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Problem & solution reads
Problem & solution posts on Yomu, summarised newest first.
- 891 posts
- 20 companies
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Problem & solution posts
Upgrading Checkout Blocks app to Polaris web components
Checkout Blocks upgraded all five UI extensions from React and Shopify’s legacy Remote UI bridge to remote-dom with Preact and Polaris web components, while moving from JavaScript to TypeScript and API version 2026-01. The migration addressed checkout’s performance sensitivity and Shopify’s requirement that deployments move off versions earlier than 2026-01. A parallel core-next library allowed extensions to transition incrementally, with shared providers, telemetry, hooks, helpers, and rules adapted before legacy core was removed. To meet the remote-dom CLI’s 64KB gzip limit, the team dropped react-reconciler, replaced liquidjs with a smaller parser validated against more than 42,000 lines of merchant configurations, replaced dayjs, and retained markdown-to-jsx through Preact compatibility. Transferred bundles fell 40% to 85%, while weighted ELT improved 8% at P50 and 7% at P90.
How Stripe uses graph search and state machines to auto-remediate a global database fleet
Stripe describes how its Document Database fleet previously handled degraded MongoDB shards with single-step, hard-coded plugins that struggled with combined failures, intermediate states, and more than 40 shard layouts. The replacement models shard topology and health as a directed graph, with infrastructure operations as edges and safety invariants defining valid states. A simulator uses breadth-first search to find minimum-operation recovery paths, while an abstraction over Temporal contexts lets the same remediation logic run in memory without real infrastructure calls; later, the system also uses Dijkstra's algorithm. The system reduced pager volume by 30%—about 200 pages annually—reclaimed 12 days of fleet health each year, and handled a new layout with zero code changes, according to the post.
Migrating from Apollo Tooling to GraphQL Codegen at Yelp
Yelp migrated its frontend React monorepo from the deprecated apollo-tooling to GraphQL Codegen to ensure ongoing maintenance and build reliability. Because the monorepo spans over 500 packages with thousands of generated type files and custom nested type imports, the Client Data team prioritized a transparent migration that avoided codebase-wide changes. The team rejected approaches requiring syntax updates or altered import paths, choosing instead to configure GraphQL Codegen and contribute upstream plugin changes so output structures and naming conventions matched the legacy output. Migrating packages incrementally through small pull requests enabled continuous feedback while allowing developers across dozens of teams to maintain their workflows without editing existing code. Completing the migration to GraphQL Codegen v6 simplified Yelp's build tooling, eliminated deprecated dependencies, and left upstream compatibility enhancements available for the wider open-source community.
Why Trainy migrated from Amazon RDS Postgres to ClickHouse Managed Postgres
Trainy’s Pluto is an open-source experiment tracker that separates analytics metrics in ClickHouse Cloud from relational run metadata in Postgres. Its original metadata store, Amazon RDS Postgres, was too expensive for the volume observed and contributed to slower initial page loads, so Trainy migrated roughly 40–50 GB to ClickHouse Managed Postgres, which uses local NVMe storage and keeps both databases in one account. After migration, initial page loads became 2–3 times faster, while a complex query reported as taking 10–15 minutes previously returned in five seconds; visualization queries over runs with hundreds of thousands of steps typically return in 50–100 milliseconds at the P50. Trainy reports 100% reliable ingestion with zero dropped data points and says the unified stack could support future joins between GPU metrics and run metadata, as well as governed agent access.
AI-Enabled Advisory Services for Higher Education
Higher-education call centers face costly, limited-coverage monitoring of advisor conversations and brittle, slow methods for identifying student concerns from transcripts. The proposed workflow deploys OpenAI Whisper on Databricks Model Serving, applies AI Functions for sentiment, topics, intent, and rubric scoring, and uses Unity Catalog to govern the resulting data. For advisor quality, an LLM-as-a-judge evaluates every transcript against a reference-table rubric, returns a weighted 1–5 overall score and per-criterion scores, and routes flagged calls for targeted QA review instead of random sampling. For student insights, quarterly transcript enrichment feeds an Agent Bricks Knowledge Assistant for cited reasoning over raw calls and a Genie Space for structured trend queries, while LangGraph orchestrates UC SQL functions as tools. Together, these components let non-technical advisors, mentors, and QA managers query student interactions without reaching out to a data SME.
