ISSUE 496
Stop and Smell the Tulips

"To avoid falling into workslop or other error-prone AI traps, organizations should ensure they’re prioritizing awareness, transparency and education among employees."

— Joel Carusone of NinjaOne, "Why Rushing AI Adoption Leads to Low-Quality ‘Workslop"

How Can We Solve Observability’s Data Capture and Spending Problem?

Observability must adapt and improve to often radically changing needs — and rising cost concerns. The so-called “DevOps practitioner” can be a developer, an operations team member, a site reliability engineer or a business stakeholder. What they all have in common is that they must be able to interpret telemetry data in order to make business decisions. Tune in to learn more!

Author Spotlight: Richard MacManus

Richard's journey began with '90s code, leading to the legendary ReadWriteWeb. Now he guides The New Stack's web and frontend development coverage, bringing critical historical context to our current cloud native era. Richard sees movements like the fediverse and open-source AI as a crucial return to the web's original, decentralized promise, a theme he covers with unmatched insight.

Stop and Smell the Tulips

I saw a post on one of the socials this week that made me laugh and jarred me (the best kind of social post): “Were people in 1636 told to find a way to use TULIPS in their workflows?” The AI investment wave is certainly giving 17th-century Dutch tulip mania these days.

At KubeCon + CloudNativeCon North America this month, AI was the added ingredient — the Miracle-Gro, if you will — in nearly every conversation. But in the real world of enterprise IT, it’s still (a) early days, if you’re an AI optimist or (b) what are we gonna do with all these tulips?

It was a big week for AI optimists, with Senior Editor Frederic Lardinois bringing us news from OpenAI (on Codex-Max) and Google (Gemini 3 Pro and the Antigravity agentic development platform).

On the “too many tulips” side of the ledger, there’s one of our more popular posts with readers this past week, by contributor Animesh Koratana, founder of PlayerZero, a predictive software quality company. Koratana wrote about the essential dilemma facing organizations that use AI to write code: it can move fast, but it breaks things in production.

Context is part of the problem, he argued: “Unfortunately, most LLMs operate a lot like the character Dory in ‘Finding Nemo’: They have no context from one query to the next and have extremely short memories.” By contrast, most enterprises are running on decades-old codebases. ”These systems have emergent behaviors, implicit dependencies and historical workarounds — compound interest on their technical debt.”

Inference gets the attention these days, but context is the key to making these tulips bloom. At least that’s what we’re hearing. Follow The New Stack for more gardening tips.

— Heather Joslyn, editor-in-chief, TNS

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