---
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

### [Meta’s Spark Muse 1.1 is now available on Databricks, fully governed by Unity AI Gateway](https://yomu.fyi/post/meta-s-spark-muse-1-1-is-now-available-on-databricks-fully-governed-by.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Pavithra Rao, Shaotong Li, Martin Grund, Kelly Albano
- Published: Jul 17, 2026
- Type: Announcement

Databricks announces support for Meta’s Muse Spark 1.1 through Model Provider Services (MPS) in Unity AI Gateway, addressing fragmented API keys, access controls, and usage visibility when organizations adopt new models. An MPS is a Unity Catalog securable that stores provider configuration and an encrypted API key, while callers use their Databricks credentials and the gateway attaches the key at request time. The post demonstrates registering Muse Spark through the OpenAI provider type with Meta’s API base URL and Responses API, then governing use with Unity Catalog privileges, model allowlists, policies, rate limits, usage metering, and inference tables. Requests are routed through the gateway, where access and guardrails are applied before reaching Meta; usage, spend, tokens, latency, status codes, and optionally full payloads are recorded for attribution and audit.


### [Decoding Customer Value: pLTV Models for India’s Quick Commerce Delivery Wars — part 1](https://yomu.fyi/post/decoding-customer-value-pltv-models-for-india-s-quick-commerce-deliver.md)
- Company: [Swiggy](https://yomu.fyi/company/swiggy.md)
- Author: Jayshmi A
- Published: Jul 17, 2026
- Type: Problem & solution

Swiggy’s pLTV effort addresses early-stage lifetime-value prediction for Food Delivery and Instamart, where sparse signals, zero-inflated outcomes, long tails, and trial-driven acquisition make conversion models unsuitable. It encodes 350+ pre-order signals across 12 groups in a simple multilayer perceptron with three shared hidden layers, task-specific heads, a custom MAE loss, and Adam optimization. A four-head variant added order-count predictions for each vertical, producing marginally better performance with 135K rather than 363K parameters. Evaluation emphasized ranking through Spearman correlation and decile diagonal coverage, reaching above 0.75 Spearman and 70–80% diagonal coverage. In production bidding experiments, the in-house model delivered better retention and higher gross order value per acquired user at no incremental platform cost, while a third-party platform acquired more users per dollar.


### [DE Dost: Teaching Machines to Be a Friend on the Last Mile](https://yomu.fyi/post/de-dost-teaching-machines-to-be-a-friend-on-the-last-mile.md)
- Company: [Swiggy](https://yomu.fyi/company/swiggy.md)
- Author: Aishwary Yadav
- Published: Jul 17, 2026
- Type: Explainer

DE Dost is a GenAI onboarding companion embedded in Swiggy’s delivery-executive app, designed for new DEs who receive information but lack confidence-building, immediate guidance. It supports questions in nine languages across SOPs, earnings, payouts, app navigation, shifts, and support, adapting detail and tone to conversational context. Before the first LLM call, a graph state assembles user context, preprocessing resolves references, and embedding-based semantic routing separates general knowledge-base questions from personal diagnostics. Retrieval uses authored QnA pairs plus hybrid semantic and lexical scoring to handle paraphrases and terms such as “MG” and “COD,” while guardrails constrain follow-ups to available knowledge. Evaluation spans about 800 cases per language across fleet and payout combinations, with macro accuracy trending at 90%+ and a target P50 latency of 4.5 seconds after reducing an initial 15-second pilot.


### [How We Made All Swiggy Apps Operations AI-Native](https://yomu.fyi/post/how-we-made-all-swiggy-apps-operations-ai-native.md)
- Company: [Swiggy](https://yomu.fyi/company/swiggy.md)
- Author: Raj Gohil
- Published: Jul 17, 2026
- Type: Explainer

Swiggy’s mobile engineering teams faced operational overload as releases, production signals, and reviews required switching among Jira, Confluence, Bitrise, Play Console, Crashlytics, New Relic, Google Sheets, ImageKit, and Slack. The response was an operating layer built around Slack-native agent workflows, reusable AI skills, MCPs, and scheduled jobs connected to release, observability, QA, cost, and internal mobile systems. It can generate readiness updates, group Jira work, identify missing owners and blockers, summarize vitals, analyze alerts, follow up TRM actions, and recommend RAMP, HOLD, or ROLLBACK for staged rollouts by correlating Crashlytics, Play Vitals, and adoption. Humans still approve rollout actions, while recurring channel reports make observability and accountability more consistent and reduce clerical integration work. The stated model lets agents handle mechanical load while engineers retain judgement, ownership, product quality, architecture, and execution.


