Python Integrations

We provide an accessible platform designed to empower businesses across diverse sectors to undergo digital transformation and achieve enhanced insights that foster a lasting competitive edge. Our software suite features Contxt, a robust, real-time industrial platform functioning as the core data engine, along with Nsight, our application for data integration and intelligent analytics. Throughout this journey, we ensure comprehensive support and services. At the heart of our offering lies Contxt, our adaptable data management engine focused on industrial optimization. This platform is built upon our cutting-edge ETLT technology, which facilitates data availability in under 15 seconds for any transaction across various disconnected data sources. Contxt enables developers to construct a real-time digital twin, providing live data across all applications and analyses within the organization, thereby driving significant business outcomes. Ultimately, our commitment is to equip businesses with the tools they need to thrive in an increasingly competitive landscape.

The API framework permits any agent application within the OpenAI ecosystem, encompassing all AI agents currently, to leverage GaiaNet as an alternative option. In addition, while OpenAI's API relies on a limited selection of models for general responses, each node within GaiaNet can be extensively tailored with fine-tuned models enriched by specific domain knowledge. GaiaNet operates as a decentralized computing framework that empowers individuals and enterprises to develop, implement, scale, and monetize their unique AI agents, embodying their distinct styles, values, knowledge, and expertise. This innovative system facilitates the creation of AI agents by both individuals and businesses, while each GaiaNet node forms part of a distributed and decentralized network known as GaiaNodes. These nodes utilize fine-tuned large language models that incorporate private data, as well as proprietary knowledge bases that enhance model performance for users. Moreover, decentralized AI applications make use of the GaiaNet's distributed API infrastructure, offering features such as personal AI teaching assistants that are readily available to provide insights anytime and anywhere, thereby transforming the landscape of AI interaction. As a result, users can expect a highly personalized and efficient AI experience tailored specifically to their needs and preferences.

SecureFlag provides a practical training experience in authentic development settings, catering specifically to the unique training requirements of enterprises. Supporting over 45 technologies and addressing more than 150 types of vulnerabilities, each session takes place in a fully equipped development environment. Given that more than 70% of vulnerabilities emerge during the development phase, it is essential to prioritize the creation of secure software. SecureFlag has transformed the landscape of secure coding education significantly. With immersive hands-on labs, participants gain experience in virtual environments, utilizing familiar tools and platforms. This approach enables learners to actively identify and address common security challenges through practical engagement rather than passive observation. The labs operate in genuine, virtualized settings, ensuring that participants are accustomed to the tools they would typically employ in their professional roles. Additionally, fostering a spirit of friendly competition can enhance engagement within your organization’s developer community and encourage ongoing learning. Such interactive training not only builds skills but also strengthens team collaboration in tackling security issues.

Enhance your organization's security measures through specialized training and achieve ISC2 certification for your software security professionals. Equip your entire development team—including software developers, security champions, software architects, QA engineers, and project managers—with essential software security knowledge and secure coding techniques tailored to specific programming languages. Upon completing all training modules and passing the final exam, you will be awarded a certificate recognized throughout the industry by Security Compass and ISC2. This certification can be showcased with a social media badge, allowing you to highlight your team's commitment to security excellence. Gain a deeper insight into prevalent security threats by engaging with real-world exploitation examples, and address your team's knowledge gaps with precise training that is accessible when and where it is most needed. By doing so, you can minimize inconsistencies and empower your team to achieve peak performance. Track progress and success from module to module, providing valuable insights into the skills and competencies of your team as you foster a culture of continuous improvement in software security. Ultimately, this training initiative will not only elevate individual capabilities but also enhance the overall security posture of your organization.

The Gemma family consists of advanced, lightweight models developed using the same innovative research and technology as the Gemini models. These cutting-edge models are equipped with robust security features that promote responsible and trustworthy AI applications, achieved through carefully curated data sets and thorough refinements. Notably, Gemma models excel in their various sizes—2B, 7B, 9B, and 27B—often exceeding the performance of some larger open models. With the introduction of Keras 3.0, users can experience effortless integration with JAX, TensorFlow, and PyTorch, providing flexibility in framework selection based on specific tasks. Designed for peak performance and remarkable efficiency, Gemma 2 is specifically optimized for rapid inference across a range of hardware platforms. Furthermore, the Gemma family includes diverse models that cater to distinct use cases, ensuring they adapt effectively to user requirements. These lightweight language models feature a decoder and have been trained on an extensive array of textual data, programming code, and mathematical concepts, which enhances their versatility and utility in various applications.

Roseman Labs allows you to encrypt and link multiple data sets, while protecting the privacy and commercial sensitivity. This allows you combine data sets from multiple parties, analyze them and get the insights that you need to optimize processes. Unlock the potential of your data. Roseman Labs puts the power of encryption at your fingertips with Python's simplicity. Encrypting sensitive information allows you to analyze the data while protecting privacy, commercial sensitivity and adhering GDPR regulations. With enhanced GDPR compliance, you can generate insights from sensitive commercial or personal information. Secure data privacy using the latest encryption. Roseman Labs lets you link data sets from different parties. By analyzing the combined information, you can discover which records are present in multiple data sets. This allows for new patterns to emerge.

