---
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

### [Governing agent autonomy with Auto-review](https://yomu.fyi/post/governing-agent-autonomy-with-auto-review.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: David Gomes & Travis McPeak
- Published: Jun 11, 2026
- Type: Announcement

Auto-review governs local agent autonomy as a continuum, allowing routine work to proceed while reviewing actions whose consequences depend on context, user intent, and security stakes. A small classifier agent runs before tool calls in the same RPC stream as the parent agent, and can inspect workspace contents with ReadFile, Grep, Glob, and ListDir when a command alone is insufficient. Its blocks return explanations rather than automatically prompting users, enabling the parent agent to narrow or replace a risky action. Evaluation used 6,122 deduplicated labeled rows from internal developer sessions plus synthetic high-consequence cases, and tested tool use, overrides, parse failures, and behavioral flapping. Early results show the classifier blocks around 4% of reviewed actions, while about 7% of Auto-review chats experience at least one interruption.


### [Catch visual regressions with Snapshots, now in beta](https://yomu.fyi/post/catch-visual-regressions-with-snapshots-now-in-beta.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Max Topolsky
- Published: Jun 11, 2026
- Type: Announcement

Sentry has released the beta version of Sentry Snapshots to catch visual regressions on pull requests across any platform with a frontend. The tool captures screenshots of an application across various viewports, themes, and languages, compares them against a baseline, and blocks pull requests when diffs are detected. Teams can attach custom context metadata, including test file paths, to help developers and AI agents locate test sources and generate additional test cases. By leveraging the sentry-mcp integration, REST API, and sentry-cli, workflows can retrieve base images, diff masks, and run local diff verification before pushing commits. Originally created at Emerge Tools for mobile applications, this iteration expands automated visual testing capabilities to broader frontend codebases.


### [Stop building data products. Start building data services.](https://yomu.fyi/post/stop-building-data-products-start-building-data-services.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: Jun 11, 2026
- Type: Explainer

Howden’s rapid acquisition pace exposed limits in an enterprise data model built around one product per use case, downstream quality checks, and dashboard-driven consumption. Group Chief Data Officer Barry Panayi describes shifting to open, governed data services, moving mastering and quality checks closer to ingestion, and codifying reconciliation in the Accord data model. On Databricks, the company consolidated more than 100 sources of record, standardized pipelines and shared code, and built reusable assets for cross-domain analytics, while continuing to productionize models as consistent services. The account argues that AI agents require a composable services layer, and that insight lag—the time between data existing and being usable—matters more than freshness; conversational analytics through Genie also reduced dashboard-building work.


### [Introducing Boost+](https://yomu.fyi/post/introducing-boost.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jun 11, 2026
- Type: Announcement

Boost+ is an updated promotion for delivery drivers that adds pay to eligible trips during periods and locations of high demand. The promotion applies automatically when a trip’s pickup is in a designated blue zone during listed hours, and the extra amount appears in the fare shown on the trip request. Its calculation uses estimated active time—from accepting the trip through delivery—rather than time spent online waiting; for example, a 30-minute trip at $10 per active hour receives $5 in Boost+ pay. Drivers can find upcoming opportunities in the Discover tab and on the map, view total Boost+ earnings in the trip receipt, and combine the promotion with Quests and other promotions. Availability varies by city, and time beyond the estimated active time does not qualify for Boost+ pay.


### [Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP](https://yomu.fyi/post/profiling-in-pytorch-part-2-from-nn-linear-to-a-fused-mlp.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Aritra Roy Gosthipaty, Rémi Ouazan Reboul, Sergio Paniego, Pedro Cuenca, Sayak Paul
- Published: Jun 11, 2026
- Type: Tutorial

Part 2 of the series examines how PyTorch profiler traces change from a single bias-enabled nn.Linear to a three-layer MLP with an intervening activation, using scripts run on an NVIDIA A100-SXM4-80GB GPU. It explains that nn.Linear represents y = x @ w.T + b, dispatches aten::addmm, and folds bias addition into the cuBLAS GEMM epilogue, while aten::t only rewrites weight strides and launches no GPU kernel. For the MLP, eager execution uses three GEMMs plus separate activation and multiplication kernels, whereas torch.compile fuses the latter operations and keeps the intermediate out of HBM, while leaving the GEMMs unchanged. A hand-written Liger Triton kernel provides the same fusion without Dynamo guards or compile latency, trading Inductor’s shape-specialized speed for generic execution across changing shapes.


