Open Source - Meta | Techlist.io

metaJun 30, 20262 min readCurated summary

10 Years of Meta’s Commitment to Python

Meta marks its 10th consecutive year sponsoring the Python Software Foundation (PSF), emphasizing that Python is central to its infrastructure, products, and AI work. The company views sponsorship as both a responsibility to the open-source community and a strategic investment in the long-term health, security, and innovation of the technology it relies on. ## Python’s Role at Meta - Python is Meta’s most widely used programming language. - It supports infrastructure for products including Instagram and Threads, as well as AI research and data-driven initiatives. - Meta engineers contribute directly to Python’s development, including core maintenance and Python Enhancement Proposals. - Meta’s open-source contributions include: - PyTorch, originally developed at Meta before becoming an independent foundation. - Pyrefly, a fast Python type checker and language server. - Meta expects Python to remain important as it expands AI capabilities and scales its infrastructure. ## Why Meta Supports the PSF - Open-source adoption creates a shared responsibility to maintain a healthy, secure, and sustainable ecosystem. - PSF funding supports the Developer-in-Residence program, enabling full-time developers to work on Python improvements that might otherwise be neglected or left to volunteers. - Sponsorship helps strengthen PyPI, including critical security improvements that protect package distribution and consumption. - Funding also supports education and community development through: - PyCon US workshops, summits, and discounted or free passes. - Fundraising and support for groups such as PyLadies. - Meta considers these efforts an investment in the tools, infrastructure, and people behind its own technology stack. ## Ways to Support the Python Software Foundation - Individuals can make one-time donations or become PSF members. - Membership may include voting rights and can be supported through financial contributions or volunteer time. - Organizations can become annual sponsors at different contribution levels. - Sponsorship offers public recognition, community engagement opportunities, event participation, and—in higher tiers—greater visibility and invitations to special initiatives. Meta concludes by thanking Python’s maintainers, contributors, educators, and advocates, while encouraging other individuals and organizations to help sustain the language through PSF donations, membership, or sponsorship.

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Open Source AI Machine Learning Python+3

metaMar 30, 20263 min readCurated summary

AI for American-Produced Cement and Concrete

Meta is expanding its use of AI to help concrete producers create stronger, more sustainable, and more domestically sourced mixes. Its new open-source model, Bayesian Optimization for Concrete (BOxCrete), uses existing performance data and lab results to identify promising formulations faster than traditional trial-and-error methods. Early projects show that AI can improve curing speed and reduce cracking while supporting greater use of U.S.-made cement and materials. ## The Case for AI-Designed Concrete - The U.S. produces about 400 million cubic yards of concrete annually. - Although ready-mix concrete is generally produced domestically, roughly 20–25% of cement consumption is supplied by imports. - Concrete mix designers must balance: - Structural strength - Curing speed - Workability and slump - Cost - Sustainability - Traditional design depends on laboratory experimentation, engineer judgment, and historical knowledge, making it slow and expensive to adapt. - Different cements have different chemistries, so a formulation that works with one cement may fail with another. ## Supporting Domestic Cement Production - Greater use of U.S.-made cement could strengthen domestic manufacturing, jobs, and investment. - Reshoring and foreign direct investment have returned more than 1.1 million jobs to the U.S. since 2020. - The cement and concrete sector contributes over $130 billion annually and supports approximately 600,000 jobs. - AI can help producers reformulate mixes around locally available materials without compromising performance. ## BOxCrete and Open Data - Meta is releasing BOxCrete on GitHub as an open-source model for concrete mix design. - Compared with earlier models, BOxCrete is more robust to noisy data and can predict concrete slump, an important measure of workability. - Meta is also publishing the foundational dataset used to develop the concrete mix for its Rosemount, Minnesota, data center. - The associated research paper describes the model, data, and methodology. ## Results in Minnesota - Meta, Amrize, Mortenson, and the University of Illinois used BOxCrete to design a mix for a data center foundation. - The mix used domestically sourced materials. - It reached full structural strength 43% faster than the original formulation. - It reduced cracking risk by nearly 10%. - After meeting structural requirements, the mix was approved for use in additional parts of the data center. ## Industry Partnerships in Illinois and Pennsylvania - Meta is working with Amrize and the University of Illinois to apply AI to industrial-scale concrete production. - Amrize operates 18 cement plants, 141 cement terminals, and 269 ready-mix sites across North America. - Amrize has introduced a “Made in America” cement label and announced nearly $1 billion in planned 2026 investments, partly aimed at increasing domestic cement production. - Pennsylvania-based Quadrel integrated Meta’s open-source framework into its ready-mix software. - Quadrel uses the technology for data preprocessing, batch and test normalization, feature engineering, customer-specific model training, and quality-control workflows. - Its models improve continuously as new field-test results are incorporated. ## Adaptive Experimentation - Meta’s Adaptive Experimentation platform uses Bayesian optimization to search the large space of possible concrete formulations. - The system: - Learns from historical mix designs, laboratory results, and performance metrics. - Proposes candidate mixes likely to satisfy target specifications. - Compares the performance of domestic and imported materials. - Applies technical and ingredient constraints before testing. - Updates its predictions after each new experiment. Meta’s work suggests that open-source AI can make concrete development faster, more data-driven, and better suited to domestic materials. Producers can use BOxCrete and adaptive experimentation to reduce laboratory costs, improve performance, and support more sustainable and resilient U.S. cement and concrete supply chains.

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Open Source AI Bayesian Optimization Adaptive Experimentation+4

metaMar 2, 20262 min readCurated summary

Investing in Infrastructure: Meta’s Renewed Commitment to jemalloc

Meta is renewing its commitment to jemalloc, recognizing its long-term role in delivering reliable and efficient infrastructure alongside the Linux kernel and compilers. After acknowledging that short-term decisions created technical debt and slowed development, Meta has unarchived the original repository and begun rebuilding a long-term roadmap. The effort will focus on modernization, reduced maintenance, hardware adaptation, and closer collaboration with the open-source community. ## Why jemalloc Matters - jemalloc is a high-performance memory allocator used as a foundational component of Meta’s software stack. - It has adapted to changing hardware and workloads over time. - Its impact is comparable to other core infrastructure components such as the Linux kernel and compilers. ## Reflecting on Technical Debt - Meta says recent development gradually moved away from the rigorous engineering principles needed for foundational software. - Some changes provided short-term benefits but introduced technical debt. - That debt increased maintenance burdens and slowed future progress. - Community feedback, including discussions with jemalloc founder Jason Evans, prompted Meta to reassess its stewardship. ## Renewed Development Priorities - **Technical debt reduction:** Clean up, refactor, and improve the codebase to make jemalloc more efficient, reliable, and maintainable. - **Huge-page allocation:** Continue improving the hugepage allocator (HPA) and its use of transparent hugepages (THP) to improve CPU efficiency. - **Memory efficiency:** Optimize memory packing, caching, and purging mechanisms. - **AArch64 support:** Improve out-of-the-box performance on ARM64 systems. - **Hardware and workload adaptation:** Continue evolving jemalloc for current and emerging platforms. ## Open-Source Collaboration - The original jemalloc repository has been unarchived. - Meta intends to work with the open-source community on the project’s future. - The company acknowledges that renewed trust must come through measurable improvements and sustained development. - Community members are invited to provide feedback, contributions, and collaboration. Meta’s practical next step is to demonstrate its renewed commitment through code cleanup, performance improvements, and transparent collaboration. The project’s long-term health will depend on consistent execution rather than statements alone.

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Open Source Performance Optimization Linux Memory Allocation+3