### [Enable Your SAML Requesting App for Cross App Access](https://yomu.fyi/post/enable-your-saml-requesting-app-for-cross-app-access.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Alisa Duncan
- Published: Jul 17, 2026
- Type: Tutorial

Cross App Access allows enterprise applications federated with Security Assertion Markup Language to interact with third-party resource APIs without migrating to OpenID Connect. The flow adapts the Identity Assertion Authorization Grant specification to SAML by adding three token exchange steps following the standard single sign-on handshake. First, the application exchanges the validated SAML assertion for an OAuth refresh token using Okta's token endpoint and a private key JSON Web Token. The application then uses the refresh token to request a short-lived Identity Assertion Authorization Grant token from Okta. Finally, the application presents the Identity Assertion Authorization Grant token to the third-party authorization server to obtain an OAuth access token.


### [Improvements to Cursor in Slack](https://yomu.fyi/post/improvements-to-cursor-in-slack.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Published: Jul 17, 2026
- Type: Announcement

Cursor in Slack now gives users more control over tasks by presenting a plan before work begins and reporting status as it progresses. Its Slack presentation has also changed: compact footer links replace in-message buttons, while tables, pull requests, and artifacts render more cleanly. In named multi-repo environments, Cursor can select an environment with access to related frontend, backend, and shared-code repositories; if it needs another repository, it prompts for a switch and resumes the task afterward. Cross-channel workflows let Cursor read context from other channels and threads and send updates to the original thread or a relevant channel. Together, these changes support earlier redirection, broader repository access, and task coordination across Slack workspace conversations.


### [The New DMARC is Here](https://yomu.fyi/post/the-new-dmarc-is-here.md)
- Company: [Resend](https://yomu.fyi/company/resend.md)
- Published: Jul 17, 2026
- Type: Explainer

DMARCbis is finalized as three Standards Track RFCs: RFC 9989 for the core protocol, RFC 9990 for aggregate reporting, and RFC 9991 for failure reporting. Existing DMARC records continue working as before, so most senders do not need immediate changes. The largest behavioral update replaces Public Suffix List lookups with an algorithmic DNS Tree Walk, in which receivers query \_dmarc records up the DNS hierarchy; the psd tag can declare hierarchy boundaries. The pct tag is replaced by boolean t, while np can specify a policy for non-existent subdomains, including np=reject. Reporting knobs rf and ri are removed, but rua and ruf remain available; senders using fractional pct should use a none-to-quarantine-to-reject rollout, keep reading reports, and avoid rushing to reject.


### [Q&A: How Capcom Brought Path Tracing to RE ENGINE Across PRAGMATA and Resident Evil Requiem](https://yomu.fyi/post/q-a-how-capcom-brought-path-tracing-to-re-engine-across-pragmata-and-r.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jul 16, 2026
- Type: Explainer

Capcom integrated path tracing into RE ENGINE across PRAGMATA and Resident Evil Requiem to deliver unified indirect and direct lighting. Over two years, the team built a reference path tracer validated against standard DCC tools, scaling it down into a real-time version optimized for NVIDIA DLSS Ray Reconstruction denoising. To manage rendering performance, Capcom avoided expensive AlphaTest texture lookups by creating a ScreenSpaceAlphaTest method comparing ray-hit positions against screen-space depth. Sampling noise in emissive and indoor regions is mitigated using Next Event Estimation and a custom implementation of ReSTIR GI. Replacing traditional shadow maps with continuous path tracing reduced the visual gap between gameplay and cinematics while enabling real-time light transmission through strand hair.


