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Jennifer Riggins on Muck Rack

Jennifer Riggins

(She/Her)
London
As seen in: Apple Podcasts, LinkedIn, Medium, Entrepreneur, ZDNet, Product Hunt, InfoQ, Social Media Today, DZone, The Code Project and
Covers:  Developer productivity. DevEx metrics. Platform engineering. Tech layoffs. Onboarding. Tech interviews. Diversity equity inclusion and belonging. Accessibility.
Doesn't Cover: Funding. New CEOs. Crypto. Too technical.
tech culture journalist | pro writer since 2003 | 7yo mommy | ‘inimitable’ ‘force of nature’ ‘a little bit sweary’ 'the one journo I can trust' | she/her

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Jennifer Riggins’s Biography

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Jennifer Riggins is a culture side of tech storyteller, journalist, writer, and event and podcast host, helping to share the stories where culture and technology collide and to translate the impact of the tech we are building. She has been a working writer since 2003, and is currently based in London.

Articles

Stop correcting AI code. Build the system agents need.

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By Jennifer Riggins
| The New Stack Verified
If software engineers are no longer writing code, what are they doing? That’s the question on millions of minds. AI tools are now widely used by developers, but fear of layoffs remains a concern. On the other hand, coding is only one part of software engineering. This creates an opportunity for engineers to get closer to the business and to the context of their role’s purpose within their organization and industry. “I like to think in parallels.
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Watch AWS engineers troubleshoot agentic AI with OpenTelemetry and OpenSearch

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By Jennifer Riggins
| The New Stack Verified
Your organization constantly needs more information about system performance, usage, and data while in production — or better yet, before it heads to prod. The challenge of telemetry increases with the complexity of your stack and agentic sprawl. Because “it works in the testing environment” becomes moot in the face of non-deterministic agents.
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How AI is solving the memory crunch it created

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By Jennifer Riggins
| The New Stack Verified
Memory has replaced compute as a primary constraint for modern tech teams. A perfect storm of hardware architecture limitations, semiconductor supply chain uncertainty, and changing software licensing models has left enterprises confronting increasingly memory-constrained environments. All while high-bandwidth AI workloads overload the production chain’s ability to provide sufficient memory — and when your AI token bill is starting to cost more than your salary bill.
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