March 2026: Delivered scalable data processing and experimental architectures across metatensor/metatrain and lab-cosmo/atomistic-cookbook. Key outcomes: 1) Memory-safe batch-to-disk streaming for ASEWriter/MetatensorWriter enabling large datasets (100k+ structures) without memory blowups and with interruption safety; included fix to gradient sample indices to prevent runtime errors. 2) PhACE architecture introduced: SO(3)-equivariant message-passing model with tensor products, with updates across model, trainer, modules, tests, docs, and CI. 3) Dependency updates and CI improvements in Eon-Pet-Neb for metatrain compatibility, including ignoring untracked CI artifacts and aligning pins to modern releases. 4) Regression tests and cross-repo coordination established for stability with newer dependency versions. Overall impact: boosted scalability of data processing, reliability of training pipelines, and readiness for broader adoption of PhACE. Technologies/skills demonstrated: Python, PyTorch, tensor products, SO(3) equivariance, memory-efficient streaming I/O, regression testing, CI automation, dependency management, cross-repo collaboration. Business value: enables large-scale experiments, reduces memory-related outages, accelerates model iteration and deployment readiness.

February 2026 monthly summary focusing on developer-facing deliverables across metatensor/metatensor and lab-cosmo/atomistic-cookbook. Highlights include the introduction of a Developer FAQ and Testing Guidance, a stability improvement for Valgrind in test environments, and an NEB visualization plotting enhancement that improves surface visuals and performance.

January 2026 monthly summary focusing on compatibility, packaging reliability, and maintainability across two repositories. Delivered features and fixes improved runtime compatibility, packaging stability, and upgrade readiness, delivering clear business value for users and maintainers. Key outcomes: - PyTorch v2.10 compatibility and version-reference updates across metatensor/metatensor, culminating in the release of metatensor-torch v0.8.4. - EON package recipe enhancements in lab-cosmo/atomistic-cookbook, integrating UPET and OCINEB with dependency updates and targeted code cleanup to improve maintainability and future extensibility. - Gromacs environment compatibility pin established to < 2026 to ensure stability and reproducibility in CI/environment setups. Overall impact: - Reduced upgrade friction for PyTorch users and smoother adoption of newer ML pipelines. - Improved packaging hygiene and maintainability across multiple repos, enabling faster iteration and safer dependency upgrades. - Greater environment stability and reproducibility for end users and downstream projects. Technologies/skills demonstrated: - Python packaging and dependency management - Cross-repo collaboration and release engineering - Dependency pinning and environment compatibility strategies - Code cleanup and maintainability practices

December 2025 monthly summary: Highlights across lab-cosmo/atomistic-cookbook and metatensor/metatensor. Delivered NEB workflow enhancements with eon-pet-neb integration, CLI improvements, and example scripts. Stabilized the codebase by temporarily disabling metatomic-plumed to address dependency issues. Updated external documentation URLs to improve accuracy and reliability. Overall, these efforts accelerated transition-state workflows, improved developer experience, and strengthened documentation quality across projects.

November 2025 monthly summary for metatensor/metatensor. Delivered PyTorch 2.9 compatibility across the project, released metatensor-torch v0.8.2, and optimized CI workflows with caching and uv-based Python environment management. These efforts modernize stack compatibility, accelerate feedback loops, and provide a clean release cycle with updated versioning and changelog.

Month: 2025-10 | Metatensor/metatensor: Codebase modernization focused on C++17 upgrade and header refactor, with build and dependency updates to support modern standards. Core functionality preserved while internal structure was improved for maintainability and future development.