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The Missing Step in Mobile Release Operations: Store Screenshot Management

Mobile release teams are used to managing code, builds, test results, signing credentials and deployment approvals. Store screenshots often sit outside that system. They are treated as a final design request, passed between product, marketing and engineering in a collection of chat messages and shared folders.

Test Behavior, Not Choreography

The spy from post 4 is a sharp tool. Once a test can record every interaction, it is tempting to assert on all of them. The result looks thorough, but it is usually a transcript rather than a useful specification. This post takes a test written that way, makes a change that no customer could possibly notice, and watches the test fail anyway. This is part 5 of a ten-part series. The code is in Java, Node.js, Go and Python.

Make Failure Boring with Mocks

Every codebase has a failure path nobody has run. Not through laziness, but because reproducing it requires a backend dependency to misbehave on cue. In the package notifier, the carrier must refuse, stall, or return nonsense at the exact moment the test runs. So the retry logic ships unverified and everyone hopes. The seam from post 2 already gives the test control. A seam is a place where you can change what code does without editing that code.

Did It Actually Send?

The notifier has returned a message throughout this series, which made testing almost suspiciously easy. Assert on the return value and you are done. Real notifiers do more than build strings: they send them. Once a message goes to an email provider or SMS gateway, the function may return nothing useful. When that change lands, every existing test loses the value it asserted on. This is part 4 of a ten-part series. The code is in Java, Node.js, Go and Python.

MCP Servers 1.1.0 Add Flexible HTTP Routing and CLI Connection Management

We are pleased to announce the release of MCP Servers 1.1.0, bringing new configuration options for HTTP-based deployments and expanded command-line capabilities for managing database connections. The new version makes it easier to control how MCP Servers are exposed over HTTP, host multiple MCP Servers under a single hostname, and configure connections directly from the command line.

The six pillars of AI-ready telemetry

“AI-ready” is everywhere right now, attached to nearly every product in every category. The catchy label rarely means anything specific, just as additional questions are warranted when vendors claim to be “AI-native”. After fighting through all the marketing jargon, there needs to be a standard, not a slogan. And the definition changes depending on what the data is for. AI-ready for a data warehouse and AI-ready for live operational telemetry are not the same problem.

Outrun the Threat Window: AI-accelerated Vulnerability and Patch Management

The gap between vulnerability disclosure and active exploitation is shrinking—often from weeks to mere hours. Traditional patching cycles no longer cut it. In this session, discover how AI-accelerated solutions can help you: Identify exposed assets faster Prioritize vulnerabilities by real-world risk Remediate across Windows, macOS, Linux, and hundreds of apps Verify success with a connected workflow Learn how our approach, powered by AI-driven insights and automation, can help you close the gap before attackers strike. Watch now and take control of your patch management.

How Is AI Changing IT Operations? Building Production-Ready AI Agents with Alex Zinovy

How is AI changing IT operations, and what does it take to move AI agents from impressive demos to production-ready systems? In this episode of Agents of IT, Resolve’s Zack Austin sits down with Alex Cinovoj, Founder and CTO of TechTide AI, to explore what enterprise AI looks like when it has to work in the real world. Alex brings years of hands-on IT, infrastructure, DevOps, and AI engineering experience to a conversation about the shift from experimenting with AI to building trustworthy systems that deliver measurable outcomes.

LLM token cost: pricing per token explained

LLM token cost is the price a provider charges per token a model reads or writes, quoted in dollars per million tokens. Input and output bill at separate rates, with output priced at roughly 5x input. As of September 2026, published rates range from under $0.10 to more than $180 per million tokens on top-end reasoning tiers. In late 2025, Hardik Sonetta of Thomson Reuters Labs published a warning about the most common prompt caching mistake in production.