How Bullet uses ClickHouse Cloud to give DeFi's fastest exchange real-time analytics
Bullet operates a DeFi exchange for perpetuals, spot trading, and lending, with trades settled on a blockchain while users retain custody of their assets. Its sub-millisecond trading system produces more than 150 million event rows per hour, creating a need for historical indexing, real-time analytics, and operational monitoring beyond the blockchain’s state-machine model. ClickHouse Cloud consumes events from Kafka, stores them in dedicated tables, and supports user features, leaderboard and reward calculations, P&L charts, audit logs, and Grafana-backed alerts. Replacing Databricks reduced query latency from 10–15 seconds to milliseconds, improved data freshness from 1–2 hours to under 5 seconds, and removed a separate DynamoDB serving layer at comparable cost while handling 1,000 times more data across staging, testnet, and mainnet.
Shipyard: How We Built Slack’s Next-Generation EC2 Platform
Slack developed Shipyard to modernize its EC2 infrastructure by replacing continuously updated, mutable instances with immutable deployable artifacts. The platform adopts a layered image architecture starting with a standardized base image called slack-zero, which is built using AWS Image Builder and consumed by downstream service-specific machine images. Rather than running periodic Chef jobs in the background, configuration management is restricted to image baking and initial instance provisioning phases. Fleet management is handled through the Peekaboo inventory system, metric-driven progressive rollouts via Gondola, and automated instance rotation managed by a lifecycle component known as the Reaper. Developers validate cookbook updates prior to merging using Ship Quick, which provisions real infrastructure across dedicated worker fleets to verify provisioning workflows.
Blocking Slow-Burn Attacks: Contextual Policies in Omnigent
Omnigent’s post examines how a vendor-review assistant can leak confidential pricing terms when an attacker hides an indirect prompt injection in a shared runbook. Because the malicious workflow is divided into ordinary actions, stateless checks approve each step even though the session as a whole is unsafe. The demonstration compares an unprotected run, which sends the summary externally, with a contextual policy that stores a running risk score, adds 30 for each document read, and denies email after the score exceeds 50. It also shows that agents cannot remove or disable policies, new policies require human approval, and any denial prevails when policies are combined. Runtime enforcement therefore preserves the block even when the agent has been misled.
Training Orchestrator: Unifying Model Training at Yelp
Yelp developed Training Orchestrator to standardize machine learning pipeline execution and eliminate divergent training scripts across its applied engineering teams. Previously, monolithic Spark scripts coupled training logic directly to cluster runtimes, preventing local testing, slowing debug cycles, and causing inconsistent configuration drift across projects. The new framework introduces a declarative configuration layer backed by Pydantic models, enabling static type checking and step schema validation before runtime execution starts. Training workflows are defined as modular step hierarchies that the orchestration engine compiles into directed acyclic graphs for execution in topological order. By injecting Spark and MLflow contexts into steps, the architecture ensures reproducible tracking, automated Slack alerts, and parity across local development and production environments.
Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned
Engineers at Netflix required a unified, real-time view of service dependencies to navigate distributed architecture and improve incident troubleshooting. Traditional batch systems introduced stale data, so the team created a streaming-first platform backed by reactive streams and backpressure handling to ingest flow records from multi-region Kafka streams and Server-Sent Events without data loss. The architecture partitions data into physically separate graph and columnar storage layers covering eBPF network flows, IPC metrics, and distributed traces. Network flow ingestion relies on a three-stage distributed aggregation pipeline using consistent hashing to resolve network intermediaries into logical application connections. The resulting production system serves time-travel and topology queries with sub-second latency while continuously updating dependency views.
Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler
Meta’s ads serving fleet processes more than 5 million requests per second, so millisecond-scale latency changes can affect ads relevance and advertiser ROI. During a move from kernel 6.4 with CFS to kernel 6.9, the team observed a latency regression associated with EEVDF and used sched_ext, an upstream BPF-based framework, to encode ads-specific scheduling priorities. The policy soft-partitions CPUs between latency-critical request-path threads and less-sensitive work, adjusts pool sizes with load-based heuristics, and runs as a user-space binary that loads BPF, enabling policy changes without rebuilding or reinstalling the kernel. Initial results were a 28% reduction in ads retrieval p99 latency, 3.28 megawatts of fleet power savings, and a 1.1% increase in weighted ads ranked; two later user-space updates delivered an additional 60% p99 reduction and an 18% decrease in critical-path timeout errors, with iterations shipping in days rather than months.
How Insider One uses ClickHouse Cloud to power real-time customer engagement at scale
Insider One’s Unified Customer Database (UCD) unifies event data, user attributes, identity resolution, and profiles across channels to support real-time segmentation, personalization, and analytics. The platform initially ran ClickHouse in a self-managed AWS deployment, but growth brought larger datasets, more concurrent queries, and increasing work tuning clusters and balancing performance against cost. In a proof of concept, the team migrated an anonymized dataset from one of its largest customers to ClickHouse Cloud and replayed UI segmentation, audience-count, and scheduled-campaign workloads. Average query latency fell 50–60% without significant changes, reaching 70–75% for the largest customers, while latency percentiles became more predictable; the managed service also reduced operational burden and gave the team more capacity for product development and its AI-oriented roadmap.
How to Evaluate General-Purpose Robot Policies for Real-World Deployment
Evaluating robotics foundation models accurately in simulation remains challenging due to visual domain overlap, benchmark saturation, diagnostic gaps, and small sample sizes. RoboLab addresses these deficiencies by providing a robot-agnostic simulation platform that supports rapid task creation and diagnostic evaluation suites. Tasks in RoboLab can be compiled across varying robot embodiments and target visual, procedural, and relational competencies. Statistical analysis using the Clopper-Pearson method reveals that narrowing the confidence interval around a 90 percent success rate from ten to two percentage points demands increasing rollouts from 70 to 1,030. Developed by NVIDIA Research, RoboLab powers the open-source Isaac Lab-Arena simulation framework to establish scalable evaluation for real-world robotic policies.
amazon.science
Amazon and University of Michigan give robots a sense of touch
Robotic manipulation in contact-rich settings requires accurate tactile force perception, but existing physics simulators either run too slowly for reinforcement learning or oversimplify shear forces. HydroShear addresses this tactile reality gap by incorporating path-dependent force tracking into hydroelastic contact models. By remembering object motion history across soft elastomer sensors, the GPU-parallelizable simulator computes full 3-D contact deformations, friction, and slipping dynamics. Reinforcement learning policies trained entirely in HydroShear transferred directly to a real Franka robot equipped with GelSight Mini sensors without fine-tuning. Across four challenging manipulation tasks, HydroShear achieved a 93 percent average success rate, outperforming baseline models TacSL and FOTS.
Better tools made Copilot code review worse. Here’s how we actually improved it.
When migrating GitHub Copilot code review from custom navigation tools to shared Unix-style CLI utilities—grep, glob, and view—benchmarks revealed higher review costs and fewer caught issues. Rather than an issue with the underlying tools, trace analysis showed the agent used general-purpose coding assistant instructions that triggered expansive repository browsing loops. In response, the team rewrote the tool guidance to enforce a review-specific workflow anchored to pull request diffs. The updated instructions direct the agent to narrow candidate call sites and files using batched discovery before reading minimal line ranges with view. This workflow tuning reduced average review costs by approximately 20% in production while maintaining review quality.