### [Building ClickCannon - a tool for benchmarking ClickHouse](https://yomu.fyi/post/building-clickcannon-a-tool-for-benchmarking-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 11, 2026
- Type: Benchmark

ClickCannon was created to answer how much CPU, memory, and disk are needed for ClickStack observability workloads, while accounting for schema, ingestion rate, hardware, and concurrent queries. It replays about 2 TiB of real OpenTelemetry trace data, converted to ClickHouse Native format with ZSTD compression, rather than relying only on synthetic datasets. A custom multithreaded Go reader/inserter provides control over uncompressed throughput and exposes stream metrics such as compressed and uncompressed bytes, row counts, and block counts, while the framework simulates users and query workloads during ingestion. The project evolved into an open-source, generic ClickHouse benchmarking tool for custom insert and query workloads, with fine-grained control over throughput, concurrency, and user behavior; the post reports that it can push substantial throughput from a single machine.


### [Scaling AI Through Data Fluency](https://yomu.fyi/post/scaling-ai-through-data-fluency.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: Jun 10, 2026
- Type: Explainer

Aer Lingus is redirecting a significant share of its IT and change spending from traditional maintenance toward a Databricks-powered data foundation, addressing legacy systems that trap information in departmental silos. Dave O’Donovan says the airline spent the past 18 months prioritizing platform development, governance, data quality and data literacy rather than chasing each new AI product. Databricks was selected for a unified lakehouse architecture, with data warehousing, Genie’s plain-English querying and real-time operational data intended to broaden access beyond specialist teams. At Aer Lingus’s Operations Control Center, combining sensor and operational inputs gives teams a fuller real-time view for disruption decisions, while commercial teams use live insights to adjust pricing. The transformation also includes a Data Literacy Academy, a 75/25 capacity split between foundational work and innovation, a 20-person Continuous Improvement team, and experiments with agents for business-case development and CFO review.


### [AWS and Databricks at Data + AI Summit 2026: Accelerating real-world AI innovation](https://yomu.fyi/post/aws-and-databricks-at-data-ai-summit-2026-accelerating-real-world-ai-i.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sarah Jack, Taylor Hoss
- Published: Jun 10, 2026
- Type: Announcement

AWS and Databricks describe their expanded collaboration at Data + AI Summit 2026, where AWS returns as a Legend Sponsor with sessions, demos, customer stories, and industry forums. The partnership centers on generative AI adoption, unified governance, and open data architectures, including an agentic stack that combines Amazon Bedrock, Bedrock AgentCore, Kiro, and the Databricks Data + AI Platform. A featured integration uses a governed MCP connection through Databricks Apps so AgentCore can query Unity Catalog-governed data, ask AI/BI Genie questions, and read low-latency state from Lakebase while honoring existing permissions. AWS will demonstrate these workflows at Booth #100 and present a session on federating Unity Catalog to AWS Glue, alongside customer examples including Mastercard, Talkdesk, nCino, Addepar, and Workday. Attendees can also join technical conversations, receptions, and a 14-day Databricks on AWS Marketplace trial with $400 in usage credits.