Cloud 66 offers a comprehensive solution for constructing, deploying, and scaling applications across various cloud platforms, eliminating the complexities typically associated with server management. It accommodates any programming language or framework you choose, ensuring flexibility in your development process. The platform efficiently builds, configures, and oversees all significant databases and components essential for your projects. You can deploy directly to your own accounts across a range of cloud providers or even to on-premise servers. By linking your Git repository with Cloud 66, you gain access to support for all prominent Git services, including GitHub, GitLab, Bitbucket, and Azure DevOps, whether your code is organized in a mono-repo or a multi-repo configuration. Furthermore, the platform is designed to support every programming language and framework, either through native integration or by utilizing containers. To maximize efficiency, users can also connect their cloud provider accounts directly to Cloud 66, which is compatible with all leading cloud services such as AWS, DigitalOcean, Google Cloud, Azure, and Linode. In addition, Cloud 66 intelligently analyzes your code to identify the necessary components for running your application, and it even extends its support to static site generators like Gatsby, Hugo, and Jekyll, catering to a wide range of development needs. This makes it an ideal choice for developers aiming to streamline their workflow and enhance their deployment processes.

Scale from zero to millions of events per second effortlessly. Arroyo is delivered as a single, compact binary, allowing for local development on MacOS or Linux, and seamless deployment to production environments using Docker or Kubernetes. As a pioneering stream processing engine, Arroyo has been specifically designed to simplify real-time processing, making it more accessible than traditional batch processing. Its architecture empowers anyone with SQL knowledge to create dependable, efficient, and accurate streaming pipelines. Data scientists and engineers can independently develop comprehensive real-time applications, models, and dashboards without needing a specialized team of streaming professionals. By employing SQL, users can transform, filter, aggregate, and join data streams, all while achieving sub-second response times. Your streaming pipelines should remain stable and not trigger alerts simply because Kubernetes has chosen to reschedule your pods. Built for modern, elastic cloud infrastructures, Arroyo supports everything from straightforward container runtimes like Fargate to complex, distributed setups on Kubernetes, ensuring versatility and robust performance across various environments. This innovative approach to stream processing significantly enhances the ability to manage data flows in real-time applications.

ModelOp stands at the forefront of AI governance solutions, empowering businesses to protect their AI projects, including generative AI and Large Language Models (LLMs), while promoting innovation. As corporate leaders push for swift integration of generative AI, they encounter various challenges such as financial implications, regulatory compliance, security concerns, privacy issues, ethical dilemmas, and potential brand damage. With governments at global, federal, state, and local levels rapidly establishing AI regulations and oversight, organizations must act promptly to align with these emerging guidelines aimed at mitigating AI-related risks. Engaging with AI Governance specialists can keep you updated on market dynamics, regulatory changes, news, research, and valuable perspectives that facilitate a careful navigation of the benefits and hazards of enterprise AI. ModelOp Center not only ensures organizational safety but also instills confidence among all stakeholders involved. By enhancing the processes of reporting, monitoring, and compliance across the enterprise, businesses can foster a culture of responsible AI usage. In a landscape that evolves quickly, staying informed and compliant is essential for sustainable success.

An innovative AI-driven platform that leverages cutting-edge Large Language Models (LLMs). It introduces a distinctive methodology for creating, debugging, and refining comprehensive end-to-end tests through the use of natural language. Effortlessly generate and enhance tests by simply providing natural language inputs, thereby streamlining the testing process with smart automation. The intelligent test planner automatically formulates and executes test steps based on high-level goals. Additionally, multi-language code export enables the transformation of your automated tests into all major programming languages and frameworks. Transform your actions into natural language commands to produce robust tests that meet your requirements. Articulate complex conditions and assertions using everyday language, making the process as straightforward as interacting with your team. Relay the same directives to KaneAI and observe how it efficiently automates your testing tasks. Generate comprehensive tests based solely on high-level objectives and develop them across your technology stack, ensuring thorough test coverage for both web and mobile platforms. With this platform, testing becomes not just a necessity but a seamless part of your development workflow.

Create your photonic integrated circuit within a layout-focused workflow that allows designers to utilize either a drag-and-drop interface or a script-based approach. Both methods are facilitated by a comprehensive custom IC design layout editor, which also manages the physical verification and tape-out stages. L-Edit Photonics allows for rapid photonic design creation through its intuitive drag-and-drop functionality, eliminating the need for coding. Upon finalizing the design, a netlist can be generated to support photonic simulations. The PIC design is entirely integrated within an IC layout editor, enabling users to develop layouts without writing any code, thus supporting a layout-centric approach that does not require a schematic. For those who prefer a schematic flow, S-Edit is available as an optional tool. Moreover, a simulation netlist can be produced for input into a photonic simulator, and photonic simulations are seamlessly incorporated through partnerships with various providers. Additionally, multiple foundries offer photonic PDKs to enhance design capabilities. Overall, this comprehensive workflow simplifies the photonic design process while catering to various designer preferences.

Generate secure, context-aware infrastructure as code (IaC) directly from application code without needing to modify that code. While we appreciate the benefits of infrastructure as code, there is certainly potential for enhancements. StackGen leverages the application’s existing code to produce IaC that is not only consistent and secure but also compliant with industry standards. This approach eliminates bottlenecks, reduces potential liabilities, and minimizes the risk of errors that often come from manual processes, allowing for a quicker time-to-market for your applications. By providing developers with a streamlined experience, they can focus on coding rather than having to become infrastructure specialists. Consistency, security, and policy compliance are integrated by default into the auto-generated IaC. The system generates context-aware IaC without requiring any changes to the original code, ensuring that it is properly supported and aligned with the principle of least-privileged access. There's no necessity to reconstruct your existing pipelines, as StackGen seamlessly integrates into your current workflows, bridging the gaps between teams. This empowers developers to automatically create IaC that adheres to your established provisioning checklist, enhancing overall efficiency and collaboration. Ultimately, this innovative approach not only accelerates development but also strengthens security protocols across the board.

