Quinnipiac and Verndale Use AI Agents to Repurpose University Content
Quinnipiac University and Verndale detailed an AI enabled content operating model in a press release that turns existing stories on QU.edu into reusable digital content. The workflows create short summaries, extract faculty media mentions, surface testimonials and build photo galleries.
The system connects Quinnipiac Today, Mark and Optimizely CMS. AI agents complete structured tasks, while marketers remain responsible for strategy, creative work and final approval.
Quinnipiac and Verndale reported a 33% improvement in messaging recall, a 75% improvement in user experience, a 90% increase in article optimization and 91 minutes of additional productivity per marketer each week. They also reported a 25% improvement in AI readiness. The project was presented at Opticon 2026.
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Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
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