## OCDevel AI Podcast — Linear Regression (Phase 1: Your First Real Model) This episode opens with a quick news brief, then settles into the anchor tutorial: **linear regression**, the first real model in the course. Intuition first, then a hands-on scikit-learn workflow. ### News brief (June 22–28, 2026) - **OpenAI GPT-5.6 "Sol / Terra / Luna"** — limited release June 26. A three-tier family (flagship / balanced / fast-cheap) with new "max" and "ultra" reasoning modes; reported pricing and gains. [9to5Mac](https://9to5mac.com/2026/06/26/openai-upgrading-chatgpt-and-codex-with-new-gpt-5-6-models-in-limited-release/) - **Anthropic Claude Tag** — June 23. A "virtual teammate" you @-mention in Slack; runs on Opus 4.8. [Anthropic](https://www.anthropic.com/news/introducing-claude-tag) · [Fortune](https://fortune.com/2026/06/23/anthropic-claude-tag-virtual-employee-tool-slack/) - **SpaceX × Reflection AI** — June 22. ~$6.3B compute deal for Nvidia GB300s at Colossus 2 near Memphis. [TechCrunch](https://techcrunch.com/2026/06/22/spacex-inks-compute-deal-with-reflection-ai-an-open-source-ai-lab/) · [CNBC](https://www.cnbc.com/2026/06/22/spacex-ai-colossus-data-center-reflection.html) - **OpenAI "Daybreak" / GPT-5.5-Cyber** — June 22. Defensive-security model, Codex Security plugin, "Patch the Planet." [Axios](https://www.axios.com/2026/06/22/openai-rolls-out-more-capable-version-of-cyber-model) · [Infosecurity](https://www.infosecurity-magazine.com/news/openai-daybreak-gpt-5-5-cyber/) - **DeepMind talent moves** — Jumper to Anthropic, Shazeer to OpenAI. [TechCrunch](https://techcrunch.com/2026/06/20/nobel-laureate-john-jumper-is-leaving-deepmind-for-rival-anthropic/) · [Fortune](https://fortune.com/2026/06/23/google-deepmind-ai-researcher-departures-raise-doubts-about-ability-to-win-the-ai-race-shazeer-jumper-eye-on-ai/) - **ByteDance Seedance 2.5** — June 23, reportedly 30-second single-pass video. ### Tutorial: Linear Regression - **Regression vs classification**; simple vs multiple regression; the hypothesis `y = w·x + b` and why that dot product is the atom of everything downstream. - **The scikit-learn one-liner**: fit / predict / score on [`LinearRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) (stable 1.9.0). Worked on `fetch_california_housing` (not the removed Boston set), honest test R² ≈ 0.6. - **Reading coefficients** (ceteris paribus), the units/scaling caveat, and why magnitude ≠ importance. - **What fitting does**: residuals, OLS, MSE, and *why squared* (penalty, differentiability, convexity, the Gaussian/MLE link). Closed-form normal equation vs gradient descent. - **Evaluation**: R², MSE, RMSE, MAE — computed on held-out test data. - **Assumptions** (the classic five) and the master diagnostic, the residual plot. - **Pitfalls**: correlation ≠ causation, extrapolation, outliers, multicollinearity, and [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet) — always plot your data. - **History**: [least squares](https://en.wikipedia.org/wiki/Least_squares) (Legendre 1805, Gauss 1809) and Galton's "[regression toward the mean](https://en.wikipedia.org/wiki/Regression_toward_the_mean)." - **Forward**: logistic regression → the single neuron → neural networks.