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Sprep turns any document into a ready-to-listen podcast in minutes. Upload a PDF, paste a URL, or drop in a set of notes. Sprep scripts it, voices it, and produces a clean audio briefing you can share, publish, or distribute internally. No recording equipment. No editing. No studio. Built for teams that need audio at scale and individuals who learn better by listening, Sprep sits at the intersection of productivity and compliance. Unlike consumer tools like NotebookLM, Sprep is built for business and education environments where data handling, access controls, and auditability actually matter. Your documents stay yours. Whether you are converting clinical publications into audio for medical teams, turning internal knowledge bases into on-demand briefings, or repurposing written content into podcast episodes for a growing audience, Sprep handles the production layer so you can focus on the content itself. The result is studio-quality audio from any written input. Faster than recording, cleaner than text, and ready for the way people actually consume information today.

Eliminate unnecessary paper records with the LabVantage Electronic Laboratory Notebook (ELN) and Laboratory Execution System (LES). Capture, organize, manage and collaborate experiments and test execution workflows across your organization with ease. With features designed to increase productivity while reducing errors, you can replace your paper lab notebooks and paper method worksheets with confidence. The LabVantage ELN and LES are fully embedded in LabVantage to provide a comprehensive and integrated lab automation solution.

Alchemy is enterprise software that helps product and technical teams make better products faster. It is specifically built for companies that develop new products and product applications. Alchemy's configurable electronic lab notebook (ELN) powered by real-time AI insights helps you to accelerate innovation and ensure that everything you develop meets technical requirements and commercial objectives. Its integrated testing center and laboratory information management system (LIMS) help you to streamline your workflows and collect AI-ready data at every step. Alchemy's connected sales & service module gives everyone real-time visibility into client requests and related lab work, so you can work together seamlessly to deliver better products to market faster. Alchemy's configuration center makes it easy to configure your properties and workflows to match your development processes exactly.

The PyTorch 0.3 Python 2.7 CPU Notebook is a pre-configured and fully integrated runtime environment designed for machine learning and data science applications. It combines PyTorch 0.3, an open-source machine learning library, with Python 2.7 and Jupyter Notebook, a browser-based interactive platform for programming and data analysis. This stack is optimized for CPU performance, providing a stable and tested execution environment suitable for both short and long-running high-performance tasks. It can be seamlessly integrated into continuous integration and deployment workflows, making it ideal for training, inference, or running as an API service. Key Features and Functionality: - Integrated Software Stack: Combines PyTorch 0.3, Python 2.7, and Jupyter Notebook for a cohesive development environment. - CPU Optimization: Tailored for high-performance tasks on CPU, ensuring efficient resource utilization. - Versatile Deployment Options: Can be installed on new or existing Linux servers, run as a virtual machine, used as a base image for Docker or Vagrant, or launched on various cloud platforms. - Development Tools: Includes a development preset with essential tools like C compiler and make for program development and building. Primary Value and User Solutions: This product addresses the need for a ready-to-use, stable, and high-performance environment for machine learning and data science projects. By integrating essential tools and libraries, it reduces setup time and complexity, allowing users to focus on development and research. Its CPU optimization ensures efficient execution of tasks without the need for specialized hardware, making it accessible for a wide range of users and applications.

Online LIMS is an international software technology company focused on laboratory automation and configurable laboratory interfacing tools. We deliver innovative, effective and cost efficient solutions and services to a wide range of customers in the environmental, life sciences and geo-chemical laboratory analysis fields.

Decipad revolutionizes the way individuals and teams interact with numbers by offering an intuitive, low-code platform for data modeling and interactive storytelling. With Decipad, creating dynamic plans, models, and reports is not just efficient but enjoyable. Its smart document environment, akin to a virtual notebook, effortlessly blends text, formulas, and visualizations, enabling users to express numbers with meaning and foster a deeper understanding across the team. Decipad's natural language processing and low-code blocks make modeling both fun and powerful, allowing for a human-centric approach to formulating variables and equations. Users can connect data sources directly to their document, ensuring automatic updates and real-time insights. Beyond mere data representation, Decipad empowers users to craft scenarios, present playable stories, and drive richer engagement with both team members and customers. Whether you are a student, teacher, founder, or a professional, Decipad invites you to explore the world of numbers in a playful, meaningful, and collaborative way, transforming static data into interactive narratives that inspire action and communicate value effectively

MyInfo is a personal information manager developed by Milenix Software. MyInfo collects, organizes, edit, stores, and retrieves personal-reference information like text documents, web snippets, e-mails, notes, and files from other applications. The latest version offers speed improvements, various perspectives from which to organize data trees, a modern user interface, multiple attachments per note, multiple sections per notebook and more. MyInfo uses both hierarchical, and folder-like structures along with tags, categories and other meta-information for organizing its content. MyInfo can import data from a range of third-party applications, including AskSam The software can be used as a free-form personal information manager, personal wiki, outliner, knowledge database, game master tool, GTD filing system. Overall, MyInfo is an outstanding tool for anyone who needs to organize notes, class or business material or documents, catalogue and search PDFs, write multi-chapter books or essays, and collect web-data.

