Systems | Development | Analytics | API | Testing

One of the key tasks of a finance/banking/insurance QA team is to ensure that defects never slip into production. Automation testing plays a critical role in accomplishing that task. However, now that we have AI, the industry is changing. Fast. Just automation testing is not enough. It should now be augmented by the capabilities of AI. When done right, AI-augmented automation testing is a game-changer for QA teams in finance firms. Here's the playbook to help your team achieve exactly just that.

One of our recurring jobs at Tideways is to ensure that all of our instrumentation works with new and upcoming PHP versions. For us, “working” doesn’t just mean that the results are correct, but that your PHP extension is fast, gathering insights for our customers with a minimal performance overhead.

In September 2025, we brought AI Engineer to Paris for a two-day conference with 700+ attendees, 25 sponsors, over 47 talks cross 5 tracks. This is the third post in a 4-part series on the event: If you missed AIE Paris, you’ll be able to watch the replays on the Koyeb YouTube channel. Main Stage Day 1 Main Stage Day 2 If you’re not familiar, the team behind the AI Engineer has been running AI Engineer conferences in the US for the past three years.

Some topics are just too trendy to overlook, and AI in testing is definitely one of them. A few weeks ago, we shared a blog post introducing the integration between an MCP server and OctoPerf, highlighting the many benefits it brings. To illustrate this in action, we recently hosted a webinar led by Thomas Pitteman, performance testing expert at Adeo and OctoPerf power user.

2025 will be remembered as the year AI wrote poetry, passed medical exams, and stumbled with spectacular blunders. For every impressive breakthrough, there was an equally impressive facepalm.

Real-time data synchronization is critical for businesses that rely on APIs to connect systems like CRMs, billing platforms, and inventory databases. It ensures that updates happen instantly, preventing issues like outdated information, missed opportunities, or operational inefficiencies. Here’s a quick breakdown of how to choose the right tool: Integration: Look for tools with built-in connectors for databases (e.g., SQL Server, MySQL) and cloud platforms (e.g., Snowflake, Amazon RDS).

It’s 2 AM after the export workflow failed again. Your users are still waiting for their invoices because your team is in the office manually reformatting 500 of them. By morning, their compliance officer is busy calling when the system times out trying to generate a 100-page report. And before you’ve even had coffee, your biggest customer questions data accuracy because last month’s statement looks nothing like this month’s. The cost?

Migrating from self-managed Apache Kafka connectors to fully managed connectors has been a persistent challenge for data teams working on Confluent Cloud. While Confluent-managed connectors deliver enterprise-grade features, seamless upgrades, and comprehensive support that add up to significant development and operations cost savings, the journey to get there often feels daunting and opaque.

As a product manager, you want analytics that add value without adding complexity. Automated insights do the heavy lifting inside your app, surfacing key changes for your users without making them dig. The result is happier customers, fewer support tickets, and more time for you to focus on roadmap and innovation.