Kelverion Talks Episode 1
In this episode Kelverion discusses IT Automation vs AI: the what, why and when to use either or both.
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GitOps & Harness AI
Harness AI understands GitOps entities and can detect misconfigurations in manifests, identify missing dependencies or clusters, diagnose connectivity issues, and suggest fixes in context. With the expansion of the "Ask AI" assistant into GitOps, teams can troubleshoot issues directly where they occur, not after the fact.
#054 - From Shiny Objects to FinOps: Taming Cloud Costs in the AI Era with Josh Schlanger (CloudX...
In this episode of the Kubernetes for Humans podcast, we are joined by infrastructure and FinOps expert Josh Schlanger. Drawing on over 15 years of experience across Martech, e-commerce, and health tech, Josh shares why solving core business problems should always take priority over chasing new, "shiny object" technologies.
Women's Day Panel: Navigating the Future of Engineering in the Age of AI
How is AI reshaping engineering—and what does it mean for the future of work? At our first GTA Boston Hub event of the year, we brought together engineering leaders from Boston Consulting Group and Athenahealth to dive into one of the most pressing topics today: the rise of generative AI. In this panel, we explore: Key takeaway: This isn’t “human vs AI”—it’s human augmented by AI. The real advantage lies in how we adapt, collaborate, and lead in this new era.
AWS VPC Peering Vs. Transit Gateway: Which To Choose And Why [2026]
VPC peering can be simple and cost-effective in smaller setups. For growing multi-account platforms, Transit Gateway can offer predictable structure and centralized governance. But that’s not all. AWS VPC peering connects two VPCs directly with no hourly fee — simple and cost-effective at small scale, but it creates an unmanageable mesh as your VPC count grows.
Build Numbers That Actually Make Sense: Branch-Scoped Sequence IDs in Harness CI | Harness Blog
You're tagging Docker images with build numbers. -Build is your latest production release on main. A developer pushes a hotfix to release-v2.1, that run becomes build. -Another merges to develop, build. A week later someone asks: "What build number are we on for production?" You check the registry. -You see,,, on main. The numbers in between? Scattered across feature branches that may never ship. Your build numbers have stopped telling a useful story.
Jensen Huang's warning: lead the AI transition - or finance it
The wrong people got the most attention from Jensen Huang’s comments last week. Huang told the All-In Podcast that he’d be “deeply alarmed” if a $500,000 engineer consumed less than $250,000 in AI tokens annually. Within 48 hours, the discourse collapsed into a compensation debate.
£10M Investment in UK AI Infrastructure | Pulsant CEO Talks to Data Centre Solutions
Join Pulsant CEO Rob Coupland in an exclusive interview with Phil Alsop, Editor at Data Centre Solutions (@datacentres), as they explore Pulsant’s £10 million investment in the Milton Keynes data centre. This upgrade delivers high-density, sovereign computing capacity, helping businesses accelerate AI and tech projects while keeping data secure and local. Rob also shares plans to expand this high-density model across the UK, supporting enterprise AI at scale and boosting local economies.
AI Deployment in Production: Orchestrate LLMs, RAG, Agents | Harness Blog
For the past few years, the narrative around Artificial Intelligence has been dominated by what I like to call the "magic box" illusion. We assumed that deploying AI simply meant passing a user’s question through an API key to a Large Language Model (LLM) and waiting for a brilliant answer.
Groq vs. GPUs: The future of AI inference in 2026
Back in 2016, Jonathan Ross founded Groq, the AI chip startup, which went on to enter a non-exclusive licensing agreement with NVIDIA for Groq’s inference technology (as part of a $20 billion deal). The name ‘Groq’ is commonly confused with X (formerly Twitter)’s Grok, which was launched in 2023 as a Gen AI chatbot. As demand for real-time AI continues to grow, inference has become one of the most important and expensive parts of the machine learning lifecycle.