---
title: "Latest reads"
description: "The engineering internet, summarised so you can actually read it."
---

# Latest reads
> The engineering internet, summarised so you can actually read it.

## Articles

### [Provision a production-ready dev stack from your terminal](https://yomu.fyi/post/provision-a-production-ready-dev-stack-from-your-terminal.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Rami Banna
- Published: Mar 26, 2026
- Type: Announcement

Stripe Projects, a new tool in the Stripe CLI, addresses the manual, fragmented work of turning a local prototype into a production-ready environment. It manages a project manifest and Stripe-backed record for attached services, provider accounts, and required credentials, while an integration protocol standardizes provisioning, plan selection, upgrades, and credential handoff. The developer preview supports services from providers including Vercel, Railway, Supabase, Neon, PlanetScale, Turso, Chroma, PostHog, Clerk, and Runloop; resources remain in accounts owned by the developer, and credentials can be synced to local development, CI, or a team secret manager. Through CLI commands such as stripe projects init, add, upgrade, and env --pull, developers or coding agents can repeat setup, audit changes, and upgrade supported plans using tokenized payment credentials, although payment handoff is limited to supported providers and plans in the US, EU, UK, and Canada.


### [Top 10 best practices tips for ClickHouse](https://yomu.fyi/post/top-10-best-practices-tips-for-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Yonatan Dolan
- Published: Mar 26, 2026
- Type: Explainer

ClickHouse delivers rapid analytical queries across massive datasets, but suboptimal schemas and queries can significantly reduce performance and storage efficiency. Aligning table definitions with ClickHouse storage and execution mechanics resolves these bottlenecks. Key practices include placing low-cardinality columns first in the ORDER BY clause to enable granule pruning, and selecting optimal data types while avoiding Nullable wrappers to cut memory overhead. Developers should treat partitioning as a data management tool for TTL drops or merge engines rather than a generic speed booster, preventing fragmented parts from slowing query execution. Recent improvements like the Analyzer query planner have also made joins viable, though dictionaries remain faster for static lookups.


### [More Visibility, Less Guesswork: ClickHouse Cloud's New Monitoring Capabilities](https://yomu.fyi/post/more-visibility-less-guesswork-clickhouse-cloud-s-new-monitoring-capab.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mihir Gokhale
- Published: Mar 25, 2026
- Type: Announcement

ClickHouse Cloud released new monitoring capabilities and dashboards to give platform administrators deeper visibility into deployment health and server behavior. A newly introduced Overview page consolidates critical signals into a unified view for rapid health checks. Additionally, an updated Infrastructure page tracks service scaling over time, presenting CPU and memory utilization with new aggregation options alongside modals explaining automatic scaling decisions. The platform now sends automated email and Slack notifications when deployments encounter risks such as excessive parts, failed mutations, or query concurrency saturation. These additions allow administrators to proactively identify operational bottlenecks and configure scaling limits without spending excessive time searching for telemetry data.


### [Announcing Monitors: Opening the AI black box](https://yomu.fyi/post/announcing-monitors-opening-the-ai-black-box.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Alissa Tyrangiel
- Published: Mar 25, 2026
- Type: Announcement

Intercom announced Monitors, a new Fin product area intended to complete its observability suite alongside Insights and Recommendations. It addresses the limits of random sampling, CSAT, and spreadsheet-driven quality assurance by letting teams select conversations with filters or natural-language criteria, including risk signals, and maintain repeatable samples. Custom Scorecards then define business-specific evaluation criteria, weighting, and critical failures, with scoring performed by AI, humans, or both to produce an overall quality score. Flagged conversations enter a Review Queue for assignment, notes, status tracking, and follow-up fixes, while reporting compares scores with CX Score, resolution rate, Monitors, and Scorecards over time. Monitors for Fin conversations is live, with human-agent QA, real-time alerts, and knowledge-base evaluation identified as planned additions.