Runyour AI offers an ideal platform for artificial intelligence research, encompassing everything from machine rentals to tailored templates and dedicated servers. This AI cloud service ensures straightforward access to GPU resources and research settings specifically designed for AI pursuits. Users can rent an array of high-performance GPU machines at competitive rates, and there's even an option to monetize personal GPUs by registering them on the platform. Their transparent billing system allows users to pay only for the resources consumed, monitored in real-time down to the minute. Catering to everyone from casual hobbyists to expert researchers, Runyour AI provides specialized GPU solutions to meet diverse project requirements. The platform is user-friendly enough for beginners, making it easy to navigate for first-time users. By leveraging Runyour AI's GPU machines, you can initiate your AI research journey with minimal hassle, ensuring you can focus on your innovative ideas. With a design that prioritizes quick access to GPUs, it delivers a fluid research environment ideal for both machine learning and AI development.

Introducing an innovative reactive notebook designed for Python, which allows you to conduct repeatable experiments, run scripts seamlessly, launch applications, and manage versions using git. 🚀 Comprehensive: it serves as a substitute for jupyter, streamlit, jupytext, ipywidgets, papermill, and additional tools. ⚡️ Dynamic: when you execute a cell, marimo automatically runs all related cells or flags them as outdated. 🖐️ Engaging: easily connect sliders, tables, and plots to your Python code without the need for callbacks. 🔬 Reliable: ensures no hidden states, guarantees deterministic execution, and includes built-in package management for consistency. 🏃 Functional: capable of being executed as a Python script, allowing for customization via CLI arguments. 🛜 Accessible: can be transformed into an interactive web application or presentation, and functions in the browser using WASM. 🛢️ Tailored for data: efficiently query dataframes and databases using SQL, plus filter and search through dataframes effortlessly. 🐍 git-compatible: stores notebooks as .py files, making version control straightforward. ⌨️ A contemporary editor: features include GitHub Copilot, AI helpers, vim keybindings, a variable explorer, and an array of other enhancements to streamline your workflow. With these capabilities, this notebook elevates the way you work with Python, promoting a more efficient and collaborative coding environment.

Outspeed delivers advanced networking and inference capabilities designed to facilitate the rapid development of voice and video AI applications in real-time. This includes AI-driven speech recognition, natural language processing, and text-to-speech technologies that power intelligent voice assistants, automated transcription services, and voice-operated systems. Users can create engaging interactive digital avatars for use as virtual hosts, educational tutors, or customer support representatives. The platform supports real-time animation and fosters natural conversations, enhancing the quality of digital interactions. Additionally, it offers real-time visual AI solutions for various applications, including quality control, surveillance, contactless interactions, and medical imaging assessments. With the ability to swiftly process and analyze video streams and images with precision, it excels in producing high-quality results. Furthermore, the platform enables AI-based content generation, allowing developers to create extensive and intricate digital environments efficiently. This feature is particularly beneficial for game development, architectural visualizations, and virtual reality scenarios. Adapt's versatile SDK and infrastructure further empower users to design custom multimodal AI solutions by integrating different AI models, data sources, and interaction methods, paving the way for groundbreaking applications. The combination of these capabilities positions Outspeed as a leader in the AI technology landscape.

Poolside is developing cutting-edge AI tailored for the complexities of contemporary software engineering. Our model is designed to address the unique challenges faced in this field. You can customize our model based on your organization's specific software development practices, libraries, APIs, and knowledge repositories. This proprietary system continually adapts to reflect the coding styles of your developers, effectively transforming your company into an AI powerhouse. We are creating foundational models, an API, and an assistant to empower your developers with the capabilities of generative AI. The Poolside stack is compatible with your existing infrastructure, ensuring that no data or code leaves your security perimeter. This solution is particularly suited for highly regulated sectors such as financial services, defense, and technology, along with retail, tech, and systems integrators. By incorporating your codebases, documentation, and knowledge assets, our model is finely tuned to meet the specific needs of your development teams and business objectives. Furthermore, Poolside is installed within your environment, allowing for secure and private integration with your data, thereby enhancing operational efficiency and innovation. Ultimately, this approach guarantees that your AI solution evolves alongside your organization’s growth and requirements.

Enhance your cyber security measures with the Rainforest platform, which is designed to protect your innovations and instill confidence as you navigate the digital landscape securely. With rapid implementation and swift results, Rainforest offers a solution that is far less complex than traditional options, saving companies both time and resources. The platform allows for a seamless integration process, enabling your team to focus on resolving issues rather than getting bogged down in implementation. Utilizing advanced AI, our trained models provide insightful fix suggestions, making it easier for your team to tackle challenges effectively. With seven distinct application analyses that cover comprehensive application security, local code evaluations, and AI-driven recommendations, you can expect quick vulnerability detection and effective remediation for strong application defense. Furthermore, continuous cloud security posture management identifies misconfigurations and vulnerabilities in real-time, making it simple to enhance your cloud security effortlessly. Ultimately, Rainforest empowers organizations to operate securely and confidently in an increasingly complex digital environment.

Organizations focused on privacy collaborate with Decentriq to ensure robust data protection. Utilizing cutting-edge encryption and privacy-enhancing technologies, including synthetic data, differential privacy, and confidential computing, you retain complete control over your data at all times. The implementation of end-to-end encryption guarantees that your data remains confidential from all external parties. Importantly, Decentriq is unable to view or access your data directly. Furthermore, remote attestation provides assurance that your information is securely encrypted and that only authorized analyses are conducted. The platform is built in partnership with top-tier hardware and infrastructure providers, ensuring optimal performance. It is specifically designed to accommodate even the most sophisticated AI and machine learning models, keeping your data secure regardless of the complexities involved. With processing speeds that rival traditional cloud solutions, you can enjoy both scalability and superior data protection. Additionally, our expanding network of data connectors facilitates more efficient workflows across various leading data platforms, enhancing overall productivity.

