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datadog

How to measure developer experience (DevEx) in the AI era

May 22, 2026 By Datadog In Datadog
As AI coding assistants dramatically inflate PR counts, commit frequency, and lines of code, the limitations of individual output metrics have never been more apparent. A developer can now produce significantly more lines per session, but higher volume doesn’t guarantee that the code is stable, maintainable, or successfully running in production. GitClear analyzed over 200 million lines of code and found that code churn nearly doubled following widespread AI adoption.
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How Copilot integration services redefines corporate workflow

How Copilot integration services redefines corporate workflow

May 22, 2026 By OpsMatters In OpsMatters
The common situation of most businesses today is to be drowning in data, yet starved for efficiency. Underutilization of data, where valuable corporate information is locked within disconnected applications, has led employees to act as bridges between the software systems. Microsoft Copilot is often touted as one answer to this, and with its current ecosystem, it may just be the best one. It can use AI, not as a passive chat, but as an active, intelligent agent that unifies corporate data and helps automate cross-platform workflows.
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devart

Devart Brings AI Agents Closer to Enterprise Data with New MCP Server Product Line

May 21, 2026 By dotConnect Team In Devart
We are excited to announce the release of the brand new line of MCP Servers (Model Context Protocol), designed to connect AI assistants, AI agents, and large language models directly to enterprise databases and cloud business platforms. The release includes 19 specialized MCP Servers and the flagship Universal MCP Server, which enables AI access to virtually any data source through the ODBC standard.
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Inside the Grafana AI Team Weekly: Workspaces and Investigations (April 28, 2026)

May 21, 2026 By Grafana In Grafana
This is an excerpt from a real AI team weekly meeting where we talk about the stuff we build and occasionally also demo them! In this one, Staff Product Design Engineer Ben Darlow demos improvements to Workspace Home. Staff Software Engineer Sonia Aguilar and Principal Software Engineer Sven Großmann also demo a new dependency graph view for Investigations. We're showing parts of our team meetings to build in public in some small way and give you a sneak preview of what's to come. But not all features we show may make it to production! You've been warned. :)
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ribbon

From Watching AI Search to Engineering for It: What Q1 2026 Taught Us About Real Digital Demand

May 21, 2026 By Paul Leininger In Ribbon
Last year, I wrote about how AI-driven search trends reshaped my digital marketing strategy in ways I hadn’t seen in two decades. At the time, the story was mostly observational: traffic patterns were changing, conversions were holding, and AI-generated search answers were clearly influencing buyer behavior. Fast-forward to the first quarter of 2026, and one thing is clear — this shift didn’t slow down; it accelerated.
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Inside the Anthropic + Claude Code Hype at AWS Summit London: Live Laugh Logs ep. 2

May 21, 2026 By Coralogix In Coralogix
Are companies blowing through their entire 2026 AI budget in a matter of months? Welcome to Episode 2 of Live Laugh Logs, the podcast from Annie, Lewis, and Andre from the Coralogix Developer Relations team, where we get together and recap everything going on in our worlds!
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canonical

Canonical announces fully Managed Kubeflow AI operations platform on the Microsoft Azure Marketplace

May 21, 2026 By Massimiliano Gori In Canonical
Canonical, the publisher of Ubuntu, today announced the general availability (GA) of Managed Kubeflow on the Microsoft Azure Marketplace. This solution enables AI teams to get a fully managed, production-ready MLOps platform in their own tenant. Upstream Kubeflow is a powerful tool for machine learning, but it remains notoriously challenging to deploy and maintain.
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canonical

Developing web apps with local LLM inference

May 21, 2026 By Abdelrahman Hosny In Canonical
I’ve yet to meet a developer that enjoys working with metered AI APIs. The need to pay for every API call in development works in direct opposition to the ethos of rapid iteration, and it’s easy for the costs to get out of hand. That’s why Canonical has created a different approach to building AI-powered applications; one where the model lives inside your app, not behind a pay-per-token HTTP call.
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sematext

Using AI to Instrument Applications with OpenTelemetry

May 21, 2026 By Sematext In Sematext
OpenTelemetry is one of the best things that’s happened to observability in the last decade. It’s open. It has SDKs for every language that matters. It’s vendor neutral. The OTel community has been doing the hard work of standardizing how applications emit telemetry, so that you, the engineer, don’t have to learn five different agent formats to monitor five different services.
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iOPEX

From AI Sprawl to Orchestration: Delivering Intelligence as a Service

May 21, 2026 By Sarang Tuteja , Regional Managing Director, Europe In iOPEX
Most enterprise AI deployments were never designed to coexist. They were designed to prove a point, respond to a board directive, or secure a budget. The result, two years into the generative AI cycle, is an expanding estate of disconnected models, fragmented pilots, and overlapping capabilities that collectively deliver far less value than the sum of their parts. HFS Research calls it "death by a thousand POCs". The more precise description is architectural negligence at an enterprise scale.
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