More AI Engineering Podcast episodes
Published 8 Feb 2026
Duration: 00:59:25
A podcast delves into Voice AI complexities, introducing an approach that leverages specialized models for efficiency, accuracy, and scalability in addressing emotional and contextual nuances in human speech.
SummaryIn this episode of the AI Engineering Podcast, Carter Huffman, co-founder and CTO of Modulate, discusses the engineering behind low-latency, hi...
The podcast explores the complexities of Voice AI, emphasizing the challenges posed by the emotional and contextual subtleties present in human speech. It presents the Ensemble Listening Model (ELM) as a novel architecture designed to overcome limitations in cost, processing power, and audio quality variability. ELM uses a dynamic ensemble of small, specialized models tailored to different audio distributions, enabling efficient, accurate, and scalable voice analysis through real-time model selection and the inclusion of structured memory and feedback mechanisms to ensure consistency.
The discussion also highlights the advantages of using ensemble models over large foundation models, such as cost-efficiency, reduced instances of hallucination, and the potential for more distributed and modular AI systems. However, the podcast also addresses challenges in model validation, observability, and identifying failure modes. It underscores the importance of integrating domain-specific knowledge and advanced architectures like transformers to manage the complexities of high-dimensional audio data effectively.
25 Feb 2026 Kubernetes, Compliance, and Control: The Operational Backbone of AI Sovereignty
This podcast discusses the Bruin framework for improving AI development and control through open-source tools and infrastructure, with a focus on national sovereignty over AI systems and data.
15 Feb 2026 From Blind Spots to Observability: Operationalizing LLM Apps with OpenLit
Current AI app development trends are hindered by challenges such as vendor lock-in, unclear value creation, and limited understanding of AI behavior, but open-source tools like OpenLit can help address these issues.
27 Jan 2026 GPU Clouds, Aggregators, and the New Economics of AI Compute
Bruin, an open-source AI/ML data infrastructure framework, addresses GPU cloud market dynamics, technical challenges like Kubernetes portability and data gravity, and evolving trends in LLM tooling, infrastructure gaps, and hardware competition.
20 Jan 2026 The Future of Dev Experience: Spotifys Playbook for OrganizationScale AI
Spotify's engineering and AI integration focuses on distributed architecture, collaborative tools like Backstage, monorepo standardization, AI agents for code generation and operations, challenges in cross-team collaboration and reliability, and expanding AI beyond coding into product development and documentation while balancing innovation with rigorous testing and human oversight.
5 Jan 2026 Generative AI Meets Accessibility: Benchmarks, Breakthroughs, and Blind Spots with Joe Devon
AI tools can both enhance and challenge accessibility in digital environments, but better standards and tools are needed to ensure inclusivity and accessibility in various technologies.