---
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

### [5 non-sponsor brand moments during this football summer](https://yomu.fyi/post/5-non-sponsor-brand-moments-during-this-football-summer.md)
- Company: [Brandwatch](https://yomu.fyi/company/brandwatch.md)
- Author: Ksenia Newton
- Published: Jul 16, 2026
- Type: Explainer

This analysis examines how five non-sponsor brands used football-related moments to gain attention without buying official tournament partnerships. Consumer Research tracked brand mentions in public social and news content from June 11 to July 8, identifying responses to clean-stadium rules, fan travel, beer shortages, and rising ranch interest. Levi’s covered-logo campaign generated 20.1k mentions, while Heinz’s taped condiment packaging produced 5.1k; Miller Lite and Delta reacted to the Tartan Army with a beer barge and airport welcome. Kraft followed a TSA post with a limited-edition “TSA-Compliant Ranch” kit, recording over 4k mentions and a 3,998% increase over the previous timeframe. The examples support a model based on speed, authenticity, and real-time listening, with social and search signals helping brands act while trends are building.


### [Introducing Apache Spark 4.2](https://yomu.fyi/post/introducing-apache-spark-4-2.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Wenchen Fan, Andreas Neumann, Serge Rielau, Szehon Ho, Gengliang Wang, Linhong Liu, Hyukjin Kwon, Jerry Peng, DB Tsai, Xiao Li, Reynold Xin
- Published: Jul 16, 2026
- Type: Announcement

Apache Spark 4.2 extends the engine’s role in modern data and AI workloads with governed metrics, vector and top-K primitives, Arrow-first Python execution, native change data capture, and stronger streaming foundations. Metric views provide shared business definitions, while Spark Connect uses gRPC and Arrow to let remote clients submit logical plans without a full Spark runtime. Spark SQL adds vector similarity functions, NEAREST BY, geospatial types, sketches, and time-series features; Python interoperability includes Arrow UDFs and can move Spark DataFrames into supported Arrow-native tools without copying or serializing underlying data. Spark Declarative Pipelines adds Auto CDC for SCD Type 1 targets, while Data Source V2 standardizes change streams through CHANGES and expands row-level operations and schema evolution. The release also includes operational updates such as Web UI modernization, Kubernetes improvements, JDK 25 support, and dependency upgrades.


### [Unified Logs is now in open beta](https://yomu.fyi/post/unified-logs-is-now-in-open-beta.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: jordi, joshenlim, kemal\_y, steven\_eubank
- Published: Jul 16, 2026
- Type: Announcement

Unified Logs enters open beta, bringing events from the API gateway, Postgres, Auth, Storage, PostgREST, Realtime, and connection poolers into one searchable dashboard view. Before this, debugging a single request required separate log pages, service-specific filters, and manual correlation across tabs, which slowed investigation and risked losing context. Users can filter by log type, level, status, method, or pathname, search event messages, share URL-based filter states, stream new events live, and zoom into timeline ranges colored by log level. Clicking a row opens a detail panel that presents network timing, gateway, database query, and Auth blocks alongside raw JSON, while Log Drains forward events to external destinations. The feature is rolling out to all projects during open beta, with the classic Logs Explorer still available and feedback requested.


### [Build a Secure C# MCP App with Cross App Access (XAA)](https://yomu.fyi/post/build-a-secure-c-mcp-app-with-cross-app-access-xaa.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Aasawari Sahasrabuddhe
- Published: Jul 16, 2026
- Type: Tutorial

Cross App Access (XAA) addresses the gap between a user’s authenticated identity and a downstream MCP server, API, or agent tool that must trust that identity without another login. The described flow uses OIDC and PKCE for sign-in, then RFC 8693 token exchange to turn the ID token into a JWT Authorization Grant (JAG), followed by RFC 7523 exchange at the MCP authorization server for a scoped bearer token. A C# MCP SDK IdentityAssertionGrantProvider encapsulates both exchanges, while ASP.NET Core OIDC middleware persists the ID token and an HTTP transport presents the resulting token to the MCP server. The sample connects to a Todo resource through the xaa.dev playground, completes MCP initialization, lists resources, and reads todo0://todos; the article states that the end-to-end implementation fits in under 50 lines of C#.


