Systems | Development | Analytics | API | Testing

Until now, sending push notifications to a React Native app with Ably meant registering devices manually through the push admin API, usually from a server. The new plugin moves activation where it belongs: onto the device. We've added first class support for React Native apps receiving push notifications through the Ably PubSub JavaScript SDK.

When you want to know the last time some user was seen or some resource was accessed/viewed, then usually you reach to a timestamp column and update this to the current time whenever it’s seen or accessed. You may have written this kind of SQL statement (or the equivalent) in your ORM yourself a few times: It looks innocent, but even on reasonably small tables, this can cause slow UPDATE statements regularly.

Playwright is a browser automation framework that also ships a built-in HTTP client for API testing. That HTTP client, called APIRequestContext, lets you send requests and assert on responses directly inside a Playwright test – no browser, no separate tool. If your team already uses Playwright for end-to-end browser tests, the API layer requires no separate testing framework. It runs inside the same suite, the same CI job, the same configuration.

I spent the day at NVIDIA thinking I’d be learning about all the newest models and RAM throughput achievements. Instead, a version of the future started to crystallize where AI really is good enough, pervasive and cheap(er). Let me share some of my observations and conclusions.

The pipeline is in the CRM. The forecast isn’t.

The Internet of Medical Things (IoMT) is growing faster than ever. According to 2026 data from The Business Research Company, the global IoMT market has reached over $124 billion this year and is on track to hit nearly $300 billion by 2030. This growth is happening because healthcare is moving outside hospital walls and into patients' homes through remote monitoring. But for engineering teams, connecting these devices is a massive headache.

Model Context Protocol allows AI models like Claude to communicate with the outside world. But MCP debugging has been one of our steepest learning curves at Bugfender. Several different layers need to work together at the same time and if one thing breaks, it can scupper the whole workflow. That’s why we’re here today. To pass on hard-won knowledge, so you can jump the curve.

As AI rapidly integrates into software development, from code generation to interface review, it promises unmatched efficiency.

For as long as there’s been a rental market, there’s been a version of this question: is the number on the listing actually the number? Ask anyone who’s built a pricing model, a forecasting tool, or a CPI estimate on top of rental data, and you’ll get the same answer — probably not, and there was never a good way to check.

PropTech teams sometimes plan an AI initiative by starting with the wrong question. They ask which model to use before they can answer a more basic one: is our property data ready for any of it? AI pilots may stall not because the models underperform, but because the property data feeding them is fragmented across MLS feeds, PMS records, and CRM exports, duplicated across sources, and missing the ownership, tax, and location context a model needs to reason.