1. Develop your strategy
Everything begins by understanding who you are and the goals you're trying to achieve. We have proven workflows to uncover who you are, your goals, what's working, and the aspects of your story developers need to know.
2. Plan your activation
Once we have an understanding of who you are and how we can help you, we'll begin to shape what to say, how to say it, and where. Our team has years of growth and marketing experience to develop a campaign focused your goals.
3. Build your following
Developers follow our content to explore new ideas and discover new tools. We make sure your brand and product are a part of their journey. We're here to guide and help you every step of the way.
Podcast sponsorship examples
Linode, Zeus-like power
Linode, Dedicated CPUs
Digital Ocean, Product Lineup
Rollbar, CircleCI - Paul Biggar
Team Culture / Hiring
Indeed, Darren Nix
Rollbar, Move fast and fix things
Fastly, Network-wide pre-roll
News sponsorship examples
If you are looking for insights into the marketing technology landscape and combing through the 7,000+ companies in the MarTech 5000 sounds like too much work, we have good news. In this post Segment uncovers which companies in the MarTech 5000 are growing rapidly and which old categories are ripe for disruption. Today, we’d like to share these insights with you, comparing each tool along with its competitors, and highlighting who’s growing the fastest. We’ll include some light commentary, including where we see the highest potential, but for the most part, these graphs speak for themselves. We’re also doing our small part to help drive the growth of the 2019 category winners and beyond. This year, we’re opening up the Segment platform to partners, and our Developer Center is available in beta for you to start building integrations today.
To commemorate the 2019 PyCon conference and the worldwide Python community, Lisa Tagliaferri and Brian Boucheron from DigitalOcean have put together a free eBook of Python machine learning projects! As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions — sometimes without final input from humans who may be impacted by these findings — it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all.
Every day we influence developers at some of the most innovative tech companies in the world.