---
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

### [When predicting the next hit requires more than intuition](https://yomu.fyi/post/when-predicting-the-next-hit-requires-more-than-intuition.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Elena Tesser
- Published: Apr 30, 2026
- Type: Problem & solution

Content investment decisions in entertainment commit substantial funds despite limited information, because leaders often combine executive instinct, competitive benchmarks, and historical performance data. Although companies hold viewing and subscriber signals such as episode completion, skip patterns, genre performance by demographic, acquisition, and retention, those insights are frequently buried in dashboards and spreadsheets. The post presents Databricks Genie as a natural-language interface that lets content leaders query governed performance data directly, including comparisons linking content types with 90-day retention, subscriber demographics, lifetime value, acquisition source, and historical greenlights. It argues that current, conversational access can complement creative judgment by reducing dependence on delayed analyst recommendations, while external market data can add competitive context.


### [Data Mesh at Grab (Part II): The foundational tools behind certification](https://yomu.fyi/post/data-mesh-at-grab-part-ii-the-foundational-tools-behind-certification.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Aezo Teo
- Published: Apr 30, 2026
- Type: Explainer

Grab’s Signals Marketplace uses three foundational platforms to make data certification operational across its data mesh: Hubble, Genchi, and the Data Contract Registry. Hubble, built on DataHub, catalogs assets, ownership, documentation, lineage, contracts, and certification status, while its event-driven engine recalculates certification as metadata and upstream health change. Genchi provides self-service observability and tests for freshness, volume, schema, completeness, and business checks; the registry manages explicit expectations between producers and consumers. Together, these signals let Hubble classify assets as Uncertified, Certified, CertifiedPlus, or Revoked, giving consumers a workflow centered on certified data rather than hearsay. The post reports that P80 datasets, the tables accounting for 80% of queries, fell by over 58% in one year after the Signals Marketplace campaign began in 2024, and positions the foundation as support for AI agents and automated analytics.


### [Welcoming Diel Duarte, our new Open source Engineer](https://yomu.fyi/post/welcoming-diel-duarte-our-new-open-source-engineer.md)
- Company: [Resend](https://yomu.fyi/company/resend.md)
- Published: Apr 30, 2026
- Type: Announcement

Resend introduces Diel Duarte as its new open source engineer, bringing more than 13 years of experience building web applications and SDKs. His focus is developer experience, especially API design with clean ergonomics that lets developers concentrate on building rather than wrestling with interfaces. Diel describes a path from studying programming and computer fundamentals at Cotemig to following Resend as a user and joining after an interview process he found unusually human; he cites Resend’s DX and craft, along with Zeno’s open source work, as major references. Outside work, he draws inspiration from deliberately curated feeds, new languages and APIs, visual-design communities, and creator friends, while keeping music and interior design as interests; he names Warp Terminal as his favorite tool and Japan as a memorable destination.


### [Approximate answers, exact decisions: New sketch functions for analytics](https://yomu.fyi/post/approximate-answers-exact-decisions-new-sketch-functions-for-analytics.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Daniel Tenedorio, Kent Marten, Gengliang Wang, Chenhao Li
- Published: Apr 29, 2026
- Type: Announcement

Databricks now supports four sketch function families, built on Apache DataSketches, for replacing expensive exact percentiles, distinct counts, set operations, top-K rankings, and metric aggregations with bounded-memory approximations. KLL sketches answer quantiles, Theta sketches support unions, intersections, and differences, approximate top-K sketches track frequent items, and Tuple sketches combine distinct counting with aggregated metrics such as customer revenue. The sketches can be built during ETL, stored as columns in Delta tables, and merged on read, allowing dashboards and streaming pipelines to avoid rescanning raw data; SQL, DataFrame, and Structured Streaming pipelines are supported. The stated trade-off is configurable 1–2% relative error, with the source citing a 1000x speedup for applicable workloads. Exact methods remain appropriate for financial auditing, compliance reporting, and other precision-required uses.


