Exploring the “quiet failures” that keep enterprise AI pilots from delivering real-world value—things like brittle speech models, opaque pipelines, misaligned benchmarks, rising infrastructure costs, and unclear ROI attribution. Drawing on real deployments in speech recognition, retrieval-augmented generation, evaluation systems, and backend infrastructure, this paper pinpoints where AI projects stumble and offers practical engineering solutions. Whether you’re an AI architect, product manager, or technical lead, you’ll find concrete insights to help your organization move from promising prototypes to reliable, scalable systems.
June 2nd, 2025
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Meta’s LLaMa 4 pushes the boundaries of AI with a staggering 10 million token context window and native multimodal capabilities—text, image, audio, and code. Built on a Mixture-of-Experts architecture, it promises faster outputs, deeper context, and enterprise-ready scalability. But how does it perform against rivals like Gemini 2.5 or ChatGPT 4.0 ? Where does it shine and where does it stumble? M37Labs dives deep into the architecture, performance, and real-world potential of LLaMa 4 in this exclusive research analysis.
May 15th, 2025
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