A highly-technical, daily show covering all that happened in the world of AI Engineering. Curated from around the internet, powered by Jellypod.
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Contact path
Verified email
Booking probability
40%
Guest openness
Selective
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Signup to Generate a PitchA highly-technical, daily show covering all that happened in the world of AI Engineering. Curated from around the internet, powered by Jellypod.
Recent episodes include "Why Kubernetes Fails AI Agents" (18 July 2026, 1 minute), "Kimi K3: The Open-Weights Giant Taking Over Frontend Coding" (17 July 2026, 7 minutes), and "Levitating Labs, Reasoning Tokens, and the Future of Discovery" (16 July 2026, 8 minutes).
The AI Engineering Podcast publishes roughly daily, with episodes averaging 6 minutes across 35 episodes.
Booking probability for The AI Engineering Podcast is 40%, and the host is rated Selective on guests. The AI Engineering Podcast requires a Pod Score of 80 or higher. The AI Engineering Podcast's contact route on record: verified email.
The strongest fits for The AI Engineering Podcast are IT Services & Software Development and Cloud, Data & Web Hosting. The AI Engineering Podcast has a news audience.
The AI Engineering Podcast is a news podcast with 35 episodes published.
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
The AI Engineering Podcast
<p>This episode breaks down why Docker and Kubernetes are the wrong fit for AI agents, from container breakout risks to the pain of stateless restarts and slow recovery. It then explores how MicroVMs, NVMe caching, and...
The AI Engineering Podcast
<p>We break down Moonshot AI’s Kimi K3, the 2.8-trillion-parameter open-weights giant that shot to the top of the Frontend Code Arena while only landing mid-pack in general chat. Plus: why its vision-in-the-loop coding...
The AI Engineering Podcast
<p>This episode explores how Lila Sciences is turning the lab into a data center, using automated hardware, legacy instruments, and iterative physical feedback to accelerate discovery. It also dives into generalist AI...
The AI Engineering Podcast
<p>We unpack Thinking Machines Lab’s massive open-weight model release, from its trillion-scale Mixture-of-Experts architecture and local deployment support to what it means for developer sovereignty. Then we dive into...
The AI Engineering Podcast
<p>This episode explores why today’s AI coding agents need endurance testing, better tracing, and dynamic web evaluations to survive real-world production workflows. It also dives into active perception in multimodal...
The AI Engineering Podcast
<p>We dig into the chaos of GPT-5.6’s 36 configuration variants, why cheaper models can outperform pricier tiers with the right reasoning settings, and how endless parameter tuning is slowing teams down.</p><p>Then we...
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