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GitLens vs VS Code Git Graph Ranked for Solo Devs
Choosing the right Git extension for your VS Code setup can make the difference between a smooth workflow and hours lost hunting for context. GitLens, developed by GitKraken, and VS Code Git Graph both aim to enhance your Git experience, but they approach the problem differently. This article ranks both extensions across key workflow scenarios – merge conflicts, commit history, code review, debugging, UX, and performance – so you can pick the right tool for how you work.
AI Productivity Metrics Dashboard for Engineering Managers (2026)
Measuring AI’s impact on your engineering team is harder than it sounds. Headlines claim AI writes 30% of code and doubles productivity, but those numbers rarely match what you see on the ground. Without a dedicated dashboard that blends leading indicators, anti-gaming safeguards, and ROI reporting, you cannot answer the question that matters most: is AI helping your team ship better software faster?
What Vera Rubin means for AI infrastructure in 2027
Every so often, NVIDIA releases something that quietly changes the direction of the industry. CUDA did it. DGX did it. NVLink did it. Vera Rubin feels like one of those moments again. At first glance, Rubin looks like the natural successor to Blackwell. Faster GPUs, larger memory pools, and eye watering performance numbers. But the more you dig into the architecture, the clearer it becomes that NVIDIA is not simply shipping another accelerator generation.
What a Context Graph Actually Is, and How to Build One | Harness Blog
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.
Core Java vs Enterprise Java: Jakarta EE, Spring Boot & Modern Trade-offs [2026 Guide] | Harness Blog
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.
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 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.
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.