Systems | Development | Analytics | API | Testing

A real estate acquisition often brings another data environment with it. That can mean a second CRM, a second PMS or ERP instance with its own chart of accounts, and a second set of property and client IDs that were never built to reference each other. Add in a spreadsheet or two, built to keep the two businesses reporting the same numbers in the meantime, and the picture gets messier still.

We're pulling back the curtain on a project we've been working on for some time now: the next evolution of the Kong brand. You may have noticed some changes recently to our site, swag, or socials. But today it's official: we're announcing Kong's rebrand and introducing our new mascot, Karl. Without further ado, let's dig in. Along the way, we'll give some insight into the "why" behind it all, while looking back at how things have changed since the early days of Kong. We were the API connectivity company.

AI agents are proliferating faster than most institutions can govern them and the primary challenge is quickly becoming an "accountability gap." Disconnected pilots rarely scale into accountable, auditable operations. The financial services industry is currently at a tipping point: banks must bridge the gap between initial AI enthusiasm and operational reality.

Over the past few decades of digital transformation, organizations have accumulated separate tools for separate jobs: RPA for UI automation, BPM for workflow management, iPaaS for API integrations, and other point solutions. These investments have delivered undeniable productivity gains. But they've also created challenges: systems that don’t talk to each other, processes that break across integrations, and IT teams that spend more time maintaining the technology rather than innovating with it.

AI coding tools are becoming increasingly capable of writing software that compiles, passes tests, and solves real engineering problems. But generating working code is only part of what these systems now do. When an AI assistant creates a Node.js project, it may also influence decisions about: Those decisions can survive much longer than the generated code itself.

The forecasting model is often the smaller part of the job. In production, what makes predictive analytics in real estate reliable is the infrastructure around it: governed historical data, unified business entities, feature pipelines, model serving, monitoring, and integration into the systems where people work.

Namespace, Avrea, and Blacksmith are among the fastest GitHub Actions runner providers on the market right now. According to Avrea's August 2026 benchmarks, they all score within about 1% of each other on raw CPU. The same benchmark shows warm builds (populated cache) beating GitHub's default cold start (cache disabled) by anywhere from 5x to more than 140x, depending on the project.

Memory leaks are the bugs that really creep up on you. One minute your app is running perfectly, then suddenly it slows to a crawl or crashes. We don’t get a warning with memory leaks, so we need to prepare proactively and be ready when they hit. This means we need to: This guide is here for all of that.