AI - 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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AI Machine Learning Open Source Python+3

metaApr 8, 20262 min readCurated summary

Trust But Canary: Configuration Safety at Scale

As AI accelerates software development, stronger safeguards are needed to prevent faster mistakes from becoming larger incidents. Meta’s Configurations team uses canarying, progressive rollouts, health checks, and monitoring to detect regressions early. Data and AI also help reduce alert noise and speed up identifying the changes responsible for failures. ## Safe Configuration Rollouts - Meta deploys configuration changes gradually rather than releasing them everywhere at once. - Canarying exposes changes to a small subset of systems or users first. - Progressive rollouts expand the deployment only when monitoring indicates that the change is healthy. - These practices limit the impact of faulty configurations and provide opportunities to stop or reverse a rollout. ## Monitoring and Health Checks - Automated health checks and operational signals help identify regressions soon after deployment. - Monitoring provides evidence for deciding whether a rollout should continue, pause, or be rolled back. - Early detection is especially important at Meta’s scale, where a small configuration error can affect many systems. ## Learning from Incidents - Incident reviews focus on improving tools, processes, and safeguards rather than assigning blame to individuals. - The goal is to make future failures less likely and reduce their potential impact. - These reviews turn operational problems into improvements across the configuration management system. ## AI-Assisted Operations - Data-driven techniques reduce alert noise so engineers can focus on meaningful signals. - AI and machine learning help speed up bisection, narrowing down which change introduced a problem. - Faster diagnosis can shorten recovery times and make progressive deployment practices more effective. The episode recommends combining gradual releases, strong observability, blameless incident reviews, and AI-assisted analysis to keep increasingly rapid development safe at scale.

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AI Machine Learning Incident Management Canary Deployment+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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AI Open Source Bayesian Optimization Adaptive Experimentation+4

metaJan 14, 2026Original article

Adapting the Facebook Reels RecSys AI Model Based on User Feedback (opens in new tab)

Meta has enhanced the Facebook Reels recommendation engine by shifting focus from traditional engagement signals, like watch time and likes, to direct user feedback. By implementing the User True Interest Survey (UTIS) model, the system now prioritizes content that aligns with genuine user preferences rather than just short-term interactions. This shift has resulted in significant improvements in recommendation relevance, high-quality content delivery, and long-term user retention. **Limitations of Engagement-Based Metrics** * Traditional signals like "likes" and "watch time" are often noisy and may not reflect a user’s actual long-term interests. * Models optimized solely for engagement tend to favor short-term value over the long-term utility of the product. * Internal research found that previous heuristic-based interest models only achieved 48.3% precision in identifying what users truly care about. * Effective interest matching requires understanding nuanced factors such as production style, mood, audio, and motivation, which implicit signals often miss. **The User True Interest Survey (UTIS) Model** * Meta collects direct feedback via randomized, single-question surveys asking users to rate video interest on a 1–5 scale. * The raw survey data is binarized to denoise responses and weighted to correct for sampling and nonresponse bias. * The UTIS model functions as a lightweight "alignment model layer" built on top of the main multi-task ranking system. * The architecture uses existing model predictions as input features, supplemented by engineered features that capture content attributes and user behavior. **Integration into the Ranking Funnel** * **Late Stage Ranking (LSR):** The UTIS score is used as an additional input feature in the final value formula, allowing the system to boost high-interest videos and demote low-interest ones. * **Early Stage Ranking (Retrieval):** The model aggregates survey data to reconstruct user interest profiles, helping the system source more relevant candidates during the initial retrieval phase. * **Knowledge Distillation:** Large sequence-based retrieval models are aligned using UTIS predictions as labels through distillation objectives. **Performance and Impact** * The deployment of UTIS has led to a measurable increase in the delivery of niche, high-quality content. * Generic, popularity-based recommendations that often lack depth have been reduced. * Meta observed robust improvements across core metrics, including higher follow rates, more shares, and increased user retention. * The system now offers better interpretability, allowing engineers to understand which specific factors contribute to a user’s sense of "interest match." To continue improving the Reels ecosystem, Meta is focusing on doubling down on personalization by tackling challenges related to sparse data and sampling bias while exploring more advanced AI architectures to further diversify recommendations.