### [Your AI is ready. Your data foundation probably isn’t](https://yomu.fyi/post/your-ai-is-ready-your-data-foundation-probably-isn-t.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: CIO.com
- Published: Jul 16, 2026
- Type: Explainer

Cushman & Wakefield’s enterprise AI program addresses fragmented experiments, disconnected data, and uneven organizational maturity across a 53,000-person workforce. Over four years, Chief Digital and Information Officer Sal Companieh used a product operating model, business-linked accountability, co-created investment decisions, and shared architecture standards to build a common foundation while preserving business-unit flexibility. Databricks supports that strategy as a partner and platform, with its intelligence layer and Genie helping employees query trusted data in natural language, examine quality and governance, and monitor compliance. The company says the time from idea to outcome has fallen from months to days, while client and acquisition onboarding has materially accelerated. Companieh identifies human behavior, education, and trust—not technology alone—as essential to making change durable.


### [From experiment to insight: how Dotmatics Luma and Databricks make AI-ready science a reality](https://yomu.fyi/post/from-experiment-to-insight-how-dotmatics-luma-and-databricks-make-ai-r.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ryan Bernhardt, Michael Fritz
- Published: Jul 16, 2026
- Type: Explainer

Scientific workflows generate data across instruments, teams, and partner networks, but siloed systems can strip away metadata, lineage, and experimental context. Luma, Dotmatics’ scientific intelligence platform, captures outputs continuously and harmonizes them into structured, FAIR-compliant records, while Databricks supplies scalable storage, governance, and enterprise data and AI infrastructure. Running natively on Databricks, Luma preserves a continuous digital thread across experiment design, acquisition, analysis, reporting, and sharing, with Delta Sharing supporting governed exchange with collaborators. Chromatography illustrates the approach: connectivity across vendor systems and Virscidian’s Analytical Studio automate LC/MS processing while retaining context and adding dashboards, registration, and management tools. In one pharmaceutical deployment, Luma connected about 1,500 of more than 5,000 instruments across four LC/MS vendors, enabling cross-vendor performance trending, unified purity analysis, and a foundation for AI and machine learning.


### [How To Set Up a Staging Environment on Heroku in 3 Easy Steps](https://yomu.fyi/post/how-to-set-up-a-staging-environment-on-heroku-in-3-easy-steps.md)
- Company: [Heroku](https://yomu.fyi/company/heroku.md)
- Author: Andy Smith
- Published: Jul 16, 2026
- Type: Tutorial

The guide explains how to add a Heroku staging environment so changes are tested in a near-identical, isolated replica before production. It uses a pipeline with production and staging apps, disables production automatic deployments, connects staging to GitHub, and enables automatic builds, with Review Apps offered for pull requests. The setup applies Twelve-Factor practices by separating config vars and databases, recommending migrations into a fresh Postgres database or warning that pg:copy overwrites staging data and can create compliance risks. After validation, slug promotion sends the exact compiled staging asset to production without recompiling, while numbered releases support rollback; database changes must still be reversed separately.


### [The skills gap behind agentic AI — and how Databricks is closing it with a new context engineer certification and agent trainings](https://yomu.fyi/post/the-skills-gap-behind-agentic-ai-and-how-databricks-is-closing-it-with.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Rachel Canetta, Trang Le
- Published: Jul 16, 2026
- Type: Announcement

Databricks introduced the Databricks Certified Context Engineer Associate beta exam to validate skills for building reliable, production-grade AI agent systems as organizations scale agentic AI. Context engineering is presented as the practice of curating, maintaining, and filtering tokens, memory banks, and tool parameters so an LLM can solve a task; the certification is intended to benchmark that technical skill set. Databricks also added AI Agent Fundamentals, Building Retrieval Agents on Databricks, and Agent Evaluation, covering agent reasoning, retrieval-augmented architectures, and systematic performance testing and improvement. Its AI-first certification-prep guide is available on every certification page, works with free tiers of major LLMs, includes guardrails and disclaimers about hallucinations, and shows candidates how to use Free Edition for hands-on practice; registration for the Context Engineer Associate is open, with the first exam scheduled for July 29, 2026.


