Best AI tools for< Create Value >

ValueProp.Dev is an AI-powered tool that helps businesses create Value Proposition Canvases by analyzing company descriptions. The tool generates detailed Value Proposition Canvases based on customer jobs, pains, gains, products, and services. It simplifies the process of identifying and designing value propositions that resonate with target customers, ultimately aiding businesses in improving their offerings and making strategic decisions.

OpenServ is a platform that empowers autonomy by providing a hub to find, curate, and employ teams of autonomous agents. Users can hire custom AI workforces to enhance productivity and revolutionize the way value is created. The platform allows users to browse autonomous AI agents, create custom teams, integrate favorite apps, leverage AI workforce, and monetize skills. OpenServ offers a developer-friendly environment with customizable and technology-agnostic features, enabling users to host, create, and monetize agents. The platform aims to streamline tasks, enhance collaboration, and maximize flexibility in utilizing AI technologies.

DEUS is a data and artificial intelligence company that empowers organizations to advance value creation by unlocking the true value within their data and applying AI services. They offer services in data science, engineering, design, and strategy, partnering with organizations to benefit people, business, and society. DEUS also focuses on addressing wicked problems and societal challenges through human-centered artificial intelligence initiatives. They help organizations launch AI projects that create real value and partner across the product and service lifecycle.

ML6 is an AI strategy and services provider that partners with organizations to leverage innovative AI technology for business transformation. They offer tailored AI solutions to drive efficiency, innovation, and growth, with a focus on autonomous AI agents as collaborative tools. ML6 specializes in shaping AI strategies, building custom AI solutions, and ensuring data and AI governance. With over 10 years of AI expertise and a team of 200+ AI experts, ML6 works with global clients across various industries to create value through AI.

Thales Labs is a premier AI research lab and incubator empowering entrepreneurs and domain experts to revolutionize industries with large language models and web3. They focus on fostering innovation in sectors like Insurance, Finance, Healthcare, Pharma, Law, and Journalism. The user-friendly app allows experts to build AI applications using their natural language skills, with support from skilled engineers for complex challenges. Join Thales Labs to transform industries, unlock new opportunities, and create value with AI-driven innovation.

Fetch.ai Innovation Lab is a leading platform advancing artificial intelligence and driving innovation to create value at scale. The lab unites academic institutes, research teams, and businesses to develop and expand advanced AI solutions. It fosters a collaborative environment that supports impactful projects and pushes the boundaries of what's possible with AI. The lab offers resources, support, and networking opportunities to drive groundbreaking ideas and growth in the AI ecosystem.

CodiumAI is an AI-powered tool that helps developers write better code by generating meaningful tests, finding edge cases and suspicious behaviors, and suggesting improvements. It integrates with popular IDEs and Git platforms, and supports a wide range of programming languages. CodiumAI is designed to help developers save time, improve code quality, and stay confident in their code.

StartNew.app is a business tool that uses AI to help entrepreneurs and startups develop marketing strategies and business plans. It offers a range of features to help users understand their target audience, position their product, and create a value proposition. StartNew.app also includes a business plan generator that allows users to create a comprehensive business plan with just a few clicks.

Focus Group Simulator is an AI tool designed to generate market insights instantly by simulating focus groups. By combining the power of LLMs to personate target segments with marketing quants analysis and best marketing frameworks, the tool provides valuable insights for businesses, especially startups. It helps identify low-hanging-fruit segments and offers guidance on product development, pricing, and promotion strategies to create more value and avoid waste. Users can customize simulations and engage with the team for further enhancements.

Halfspace is an award-winning data, advanced analytics, and AI company that helps companies drive growth by leveraging applied Advanced Analytics & Artificial Intelligence. Their team, consisting of physicists, engineers, computer scientists, and designers, is data-driven to the core and captures value from data to unleash maximum potential. They work with bold and ambitious clients to create long-term value, focusing on quality above all.

Existential is an AI-powered career exploration platform that helps you discover your true calling. Our platform uses a combination of psychometric assessments, AI algorithms, and expert career counseling to help you identify your strengths, interests, and values. We then match you with a personalized list of career paths that are a good fit for you.