### [Companies winning with AI built the data layer first](https://yomu.fyi/post/companies-winning-with-ai-built-the-data-layer-first.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: Apr 29, 2026
- Type: Explainer

Trinity Industries’ experience is presented as a case for treating the data layer, rather than models or dashboards, as the foundation of enterprise AI. The railcar manufacturer migrated 95% of its enterprise data to a single Databricks lakehouse, adopted Medallion architecture, moved transformations upstream, and retired legacy dashboards containing nearly 600 measures. That consolidation supports streaming ETA predictions, procurement agents, and Genie conversational analytics: the ETA model is reported as 50% more accurate than industry ETAs, agents helped increase on-time material delivery by 15%, and Genie handles more than 1,000 questions monthly. The migration took close to a year, followed by six to eight months of additional work, but the account argues that trusted, standardized data enables experimentation, automation, and broader employee access to analysis.


### [Massachusetts Driver Benefits](https://yomu.fyi/post/massachusetts-driver-benefits.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Apr 29, 2026
- Type: Announcement

In June 2024, Uber reached an agreement with Massachusetts Attorney General Andrea Campbell that adds protections and benefits for independent Massachusetts Uber Rides drivers. For Rides trips beginning in Massachusetts, drivers are guaranteed $34.48 per hour of active time, calculated over two-week periods, with supplemental payments when trip fares and included incentives fall short. Beginning November 1, 2024, drivers can accrue up to 40 hours of paid sick leave annually at $21.22 per hour, subject to the stated accrual and claiming rules. The post also describes occupational accident insurance, Paid Family Medical Leave payments, a portable health fund with quarterly eligibility, and account-deactivation appeals. It explains how active time, sick leave, guaranteed earnings, and health-fund participation are viewed or claimed through Uber’s systems and related administrators.


### [Rethinking SQL ETL for modern data platforms](https://yomu.fyi/post/rethinking-sql-etl-for-modern-data-platforms.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Matt Jones, Shanelle Roman
- Published: Apr 29, 2026
- Type: Problem & solution

SQL ETL is often distributed across a warehouse, transformation framework, orchestrator, monitoring, lineage, and data-quality systems, creating operational fragmentation as data teams scale. The post proposes a unified platform model that combines execution, orchestration, observability, governance, and optimization while supporting analytics engineers, warehouse engineers, and analysts through dbt, scripts and stored procedures, Materialized Views, declarative pipelines, and no-code tools. Databricks implements this model with shared execution, governance, and observability, using serverless infrastructure and AI-driven optimization alongside open table formats and ANSI SQL. The stated benefits include reduced operational overhead, support for batch and real-time workloads, and incremental modernization rather than disruptive rewrites. Reported examples include HP’s 32% cloud savings and 36% lower combined job runtime after moving to serverless compute, and Adobe reducing query execution from eight minutes to three seconds with Materialized Views.


### [Stripe data now available on Databricks via Databricks Marketplace](https://yomu.fyi/post/stripe-data-now-available-on-databricks-via-databricks-marketplace.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Justin Fenton, Harish Gaur, Matt Napoli
- Published: Apr 29, 2026
- Type: Announcement

Stripe Data Pipeline is now available on Databricks Marketplace through Delta Sharing, allowing payment and business data to be analyzed in a Databricks workspace without ETL. Stripe data—including transaction records, customer histories, subscriptions, refunds, and payouts—remains in Stripe’s infrastructure and is queried directly through Unity Catalog, where it can be joined with other tables. The approach replaces polling integrations and custom jobs, avoiding per-call charges, connector licensing fees, data duplication, and maintenance code while providing row- and column-level access controls, audit trails, and compliance features. The shared data is positioned for AI-native uses such as continuous transaction monitoring, churn scoring, LLM-powered retention workflows, Genie queries, and finance analytics apps. Getting started involves visiting the Stripe Data Pipeline listing on Databricks Marketplace.