### [Announcing the Public Preview of Custom URLs](https://yomu.fyi/post/announcing-the-public-preview-of-custom-urls.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Gordon Wang, Steve Costa, Ankit Mishra
- Published: Jun 10, 2026
- Type: Announcement

Databricks has announced the public preview of Custom URLs, giving each account a single branded domain that serves its workspaces. Previously, workspace-specific URLs made navigation, sharing, bookmarking, and account-wide features more cumbersome, while users had to log in repeatedly when switching workspaces. Custom URLs use a shared account session to verify access and create workspace sessions seamlessly, while preserving authorization boundaries and keeping existing per-workspace URLs functional. The feature provides a unified Genie entry point, cross-workspace Unity Catalog lineage, and an account-level URL that remains stable during disaster-recovery failover, including for downstream tools using existing connection strings. Activation requires Unified Login; Frontend Private Link workspaces fall back to per-workspace URLs, and account admins can claim a URL, enable it, and optionally turn on automatic redirects.


### [How Rivian drives trusted, AI-powered decisions at the speed of thought with Databricks](https://yomu.fyi/post/how-rivian-drives-trusted-ai-powered-decisions-at-the-speed-of-thought.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Romit Jadhwani, Saritha Suresh, Miranda Luna, Julia Powell
- Published: Jun 10, 2026
- Type: Explainer

Rivian is building electric vehicles and services that require fast, trusted decisions across manufacturing, supply chain, finance, service and operational planning, while business users need reliable metrics and insights. Using Databricks AI/BI, Genie, Unity Catalog metric views, Databricks Apps and AI-assisted engineering, the company is consolidating dashboards, semantic definitions, permissions, sensitive data and AI-powered workflows on one governed foundation. Rivian migrated a massive multi-domain dashboard base in less than six months, is standardizing more than 50 metrics, and worked with Databricks as a design partner on roughly 58 product features. The resulting self-service analytics and operational applications cut supply-chain monitoring time by 60 to 70%, reduce inventory investigations from over 30 minutes to under two, predict stock-out risk more than four days ahead, and reduce some ingestion setup time by more than 60%, supporting AI-powered decisions without competing versions of the truth.


### [Piloting a new offline heatmap with delivery zones](https://yomu.fyi/post/piloting-a-new-offline-heatmap-with-delivery-zones.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jun 10, 2026
- Type: Announcement

The Driver app is piloting a new offline heatmap on its home screen in select US cities. It shows where couriers can go online instantly and indicates demand levels intended to help them find trips more quickly. Each outlined delivery zone is color-coded red for high demand, orange for medium demand, yellow for low demand, or grey for areas open only to Platinum and Diamond couriers. The map refreshes every 10 minutes; couriers can tap a zone to see its name or navigate there, then see nearby busy restaurants and stores after going online. The announcement lists participating locations across the United States.


### [Metric Semantic Layer: How Lyft Governs and Scales Key Data Definitions](https://yomu.fyi/post/metric-semantic-layer-how-lyft-governs-and-scales-key-data-definitions.md)
- Company: [Lyft](https://yomu.fyi/company/lyft.md)
- Author: Iraklikhorguani
- Published: Jun 10, 2026
- Type: Problem & solution

As Lyft scaled, different teams developed conflicting definitions for key business metrics due to the lack of centralized version control and shared standards. To resolve this, Lyft implemented an internal Metric Semantic Layer as a Python package that stores authoritative metric definitions in YAML files with Jinja SQL templates. The system restricts onboarding to Golden Metrics used across multiple applications, requiring team-based approval from both Business and Operational Owners for any definition changes. Standardized definitions are exposed through Python APIs, integrated into the Amundsen data catalog and self-service user interfaces, and surfaced to AI tools via a Model Context Protocol.