### [Scaling to 1 million concurrent sandboxes in seconds](https://yomu.fyi/post/scaling-to-1-million-concurrent-sandboxes-in-seconds.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Jul 16, 2026
- Type: Problem & solution

Modal rebuilt its sandbox platform to support millions of concurrent sandboxes and tens of thousands of creations per second, removing all central bottlenecks from the control plane. The new architecture eliminates central coordination and durable datastores from the critical path, using horizontally scaled scheduling servers that run a fast load-balancing algorithm against in-memory cached worker state from Redis streams. Workers are contacted directly via RPC to create sandboxes, and sandbox metadata is written asynchronously, avoiding O(sandboxes) and O(nodes) operations that limited previous systems. In a benchmark, Modal created 1 million sandboxes in under a minute with median time-to-interactivity under half a second, though the long tail is attributed to kernel and network contention like the rtnl lock. The system is designed to scale without practical limits, supporting use cases like reinforcement learning and background agents that require massive concurrent sandbox creation.


### [What does 99.9% uptime mean for inference?](https://yomu.fyi/post/what-does-99-9-uptime-mean-for-inference.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Jul 16, 2026
- Type: Explainer

The post defines three inference reliability tiers by the failure domains each must survive: 99% requires surviving node-level GPU failures like ECC errors and thermal throttling through automated health checks and fast replica replacement within a single data center. 99.9% requires surviving a full data-center failure by deploying weights across two facilities with enough capacity on each side and routing live traffic to both continuously, not a cold standby. 99.99% requires multi-region deployment with AZ redundancy and reserved failover capacity. The authors argue that vague SLA definitions hide which failure domains are covered, and that infrastructure ownership—whether a provider controls its hardware or rents from a hyperscaler—determines how quickly failures are resolved.


### [Evolving Spec-Driven Development: Conductor Now Supports Antigravity](https://yomu.fyi/post/evolving-spec-driven-development-conductor-now-supports-antigravity.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Mahima Shanware, Sherzat Aitbayev, Jay Kornder
- Published: Jul 16, 2026
- Type: Announcement

Conductor, introduced as a Gemini CLI extension for spec-driven development (SDD), is evolving into a plugin that supports Antigravity CLI and other tools. The change moves project awareness from ephemeral chat logs into persistent, version-controlled Markdown artifacts while replacing strict command sequences with conversational creation and iteration of context, specs, and plans. Plugins package skills, rules, MCP servers, and hooks, and the Conductor Plugin can update project context and mark plan tasks complete as discussions progress, while preserving existing commands, specs, plans, shared configuration, and development tracks. The post reports a higher success rate on the most complex subset of TerminalBench tasks than a user not using SDD, and gives an Antigravity installation command: agy plugins install https://github.com/gemini-cli-extensions/conductor.


### [Building scalable AI agents with modular prompt transpilation](https://yomu.fyi/post/building-scalable-ai-agents-with-modular-prompt-transpilation.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Simerus Mahesh
- Published: Jul 16, 2026
- Type: Problem & solution

Monolithic system prompts become difficult to reason about, test, and change as teams add safety policies, domain rules, formatting requirements, and escalation behaviors. The proposed approach treats prompts as software artifacts: modular skill files are composed with includes, variables, and macros, then transpiled into deterministic artifacts. Build-time validation checks missing imports, undefined variables, and circular dependencies, while dependency graphs and CI/CD golden-file comparisons catch drift before deployment. At runtime, progressive disclosure keeps a stable control plane loaded while agents retrieve task-specific skills, reducing context exhaustion and noise. Agents can propose new skill modules or import changes through pull requests, but human review and evaluation remain part of the merge process.


