Systems | Development | Analytics | API | Testing

Rate limiting and throttling are two key strategies for managing API traffic in multi-tenant systems. While both aim to maintain performance and prevent misuse, they work in different ways: Rate Limiting: Sets strict caps on the number of requests a user can make within a specific time frame. Extra requests are rejected outright with an HTTP 429 status code. Throttling: Dynamically adjusts request processing speeds based on system load. Extra requests are slowed down or queued rather than blocked.

TL;DR – No flaky builds because of “data‑center air” at Bitrise! We built our own chassis that can talk to the Bitrise scheduler, can pre‑cool itself, and squeezes 32 Mac minis into a 6 rack unit oversized pizza box—while looking downright gorgeous doing it. We call it the mini Orchard.

Businesses need to be able to respond quickly to macroeconomic uncertainty and changing market conditions, whether those are caused by trade disputes, public health crises like the COVID-19 pandemic, or simply abrupt changes in consumer demand. Amid that swirl of disruptions, technology leaders are also being pressured by management and investors to optimize their businesses and drive as much efficiency as possible.

Every new protocol introduces complexities—onboarding new tools and SDKs, adapting to new processes, evaluating security and performance implications, ensuring compatibility with existing systems, and overcoming the learning curve. So when a new protocol shows up, the first question to ask is: is it really necessary? Let’s ask that about MCP—what problem is it trying to solve?

The Fivetran binary log reader optimizes change data capture for superior performance when replicating from an Oracle database.

Living near Frankfurt in central Germany, I often drive through the bustling streets of this financial hub, where every business seems to pulse with ambition. A few months ago, I helped a friend who works at a small logistics company in Offenbach. His ERP system was struggling to keep up with growing orders, causing delays that frustrated his team and clients.

Automation has long been a key driver of efficiency. Traditional RPA and IDP technology promised to relieve carriers from rekeying, extracting data from forms, and other repetitive tasks. At Appian, we saw early that automation in isolation doesn't achieve transformative outcomes. Why? Because AI has too often been deployed at the edges of workflows: copilots, chatbots, or analytics dashboards that assist us when prompted.

You know the feeling, you push a new feature, and your tests should catch any issues. But instead, they fail inconsistently, slow down your pipeline, or worse… pass when they shouldn’t. End-to-end testing is always a risk. That's precisely the reason why more teams are turning to end-to-end testing with Playwright. Unlike clunky legacy frameworks, the Playwright testing framework was built for speed, reliability, and cross-browser accuracy all out of the box.

Amazon Web Services (AWS) had a well-documented failure of the S3 service in its US East region this week. The knock-on effects of the S3 outage took out many of Amazon’s other services and caused serious and long-lasting outages for many high-profile services including Quora, Trello, GitLab, and many others. Overall Amazon provides an outstanding set of services, and as anyone knows these things happen, the key is to architect your solution so that it can tolerate these kinds of failures.

Let’s say you’re renovating a house. It’s a big project, and you’ll need a lot of planning, expertise, and equipment. Now imagine you live in a world where the hammers you bought at one store don’t match up with the nails you bought at another, and certain wrenches wouldn’t work on anything but the parts they came with. What a pain, right?