Operations | Monitoring | ITSM | DevOps | Cloud

A few weeks ago we had a bug with Session Replay. Replays were failing in some browsers once more than 1,000 video segments loaded. We had no idea how often it happened or who was hitting it, and because the failure didn’t always produce an error, we had no way to find affected users to reproduce it. Before, we could’ve answered this with spans or logs, but it’s clunky — spans are often sampled, so you can miss outliers; logs are less structured and tend to change over time.

For most of JMS's lifetime, writing a simple producer required creating a ConnectionFactory, creating a Connection, starting it, creating a Session, creating a MessageProducer, creating a Message, calling send(), and then closing the producer, session, and connection with the close calls safely wrapped in finally blocks to prevent resource leaks. Every developer knew the pattern. Every developer wrote it slightly differently. Every code review had the same comments about resource management.

This article is a high-level overview of the Coralogix CLI. For a deeper look at how it works in practice, read the full technical deep dive here. Agent-driven investigation sounds simple: read the alert, query the data, return the cause. In reality, most agents either overload their context window with raw logs or guess at queries and return incorrect results.

It was 11:47pm on a Thursday night, and a senior platform engineer at a large North American bank was rolling back a ‘simple’ configuration change. The change itself was small, a routine update approved through the usual review process, but when it was applied, pods began cycling and connections started dropping. For the next three seconds, mobile banking sessions already mid-transaction dropped. Customer support lit up.

A few weeks ago, we shipped cascading replication for PostgreSQL, MySQL and Redis on Cloud 66. Customers can now build replication chains: a primary streaming to a middle replica, which in turn streams to leaves. It reduces load on the primary, supports geographic distribution, and stops you from melting your network when you have a large fan-out of replicas all pulling WAL from the same machine. PostgreSQL has supported cascading replication natively since version 9.1, which shipped over a decade ago.

AI adoption costs are going parabolic. The companies that can see what they're spending will invest with confidence. Everyone else is flying blind. Every company adopting AI is facing the same problem: the cost of AI adoption in products, in operations, and especially in engineering is accelerating with no alignment between spend and value. The competitive pressure is real. Companies that don’t invest in AI will be displaced by those that do. But the investment itself is becoming inscrutable.

SQL Analytics has always been one of my favourite SquaredUp features. That's not just because I can use raw SQL to achieve complex data transformations. The fact that I can run SQL queries over data from all sorts of sources — not just relational databases, gives incredible power and flexibility. The great news is that SQL Analytics now ships with our AI-driven SmartAssist technology.

Most people assume the modern enterprise runs cleanly on the dashboards and cloud consoles that dominate today’s digital workspaces. Anyone who operates these environments understands a more complicated truth. The real work happens beneath those surfaces, in systems few people notice until something slips. Across industries, engineers face the same recurring scenario: a routine shift disrupted by signals of degradation somewhere in the environment.

If you spend most of your day in branches and pull requests, the platforms you pick decide how much friction you carry. The “version control platforms” label covers two different things: the hosting service where your code lives, and the client you use to interact with it locally. They both matter, and they don’t always pull in the same direction.

If you’ve been online in the last fortnight, you’ve probably seen ServiceNow’s “Kevin” memo, the fictional 2028 post-mortem about an enterprise where the AI agents won, the governance team was eliminated, and a single AI governance lead named Kevin spent two years filing risk assessments that were auto-resolved before anyone read them.