---
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

### [Video Call Integration on Different Platforms](https://yomu.fyi/post/video-call-integration-on-different-platforms.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Donal Toomey
- Published: Aug 20, 2026
- Type: Tutorial

Video calling can add direct, personal communication to customer and patient interactions without moving conversations to a third-party app or website. The post outlines an integration path based on choosing a platform, selecting development-specific SDKs, and either building a custom application or deploying and adapting an open-source one. Twilio is presented as a cloud communications platform offering APIs, SDKs, reference apps, and documentation, with SDKs for JavaScript, Android, and iOS; listed build examples also use Svelte, Go, Python, ASP.NET Core, Angular, Node.js, TypeScript, and Express. Open-source options provide features such as screen sharing and network-condition monitoring, while Twilio Video supports branded virtual backgrounds and layouts, but the source does not report implementation results.


### [Shopify powers observability for global-scale commerce with ClickHouse](https://yomu.fyi/post/shopify-powers-observability-for-global-scale-commerce-with-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 20, 2026
- Type: Problem & solution

Shopify rebuilt its observability infrastructure after metrics, logs, and traces became fragmented across vendors, with unpredictable costs and limited cross-signal analysis. It created Observe, a unified platform that runs metrics, logs, traces, profiles, and exceptions on self-hosted ClickHouse, treating them as structured, time-ordered, high-dimensional events and serving roughly 50 million events per second normally and 100 million at BFCM peak, or about 110 GB/s. Kafka buffers telemetry for large synchronous durable writes, while materialized views support flexible schemas, autocomplete, typed hot keys, and identifier-based correlation; the latter reportedly makes targeted searches 10x faster. Shopify reports a 16x query improvement out of the box, exceeding 30x at peak performance, with data queryable in under a minute, and says it would likely choose ClickHouse Cloud if starting again because managed operations could reduce the self-hosting burden.


### [Enterprise-Managed Authorization for your MCP server: Setting Up and Testing XAA in Auth0](https://yomu.fyi/post/enterprise-managed-authorization-for-your-mcp-server-setting-up-and-te.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Miguel Pedregosa
- Published: Aug 20, 2026
- Type: Tutorial

Connecting AI agents to enterprise APIs and MCP servers creates governance challenges when relying on traditional OAuth consent prompts. Cross App Access shifts authorization decisions to the enterprise identity provider, allowing IT administrators to manage agent permissions centrally. In this architecture, an identity provider like Okta authenticates the user and issues an Identity Assertion JWT Authorization Grant based on administrative policy. The requesting AI agent presents this assertion to Auth0, which validates the signature and exchanges it for a standard access token via a JWT-bearer grant. Protected APIs can then validate the final token normally without requiring code changes or direct user consent interactions.


### [Enterprise Readiness Is Not What You Support. It Is What Your Customers Can Own](https://yomu.fyi/post/enterprise-readiness-is-not-what-you-support-it-is-what-your-customers.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Sheena Allan
- Published: Aug 20, 2026
- Type: Problem & solution

Workplace operations platform SafetyCulture previously relied on a multi-team, manual onboarding process for enterprise single sign-on setups across diverse identity providers, which created operational overhead and accounted for approximately 30 percent of engineering support tickets. To resolve these scaling bottlenecks, the engineering team integrated Auth0's Self-Service Enterprise Configuration, implementing a step-by-step wizard for OpenID Connect and custom SAML setups. The resulting workflow allows customer administrators to configure identity settings, test logins before committing, and manually toggle production activation. Moving from proof-of-concept to general availability took two months, achieving a 50 percent unassisted completion rate. Four months post-launch, SafetyCulture recorded a 60 percent reduction in SSO setup-related customer support tickets.


### [Multilingual Knowledge Transfer under Data Constraints via Lexical Interventions](https://yomu.fyi/post/multilingual-knowledge-transfer-under-data-constraints-via-lexical-int.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Aug 20, 2026
- Type: Problem & solution

Cross-lingual knowledge transfer is critical for training multilingual language models when target-language data is scarce and downstream tasks demand reasoning or world knowledge. Existing transfer approaches often depend on scarce resources such as large parallel corpora, translation systems, auxiliary models, or extra training phases. To address this limitation, the authors introduce LINK, a data-level intervention method that operates directly during pretraining via bilingual vocabularies. LINK swaps randomly selected words in a portion of the high-resource English corpus with word-level translations according to a specified replacement ratio. Across evaluations spanning eight languages and five model sizes, the technique delivered notable performance gains on downstream target-language tasks while achieving up to a 2x speedup in training to reach equivalent performance.