### [Databricks and Stripe projects: Infrastructure built for agents](https://yomu.fyi/post/databricks-and-stripe-projects-infrastructure-built-for-agents.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Brad Van Vugt, Guillaume Rivals
- Published: Apr 29, 2026
- Type: Announcement

The post announces Databricks as a launch partner for Stripe Projects, an agent-first CLI that lets AI agents discover, provision, and pay for Neon databases without human interaction. It addresses the manual work still required to provision services, navigate interfaces, configure accounts, and enter payment details, which can slow autonomous app development. With Lakebase architecture, Neon can provide production-ready Postgres in under 350ms, while decoupled compute and storage support rapid creation and teardown of OLTP databases. Serverless scaling adjusts compute to traffic and can scale to zero, while zero-copy branching creates isolated production-data branches for testing, migrations, and prompt experiments without risking the primary environment. The post also notes Databricks’ Stripe Data Pipeline is available in the Databricks Marketplace for analyzing Stripe data without ETL.


### [Granite 4.1 LLMs: How They’re Built](https://yomu.fyi/post/granite-4-1-llms-how-they-re-built.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Yousaf Shah
- Published: Apr 29, 2026
- Type: Explainer

Granite 4.1 is a family of dense decoder-only language models in 3B, 8B, and 30B sizes, trained on approximately 15T tokens. Its five-phase pre-training pipeline moves from broad web-scale data to progressively curated mixtures, increases math and code emphasis, and extends context from 4K to 512K tokens through staged long-context training. Supervised fine-tuning uses about 4.1M curated samples evaluated with LLM-as-Judge and rule-based checks, while reinforcement learning uses on-policy GRPO with DAPO loss. The 8B instruct model matches or surpasses the earlier Granite 4.0-H-Small 32B-A9B MoE model despite its simpler dense architecture and fewer parameters, and the Granite 4.1 models are released under Apache 2.0.


### [Agents are ready, but your architecture probably isn't](https://yomu.fyi/post/agents-are-ready-but-your-architecture-probably-isn-t.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Catherine Brown
- Published: Apr 29, 2026
- Type: Explainer

Enterprise AI initiatives often produce activity rather than value because organizations start with technology instead of a defined outcome and overlook data architecture, governance, and semantic context. Agentic systems add risk when they can send messages, update records, place orders, or delete records, making permissions and situational controls essential. The discussion argues that dashboards and batch pipelines are poorly matched to low-latency, high-scale agent workloads, which require transactional infrastructure alongside existing analytics. Lakebase is presented as that transactional foundation, while AgentBricks, Databricks Apps, and Genie provide agent development and monitoring, application delivery, and conversational data access. The recommended path is to define success first, isolate a focused pilot, learn what works, and redesign underlying processes rather than merely add AI to them.


### [Interoperability between Unity Catalog and Google BigQuery via catalog federation](https://yomu.fyi/post/interoperability-between-unity-catalog-and-google-bigquery-via-catalog.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: John Spencer, Jason Reid
- Published: Apr 29, 2026
- Type: Announcement

Databricks and Google Cloud announce bidirectional catalog federation that lets customers access the same data from Unity Catalog or BigQuery without duplication. The interoperability uses Apache Iceberg REST catalog APIs, enabling both platforms to read and write the same tables through a common open interface. Google Cloud's catalog federation is in preview for reading Unity Catalog-managed tables from BigQuery, while Google Cloud's Lakehouse federation is in private preview for Databricks users to govern and read foreign Iceberg tables. Unity Catalog's policies, fine-grained access controls, and lineage tracking apply to federated data, and users can query it with Genie using natural language. The announcement positions open standards as a path to interoperability across data platforms and previews future catalog metadata exchange through Iceberg REST catalog APIs.


