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The Regional Data Centre Revolution Powered by AI Demand

The Regional Data Centre Revolution Powered by AI Demand

Apr 21, 2026 By Pulsant In Pulsant
London still hosts the biggest concentration of UK data centre capacity, but the centre of gravity is starting to move. AI workloads are changing the infrastructure maths, pushing power, space and planning considerations up the decision list. That is exactly where regional locations start to look like the sensible option. Government data shows how concentrated the market remains: as of autumn 2024, London is estimated at 1,048MW of colocation IT load. Compare that with 44MW in the East of England, 17MW in the North East and 30MW in Scotland. The gap is huge, yet it is not a permanent advantage.
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VictoriaMetrics

What's New in VictoriaMetrics Cloud Q1 2026? Logs, MCP Server, Better Alerting, and... a Secret Project

Apr 21, 2026 By Jose Gomez-Selles In VictoriaMetrics
Q1 2026 has been one of our most eventful quarters yet for VictoriaMetrics Cloud. We shipped something we have been building towards for a long time, crossed a few infrastructure milestones, and started clearing the path for what is coming next to the most performant observability stack.
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meshiq

The Modern Messaging Primer: Navigating the Shift from Legacy Middleware to Open Source Innovation

Apr 21, 2026 By meshIQ In meshIQ
The shift from legacy middleware to open-source innovation promises agility and cost savings, but introduces the 'Modernization Tax'—operational complexity that requires new approaches to observability, governance, and management across hybrid messaging environments.
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iOPEX

Before You Deploy Another Agent, Read This

Apr 21, 2026 By iOPEX Technologies , In iOPEX
Enterprise boardrooms are not debating whether to adopt agentic AI anymore. The debate has moved to a harder question: why do so many agentic deployments stall between pilot and production? ServiceNow's Enterprise AI Maturity Index 2026 puts a number to it. Most enterprises that have invested in AI tooling report that their biggest obstacle is not model quality or compute cost. It is the infrastructure that those agents are expected to operate within. The models are capable.
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harness

Building for the Agentic Era: Engineering Excellence at Harness | Harness Blog

Apr 21, 2026 By Juveria Kanodia In Harness
As AI agents become ubiquitous across the software development lifecycle, engineering teams must do more than adopt new tools; they must redesign how they build, verify, and operate software. This post distills the vision, priorities, and best practices that guide engineering excellence at Harness. Different products sit at the heart of the Harness platform.
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cloudzero

Claude Opus 4.7 Pricing In 2026: What It Actually Costs (And Whether It's Worth It)

Apr 21, 2026 By Lyne Carolyne In CloudZero
Claude Opus 4.7 holds at $5/$25 per million tokens — but a new tokenizer inflates costs up to 35% on identical text. Here's what Opus 4.7 actually costs at production scale, how it compares to Sonnet 4.6, and the six levers that determine where your bill lands.
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grafana

Introducing o11y-bench: an open benchmark for AI agents running observability workflows

Apr 21, 2026 By Yasir Ekinci In Grafana
Evaluating agents is hard. Verifying observability tasks is harder. Yes, AI agents have gotten dramatically and quantifiably better at coding and tool use, but observability presents a different kind of challenge. In a real incident, the hard part is rarely just writing a query. It's deciding which signal matters, figuring out whether a spike is noise or symptom, correlating metrics with logs and traces, and sometimes making a change in Grafana without breaking the dashboard another engineer depends on.
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grafana

AI Observability in Grafana Cloud: A complete solution for monitoring your agentic workloads

Apr 21, 2026 By Maurice Rochau In Grafana
The observability industry has developed great tools for using metrics, logs, traces, and profiles to monitor the cloud native applications that have dominated the last decade of software development. But when it comes to understanding what an AI system is actually doing, we’re often left reading raw conversations, guessing at quality, and reacting too late. And that’s a problem.
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