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What is Turing’s Torch: AI Weekly?
Turing’s Torch: AI Weekly is Jonathan Harris’s no-hype artificial intelligence podcast. Each episode turns the week’s AI news into clear context for readers, business owners, creators, and anyone who wants the practical version rather than the circus version.
Latest: current episode Listen now.
Want the written version?
The newsletter is the same editorial approach — condensed, curated, and easier to skim.
Prefer deep dives?
The eBooks are topic-specific, practical, and designed to be useful, not impressive.
Latest three episodes
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24 July 2026 · 59:47
Agentic AI, Model Hype, and MCP and Agent Skills
Jonathan Harris cuts through Agentic AI, Model Hype, MCP and Agent Skills, Dirty Data, and Workflow Automation in this 60-minute Turing’s Torch: Artificial Intelligence Weekly briefing.
10 July 2026 · 24:38
Model Hype, Dirty Data, and Workflow Automation
Jonathan Harris cuts through Model Hype, Dirty Data, Workflow Automation, and AI Governance in this 45-minute Turing’s Torch: Artificial Intelligence Weekly briefing.
3 July 2026 · 24:04
AI Agent Skills and Model Hype
What matters is discerning the practical utility of AI advancements from the prevailing hype.
2026-07-10
Model Hype, Dirty Data, and Workflow Automation
Jonathan Harris cuts through Model Hype, Dirty Data, Workflow Automation, and AI Governance in this 45-minute Turing’s Torch: Artificial Intelligence Weekly briefing. What matters is simple: what is useful, what is undercooked, and who carries the risk once the demo glow wears off. Expect plain-English context on power, money, data, labour and control, with the usual vendor fireworks left outside where they belong.
2026-07-03
AI Agent Skills and Model Hype
What matters is discerning the practical utility of AI advancements from the prevailing hype. Jonathan Harris examines how new AI models and agent skills are being implemented, often revealing that the real challenge lies in the surrounding infrastructure and governance, not just the core technology. This week explores the tension between convenience and control, the complexities of decentralised AI, and the critical need for robust data engineering and benchmark hygiene to ensure genuine capability.
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