The best standup bot for keeping your team on track during daily async standup meetings. Sup can facilitate a standup meeting, retrospective meeting, or other meetings asynchronously for your team using either a chat-based interface or a dialogue window.
Conduct standups & follow-ups: Sup provides team standup updates. You can see how everything works with direct questions and answers. Asynchronous standups and multiple follow-ups are a click away.
Vacation Tracker: Request, approve, view, and manage vacations with Sup. Holiday tracking is easy with regular updates and a dashboard of employee holiday analytics.
Create surveys & polls: Remote working can become a lot easier when you can make quick decisions based on surveys and quick polls. Make it simple for your team to voice their opinions.
Track team mood: Sup? Is that what you want to ask your team? Using mood tracking, understand your team's emotions. To gauge team morale, use it with a follow-up like standups. The anonymity of responses allows for honest answers.
Integrations: Sup x GoogleSheets. Sup integrates with Google Sheets to create a new Google Sheet file at the end of every month and sync the follow-up responses. Trusted by small and big-leaguers like Iterable, Adobe, PWC, Stripe, MailChimp, Starbucks, Mixpanel, Dell, Warner Bros, Wise, Perceptyx, Udaan, and more.
We use Sup bot extensively in our team to facilitate standup, End-of-day follow-ups, holiday tracking - all without leaving Slack.
Based on our record, OpenCV seems to be more popular. It has been mentiond 60 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but... - Source: dev.to / 4 days ago
Ideal For: Computer vision, NLP, deep learning, and machine learning. - Source: dev.to / 17 days ago
Almost everyone has heard of libraries like OpenCV, Pytorch, and Torchvision. But there have been incredible leaps and bounds in other libraries to help support new tasks that have helped push research even further. It would be impossible to thank each and every project and the thousands of contributors who have helped make the entire community better. MedSAM2 has been helping bring the awesomeness of SAM2 to the... - Source: dev.to / 5 months ago
OpenCV is an open-source computer vision and machine learning software library that allows users to perform various ML tasks, from processing images and videos to identifying objects, faces, or handwriting. Besides object detection, this platform can also be used for complex computer vision tasks like Geometry-based monocular or stereo computer vision. - Source: dev.to / 6 months ago
This library is used for image and video processing, offering functions for tasks like object detection, filtering, and transformations in computer vision. - Source: dev.to / 8 months ago
Project Burndown - Burndown is project management, automated. Our smart scheduling technology constantly manages your team's schedule - based on your priorities, progress, and capacity - so you don’t have to.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Standup Bot - An easy to use bot that automates your team’s standups, check-ins or any kind of recurring status update meetings, without breaking the bank.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Hive - Seamless project management and collaboration for your team.
NumPy - NumPy is the fundamental package for scientific computing with Python