We empower businesses to harness the potential of first-party data collaboration while minimizing associated risks. By converting consumer data from a potential burden into a valuable revenue-generating asset, organizations can flourish in a landscape that has moved beyond traditional cookies. Our approach allows for enhanced collaboration with additional partners, thereby maximizing value for customers. Furthermore, we facilitate financial inclusion and boost revenue through innovative partnerships that utilize alternative data sources. Our solution improves underwriting accuracy and optimizes profitability by incorporating these diverse data streams. Each participant employs our secure desktop application to anonymize, tokenize, and safeguard all personally identifiable information within their consumer data, ensuring it remains protected in their local environment. This procedure produces US-patented crypto-IDs for each anonymized consumer profile, allowing for the secure matching of shared consumers across various datasets in our neutral Cloud environment. With our cutting-edge technology, we are at the forefront of the next evolution in consumer data management and collaboration. This ensures that businesses can thrive in a data-driven future while maintaining the highest standards of privacy and security.

Actian Ingres is a highly dependable SQL-standard transactional database known for its exceptional reliability and performance. Now, it has evolved into a hybrid database that seamlessly integrates transactional and analytical processing, boasting the remarkable X100 analytics engine. By supporting both row-based and columnar storage formats, Ingres enables organizations to conduct transaction processing and operational analytics within a single platform efficiently. This solution is recognized as the most reliable transactional database in the industry, offering a low total cost of ownership alongside round-the-clock global support and outstanding customer satisfaction. With a solid history of success, it underpins thousands of enterprises that manage billions of transactions across years of updates and migrations, ensuring that businesses can rely on it for their critical operations. Its continuous innovation and commitment to excellence solidify its position as a leader in the database market.

All of your data requirements are met through a comprehensive and robust platform. It encompasses everything from data ingestion and integration to transformation, storage, knowledge cataloging, graph networks, data analytics, governance, monitoring, and sharing. This AI/ML platform empowers enterprises to train, test, troubleshoot, deploy, and manage models at scale, enhancing productivity while ensuring reliable performance in production environments. With a focus on simplifying the model training process, it offers options such as AutoML for automated solutions or CustomML for developing tailored models from the ground up. This innovative approach enables a seamless connection between essential machine learning logic and data, facilitating an integrated exploration of potential actions. Additionally, it is compatible with your existing protocols and authorization frameworks, ensuring smooth integration. The platform features default propagation and extensive configurability, catering to your specific needs. Utilize the internal lineage system for effective alerting and impact analysis while being securely woven into the overall security framework, providing immutable tracking of all activities. This ensures that your data remains secure and compliant while maximizing the platform's functionalities.

The intelligent semantic layer merges data with its business context and interconnections, consolidates metrics, and speeds up the production of data products by allowing for SQL queries that are 90% shorter. Users can easily model the data using familiar business terminology, creating a shared understanding and aligning the metrics with business objectives. By defining semantic relationships that replace traditional JOIN operations, queries become significantly more straightforward. Hierarchies and classifications are utilized to enhance data comprehension. The system automatically aligns data with the semantic model, enabling the integration of various data sources through a robust distributed SQL engine that supports large-scale querying. Data can be accessed as an interconnected semantic graph, improving performance while reducing computing expenses through an advanced caching engine and materialized views. Users gain from sophisticated query optimization techniques. Additionally, Timbr allows connectivity to a wide range of cloud services, data lakes, data warehouses, databases, and diverse file formats, ensuring a seamless experience with your data sources. When executing a query, Timbr not only optimizes it but also efficiently delegates the task to the backend for improved processing. This comprehensive approach ensures that users can work with their data more effectively and with greater agility.

Many commonly used dashboards, reporting, and ETL tools often face challenges with connectivity to specific data sources, which can create significant integration hurdles for users. To address this issue, Simba provides ready-to-use, standards-compliant drivers that facilitate seamless connectivity, streamlining the integration process. Organizations that supply data to their customers frequently find it difficult to ensure smooth and straightforward data access for their users. By leveraging Simba’s SDK, developers are empowered to create custom, standards-based drivers, which makes access to data more user-friendly compared to traditional methods like CSV exports or API integrations. Moreover, unique backend requirements, such as those dictated by particular applications or internal workflows, can further complicate connectivity efforts. With the aid of Simba’s SDK or managed services, it becomes possible to develop drivers that are specifically tailored to address these unique needs. Furthermore, Simba offers extensive ODBC/JDBC compatibility that supports a diverse array of applications and data tools, enhancing their functionality. By incorporating Simba Drivers into these platforms, organizations can significantly improve their connectivity capabilities, opening the door to a broader range of data sources for their users. This ultimately leads to a more integrated and efficient data environment for businesses.

Data contracts play a crucial role in enhancing the interaction between data teams and developers. Rather than merely identifying issues after they arise, it’s essential to proactively prevent them at the application level. Utilize AI-powered asset registration to monitor every alteration from all data sources. Amplify the success of data initiatives by ensuring visibility upstream and conducting thorough impact analyses. By implementing data governance as code and data contracts, both data ownership and management can be shifted left. Establishing trust in data is also vital, achieved through prompt communication regarding data quality standards and any modifications. Our AI-driven technology allows for the elimination of data problems right at their origin, ensuring a smoother workflow. Gable serves as a B2B data infrastructure SaaS that provides a collaborative platform specifically designed for the creation and enforcement of data contracts. These ‘data contracts’ are essentially API-based agreements between software engineers managing upstream data sources and the data engineers or analysts who utilize that data for machine learning model development and analytics. With Gable, organizations can streamline their data processes, ultimately fostering a culture of trust and efficiency.