### [Reducing our monorepo size to improve developer velocity](https://yomu.fyi/post/reducing-our-monorepo-size-to-improve-developer-velocity.md)
- Company: [Dropbox](https://yomu.fyi/company/dropbox.md)
- Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel,Ishan Mishra
- Published: Mar 25, 2026

Dropbox reduced its core server monorepo size from 87GB to 20GB after approaching GitHub's 100GB limit and suffering from hour-long clone times. The excessive bloat was traced to Git's delta compression heuristics interacting poorly with internationalization directory paths, generating inefficient diffs across different languages. By collaborating with GitHub to run an aggressive server-side repack with tuned window and depth settings, Dropbox cut clone times to under 15 minutes.


### [Beyond A/B Testing: Using Surrogacy and Region-Splits to Measure Long-Term Effects in Marketplaces](https://yomu.fyi/post/beyond-a-b-testing-using-surrogacy-and-region-splits-to-measure-long-t.md)
- Company: [Lyft](https://yomu.fyi/company/lyft.md)
- Author: Iraklikhorguani
- Published: Mar 25, 2026
- Type: Explainer

Lyft describes a framework for estimating long-term effects of pricing and incentive decisions in its two-sided marketplace, where direct and market-mediated consequences can diverge. The method first uses residualized observational regressions to estimate how policy changes alter distributions of negative experiences such as wait times, surge, cancellations, idleness, and hourly earnings, validating those responses with switch-back experiments. It then applies Augmented Inverse Probability Weighting to estimate how these experiences affect future outcomes, summarizes the relationship with a surrogacy index, and checks calibration using user-split experiments. Finally, Lyft combines mediated and separately estimated direct effects and uses region-split experiments for end-to-end validation, selecting treated and control regions with a forward-selection algorithm inspired by FDiD. The framework is intended to support scenario planning, budget allocation, and continuous calibration of long-run marketplace forecasts.


### [Run cloud agents in your own infrastructure](https://yomu.fyi/post/run-cloud-agents-in-your-own-infrastructure.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Katia Bazzi
- Published: Mar 25, 2026
- Type: Announcement

Cursor has made self-hosted cloud agents generally available, allowing code, tool execution, and build artifacts to remain within an organization’s network. Each agent runs in an isolated virtual machine or dedicated worker with a terminal, browser, and full desktop; it can clone repositories, configure environments, write and test code, and push changes for review. A worker connects outbound to Cursor’s cloud over HTTPS, receives tool calls from the agent harness, and returns execution results, while organizations can run long-lived or single-use workers and manage large fleets through a Helm chart, Kubernetes operator, or fleet management API. The product retains cloud-agent capabilities including multi-model support, plugins, and team permissions, while giving teams access to internal caches, dependencies, endpoints, and security controls without maintaining a separate agent stack.


### [ClickHouse is data lake ready](https://yomu.fyi/post/clickhouse-is-data-lake-ready.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Karolina Ruiz Rogelj, Melvyn Peignon
- Published: Mar 25, 2026
- Type: Announcement

ClickHouse announced expanded data lake capabilities, enabling direct querying, accelerated ingestion into MergeTree, and writes across Apache Iceberg and Delta Lake tables. Open lake architectures provide storage portability across cloud providers and catalogs, but standard query engines often struggle with latency for real-time workloads. Over two years of development, ClickHouse engineered a native Parquet reader with page-level parallelism, integrated the Delta Rust Kernel, and introduced support for schema evolution, time travel, and DML operations. The platform functions across AWS Glue, Unity Catalog, Polaris, and Iceberg REST Catalog on storage systems including Amazon S3, Google Cloud Storage, and Azure Blob Storage. These enhancements allow organizations to execute federated, high-concurrency SQL queries across multiple catalogs or write processed analytical results back to their lakes without vendor lock-in.


