---
title: "Performance"
description: "817 posts about Performance, summarised, each linking to the original."
---

# Performance
> 817 posts about Performance, summarised, each linking to the original.

## Articles

### [Flow generation through natural language: An agentic modeling approach](https://yomu.fyi/post/flow-generation-through-natural-language-an-agentic-modeling-approach.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Apr 22, 2026
- Type: Explainer

Shopify built a tool-calling agent for Sidekick that converts natural-language requests into Shopify Flow workflows, addressing the cold-start problem by working backward from thousands of anonymized production workflows. The team generated synthetic queries and tool trajectories, fine-tuned Qwen3-32B, and changed Flow’s nested JSON DSL into an equivalent Python representation connected by a round-trip transpiler. Using Python improved syntactic correctness by 22 points and semantic correctness by 13 points on a 300-example benchmark, while matching production tool names, ordering, responses, and prompts proved essential to avoid degradation. Production evaluation exposed a 35% gap masked by benchmark parity, so Shopify built a weekly feedback loop that scores conversations, filters training data, identifies slice gaps, retrains, and deploys. The resulting agent serves most production traffic, runs 2.2 times faster at 68% lower cost, and outperforms closed models.


### [Index sharding in ClickHouse Cloud: Petabyte-scale data needs petabyte-scale indexing](https://yomu.fyi/post/index-sharding-in-clickhouse-cloud-petabyte-scale-data-needs-petabyte.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: James Cunningham
- Published: Apr 21, 2026
- Type: Problem & solution

In large-scale ClickHouse Cloud deployments, replicas traditionally load entire primary and secondary indexes from object storage into working memory, creating massive redundant memory overhead as fleets expand. Index sharding resolves this bottleneck by distributing the index analysis phase across available replicas using consistent hashing over data parts. Instead of every node loading full index structures, each replica loads and analyzes only its assigned fraction of primary keys, bloom filters, text indexes, or vector indexes. The query initiator merges the resulting granule match ranges, allowing parallel replicas to stream data without any single machine retaining the whole index. This approach freed working memory for query execution and accelerated index analysis by up to 7.7x in benchmark tests on a 50 billion row table.


### [Keeping the Cursor app stable](https://yomu.fyi/post/keeping-the-cursor-app-stable.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Andrew Chan & Kevin Nguyen
- Published: Apr 21, 2026
- Type: Explainer

Cursor's desktop app faces disruptive crashes as usage and features such as subagents, instant grep, and browser use have expanded, with most failures caused by out-of-memory conditions. The team measures instability across Electron's multi-process architecture using telemetry, per-session and per-request crash rates, minidumps, stack traces, crash breadcrumbs, and dashboards that update within minutes. Its two-pronged debugging strategy combines feature-level experiments and oversize-message instrumentation with bottom-up analysis based on a crash watcher, Chrome DevTools Protocol (CDP) stacks, automated daily investigations, opt-in heap snapshots, and low-rate allocation profiling. Findings led to chunking large data, killswitches, lifecycle fixes for leaks, and process isolation for extensions, while prevention uses Bugbot rules, stress tests, garbage collection, performance tests, and automated rollbacks. OOM-per-session fell 80% from its late-February peak and OOM-per-request fell 73% since March 1.


### [QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard](https://yomu.fyi/post/qimma-a-quality-first-arabic-llm-leaderboard.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Leen AlQadi, Ahmed Alzubaidi, Mohammed Alyafeai, Maitha Alhammadi, Shaikha Alsuwaidi, Omar saif alkaabi, Basma Boussaha, Hakim Hacid
- Published: Apr 21, 2026
- Type: Benchmark

QIMMA is an Arabic LLM leaderboard built to address fragmented evaluation, translation artifacts, weak benchmark quality checks, limited coverage, and reproducibility gaps. It treats benchmark validation as a prerequisite for reported scores rather than an afterthought. The suite consolidates 109 subsets from 14 source benchmarks into more than 52,000 samples across seven domains, with 99% native Arabic content, Arabic-adapted HumanEval+ and MBPP+ coding tasks, and six prompt templates. Before model evaluation, Qwen3-235B-A22B-Instruct and DeepSeek-V3-671B assess samples using a 10-point rubric, flagged cases receive native-speaker review, and code statements are refined without changing task identifiers, reference solutions, or tests. In the April 2026 rankings, Qwen/Qwen3.5-397B-A17B-FP8 ranks first, while Arabic-specialized models lead cultural and linguistic tasks and multilingual models lead coding.


