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AI inference vs. training: What they are and how they differ

Jun 4, 2026 By Jubril Oyetunji In Civo

AI inference and training are terms you'd run into if you have been around software engineering or even just scrolled through the news. Both are integral to delivering the AI-powered experiences we have come to expect from many of the applications we use daily. According to McKinsey, by 2030 inference will overtake training as the dominant workload in AI data centers, making up more than half of all AI compute and roughly 30-40% of total data center demand.

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The skill gap AI can't close, with Hywel Carver of Skiller Whale

Jun 4, 2026 By CircleCI In CircleCI

If 90% of code is now AI-generated, what happens to the engineers who never had to write it themselves? The ability to review code well, to catch what the LLM missed, depends on mental models that only come from practice. That tension sits at the center of this conversation.

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AI Chat Detection Is Here in AirDroid Parental Control!

Jun 4, 2026 By AirDroidOfficial In AirDroid

Say hello to AI Chat Detection! And learn how to receive alerts about potentially risky conversations in your child's ChatGPT, Gemini, and Character.AI.

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Autonomous IT Is Here. Are You Prepared?

Jun 4, 2026 By Sean Malvey In Nexthink

Enterprise IT was built for a more predictable workplace, where support began when an employee reported a problem and IT worked backward from the details they could provide. That model made sense when devices, applications, and ways of working were easier to control. Today, the digital workplace moves too quickly for IT to rely on reported issues alone. By the time a ticket appears, employees may have already lost time, worked around the problem, abandoned the tool, or turned to an unmanaged alternative.

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Rocky reads your stack, not just your check

Jun 4, 2026 By Stefan Judis In Checkly

You get an infrastructure alert. Something's off, so you task your agent to investigate, and it ends up grepping through the logs. You both wonder if there's customer impact. There’s a simple rule: AI agents in the incident loop are only as good as the signal you feed them. And logs alone aren't a great signal.

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Checkly

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Introducing Bits Agent Builder: Build agentic workflows for alert response and remediation

Jun 4, 2026 By Amber Tunnell In Datadog

Building automated workflows that adapt to real-world complexity can be a challenge. As systems scale and scenarios multiply, teams often end up hardcoding endless logic branches just to handle every potential outcome. That’s why we’re introducing Bits Agent Builder, a powerful new tool that lets you create custom AI agents that are fully hosted by Datadog.

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AI Observability Deep Dive Demo | Grafana Cloud

Jun 4, 2026 By Grafana In Grafana

Grafana AI Observability is our new database and platform for observing AI Agents. Over the past year at Grafana Labs, we built Agents and we needed a way to understand how they are performing, what are the costs associated with them, what's the error rate or time to the first token as well as how they are behaving. Grafana Staff Engineer, Ivana Hučková provides a deep dive demo on how Grafana AI Observability connects our experience building Agents with our experience building observability systems.

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iFrame Expands AI Infrastructure Offering With Hosted Inference Service for Open-Weight Models

Jun 4, 2026 By OpsMatters In OpsMatters

Organizations looking to reduce AI operating costs while maintaining performance are increasingly turning to open-weight models. This trend accelerated throughout 2024 as businesses sought alternatives to expensive proprietary systems and greater control over their AI infrastructure.

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Why AI Evaluation Is Becoming a Business Priority, Not Just a Technical Task

Jun 4, 2026 By OpsMatters In OpsMatters

Artificial intelligence products are evolving at a pace that challenges traditional quality assurance and validation processes. As organizations race to release new AI-powered features, many product teams face the same question: how do they know a system is ready for real-world use? As reported by AI Journal, conversations with product leaders across different sectors reveal a growing focus on AI evaluation as a critical part of product development. Their experiences highlight the challenges of balancing innovation, risk management, customer expectations, and future regulatory requirements.

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What Enterprise AI Gets Wrong About Usage

Jun 3, 2026 By Dallon Robinette In Selector

AI is moving out of the experimental phase and into the everyday rhythm of work. Teams are no longer using it occasionally for novelty or quick wins, but instead are exploring more robust use cases to investigate issues, answer questions faster, surface context, and help them move through complex workflows with more confidence. That’s the shift that most organizations’ leadership teams have been asking for.

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