Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on APIs, Mobile, AI, Machine Learning, IoT, Open Source and more!

Microsoft Took 8 Months to Fix This Copilot Vulnerability

Microsoft finally patched a critical Copilot vulnerability nearly eight months after researchers first disclosed it — and the way the attack worked raises some unsettling questions about AI memory. The vulnerability chained together multiple flaws that could allow a malicious prompt hidden inside a webpage to be pulled into Copilot simply by asking it to summarize the page. From there, the attack could potentially access connected data from services like Gmail, Google Drive, and Google Calendar and exfiltrate that information using Copilot’s own capabilities. But the most concerning part may have been persistence.

Agent vs Agentless Monitoring and How to Decide What Goes Where

Why does half the infrastructure end up returning no monitoring data? The standard plan is to install collection software on everything, which moves quickly across servers and stops dead at the first device running closed firmware. Storage arrays, firewalls, and switches will never accept an install, and the rollout stalls there. That plan usually gets set once for the whole environment, with a single collection model applied to hardware it was never suited for.

AI cost calculator: estimate your total spend

An AI cost calculator for the whole wallet adds four lanes: seats and subscriptions, API and token usage, cloud AI services, and GPU infrastructure. Average 2026 totals run $25 per employee per month at light adoption, $100 to $150 at active adoption, and $300 or more at AI-heavy companies. Getting to your number takes four lane subtotals and three corrections.

Prompts, skills, and the AGENTS.md nobody wants to write (and how Anthropic writes theirs)

You’ve watched Claude Code compact a conversation. The context bar fills, it pauses, a summary appears, and it carries on like nothing happened. You probably assumed a housekeeping script trimmed the transcript in the background. It didn’t. The model compacted itself. When the window fills, Claude Code sends a long, specific prompt telling the model how to summarize its own conversation. Then it does, same model, same turn. The thing managing your context window is just another instruction.

Is Your UV DTF Printer Ready for Growth? 7 Production Bottlenecks

Buying a UV DTF printer is often treated as a simple equipment decision: compare specifications, choose a machine, and start producing. In practice, the printer itself is only one part of a much larger production system. A machine that works perfectly for ten orders a week may become frustrating when demand doubles. The problem is not always that the printer is too slow. Supplies, workspace, maintenance routines, file preparation, finishing, replacement parts, and operator time can all become the real constraint.

One DTF Printer or Two? A Smarter Capacity Plan for Growing Print Shops

Buying more DTF printing capacity sounds simple: if orders are increasing, buy a faster machine. In practice, growing print shops face a more important decision. Should you replace the current printer with a higher-output model, or keep it and add a second production machine? The better answer depends on more than print speed. Order concentration, maintenance windows, rush-job frequency, operator capacity, artwork mix, and the cost of production downtime all affect which setup gives a shop more usable capacity.

Before You Buy a Hydraulic Heat Press: Run This DTF Production Bottleneck Audit

A hydraulic heat press makes sense when the press station is a measurable production constraint, not simply because a shop is getting busier. Before upgrading, track where orders wait, how much operator time pressing requires, how often work is re-pressed, and whether the press can keep pace with printing and garment preparation. If the queue consistently forms at the press, an upgrade may solve a real workflow problem. If delays start somewhere else, a new press may only move the bottleneck.

Why Growing B2B and DTC Brands Are Rethinking Their Ecommerce Infrastructure in 2026

A growing number of B2B and DTC brands are running the same calculation this year: what their ecommerce stack actually costs once every app subscription, integration fix, and developer hour gets added to the platform fee. The answer is pushing a broader look at ecommerce infrastructure itself, not just which platform sits underneath it.

Headless vs. Traditional Web Architecture: What DevOps Teams Need to Consider

DevOps teams face a critical architectural decision when building modern web applications: should they stick with traditional, monolithic systems or embrace headless architecture? This choice affects everything from deployment workflows to team collaboration, performance optimization, and long-term maintenance costs. Understanding the technical and operational implications of each approach helps teams make informed decisions that align with their specific requirements.

Building AI Systems That Survive an Audit: Evidence Trails, Traceability and Compliance by Design

A model returns an answer with a confidence score of 0.94. The team ships it. Six months later someone asks why the system produced that specific answer, and nobody can reconstruct it. For years accuracy was the only number that mattered in machine learning. Get the error rate down, ship the model, move on. In regulated domains that is no longer enough. The harder question is whether you can defend a single decision after it has been made. Most systems were never built to answer that, and by the time someone asks, the information needed is already gone.