/ THE FEED /

Read what ships.

Deep, recent reads ranked by depth and freshness.

  1. We gave AI agents a mobile dev environment

    Engineering teams outgrew their local machines while running parallel AI mobile development agents, constrained by heavy builds and lack of iOS tools on Linux. To solve this, they built Remote Dev using warm pools of remote Macs connected via SSH alongside separate Linux VMs for Android virtualization, paired with agent skills and live browser streaming. This setup shifted workflow bottlenecks, increasing AI-merged pull requests from 6 percent to 61 percent.

    DEPTH ■■■■■9 min read5 days ago

    ArchitectureMobile

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  2. App stability at scale with a layered defense

    To maintain app stability amidst rapid weekly releases and AI-generated code, Grab uses a layered defense strategy covering prevention, release gates, and incident response. This pipeline includes static analysis via custom lint rules, multiple tiers of automated and manual testing, a pre-production early access build, staged rollouts, and feature flags with instant kill switches. As a result, critical defects are caught early in the development lifecycle before reaching the wider production user base.

    DEPTH ■■■■■16 min read6 days ago

    MobileReliability

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  3. How Shopify turns open models into task experts

    Shopify uses a continual learning flywheel to post-train open models, such as NVIDIA Nemotron, to serve as dedicated subagents for specific tasks like GraphQL generation. By optimizing inference and utilizing automated self-healing to generate training data from production failures, the team successfully created task experts that outperform frontier models while reducing serving costs by 90-98% and improving response times.

    DEPTH ■■■■■5 min read6 days ago

    Architecture

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  4. SBD: We Have a Filesystem at Home

    Fly.io replaced a custom JuiceFS-based filesystem for their Sprites infrastructure with the Sprites Block Device (SBD) to eliminate complex, out-of-sync metadata ledgers. SBD presents object storage as a block device over NBD, allowing standard ext4 to manage the filesystem and simplifying checkpoints and restores. The result is significantly improved reliability and a much simpler storage architecture.

    DEPTH ■■■■■6 min read5 days ago

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  5. Cross-store product clustering across millions of stores

    Shopify needed to group identical product listings from millions of different merchants under a single global identifier. To solve this at scale without comparing every possible pair, the team implemented a multi-stage pipeline using vector retrieval, a tiered scoring system of ML models, and a bounded graph clustering approach. This architecture successfully standardizes product data across the entire platform while controlling computational costs.

    DEPTH ■■■■■13 min read7 days ago

    Architecture

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  6. Building Overexposed: understanding application exposure

    The team built an automated scanner called Overexposed to track internet-facing services and verify access controls across Kubernetes and Cloud Run. By querying live cloud inventories and probing endpoints like an outsider, the scanner classifies exposure while avoiding false assumptions from static config files. As a result, the engineering organization improved accuracy and significantly reduced inconclusive findings over time.

    DEPTH ■■■■■8 min read7 days ago

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  7. Powering AI-led research through simulation

    The engineering team built a self-contained simulation platform called sim-rs to model marketplace dispatch dynamics and accelerate experimentation. By connecting automated software agents to this simulator with strict safety safeguards, the team successfully optimized strategy policies and simulator performance. This closed-loop lab environment allows agents to test ideas rapidly while ensuring that only valid, production-ready improvements are produced.

    DEPTH ■■■■■10 min read3 weeks ago

    Machine Learning

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  8. The making of the Yelp Assistant UI

    Yelp developed a chat interface called Yelp Assistant using a hybrid approach combining native UI components and a server-driven UI framework called CHAOS. This architecture allows the team to dynamically update the chat experience without waiting for app store releases. The system separates views from data using reactive datasets and MessageEvents to manage the chat loop, state, and user interactions.

    DEPTH ■■■■■12 min read4 weeks ago

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  9. ShopGym: Realistic, reproducible sandboxes for shopping agents

    Training and evaluating shopping agents is difficult due to changing live store environments, bot detection, and limited hand-built test stores. The team developed ShopGym, a framework that converts live storefronts into self-contained, anonymized sandbox shops and generates benchmark tasks. Evaluation showed that synthetic shops preserve key structural properties and behavioral alignment with live stores, giving agents a reliable environment to practice and improve.

    DEPTH ■■■■■6 min read3 weeks ago

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  10. Smarter personalization: How property data helps us understand user price sensitivity

    Existing user personalization systems fail to systematically account for residential property values, leading to one-dimensional profiles and regional blind spots. A conceptual four-step method is proposed that combines public property data, large language models, building-level price estimation, and hierarchical clustering to classify user affluence. The approach remains unimplemented, with future viability depending on technical validation, data quality, and privacy approvals.

    DEPTH ■■■■■4 min read3 weeks ago

    Data EngineeringMachine Learning

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  11. Helix: The internal tool powering our Shopify app's native migration

    Shopify is migrating its mobile apps from React Native to native Swift and Kotlin using an internal LLM-powered tool called Helix. Helix breaks migrations down into small checkpoints and enforces strict quality gates covering behavior, UI equivalence, architectural compliance, and human review. This iterative loop allows the team to reliably generate and migrate hundreds of screens to native code.

    DEPTH ■■■■■8 min read5 weeks ago

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  12. Automated merchant review summary system with integrated feedback

    To help users navigate large volumes of unstructured merchant reviews, the engineering team built an automated review summary system using representative sampling, large language models, and automated quality control checks. The pipeline generates overall and topic-based summaries while incorporating maintenance triggers based on new review counts and consumer feedback. This approach reduces the text consumers need to scan while ensuring claims remain grounded in source data.

    DEPTH ■■■■■5 min read4 weeks ago

    Machine Learning

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  13. Migrating Shop app from React Native to native

    Shop migrated its mobile app from React Native to native Swift and Kotlin by leveraging AI coding agents to accelerate the rewrite. The migration resulted in improved startup times, higher session stability, smaller Android app sizes, and smoother rendering performance.

    DEPTH ■■■■■5 min read6 weeks ago

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  14. Native is now the future of mobile at Shopify

    Shopify is migrating its mobile applications from React Native back to native Swift and Kotlin. The team determined that the emergence of advanced coding agents significantly reduces the cost of maintaining platform parity, making native development the superior choice again. To execute this migration efficiently, Shopify developed a system called Helix and a CLI-driven, agent-addressable architecture to enable fast, autonomous testing.

    DEPTH ■■■■■10 min read6 weeks ago

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  15. GrabMart data for faster disease outbreak detection

    The organization proposes a conceptual data pipeline to analyze aggregated GrabMart transaction data for symptom-related product demand, aiming to supplement clinical surveillance for disease outbreaks. The design relies on data aggregation, statistical baseline comparisons, and privacy safeguards like differential privacy without involving individual health inference. The system remains in an early planning phase requiring rigorous validation, privacy review, and public health oversight.

    DEPTH ■■■■■5 min read2 weeks ago

    Data EngineeringMachine Learning

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  16. How River takes security work from a fix to merge

    Shopify developed an AI agent named River to automate security vulnerability remediation from dependency upgrades to pull request merges. The agent validates live states, replays and repairs fixes on current repository heads, and escalates context-dependent decisions to human engineers. This workflow reduced the open issue backlog by about 70 percent and increased freshness-gated security merges significantly.

    DEPTH ■■■■■9 min read7 weeks ago

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