### [Scaling Laws for Mixture Pretraining Under Data Constraints](https://yomu.fyi/post/scaling-laws-for-mixture-pretraining-under-data-constraints.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Aug 20, 2026
- Type: Problem & solution

Scaling language models demands large volumes of data, but high-value target sources like low-resource languages and specialized domains remain inherently scarce. Practitioners frequently combine limited target data with abundant generic data, creating a balance between domain underexposure and excessive repetition leading to overfitting. An empirical evaluation across more than 2,000 language-model training runs demonstrates that mixture training tolerates substantially higher data repetition than single-source training, allowing scarce target corpora to be repeated 15 to 20 times. Researchers introduced a repetition-aware mixture scaling law that accounts for both the diminishing returns of repeated target tokens and the regularizing effect of generic data. Optimizing this scaling law offers a principled framework for calculating effective mixture configurations and practical pretraining recommendations under severe data constraints.


### [Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR](https://yomu.fyi/post/progressive-refinement-an-iterative-pseudo-labeling-approach-for-manda.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Aug 20, 2026
- Type: Problem & solution

Code-switching automatic speech recognition faces major performance challenges due to the scarcity of code-switching training data. To mitigate this data limitation, an iterative pseudo-labeling framework leverages a large unlabeled audio corpus to generate semi-supervised speech datasets. The system structure executes across three sequential phases: pseudo-label generation, two-stage bilingual model pre-training with supervised code-switching fine-tuning, and progressive iterative improvements. Successive refinement cycles further enhance the neural model's recognition accuracy when processing complex code-switching scenarios across alternating languages. When evaluated on the SEAME benchmark, this training pipeline achieved Mix Error Rate reductions of 6.35 percent on the devman subset and 8.29 percent on the devsge subset.


### [Building Menu Vision: Real-Time Dish Recognition](https://yomu.fyi/post/building-menu-vision-real-time-dish-recognition.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Arpitha Dudi, Growth Tech Lead
- Published: Aug 20, 2026
- Type: Problem & solution

Yelp developed Menu Vision to help diners recognize menu items and view corresponding user-generated photos and reviews in real time using their phone cameras. The feature evolved from a two-day hackathon Android prototype into a production system deployed across iOS and Android. Client devices perform all optical character recognition, text cleanup, and dish matching locally using native machine learning frameworks, while dish metadata is prefetched upon opening a restaurant page. Backend pipelines combine partner data, reviews, and photo captions into a Cassandra database, filtering exclusively for dishes with photos. To handle optical character recognition errors and name variations, the matching pipeline runs exact matching, bidirectional substring matching, and Jaro-Winkler similarity matching.


### [Developing NVIDIA Holoscan Applications with CLI, Skills, and AI Coding Agents](https://yomu.fyi/post/developing-nvidia-holoscan-applications-with-cli-skills-and-ai-coding.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Aug 19, 2026
- Type: Tutorial

NVIDIA Holoscan is a platform for real-time AI applications at the edge, and HoloHub provides reference applications and components. This post describes an engineer-guided workflow in which a coding agent builds a separate Python endoscopic tool segmentation application from the existing MONAI model, sample video, and Holoscan patterns. The Holoscan CLI, invoked through ./holohub, exposes shared commands for scaffolding, running, modes, tests, and benchmarking, while development skills and documentation guide the agent through project-specific operations. The application connects video replay, preprocessing, TensorRT inference, segmentation postprocessing, telemetry, and HoloViz visualization, with visual, smoke, and benchmark modes; the post reports that the CLI-plus-skills-plus-docs/examples configuration produced a separate application in 40 minutes using 11M tokens, and concludes that this combination offered the lowest resource overhead among the compared configurations.


