Top 7 Dev Tools for AI Startups
Tags: ai, startup, programming
Published: 2021-12-30 18:47:35

There is no shortage of dev tools to play around with these days. While many offer invaluable time-saving and organizational advantages, it can be difficult to sift through all of the options to figure out what works best for you. 

As an [AI startup](https://www.assemblyai.com/), our Deep Learning and engineering teams have tried each of these tools as we work to optimize our [Speech-to-Text API](https://www.assemblyai.com/blog/the-top-free-speech-to-text-apis-and-open-source-engines/). To help you figure out which of the tools might suite your work best, we’ve created a short list of our go-to, and most valuable, dev tools–including Neovim, Bugsnag, Tailscale, Github’s Command Line Client, Comet-ml, Jupyter-notebook, and pandas. 

Here’s what our team had to say about each one:

## Top 7 Dev Tools for AI Startups

## 1. Neovim 

[Neovim](https://neovim.io/) is a hyperextensible, Vim-based text editor which is fully compatible with Vim’s editing model and Vimscript v1. Neovim has strong defaults, one build-type and one command, a built-in terminal emulator, and modern terminal features like bracketed paste, focus events, and cursor styling. All these work together to make your daily work life easier.

## 2. Bugsnag

As an error monitoring and reporting tool, [Bugsnag](https://www.bugsnag.com/) is invaluable for our research team. Basically, Bugsnag acts as your command center for both error monitoring and app stability. When you encounter an error, you can run end-to-end diagnostics to replicate it and determine the fix. Bugsnag’s UI is also intuitive and easy to use.

## 3. Tailscale

Security is top priority for any startup, and [Tailscale](https://tailscale.com/) makes it easy to achieve peace of mind. Top features of this zero-config VPN include support for SSO, multi-factor authentication, easy deauthorization when needed, a stable IP and auto-assigned domain, and an intuitive interface. Bottom line, Tailscale makes it infinitely easier for us to access protected resources like our databases. 

## 4. GitHub’s Command Line Client

Our team loves using [Github’s Command Line Client](https://github.com/cli/cli) for increased efficiency and time saving. The Command Line Client works by bringing pull requests, issues, and more right to the terminal next to where you’re already working, so you can see your entire GitHub workflow in one place. You can also call GitHub to script almost any action and set a custom alias for commands. It can install directly on Windows, Linux, or macOS, and is also available for repositories hosted on GitHub.com and GitHub Enterprise Server 2.20+.

## 5. Comet-ml

[Comet-ml](https://www.comet.ml/site/) compares doing Machine Learning with its product to “building with legos” because it lets you customize the platform in a way that works best for you. We love using it because it lets you manage, visualize, and optimize your entire Machine Learning lifecycle. You can also easily compare experiments to help you better understand differences in how your models perform and even get alerts when something goes wrong or needs to be debugged. 

## 6. Jupyter-notebook

As part of [Project Jupyter](https://jupyter.org/), Jupyter Notebook facilitates easy computational-document creation and sharing. We love that it offers multilingual support – including Python, Julia, R, and Scala – and lets you share notebooks via GitHub, email, or Dropbox. You can also integrate data from other data tools like pandas, scikit-learn, TensorFlow, Apache Spark, and more.
 

## 7. pandas

Built on top of Python, [pandas](https://pandas.pydata.org/) is an open source data analysis and manipulation tool, similar to NumPy. While it relies on NumPy arrays for much of its manipulation and computation, pandas makes it easier to visualize and explore data, helping our team make better sense of the large amounts of data we work with on a daily basis.