Invert provides a comprehensive platform for gathering, refining, and contextualizing data, guaranteeing that every analysis and insight emerges from dependable and well-structured information. By standardizing all your bioprocess data, Invert equips you with robust built-in tools for analysis, machine learning, and modeling. The journey to clean, standardized data is merely the starting point. Dive into our extensive suite of data management, analytical, and modeling resources. Eliminate tedious manual processes within spreadsheets or statistical applications. Utilize powerful statistical capabilities to perform calculations effortlessly. Generate reports automatically based on the latest runs, enhancing efficiency. Incorporate interactive visualizations, computations, and notes to facilitate collaboration with both internal teams and external partners. Optimize the planning, coordination, and execution of experiments seamlessly. Access the precise data you require and conduct thorough analyses as desired. From the stages of integration to analysis and modeling, every tool you need to effectively organize and interpret your data is right at your fingertips. Invert empowers you to not only handle data but also to derive meaningful insights that drive innovation.

Literal AI is a collaborative platform crafted to support engineering and product teams in the creation of production-ready Large Language Model (LLM) applications. It features an array of tools focused on observability, evaluation, and analytics, which allows for efficient monitoring, optimization, and integration of different prompt versions. Among its noteworthy functionalities are multimodal logging, which incorporates vision, audio, and video, as well as prompt management that includes versioning and A/B testing features. Additionally, it offers a prompt playground that allows users to experiment with various LLM providers and configurations. Literal AI is designed to integrate effortlessly with a variety of LLM providers and AI frameworks, including OpenAI, LangChain, and LlamaIndex, and comes equipped with SDKs in both Python and TypeScript for straightforward code instrumentation. The platform further facilitates the development of experiments against datasets, promoting ongoing enhancements and minimizing the risk of regressions in LLM applications. With these capabilities, teams can not only streamline their workflows but also foster innovation and ensure high-quality outputs in their projects.

TheCodeground serves as a comprehensive online integrated development environment, providing a variety of tools for coding practice and collaboration in real time. It accommodates numerous programming languages such as Rust, GoLang, Node.js, Python, Java, HTML, CSS, and JavaScript. Users benefit from features like live code sharing, code interviews, and a Reads section filled with valuable articles. The platform's interface resembles that of Visual Studio Code, incorporating useful elements like autocomplete, JSON differentiation, and a JWT decoder to elevate the coding experience. Accessible directly through web browsers, it also offers a desktop application for Mac, Windows, and Linux systems. With The Code Ground, users can effortlessly code on any device without tedious setup processes. The cloud-based architecture guarantees quick execution, a wealth of tools, and a seamless coding journey. The CodeGround is designed to provide developers with all the essential resources for effective coding and precise data management, ensuring they can focus on their projects without distraction.

PromptQL is an innovative platform created by Hasura that empowers Large Language Models (LLMs) to interact seamlessly with structured data through intelligent query planning. This methodology enhances the capability of AI agents to retrieve and process information in a manner akin to human reasoning, significantly improving their response to intricate, real-world inquiries. By equipping LLMs with access to a Python runtime and a uniform SQL interface, PromptQL ensures precise data querying and manipulation. The platform is designed to integrate with a variety of data sources such as GitHub repositories and PostgreSQL databases, enabling users to create customized AI assistants that cater to their unique requirements. By addressing the shortcomings of conventional search-based retrieval methods, PromptQL allows AI agents to execute tasks like collecting pertinent emails and accurately classifying follow-ups. Users can easily begin their journey by connecting their data sources, inputting their LLM API key, and engaging in AI-driven development. This flexibility positions PromptQL as a vital tool for anyone looking to enhance their data-driven applications with intelligent automation.

DataHawk consolidates all your marketplace information into one platform, converting it into straightforward actionable insights that help you enhance profitability and revenue across various channels. With a comprehensive end-to-end solution, integrated artificial intelligence, and user-friendly dashboards, ecommerce analytics becomes significantly more robust and accessible. You can work with the most extensive marketplace data available, all in one convenient location. Access to enriched historical data allows for improved trend tracking, threat avoidance, and opportunity identification. The platform is customizable and adaptable to meet unique business challenges and emerging requirements seamlessly. You can also integrate your preferred data and analytics tools into the system. Supported by seasoned ecommerce and data specialists, DataHawk understands the specific hurdles you encounter because they have experienced similar challenges themselves. Utilizing machine learning and AI, DataHawk continuously monitors data, assists in decision-making processes, identifies and prioritizes necessary actions, and even automates those actions to streamline operations. This comprehensive approach ensures you are always ahead in the competitive ecommerce landscape.

Transitioning from development to production, as well as from traditional data engineering to artificial intelligence, requires securing the various environments, pipelines, tools, and open-source components integral to your data and AI supply chain. It is essential to continuously identify, prevent, and rectify security and compliance vulnerabilities in AI before they reach production. In addition, monitoring AI applications in real-time allows for the detection and mitigation of adversarial AI attacks while enforcing specific application guardrails. Noma integrates smoothly across your data and AI supply chain and applications, providing a detailed map of all data pipelines, notebooks, MLOps tools, open-source AI elements, and both first- and third-party models along with datasets, thereby automatically generating a thorough AI/ML bill of materials (BOM). Additionally, Noma constantly identifies and offers actionable solutions for security issues, including misconfigurations, AI-related vulnerabilities, and non-compliant training data usage throughout your data and AI supply chain. This proactive approach enables organizations to enhance their AI security posture effectively, ensuring that potential threats are addressed before they can impact production. Ultimately, adopting such measures not only fortifies security but also boosts overall confidence in AI systems.