### [ClickHouse expands strategic collaboration with Google Cloud, bringing deeper integration, cloud flexibility, and next-generation performance](https://yomu.fyi/post/clickhouse-expands-strategic-collaboration-with-google-cloud-bringing.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Tanya Bragin
- Published: Apr 21, 2026
- Type: Announcement

ClickHouse announced an expanded strategic collaboration with Google Cloud introducing four core architectural milestones. First, native integration with Google Cloud Lakehouse allows direct sub-second queries across data lakes and ClickHouse deployments without ETL pipelines or data duplication. Second, the Bring Your Own Cloud deployment model lets organizations run managed ClickHouse Cloud inside their own Google Cloud Virtual Private Cloud to retain data sovereignty and network controls. Third, ClickHouse Cloud is migrating automatically to Google's custom Arm-based Axion processors, boosting query throughput and cost efficiency for analytical workloads without requiring application changes. Finally, a new ClickHouse MCP server integration connects ClickHouse with Google Antigravity to enhance developer and analyst workflows.


### [“A generational leap”: How Trio unified payment analytics and cut storage by 88% with ClickHouse Cloud](https://yomu.fyi/post/a-generational-leap-how-trio-unified-payment-analytics-and-cut-storage.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 17, 2026
- Type: Problem & solution

Brazilian payment processor Trio faced severe data challenges after scaling and acquiring PayBrokers, processing over 243 million payments in early 2025 across disparate legacy databases. Because financial reconciliation and compliance demand absolute precision, the engineering team unified their analytics layer by migrating to ClickHouse Cloud. To handle out-of-order and duplicate events from their Redpanda streaming pipelines, Trio implemented sliding-window refreshable materialized views alongside ReplacingMergeTree engines rather than recomputing entire historical datasets. A custom ETL service manages schema drift before inserting clean records into ClickHouse, while batch ingestion is coordinated through Airflow. Consequently, Trio reduced storage consumption by approximately 88% through columnar compression, ingested a 5-billion-row migration without issue, and maintained real-time query performance across more than one billion daily ledger data points.


### [April 2026 newsletter](https://yomu.fyi/post/april-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Apr 16, 2026
- Type: Announcement

ClickHouse 26.3 LTS introduces experimental materialized common table expressions, WebAssembly user-defined functions, and enables asynchronous inserts by default. Alongside the release, Alasdair Brown launched clickhousectl to manage local installations and cloud deployments with built-in agentic development features and authentication safeguards. User case studies show Goldsky accelerating historical blockchain backfills by twelve times through direct ClickHouse reads using Apache Arrow, replacing their prior Kafka and Avro architecture. Additionally, the engineering team redesigned the full-text search index with front-coding compression, a sparse lookup index, and adaptive posting lists to handle expensive random reads on object storage. ClickHouse also previewed community architecture patterns for lightweight SIEM setups using Clickdetect and Wazuh log collection.


### [Frontend Engineering at Palantir: Polar Scaled Tiles in Zodiac](https://yomu.fyi/post/frontend-engineering-at-palantir-polar-scaled-tiles-in-zodiac.md)
- Company: [Palantir](https://yomu.fyi/company/palantir.md)
- Author: Palantir
- Published: Apr 15, 2026
- Type: Explainer

Zodiac, Palantir’s custom 3D globe library built on Three.js, encountered severe performance degradation near the Arctic because latitude/longitude tiles converge into tiny slivers and create excessive geometry. Standard equal-angular tiling was replaced internally with polar-scaled tiles whose widths expand toward the poles so their surface areas remain closer to equatorial tiles. The design uses spherical surface-area calculations, power-of-two merge spans, and dynamic quadtree expansion; client-side texture compositing handles standard geodetic or Web Mercator sources, while heightmaps are combined and resampled for wider tiles. The change reduced polar geometry, draw calls, and textures by over 90% and made frame time more consistent, though texture loading remains the next bottleneck and native polar tiles would remove stitching overhead.


