We’re back with another employee spotlight! Last month, we spoke to Senior Front-End Developer Mark Smith, who works out of our San Francisco office. This month, we (virtually) crossed over to the opposite coast and spent some time getting to know Marguerite des Trois Maisons, who works out of our new Toronto office as the product owner on our Site Reliability Engineering (SRE) team.

Great news: we’ve updated the Sentry’s GitHub and GitHub Enterprise integrations! With the growing relationship between GitHub and Microsoft’s developer stack, Sentry will help users save time, squash bugs, and work more efficiently through the many different parts of the development workflow. The features you already use in our existing integration are still there, including resolve via commit or via pull request, issue tracking, and suggested assignee via commit.

Money matters in IT. After all, building up computer networks and properly maintaining the digital devices companies depend on doesn't come cheap. And to ensure organizations and employees are supported by a solid and secure tech foundation, IT departments need to regularly invest in hardware, software, and services.

Sentry is rolling out additional support and features for Jira and Bitbucket, including two-way sync and org-level settings. These updated integrations will improve the experience for any developer who has Sentry and Atlassian in their workflow by increasing observability into their applications and decreasing setup time.

More and more organizations are adopting DevOps practices, and it’s been well documented that DevOps is more of a journey than a true destination. Development teams release software more often on increasingly ephemeral infrastructure, making communication and agility a business requirement. PagerDuty helps modern DevOps teams automate their incident resolution process so they can deliver better software, faster.

Continuous delivery (CD) is more than a buzzword. In fact, CD today drives development and operations (DevOps) teams at enterprises around the world.

At first glance, applying machine learning to Continuous Delivery might sound a bit like cracking a peanut with a sledgehammer. I mean, how hard can deployment automation actually be? As it turns out, it’s way more complex than we think.