### [Expanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web Search](https://yomu.fyi/post/expanding-choice-in-gemini-enterprise-agent-platform-introducing-groun.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Guangsha Shi
- Published: Jul 16, 2026
- Type: Announcement

Gemini Enterprise Agent Platform now natively integrates Parallel Web Systems as a web-grounding provider, expanding developers’ choice of sources for real-time, verifiable information. Grounding with Parallel Web Search connects Gemini models to Parallel’s structured, LLM-optimized search results, combines the provider’s web index with Gemini’s prompt decomposition and synthesis, and adds precise citation annotations. The capability is available through the Gemini API and Agent Studio, and can be subscribed to through Google Cloud Marketplace with usage charged on an existing Google Cloud invoice; zero data retention is available for sensitive workloads. Developers can make programmatic calls at scale, extract and cache web data, post-process results with other LLMs, and build catalog enrichment, autonomous-agent, and multi-agent workflows. Setup requires subscribing, accepting the terms, reviewing pricing, and selecting Parallel Web Search under Agent Studio’s partner-grounding settings.


### [Upgrading Checkout Blocks app to Polaris web components](https://yomu.fyi/post/upgrading-checkout-blocks-app-to-polaris-web-components.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Jul 16, 2026
- Type: Problem & solution

Checkout Blocks upgraded all five UI extensions from React and Shopify’s legacy Remote UI bridge to remote-dom with Preact and Polaris web components, while moving from JavaScript to TypeScript and API version 2026-01. The migration addressed checkout’s performance sensitivity and Shopify’s requirement that deployments move off versions earlier than 2026-01. A parallel core-next library allowed extensions to transition incrementally, with shared providers, telemetry, hooks, helpers, and rules adapted before legacy core was removed. To meet the remote-dom CLI’s 64KB gzip limit, the team dropped react-reconciler, replaced liquidjs with a smaller parser validated against more than 42,000 lines of merchant configurations, replaced dayjs, and retained markdown-to-jsx through Preact compatibility. Transferred bundles fell 40% to 85%, while weighted ELT improved 8% at P50 and 7% at P90.


### [Security incident disclosure — July 2026](https://yomu.fyi/post/security-incident-disclosure-july-2026.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: system
- Published: Jul 16, 2026
- Type: Incident / postmortem

An intrusion into part of production infrastructure was driven end to end by an autonomous AI agent system, accessing a limited set of internal datasets and several credentials used by services. The attack began through two code-execution paths in dataset processing, then escalated from a worker to node-level access and moved laterally across internal clusters while running thousands of actions in short-lived sandboxes. AI-assisted anomaly detection surfaced the compromise, and LLM-driven analysis of more than 17,000 attacker events reconstructed the timeline, identified indicators, mapped credentials, and separated impact from decoys. Hosted frontier models initially blocked forensic requests containing attack artifacts, so the investigation used zai-org/GLM-5.2 on internal infrastructure. The initial vulnerabilities were closed, affected infrastructure rebuilt, credentials rotated, and cluster controls and alerting strengthened; assessment of possible partner or customer impact remains ongoing.


### [July 2026 newsletter](https://yomu.fyi/post/july-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 16, 2026
- Type: Announcement

The July 2026 newsletter rounds up ClickHouse releases, community projects, benchmarks, engineering work, and upcoming events in real-time data warehousing. Version 26.6 adds hypothetical skip indexes, cascading refreshable materialized-view dependencies, experimental continuous queries, AI embedding functions, PNG output, CLI documentation, and geospatial improvements. A CostBench comparison running the full analytics path over 28 hours found better cost-performance for ClickHouse Cloud than Snowflake Interactive Tables, which required a continuously running refresh warehouse for pre-aggregated data. Other features cover conversational analytics with ClickHouse Cloud, LibreChat, and MCP; Postgres change-data capture through Debezium and Kafka; local agent queries with chDB; high-concurrency tuning; and Silk, whose fiber runtime targets lower I/O tail latency. Quick reads report ingestion, caching, security, and multi-tenant SQL developments.


