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Machine Learningremove filterAWSremove filterGitHubremove filterGrabremove filterSentryremove filterAmazon Web Servicesremove filterClear all

  1. 01Amazon Web Services · architecture

    Automate medical coding with Step Functions and Amazon Bedrock AgentCore

    Automating medical coding requires a hybrid approach that combines deterministic orchestration with bounded agentic reasoning to ensure safety, auditability, and human oversight. The team built a workflow using AWS Step Functions, AWS Lambda, and Amazon Bedrock AgentCore that evaluates terms cheapest-first via block-lists, exact matching, and finally an LLM agent for ambiguous cases. This design successfully autocodes confident cases while routing ambiguous ones to human reviewers with full audit trails and decision explanations.

    DEPTH ■■■■■15 min read5 days ago

    AWS

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  2. 02Grab · architecture

    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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  3. 03GitHub · performance

    How we make AI coding more cost efficient without sacrificing task quality

    GitHub optimized GitHub Copilot to improve cost efficiency without sacrificing task quality by evaluating changes across the entire agentic workflow. The team implemented four targeted adjustments including selective output compression, removing unused line-number formatting, shortening prompts via meta-prompting with behavioral tests, and delivering background work completions directly. These modifications reduced token usage and model inference costs globally without causing task-success regressions.

    DEPTH ■■■■■9 min read7 weeks ago

    Machine Learning

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  4. 04Grab · architecture

    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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  5. 05Grab · architecture

    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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  6. 06Grab · architecture

    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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