### [Unified context: The missing layer for enterprise AI coworkers](https://yomu.fyi/post/unified-context-the-missing-layer-for-enterprise-ai-coworkers.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Cynthya Peranandam, Christy Maver
- Published: Jul 16, 2026
- Type: Explainer

Enterprise AI assistants often produce fluent answers yet fail to improve forecast calls, deal reviews, and operational standups because decision context is scattered across systems, teams, and competing definitions. Genie One addresses this by using a shared context layer spanning Databricks data, documents, SaaS applications, and operational systems, allowing questions and follow-up work to retain business meaning. Genie Ontology organizes terms, metrics, entities, and relationships into a living knowledge graph, learning from data, dashboards, queries, documents, and connected applications while ranking definitions and signals using usage and certified-asset links. Together with Unity Catalog, it applies permissions, certified data, shared definitions, and governance controls to answers, actions, and agents. The stated outcome is faster movement from decision preparation to action, with less manual reconciliation while preserving accuracy and control.


### [What happens in the milliseconds after you tap pay](https://yomu.fyi/post/what-happens-in-the-milliseconds-after-you-tap-pay.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Harsha Pasala, Subhadip Chanda
- Published: Jul 16, 2026
- Type: Tutorial

At checkout, fraud scoring must combine transaction inference with customer-specific rules while keeping latency low enough for an interactive payment. The retail-app sample pairs a FastAPI backend and React frontend in a Databricks App with Model Serving route optimization and Lakebase Postgres, where the model retrieves historical features and the backend reads profile controls. A transaction is scored first, then checked against daily spending, international-transaction, and country rules; pooled OAuth-authenticated connections and token-rotation handling avoid repeated handshakes and stale credentials. In the supplied benchmark, route-optimized calls reached 27.2 ms p50 and 37.3 ms p95 end-to-end, with feature lookup at 8.9 ms p50, CatBoost inference at 0.4 ms, and network overhead at 17.4 ms p50; actual performance varies by model.


### [Integrating Context-Aware Video AI Agents Into Enterprise Workflows](https://yomu.fyi/post/integrating-context-aware-video-ai-agents-into-enterprise-workflows.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Jul 16, 2026
- Type: Problem & solution

Video analytics AI agents often struggle to integrate into enterprise workflows because video platforms, organizational knowledge bases, and operational systems are isolated. To resolve this, NVIDIA NemoClaw orchestrates the Metropolis Video Search and Summarization blueprint alongside the Retrieval-Augmented Generation blueprint into an automated pipeline. Upfront human-in-the-loop interactions collect analysis parameters, objects of interest, and relevant retrieval queries before video ingestion begins. The system combines hierarchical video summaries with contextual enterprise reference documents to generate structured reports with timestamps and citations. Finally, NemoClaw executes programmatic downstream tasks, such as creating prioritized Jira tickets or dispatching industrial maintenance work orders directly into management tools.


### [Scaling Agentic AI Factories Through Extreme Co-Design with NVIDIA BlueField](https://yomu.fyi/post/scaling-agentic-ai-factories-through-extreme-co-design-with-nvidia-blu.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jul 16, 2026
- Type: Explainer

Agentic artificial intelligence workflows transform data center infrastructure by turning context management, security enforcement, and inter-service communication into active components of the inference data path. To prevent host central processing units and graphics processing units from stalling on these tasks, NVIDIA introduces BlueField-4 data processing units, Vera BlueField-4 STX storage processors, and the DOCA software platform. The BlueField-4 processor offloads Layer 3 routing, encryption, and telemetry using a sixty-four-core Grace processor with up to eight hundred gigabits per second connectivity. Simultaneously, the Vera BlueField-4 STX powers the NVIDIA CMX context memory platform alongside DOCA Memos to store, protect, and share key-value cache across flash storage tiers. This co-designed infrastructure isolates tenant traffic and accelerates context reuse without degrading host-side agent execution.