Findings is a note taking app designed for scientists.

elucidaid is an electronic lab notebook, designed to help users organize all their laboratory notes in one place.

Chemia is a cost effective, compliant and turnkey Electronic Lab Notebook, used in API, Formulation and Analytical Labs, during the Drug Discovery and Development process.

Adaptive Scheduler is an advanced job scheduling solution designed to efficiently manage and execute numerous adaptive learning tasks across large-scale computing clusters, ranging from 10,000 to over 100,000 cores. It seamlessly integrates with the Adaptive package, facilitating the parallel execution of `adaptive.Learner` instances using various backends such as `mpi4py.futures`, `ipyparallel`, `loky`, `concurrent.futures.ProcessPoolExecutor`, or `dask.distributed`. This scheduler is particularly adept at handling the challenges associated with high-core-count computations, ensuring optimal performance and resource utilization. Key Features and Functionality: - Scalability: Capable of efficiently managing computations on clusters exceeding 30,000 cores, addressing the limitations of traditional parallel computing tools that struggle with high core counts. - Adaptive Integration: Designed to work seamlessly with the Adaptive package, allowing for the execution of adaptive sampling algorithms that require real-time feedback and decision-making. - Fault Tolerance: Automatically handles job failures by rescheduling tasks, ensuring minimal data loss and continuous computation, even in the event of node crashes or evictions. - Minimal File System Load: Optimized to reduce the burden on the file system, enhancing overall system performance and reliability. - Automated Job Management: Eliminates the need for manual job script creation and submission by automating these processes, thereby reducing boilerplate code and potential errors. - Preservation of Computational State: Maintains Python kernel and variable states within jobs, allowing for consistent and uninterrupted computations without the need for reinitialization. - Computation Locality: Ensures that jobs continue to run independently, even if the central job manager fails, by maximizing computation locality and reducing inter-process communication overhead. Primary Value and Problem Solved: Adaptive Scheduler addresses the critical challenge of executing large-scale, adaptive computations that require real-time feedback and decision-making. Traditional parallel computing tools often falter under the demands of high-core-count environments due to centralized scheduling bottlenecks and communication overhead. By decentralizing job management and optimizing resource allocation, Adaptive Scheduler enables researchers and engineers to perform massive parallel computations efficiently. This capability is particularly valuable in fields such as quantum device simulations, where adaptive sampling algorithms necessitate dynamic and responsive computation strategies. By providing a robust, fault-tolerant, and scalable solution, Adaptive Scheduler empowers users to harness the full potential of modern supercomputing resources directly from a Jupyter notebook environment.

Scilligence Electronic Laboratory Notebook (ELN) is a powerful platform for facilitating internal R&D and managing external collaborations. It comes with advanced informatics for small molecules, biologics, and conjugates such as antibody-drug conjugates (ADCs).

Securely access Jupyter notebooks, RStudio Server or a Terminal session from your browser. For additional flexibility, connect to a full-featured Ubuntu 21.04 Desktop environment through your browser or a native client. Ships with the latest versions of Google Chrome, Visual Studio Code, Docker.

Labii Inc. is addressing research data documentation and management difficulties within biotech companies and academic labs. Labii integrates LIMS and ELN.

Turn your dataframe into an interactive data app for visual analysis. With pygwalker, you can use one line of python code to turn your dataframe in juypter notebook to a embedded visualization app in the cell where you can use simple drag and drop operations or chat interface to visualize your data.

Wallaroo.AI is a universal AI inference platform designed to streamline the deployment, management, and optimization of machine learning (ML models across diverse environments, including cloud, on-premise, and edge locations. By simplifying the ML lifecycle, Wallaroo.AI enables enterprises to operationalize AI efficiently, reducing time-to-value and infrastructure costs while enhancing performance and scalability. Key Features and Functionality: - Rapid Deployment: Deploy ML models from testing to production in minutes using self-service tools and automated workflows. - High-Performance Inference Engine: Utilize a Rust-based server capable of processing hundreds of thousands of events per second on a single server, significantly outperforming industry standards. - Comprehensive Observability: Gain real-time insights with advanced monitoring, audit logs, and drift detection to ensure optimal model performance. - Scalability and Flexibility: Support for distributed computing on x86, ARM, CPU, and GPU hardware, with workload autoscaling to optimize resource usage. - Seamless Integration: Integrate effortlessly with existing ML toolchains, including notebooks, model registries, and experiment tracking systems. - Automated Workload Orchestration: Automate, schedule, and execute combined data and ML inferencing workflows, enabling AI teams to scale their ML workflows by 5-10x while freeing up 40% of their weekly time. Primary Value and Problem Solved: Wallaroo.AI addresses the critical challenge of transitioning ML models from prototype to production—a process where 85% of initiatives often fail to materialize. By providing a unified platform that simplifies deployment, enhances performance, and reduces operational complexity, Wallaroo.AI empowers enterprises to accelerate AI adoption, achieve faster return on investment, and gain a competitive edge in their respective industries.

