This episode traces how prompt engineering evolved from informal tricks (tipping, role-playing, "take a deep breath") into three structured reasoning frameworks — Chain of Thought, Self-Consistency, and Tree of Thoughts — that dramatically improved LLM performance without changing the models themselves, culminating in the insight that intelligence in these systems is a latent resource unlocked by better scaffolding, not better weights.

Credits

Cover Art by Brianna Williams

TMOM Intro Music by Danny Meza

A special thank you to these talented artists for their contributions to the show.

Links and Reference

• Chain of Thought Prompting: Wei, J., Wang, X., Schuurmans, D., et al. (2022). "Chain-of-Thought Prompting ElicitsReasoning in Large Language Models." NeurIPS 2022. arXiv: 2201.11903

• Self-Consistency: Wang, X., Wei, J., Schuurmans, D., et al. (2022). "Self-Consistency Improves Chain of Thought Reasoning in Language Models." ICLR 2023. arXiv: 2203.11171

• Tree of Thoughts: Yao, S., Yu, D., Zhao, J., et al. (2023). "Tree of Thoughts: Deliberate Problem Solving with Large Language Models." NeurIPS 2023. arXiv: 2305.10601

• "Take a deep breath and think carefully" improves performance:: Yang, C., Wang, X., Lu, Y., et al. (2023). "Large Language Models as Optimizers." arXiv:2309.03409.

• Christmas / holiday performance degradation caveat: This claim was popularized on social media and discussed on platforms like X/Twitter and Hacker News in late 2023. A blog post by Rob Lynch (December 2023) ran some informal tests. No peer-reviewed

• study has definitively confirmed this effect. Consider adding a caveat.

• Cleverbot:: Cleverbot (1997–2023). Originally created by Rollo Carpenter. Website: cleverbot.com (now defunct).

• OpenClaw acquisition by OpenAI: TechCrunch (Feb 15, 2026): "OpenClaw creator Peter Steinberger joins OpenAI."

• NIST AI Agent Standards Initiative: NIST (Feb 17, 2026): "Announcing the AI Agent Standards Initiative for Interoperable and Secure Innovation." https://www.nist.gov/caisi/ai-agent-standards-initiative

• OpenAI o1 as the first "thinking model": "Learning to Reason with LLMs" — announcement of o1 model family.

• Kimi K 2.5 as an agentic coding model: Moonshot AI (2025/2026). Kimi K 2.5 — a model optimized for agentic coding tasks. Release details from Moonshot AI's official announcements.

• Claude sub-agents / Cowork launch:: Anthropic (Feb 2026): Claude Cowork launch. Also: Claude Code sub-agent capabilities announced alongside Opus 4.6.

Abandoned Episode Titles

"My Grandmother Used to Read Me Windows Keys as Bedtime Stories"

"Take a Deep Breath, You're a Spreadsheet"

"Inception, but It's Math Homework"

This season finale episode features interviews with two SSU computer science capstone teams applying AI/ML to real-world problems: Sean Belingheri's edge computing project using YOLO on a Raspberry Pi to identify queen bees for hobbyist beekeepers, and "The Woods Boys" team using satellite data from Google Earth Engine with multiple ML classifiers to automate land cover classification in Sonoma County.

Credits

Cover Art by Brianna Williams

TMOM Intro Music by Danny Meza

A special thank you to these talented artists for their contributions to the show.

Links and Reference

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YOLO (You Only Look Once) Object Detection: https://docs.ultralytics.com/ (Official Ultralytics YOLO Documentation)

HOG-PCA-SVM Pipeline: https://ieeexplore.ieee.org/document/8971585/

Raspberry Pi 5: https://www.raspberrypi.com/products/raspberry-pi-5/

Honeybee Democracy (Book): https://press.princeton.edu/books/hardcover/9780691147215/honeybee-democracy

NVIDIA Jetson Nano: https://developer.nvidia.com/embedded/jetson-nano

Google Earth Engine: https://earthengine.google.com/

COCO Dataset: https://cocodataset.org/

QGIS: https://qgis.org/

Google Colab: https://colab.research.google.com/

Royal Jelly (Beekeeping): https://en.wikipedia.org/wiki/Royal_jelly

Confusion Matrix: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html

Shapefile (GIS): https://en.wikipedia.org/wiki/Shapefile