effective marketing campaigns, and measure the impact of your efforts.

MisguidedAttention is a collection of prompts designed to challenge the reasoning abilities of large language models by presenting them with modified versions of well-known thought experiments, riddles, and paradoxes. The goal is to assess the logical deduction capabilities of these models and observe any shortcomings or fallacies in their responses. The repository includes a variety of prompts that test different aspects of reasoning, such as decision-making, probability assessment, and problem-solving. By analyzing how language models handle these challenges, researchers can gain insights into their reasoning processes and potential biases.

LangCheck is a Python library that provides a suite of metrics and tools for evaluating the quality of text generated by large language models (LLMs). It includes metrics for evaluating text fluency, sentiment, toxicity, factual consistency, and more. LangCheck also provides tools for visualizing metrics, augmenting data, and writing unit tests for LLM applications. With LangCheck, you can quickly and easily assess the quality of LLM-generated text and identify areas for improvement.

DuoAttention is a framework designed to optimize long-context large language models (LLMs) by reducing memory and latency during inference without compromising their long-context abilities. It introduces a concept of Retrieval Heads and Streaming Heads to efficiently manage attention across tokens. By applying a full Key and Value (KV) cache to retrieval heads and a lightweight, constant-length KV cache to streaming heads, DuoAttention achieves significant reductions in memory usage and decoding time for LLMs. The framework uses an optimization-based algorithm with synthetic data to accurately identify retrieval heads, enabling efficient inference with minimal accuracy loss compared to full attention. DuoAttention also supports quantization techniques for further memory optimization, allowing for decoding of up to 3.3 million tokens on a single GPU.

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

Fairlearn is a Python package designed to help developers assess and mitigate fairness issues in artificial intelligence (AI) systems. It provides mitigation algorithms and metrics for model assessment. Fairlearn focuses on two types of harms: allocation harms and quality-of-service harms. The package follows the group fairness approach, aiming to identify groups at risk of experiencing harms and ensuring comparable behavior across these groups. Fairlearn consists of metrics for assessing model impacts and algorithms for mitigating unfairness in various AI tasks under different fairness definitions.

Holloway's Upscaler is a consolidation of various compiled open-source AI image/video upscaling products for a CLI-friendly image and video upscaling program. It provides low-cost AI upscaling software that can run locally on a laptop, programmable for albums and videos, reliable for large video files, and works without GUI overheads. The repository supports hardware testing on various systems and provides important notes on GPU compatibility, video types, and image decoding bugs. Dependencies include ffmpeg and ffprobe for video processing. The user manual covers installation, setup pathing, calling for help, upscaling images and videos, and contributing back to the project. Benchmarks are provided for performance evaluation on different hardware setups.

ceLLama is a streamlined automation pipeline for cell type annotations using large-language models (LLMs). It operates locally to ensure privacy, provides comprehensive analysis by considering negative genes, offers efficient processing speed, and generates customized reports. Ideal for quick and preliminary cell type checks.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

x-crawl is a flexible Node.js AI-assisted crawler library that offers powerful AI assistance functions to make crawler work more efficient, intelligent, and convenient. It consists of a crawler API and various functions that can work normally even without relying on AI. The AI component is currently based on a large AI model provided by OpenAI, simplifying many tedious operations. The library supports crawling dynamic pages, static pages, interface data, and file data, with features like control page operations, device fingerprinting, asynchronous sync, interval crawling, failed retry handling, rotation proxy, priority queue, crawl information control, and TypeScript support.

Vertex AI is a platform for end-to-end model development. It consist of core components that make the processes of MLOps possible for design patterns of all types.

Zshot is a highly customizable framework for performing Zero and Few shot named entity and relationships recognition. It can be used for mentions extraction, wikification, zero and few shot named entity recognition, zero and few shot named relationship recognition, and visualization of zero-shot NER and RE extraction. The framework consists of two main components: the mentions extractor and the linker. There are multiple mentions extractors and linkers available, each serving a specific purpose. Zshot also includes a relations extractor and a knowledge extractor for extracting relations among entities and performing entity classification. The tool requires Python 3.6+ and dependencies like spacy, torch, transformers, evaluate, and datasets for evaluation over datasets like OntoNotes. Optional dependencies include flair and blink for additional functionalities. Zshot provides examples, tutorials, and evaluation methods to assess the performance of the components.