### [Inside VAKRA: Reasoning, Tool Use, and Failure Modes of Agents](https://yomu.fyi/post/inside-vakra-reasoning-tool-use-and-failure-modes-of-agents.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Ankita Naik, danish, Ben, Anupama Murthi, Praveen Venkateswaran, Siyu, Ayhan Sebin
- Published: Apr 15, 2026
- Type: Benchmark

VAKRA is an executable, tool-grounded benchmark for evaluating whether AI agents can complete enterprise-like workflows that combine API calls, document retrieval, and natural-language constraints. It provides more than 8,000 locally hosted APIs backed by real databases across 62 domains, plus domain-aligned documents, and uses full execution traces to assess multi-step behavior. The four capability groups cover API chaining, dashboard-API tool selection, multi-hop dashboard reasoning, and multi-hop multi-source reasoning with multi-turn dialogue and optional tool-use policies; tasks can require three to seven reasoning steps, while some segments specify one to twelve tool calls or up to five logical hops. Reported analyses find that models perform best on single-hop questions, degrade as API hops increase, and often violate policies or fail to retrieve enough information when constraints restrict the relevant source, exposing a gap between isolated tool competence and end-to-end reliability.


### [Autoresearch isn’t just for training models](https://yomu.fyi/post/autoresearch-isn-t-just-for-training-models.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Apr 15, 2026
- Type: Problem & solution

Repeated visual-regression failures made Polaris CI feedback cycles take about 30 minutes, prompting an attempt to optimize build time rather than fix individual tasks. The approach uses Pi to create an Autoresearch extension that repeatedly forms a hypothesis, measures the build against a 19.1-second baseline, and keeps only changes that improve the metric, discarding crashes and slower runs. The loop identified wasted VRT work: the full component pipeline ran before Storybook recompilation, while TypeScript transformed all 580 component files although 105 needed it. Removing those inefficiencies made the build 65% faster, although the system also produced unacceptable hacks that were discarded. The extension later gained multi-metric support, consistent iteration scripts, auto commits, and open-source adoption, with reported internal improvements including unit tests running 300 times faster and Playwright tests becoming faster.


### [Index-based pruning in ClickHouse](https://yomu.fyi/post/index-based-pruning-in-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Apr 15, 2026
- Type: Explainer

ClickHouse accelerates analytical queries by minimizing the amount of data read from disk across granules. A primary key determines data part sort order and records the first value per granule, enabling binary search to skip unneeded granules during filtered queries. For non-primary key filters, lightweight projections act like secondary indexes by storing alternative sort keys alongside base table offset pointers without duplicating full table rows. Skip indexes, such as minmax indexes, record column boundaries per granule and operate without disk duplication, though effective pruning requires some correlation with the primary key. Demonstrations using a UK property price dataset show how primary key filters and minmax skip indexes eliminate large fractions of total granules.


### [Mintlify boosts NPS 30% and saves 60% with real-time analytics on ClickHouse Cloud](https://yomu.fyi/post/mintlify-boosts-nps-30-and-saves-60-with-real-time-analytics-on-clickh.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 14, 2026
- Type: Problem & solution

Documentation platform Mintlify experienced major dashboard performance degradation and rate limiting under PostHog as multi-tenant analytics traffic grew rapidly. Because PostHog lacked incremental materialized views, queries scanned full raw datasets and took tens of seconds to load. Mintlify migrated its customer-facing analytics infrastructure to ClickHouse Cloud, ingesting events through Amazon MSK and ClickPipes into a single events table backing incremental materialized views. The team executed the transition over two weeks by dual-writing events and replicating the existing PostHog schema to preserve API surface compatibility. The migration reduced dashboard query latency to sub-one-second, decreased related support bug reports to zero, lifted NPS by an estimated 30%, and lowered analytics infrastructure costs by approximately 60%.