Accelerating End-to-End Co-Folding Performance with NVIDIA BioNeMo Agent Toolkit
Biomolecular structure prediction and co-folding pipelines encounter latency and memory ceilings during sequence alignment and multi-residue inference. NVIDIA addresses these constraints through the BioNeMo Agent Toolkit, which connects AI agents to accelerated biological computing components. The pipeline offloads multiple sequence alignment to MMseqs2-GPU, while cuEquivariance and the OpenFold3 NIM accelerate core geometric attention kernels to cut inference latency up to fourfold. To handle large molecular assemblies beyond single-GPU memory limits, the Fold-CP framework distributes context across multiple GPUs with O(N²/P) memory scaling. Together, these tools enable the prediction of complexes up to 32,000 tokens on 64 NVIDIA B300 GPUs, making large-scale virtual screening and ribosome-scale modeling computationally feasible.
How Swiggy Migrated from protobuf-java to Square Wire
Swiggy migrated from protobuf-java to Square Wire after profiling showed that the runtime and protoc-generated code consumed 6.735 MB of Android app size. protobuf-javalite was rejected because Swiggy relies extensively on google.protobuf.Any, while Wire retained support for Any and other well-known types with a smaller runtime and immutable Kotlin models. The migration required updating snake_case property access, message construction, serialization calls, ProGuard rules, and a custom pipeline that compiled all schemas together before publishing package-specific AARs to AWS CodeArtifact. The team also fixed repeated enum fields with [packed = true] after discovering a silent wire-format mismatch. It reduced Swiggy’s app size by 3.52 MB and produced measurable reductions for Instamart and Toing.
Scaling Android CI: From 44 → 10 minutes — A Deep Dive into Build Time Optimization and Best…
An Android monorepo’s CI pipeline averaged about 35 minutes in its build phase, with a P90 of roughly 44 minutes, as repeated dependency downloads, environment-driven invalidations, variant bloat, and machine limits increased work. The team reworked caching with branch- and architecture-aware keys, release/develop fallbacks, cached SDK and dependency data, and a Gradle Remote Cache, while filtering repositories and restricting CI to required variants. It also tuned 32GB runners to a 16GB heap and 12GB Metaspace, disabled configuration-cache saves in ephemeral CI, upgraded Kotlin, AGP 8.10.1, and Gradle 8.11.1, and moved critical workflows to Apple Silicon. Gradle tasks fell from 3399 to 2745, while remote-cache hit rates reached P50 44% and P90 56%. After the Apple Silicon refresh, total CI time reached P50 10 and P90 11 minutes, average credits per build dropped from 55 to 43, and a comparable rollout window saved about 5800 credits.
Scaling Grab's Data Lake: Our journey to Apache Iceberg adoption
Grab is transitioning its petabyte-scale data lake from Hive Parquet tables managed through the Hive Metastore to a table-centric architecture centered on Apache Iceberg, while retaining storage-compute separation on S3. The move addresses catalog listing latency, severe small-file fragmentation, manual partition registration, absent native ACID operations, and catalog state becoming stale after direct S3 changes. Rather than converting the entire lake at once, Grab prioritizes high-value tables and uses features such as Z-ordering; representative workloads cut one query from 70 seconds to 6, reduced daily S3 API costs by up to 95%, and halved cluster resource usage. Its open-sourced UnifiedSparkCatalog detects table formats through metastore metadata or path inference and routes operations to the appropriate Spark catalog without format-specific prefixes. Deployment also required resolving Hive lock contention, timestamp incompatibilities, and storage-tier cost spikes.
From Day 1 to Production: Building Lyft’s Analytics & Rides Intelligence Assistant as Onboarding…
Lyft developed Aria, an AI-powered assistant allowing approved employees to query ride analytics via natural language through a backend with LangGraph orchestration and SQL generation. However, Aria's user interface was limited to a Streamlit prototype that lacked standard service framework integration, Lyft authentication, and multi-user scalability. To resolve these limitations, a new engineer built a production-grade web client from scratch using Lyft's internal Node.js framework and Next.js as a three-week onboarding assignment. The implementation involved configuring Envoy routing, resolving authentication plugin incompatibilities, debugging service connectivity with Grafana, and managing interface state with an XState state machine. Aria was successfully hardened and launched into production, expanding natural language analytics access across the company.
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