Simple-OpenAI is a Java library that provides a simple way to interact with the OpenAI API. It offers consistent interfaces for various OpenAI services like Audio, Chat Completion, Image Generation, and more. The library uses CleverClient for HTTP communication, Jackson for JSON parsing, and Lombok to reduce boilerplate code. It supports asynchronous requests and provides methods for synchronous calls as well. Users can easily create objects to communicate with the OpenAI API and perform tasks like text-to-speech, transcription, image generation, and chat completions.

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

MMOS (Mix of Minimal Optimal Sets) is a dataset designed for math reasoning tasks, offering higher performance and lower construction costs. It includes various models and data subsets for tasks like arithmetic reasoning and math word problem solving. The dataset is used to identify minimal optimal sets through reasoning paths and statistical analysis, with a focus on QA-pairs generated from open-source datasets. MMOS also provides an auto problem generator for testing model robustness and scripts for training and inference.

Invariant Analyzer is an open-source scanner designed for LLM-based AI agents to find bugs, vulnerabilities, and security threats. It scans agent execution traces to identify issues like looping behavior, data leaks, prompt injections, and unsafe code execution. The tool offers a library of built-in checkers, an expressive policy language, data flow analysis, real-time monitoring, and extensible architecture for custom checkers. It helps developers debug AI agents, scan for security violations, and prevent security issues and data breaches during runtime. The analyzer leverages deep contextual understanding and a purpose-built rule matching engine for security policy enforcement.

The collection of pre-trained, state-of-the-art AI models. ailia SDK is a self-contained, cross-platform, high-speed inference SDK for AI. The ailia SDK provides a consistent C++ API across Windows, Mac, Linux, iOS, Android, Jetson, and Raspberry Pi platforms. It also supports Unity (C#), Python, Rust, Flutter(Dart) and JNI for efficient AI implementation. The ailia SDK makes extensive use of the GPU through Vulkan and Metal to enable accelerated computing. # Supported models 323 models as of April 8th, 2024

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.

Do-Not-Answer is an open-source dataset curated to evaluate Large Language Models' safety mechanisms at a low cost. It consists of prompts to which responsible language models do not answer. The dataset includes human annotations and model-based evaluation using a fine-tuned BERT-like evaluator. The dataset covers 61 specific harms and collects 939 instructions across five risk areas and 12 harm types. Response assessment is done for six models, categorizing responses into harmfulness and action categories. Both human and automatic evaluations show the safety of models across different risk areas. The dataset also includes a Chinese version with 1,014 questions for evaluating Chinese LLMs' risk perception and sensitivity to specific words and phrases.

Moonshot is a simple and modular tool developed by the AI Verify Foundation to evaluate Language Model Models (LLMs) and LLM applications. It brings Benchmarking and Red-Teaming together to assist AI developers, compliance teams, and AI system owners in assessing LLM performance. Moonshot can be accessed through various interfaces including User-friendly Web UI, Interactive Command Line Interface, and seamless integration into MLOps workflows via Library APIs or Web APIs. It offers features like benchmarking LLMs from popular model providers, running relevant tests, creating custom cookbooks and recipes, and automating Red Teaming to identify vulnerabilities in AI systems.

Numismatic expert discussing coins and currency history.

Expert in numismatics, sharing coin history and collector tips

The World's Most Powerful Coin Grading and Identification AI (v1.33 Alpha)

Unearth hidden crypto gems with AI-driven analysis of low-cap coins poised for growth. Smart, insightful, your go-to for bullish potential

Expert in retail industry discussions and consumer goods insights.

Expert in analyzing social media for market trends, brand reputation, and consumer sentiment.

Provides tailored water conservation strategies for diverse regions.​

I identify various rocks from images and advise consulting a geologist for certainty.

Identify and Confirm Interior Signage Code Details & Requirements. Federal, California ADA Signage Codes (NY Coming Soon)

This interactive inquiry will help identify your relationship attachment style.

Identify your sources of fear and anger and convert those emotions into concrete next steps. Tested and approved by the real Matt Mochary!

Identify and clarify personal values to cultivate a strong sense of purpose and self-confidence

Analyzes tech startups using SWOT, PEST, and 5 Forces, in Japanese.

Casual, positive advisor for detailed software partnership strategies.

I identify flaws in debates and outline reasoning errors.

This GPT answers all the questions related to Google Search Console

Analyzes customer churn and provides strategies to improve loyalty and retention.

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%.

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Business consultant conducting Five forces analysis.You can easily create a draft for consideration.