HeyJaredSonu Kapoor
- Indexed articles, last 90 days
- 3
- Latest publication
- Aug 18, 2026
- Outlet visibility, for InfoWorld
- Top 500K sites
- Earliest in this view
- Jul 16, 2026
Latest articles
A smarter, structured approach to generative UI (opens the original)
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There is an important distinction between using AI to generate UI code during development and letting AI generate executable UI at run time. If a developer uses ChatGPT, GitHub Copilot, Cursor, or another AI coding assistant to generate a component, template, stylesheet, test, or even an entire feature, that can be a productive workflow. The generated code still enters the normal software development process. A developer can review it, edit it, test it, commit it, and maintain it like any other
AI agents need security regression testing, not another checklist (opens the original)
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AI agents are being connected to real systems faster than most organizations are learning how to secure them. That should concern us. The first wave of AI security discussion has been useful, but limited. The industry has learned the vocabulary: prompt injection, indirect prompt injection, tool misuse, data leakage, excessive agency, unsafe retrieval, and broken authorization boundaries. These terms are important because they give teams a way to talk about risk. The harder problem is what happen
Node.js security starts before CI (opens the original)
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In many teams, dependency security still happens after the most important trust decision has already been made. A package is added, the lockfile changes, the feature moves forward, and only later does the pipeline ask whether the application should have trusted that code in the first place. That workflow made sense when dependency security was mostly viewed as a compliance check. Run a scanner. Produce a report. Fail the build if the risk crosses a threshold. Let someone decide what to do next.
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Top 500K sites
For InfoWorld, the outlet · Measured Aug 1, 2026
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