Systems | Development | Analytics | API | Testing

Remember when “going shopping” meant getting in the car? This fall, filling the tank feels like applying for a small loan. U.S. regular gasoline averaged about $4.48 a gallon for the week of September 21, 2026. A round trip to the store starts competing with free shipping. And free shipping never needs a parking spot. That doesn’t tell us how many shoppers will move online this holiday season.

The first number from my local PostgreSQL 16 test was roughly 1,600 statements per second. It looked impressive. It was also the least useful result in the run. The useful part was the workload. It came from queries the demo app had actually sent: the same prepared statements, parameters, reads and writes. A synthetic benchmark tells you how PostgreSQL handles a synthetic workload. It does not tell you whether your migration just broke the UPDATE your app depends on.

Load testing for progressive web apps requires a fundamentally different approach than traditional web applications. Relying on legacy test scripts risks overlooking critical performance issues. Features like service worker caching and offline functionality can distort response times unless your tests are designed to account for them. Ignoring these aspects often leads to misleading results and an inaccurate view of real-world performance.

"Manual tester" lands differently depending on who's wearing the title, and both reactions are usually right. Ask someone stuck clicking through the same regression suite every release, no ownership, no say in what gets tested, and "manual tester" feels like a job you're trying to escape. Ask someone who can look at a new feature and instantly spot the edge case nobody thought to script, and the same title undersells what they actually do. Same word. Two different jobs.

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.