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What a Modern Business Tech Stack Should Look Like

In today's fast digital market, the real question is not if your business needs tech, but which tech will help you move forward. A modern business tech stack is more than a pile of apps. It's a carefully chosen set of digital tools and systems (like programming languages, frameworks, databases, front-end and back-end tools, and APIs) that work together to run day-to-day operations, support new ideas, and help the business grow.

5 Leading Replacements for AWS DMS in Streaming Workloads

Streaming workloads impose different requirements than classic migration projects. A platform that can support a one-time move from one system to another is not always the right fit when data must flow continuously, stay current, recover cleanly, and serve downstream analytics, applications, or AI use cases without long delays. That is the real shift behind this category. The question is no longer only whether data can be replicated.

Boom Truck Rental: Factors Such as Weight Capacity Are Important Considerations

Working in the warehousing, maintenance, and construction fields carries its own risks due to the work at height. Earlier, ladders and scaffolding were your best companions. Due to advancements in engineering and technology, you can now access well-equipped aerial boom lifts to perform your tasks safely, even at very high elevations. You don't have to buy them. Instead, you can approach a boom truck rental company for options. Various models may be available, each designed to serve a specific purpose. Examine each model for its condition, reach, and lifespan. And, please pay attention to its weight capacity.

AI, Platforms, and the Future of Value Delivery: A Conversation with ServiceNow

How do enterprises turn AI from experimental potential into real-world software delivery value — without slowing down, breaking security, or sacrificing reliability? At {unscripted} 2025, Amit Zavery — President, Chief Product Officer, and COO of ServiceNow — joined Harness CEO and Founder Jyoti Bansal for a candid fireside chat on the future of AI in the enterprise, the role of platforms in unlocking developer productivity, and why"AI-native" only works when speed, security, and reliability move together.

Why Network Operations Needs Data-Centric AI

The discussion around AI in infrastructure and operations has become increasingly model-centric. Teams want to know what model a platform uses, how current it is, how much reasoning capacity it has, and how quickly it can be updated as the model landscape shifts. Those are reasonable questions, but they tend to arrive too early. In production operations, the more consequential question is what happens to the data before any model is asked to interpret it.

Anthropic Shipped An Enterprise Analytics API. We Shipped the Claude Adapter Today.

Anthropic just shipped an Enterprise Analytics API with user-level token and cost data. Today, we're shipping the CloudZero adapter that maps that data to teams, budgets, and cost centers — so Claude spend gets the same accountability as the rest of your stack. Anthropic released the first beta of its Enterprise Analytics API this week. Admins can pull token usage and dollar cost through a programmatic endpoint, broken down by user, model, context window, region, and product surface.

Why agentic AI development needs reliability guardrails

AI has massively accelerated code deployment. In fact, since the introduction of agentic coding, GitHub has seen exponential growth in PRs, commits, and new repos. What they originally predicted would require 10X capacity, they’re now estimating it’s going to require 30X capacity, and the biggest driver is agentic development. Companies across industries are building agentic pipelines to ship features faster than ever before. That acceleration isn’t without risk.

There's an npm-shaped hole in the AI tooling stack

I've had this same conversation with 60+ engineering teams in the last six months. A team adopts AI tooling. One developer figures out how to use it well, builds up a vault of skills, MCP configs, and slash commands that 10x their output. The rest of the team has whatever they can scavenge from a shared Notion doc.

When your agents hallucinate at 2 am, it is not a model problem

The first time an AI assistant suggests "restart the service" during a live incident and nobody on the bridge can tell whether that suggestion came from a current runbook, a stale wiki page, or thin air, you stop caring about model benchmarks. You start caring about what the agent actually knew, where that knowledge came from, and whether you can trust the chain of reasoning behind it.