Typo
Linear
DevDynamics.ai
Zeda.io
LinearB
CodeRabbit
Google Fonts
Canny.io
NumPy
Pandas
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
Figure Eight
Typo is an AI-driven software delivery management platform designed to empower development teams with unparalleled real-time SDLC (Software Development Life Cycle) visibility, automated code reviews, and Developer Experience (DevEX) insights. Our platform helps teams code better, deploy faster, and stay perfectly aligned with business objectives.
Typo seamlessly integrates with your existing tool stack in under 30 seconds, providing immediate value with:
Join over 1,000 high-performing engineering teams across the globe who trust Typo to deliver reliable software faster.
Start your 14-day free trial now and experience the benefits of Typo firsthand: https://typoapp.io/
Choose the pricing plan that best suits the needs of your tech team: https://typoapp.io/pricing
TypoTypo's answer
Typo's answer
Typo is most suitable for these managerial roles: CTO (Chief Technology Officer), VP of Engineering (Vice President of Engineering), Head of Engineering, EM (Engineering Manager), TL (Technical Leader)
Typo's answer
Our founders, Kshitij and Varun, have first-hand experience with well-functioning engineering teams that consistently ship products and those that face challenges.
They've gone from being developers to engineering managers and now founders, recognizing that the best teams prioritize transparent workflows, continuous improvement, and the use of effective tools to make it happen.
In their roles leading and scaling engineering teams, they shared a common frustration - every other business team had access to metrics & workflows that efficiently resolved their operational issues, thereby showing their teams's impact on the business - the same was not true for engineering teams.
Typo was created to support engineering teams with the visibility & automation they need to build efficient software delivery & high-performing teams.
Our unwavering mission is to support in building high-performing dev teams & revolutionising the software delivery cycle - ship reliable software fast. This requires complete visibility, fast dev cycles, streamlined processes, and high-quality code - with an amazing developer experience.
By seamlessly plugging into your dev tool stack, Typo leverages AI to generate real-time engineering insights, automating code reviews & improvement goals, and bringing a unique way to measure & improve developer experience. This transformation fuels development, ensures game-changing business outcomes and propels unprecedented growth.
Typo's answer
Typo's answer
Typo's answer
Based on our record, NumPy seems to be a lot more popular than Typo. While we know about 122 links to NumPy, we've tracked only 6 mentions of Typo. 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.
You should first try talking with your team to understand the situation & their problems. Measuring individual engagement can easily be done through regular check-ins & feedback. There's this tool that we use and it does both - measuring performance & anonymous check-ins/feedback, you can give it a try - Typo. Source: about 3 years ago
Some recommended dashboard tools that you can try: Jellyfish, Typo, LinearB. Source: about 3 years ago
Hey u/varma-v here are a few tools that do exactly this - linearb.io, typoapp.io,etc. Source: about 3 years ago
It's important to identify the bottlenecks in this situation, where your team is invested & which tasks are taking the most from the development time. At our company, we're using Typo to measure these metrics (https://typoapp.io/). Source: about 3 years ago
This is not a promotion, but I think that this tool we made might benefit you as a tech manager: Typo website. Source: over 3 years ago
Unmatched integration with ML/AI ecosystems through NumPy, TensorFlow, and PyTorch. - Source: dev.to / 9 months ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโฆ. - Source: dev.to / 10 months ago
AI starts with math and coding. You donโt need a PhDโjust high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Pythonโs syntax is straightforward. - Source: dev.to / 11 months ago
The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / over 1 year ago
This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / almost 2 years ago
Linear - Streamlined issue tracking for software teams
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
DevDynamics.ai - Engineering metrics and insights to improve velocity, productivity and team health.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Zeda.io - Zeda.io is a product management tool that brings all the things needed to define, manage, and collaborate on your product at one place.
OpenCV - OpenCV is the world's biggest computer vision library