### [How Unity Catalog managed tables bring interoperability, performance, and unified governance to the Lakehouse](https://yomu.fyi/post/how-unity-catalog-managed-tables-bring-interoperability-performance-an.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Alex Jiang, Tathagata “TD” Das
- Published: Jul 16, 2026
- Type: Announcement

Unity Catalog external access to Unity Catalog managed Delta tables is now in Public Preview, allowing external engines to create, read, and write while governance remains centralized. The announcement addresses the previous multi-engine trade-off: external tables enabled access but lacked managed-table performance optimizations and governance guarantees. Catalog commits coordinate writes through Unity Catalog, making it the source of truth for table state and enabling safe external writes, multi-statement transactions, and auditing of external operations. Predictive Optimization cleans storage, collects query statistics, and selects Liquid clustering columns as query patterns change; the post says these capabilities can deliver up to 50% storage cost savings and 20x faster queries. Support includes Spark, Flink, Starburst, DuckDB, and StreamNative, with open APIs and Delta Kernel extending integrations across Databricks UC and UC OSS.


### [AI Agents Explained: How to Build with Them Safely](https://yomu.fyi/post/ai-agents-explained-how-to-build-with-them-safely.md)
- Company: [Docker](https://yomu.fyi/company/docker.md)
- Author: Jin Kim
- Published: Jul 16, 2026
- Type: Explainer

AI agents combine reasoning models, external tools, memory, orchestration, and execution environments to autonomously pursue goals across iterative perceive-reason-act loops. While simple chatbots only generate text responses to direct prompts, autonomous agents execute multi-step operations such as querying databases, modifying codebases, and executing shell commands without requiring continuous manual approvals. However, this unchecked operational autonomy significantly widens the blast radius when models encounter logic errors or vague instructions, risking destructive filesystem operations and credential leaks. Safe deployment requires shifting focus from model-level guardrails to infrastructure-level containment strategies, such as scoping tool access and isolating execution inside disposable microVM sandboxes. Controlling networking boundaries, file access, and credential exposure ensures teams achieve automated execution velocity without exposing developer host environments to uncontained systemic risk.


### [Newer Models, Same Advantage](https://yomu.fyi/post/newer-models-same-advantage.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Erick Lachmann, Gabriel Pimenta de Freitas Cardoso, Francisco de Almeida Rocha Alves, Victor Gabriel Ferreira Barbosa
- Published: Jul 16, 2026
- Type: Benchmark

DharmaOCR, a Brazilian Portuguese-focused OCR model, is evaluated against newer Mistral OCR4 and Unlimited-OCR systems on a Portuguese-only benchmark. The article attributes its performance to a two-stage training pipeline: supervised fine-tuning on varied Portuguese documents, followed by Direct Preference Optimization (DPO) using comparative output preferences. Fine-tuning concentrates model capacity on Brazilian Portuguese vocabulary, syntax, and document structures, while DPO targets repetitive or incoherent generation, reducing inference time and cost and improving stability according to the article. DharmaOCR scored 0.925, compared with 0.798 for Mistral OCR4 and 0.7587 for Unlimited-OCR, while examples from handwritten ENEM essays show the competitors misreading Brazilian names and phrases that DharmaOCR handled correctly. The conclusion is that newer architectures can raise absolute performance, but directing finite resources at one domain can preserve a measurable specialist advantage.


### [Your agent should understand what you see](https://yomu.fyi/post/your-agent-should-understand-what-you-see.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Mihir Mavalankar
- Published: Jul 16, 2026
- Type: Problem & solution

Sentry's Seer Agent initially parsed user screens using DOM-scraped character-by-character ASCII grids, leading to severe token consumption, model context rot, and an inability to support interactive actions. To resolve these bottlenecks, Sentry replaced visual text scraping with a composable semantic context system where React UI components directly declare their state, metadata, and interactive affordances. A root React context provider maintains a registry of component nodes organized hierarchically through higher-order wrappers, serializing the resulting structure to JSON and backend-generated Markdown within the system prompt. Across twenty-four live pages, this approach reduced dashboard context tokens from roughly 5,500 to 1,300 while maintaining identical satisfaction rates and tool-call volumes across more than 14,000 evaluated conversations.


[Newer posts](https://yomu.fyi/posts/page/92.md) · [Older posts](https://yomu.fyi/posts/page/94.md)