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By Mridhula Venkat
AI coding tools made code generation faster. Measuring what actually ships is the hard part. Over the last eighteen months, tools like Cursor, Claude Code, Copilot, and Windsurf have fundamentally changed how software gets built. AI-generated pull requests are increasing, developers are producing more code than ever before, and workflows that once took hours now happen in minutes. But most organizations struggle to clearly explain what that investment is actually producing.
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By Harish Doddala
Gartner expects worldwide AI software spending to hit $2.59 trillion in 2026, 47% more than organizations spent last year. The dollars are real and growing fast. But most organizations still can't measure the ROI of that spend. The problem has two sides: developers and infrastructure. On the developer side, engineers are using AI to write nearly every line of new code, and leaders have no way to tell whether that spend is producing software that ships.
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By Kelsey Rosen
Companies are shipping AI features at a pace cloud teams have rarely seen. New agents, new copilots, new flows powered by language models, all moving from prototype to production in weeks. The spend that comes with it is real and accelerating, and most teams are seeing it on the invoice before they see it anywhere else. The question is no longer how much you're spending on AI. It's whether each dollar is producing a real outcome, and whether you can govern that spend before the next invoice arrives.
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By Debaditya Chatterjee
Key Takeaway: Harness AI Test Automation now runs existing Playwright suites without code changes, adds AI-powered failure triage, and integrates test results directly into build and deployment pipelines.
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By Animesh Pathak
The NoSQL Storm explores core Database DevOps concepts and the real-world challenges teams face while managing modern NoSQL environments. It highlights the complexities of schema evolution, scaling distributed systems, and maintaining operational reliability as applications grow.
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By Chinmay Gaikwad
Continuous integration (CI) costs can escalate quickly as engineering teams scale. While most organizations focus on cloud bills, the true cost of CI includes slow build times, developer wait time, inefficient test execution, and overprovisioned infrastructure. CI cost optimization is the practice of reducing the total cost of CI pipelines by improving build efficiency, minimizing compute usage, and eliminating unnecessary work without slowing down development.
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By Shibam Dhar
Three weeks into a platform modernization project, this question landed in my inbox: "Why does our deployment pipeline take 40 minutes instead of four?" This is artifact repository sprawl in practice, and it does more than slow pipelines. It fragments your security posture, your compliance evidence, and your ability to answer basic questions like "what's actually running in production right now?".
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By Roshan Piyush
Shai-Hulud is back - this time being lighter, faster and more automated than before. This new wave, termed as Mini Shai-Hulud, has affected a number of packages from tanstack, uipath, opensearch-project and mistralai among others over the past few weeks, with the latest series of major compromises coming on 19th May, 2026 on major organizations openclaw-cn and antv. Check an extensive list of affected packages here.
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By Dewan Ahmed
Engineers have been shipping pieces of "the graph" for years. Service maps. Dependency graphs. Knowledge graphs. RDF triples. The newest entrant is the context graph, and the reason it shows up now is specific: software is increasingly executed by agents, and agents need a model of how work actually happens, not just an index of what exists.
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By Dewan Ahmed
When you're architecting an enterprise Java application, one decision quietly shapes everything downstream: runtime footprint, deployment pipelines, and how your platform team handles incidents at 3 a.m. For two decades, that decision was framed as Java SE vs Java EE. In 2026, that framing has quietly inverted.
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By Harness
AI-driven attacks aren't theoretical anymore. Here's how to prepare your team. What would you add to the prep list? Drop it below.
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By Harness
This video shows how easy it is to migrate off Jenkins to Harness. Getting started today: harness.io/jenkins.
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By Harness
AI coding agents may not replace open source libraries overnight. But Adam Arellano, Field CTO at Harness, thinks models like Mythos could expose a bigger problem: finding bugs, vulnerabilities, and edge cases faster than maintainers can keep up. That might be the real threat to tools and libraries.
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By Harness
See how to configure Harness so that OPA policies are evaluated in your infrastructure.
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By Harness
This example hows how to use Harness CI/CD to build a Cobol application and deploy it to a mainframe running z/OS. We leverage IBM DBB and Wazi Deploy in this example.
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By Harness
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.
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By Harness
What happens when AI interviews a tech leader? You get unexpectedly honest answers. Harness General Counsel Hanna Steinbach sat down with ChatGPT — and skipped the corporate script. From the realities of parenting while leading a legal team at a high-growth startup, to the daily habits that keep her grounded, this is the kind of candid leadership perspective you rarely see. Oh, and she's definitely the person sprinting to the gate right as boarding starts.
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By Harness
Most engineers can recite these three terms. Fewer know how they actually connect during a load test. If your team is running performance tests without mapping results to SLOs, you're collecting data without a pass/fail signal. This short gives you the mental model to turn load test output into something your SLA can actually depend on.
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By Harness
AI for Development Isn't New. AI for Delivery Is! AI coding assistants have transformed how teams create software. But innovation only delivers business value when code moves quickly and safely from commit to production and into customers' hands. In AI-Native Software Delivery, Harness Field CTO Nick Durkin and DevOps veterans Eric Minick and Chinmay Gaikwad present a practical guide to applying AI across the entire software delivery lifecycle.
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By Harness
Organizations everywhere are racing to modernize DevOps and elevate the developer experience, but how close are they to actually delivering?We surveyed over 650 engineering leaders to find out. The result is The State of Software Engineering Excellence 2025, a report that uncovers the hidden challenges, gaps, and opportunities shaping today's software teams.
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By Harness
This comprehensive whitepaper shows you how modern software delivery platforms solve these challenges.
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By Harness
Modern systems are more complex-and more fragile-than ever before. Whether it's scaling challenges, dependency failures, or unpredictable outages, reliability is no longer optional. It's a competitive edge. This eBook provides a practical blueprint for successfully adopting Chaos Engineering, with strategies proven to work across engineering, SRE, and QA teams. Learn how to overcome internal blockers, align ownership, and embed resilience testing directly into your software delivery lifecycle.
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By Harness
You're adopting AI code generation tools to enhance your engineering team's output, but how do you quantify the real return on investment? Without precise measurement, you're navigating in the dark, unable to identify true productivity gains or pinpoint areas for optimization. Justifying these critical AI investments becomes difficult.
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Harness delivers intelligent AI automation, so your team ships code faster, safer, and smarter.
Don't let your pipeline become the bottleneck as developers and AI coding agents generate more code. Harness AI intelligently automates, safeguards, and accelerates software delivery at any scale.
- AI for DevOps & Automation: Unleash developer productivity with AI that understands your DevOps ecosystem. Harness combines the industry's fastest, most secure CI/CD with developer self-service to automate pipelines, infrastructure, and the entire path from code to production.
- AI for Testing & Resilience: Release software confidently using AI-powered predictive analytics and testing. Make every change fast, safe, and resilient, so your teams can focus on shipping quality code instead of chasing bugs and triaging outages.
- AI for Security & Compliance: Make secure software your new default. From application and API discovery to AI-powered threat prevention, Harness uses contextual insights and agentic workflows to detect and mitigate risks from build to post-deployment.
AI for Everything After Code.