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Shipped: Catch a broken regex before it breaks your rules

Let’s say you want a Matches condition that picks up “prod”, “PROD”, and “Prod”, so you write ‘Matches: (?i)prod’ and check it in an online regex tester before publishing. It looks fine. Python accepts it, and so does JavaScript. Dimension Studio used to accept it too, and the rule is published. Then your data stops updating. The new pattern is what’s keeping it from materializing, but nothing tells you that.

Cloud Asked What It Cost, AI Is Asking What It's Worth | Harness Blog

AI has quickly become one of the largest and fastest-growing enterprise expenses, exposing many of the same governance and visibility challenges organizations previously faced with cloud. Based on findings from the 2026 State of AI in FinOps report, we explore how mature organizations improve AI cost ownership, reduce waste, and build a culture focused on measurable business value.

How to standardize app delivery across AWS, Azure, and GCP

Running workloads across AWS, Azure, and GCP is the operational reality for most enterprise engineering teams. The challenge isn't the providers themselves, it's what happens when each one accumulates its own delivery pipeline, its own security configuration, and its own environment management tooling. What starts as provider flexibility quietly becomes provider-specific complexity, multiplied across every team that ships.

A default is not a decision: cut AI model costs with CloudZero's free, open-source Model Rightsizer

Only 22% of finance leaders can tie their AI spend to a business outcome, according to CloudZero’s 2026 finance survey. When AI ROI falls short, it usually isn’t because a company is doing too much AI. It’s that no one is watching which model runs which task, and that one choice accounts for a large part of the cost. Here’s why it happens.

Model Rightsizer: the agent that stops your other agents from defaulting to Fable

Model Rightsizer is an open-source Claude Code sub-agent from CloudZero that scores each task on capability need versus cost pressure, then routes it to the smallest model that can handle it. In its first week, it cut Opus spend 75% while shifting 234x more work to Sonnet. Every Claude Code agent you run has to answer a question it usually never gets asked: does this task need the smartest model available, or are you paying Fable prices to rename a variable across three files?

Software is a team sport. AI tooling forgot that - Upsun Product Highlights

AI tools made individual developers faster. Teams still aren't shipping more product. That gap is the whole reason Upsun Dispatch exists. In this Product Highlights conversation, Kateryna Dvornichenko, a product manager at Upsun who has spent the past several months building Upsun Dispatch, explains why the tooling market got the unit wrong. Her take: "Making software is a team sport." We get into.

Security Camera Local Storage vs Cloud Subscriptions for Homeowners

If you want privacy and no monthly fees, choose local storage. If off-site backup matters most, choose cloud. If you need both, use a hybrid setup. Since people want to supervise their houses, check the driveway, and keep an eye on their backyards, they tend to buy a security camera to help them. But once the cameras are installed, a new problem quickly emerges: where exactly are all the recorded videos stored? This is precisely where the choice between local storage and cloud storage subscription plans for security cameras has a critical impact.

Cloud Outage Response: Lessons From July's Bad Week

In a single week, two of the largest cloud providers on earth failed at almost the same time, and a good chunk of the internet went with them. Effective cloud outage response stopped being a theoretical exercise and became the difference between a calm 30 minutes and a chaotic afternoon for thousands of on-call engineers. On July 23, 2026, a maintenance bug inside Microsoft Azure pulled IP routes off more devices than intended in the West US region, cutting Microsoft 365 access for millions.

How to prove the business value of AI

Proving AI business value means sorting every AI investment into one of four buckets - revenue growth, cost avoidance, productivity gain, or risk reduction - then tracking spend at the unit level (per feature, customer, or team) so each dollar has a traceable return. Most companies measure one bucket well and leave the rest unattributed. That gap is why the same AI deployment can look like a $40M win and a public reversal at the same time.