Is Your AI Coding Agent Actually Boosting Productivity
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Working as a remote engineer at Cribl | Building the AI Platform for Telemetry
Learn what it’s like to work as an engineer at Cribl, a remote-first company building the AI platform for IT and security data. In this recruiting video, Cribl’s engineering and support leaders share how fully distributed teams collaborate, solve hard data problems, and grow their careers while working from around the world. You’ll hear from managers and leaders in site reliability engineering, security incubation, and technical support about.
Chunk sidecars: Inner Loop Validation for AI Coding Agents
Your agent writes code fast, but you shouldn't have to see it until it's right. Chunk sidecars are lightweight microVMs that work inside the agent loop, requiring agents to pass pre-push validation in a CI-like environment before they declare they're "done." That means no massive CI pile-ups, no long round-trips that risk resetting your agent's context. You're sending code you already know is good.
Untested AI is unshippable AI, with Laurie Voss of Arize
Most AI applications in production right now were shipped on vibes. A developer ran a few queries, liked what they saw, and pushed to prod. Laurie Voss argues that's the core reason so many AI products feel broken, and the fix is simpler to name than it is to do: write the tests.
IT Insider: Solving AI Accountability Crisis
This episode of Ivanti's "IT Insider" series that explores the critical challenges organizations face when moving from AI experimentation to full-scale deployment. The discussion centers on three main themes: The AI Governance Gap: Guests Brooke Johnson and Sterling Parker discuss the "governance gap" where organizations deploy AI faster than they can establish policies. They highlight the risks of Shadow AI (unsupervised AI use) and the importance of having an AI Governance Council to ensure responsible use.
The Invisible IT Department: How to Deliver Friction-Free Experiences with Agentic AI
Every enterprise has bought AI, but many are still waiting for their investment to pay off. Ivanti’s 2026 AI Maturity Report found that only 2% of organizations say they currently have no AI use at all. As the majority of organizations move beyond the AI experimentation stage, the real competitive differentiator is if that AI is providing continuous, business value at scale.
How to build a secure AI agent sandbox with relaxAI and Claude Code
AI agents are powerful. They're also unpredictable, non-deterministic, and capable of doing things you didn't ask them to do, as the Rome Alibaba and Claude Mythos case studies make very clear. The answer isn't to avoid agentic AI. It's to run it properly. In this demo, Ben Norris, founding engineer at relaxAI, shows how to build a fully sandboxed AI agent environment from scratch, an ephemeral Civo VM provisioned via Terraform and GitHub Actions, locked down with egress policies, an unprivileged Linux user, and hard resource caps, running a Claude Code session pointed at the relaxAI API.
Observability for a Privacy-first AI Wearable | Grafana Everywhere
Trust is everything when AI gets personal. Golden Grot Award winner and NeoSapien co-founder and CEO Dhananjay Yadav shares how his team uses Grafana Assistant to ensure the privacy-first AI wearable delivers a seamless, reliable experience without compromising its mission. Because when AI moves closer to our everyday lives, teams need to know what’s happening — and users need to trust that it’s working as intended.
Klaudia Under the Hood: How We Built an AI SRE That Actually Earns Trust
In reliability engineering, being ‘mostly right’ is a liability. An AI SRE that sometimes misses the root cause or gives a confident, wrong answer at 2:17 AM has no place in an enterprise cloud environment. In this context, silence is better than noise. That’s the bar Klaudia is built to clear: genuine reliability that you can trust in production. The kind of reliability that earns a place alongside your best engineers. Getting there requires more than just a capable model.
Why Custom Route Optimization Software Outperforms Generic TMS Logic
Most logistics companies running fleet routing and scheduling software already know, at some level, that the routing output is not quite right. Not wrong in ways that cause obvious failures - just consistently suboptimal in ways that dispatchers compensate for manually, shift after shift. A fleet with mixed vehicle classes that the engine treats as equivalent. Delivery windows that get re-optimised at dispatch and then fall apart when a customer calls at 10 a.m. to reschedule. Hazmat constraints encoded as exclusion zones rather than permit-specific corridor logic. These are not edge cases.