AI Machine Learning Recommendation Systems Information Retrieval+5

metaDec 19, 2025Original article

DrP: Meta's Root Cause Analysis Platform at Scale (opens in new tab)

DrP is Meta’s programmatic root cause analysis (RCA) platform designed to automate incident investigations and reduce the burden of manual on-call tasks. By codifying investigation playbooks into executable "analyzers," the platform significantly lowers the mean time to resolve (MTTR) by 20% to 80% for over 300 teams. This systematic approach replaces outdated manual scripts with a scalable backend that executes 50,000 automated analyses daily, providing immediate context when alerts fire. ## Architecture and Core Components * **Expressive SDK:** Provides a framework for engineers to codify investigation workflows into "analyzers," utilizing a rich library of helper functions and machine learning algorithms. * **Built-in Analysis Tools:** The platform includes native support for anomaly detection, event isolation, time-series correlation, and dimension analysis to identify specific problem areas. * **Scalable Backend:** A multi-tenant execution environment manages a worker pool that handles thousands of requests securely and asynchronously. * **Workflow Integration:** DrP is integrated directly into Meta’s internal alerting and incident management systems, allowing for automatic triggering without human intervention. ## Authoring and Verification Workflow * **Template Bootstrapping:** Engineers use the SDK to generate boilerplate code that captures required input parameters and context in a type-safe manner. * **Analyzer Chaining:** The system allows for seamless dependency analysis by passing context between different analyzers, enabling investigations to span multiple interconnected services. * **Automated Backtesting:** Before deployment, analyzers undergo automated backtesting integrated into the code review process to ensure accuracy and performance. * **Decision Tree Logic:** Investigation steps are modeled as decision trees within the code, allowing the analyzer to follow different paths based on the data it retrieves. ## Execution and Post-Processing * **Trigger-based Analysis:** When an alert is activated, the backend automatically queues the relevant analyzer, ensuring findings are available as soon as an engineer begins triaging. * **Automated Mitigation:** A post-processing system can take direct action based on investigation results, such as creating tasks or submitting pull requests to resolve identified issues. * **DrP Insights:** This system periodically reviews historical analysis outputs to identify and rank the top causes of alerts, helping teams prioritize long-term reliability fixes. * **Alert Annotation:** Results are presented in both human-readable text and machine-readable formats, directly annotating the incident logs for the on-call responder. ## Practical Conclusion Organizations managing large-scale distributed systems should transition from static markdown playbooks to executable investigation code. By implementing a programmatic RCA framework like DrP, teams can scale their troubleshooting expertise and significantly reduce "on-call fatigue" by automating the repetitive triage steps that typically consume the first hour of an incident.

AI Machine Learning Observability Automation+4

metaDec 15, 2025Original article

How AI Is Transforming the Adoption of Secure-by-Default Mobile Frameworks (opens in new tab)

