Reading room.
Engineering blogs & AI research
A reading room from engineering teams and AI labs: titles, short feed excerpts, and links to the originals. For my own essays, explore the writing archive.
- 1359 articles tracked
- 37 sources
- Refresh scheduled daily
Sources
App stability at scale with a layered defense
Introduction Artificial intelligence (AI) coding assistants have changed how fast we can ship. They have not changed how mobile users experience a crash. That gap is the pressure every mobile stability program now operat…
GrabMart data for faster disease outbreak detection
Abstract Retail purchases can supplement clinical surveillance because changes in demand for some over-the-counter (OTC) products may appear before confirmed case reports. This article describes an early-stage concept fo…
Smarter personalization: How property data helps us understand user price sensitivity
Introduction Existing systems estimate user price sensitivity primarily from spending behavior or demographic proxies. They do not systematically account for residential property values, which can indicate a user’s finan…
Powering AI-led research through simulation
The short story Consider a Friday evening. A food order arrives from a mall in the city center. One driver is nearby; another is finishing a drop-off and will be available shortly; a second order from the same mall may o…
Automated merchant review summary system with integrated feedback
Introduction Merchant reviews contain useful details about food quality, portion size, packaging, and value. Finding these details often requires reading many comments. Aggregate ratings simplify comparisons, but they do…
Data Mesh at Grab (Part III): Operationalizing data reliability with automated DPIs
Introduction In the first two parts of this series , we described how Grab approaches data mesh through the Signals Marketplace: a way for teams to publish, discover, and reuse trusted data products across domains. Part…
Building Jarvis Pro: Route first, answer later
Introduction The first Jarvis Pro prototype could produce answers that sounded right. That was the problem. One early answer looked polished: it named the merchant, summarized the week, and recommended pushing promotions…
Grab Bench: Evaluating AI on Grab-shaped production work
Introduction What worried us wasn’t the hallucination, it was the subtle plausibility. Answers an engineer could easily read past and accept: a right-looking Structured Query Language (SQL) query, a plausible tool call,…
How AI is transforming analytics at Grab
Introduction At Grab, analytics sits close to almost every decision that matters. Our north star is the democratisation of intelligence, ensuring that anyone making a business call has immediate access to trustworthy ans…
Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions
Introduction The efficacy of semantic search relies on the accuracy of the underlying Knowledge Graph (KG). In high-velocity domains like on-demand food delivery or e-commerce, the catalog of entities like dishes, produc…
Agent platform (Part 1): How we help Grab build and run AI agents at scale
Part 1: From one support bot to a framework At Grab, AI agents have evolved from interesting team prototypes into production services used every day by millions of merchants, drivers, and consumers. Today, more than 500…
Scaling Grab's Data Lake: Our journey to Apache Iceberg adoption
Introduction: The evolution of Grab’s Data Lake At Grab’s scale, managing petabytes of data across billions of S3 objects demands more than a storage layer. It demands a robust architectural primitive that supports the h…
Migrating Counter Service storage: Design choices and learnings
Introduction Counter Service is used across Grab’s anti-fraud platform to answer time-windowed count questions, such as recent ride requests by a user or failed payment attempts on a card. The service handles tens of tho…
Scaling out Distroless adoption With AI
Distroless adoption at Grab Grab is migrating from heavy base images to Distroless images to reduce security risks. By limiting each container to the application and its runtime dependencies, we shed non-essential binari…
Palana (Part 2): Architecting isolation, identity, and auditability for AI agents
Introduction In Part 1 , we introduced Palana , Grab’s Kubernetes-native secure execution platform for autonomous AI agents. We discussed the underlying need for isolated environments and covered its core design principl…
Palana (Part 1): Why Grab built a secure platform for autonomous AI Agents
Abstract Artificial intelligence (AI) agents are moving from experiments into everyday engineering workflows. They can read code, call application programming interfaces (APIs), run tests, create merge requests, answer S…
From decentralized Docs-as-Code to a centralized repository: Evolving Grab's documentation strategy
Introduction: The journey of documentation at Grab In early 2021, Grab adopted a Docs-as-Code approach to address gaps in our technical documentation processes, as illustrated in our blog post Embracing a Docs-as-Code .…
The Hugo evolution: Engineering Grab's unified, one-click data ingestion platform with Apache Flink
Introduction Data drives every decision we make at Grab. As our operations scale, so does our need for robust, real-time data ingestion and processing frameworks. Enter Hugo: our self-service data platform that has long…
How AI is transforming analytics at Grab
Introduction At Grab, analytics sits close to almost every decision that matters. Our north star is the democratization of intelligence, ensuring that anyone making a business call has immediate access to trustworthy ans…
Scaling developer experience: How we improved Android Studio in a large monorepo
Introduction Long integrated development environment (IDE) sync/indexing times can quietly erode developer productivity, making code navigation sluggish, spiking memory usage, and slowing down Jetpack Compose preview upd…
Enhancing Flink deployment with shadow testing
Introduction Ensuring the reliability of Apache Flink deployments in Grab is crucial for the availability of our business-critical, real-time applications. While all applications are tested in a staging environment befor…
Data Mesh at Grab (Part II): The foundational tools behind certification
Introduction In Part I , we discussed why Grab is investing in a data mesh, referred to as the Signals Marketplace within Grab, as part of our evolving data culture. We also explained how data certification aids teams in…
From firefighting to building: How AI agents restored our team’s core productivity
Abstract Grab’s Analytics Data Warehouse (ADW) team supports over 1,000 users each month. These users support an extensive repository of more than 15,000 tables, which powers approximately 50% of all queries within our d…
Enabling R8 optimization at scale with AI-assisted debugging
Grab is Southeast Asia’s leading superapp, providing a suite of services that bring essential needs to users throughout the region. Its offerings include ride-hailing, food delivery, parcel delivery, mobile payments, and…
Reclaiming Terabytes: Optimizing Android image caching with TLRU
Introduction In a previous post, we discussed Project Bonsai , our initiative to reduce the Grab app’s download size. We successfully reduced the Android Application Package (APK) download size by 26%. This reduction off…
Cursor at Grab: Adoption and impact
Adoption overview The illustration below encapsulates how Cursor is scaled across Grab, achieving rapid and widespread adoption that accelerated software development and empowered non-technical teams to build solutions.…