May 2026 monthly summary: Delivered major feature integration and notebook modernization across two repositories, driving expanded capabilities and improved user clarity while maintaining stability. OpenCode Go provider added to marimo as a built-in AI model provider with a low-cost subscription path. PyMC overview notebook updated to version 6 to reflect recent metadata and execution changes. No critical bugs fixed this period; focus was on feature delivery and documentation to support scale. Business impact includes broader model options for users, potential monetization through additional providers, and improved onboarding with better documentation and API clarity.

April 2026 monthly summary: Migration of model_table into the core PyMC library, enabling richer model representations and improved user usability. Consolidated model-table functionality by moving from pymc-extras to main PyMC, delivering a richer summary of variables, expressions, and dimensions. No major bugs fixed this month. Ongoing focus on API stability and maintainability.

March 2026 — Delivered targeted improvements to the Marimo progress bar in pymc, enhancing observability and reliability for long-running sampling tasks. The changes refined display of statistics, corrected naming conventions, and improved handling of speed and sampling metrics. Expanded test coverage ensures robustness of progress-tracking features across scenarios, reducing debugging time for users and contributors.

January 2026 monthly summary for pymc-devs/pymc focusing on user-facing sampling enhancements, progress-tracking improvements, and MCMC performance/stability fixes that directly impact reliability, UX, and throughput.

December 2025 monthly summary focused on delivering high-impact features in probabilistic modeling libraries (pymc) and tensor utilities (pytensor), elevating business value through scalable GP capabilities, improved tensor handling, and strengthened test coverage. Work emphasized numerical stability, correctness, and maintainability to support reliable modeling for production workflows.

Month: 2025-05 — Repository hygiene improvements for AllenDowney/pymc. Delivered cleanup of obsolete documentation and a targeted update to .gitignore to exclude transient and configuration directories (.jupyter, .claude). These changes reduce repo noise, lower risk of accidentally tracking unnecessary files, and improve maintainability, onboarding, and readiness for future releases.

February 2025: Delivered PyMC probability distributions documentation update in AllenDowney/pymc, clarifying logp and random usage, introducing draw as the standard sampling function, and updating CustomDist to use the dist parameter for logp functions. This improves API clarity, onboarding, and reduces support overhead.