In Part 1 of a two-part series, guest host Sam Witteveen joins Allen to catch up and dive deep into the rapidly evolving world of AI models. Sam shares his fascinating journey from being a successful pop songwriter to becoming a Machine Learning Google Developer Expert (GDE) and running the massive Machine Learning Singapore meetup.

The conversation shifts to the latest AI developments, exploring the "Big Three" model builders—Anthropic, OpenAI, and Google. Sam and Allen discuss the frenetic pace of new model releases, changes to the Gemini 3 API, and how developers navigate the trade-offs between intelligence, latency, and cost.

Finally, they pull back the curtain on how these models are actually trained today. Discover why models are no longer trying to be "fact machines" and how post-training breakthroughs, code execution sandboxes, and Reinforcement Learning (RL) environments are dramatically improving AI capabilities. Stay tuned for the end of the episode, where they hint at what's coming in Part 2!

Timestamps:

[00:00:00] Introduction and catching up

[00:01:33] Sam's fascinating journey from pop music to machine learning

[00:05:23] Running the massive Machine Learning Singapore meetup

[00:07:42] Stumbling into YouTube and teaching AI with Google Colab

[00:12:38] Analyzing the "Big Three" AI models and rapid release cycles

[00:17:52] Gemini 3 API updates, Flash models, and thinking levels

[00:22:00] Tool use, knowledge cutoffs, and why LLMs aren't fact machines

[00:26:00] How post-training and code sandboxes revolutionized AI

[00:32:00] Scaling Reinforcement Learning (RL) environments for design

[00:34:04] Structured outputs and the return to predictable rules

[00:36:43] Tune in next time for more! And where to find Sam online

Hashtags:

#TwoVoiceDevs #AI #MachineLearning #DeepLearning #LLM #GoogleGemini #Gemini #OpenAI #ChatGPT #Anthropic #Claude #ReinforcementLearning #RAG #Developers #SamWitteveen