AWS ParallelCluster is a free, open-source tool designed for efficient management and deployment of High-Performance Computing (HPC) clusters within the AWS environment. It streamlines the configuration of essential components such as compute nodes, shared filesystems, and job schedulers, while accommodating various instance types and job submission queues. Users have the flexibility to engage with ParallelCluster using a graphical user interface, command-line interface, or API, which allows for customizable cluster setups and oversight. The tool also works seamlessly with job schedulers like AWS Batch and Slurm, making it easier to transition existing HPC workloads to the cloud with minimal adjustments. Users incur no additional costs for the tool itself, only paying for the AWS resources their applications utilize. With AWS ParallelCluster, users can effectively manage their computing needs through a straightforward text file that allows for the modeling, provisioning, and dynamic scaling of necessary resources in a secure and automated fashion. This ease of use significantly enhances productivity and optimizes resource allocation for various computational tasks.

Enhance the efficiency of daily operations while simplifying the administration of Cisco UCS infrastructure. By automating routine processes, you can also lower operational costs significantly. Over 50,000 customers around the globe have experienced substantial advantages from this solution. Cisco UCS Manager integrates flawlessly with Cisco Intersight, a cloud-based systems management platform, facilitating even greater operational efficiencies. It is designed to support the comprehensive Cisco UCS server lineup, allowing for server, fabric, and storage provisioning, along with device discovery, inventory management, configuration, diagnostics, monitoring, fault detection, auditing, and data collection. Furthermore, you can leverage the capabilities of Cisco UCS Manager on a global scale, managing thousands of servers across various domains with Cisco Intersight Infrastructure Service. This innovative open platform approaches infrastructure management as code, significantly enhancing the capabilities of existing management tools through a wide and established partner ecosystem. As a result, organizations can optimize their IT operations and drive better performance across their infrastructures.

Konfig is an innovative developer tool designed to streamline the creation of SDKs, documentation, demos, and tutorials for REST APIs, making the onboarding process for external developers more efficient. By simply importing an OpenAPI Specification or Postman Collection, Konfig can automatically generate SDKs in widely-used programming languages such as TypeScript, Python, Java, C#, PHP, Ruby, Go, Swift, and Dart. The platform enhances the quality of these SDKs by utilizing a linter to detect and correct errors in the OpenAPI Specification and by generating test cases to ensure that updates to the API do not disrupt existing SDK functionality. Furthermore, Konfig produces intuitive, branded documentation that updates automatically in response to any modifications in the API specification, thereby ensuring consistency between the documentation and the generated SDKs. Users can also create interactive demos and tutorials using familiar Markdown, which includes the capability to execute code directly in the browser for a more immersive learning experience. Overall, Konfig not only simplifies the technical aspects of API integration but also enriches the educational resources available to developers.

MLBox is an advanced Python library designed for Automated Machine Learning. This library offers a variety of features, including rapid data reading, efficient distributed preprocessing, comprehensive data cleaning, robust feature selection, and effective leak detection. It excels in hyper-parameter optimization within high-dimensional spaces and includes cutting-edge predictive models for both classification and regression tasks, such as Deep Learning, Stacking, and LightGBM, along with model interpretation for predictions. The core MLBox package is divided into three sub-packages: preprocessing, optimization, and prediction. Each sub-package serves a specific purpose: the preprocessing module focuses on data reading and preparation, the optimization module tests and fine-tunes various learners, and the prediction module handles target predictions on test datasets, ensuring a streamlined workflow for machine learning practitioners. Overall, MLBox simplifies the machine learning process, making it accessible and efficient for users.

Ludwig serves as a low-code platform specifically designed for the development of tailored AI models, including large language models (LLMs) and various deep neural networks. With Ludwig, creating custom models becomes a straightforward task; you only need a simple declarative YAML configuration file to train an advanced LLM using your own data. It offers comprehensive support for learning across multiple tasks and modalities. The framework includes thorough configuration validation to identify invalid parameter combinations and avert potential runtime errors. Engineered for scalability and performance, it features automatic batch size determination, distributed training capabilities (including DDP and DeepSpeed), parameter-efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and the ability to handle larger-than-memory datasets. Users enjoy expert-level control, allowing them to manage every aspect of their models, including activation functions. Additionally, Ludwig facilitates hyperparameter optimization, offers insights into explainability, and provides detailed metric visualizations. Its modular and extensible architecture enables users to experiment with various model designs, tasks, features, and modalities with minimal adjustments in the configuration, making it feel like a set of building blocks for deep learning innovations. Ultimately, Ludwig empowers developers to push the boundaries of AI model creation while maintaining ease of use.

AutoKeras, an AutoML framework built on Keras, is designed by the DATA Lab at Texas A&M University. Its primary objective is to democratize machine learning, making it accessible to a wider audience. With an exceptionally user-friendly interface, AutoKeras facilitates a variety of tasks, enabling users to engage with machine learning effortlessly. This innovative approach removes many barriers, allowing individuals without extensive technical knowledge to leverage advanced machine learning techniques.

Sayari delivers essential context that fosters assured conclusions, facilitates information sharing, and enhances reporting. By equipping analysts and compliance teams, it enables leaders to make well-informed choices that enhance the safety of trade environments. The platform was specifically crafted to aid these teams in swiftly grasping the intricate relationships among various entities, thus providing deeper insights into international commerce and supply chains. Users can efficiently uncover entities associated with designated parties or high-risk regions by utilizing integrated watchlists, trade and maritime data, graph analytics, and geospatial search tools. Moreover, it accelerates cross-border investigations into unlawful financial activities through comprehensive beneficial ownership data sourced from offshore and high-risk jurisdictions. Additionally, it allows for the immediate identification of entities connected to designated parties, enriching networks with fresh investigative insights while ensuring the preservation of a court-admissible documentation trail. This comprehensive approach not only streamlines compliance efforts but also significantly enhances the overall understanding of complex global trade dynamics.

