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Introducing Atatus MCP Server: Connect AI Agents to Your Observability Data

AI coding assistants like Claude, Cursor, Codex, GitHub Copilot have become standard tools in the modern engineering workflow. Developers use them to write code, generate tests, and review pull requests. But when something breaks in production, these assistants hit a wall: they have no access to your actual system state. They can reason about logs, traces, and metrics. They just can't see yours.

How IT Teams Can Cut AI Token Costs with Deterministic Workflows

In our previous post on AI tokenomics, we looked at the rising cost challenge behind token-based AI systems. When enterprise IT teams rely on AI to reason through the same repeatable work over and over again, the costs to resolve those tasks may increase to an unreasonable level. That is where a deterministic IT automation platform becomes essential. A deterministic workflow follows predefined logic, meaning that given the same inputs and conditions, it produces the same expected result.

6 Ways to Use the Hyperping MCP Server

When something goes down, the last thing you want is to alt-tab between a monitoring dashboard, your on-call tool, and three Slack threads to figure out what is happening and who owns it. That context is usually all there. It is just scattered. The Hyperping MCP server fixes that by putting your monitoring data inside the AI tools you already work in. Your agent can read monitor state, outage timelines, SLAs, and on-call schedules, and answer the questions you would normally chase across tabs.

What Makes a Reliable CNC Machining Partner in China? 5 Criteria Operations Teams Use

In 2026, sourcing CNC machined parts from China is not just a procurement decision. It is an operations decision. The wrong manufacturing partner can create late deliveries, unclear ownership, drawing errors, quality disputes and a production schedule that turns into a daily escalation.

Created Data-Driven Operations: Tech Systems Behind Restaurant Success

Modern kitchen management requires extreme precision to maintain daily service standards. Floor managers track multiple operational metrics to keep kitchen stations moving smoothly throughout busy shifts. Every single dining shift brings unique tracking demands that can overwhelm team members. Digital infrastructure helps commercial kitchens maintain high food quality without dropping the ball on service speed.

Ray-Ban Meta Glasses and the Expanding Wearable Technology Ecosystem

Wearable technology is no longer limited to step counters and fitness trackers. It has grown into a wider ecosystem of smartwatches, earbuds, rings, health monitors, and smart glasses that help people stay connected without always reaching for a phone. That shift is one reason Ray-Ban camera glasses fit into a much bigger conversation. They are not just eyewear with a camera built in. They represent a growing move toward technology that feels more natural, more personal, and easier to use throughout the day.

How to Choose a Cloud Migration Partner in New Jersey: What IT Leaders Need to Verify

A failed cloud migration does not announce itself in advance. Data loss, extended downtime, misconfigured security controls, and compliance gaps surface during or after the move, when reversing course is expensive and the business is already affected. For New Jersey organisations in financial services, healthcare, legal, and manufacturing, the stakes are high enough that choosing the right migration partner is at least as important as choosing the right cloud platform. The hard part is separating providers who can execute a migration cleanly from those who can describe one convincingly.

Telegram Lead Generation Funnel: From Channel to Customer

Telegram has evolved far beyond a messaging application. Today, it is a powerful customer acquisition channel that helps businesses attract, qualify, nurture, and convert prospects inside a single ecosystem. As competition on traditional advertising platforms continues to increase, many companies are turning to Telegram to build direct relationships with potential customers and create more efficient lead generation funnels.

Which Bugs AI Agents Fix Better With Traffic

In the first experiment, I wanted a baseline: if an AI coding agent gets the same production signal a human would get, can it fix bugs in a codebase it has never seen? Yes, but only when I gave it better context. With only an alert, the agent passed 51% of the runtime tests. When I added captured traffic, the actual request and response for the failing call, it climbed to 77%. This post is the second pass.