Operations | Monitoring | ITSM | DevOps | Cloud

Tech companies and brands love calling AI “intelligent.” But is it really? AI doesn’t decide what matters. Humans do. We decide what’s important, then feed prompts, data, and instructions into AI models so they work the way they do. At the end of the day, AI is obedient to human intelligence, not the other way around. And it’s on us to use it in ways that actually matter, instead of dismissing it or freaking out that it’s going to replace humans.

DataReader and DataSet are two significant data access models that can greatly impact the performance, scalability, and responsiveness of your.NET application. The connected model, powered by DataReader, keeps a live connection open and streams data forward-only to maximize speed and minimize memory usage. The disconnected model, implemented through DataSet, takes the opposite approach. It loads data into memory so you can edit and reuse it without constant database interaction.

InfluxData prides itself on prioritizing developer happiness. A key ingredient to that formula is providing client libraries that let users interact with the database in their chosen language and library. Data analysis is the task most broadly associated with Python use cases, accounting for 58% of Python tasks, so it makes sense that Pandas is the second most popular library for Python users.

Inventory management refers to all the processes that enable an organization to track, control, organize, and optimize its physical assets throughout their lifecycle. It plays a key role in the operational, financial, and logistical performance of businesses, regardless of their size or industry.

Event Intelligence Solutions are redefining how organizations manage complexity and risk across digital ecosystems. Their true power lies not only in detecting anomalies or suppressing noise, but in providing actionable, explainable intelligence that connects IT events to business impact.

When we launched Seer, our AI debugging agent, we built it on a core belief: production context is essential for understanding the complex failure modes of real-world software. Seer uses the detailed telemetry that Sentry collects (errors, spans, logs, metrics, and more) to accurately root cause and fix bugs. Because this telemetry is trace-connected, Seer can deterministically traverse all the data relevant to a problem rather than relying exclusively on imprecise time-range searches.

Distributed tracing has become essential for modern software teams. As applications evolve into complex distributed systems with microservices, APIs, databases, and third-party integrations, understanding how a single user request travels through your entire stack is no longer optional, it’s critical for maintaining performance, reliability, and user satisfaction.

For many IT directors, the service desk feels permanently stretched. It’s a math problem that is forever in motion. Every quarter brings new apps, new devices, new access rules, and new ways for small issues to become daily interruptions. Even when tooling improves, the queue still grows because the work expands with the environment. The pressure shows up in familiar places, like rising ticket counts, tighter SLAs, and a large backlog of projects that need help.

We’re excited to announce a new integration between Bindplane and Statsig, making it easier to collect, process, and route OpenTelemetry signals into Statsig at scale. This integration provides a seamless way to connect Statsig with the OpenTelemetry ecosystem using Bindplane’s vendor-neutral, OpenTelemetry-native telemetry pipeline. Focus on product insight, not collector operations.