Read what ships.

01Grab · architecture

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 read4 days ago

MobileReliability

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02Honeycomb · architecture

OpenTelemetry Collector Configuration for LLM Observability

LLM applications emit bulky telemetry containing sensitive user content and disparate attribute names. The team uses an OpenTelemetry Collector pipeline to normalize, enrich, redact, and export this data to Honeycomb. This centralizes control, protects data before it leaves the network, and maintains consistent observability.

DEPTH ■■■■■12 min read2 weeks ago

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03Grab · 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 read2 weeks ago

Machine Learning

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04Honeycomb · practice

How Honeycomb Private Cloud Drinks From the Fire Hose

The Honeycomb Private Cloud team struggled to manually track and integrate rapid infrastructure and configuration changes produced by upstream teams. To solve this, they built custom scripts to generate automated drift reports and eventually integrated AI to help triage and track the necessary follow-up work. As a result, the team successfully scaled their integration process into a reliable daily workflow.

DEPTH ■■■■■10 min read2 weeks ago

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05Grab · architecture

Data Mesh at Grab (Part III): Operationalizing data reliability with automated DPIs

Grab addressed runtime data reliability issues in its data mesh by implementing automated Data Production Issues (DPIs). The system uses an incident orchestrator to detect contract breaches, a Data Health API for cross-platform root cause analysis, and Hugo for automated remediation. This approach resolved 86.9 percent of incidents automatically and made mean time to resolve six times faster.

DEPTH ■■■■■12 min read8 weeks ago

DatabasesObservability

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