### [How Stripe uses graph search and state machines to auto-remediate a global database fleet](https://yomu.fyi/post/how-stripe-uses-graph-search-and-state-machines-to-auto-remediate-a-gl.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Pragya Mehta
- Published: Jul 16, 2026
- Type: Problem & solution

Stripe describes how its Document Database fleet previously handled degraded MongoDB shards with single-step, hard-coded plugins that struggled with combined failures, intermediate states, and more than 40 shard layouts. The replacement models shard topology and health as a directed graph, with infrastructure operations as edges and safety invariants defining valid states. A simulator uses breadth-first search to find minimum-operation recovery paths, while an abstraction over Temporal contexts lets the same remediation logic run in memory without real infrastructure calls; later, the system also uses Dijkstra's algorithm. The system reduced pager volume by 30%—about 200 pages annually—reclaimed 12 days of fleet health each year, and handled a new layout with zero code changes, according to the post.


### [Migrating from Apollo Tooling to GraphQL Codegen at Yelp](https://yomu.fyi/post/migrating-from-apollo-tooling-to-graphql-codegen-at-yelp.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Igor Kusakov, Software Engineer
- Published: Jul 16, 2026
- Type: Problem & solution

Yelp migrated its frontend React monorepo from the deprecated apollo-tooling to GraphQL Codegen to ensure ongoing maintenance and build reliability. Because the monorepo spans over 500 packages with thousands of generated type files and custom nested type imports, the Client Data team prioritized a transparent migration that avoided codebase-wide changes. The team rejected approaches requiring syntax updates or altered import paths, choosing instead to configure GraphQL Codegen and contribute upstream plugin changes so output structures and naming conventions matched the legacy output. Migrating packages incrementally through small pull requests enabled continuous feedback while allowing developers across dozens of teams to maintain their workflows without editing existing code. Completing the migration to GraphQL Codegen v6 simplified Yelp's build tooling, eliminated deprecated dependencies, and left upstream compatibility enhancements available for the wider open-source community.


### [Build a Multi-Camera 3D Tracking Application with NVIDIA DeepStream 9.1 Skills](https://yomu.fyi/post/build-a-multi-camera-3d-tracking-application-with-nvidia-deepstream-9.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Jul 15, 2026
- Type: Announcement

NVIDIA DeepStream 9.1 introduces AutoMagicCalib and Multi-View 3D Tracking skills to resolve the limitations of single-camera 2D tracking and manual camera calibration in large-scale video analytics. Multi-View 3D Tracking projects detections into a shared 3D coordinate system, matching tracklets across cameras via MQTT and maintaining globally consistent object IDs. AutoMagicCalib automates camera intrinsic and extrinsic parameter estimation by analyzing object trajectories across video feeds, optionally using a Visual Geometry Grounded Transformer. These capabilities are integrated into 13 agentic skills that allow coding agents to set up, configure, and execute end-to-end vision AI pipelines from natural language prompts. Outputs include an on-screen display grid, bird's-eye view trajectories, and per-frame protobuf metadata streamed through Kafka.


### [Introducing Claude Tag](https://yomu.fyi/post/introducing-claude-tag.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 15, 2026
- Type: Announcement

Claude Tag is a Slack-based way for teams to delegate work to Claude by tagging @Claude in shared channels, with access to selected tools, data, and codebases. Claude breaks requests into stages, uses permitted tools, replies in threads, retains relevant channel context, and can pursue scheduled tasks asynchronously; ambient behavior can also surface information and follow up on unresolved work. Administrators scope Claude identities, memories, tools, and information by channel, set organization- and channel-level token-spend limits, and review activity logs, while users can also message Claude privately. Available in beta to Claude Enterprise and Team customers, Claude Tag replaces the existing Claude in Slack app, works with Opus 4.8, and is credited with creating 65% of the product team’s code through Anthropic’s internal version.


