Operations | Monitoring | ITSM | DevOps | Cloud

How Qovery uses Qovery to speed up its AI project

Discover how Qovery leverages its own platform to accelerate AI development. Learn how an AI specialist deployed a complex stack; including LLMs, QDrant, and KEDA - in just one day without needing deep DevOps or Kubernetes expertise. See how the "dogfooding" approach fuels innovation for our DevOps Copilot.

Datadog acquires Propolis

Generative AI enables teams to write and ship code faster than ever. But current methods for testing and quality assurance have not evolved to match the new pace and scale of deployments. Manual and deterministic testing paths quickly become obsolete when new features are released, and they fundamentally can’t test AI outputs, leaving a massive untested surface area. To keep up, teams need new testing methods that can define what goals users have, and ensure that their outcomes match.

Why Context, Not Prompts, Determines AI Agent Performance

Prompt engineering improves single responses, but agent performance is determined by how execution context is captured, replayed, and constrained over time. For the past few years, enterprises have obsessed over prompts, with entire roles emerging around their design and an ecosystem of tooling and templates following close behind. This focus delivered early gains because it allowed teams to rapidly improve outputs without modifying the surrounding system. Over time, those gains flattened.

The Hidden Cost of 30% AI-Generated Code #speedscale #aicoding #devops #technews #ai

AI now writes 30% of Big Tech’s code, but the resulting surge in defects is crashing platforms like AWS and GitHub. Manual testing can no longer keep up with this velocity; it's time to deploy AI Quality Agents to save our systems. Is AI speed worth the decline in code quality, or are we headed for a breaking point? Let me know if you’ve noticed more bugs in your workflow lately. Video collab with @ScottMooreConsultingLLC.

Scaling AI Reliability: Real world lessons from Mistral AI

How does one of the world's leading AI companies keep its infrastructure reliable while shipping new models constantly? In this webinar, Devon Mizelle, Senior SRE at Mistral AI, shares the real story. Devon walks through how Mistral built an automated system that generates synthetic checks for every model the moment it goes live—no manual configuration, no forgotten monitors, no inconsistent alerting. Using monitoring as code, his team eliminated the toil of maintaining hundreds of checks across a rapidly evolving model ecosystem.

Intelligent Voice Agents: Transforming Operational Efficiency in Business Communication

Businesses today operate in an environment where customers expect immediate, personalised responses across all communication channels. Traditional phone systems and support teams often struggle to meet these demands without incurring significant costs or operational strain. As a result, intelligent voice agents - AI-powered systems designed to conduct natural conversations - are emerging as a strategic solution to drive operational efficiency and enhance customer engagement.

Why the 2026 Google Ads Specialist is Now an "AI Strategist"

The days of simply "picking the right keywords" are officially behind us. As we navigate 2026, the Google Ads interface has transformed from a tool of manual levers into a sophisticated AI powerhouse. For businesses, this means the role of a Google Ads Specialist has fundamentally shifted. They are no longer just "account managers"; they are data architects and AI co-pilots. If you're wondering why your old PPC strategies aren't hitting the same ROAS (Return on Ad Spend) targets, it's time to look at how the role of the specialist has evolved.
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Meet AlmaIQ: The AI Concierge Simplifying Employee Support

Almaden has exciting news to make life easier for enterprise employees: AlmaIQ . Unlike other virtual assistants that are complex to set up and maintain, AlmaIQ is simple. Acting like a "concierge" or personal assistant, it answers questions from computer issues to corporate processes, instantly and without complication in the user's native language.

AI is not intelligent. It's obedient.

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.