JarvisLabs.ai is a GPU cloud platform designed to simplify and accelerate AI development by providing seamless access to powerful GPU resources. Catering to AI engineers, researchers, and creative professionals, it offers a user-friendly environment that eliminates the complexities of cloud setup. With JarvisLabs.ai, users can launch their preferred Python environments in seconds, choose from a variety of modern NVIDIA GPUs—including A100, A6000, RTX5000, and RTX6000—and utilize pre-installed frameworks like PyTorch, TensorFlow, and Fastai. The platform supports Jupyter notebooks directly in the browser and allows SSH access, enabling users to continue coding from their laptops. This streamlined approach empowers users to focus on their AI projects without the overhead of managing hardware infrastructure. Key Features and Functionality: - Instant GPU Access: Deploy GPU instances rapidly, reducing setup time and accelerating project timelines. - Diverse GPU Selection: Access a range of NVIDIA GPUs, including A100, A6000, RTX5000, and RTX6000, to meet various computational needs. - Pre-Configured Environments: Utilize environments with pre-installed AI frameworks such as PyTorch, TensorFlow, and Fastai, facilitating immediate productivity. - Flexible Development Interfaces: Work seamlessly with Jupyter notebooks in the browser or connect via SSH for a personalized development experience. - Cost-Effective Pricing: Benefit from a pay-as-you-go model, ensuring affordability and scalability for projects of all sizes. Primary Value and User Solutions: JarvisLabs.ai addresses the challenges AI practitioners face in setting up and managing GPU infrastructure. By offering a one-click solution to access high-performance GPUs with pre-configured environments, it eliminates the need for extensive hardware management and configuration. This enables users to focus on developing and deploying AI models efficiently, reducing time-to-market and operational costs. The platform's affordability and ease of use make advanced AI development accessible to startups, educational institutions, and individual researchers, democratizing the field of artificial intelligence.

Studylog is in-vivo study management software used by pharmaceutical companies, biotech firms, contract research organizations (CROs), and academic research institutions to manage the workflow of in-vivo research studies. The platform covers the study lifecycle from design through reporting, including study design and scheduling, task and measurement tracking, data acquisition, randomization, data analysis, and report generation. The software is used to replace spreadsheet-based tracking methods with a centralized database that stores study data, protocols, and results in a single system. Data can be entered manually or collected automatically through integrations with wired and wireless scales, calipers, and blood analyzers. Studylog also integrates with GraphPad Prism for graphing and statistical analysis, and supports bioluminescence imaging (BLI) data. Key functionality includes: - Study design tools, including reusable study templates and the ability to clone prior studies for follow-up research - Task and measurement scheduling with timers and completion tracking - Multiple randomization methods (stratified, matched distribution, multi-parameter, and pure random), with options to randomize by parameters such as age, body weight, or tumor volume - Automated data collection from connected laboratory devices - Built-in graphing, including box plots, group mean line graphs, and Kaplan-Meier survival curves - A calendar view for scheduling and tracking tasks across multiple concurrent studies - Standardized dictionaries and nomenclature for consistent data entry across studies - Access controls and permissions by user, team, and study - A web API for integration with electronic lab notebooks (ELNs) and other systems Studylog is offered as a desktop application and includes offline access, allowing users to continue working with synchronized study data when network connectivity is unavailable. The company has provided in-vivo study management software for more than 20 years and offers customer support staffed by former in-vivo researchers.

Stop runaway AI agent loops and surprise developer bills before they hit your monthly invoice. Cognocient AI is an AI FinOps platform that sits in front of your LLM infrastructure to enforce real-time budget caps, eliminate code-loop waste, and attribute every dollar spent to specific business outcomes. Cognocient works as a proxy between your application code and AI providers such as OpenAI, Anthropic, and Gemini, so every request is metered as it happens. Teams get real-time cost attribution by project, team, feature, or customer. Budgets are enforced before a request is sent, not after the invoice arrives. Cost controls for agentic loops stop runaway agents and retry storms from burning through spend. FOCUS-aligned reporting and exports bring AI spend into existing FinOps and chargeback workflows. Why tech teams choose Cognocient AI: • Two-Minute Setup: Change exactly one base URL in your existing SDK configuration to route, balance, and secure your AI traffic. • Failure Loop Breaker: Protect your credit card from autonomous agents with automatic triggers that trip after consecutive errors or tool-call loops. • No Content Storing: Built with enterprise security in mind, the proxy tracks call metadata (costs, tokens, latency) without storing your underlying prompts or responses. • CI Cost Gates: Catch code regressions early by failing test builds if an evaluation run exceeds your designated financial threshold. From black-box developer spend to boardroom clarity, Cognocient AI unifies engineering efficiency with complete financial accountability.

ParallelM's MCenter helps Data Scientists deploy, manage and govern ML models in production. Just import your existing model from your favorite notebook and then create data connections or a REST endpoint for model serving with the drag-and-drop pipeline builder. Advanced monitoring automatically creates alerts when models are not operating as expected due to changing data. With built-in model governance, every action is controlled and tracked including model versioning and who can promote models into production to ensure compliance with regulations.