App stability at scale with a layered defense
AI coding assistants have changed how fast we ship, not how users experience a crash. Here's how Grab keeps its superapp stable with a layered defense: lint rules, tests, an internal pre-release build, staged rollouts, feature flags, and fast on-call triage.
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Articles
App stability at scale with a layered defense
GrabMart data for faster disease outbreak detection
Smarter personalization: How property data helps us understand user price sensitivity
Powering AI-led research through simulation
Automated merchant review summary system with integrated feedback
Data Mesh at Grab (Part III): Operationalizing data reliability with automated DPIs
Building Jarvis Pro: Route first, answer later
Grab Bench: Evaluating AI on Grab-shaped production work
How AI is transforming analytics at Grab
Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions
Agent platform (Part 1): How we help Grab build and run AI agents at scale
Scaling Grab's Data Lake: Our journey to Apache Iceberg adoption
Migrating Counter Service storage: Design choices and learnings
Scaling out Distroless adoption With AI
Palana (Part 2): Architecting isolation, identity, and auditability for AI agents
Palana (Part 1): Why Grab built a secure platform for autonomous AI Agents
From decentralized Docs-as-Code to a centralized repository: Evolving Grab's documentation strategy
The Hugo evolution: Engineering Grab's unified, one-click data ingestion platform with Apache Flink
Scaling developer experience: How we improved Android Studio in a large monorepo
Enhancing Flink deployment with shadow testing
Data Mesh at Grab (Part II): The foundational tools behind certification
From firefighting to building: How AI agents restored our team’s core productivity
Enabling R8 optimization at scale with AI-assisted debugging
Reclaiming Terabytes: Optimizing Android image caching with TLRU
Cursor at Grab: Adoption and impact
Docker lazy loading at Grab: Accelerating container startup times
From deployment slop to production reality: How BriX bridges the gap with enterprise-grade AI infrastructure
Demystifying user journeys: Revolutionizing troubleshooting with auto tracking
How Grab is accelerating growth with real-time personalization using Customer Data Platform scenarios
A Decade of Defense: Celebrating Grab's 10th Year Bug Bounty Program
Real-time data quality monitoring: Kafka stream contracts with syntactic and semantic test
SpellVault’s evolution: Beyond LLM apps, towards the agentic future
Grab's Mac Cloud Exit supercharges macOS CI/CD
How we built a custom vision LLM to improve document processing at Grab
Machine-learning predictive autoscaling for Flink
Modernising Grab’s model serving platform with NVIDIA Triton Inference Server
Highly concurrent in-memory counter in GoLang
User foundation models for Grab
Powering Partner Gateway metrics with Apache Pinot
Taming the monorepo beast: Our journey to a leaner, faster GitLab repo
Data mesh at Grab part I: Building trust through certification
The evolution of Grab's machine learning feature store
Grab's service mesh evolution: From Consul to Istio
DispatchGym: Grab’s reinforcement learning research framework
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Evaluating performance impact of removing Redis-cache from a Scylla-backed service
Facilitating Docs-as-Code implementation for users unfamiliar with Markdown
Improving Hugo stability and addressing oncall challenges through automation
Building a Spark observability product with StarRocks: Real-time and historical performance analysis
TechDocs at Grab: Cultivating a culture of quality documentation
Grab AI Gateway: Connecting Grabbers to multiple GenAI providers
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Supercharging LLM application development with LLM-Kit
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LLM-assisted vector similarity search
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No version left behind: Our epic journey of GitLab upgrades
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Iris - Turning observations into actionable insights for enhanced decision making
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Enabling near real-time data analytics on the data lake
The journey of building a comprehensive attribution platform
Managing dynamic marketplace content at scale: Grab's approach to content moderation
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Kafka on Kubernetes: Reloaded for fault tolerance