### [Social Media Analytics: Key Insights for Business Success](https://yomu.fyi/post/social-media-analytics-key-insights-for-business-success.md)
- Company: [Brandwatch](https://yomu.fyi/company/brandwatch.md)
- Author: Yasmin Pierre
- Published: Mar 24, 2026
- Type: Tutorial

Social media analytics involves gathering, analyzing, and interpreting social media data to understand audience behavior, evaluate channels and campaigns, and guide future plans. The guide covers the workflow from collection through interpretation and strategic adjustment, including engagement, reach, sentiment, audience, competitor, and conversion metrics. It recommends tools such as Brandwatch, Google Analytics, Sprout Social, Hootsuite Analytics, and Meta Business Suite, while noting that tool choice depends on needs and budget; integrations can centralize multiple platforms. The guidance emphasizes aligning metrics with business goals, automating collection, combining cross-platform data, benchmarking competitors, using historical trends, and turning findings into content and campaign changes. It also addresses attribution, platform changes, privacy, and emerging uses of AI, predictive, real-time, unified, video, and audio analytics.


### [eBay Partners with Meta for New Affiliate Program, Boosting Social Commerce for Sellers and Creators](https://yomu.fyi/post/ebay-partners-with-meta-for-new-affiliate-program-boosting-social-comm.md)
- Company: [Ebay](https://yomu.fyi/company/ebay.md)
- Author: Adrian Fung, Global Chief Marketing Officer, eBay
- Published: Mar 24, 2026
- Type: Announcement

eBay is joining Facebook’s affiliate partnerships as a beta partner, allowing creators across Facebook’s global ecosystem to feature eBay inventory directly in content. Creators can access and embed listings in Reels, photos, and text posts, and earn a commission when followers click a linked listing and complete a purchase on eBay. The integration is intended to make discovery more direct within Facebook, helping sellers reach engaged buyers, expand market reach, and generate incremental demand through creator-led storytelling. The beta is scheduled to begin in the coming months, offering sellers a new visibility channel and creators an additional monetization stream as social commerce and the creator economy continue to evolve.


### [Intelligent security at ClickHouse speed: How Cogent Security built an AI-native vulnerability management platform](https://yomu.fyi/post/intelligent-security-at-clickhouse-speed-how-cogent-security-built-an.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 24, 2026
- Type: Problem & solution

Enterprise security defenders face shrinking exploitation windows alongside massive volumes of vulnerability findings across disconnected tools. To overcome query bottlenecks from traditional relational databases, Cogent Security designed a lakehouse serving architecture using Apache Iceberg and per-customer ClickHouse instances. The architecture relies on query-driven denormalized tables, custom compression codecs, and pre-computed projections to serve hundreds of millions of rows with sub-second latency. On top of this data layer, Cogent implemented an agentic text-to-SQL system that iteratively queries live data, references an in-house Ontology Service for semantic context, and applies majority-voted evaluations. This combination improved text-to-SQL accuracy from 40% to 94% while providing sub-three-second response times across 98.7% of queries at 100 million rows.


### [A New Framework for Evaluating Voice Agents (EVA)](https://yomu.fyi/post/a-new-framework-for-evaluating-voice-agents-eva.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tara Bogavelli, Gabrielle Gauthier Melancon, Katrina Stankiewicz, Nifemi Bamgbose, Hoang Nguyen, Raghav Mehndiratta, Hari Subramani, Fanny Riols
- Published: Mar 24, 2026
- Type: Benchmark

EVA is an end-to-end framework for evaluating conversational voice agents across task accuracy and spoken conversational experience. It simulates multi-turn live-audio conversations with a user simulator, the evaluated voice agent, deterministic tool execution, validation, and metrics derived from recordings, transcripts, and tool-call logs. The framework reports EVA-A for Accuracy and EVA-X for Experience, combines code-based and LLM- or LALM-as-Judge metrics, and uses pass@3 and pass^3 across three trials to capture peak performance and consistency. Its initial release contains 50 reproducible English-language airline scenarios and 15 tools covering rebooking, itinerary changes, cancellations, standby, and vouchers. Results for 20 cascade and audio-native systems show a consistent tradeoff: agents stronger at task completion tend to provide worse experiences, while a substantial gap between pass@3 and pass^3 indicates inconsistent success.