Coachvox AI is a tool that allows you to create an AI version of yourself to generate leads, engage with your audience, and provide 24/7 support. It is trained on your content and style, so it can answer questions and provide information in a way that is unique to you. Coachvox AI can be used as a lead magnet, a value-add for existing clients, a paid product, or an internal resource for your team.

Gradient is an AI automation platform designed specifically for enterprise AI purposes. It offers a seamless way to automate manual workflows with minimal effort, providing business intuition and industry expertise. The platform ensures unmatched compliance with various regulations and prioritizes privacy and security. Gradient's Agent Foundry enables users to automate tasks, integrate data, and optimize workflows efficiently, making it a valuable tool for modern enterprises.

The Value Chain Generator® is an AI & Big Data platform for circular bioeconomy that helps companies, waste processors, and regions maximize the value and minimize the carbon footprint of by-products and waste. It uses global techno-economic and climate intelligence to identify circular opportunities, match with suitable partners and technologies, and create profitable and impactful solutions. The platform accelerates the circular transition by integrating local industries through technology, reducing waste, and increasing profits.

Distribute is an AI-powered digital sales platform that enables users to create personalized, interactive sales rooms and content pages quickly and efficiently. It offers features like AI microsites, lead magnets, video prospecting, and personalized deal rooms. The platform helps users streamline their sales workflow, improve engagement with prospects, and track content performance. Distribute aims to revolutionize the way sales content is created and shared, providing a one-stop solution for generating effective sales materials.

RIDO Protocol is a decentralized data protocol that allows users to extract value from their personal data in Web2 and Web3. It provides users with a variety of features, including programmable data generation, programmable access control, and cross-application data sharing. RIDO also has a data marketplace where users can list or offer their data information and ownership. Additionally, RIDO has a DataFi protocol which promotes the flowing of data information and value.

Ohboiler is an AI-powered custom template tool that allows users to create reusable templates quickly and easily. It blends new content with templates, enabling users to update only the key values. From daily emails to kids' stories, Ohboiler works seamlessly with AI at mind-boggling speed. It offers an Inline Variable Editor for pre-processing boilerplates and templates, ensuring fast results within 3 seconds. Users can interact with AI through various UIs, maintaining a human touch in their content. Ohboiler is not just a Template Maker but also an App Maker, offering over 100 templates for unlimited access and customization.

Rosetta AI is an e-commerce marketing platform that uses artificial intelligence to help businesses acquire qualified traffic, personalize interactions, and strengthen member repurchases. It offers a range of solutions, including personalized recommendations, AI-powered advertising, interactive experiences, product label generation, and automated marketing. Rosetta AI is used by over 2000 enterprise and SMB websites worldwide and has helped clients increase their click-through rate, average order value, check-out conversion, and revenue.

QuData is an AI and ML solutions provider that helps businesses enhance their value through AI/ML implementation, product design, QA, and consultancy services. They offer a range of services including ChatGPT integration, speech synthesis, speech recognition, image analysis, text analysis, predictive analytics, big data analysis, innovative research, and DevOps solutions. QuData has extensive experience in machine learning and artificial intelligence, enabling them to create high-quality solutions for specific industries, helping customers save development costs and achieve their business goals.

Sequens.ai is an AI application that generates AI contents at scale, checked by B2B marketers. It provides value to your audience across all channels by utilizing fine-tuned AI technology and expert reviews for SEO, social media, and lead generation contents. The platform offers a seamless process from content creation to publication, helping businesses enhance their marketing strategies and engage their audience effectively.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.

LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

pgvecto.rs is a Postgres extension written in Rust that provides vector similarity search functions. It offers ultra-low-latency, high-precision vector search capabilities, including sparse vector search and full-text search. With complete SQL support, async indexing, and easy data management, it simplifies data handling. The extension supports various data types like FP16/INT8, binary vectors, and Matryoshka embeddings. It ensures system performance with production-ready features, high availability, and resource efficiency. Security and permissions are managed through easy access control. The tool allows users to create tables with vector columns, insert vector data, and calculate distances between vectors using different operators. It also supports half-precision floating-point numbers for better performance and memory usage optimization.

