Startups Using Linux in Boston

Via their job posts and information submitted by startups themselves, these are the Boston Linux startups we've found.

Interested in other technologies? Browse or search all of the built-in-boston tech stacks we've curated.

Tech Stack Highlights

TypeScript – With TypeScript’s compile time checking we have seen increased quality in our software with the ease of JavaScript. We also use CircleCI continuous automated building, testing and deployment which further increases our quality and development speed.

NodeJS – Our cloud-based Microservices are primarily written in NodeJS. NodeJS allows us to develop scalable and easily deployable software for our business logic and high level robot behaviors.

ROS – Our robots run on the ROS with customizations to greatly improve performance and reliability. With LIDAR, 3D depth cameras, odometry and other sensors our robots are able to move autonomously in our customer’s facilities.

OpenCV – With 256 CUDA cores on our robot you will have access to more than a teraflop of compute on our robot. Some of our highest performance algorithms utilize OpenCV to take advantage of these parallel cores.

Angular – Our graphical interfaces are built as Angular applications running on Electron giving us ease of development running on multiple platforms and fully extensible as we design new features.

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Tech Stack Highlights

Ruby / Rails – We use Ruby and Rails for our web facing applications, as well as the API underlying the platform. The entire platform is deployed to Amazon AWS. We haven’t made the jump to Rails 5 yet for existing applications, but it’s on the radar.

AWS – The entire drizly platform is built on AWS. We use a variety of different services, including SNS, Redshift and Lambda, which are used heavily in our data pipeline.

React – Where possible new frontend development is done in React. We have some SPA style projects built in React, as well as a significant number of components on our e-commerce site utilizing react.

Mysql/Postgres/Redshift – Depending on the application we use a slightly different data-store. We like to be flexible, and find the right solution for the problem at hand.

SQS – We utilize SQS heavily as a background job processing system. We’ve found it to be stable, and durable, and performant enough for our use cases.

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Sensor & computer-vision predictive analytics system for controlled-environment agriculture.

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“Human behavior prediction technology” for autonomous vehicles.

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Developing AI/AR software for easier repurposing of companies’ robotics systems.

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Processor-level cybersecurity.

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“Universal adapter for banks.” Connecting financial institutions’ existing systems to a standardized API used by a platform of FinTech vendors.

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Ballot visualization software, providing quick, transparent review of scanned ballots and how they were counted.

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Adding area-awareness to existing industrial robots for safer, more efficient manufacturing.

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