Appsmith
Appsmith enables organizations to create custom internal applications quickly with minimal coding. The platform allows users to build applications by connecting data sources, APIs, and workflows through a user-friendly drag-and-drop interface. Appsmith's flexibility with JavaScript lets developers fully customize components, while the open-source architecture and enterprise security features ensure scalability and compliance. With self-hosting and cloud deployment options, businesses can choose the best setup for their needs, whether for simple dashboards or complex business applications.
Appsmith offers a comprehensive solution for creating and deploying custom AI agents that can automate key business processes. Designed for sales, support, and people management teams, the platform allows companies to embed conversational agents into their systems. Appsmith's AI agents enhance operational efficiency by managing routine tasks, providing real-time insights, and boosting team productivity, all while leveraging secure data.
Learn more
JOpt.TourOptimizer
If you are developing software for Logistics Dispatch Solutions, which contain challenges:
-For staff dispatching, such as sales reps, mobile service, or workforce?
-For truck shipment allocation in daily transportation and logistics (scheduling, tour optimization, etc.)?
-For waste management and District Planning?
-Generally, highly constrained problem sets?
And your product does not have an automized optimization engine?
Then JOpt is the perfect fit for your product and can help you to save money, time, and workforce, letting you concentrate on your core business.
JOpt.TourOptimizer is an adaptable component to solve VRP, CVRP, and VRPTW class problems for any route optimization in logistics or similar fields. It comes as a Java library or in Docker Container utilizing the Spring Framework and Swagger.
Learn more
regon
litex.regon serves as a user-friendly interface for the Polish REGON database by providing a straightforward, Python-based wrapper. To utilize its SOAP API, users must obtain a user key from the administrators of REGON. The REGONAPI requires just one argument, which is the service URL provided by these administrators. Once logged in, users can initiate queries against the database. Queries can be made using a single REGON number (which can be either 9 or 14 digits), a single 10-digit KRS number, or a single NIP (which consists of a 10-digit string). Furthermore, users can query collections of REGONs, KRSs, or NIPs, ensuring that all items in a collection conform to the respective length requirements. The method only processes one parameter at a time, prioritizing the first one provided from the list. Additionally, users can request a more comprehensive report by including the detailed=True parameter, which prompts the search method to return a default detailed report. If a user is familiar with the REGON of a specific business entity and the name of the detailed report, they can retrieve the complete report directly, enhancing the accessibility of information within the database. This feature makes litex.regon a valuable tool for anyone needing detailed insights into Polish business entities.
Learn more
broot
The ROOT data analysis framework is widely utilized in High Energy Physics (HEP) and features its own file output format (.root). It seamlessly integrates with software developed in C++, while for Python users, there is an interface called pyROOT. However, pyROOT has compatibility issues with python3.4. To address this, broot is a compact library designed to transform data stored in Python's numpy ndarrays into ROOT files, structuring them with a branch for each array. This library aims to offer a standardized approach for exporting Python numpy data structures into ROOT files. Furthermore, it is designed to be portable and compatible with both Python2 and Python3, as well as ROOT versions 5 and 6, without necessitating changes to the ROOT components themselves—only a standard installation is needed. Users should find that installing the library requires minimal effort, as they only need to compile the library once or choose to install it as a Python package, making it a convenient tool for data analysis. Additionally, this ease of use encourages more researchers to adopt ROOT in their workflows.
Learn more