Operations | Monitoring | ITSM | DevOps | Cloud

Fine-tuning Large Language Models (LLMs) on private, domain-specific data can unlock significant value for your specific use case. When done correctly, you can create AI apps that understand your organization’s unique context. These apps can speak your brand’s voice and deliver remarkably accurate results that general models cannot match. However, finetuning is not always the right solution. Many teams rush into this complex technique without exploring simpler alternatives first.

dbt is one of the most popular solutions for data transformations and modeling. Many commercial data pipelines rely on dozens, or even hundreds, of individual dbt jobs. Data engineers, data platform engineers, and analytics engineers who own these pipelines need to maintain a testing framework to prevent mistakes in data processing that can compromise analysis.

InfluxDB is a widely used time-series database designed for storing and querying metrics, events, and telemetry data. It’s commonly used for infrastructure monitoring, application instrumentation, and IoT-style workloads where time-based data is central. In many environments, InfluxDB already exists as part of the monitoring or data collection pipeline, and the primary need is simply to visualize that data effectively.

In 2025, DevOps teams faced a pivotal moment. The era of treating security as an afterthought was over. Practically overnight, airtight protection became a non-negotiable requirement across every layer of the technology stack, whether on prem, in the cloud, or at the network’s edge. For many teams, this wasn’t just a technical hurdle; it was a daily source of stress.

As our applications grow from simple side projects into complex distributed systems with many users, the “old way” of console.log debugging isn’t going to hold up. To build truly observable systems, we have to transition from simple text logs to structured, queryable, trace-connected events.

Technical debt refers to the future costs and limitations incurred when organizations opt for short-term solutions over robust, long-term scalable architectures. For the middle mile, technical debt often manifests as equipment or network designs that restrict long-term flexibility, scalability, or interoperability.

We are witnessing a fundamental transformation in how software is built. The industry has moved beyond the experimental phase of Machine Learning Operations and entered a complex new reality: the era of the AI Software Supply Chain. The adoption metrics confirm this shift is irreversible. Google reports that 90% of tech workers are now using AI as part of their daily work. Similarly, McKinsey data reveals that 88% of organizations use AI in at least one business function.

Our December Harness AI updates focus specifically on enhancements to Harness AI governance. You can review our Harness AI November updates here.

Note: this post was co-authored by Nicholas Borg, 3CX Product Manager. 3CX provides a robust, flexible IP PBX platform used by organizations of all sizes to power their contact centers. It offers detailed call activity, agent performance metrics, and operational insights — all of which become even more powerful when visualized.

Even with a powerful database or visualization layer, performance can suffer if queries aren’t optimized or system settings aren’t tuned. The new Mimir Query Engine in Grafana Cloud improves query efficiency, but there are still best practices you can follow to keep dashboards fast and responsive—whether your data source is hosted in Grafana Cloud or running on-premises.