### [Speeding up GPU kernels by 38% with a multi-agent system](https://yomu.fyi/post/speeding-up-gpu-kernels-by-38-with-a-multi-agent-system.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Wilson Lin, Sahil Modi, Yuan Zhang & Edward Lin
- Published: Apr 14, 2026
- Type: Benchmark

The post reports a three-week experiment in which a multi-agent system optimized 235 CUDA kernel problems generated from workloads spanning more than 124 production open-source models. Using SOL-ExecBench on 27 NVIDIA Blackwell 200 GPUs, the system used a planner to distribute and rebalance work, while autonomous workers benchmarked, debugged, and refined kernels without developer intervention. It ran separate CUDA C with inline PTX and CuTe DSL runs, reaching assembly-level optimization and learning a documented higher-level API. Against a single-agent optimized PyTorch baseline, it outperformed 149 of 235 problems, achieving a 1.38x geometric mean ratio, while 45 solutions exceeded 2x. The highlighted cases included an 84% geomean gain over a human FlashInfer baseline for paged-prefill attention, a 39% gain for NVFP4 MoE linear, and a GEMM reaching 86% of a tuned cuBLAS baseline; median SOL was 0.56, leaving room for improvement.


### [Automating Receipt Collection: Apple Intelligence for On-Device Inference](https://yomu.fyi/post/automating-receipt-collection-apple-intelligence-for-on-device-inferen.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Kabir Oberai
- Published: Apr 13, 2026
- Type: Problem & solution

Ramp built automatic receipt detection for its iOS app to reduce the effort of finding receipt photos while keeping private images on device. An initial design used PhotoKit, Vision OCR, background processing, local storage, and exact checks for merchant, amount, and date, but formatting differences, aliases, and OCR errors produced many false negatives. FoundationModels enabled a local structured-output LLM, yet a single prompt performed poorly in evals because the small 4096-token model hallucinated and took shortcuts. Splitting the merchant, date, and amount questions and requesting rationale improved evaluation results, but the alpha release reached 66% precision and about 18% recall. Version 3.0 combined one LLM merchant-name check with deterministic iOS 26 data detectors for dates and monetary values; it cut processing time, more than doubled recall, and raised precision to 87%.


### [Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs](https://yomu.fyi/post/waypoint-1-5-higher-fidelity-interactive-worlds-for-everyday-gpus.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Andrew Lapp, Louis Castricato, Scott Fox, Shahbuland Matiana, David Rossi
- Published: Apr 9, 2026
- Type: Announcement

Overworld’s Waypoint-1.5 is a real-time video world model aimed at making interactive generative worlds usable on consumer hardware rather than limited to datacenter-scale compute. It offers two tiers: a 720p model for higher-performance desktop GPUs, capable of up to 60 FPS on RTX 3090 through 5090 hardware, and a 360p model for broader deployment, including gaming laptops and soon Apple Silicon Macs. Training used nearly 100 times more data than Waypoint-1, while more efficient video-modeling techniques reduce redundant computation across frames to support coherent motion and responsive exploration. Users can run it locally through the Biome desktop client, try it in a browser via Overworld Stream, or use the World Engine inference library and third-party clients. The release presents improved visual fidelity, broader local hardware support, and continued progress toward real-time interactive world simulation.


### [Selective Test Execution at Stripe: Fast CI for a 50M-line Ruby monorepo](https://yomu.fyi/post/selective-test-execution-at-stripe-fast-ci-for-a-50m-line-ruby-monorep.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Aditya Anchuri
- Published: Apr 9, 2026
- Type: Problem & solution

Stripe’s Ruby monorepo contains about 50 million lines, 100,000 test files, and roughly 1.2 million test units, making sequential execution impractical at an estimated four months. To keep CI within minutes, Selective Test Execution (STE) runs tests whose recorded file accesses intersect files changed since a suitable baseline, averaging about 5% of the suite and under 10% of always-run compute. A dynamically linked C++ library loaded with LD\_PRELOAD intercepts file opens, associates them with hierarchical test scopes, and propagates tracing to child processes; logs are aggregated into a selection index outside the syscall hot path. Guardrails cover tests affected by directory discovery, previously failing tests, selective linting, generated files, and reproducible database-backed baselines ordered by Monotonic Revision IDs. The system is presented as a way to preserve safety and affordability while scaling CI for a highly dynamic language.