### [Smarter Auto-Scaling for ClickHouse: The Two-Window Approach](https://yomu.fyi/post/smarter-auto-scaling-for-clickhouse-the-two-window-approach.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Ashwath Singh, Manas Alekar
- Published: Mar 24, 2026
- Type: Problem & solution

ClickHouse Cloud required a vertical auto-scaling mechanism that preserves rapid scale-ups while avoiding prolonged over-provisioning after traffic drops. Operating on a single 30-hour lookback window forced scale-downs to take up to 30 hours, whereas an earlier fixed-factor CPU algorithm caused severe oscillations. The engineering team deployed a dual-window recommender pairing a 3-hour window for responsive scale-downs with a 30-hour window for single-step scale-ups. Alongside a trend-based hunting check to resolve conflicting window recommendations, the team introduced target-tracking CPU scaling using geometric mean watermarks and 10-minute median smoothing. This dual-window architecture reduced scale-down latency to 3 hours, prevented scaling oscillations, and significantly reduced infrastructure costs for variable database workloads.


### [Building high-performance full-text search for object storage](https://yomu.fyi/post/building-high-performance-full-text-search-for-object-storage.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Elmi Ahmadov, Jimmy Aguilar, and George Larionov
- Published: Mar 24, 2026
- Type: Problem & solution

ClickHouse redesigned its native full-text index to address latency bottlenecks caused by random read patterns on remote object storage. Instead of using a Finite State Transducer dictionary, the new architecture organizes indexed tokens into fixed-size sorted blocks compressed with front-coding. Each data part separates index data across a small in-memory sparse index file, a sequential dictionary file, and a posting list file. This layout allows the query engine to pinpoint token locations with minimal random I/O and resolve multiple full-text functions directly from index files. Consequently, ClickHouse Cloud maintains predictable search latency across shared object storage while matching local disk performance and scaling through parallel query execution.


### [Fast regex search: indexing text for agent tools](https://yomu.fyi/post/fast-regex-search-indexing-text-for-agent-tools.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Vicent Marti
- Published: Mar 23, 2026
- Type: Explainer

Agentic coding has revived grep as a critical way to gather source-code context, but ripgrep can stall on large monorepos because it scans every file. The proposed remedy is a local inverted index of overlapping character trigrams, with regular expressions parsed into required and alternative trigram constraints that narrow candidate files before exact matching. The index stores posting lists in one disk file and a sorted hash-to-offset lookup table in another, memory-mapping only the table for binary-search lookups; hash collisions can broaden results but cannot produce incorrect ones. Freshness is prioritized because agents often search for text they have just written, and the client index layers user and agent changes over a Git commit. The reported conclusion is that instant local search creates qualitative workflow improvements, especially for bug investigation and large Enterprise repositories.


### [Structured Logging in .NET with Serilog and ClickHouse](https://yomu.fyi/post/structured-logging-in-net-with-serilog-and-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Alex Soffronow Pagonidis
- Published: Mar 23, 2026
- Type: Tutorial

Traditional logging solutions like Seq, ELK, and cloud platforms often face scaling bottlenecks, including slower queries, higher operational costs, and complex pipeline management. To address this, an ASP.NET service can write structured logs directly to ClickHouse using Serilog and the Serilog.Sinks.ClickHouse library. The sink transmits data over HTTP and provides a fluent C# builder to configure table schemas, typed JSON columns, full-text indexes, and MergeTree engine parameters. ClickHouse stores log data in a columnar format that delivers 10 to 20 times compression while enabling direct SQL queries, JSON property filtering, and real-time aggregations through materialized views. Furthermore, the sink includes an in-memory buffer to handle transient network drops with configurable batching, retry mechanisms, and callback hooks for failure alerting.


