Systems | Development | Analytics | API | Testing

Every analyst has a version of this “Monday blues.” A leader pings asking for the quarterly forecast, wants it broken out by region, and needs it for a call in thirty minutes. The fastest way to do this? Pull the data out of your tool, paste it into Excel, and build the report there. Thirty minutes later, you've got the report: three new formula columns, a pivot view summarizing the regional split, and conditional formatting flagging the outliers.

When a QA team outgrows a spreadsheet, the fix is rarely more spreadsheets. It is one place where test cases, execution history, defects, and approvals live together, so a release manager does not have to reconstruct who tested what from three different tools the night before a launch. G2's Fall 2026 reports put a number on how well Katalon does that.

AI agents are getting remarkably good at fixing code. Give an agent a well-defined performance problem, enough runtime data, and access to the relevant codebase, and it can often trace the evidence back to a function, suggest an optimization, validate the change, and produce a useful patch. That workflow is no longer particularly difficult to imagine. In some cases, it is already practical. Production debugging, however, has never been only about fixing the code directly in front of you.

Benchmarking 5 interfaces to 1 dbt project: what each fixed, what it cost, and which model to put behind it.

Test libraries that once felt instant can become harder to manage as they grow: test cases take longer to open, reports take longer to build, and every sprint adds more data to an already bloated Jira environment. That’s usually the moment when a testing leader starts evaluating whether the test management tool installed years ago still fits the way the organization tests today. Your team has already decided to run testing inside Jira.

Grape handles exceptions with rescue_from blocks declared on your API class: rescue_from :all catches every exception, while rescue_from ActiveRecord::RecordNotFound targets one class. Inside an endpoint, error!("Not Found", 404) halts with a formatted error, defaulting to status 500. Without a handler, invalid params return 400 and other exceptions bubble up to Rack. This guide covers Grape 3.3. Every response body, status code, and error message was verified on Grape 3.3.5, Rack 3.2.7, and Ruby 3.4.10.

That's Nathan Hillyer, Director of Engineering at ForeFlight, on what happened after his team started running their GitHub Actions workflows on Bitrise Build Hub. Until last month, trying that for yourself meant talking to sales first. That’s fine for a 300-person engineering org evaluating a CI overhaul, but it made no sense for the two-pizza team that just wants to know whether changing one line of YAML will meaningfully speed up their Xcode builds. That's fixed.

For much of the past decade, the PropTech default was straightforward: buy the commodity software and reserve engineering capacity for what differentiated the product. AI-assisted development is moving that line, and the answer looks less obvious than it did two years ago. For the broader framework, including cost, integrations, vendor lock-in, data ownership, and long-term maintenance, see our guide to build vs buy real estate software.

Data interoperability enables systems to exchange and reuse information across platforms. Healthcare interoperability extends that capability with clinical context, patient identity, privacy, and compliance controls. This guide answers what is data interoperability, what is healthcare interoperability, and how the two relate. In 2026, the distinction supports regulatory readiness, reliable AI, and safer data exchange.

The mobile sector has undergone massive transformations since the early days. What used to be a predictable verification exercise is now an intricate web of competing hardware manufacturers, custom firmware distributions, distinct chipset designs, and fast software update schedules. Modern software teams face hundreds of real-world variables every time they prepare a public build. Managing device fragmentation stands as one of the most demanding technical roadblocks in software verification.