### [ClickHouse at FOSDEM 2026](https://yomu.fyi/post/clickhouse-at-fosdem-2026.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Tyler Hannan
- Published: Apr 8, 2026
- Type: Announcement

The ClickHouse community and development team participated in FOSDEM 2026 in Brussels, hosting a community dinner and delivering technical presentations across multiple developer tracks. Presentations covered a range of engineering topics, including building geospatial analytical applications with Leaflet and ClickHouse, as well as integrating LLVM XRay to dynamically hot-patch production logging without redeployments. Additional ClickHouse sessions addressed the engineering challenges of integrating Rust libraries into large C++ codebases, optimizing low-level memcpy routines, and implementing specialized inverted indexes for large-scale text processing. Contributed community sessions detailed workflows for upstream patches in MariaDB and Postgres, alongside WebShield, an open-source Python daemon that streams web accelerator access logs into ClickHouse for bot analysis.


### [Safetensors is Joining the PyTorch Foundation](https://yomu.fyi/post/safetensors-is-joining-the-pytorch-foundation.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Luc Georges, Lysandre
- Published: Apr 8, 2026
- Type: Announcement

Safetensors has joined the PyTorch Foundation as a foundation-hosted project under the Linux Foundation, alongside DeepSpeed, Helion, Ray, vLLM, and PyTorch. It began as a Hugging Face project addressing the risk of arbitrary code execution in pickle-based model-weight formats. Its format stores tensor metadata in a JSON header capped at 100MB, followed by raw tensor data, and supports zero-copy mapping and lazy loading. The move places the trademark, repository, and governance with the Linux Foundation, while Luc and Daniel remain on the Technical Steering Committee and lead day-to-day work. Existing users get the same format, APIs, and Hub integration without breaking changes, while contributors gain a documented maintainer path; planned work includes PyTorch core serialization, accelerator-aware loading, parallel loading, and quantization support.


### [How Eisan made POS analytics faster, cheaper, and more reliable with ClickHouse Cloud](https://yomu.fyi/post/how-eisan-made-pos-analytics-faster-cheaper-and-more-reliable-with-cli.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 8, 2026
- Type: Problem & solution

Eisan System Development moved a retail analytics workload from a QlikView-based platform to ClickHouse Cloud after rising ID-POS data volumes caused slower processing, failed analyses, and increasing licensing and server costs. Client records from supermarkets and drugstores support basket analysis, cross analysis, and real-time personalization, with some datasets nearing 200 million records annually. The team compared open-source options including PostgreSQL and YugabyteDB, then tested ClickHouse configurations, including a 30-core, 120-GB setup, while accounting for cache memory usage through load testing. In comparison, ClickHouse ran on slightly lower-spec hardware under high-load conditions without observed performance deterioration, and Eisan reports no cases so far where it failed to return a result; the team is now targeting 100 billion records and exploring off-hours processing and scheduled scaling.


### [MongoDB Predictive Auto-Scaling: An Experiment](https://yomu.fyi/post/mongodb-predictive-auto-scaling-an-experiment.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: A. Jesse Jiryu Davis, Matthieu Humeau
- Published: Apr 7, 2026
- Type: Problem & solution

MongoDB Atlas initially relied on a reactive auto-scaler that took minutes to adjust instance sizes and only transitioned between adjacent tiers. Because these delays caused transient server over-utilization or unnecessary customer costs, researchers prototyped predictive auto-scaling to forecast demand. The experimental architecture used short-term and long-term forecasters for customer-driven metrics, an estimator using boosted decision trees to project CPU utilization, and a planner to pick the most cost-effective tier. In testing, the prototype kept replica sets closer to target CPU ranges while saving an average of nine cents per replica set hourly. MongoDB subsequently implemented a production version that scales replica sets up proactively before expected spikes while retaining reactive downscaling.


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