SimplerLLM is an open-source Python library that simplifies interactions with Large Language Models (LLMs) for researchers and beginners. It provides a unified interface for different LLM providers, tools for enhancing language model capabilities, and easy development of AI-powered tools and apps. The library offers features like unified LLM interface, generic text loader, RapidAPI connector, SERP integration, prompt template builder, and more. Users can easily set up environment variables, create LLM instances, use tools like SERP, generic text loader, calling RapidAPI APIs, and prompt template builder. Additionally, the library includes chunking functions to split texts into manageable chunks based on different criteria. Future updates will bring more tools, interactions with local LLMs, prompt optimization, response evaluation, GPT Trainer, document chunker, advanced document loader, integration with more providers, Simple RAG with SimplerVectors, integration with vector databases, agent builder, and LLM server.

Mo AI Studio is an enterprise-level AI agent running platform that enables the operation of customized intelligent AI agents with system-level capabilities. It supports various IDEs and programming languages, allows modification of multiple files with reasoning, cross-project context modifications, customizable agents, system-level file operations, document writing, question answering, knowledge sharing, and flexible output processors. The platform also offers various setters and a custom component publishing feature. Mo AI Studio is a fusion of artificial intelligence and human creativity, designed to bring unprecedented efficiency and innovation to enterprises.

The Pinecone AWS Reference Architecture with Pulumi is a distributed system designed for vector-database-enabled semantic search over Postgres records. It serves as a starting point for specific use cases or as a learning resource. The architecture is permissively licensed and supported by Pinecone's open-source team, facilitating the setup of high-scale use cases for Pinecone's scalable vector database.

Glide is a cloud-native LLM gateway that provides a unified REST API for accessing various large language models (LLMs) from different providers. It handles LLMOps tasks such as model failover, caching, key management, and more, making it easy to integrate LLMs into applications. Glide supports popular LLM providers like OpenAI, Anthropic, Azure OpenAI, AWS Bedrock (Titan), Cohere, Google Gemini, OctoML, and Ollama. It offers high availability, performance, and observability, and provides SDKs for Python and NodeJS to simplify integration.

HolmesGPT is an open-source DevOps assistant powered by OpenAI or any tool-calling LLM of your choice. It helps in troubleshooting Kubernetes, incident response, ticket management, automated investigation, and runbook automation in plain English. The tool connects to existing observability data, is compliance-friendly, provides transparent results, supports extensible data sources, runbook automation, and integrates with existing workflows. Users can install HolmesGPT using Brew, prebuilt Docker container, Python Poetry, or Docker. The tool requires an API key for functioning and supports OpenAI, Azure AI, and self-hosted LLMs.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

Agently is a development framework that helps developers build AI agent native application really fast. You can use and build AI agent in your code in an extremely simple way. You can create an AI agent instance then interact with it like calling a function in very few codes like this below. Click the run button below and witness the magic. It's just that simple: python # Import and Init Settings import Agently agent = Agently.create_agent() agent\ .set_settings("current_model", "OpenAI")\ .set_settings("model.OpenAI.auth", {"api_key": ""}) # Interact with the agent instance like calling a function result = agent\ .input("Give me 3 words")\ .output([("String", "one word")])\ .start() print(result) ['apple', 'banana', 'carrot'] And you may notice that when we print the value of `result`, the value is a `list` just like the format of parameter we put into the `.output()`. In Agently framework we've done a lot of work like this to make it easier for application developers to integrate Agent instances into their business code. This will allow application developers to focus on how to build their business logic instead of figure out how to cater to language models or how to keep models satisfied.

RD-Agent is a tool designed to automate critical aspects of industrial R&D processes, focusing on data-driven scenarios to streamline model and data development. It aims to propose new ideas ('R') and implement them ('D') automatically, leading to solutions of significant industrial value. The tool supports scenarios like Automated Quantitative Trading, Data Mining Agent, Research Copilot, and more, with a framework to push the boundaries of research in data science. Users can create a Conda environment, install the RDAgent package from PyPI, configure GPT model, and run various applications for tasks like quantitative trading, model evolution, medical prediction, and more. The tool is intended to enhance R&D processes and boost productivity in industrial settings.

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

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=