Managing Customer Releases with Feature Flags instead of Branches

At Blend, we offer a white-label consumer lending platform that streamlines the otherwise manual, paper-based, and generally painful borrowing process. One challenge inherent in our business model and industry is serving a diverse set of lenders with a single product — from small independents to the largest banks — each with different levels of comfort in accepting changes to the product. Some lenders want the latest functionality as soon as it’s available, while others prefer to test every user-facing change in our beta environment for a month or more before allowing it to be promoted to production.

Moving to Multitenancy

Here at Blend, we recently shifted to a multitenant paradigm for our core application. That is to say we moved from a paradigm where a single instance of our app served traffic from a single customer to one where a single instance can serve any number of them. Why didn’t we start that way? If you have a system where customers need to interact with each other, multitenancy is necessary from the start.

Continuous delivery as code with Jenkins DSL

At Blend, we’re dedicated to bringing simplicity and transparency to consumer finance. In our six years building a lending platform better suited for the 21st century — one that delivers greater security and transparency and is accessible to all consumers — we’ve grown a lot. Just two years ago our team was barely 100 employees total, but today, we’re at more than 350, including 90 engineers.

How we stopped wasting time building custom integrations

At Blend, we’re always working to increase transparency and equity in access to consumer lending and lending-adjacent markets. The current technical ecosystem of consumer lending is disjointed. Much like bridges bring communities together in the real world, much of what Blend does depends on our ability first to construct virtual bridges (integrations) between these existing, disjointed systems, and second, to create a unified experience for our users.

Predicting Application Submission with a Recurrent Neural Network

At Blend, we’re building tools to power a frictionless, more accessible consumer lending ecosystem. We want to ensure everyone who wants a loan (and qualifies for one) can apply easily. Applying for a mortgage is a complicated and lengthy process that requires close collaboration between borrowers and loan officers. It is critical for lenders to provide timely help to borrowers, but it’s difficult to know when to step in. One project we worked on over the past few sprints aimed to tackle that ambiguity.

Centralizing Logs in an Isolated AWS Account

As a member of the information security team here at Blend, I recently teamed up with the business analytics team to re-architect our log pipeline to increase the security and availability of both the log delivery system and access to the logs themselves. Since the logs provide crucial insight into the production environment for many different teams at Blend, we found ourselves with a list of different requirements to accommodate each team.