Revamp banking functions across various sectors, including retail, corporate, small and medium enterprises, Islamic banking, microfinance, and distinct financial entities. Oracle FLEXCUBE empowers banks to initiate their digital transformation journeys, ensuring they remain relevant, competitive, and compliant amidst the rapidly changing landscape of the banking sector. It enables the launch and management of a broad spectrum of customized products and services, facilitating efficient growth and expansion. Banks can swiftly adjust their operations to accommodate shifting customer expectations, business needs, and regulatory demands. Furthermore, the platform allows for seamless collaboration within ecosystems to create and enhance innovative financial products and services. It ensures operational efficiency at a large scale while providing flexibility in deployment and operations. The onboarding process for new customers is made simple, and personalized product offerings can be provided effortlessly. Additionally, it accelerates the decision-making process for lending products, reducing the time required for approvals and decisions. Moreover, the corporate lending lifecycle is streamlined, covering everything from origination to the management of credit, collateral, syndication, and beyond, thereby enhancing overall operational effectiveness. This comprehensive approach ultimately positions banks to thrive in an increasingly competitive market.

HACARUS' AI Core stands out because it can generate highly precise defect detection models using minimal data sets that consist solely of quality information, and it can easily adapt to evolving production environments with minor modifications. The HACARUS Check AI software is an all-encompassing inspection solution designed with the operator's needs at the forefront. This user-friendly application boasts a complete graphical interface, facilitating the effortless creation of robust AI models. To train a new inspection model, only a small set of high-quality data is necessary, and the resulting predictions are delivered almost instantaneously. Additionally, the detected defects are highlighted through useful features, including bounding boxes and heat maps, enhancing the user's ability to identify issues quickly. This innovative approach not only streamlines the inspection process but also ensures higher reliability in defect detection.

Prompt Flow is a comprehensive suite of development tools aimed at optimizing the entire development lifecycle of AI applications built on LLMs, encompassing everything from concept creation and prototyping to testing, evaluation, and final deployment. By simplifying the prompt engineering process, it empowers users to develop high-quality LLM applications efficiently. Users can design workflows that seamlessly combine LLMs, prompts, Python scripts, and various other tools into a cohesive executable flow. This platform enhances the debugging and iterative process, particularly by allowing users to easily trace interactions with LLMs. Furthermore, it provides capabilities to assess the performance and quality of flows using extensive datasets, while integrating the evaluation phase into your CI/CD pipeline to maintain high standards. The deployment process is streamlined, enabling users to effortlessly transfer their flows to their preferred serving platform or integrate them directly into their application code. Collaboration among team members is also improved through the utilization of the cloud-based version of Prompt Flow available on Azure AI, making it easier to work together on projects. This holistic approach to development not only enhances efficiency but also fosters innovation in LLM application creation.

Tumeryk Inc. focuses on cutting-edge security solutions for generative AI, providing tools such as the AI Trust Score that facilitates real-time monitoring, risk assessment, and regulatory compliance. Our innovative platform enables businesses to safeguard their AI systems, ensuring that deployments are not only reliable and trustworthy but also adhere to established policies. The AI Trust Score assesses the potential risks of utilizing generative AI technologies, which aids organizations in complying with important regulations like the EU AI Act, ISO 42001, and NIST RMF 600.1. This score evaluates the dependability of responses generated by AI, considering various risks such as bias, susceptibility to jailbreak exploits, irrelevance, harmful content, potential leaks of Personally Identifiable Information (PII), and instances of hallucination. Additionally, it can be seamlessly incorporated into existing business workflows, enabling companies to make informed decisions on whether to accept, flag, or reject AI-generated content, thereby helping to reduce the risks tied to such technologies. By implementing this score, organizations can foster a safer environment for AI deployment, ultimately enhancing public trust in automated systems.

Oracle NoSQL Database is specifically engineered to manage applications that demand high data throughput and quick response times, along with adaptable data structures. It accommodates various data types including JSON, tables, and key-value formats, and functions in both on-premises installations and cloud environments. The database is designed to scale dynamically in response to fluctuating workloads, offering distributed storage across multiple shards to guarantee both high availability and swift failover capabilities. With support for programming languages such as Python, Node.js, Java, C, and C#, as well as REST API drivers, it simplifies the development process for applications. Furthermore, it seamlessly integrates with other Oracle products like IoT, Golden Gate, and Fusion Middleware, enhancing its utility. The Oracle NoSQL Database Cloud Service is a completely managed solution, allowing developers to concentrate on creating applications without the burden of managing backend infrastructure. This service eliminates the complexities associated with infrastructure management, enabling teams to innovate and deploy solutions more efficiently.

Smolagents is a framework designed for AI agents that streamlines the development and implementation of intelligent agents with minimal coding effort. It allows for the use of code-first agents that run Python code snippets to accomplish tasks more efficiently than conventional JSON-based methods. By integrating with popular large language models, including those from Hugging Face and OpenAI, developers can create agents capable of managing workflows, invoking functions, and interacting with external systems seamlessly. The framework prioritizes user-friendliness, enabling users to define and execute agents in just a few lines of code. It also offers secure execution environments, such as sandboxed spaces, ensuring safe code execution. Moreover, Smolagents fosters collaboration by providing deep integration with the Hugging Face Hub, facilitating the sharing and importing of various tools. With support for a wide range of applications, from basic tasks to complex multi-agent workflows, it delivers both flexibility and significant performance enhancements. As a result, developers can harness the power of AI more effectively than ever before.

