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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 read3 days ago

    ArchitectureMobile

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  2. Why Shopify Migrated From React Native

    Shopify migrated its mobile applications from React Native to native Swift and Kotlin because AI coding agents made the cost of duplicating features across platforms negligible. Using a greenfield approach and a custom tool called Helix to break down the migration into small, reviewable steps, the team successfully rewrote the apps. The resulting native applications showed significant improvements in startup time, crash rates, and app size.

    DEPTH ■■■■■11 min read5 days ago

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  3. Beyond container image signing: How we enforce software supply chain policies at Datadog

    Container image signing alone cannot detect upstream security compromises occurring before the image is signed. To address this, the engineering team built an internal system called Turnstile that generates cryptographically signed attestations across an artifact's life cycle and evaluates them against environment-specific policies at deployment time. This approach enables the organization to enforce strict security and compliance requirements at scale without disrupting developer workflows.

    DEPTH ■■■■■15 min read5 days ago

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  4. 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 read4 days ago

    Architecture

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  5. How Uber Built a Genie to Answer Questions Correctly

    Uber built an internal AI assistant called Genie to answer engineering support questions directly within Slack. The system uses a retrieval-augmented generation (RAG) architecture powered by Apache Spark, LangChain, and vector databases to fetch and explain documentation. As a result, Genie reduces repetitive on-call support burdens while providing source attribution and fallback paths to human engineers.

    DEPTH ■■■■■13 min read6 days ago

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  6. 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 read5 days ago

    Architecture

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  7. 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 read5 days ago

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  8. LLM Prompt Caching Versus Semantic Caching

    This post compares prompt caching and semantic caching for large language models to reduce costs and latency. Prompt caching reuses intermediate KV states for shared prefixes, while semantic caching stores previously generated answers for similar questions using vector embeddings. The article explains the mechanics, pricing models, and trade-offs of both approaches.

    DEPTH ■■■■■16 min read4 days ago

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  9. How we extended Apache DataFusion to execute one query across many machines

    Datadog needed to execute interactive queries across large datasets that exceeded single-node limits while maintaining low latency. To solve this, the team built Distributed DataFusion, an open-source framework extending Apache DataFusion across multiple machines using zero-copy streaming via Apache Arrow and Substrait. As a result, heavy queries complete up to 10 times faster without imposing distribution overhead on lightweight queries.

    DEPTH ■■■■■12 min read3 weeks ago

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  10. How Netflix Taught an LLM to Recommend Movies So That You Keep Watching

    Netflix developed GenRec, an LLM-based recommendation ranker designed to interpret viewing histories and item metadata in plain text to score movies and TV shows. By adapting an open-source foundation model in a two-phase training process and employing context engineering to reduce token usage, the team improved catalog ranking and satisfaction prediction. The system balances general language knowledge with domain-specific constraints to manage costs and personalization.

    DEPTH ■■■■■15 min read2 weeks ago

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  11. How we built an async-aware Python profiler

    Traditional Python profilers treat asyncio tasks as independent stacks, obscuring logical execution paths and relationships. The Datadog team built an async-aware profiling model that tracks task dependencies to reconstruct unified execution paths ("stacked stacks") while reducing profiling overhead by over 60 percent. This approach provides clearer performance visibility for asynchronous applications without excessive production latency.

    DEPTH ■■■■■19 min read3 weeks ago

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  12. Why LLMs Agree With You Even When You’re Wrong

    Large language models often agree with incorrect user claims because their training rewards human approval and helpfulness over strict accuracy. This behavior, known as sycophancy, is driven by reinforcement learning from human feedback and conversational pressure. Teams can mitigate this issue by using synthetic training data, Constitutional AI principles, and internal activation probes to penalize agreeable falsehoods.

    DEPTH ■■■■■13 min read2 weeks ago

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  13. 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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  14. How to Build Models Without Language, with Logical Intelligence

    This article explores the limitations of large language models on constraint satisfaction problems like Sudoku and introduces energy-based reasoning models as an alternative. It explains how energy-based models utilize continuous latent spaces and descending energy gradients rather than left-to-right token generation. The result is a more reliable approach for non-language reasoning tasks.

    DEPTH ■■■■■19 min read13 days ago

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  15. 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 read4 weeks ago

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  16. Making Rust observability reliable at scale with OpenTelemetry

    Datadog engineers faced inconsistent tracing, broken spans, and context loss across Rust services due to fragmented tooling and unopinionated OpenTelemetry configurations. To fix this, they contributed upstream to OpenTelemetry to improve interoperability and built an opinionated Rust tracing library called dd-trace-rs. This reduced backend ingestion volume by 20x while increasing the rate of indexed spans per service by 3x.

    DEPTH ■■■■■10 min read7 weeks ago

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  17. How DoorDash Built a Toolbox for AI Agents

    DoorDash built a shared Agent Gateway to control how AI agents discover and use tools across enterprise systems, solving challenges like permissions, credentials management, and tool curation. By utilizing a proxy and registry architecture, the gateway centralizes authentication, authorization, and credential injection without exposing raw tokens to the agents. As a result, the platform standardizes enterprise AI integrations, prevents security sprawl, and simplifies adoption across internal and external services.

    DEPTH ■■■■■15 min read3 weeks ago

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  18. 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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  19. AI Agents Can Think. Now They Can Pay.

    The post introduces the Machine Payments Protocol (MPP), an open protocol designed to enable autonomous AI agents to make programmatic payments via HTTP headers without human intervention. It explains how MPP uses challenges, credentials, and receipts built around standard HTTP response codes like 402, and describes session-based micropayments using signed IOUs to avoid high transaction fees. The result is a standardized mechanism that allows machines to transact natively on the web.

    DEPTH ■■■■■14 min read3 weeks ago

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  20. 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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