Checksum.ai
Engineering teams shipping with AI have a new bottleneck: validation. Code output has accelerated. Quality hasn't. Checksum closes the gap.
Checksum is a continuous quality platform with a suite of AI agents that handle testing end-to-end, at every stage of the development lifecycle. Where most tools wait for a human to trigger them, Checksum runs autonomously in the background, generating tests, executing them, and repairing failures without manual intervention. Seventy percent of test failures are resolved automatically through real-time auto-recovery.
The platform covers every layer: end-to-end UI flows via Playwright, API endpoint chains, and targeted CI tests scoped to exactly what changed in a PR. All tests land as real code in your repository and are delivered as standard Playwright, owned by your team.
Checksum is fine-tuned on 1.5+ million test runs and integrates natively with Cursor, Claude Code, and 100+ AI coding agents. Type /checksum and your coding agent's output gets tested before it ever reaches review. Generation and healing happen on Checksum's cloud infrastructure which means no LLM tokens consumed, no local resources required.
The result: test suites that stay green as the product evolves, fewer regressions reaching production, and release confidence that scales alongside AI output.
Learn more
qTest
Software testing must be centrally managed and visible from conception to production in order to make software releases more secure and faster. Tricentis qTest enables teams to collaborate and ship faster, with less risk, by unifying, managing, and scaling testing across the enterprise. Robust testing includes a variety of testing tools, teams, test types, and testing methods. Tricentis qTest combines them all so that teams can release more confidently and reduce risk. It also helps identify opportunities to move faster - collectively. Automate more testing, increase the release velocity, and bring together teams throughout the software development process. Native DevOps integrations such as Jira, Jenkins and GitHub keep QA and development in sync. With a full audit trail, trace defects and tests back to development and requirements. Align teams with cross-project reporting.
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
openpyxl
Openpyxl is a Python library designed for reading and writing Excel 2010 files in formats such as xlsx, xlsm, xltx, and xltm. The library was developed due to the absence of a native solution for handling Office Open XML files in Python, and it owes its origins to the PHPExcel project. It is important to note that openpyxl does not provide protection against certain vulnerabilities like quadratic blowup or billion laughs XML attacks by default, but these risks can be mitigated by installing the defusedxml library. To install openpyxl, you can use pip, and it's recommended to perform this installation within a Python virtual environment to avoid conflicts with system packages. In some instances, you may want to work with a specific version of the library, especially if there are fixes that have not yet been released officially. Fortunately, you do not need to create an actual file on your filesystem to begin using openpyxl; simply import the Workbook class and begin your tasks. When you create sheets, they are automatically assigned names, and once you rename a worksheet, you can access it using the corresponding key from the workbook. This ease of use makes openpyxl a popular choice for many Python developers working with Excel files.
Learn more