Championing CyberSecurity: Grab's bug bounty programme in 2023
Sliding window rate limits in distributed systems
An elegant platform
Road localisation in GrabMaps
Graph modelling guidelines
Scaling marketing for merchants with targeted and intelligent promos
Stepping up marketing for advertisers: Scalable lookalike audience
Building hyperlocal GrabMaps
Streamlining Grab's Segmentation Platform with faster creation and lower latency
Unsupervised graph anomaly detection - Catching new fraudulent behaviours
Zero traffic cost for Kafka consumers
Go module proxy at Grab
PII masking for privacy-grade machine learning
Performance bottlenecks of Go application on Kubernetes with non-integer (floating) CPU allocation
How we improved our iOS CI infrastructure with observability tools
2.3x faster using the Go plugin to replace Lua virtual machine
Safer deployment of streaming applications
Message Center - Redesigning the messaging experience on the Grab superapp
Evolution of quality at Grab
How OVO determined the right technology stack for their web-based projects
Migrating from Role to Attribute-based Access Control
Securing GitOps pipelines
New zoom freezing feature for Geohash plugin
Graph service platform
Zero trust with Kafka
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Graph for fraud detection
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Automatic rule backtesting with large quantities of data
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Search architecture revamp
Embracing a Docs-as-Code approach
Graph Networks - Striking fraud syndicates in the dark
How we reduced our CI YAML files from 1800 lines to 50 lines
How Kafka Connect helps move data seamlessly
Supporting large campaigns at scale
How telematics helps Grab to improve safety
Real-time data ingestion in Grab
Abacus - Issuing points for multiple sources
Exposing a Kafka Cluster via a VPC Endpoint Service
How Grab built a scalable, high-performance ad server
Biometric authentication - Why do we need it?
Using real-world patterns to improve matching in theory and practice
Designing products and services based on Jobs to be Done
Search indexing optimisation
Automating Multi-Armed Bandit testing during feature rollout
How We Cut GrabFood.com’s Page JavaScript Asset Sizes by 3x
Protecting Personal Data in Grab's Imagery
Processing ETL tasks with Ratchet
App Modularisation at Scale
Reshaping Chat Support for Our Users
Debugging High Latency Due to Context Leaks
Building a Hyper Self-Service, Distributed Tracing and Feedback System for Rule & Machine Learning (ML) Predictions
Our Journey to Continuous Delivery at Grab (Part 2)
How We Improved Agent Chat Efficiency with Machine Learning
How Grab Leveraged Performance Marketing Automation to Improve Conversion Rates by 30%
One Small Step Closer to Containerising Service Binaries
Customer Support Workforce Routing
Serving Driver-partners Data at Scale Using Mirror Cache
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Trident - Real-time Event Processing at Scale
Pharos - Searching Nearby Drivers on Road Network at Scale
Reflecting on the Five Years of Bug Bounty at Grab
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Democratising Fare Storage at Scale Using Event Sourcing
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Designing Resilient Systems: Circuit Breakers or Retries? (Part 2)
Querying Big Data in Real-time with Presto & Grab's TalariaDB
Designing Resilient Systems: Circuit Breakers or Retries? (Part 1)
Orchestrating Chaos Using Grab's Experimentation Platform
Reliable and Scalable Feature Toggles and A/B Testing SDK at Grab
Mockers - Overcoming Testing Challenges at Grab
Journey of a Tourist via Grab
How We Designed the Quotas Microservice to Prevent Resource Abuse
Grab Senior Data Scientist Liuqin Yang Wins Beale-Orchard-Hays Prize
Building Grab’s Experimentation Platform
Introducing Grab-Kit: Distributed Service Design at Grab
How Grab Experimented with Chat to Drive Down Booking Cancellations
Deep Dive into Database Timeouts in Rails
Dealing with the Meltdown Patch at Grab
GrabShare at the Intelligent Transportation Engineering Conference
Grabbing Growth: A Growth Hacking Story
The Data and Science Behind GrabShare Part I: Verifying Potential and Developing the Algorithm
The Art of Hiring Good Engineers
Migrating Existing Datastores
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Grab's Front End Study Guide
DNS Resolution in Go and Cgo
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Grab Vietnam Careers Week
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