### [Datadog and ClickHouse partner to bring full-fidelity data to modern observability](https://yomu.fyi/post/datadog-and-clickhouse-partner-to-bring-full-fidelity-data-to-modern-o.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 10, 2026
- Type: Announcement

ClickHouse and Datadog announce a partnership aimed at reducing the tradeoff between retaining complete telemetry and keeping it accessible through familiar observability workflows. New Preview capabilities let organizations route logs directly to ClickHouse through Datadog Observability Pipelines and search those logs from the Datadog Log Explorer. Pipelines can parse, enrich, filter, transform, and redact data before storage, while ClickHouse Cloud’s separation of storage and compute supports long-term retention, independently scaled query resources, and workload isolation. Federated log search queries data directly in ClickHouse, avoiding duplicate copies or re-ingestion while allowing high-volume logs to remain searchable and historical investigations to use full-fidelity telemetry. The partnership combines Datadog’s collection and investigation experience with ClickHouse’s storage and analytics foundation; the native integration and federated search are available in Preview.


### [Jumpstart your Data Modeling with Databricks Industry Data Models](https://yomu.fyi/post/jumpstart-your-data-modeling-with-databricks-industry-data-models.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Amr Ali, Drew Triplett, Franco Patano, Shelley Shaffery
- Published: Jun 10, 2026
- Type: Announcement

Databricks is publishing a public library of 40 Lakehouse industry data models designed to provide Silver-layer foundations for analytics and machine learning. Each industry offers a Minimum Viable Model and Expanded Coverage Model derived from the same model.json, with breadth rather than attribute depth distinguishing the scopes. A rules-driven AI agent applies more than 200 structural checks across 14-plus modeling domains, enforcing hierarchy, primary and foreign keys, normalization, division balance, data types, governance tags, and acyclic relationships. The models deploy to Unity Catalog in three physical cataloging styles and include DDL, schemas, metric views, classification tags, ontology, diagrams, and synthetic data with valid references. The airline ECM example contains 19 domains, 420 products, 17,278 attributes, 420 primary keys, and 2,877 foreign keys, while the models remain customizable starting points requiring domain expertise and organizational review.


### [AI Serving Platform That Adapts to Your Model](https://yomu.fyi/post/ai-serving-platform-that-adapts-to-your-model.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Anshul Gupta
- Published: Jun 10, 2026
- Type: Problem & solution

Databricks Custom Model Serving addresses the operational burden of serving custom models, whose resource profiles, traffic patterns, and latency requirements vary widely from small CPU classifiers to large GPU-backed language models. Its fully managed platform packages MLflow models and uses isolated Kubernetes deployments, model-appropriate runtimes, and a short request path to limit interference and per-request overhead. At the center, the AutoPilot Pod Autoscaler combines active concurrency and queue signals for horizontal scaling with CPU, GPU, and memory measurements for model-aware target-concurrency adjustment, allowing one controller to adapt across workloads. Warm pools, provisioned concurrency, and zero-downtime updates address startup and deployment concerns, while reported production results include 90%+ cost savings for some customers, up to 2x improvement in p99 and p50 latency, 100K+ QPS, and 99.99% availability.


### [Agents can now provision ClickHouse and Postgres on ClickHouse Cloud](https://yomu.fyi/post/agents-can-now-provision-clickhouse-and-postgres-on-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 10, 2026
- Type: Announcement

ClickHouse Cloud is now available through Stripe Projects, a Stripe CLI workflow that lets developers and AI agents provision real infrastructure from the command line. Running stripe projects init my-app and adding a service provisions ClickHouse or managed Postgres, returns working credentials to the environment through .env, and hands control to clickhousectl; clickhousectl greater than v0.3.0 is required. The integration supports ClickHouse for real-time analytics and managed Postgres, identified as a public beta, for transactional workloads; both services use the same CLI workflow, land in the same account, and share credentials, while Postgres uses local NVMe storage and is described as offering microsecond latency and up to 10x faster performance on I/O-heavy workloads. The feature is in developer preview, and agents can continue through clickhousectl for data ingestion with ClickPipes and service queries without manual credential handling or context switching.


