Oct 02, 2026

Stop Renting Your AI's Memory — Dylan Couzon, Qdrant

Dylan Couzon, developer relations engineer at Qdrant, argues that frontier-class models now run on hardware people can own, but the memory that makes an assistant personal still lives in someone else's cloud. He covers why owning inference gives autonomy while owning memory gives continuity, the CPU, RAM, and disk model for LLMs, why retrieval beats stuffing everything into the prompt, and a live offline demo of a drone building searchable memory with Qdrant Edge, plus opt-in shared memory.

Oct 01, 2026

AI Makes Games Worse?

Piero Molino, CEO of Studio Atelico, argues that AI should be a foundational game mechanic enabling new experiences rather than a cost-cutting shortcut. He covers on-device multimodal engines built on small fine-tuned models, the economics of cloud inference, artist rights, open source in the game stack, and what skill means in an era of coding agents.

Sep 18, 2026

A Worm With 302 Neurons Inspired Their Architecture — Ramin Hasani, Liquid AI

Liquid AI began by drawing inspiration from the C. elegans worm to develop Liquid Neural Networks, focusing on continuous-time, differentiable systems for efficient intelligence. Facing scaling challenges, they developed STAR, a meta-AI system for architectural search, optimizing hybrid models for quality, memory, latency, and speed, leading to their Liquid Foundation Models (LFMs). Their core focus is on bringing AI to the edge (on-device, in-car) for cost reduction, privacy, and offline capabilities, with successful deployments at Mercedes-Benz and Shopify. Liquid is also building a platform for 'sovereign AI,' enabling enterprises to customize and own their intelligence end-to-end, and researching massively multimodal systems, efficiency across the AI stack, and novel data types like DNA.

Aug 14, 2026

IBM’s cloud collab, Meta’s Muse Glimmer & OpenAI’s upcoming Astra model

This episode explores IBM's massive AI infrastructure partnership with Together AI and NVIDIA, Meta's open-source Muse Glimmer model enabling powerful on-device AI, and OpenAI's delayed Astra model due to critical cybersecurity capabilities. Discussions cover the economics of industrial-scale AI, the implications of local vs. cloud AI, and the profound security challenges and opportunities presented by both open and closed frontier models.

Aug 07, 2026

Anthropic’s sandbox breach, EU’s AI transparency push and DeepSeek’s cost-cutting model

This episode delves into several critical developments in AI. It begins by discussing recent sandbox breaches by Anthropic and Meta, mirroring earlier incidents with OpenAI, prompting debate on whether these are mere accidents or a growing concern as models become more capable and "agentic." The conversation then shifts to the EU's new AI transparency rules, exploring the challenges and effectiveness of labeling AI-generated content. Finally, the podcast examines DeepSeek V4-Flash's impact on the AI market, questioning if its low cost and high performance will disrupt the pricing of more capable, proprietary models and drive greater commodification and on-device inference.

Aug 07, 2026

Local Models: Trust, Control, Optimization — Carter Abdallah, NVIDIA

A panel with Vincent Weisser (Prime Intellect), Lucas Atkins (Arcee AI), and Chris Alexiuk (NVIDIA) on why open models matter for trust, control, cost predictability, and data ownership. The discussion covers the distinction between trust and safety, post-training open models for specific harnesses and use cases, the open MDW license, the Linux-like role of open infrastructure alongside closed models, and predictions for open model capabilities and local AI adoption over the next year.