Conference talks, community meetups, and booth demos, mostly about the ONNX ecosystem and running AI locally.
Panelist alongside leaders from Stanford, Salesforce, and the MBZUAI Institute for Foundation Models, moderated by Intel: what changes in the infrastructure stack when agents move from prototypes to persistent, production-scale systems, and where silicon, governance, memory, and orchestration need to land.
Hands-on workshop delivered at AMD's flagship Advancing AI event in San Francisco (with Iswarya Alex) and at Advancing AI Day in Shanghai: harnessing the on-device power of the Ryzen AI Max 395 to run large coding models locally with Lemonade Server, no cloud, no API keys. Attendees architect specialized AI agents with Qwen Coder and watch them collaborate to build something they'll actually want to play.
Hands-on lab at Microsoft Build: powering real development workflows with local AI infrastructure, building and running applications with AI-driven coding agents, OpenCode as the agent and Lemonade serving the models on-device.
Live demo at the AMD booth at CES 2026: Lemonade serving large language models fully locally on AMD hardware, showing NPU- and GPU-accelerated generation on consumer machines.
Moderated the closing panel of the ONNX Community Meetup 2025 on interoperability and standardization across the AI stack.
A conversation with AMD engineers Victoria Godsoe and Krishna Sivakumar on ONNX, the TurnkeyML toolchain, and how the ONNX Model Zoo was rebuilt on top of it.
On the large gap between AI capital expenditure and revenue, why customer-facing AI products matter, and a brainstorm of AI product ideas with market and societal relevance.
A two-hour conversation on the state of LLMs: the OpenAI, Google, Microsoft, and Apple AI keynotes, the hallucination problem, how AI affects society, and whether LLMs lead to general intelligence.
With Sai Kishan Pampana (Intel): how ONNX grew from an interchange format into the interoperability layer of the AI stack, why fragmented frameworks and tools need open standards, and where the ecosystem goes next.
Presenting the relaunched ONNX Model Zoo at the ONNX Community Meetup: how TurnkeyML powers a reproducible, continuously validated model corpus for the ONNX ecosystem.
Why graph neural networks stress conventional accelerator architectures, and how Groq's deterministic LPU architecture handles the irregular compute and memory patterns GNNs present. (Recording no longer public.)
How machine learning reshaped the advertising industry's infrastructure and economics, and where the landscape is heading. (Recording no longer public.)
A tour of the ONNX ecosystem - the interchange format, ONNX Runtime, ONNX Script, and the Model Zoo - and how they fit together for teams moving models across frameworks and hardware. (Recording no longer public.)