### [EC2’s formally verified “isolation engine” provides mathematical assurance of virtual-machine isolation](https://yomu.fyi/post/ec2-8217-s-formally-verified-8220-isolation-engine-8221-provides-mathe.md)
- Company: amazon.science
- Author: Dominic Mulligan, Nathan Chong
- Published: Jun 10, 2026
- Type: Announcement

AWS has introduced M9g and M9gd EC2 instances powered by Graviton5 CPUs, featuring the formally verified Nitro Isolation Engine to enforce virtual machine isolation. To prove the engine's correctness, engineers developed 330,000 lines of machine-checked mathematics using the Isabelle/HOL proof assistant. The system architecture separates hypervisor policy from isolation mechanisms, requiring the deprivileged Nitro Hypervisor to route all guest state modifications through the minimal Rust-based engine. Functional verification uses a restricted language subset called micro Rust, Separation Logic contracts specifying total correctness, and weakest-precondition calculus from the open-source AutoCorrode library. Finally, high-level transition relations and noninterference proofs ensure that memory integrity and guest confidentiality are preserved across every execution step.


### [Graviton5’s improved design increases speed and energy efficiency — beyond Moore’s law](https://yomu.fyi/post/graviton5-8217-s-improved-design-increases-speed-and-energy-efficiency.md)
- Company: amazon.science
- Author: Ali Saidi
- Published: Jun 10, 2026
- Type: Announcement

Amazon announced the general availability of M9g and M9gd EC2 instances powered by the new Graviton5 processor. Built on a three-nanometer process, Graviton5 features 192 Neoverse V3 cores, DDR5-8800 memory support, PCIe gen6 interconnects, and 192 megabytes of level-three cache. The chip transitions from seven discrete dies to four unified chiplets connected at 420 gigabytes per second, eliminating dedicated I/O and memory controller dies. These architecture updates deliver up to 25% higher computational performance over Graviton4, with up to 35% faster performance for web applications and machine learning inference. The new instances also introduce the Nitro Isolation Engine, which uses formal verification to enforce hardware and software isolation between virtual machines.


### [Encoding Your Domain Expert: The Context Layer Behind Spotify's Data Assistant](https://yomu.fyi/post/encoding-your-domain-expert-the-context-layer-behind-spotify-s-data-as.md)
- Company: [Spotify](https://yomu.fyi/company/spotify.md)
- Author: Spotify Engineering
- Published: Jun 10, 2026
- Type: Problem & solution

Spotify developed an AI data assistant, Vedder, to scale access to over 70,000 internal datasets without overwhelming domain experts. Because raw warehouse schemas omit critical semantic nuances and overwhelm LLM context windows, engineers implemented a domain-owned context layer organized into clusters. Each cluster contains profiled datasets, expert-curated business documentation, and vetted question-and-SQL pairs that guide a ReAct-based agent. Automated query history extraction proved insufficient on its own, as domain curators accepted only 12.5% of inferred query pairs due to noise. Deployed across Slack, IDEs, and a dedicated web interface, the system maintains reliability through continuous cluster health monitoring that flags schema drift and degraded examples.


### [Announcing the Databricks storage ecosystem: Governing the enterprise data estate, wherever it lives](https://yomu.fyi/post/announcing-the-databricks-storage-ecosystem-governing-the-enterprise-d.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Rupal Jain, Denis Dubeau
- Published: Jun 10, 2026
- Type: Announcement

Databricks announces a Software-Defined Storage (SDS) Ecosystem for governing enterprise data across on-premises, private-cloud, and edge environments without requiring migration. It responds to sovereignty and regulatory constraints, data-gravity economics, latency requirements, and the need to unlock AI value in backup, archive, and other previously inaccessible data. Storage partners implement the open-source OpenSharing protocol, expose an endpoint, and connect it to Unity Catalog so Databricks Serverless Compute can access governed data where it resides. The integrations provide a unified catalog and support Serverless Compute, Genie, AgentBricks, and model training with zero data movement or duplication; MinIO is generally available, while Everpure, Qumulo, and VAST Data are in preview stages. Databricks also says commitments are in place from six additional providers and that Volumes APIs are being developed to extend OpenSharing to unstructured files for generative-AI workloads.


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