### [Build a Domain-Specific Embedding Model in Under a Day](https://yomu.fyi/post/build-a-domain-specific-embedding-model-in-under-a-day.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Steve Han, Rucha Apte, Sean Sodha, Oliver Holworthy
- Published: Mar 20, 2026
- Type: Tutorial

The tutorial presents a pipeline for adapting a general-purpose embedding model when it misses fine-grained distinctions in domain documents and labeled query–document data is unavailable. It generates synthetic question–answer pairs with NeMo Data Designer, filters them for quality, mines hard negatives with a 95% positive-score margin ceiling, and unrolls multi-hop queries for contrastive training. The recipe fine-tunes Llama-Nemotron-Embed-1B-v2, evaluates base and adapted models with BEIR metrics, and exports the result to ONNX or TensorRT. On the reported datasets, it improved Recall@10 and NDCG@10 by more than 10%; Atlassian increased Recall@60 from 0.751 to 0.951 on JIRA data. The complete workflow can run in roughly 2–3 hours for about 500 documents, using a single 80GB Ampere-or-newer GPU.


### [Making Ads Count: Using MMoE and Auxiliary Tasks to Better Connect Buyers & Sellers](https://yomu.fyi/post/making-ads-count-using-mmoe-and-auxiliary-tasks-to-better-connect-buye.md)
- Company: [Etsy](https://yomu.fyi/company/etsy.md)
- Author: Amanda Steigman
- Published: Mar 20, 2026
- Type: Explainer

Etsy’s Ads Search ranking model needed a stronger purchase-intent signal because clicks are noisy and purchases are sparse later in the buyer journey. It retained its shared feature-representation and interaction layers, then replaced the shared feed-forward network with Multigate Mixture of Experts (MMoE), using task-specific gates and towers for click-through rate (CTR) and post-click conversion rate (PCCVR). Add-to-cart was added as an auxiliary task, while favorites were rejected after producing negative transfer. Offline, the resulting model increased Purchase and Click PR AUCs by average 3.5% and 1%, respectively, and Purchase ROC AUC by 0.5%. Online, it improved purchases and purchase calibration, supplied better inputs to auto-bidding, kept model size flat through pruning, and reduced inference cost.


### [Transformation in action: Raising the bar for customer experience](https://yomu.fyi/post/transformation-in-action-raising-the-bar-for-customer-experience.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Mar 20, 2026
- Type: Explainer

AI is shifting support from reactive backlog management toward proactive experience design, as teams gain capacity through faster, more efficient service across languages and time zones. The report says 58% rank experience and satisfaction as their top 2026 priority, up from 28%, while mature deployment triples the share able to always meet expectations from 9%. Intercom’s support team expanded access, improved first-response rates, and added onboarding sessions while absorbing a 300% demand increase without expanding headcount, enabled by deep integration of Fin. Clay uses Fin across Slack, email, and in-app chat, where it handles half of roughly 7,000 monthly queries and participates in 90% of all queries. The recommended model treats support as a product, combines AI with human handoffs, anticipates needs, builds trust, and personalizes interactions.


### [Transformation in action: Why ROI becomes clearer with deeper integration](https://yomu.fyi/post/transformation-in-action-why-roi-becomes-clearer-with-deeper-integrati.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Mar 20, 2026
- Type: Explainer

Customer service teams often see early AI gains in response speed and efficiency, but deeper operational integration produces larger returns and makes value easier to measure. Research cited in the 2026 Customer Service Transformation Report says 62% of support teams have improved metrics after implementing AI, rising to 87% among teams at mature deployment; the share able to measure ROI rises from 35% to 70%. As deployment matures, teams shift from measuring time and cost savings to tracking how freed capacity supports higher-value customer work and revenue-generating activity. The proposed AI-first economic model treats AI as a workforce capability that improves human productivity, system improvement, revenue influence, and organizational agility. Intercom’s Fin illustrates the model: it is involved in 97% of customer conversations, resolves 83.5% end-to-end, and helped absorb more than 300% demand growth without scaling headcount.


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