Operations | Monitoring | ITSM | DevOps | Cloud

A frequently encountered issue when exploring database optimizations is proving that a proposed query rewrite is actually faster than the existing query. Often, proving this isn’t just as simple as running both queries and comparing their execution times. When your hot queries are measured in milliseconds, factors such as cache states, concurrent queries competing for CPU and I/O, and noisy neighbors can mask the true performance difference between query versions.

Developers and SREs who use Bitbucket Cloud often need to move between their source code, observability data, test results, and security findings to investigate a problem. Connecting an error or slow method to the correct repository, file, and commit takes time. Teams may also discover vulnerabilities, infrastructure misconfigurations, coverage regressions, or flaky tests only after code has progressed further through the software development life cycle, when remediation is more disruptive.

AI ticketing puts machine learning and language models to work on the IT help desk. An AI ticketing system reads tickets, sorts them, routes them, drafts replies, and resolves routine requests without a technician.

NestJS 12 dropped last week with ESM packages, Standard Schema validation, a rebuilt CLI, and a brand-new observability SDK. That last one is the most interesting part for anyone running NestJS in production with an APM tool. At Scout Monitoring, we instrument NestJS applications through our Node.js agent. A framework shipping its own observability layer changes the conversation for every APM vendor in the ecosystem, including us. Here is what you need to know.

An application slows down every month-end. You open Hyper-V Manager, the virtual machine looks healthy, and the ticket closes without a cause. Next month it happens again. Native consoles show live state and keep no history behind it, so nobody can prove whether the fault was the guest, the host, or a storage path shared with nine other workloads. That gap sets what a Hyper-V deployment costs to run, measured in unplanned downtime and in engineering hours spent guessing. This guide covers.

What happens when the segment that failed was never reporting to anything? The incident stops being a diagnosis and turns into a search, and the cost of that search lands on the business rather than on the network team. Network visibility measures how much of your environment can account for itself under pressure. Most teams assume their coverage is broadly complete because their monitoring platform reports thousands of healthy objects.

Lovable is fast at the part that used to take a week. Describe an application, and you have a working interface in minutes. Lovable already has an answer for the backend. Its built-in Cloud backend is enabled by default and runs on Supabase's open-source foundation, and you can connect your own Supabase project instead. Both are reasonable places to start. Neither puts the database in an account you already own, in the cloud and region you picked, next to the rest of your data platform.

A few months ago, Darragh Curran, CTO at Fin (formerly Intercom) set a public goal to double engineering productivity and nearly tripled it instead. They did so by pulling a few levers: AI writing code at scale, building an AI-driven PR review system, leveraging observability as a trust mechanism, and with leadership becoming more hands-on through the transition.