May 2026 summary for ecmwf/anemoi-core focusing on 3D graph viewer usability and correctness improvements. Delivered a UI enhancement to toggle node scaling by degree and refined node selection highlighting to reflect only edges that are currently visible, addressing a long-standing confusion in dense graphs. Fixed a bug where node selection highlighted neighbours across all edge sets; now selection respects the edge-set visibility in the UI. Changes were implemented in interactive_3d.html.jinja and related graph rendering logic, with changes taking effect immediately without a full reload. Notable commits include a feature to add the degree-scaling toggle and a fix to the node selection logic (co-authored by Francesco Zanetta). Business value centers on faster, more accurate graph exploration and reduced time to insight for data scientists and engineers.

April 2026 monthly summary for ecmwf/anemoi-core: Delivered a unified Hydra-based optimization config pipeline that centralizes learning rate, schedulers, and optimizer presets under a single training.optimization group. Implemented Hydra-based instantiation for both optimizer and scheduler, enabling runtime switching of optimizers and schedulers via config changes without code edits. Introduced ready-made presets (AdamW, AdemAMix, Zero) and a cosine scheduler preset, significantly reducing configuration duplication across presets and improving maintainability. Refactored training paths to support effective_lr and generalized scheduling for both timm and PyTorch schedulers, consolidating logic that formerly lived in private helpers. Updated tests to exercise the public configure_optimizers API and revised documentation to reflect the new optimization config structure, presets, and usage. Documentation and tests migrated to public APIs, enhancing reliability and onboarding. No explicit user-facing bug fixes this month; however, the centralization of optimization config reduces the potential for misconfigurations and defects related to duplicated inline optimizer blocks, improving stability and reproducibility.

November 2025 monthly summary for ecmwf/anemoi-core: Delivered a major configuration-schema overhaul to improve reliability and clarity of filesystem/layout definitions. Introduced a new top-level 'system' schema grouping subschemas for hardware, storage, and files; clarified roles of paths and files; renamed/realigned storage definitions to better reflect directory structures for inputs, outputs, and logs; isolated path concatenation logic to a single place, reducing brittleness across the codebase. This change enhances deploy-time stability, onboarding, and future extensibility, with alignment to issue #513 and supporting docs across training, graphs, and models. The work was executed via a coordinated, multi-author PR (commit sha: da02fe7bac8c6a69f1ac967f3b243d716abf5910).

October 2025 monthly summary for ecmwf/anemoi-core: Key feature delivered and major bug fixes with clear business value and traceable commits. The updates improved user experience, interoperability, and plotting reliability across training and visualization workflows.

July 2025 monthly summary for ecmwf/anemoi-utils: Completed a targeted refactor to robustly handle nested DotDict structures, ensuring recursive casting of nested dictionaries during initialization and updates and preserving dot-access. This addressed a root cause where nested dicts/lists of dicts were inadvertently converted to plain dicts, breaking access patterns. The change includes applying casting logic in DotDict.__getitem__ and __setitem__ and is backed by added tests to verify nested behavior. The work improves configuration reliability and reduces runtime errors in downstream components that rely on dot-access to deeply nested settings.

June 2025 Monthly Summary Overview: Focused on simplifying data ingestion workflows and strengthening graph-related reliability to improve user feedback and reduce support friction. Delivered targeted code improvements across two repositories, with an emphasis on readability, maintainability, and explicit failure modes. Key highlights include a simplification in dataset construction and an upgrade to error handling for graph creation, setting a solid foundation for faster feature delivery and more predictable behavior in production.