Systems | Development | Analytics | API | Testing

LLM Testing Checklist: 50 Validations Before Production

A financial services startup launched its AI assistant without doing a proper LLM testing checklist. Within 72 hours, it gave three customers dangerous advice, telling them to withdraw their retirement savings and invest in penny stocks. The problem? The advice was completely made up. There was no validation, no factual grounding, just confident and detailed responses that were entirely wrong. The company then spent the next six months addressing regulatory issues and rebuilding customer trust.

Mastering data ingestion with Apache Airflow: How to build reliable Pipelines

applications, and AI systems. But orchestration alone does not solve one of the biggest operational challenges: reliable data ingestion. In this live session, we explore how integrating Hevo directly into Airflow workflows creates a reliable foundation for modern ELT pipelines. Through native operators, sensors, and triggers, teams can orchestrate ingestion, monitor pipeline health, and ensure downstream analytics and AI workloads always run on trusted data.

ClearML Introduces Floating NVIDIA AI Enterprise License Management with One-click NVIDIA NIM Deployments

ClearML has announced native floating license management for NVIDIA AI Enterprise licenses with one-click deployment of NVIDIA NIM microservices across AI infrastructure. The feature, available now to ClearML enterprise customers, fundamentally changes how organizations consume NVIDIA AI Enterprise software licenses, moving from a static per-GPU assignment model to a dynamic pool that follows active workloads.

Government and Defense: Air-Gapped LLM Data Access | DreamFactory

Government and defense agencies require extreme security measures to protect sensitive data like classified intelligence and military operations. Air-gapped systems, which are physically isolated from external networks, provide a robust solution by ensuring no remote access is possible. These systems are critical for deploying large language models (LLMs) safely in secure environments, enabling advanced AI capabilities like intelligence analysis and mission planning without risking data breaches.

Automate CSV Imports to Salesforce using Integrate io

Learn how to automate CSV data imports into Salesforce using Integrate.io, a powerful no-code ETL and data pipeline platform. If your team manually uploads lead lists, trade show contacts, or partner data via CSV files, this tutorial will show you how to eliminate that repetitive work entirely. In this step-by-step walkthrough, we'll build a complete data pipeline that automatically retrieves a CSV file from an SFTP server, processes and maps the fields, and loads the records directly into Salesforce Leads, all on a scheduled basis with zero manual effort.

Application Migration Simplified: How to Optimize Data for the Cloud

Organizations over the years have seen the writing on the wall: The future is cloud. Now, these companies and their DevOps teams areevolving, innovating, and pursuing new technologies, to gain a competitive edge and create new efficiencies. One of the ways they’re doing this is through application migration to cloud. In this blog, I’ll detail the nuances of application migration and how to best manage data during it, including various challenges and their solutions.

Many talk about bringing Al into testing - what makes Katalon stand out?

What makes Katalon stand out is its tester-first approach to AI. Instead of chasing flashy demos, Katalon has spent years co-developing AI capabilities with customers, focusing on how AI fits naturally into real testing workflows. The result is AI that testers can actually adopt and trust, delivering measurable gains in productivity, speed, and efficiency in day-to-day work — Alex Martins, VP of Strategy at Katalon.

The missing transport layer in user-facing AI applications

Most AI applications start the same way: wire up an LLM, stream tokens to the browser, ship. That works for simple request-response. It breaks when sessions outlast a connection, when users switch devices, or when an agent needs to hand off to a human. The cracks appear in the delivery layer, not the model. Every serious production team discovers this independently and builds their own workaround. Those workarounds don't hold once users start hitting them in production.