### [Build programmatic agents with the Cursor SDK](https://yomu.fyi/post/build-programmatic-agents-with-the-cursor-sdk.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Roshan Sadanani
- Published: Apr 29, 2026
- Type: Announcement

Cursor’s SDK is now in public beta, letting developers build programmatic coding agents with the runtime, harness, and models used by Cursor’s desktop app, CLI, and web app through TypeScript. Agents can run locally, on Cursor’s cloud in dedicated VMs, or on self-hosted workers; cloud sessions include a repository clone, configured development environment, streaming, reconnection, and options to push branches or open pull requests. The SDK also provides codebase indexing, semantic search, grep, MCP servers, repository skills, hooks, and named subagents, while supporting every model available in Cursor, including Composer 2.5. Described uses include CI/CD automations, custom internal platforms, and customer-facing agent experiences, with starter projects for quickstarts, prototyping, kanban workflows, and a CLI; the SDK uses standard token-based consumption pricing.


### [Two commands to Sentry: now on Stripe Projects](https://yomu.fyi/post/two-commands-to-sentry-now-on-stripe-projects.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Burak Yiğit Kaya
- Published: Apr 29, 2026
- Type: Announcement

Sentry is now integrated into Stripe Projects, enabling developers and coding agents to discover, provision, and manage Sentry services directly through the command-line interface. Running initialization and addition commands automatically sets up accounts, generates necessary environment variables like auth tokens and DSNs, and scaffolds agent skill files for tools such as Claude Code and Cursor. The integration supports plan upgrades, downgrades, and the addition of the Seer AI debugging assistant with billing handled through existing Stripe payment methods. Additional CLI capabilities include minting single-use magic login URLs for the Sentry dashboard, managing multi-member team collaboration tied to Stripe organizations, and rotating project credentials directly into local environment files.


### [Evaluating AI at Scale: How Thumbtack Approaches Reliability, Safety, and Quality in GenAI](https://yomu.fyi/post/evaluating-ai-at-scale-how-thumbtack-approaches-reliability-safety-and.md)
- Company: [Thumbtack](https://yomu.fyi/company/thumbtack.md)
- Author: Thumbtack Engineering
- Published: Apr 29, 2026
- Type: Problem & solution

Thumbtack evaluates generative AI features across customer and service professional experiences to prevent unsupported claims, tone inconsistencies, and safety issues. The engineering organization transitioned from decentralized evaluation efforts to a dedicated cross-functional Evals team that provides shared infrastructure while preserving product team autonomy. System architectures combine rule-based checks, AI-as-a-judge scorers powered by DeepEval, trust and safety assessments, and crowdsourced human reviews. Teams leverage three parallel orchestration paths, including an MLflow-tracked stack, nightly Databricks batch jobs integrated with data warehouses, and multi-layer human-in-the-loop pipelines. Future milestones target centralized LLM judge registries, full task journey evaluations, automated judge-writing workflows, and cost-aware evaluation orchestration.


### [Building an RL theorem-proving workflow on Modal](https://yomu.fyi/post/building-an-rl-theorem-proving-workflow-on-modal.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Apr 29, 2026
- Type: Explainer

AE Studio built a distributed reinforcement learning pipeline on Modal to train LLMs to prove theorems in Lean. They compared Group Relative Policy Optimization, which groups attempts and applies gradient updates, with Evolution Strategies, which evaluates a population of perturbed models and updates based on success rates. The workflow used Modal’s per-function images to separate a vLLM instance on GPU for proof generation, CPU-based Lean verification, and orchestration, with .map() fanning out runs and Sandboxes isolating verification batches. ES enabled stateless checkpointing via seed histories: each worker loaded base weights from a Volume, replayed past seeds and rewards (about 200 bytes per iteration), and reconstructed the model on GPU without transferring large weight files. Early results on the lean\_workbook dataset using the Kimina-Prover-Preview-Distill-1.5B model showed ES matching or outperforming GRPO in verified proofs per iteration, especially with limited data, though gains varied.