Nextdata is an innovative operating system for data meshes that aims to decentralize the management of data, empowering organizations to effectively create, share, and oversee data products across diverse stacks and formats. By packaging data, metadata, code, and policies into versatile containers, it streamlines the data supply chain, guaranteeing that data remains useful, secure, and easily discoverable. The platform includes built-in automated policy enforcement as code, which consistently monitors and upholds data quality and compliance standards. It is designed to integrate flawlessly with existing data architectures, enabling users to configure and provision data products according to their requirements. Supporting the processing of data from any source and in any format, Nextdata facilitates advanced analytics, machine learning, and generative AI applications. Furthermore, it automatically generates and updates real-time metadata and semantic models throughout the lifecycle of the data product, significantly improving both discoverability and usability. By doing so, Nextdata not only simplifies complex data interactions but also enhances collaborative efforts within organizations, fostering a more data-driven culture.

Dendrite is a versatile platform that operates independently of any specific framework, allowing developers to design web-based tools for AI agents that can authenticate, interact with, and gather data from any online source. This innovative system mimics human browsing actions, which aids AI applications in navigating websites and retrieving information effortlessly. It features a Python SDK that equips developers with essential resources to create AI agents capable of engaging with web elements and extracting relevant data. Dendrite’s adaptable nature ensures it can seamlessly fit into any technology stack, making it an ideal choice for developers looking to improve the web interaction abilities of their AI agents. The Dendrite client synchronizes securely with website authentication sessions already established in your local browser, eliminating the need to share or store sensitive login information. Additionally, the Dendrite Vault Chrome Extension allows users to safely share their browser-based authentication sessions with the Dendrite client, further enhancing convenience and security. Ultimately, Dendrite empowers developers to create intelligent web interactions, streamlining the integration of AI into everyday online tasks.

Getgud.io is an innovative platform that converts gameplay data into targeted marketing campaigns, enhances player involvement, examines in-game behavior, fine-tunes matchmaking processes, and promotes fair play, all within a single solution. By integrating your game server with their platform without needing client-side modifications, you achieve comprehensive visibility of your game and its players. The user-friendly dashboard enables you to consolidate everything that matters to you, allowing you to query data on players, matches, and user submissions while accessing valuable insights. Additionally, you can set up actions and webhooks for specific player groups and even replay matches to observe player behavior directly. Getgud intelligently assesses in-match behavior, identifying players exhibiting toxic traits, including griefers like campers and spawn killers, as well as cheaters such as those using aim-bots and wall-hacks. You maintain authority over the thresholds for these behaviors and the corresponding actions to implement, including the option for automatic bans. Furthermore, this holistic approach ensures a more enjoyable gaming experience for all participants.

Thermo Scientific Amira Software serves as a robust and versatile solution for visualizing, analyzing, and comprehending life science and biomedical research data across a range of imaging modalities, such as optical and electron microscopy, CT, MRI, and more. Notably efficient and adaptable, Amira Software enhances advanced bioimaging workflows in diverse research domains, including structural biology, cellular biology, tissue imaging, neuroscience, preclinical studies, and bioengineering. Among its standout features are capabilities for importing and processing image data, visualization and exploration, sophisticated segmentation, measurement, quantification, and analysis, as well as tailored tools for molecular visualization, object tracking, filament tracing, meshing for finite element analysis, diffusion tensor imaging, 3D registration, and the analysis of biomaterial deformation. Additionally, Amira Software provides options for customization and integrates seamlessly with ecosystems like MATLAB, Python, and custom C++, making it a highly adaptable tool for researchers. This flexibility ensures that researchers can tailor their workflows to meet specific experimental needs and challenges.

Gemini 2.0 Flash-Lite represents the newest AI model from Google DeepMind, engineered to deliver an affordable alternative while maintaining high performance standards. As the most budget-friendly option within the Gemini 2.0 range, Flash-Lite is specifically designed for developers and enterprises in search of efficient AI functions without breaking the bank. This model accommodates multimodal inputs and boasts an impressive context window of one million tokens, which enhances its versatility for numerous applications. Currently, Flash-Lite is accessible in public preview, inviting users to investigate its capabilities for elevating their AI-focused initiatives. This initiative not only showcases innovative technology but also encourages feedback to refine its features further.

Gemini 2.0 Pro stands as the pinnacle of Google DeepMind's AI advancements, engineered to master intricate tasks like programming and complex problem resolution. As it undergoes experimental testing, this model boasts an impressive context window of two million tokens, allowing for the efficient processing and analysis of extensive data sets. One of its most remarkable attributes is its ability to integrate effortlessly with external tools such as Google Search and code execution platforms, which significantly boosts its capacity to deliver precise and thorough answers. This innovative model signifies a major leap forward in artificial intelligence, equipping both developers and users with a formidable tool for addressing demanding challenges. Furthermore, its potential applications span various industries, making it a versatile asset in the evolving landscape of AI technology.

TROCCO is an all-in-one modern data platform designed to help users seamlessly integrate, transform, orchestrate, and manage data through a unified interface. It boasts an extensive array of connectors that encompass advertising platforms such as Google Ads and Facebook Ads, cloud services like AWS Cost Explorer and Google Analytics 4, as well as various databases including MySQL and PostgreSQL, and data warehouses such as Amazon Redshift and Google BigQuery. One of its standout features is Managed ETL, which simplifies the data import process by allowing bulk ingestion of data sources and offers centralized management for ETL configurations, thereby removing the necessity for individual setup. Furthermore, TROCCO includes a data catalog that automatically collects metadata from data analysis infrastructure, creating a detailed catalog that enhances data accessibility and usage. Users have the ability to design workflows that enable them to organize a sequence of tasks, establishing an efficient order and combination to optimize data processing. This capability allows for increased productivity and ensures that users can better capitalize on their data resources.