fjo3 shares a report from the AFP: The latest heatwave sweeping across Europe is a stark reminder that it is the world's fastest-warming continent, stretching into an Arctic that is heating at an even greater pace. Britain, France, Italy and Spain have issued red alerts and health warnings for much of their territory this week as the region endures its second heat episode since May. Here is a look at why Europe is warming faster than elsewhere: The planet as a whole is around 1.4C warmer than in preindustrial times, defined as 1850-1900. By comparison, Europe is around 2.4C hotter than the preindustrial era, according to the EU's Copernicus Climate Change Service. The long-term rise in global average temperatures is mainly due to greenhouse gas emissions from burning oil, gas and coal, but it varies by regions due to a combination of factors. Land warms faster than the ocean as water can absorb more heat and cool through evaporation. Shifts in atmospheric circulation have driven more frequent and more intense heatwaves in the European summer, according to Copernicus. High-pressure systems, which bring settled weather and higher temperatures, have become more common in Europe, Copernicus director Carlo Buontempo said. [...] Another major reason is geography as Europe is connected to the Arctic, which is 3.2C warmer than in preindustrial times. The region's rising temperatures are partly due to a process known as the albedo feedback. Bright snow and ice reflect much of the sun's heat back into space, but as they melt they reveal darker, heat-absorbing surfaces such as land and the ocean. In other parts of Europe, areas where snow was very frequent in winter have seen this coverage shrink, exposing dark land. Stricter air quality regulations have reduced aerosol emissions since the 1980s. But tackling the pollutant had the side effect of contributing to global warming, as these tiny airborne particles have a cooling effect by reflecting sunlight and making clouds more reflective.

Read more of this story at Slashdot.

Tipu Qureshi — Senior Principal Engineer at AWS — joins the show fresh from the AWS Summit NYC 2026 announcements to break down how DevOps Agent is changing the way teams handle operations and release management. After 14 years across EC2, Elastic Load Balancing, AWS Support, and Networking, Tipu moved into the Agentic AI organization to build the DevOps Agent and contribute to Agent Core. We explore how the agent investigates incidents autonomously, integrates with your IDE through Kiro and Claude, and validates code changes in sandboxes before they hit production. Key takeaways: • Reactive and proactive — DevOps Agent triggers on alarms and ServiceNow incidents, but Custom Agents now run on schedules to detect anomalies before they become outages. • Context is king — Customers who integrate their Git repos, metrics, and logs get significantly more accurate root causes. Native GitHub/GitHub Enterprise support plus bring-your-own MCP for custom observability. • IDE integration — Kiro powers and Claude plugins give on-call engineers the full agentic loop: investigate, root-cause, fix, and validate without leaving the editor. • Release management — The new readiness review inspects pipeline stages, past deployment failures, and integration tests to catch issues before merge, while sandbox testing validates proposed fixes. • Multi-cloud support — Native Azure integration via IDC with RBAC, plus bring-your-own MCP and A2A for on-premises and other clouds. • Custom agents and skills — Bring domain-specific knowledge (SAP HANA failure modes, proprietary tooling) via skills from GitHub repos or the assets API, with MCP tools for full customization. • A2A bi-directional — DevOps Agent can be engaged by other agents and can reach out to other agents, enabling multi-agent escalation workflows. • Transparency — Every tool call, skill invocation, and reasoning step is captured in a journal visible to customers via API and the operator console. • What's next — Deeper integrations, automated mitigation actions with safety policies, time-bound rules for agent escalation, and script execution coming soon.

With Tipu Qureshi, Senior Principal Engineer at AWS

AWS DevOps Agent

AWS DevOps Agent — Release Management (Blog)

AWS Context — Context Intelligence for Data and AI Agents at Scale

AWS Summit NYC 2026 Announcements

Tipu Qureshi on LinkedIn

Download audio: https://op3.dev/e/dts.podtrac.com/redirect.mp3/developers.podcast.go-aws.com/media/214.mp3

119 extensions, up to 2.6 million installs, delayed execution, and payloads hidden inside image and font files. Here’s what we found, how we disrupted it, and how we’re protecting you. Browser extensions make every day browsing better — blocking ads, translating pages, and downloading videos. Millions of people trust them. But what happens when that trust is exploited? Through proactive threa...

Thinking about the horizon of autonomous agentic workflows. I visualise them as sequences of decisions that are dependent. If decision D1 is correct, D2 is more likely to be correct. If D1 is wrong, then D2 is more likely to be wrong, too. Errors propagate and compound down the sequence.

How many decisions can an agent make before the probability of an error-free end result drops off a cliff?

There are two factors here I’m considering:

1. The probability a decision is correct, P

2. The probability that an error will be caught before it propagates and compounds, C

The reliability of a decision in the sequence, R – the probability that a decision will be correct, or if it isn’t, the error will be caught before it propagates – would then be:

R = 1 – (1 – P)(1 – C)

If R = 0.99 (99% reliable), then the odds of an error-free result after 10 decisions – like, say, a few lines of code generated – are 90%. After 100, they’re just 37%. And after 1,000, they’re a miniscule 0.004%.

Physics predicts that LLMs are extremely unlikely to get significantly more reliable (see the research paper “The wall confronting Large Language Models”), though we can use them in ways that reduce the risk of errors (see my CRESS principles for context engineering).

So if we want to extend the horizons of our coding agents, we turn our attention to C – how strong are our quality gates, and how soon do decisions pass through them?

It’s really a testing/feedback problem. Again.

But even if we could get to R = 1 and extend the horizon to thousands or tens of thousands of autonomous decisions, the longer agents work without human feedback, the more decisions go unvalidated by real-world use. So it would, as far as learning where the real value is, be highly undesirable.

With the Reactive Data Layer Architecture (RDLA), you establish a clear boundary between public data APIs and private, framework-specific data-source implementations. Your presentation layer operates in a purely reactive manner, observing data changes rather than procedurally querying them. RDLA also simplifies testing by encouraging you to program to interfaces and use clean seeding patterns.

By Mervyn Anthony

Learn what's new in Visual Studio Code 1.127 (Insiders)

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