### [DeepInfra on Hugging Face Inference Providers 🔥](https://yomu.fyi/post/deepinfra-on-hugging-face-inference-providers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Aray Sultanbekova, Shang-Pin, Utemuratov, Yessen K, Oguz Vuruskaner, Célina Hanouti, Simon Brandeis, Lucain Pouget
- Published: Apr 29, 2026
- Type: Announcement

DeepInfra is now a supported Inference Provider on the Hugging Face Hub, adding its serverless inference platform and catalog of more than 100 models to model pages and client integrations. The initial integration supports conversational and text-generation tasks, with access to open-weight LLMs including DeepSeek V4, Kimi-K2.6, and GLM-5.1; support for text-to-image, text-to-video, embeddings, and other tasks will roll out soon. Users can configure provider API keys and preferences in account settings, or authenticate with a Hugging Face token so requests are routed through Hugging Face, including from the Python and JavaScript SDKs and supported agent harnesses. Direct requests are billed by the provider, while routed requests use standard provider API rates without additional markup, and Hugging Face PRO users receive $2 of monthly Inference credits.


### [From init to deploy: building with agents and Stripe Projects](https://yomu.fyi/post/from-init-to-deploy-building-with-agents-and-stripe-projects.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Anna Spysz
- Published: Apr 29, 2026
- Type: Tutorial

The post describes an internal agentic transcription app built to reduce the manual effort required to correct auto-generated subtitles for Stripe Developers videos. The app uses a Next.js interface with transcript, chat, and corrected-output panels, while Stripe Projects CLI links Vercel and OpenRouter and populates required environment variables. An LLM client supports selectable models, iterative instructions, and a transcription agent whose system prompt emphasizes preserving transcript structure; the agent can also use Stripe documentation search through MCP. In a benchmark on a 771-line meetup script, Gemini 2.5 Pro reached 99% accuracy, while the tested models reduced editing from more than 30 minutes to just over two minutes of generated edits plus a human check. The reported overall reduction was 4x, with testing costs totaling $2.54.


### [Comparing ClickHouse versions with clickhousectl](https://yomu.fyi/post/comparing-clickhouse-versions-with-clickhousectl.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Apr 29, 2026
- Type: Tutorial

Evaluating query performance differences across ClickHouse versions previously required managing Docker containers or manually downloading historical binaries from GitHub releases. The clickhousectl command-line interface simplifies this process by downloading specific ClickHouse binaries, running multiple background server instances concurrently with automatic port assignment, and executing queries across local environments. Users can copy table schemas between local instances by piping raw output from SHOW CREATE TABLE queries and replicate datasets using ClickHouse's remote table function. In a benchmark comparing ClickHouse 25.12 and 26.3 on an S3-backed Parquet dataset, version 26.3 introduced a Parquet metadata cache that reduced repeat query times from roughly 9 seconds to 1–2 seconds. System tables like system.server\_settings and system.metrics enable operators to inspect cache configurations, eviction policies, and memory consumption directly.


### [Built in, not bolted on: What AI-native actually means in cybersecurity](https://yomu.fyi/post/built-in-not-bolted-on-what-ai-native-actually-means-in-cybersecurity.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: Apr 28, 2026
- Type: Explainer

The discussion defines AI-native cybersecurity applications as systems architected with intelligence at their core, rather than traditional products with AI added later. It links tool sprawl to slower threat detection and response and argues that proprietary, context-rich security telemetry is the foundation for adaptive defense. Barracuda uses Databricks to consolidate fragmented data, normalize schemas, support real-time streaming detection, run ML operations through MLflow, and power natural-language log search across billions of security events with strict data isolation. The work began by defining customer outcomes, then progressed through incremental delivery; normalized data enabled models and agents to use cross-domain context. This approach extended across WAF-as-a-service, automated configuration, API security, and bot protection while shared outcomes aligned product, data science, engineering, and business teams.


[Newer posts](https://yomu.fyi/posts/page/136.md) · [Older posts](https://yomu.fyi/posts/page/138.md)