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Techstrong.ai — Archive · Vol 335 Issue 3

6 articles that week

Techstrong.ai, Thursday, February 19th, 2026

Creating A Compute-Ready Data Asset

Vol 335 · Issue 3 · 2026-02-19

You have a mountain of documents. Most of them are PDFs, slide decks, or internal reports. They're great for human eyes, but for an AI system, they're basically a brick wall for your AI application, hard work to ingest.

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Creating A Compute-Ready Data Asset

Techstrong.ai, Tuesday, February 17th, 2026

Why Postgres Is Emerging As The Default Database For AI Applications

Vol 335 · Issue 3 · 2026-02-17

In this Techstrong.ai Leadership Insights interview, pgEdge CEO Phillip Merrick explains how the open source Postgres database has become a de facto standard for deploying AI applications. He discusses Postgres' flexibility, ecosystem momentum, and its growing role in supporting distributed data architectures required for modern AI workloads.

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Why Postgres Is Emerging As The Default Database For AI Applications

Techstrong.ai, Tuesday, February 17th, 2026

Why Legacy Systems Are The Hidden Constraint On AI Agent Adoption

Vol 335 · Issue 3 · 2026-02-17

In this Techstrong.ai Leadership Insights interview, SnapLogic CTO Jeremiah Stone explains why legacy systems are emerging as a major barrier to deploying AI agents at scale. He discusses the integration, data access, and architectural challenges enterprises must address to successfully operationalize AI agents across existing IT environments.

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Why Legacy Systems Are The Hidden Constraint On AI Agent Adoption

Techstrong.ai, Tuesday, February 17th, 2026

How Neoclouds Are Driving More Sustainable and Cost-Efficient AI GPU Consumption

Vol 335 · Issue 3 · 2026-02-17

In this Techstrong.ai Leadership Insights interview, GMI Cloud CEO Alex Yeh explains how the rise of neocloud providers is reshaping access to GPUs required to train and run AI models. He discusses how alternative cloud infrastructure models can improve cost efficiency, optimize resource utilization, and help address sustainability challenges associated with large-scale AI workloads.

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How Neoclouds Are Driving More Sustainable and Cost-Efficient AI GPU Consumption