Meta utilizes secure-by-default frameworks to wrap potentially unsafe operating system and third-party functions, ensuring security is integrated into the development process without sacrificing developer velocity. By leveraging generative AI and automation, the company scales the adoption of these frameworks across its massive codebase, effectively mitigating risks such as Android intent hijacking. This approach balances high-level security enforcement with the practical need for friction-free developer experiences. ## Design Principles for Secure-by-Default Frameworks To ensure high adoption and long-term viability, Meta follows specific architectural guidelines when building security wrappers: * **API Mirroring:** Secure framework APIs are designed to closely resemble the existing native APIs they replace (e.g., mirroring the Android Context API). This reduces the cognitive burden on developers and simplifies the use of automated tools for code conversion. * **Reliance on Public Interfaces:** Frameworks are built exclusively on public and stable APIs. Avoiding private or undocumented OS interfaces prevents maintenance "fire drills" and ensures the frameworks remain functional across various OS updates. * **Modularity and Reach:** Rather than creating a single monolithic tool, Meta develops small, modular libraries that target specific security issues while remaining usable across all apps and platform versions. * **Friction Reduction:** Frameworks must avoid introducing excessive complexity or noticeable performance overhead in terms of CPU and RAM, as high friction often leads developers to bypass security measures entirely. ## SecureLinkLauncher: Preventing Android Intent Hijacking SecureLinkLauncher (SLL) is a primary example of a secure-by-default framework designed to stop sensitive data from leaking via the Android intent system. * **Wrapped Execution:** SLL wraps native Android methods such as `startActivity()` and `startActivityForResult()`. Instead of calling `context.startActivity(intent)`, developers use `SecureLinkLauncher.launchInternalActivity(intent, context)`. * **Scope Verification:** The framework enforces scope verification before delegating to the native API. This ensures that intents are directed to intended "family" apps rather than being intercepted by malicious third-party applications. * **Mitigating Implicit Intents:** SLL addresses the risks of untargeted intents, which can be received by any app with a matching intent-filter. By enforcing a developer-specified scope, SLL ensures that data like `SECRET_INFO` is only accessible to authorized packages. ## Scaling Adoption through AI and Automation The transition from legacy, insecure patterns to secure frameworks is managed through a combination of automated tooling and artificial intelligence. * **Automated Migration:** Generative AI identifies insecure usage patterns across Meta’s vast codebase and suggests—or automatically applies—the appropriate secure framework replacements. * **Continuous Monitoring:** Automation tools continuously scan the codebase to ensure compliance with secure-by-default standards, preventing the reintroduction of vulnerable code. * **Scaling Consistency:** By reducing the manual effort required for refactoring, AI enables consistent security enforcement across different teams and applications without slowing down the shipping cycle. For organizations managing large-scale mobile codebases, the recommended approach is to build thin, developer-friendly wrappers around risky platform APIs and utilize automated refactoring tools to drive adoption. This ensures that security becomes an invisible, default component of the development lifecycle rather than a manual checklist.

AI Generative AI Android Automation+5

metaNov 18, 2025Original article

Efficient Optimization With Ax, an Open Platform for Adaptive Experimentation (opens in new tab)

Meta has released Ax 1.0, an open-source platform designed to automate and optimize complex, resource-intensive experimentation through machine learning. By utilizing Bayesian optimization, the platform helps researchers navigate vast configuration spaces to improve AI models, infrastructure, and hardware design efficiently. The release aims to bridge the gap between sophisticated mathematical theory and the practical requirements of production-scale engineering. ## Real-World Experimentation and Utility * Ax is used extensively at Meta for diverse tasks, including tuning hyperparameter configurations, discovering optimal data mixtures for Generative AI, and optimizing compiler flags. * The platform is built to handle the logistical "overhead" of experimentation, such as managing experiment states, automating orchestration, and providing diagnostic tools. * It supports multi-objective optimization, allowing users to balance competing metrics and enforce "guardrail" constraints rather than just maximizing a single value. * Applications extend beyond software to physical engineering, such as optimizing design parameters for AR/VR hardware. ## System Insight and Analysis * Beyond finding optimal points, Ax serves as a diagnostic tool to help researchers understand the underlying behavior of their systems. * It includes built-in visualizations for Pareto frontiers, which illustrate the trade-offs between different metrics. * Sensitivity analysis tools identify which specific input parameters have the greatest impact on the final results. * The platform provides automated plots and tables to track optimization progress and visualize the effect of parameters across the entire input space. ## Technical Methodology and Architecture * Ax utilizes Bayesian optimization, an iterative approach that balances "exploration" (sampling new areas) with "exploitation" (refining known good areas). * The platform relies on **BoTorch** for its underlying Bayesian components and typically employs **Gaussian processes (GP)** as surrogate models. * GPs are preferred because they can make accurate predictions and quantify uncertainty even when provided with very few data points. * The system uses an **Expected Improvement (EI)** acquisition function to calculate the potential value of new configurations compared to the current best-known result. * This surrogate-based approach is designed to scale to high-dimensional settings involving hundreds of tunable parameters where traditional search methods are too costly. To begin implementing these methods, developers can install the platform via `pip install ax-platform`. Ax 1.0 provides a robust framework for moving cutting-edge optimization research directly into production environments.

AI Machine Learning Bayesian Optimization Adaptive Experimentation+5