### [Why Trainy migrated from Amazon RDS Postgres to ClickHouse Managed Postgres](https://yomu.fyi/post/why-trainy-migrated-from-amazon-rds-postgres-to-clickhouse-managed-pos.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 15, 2026
- Type: Problem & solution

Trainy’s Pluto is an open-source experiment tracker that separates analytics metrics in ClickHouse Cloud from relational run metadata in Postgres. Its original metadata store, Amazon RDS Postgres, was too expensive for the volume observed and contributed to slower initial page loads, so Trainy migrated roughly 40–50 GB to ClickHouse Managed Postgres, which uses local NVMe storage and keeps both databases in one account. After migration, initial page loads became 2–3 times faster, while a complex query reported as taking 10–15 minutes previously returned in five seconds; visualization queries over runs with hundreds of thousands of steps typically return in 50–100 milliseconds at the P50. Trainy reports 100% reliable ingestion with zero dropped data points and says the unified stack could support future joins between GPU metrics and run metadata, as well as governed agent access.


### [Develop Lightweight USD Runtimes Faster with AI Agents](https://yomu.fyi/post/develop-lightweight-usd-runtimes-faster-with-ai-agents.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jul 15, 2026
- Type: Explainer

Building custom Universal Scene Description (USD) implementations traditionally required adapting large existing codebases to meet specific memory footprints, application binary interfaces (ABIs), or performance requirements. The nanousd-labs project introduces an alternative approach by using AI agents to generate lightweight USD runtimes directly from the Alliance for OpenUSD machine-readable USD Core Specification. Agents parse the specification section by section, write conforming code, and validate output against specification-derived tests under engineer guidance. Written in C++ with a public C API, nanousd functions as an independent data layer that handles parsing, composition, queries, and writing without rendering pixels. This methodology enables developers to regenerate runtimes for varying deployment constraints while maintaining standard compliance through reusable workflows and skill graphs.


### [Agents for financial services](https://yomu.fyi/post/agents-for-financial-services.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 15, 2026
- Type: Announcement

Anthropic is releasing ten ready-to-run agent templates for financial services, targeting work such as pitchbook creation, KYC screening, and month-end close operations. Each template packages task-specific skills, governed data connectors, and subagents, and is available as a plugin for Claude Cowork or Claude Code, or as a cookbook for Claude Managed Agents. Claude add-ins for Excel, PowerPoint, Word, and, soon, Outlook carry context between applications, while new connectors and a Moody’s MCP app provide access to market, research, credit, and company data. The announcement says the updates pair best with Claude Opus 4.7, which leads Vals AI’s Finance Agent benchmark at 64.37%, and states that templates are available today through the financial services marketplace.


### [Inkling model from Thinking Machines Lab now on Databricks](https://yomu.fyi/post/inkling-model-from-thinking-machines-lab-now-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Mike Eastham, Yuchen Jin, Preslav Le
- Published: Jul 15, 2026
- Type: Announcement

Databricks announces that Inkling, Thinking Machines Lab’s first open-weights model, is available to enterprise customers through the Unity AI Gateway. The model is positioned for coding and agentic reasoning workflows, supports multi-modal inputs, and can be applied to enterprise data, including proprietary codebases, internal documentation, and domain-specific data. Unity AI Gateway provides centralized security, permissions, audit logging, policy enforcement, cost controls, budgets, and observability, while data remains within the governed environment; Inkling is invoked through a REST API, with SQL query support planned. Teams can try it in AI Playground, deploy a governed endpoint, connect coding agents such as Cursor, OpenCode, or Pi, and build agents with Agent Bricks, with open weights enabling customization and inference-cost optimization without per-token API pricing.


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