### [How we think about text classification in the LLM era](https://yomu.fyi/post/how-we-think-about-text-classification-in-the-llm-era.md)
- Company: [Medium](https://yomu.fyi/company/medium.md)
- Author: Raphael Montaud
- Published: Aug 19, 2026
- Type: Problem & solution

Medium revisited its aging NSFW text-classification model, which had outdated code, infrequent retraining, and poorly documented training and performance history. The replacement uses an LLM prompt containing NSFW guidelines and story content, requesting a 0-to-100 score, structured output, and a short reason rather than requiring a training set. To evaluate it, the team created a curator-labeled dataset with deliberate oversampling of NSFW stories, then compared several LLMs with the legacy model using false-positive and false-negative metrics. Some models matched or exceeded the legacy model; the team selected Mixtral 8×7b based on cost and performance and ran inference in Snowflake. The post presents this as a strong fit where training data is costly, trends are relatively stable, and only distribution-eligible stories need scoring, while topic and spam models still benefit from continuously updated datasets.


### [Building Federated Multimodal AI Workflows with NVIDIA FLARE](https://yomu.fyi/post/building-federated-multimodal-ai-workflows-with-nvidia-flare.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Aug 19, 2026
- Type: Explainer

Federating vision-language models (VLMs) across institutions requires keeping images, text, and prompts local while coordinating training and combining updates from sites with different task and modality mixes. NVIDIA FLARE addresses this workflow through server-scheduled rounds, client-local execution, and an explicit update contract, while supporting large-object externalization, pull-based tensor streaming, and disk-backed aggregation for large payloads. The post presents FedUMM as an adapter-based design in which clients train LoRA adapters over a frozen BLIP3o backbone and send only adapter updates, while full-model training remains possible when required. In reported eight-client experiments, adapter-only federation reduced per-client communication from 28.6 GB to 0.094 GB per round, improved VQA v2 by 0.7 points, and retained about 97% of centralized performance on VQA v2 and GenEval; the evaluation used simulated sites and does not establish clinical performance or formal privacy guarantees.


### [How we knew COVID was over (and what our models had to unlearn)](https://yomu.fyi/post/how-we-knew-covid-was-over-and-what-our-models-had-to-unlearn.md)
- Company: [Airbnb](https://yomu.fyi/company/airbnb.md)
- Author: Harrison Katz
- Published: Aug 19, 2026

Airbnb's forecasting team outlines their framework for managing production models that experience drift or external shocks. Rather than treating model updates as a generic retraining task, they distinguish between refitting with new data, respecifying the underlying model structure, and intentionally holding without changes. This discipline prevents teams from chasing temporary noise, carrying obsolete crisis assumptions, or over-engineering solutions during disruptions.


### [A revisit of remote Spectre attacks on Cloudflare Workers](https://yomu.fyi/post/a-revisit-of-remote-spectre-attacks-on-cloudflare-workers.md)
- Company: [Cloudflare](https://yomu.fyi/company/cloudflare.md)
- Author: Martin Schwarzl
- Published: Aug 19, 2026
- Type: Problem & solution

Cloudflare reassessed remote Spectre attack feasibility within Cloudflare Workers production workloads to evaluate defenses against modern stabilization techniques. The experimental proof of concept bypassed Dynamic Process Isolation (DyPrIs) by keeping invocations alive using WebSocket traffic, which also diluted hardware performance counter ratios below detection thresholds. By combining branch mistraining, cache line eviction, and speculative type confusion, researchers transiently accessed out-of-bounds memory via raw 64-bit TypedArray backing store pointers. This attack demonstrated cross-tenant data leakage reaching up to 12 bit/s with over 99% accuracy in production. In response, Cloudflare mitigated the vulnerability by integrating the V8 memory sandbox, deploying Memory Protection Keys for hardware-assisted in-process isolation, and redesigning DyPrIs to monitor active long-lived executions.


### [Designing effective Genie Agents from a single prompt](https://yomu.fyi/post/designing-effective-genie-agents-from-a-single-prompt.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Megan Tupper
- Published: Aug 19, 2026
- Type: Tutorial

Generic agents may select the first relevant-looking revenue table rather than the finance-maintained source because they lack business context. Genie Agents address this by using a single prompt to assemble a domain-specific agent from governed Unity Catalog context, including structured data, documents, and files. The prompt identifies the desired outcome and relevant sources, while the agent can retrieve content from PDFs, Word documents, presentations, images, and tables under the requesting user’s permissions. The recommended process is to begin with one focused, repetitive job, test it against known questions or incidents, and expand only after measuring performance. Built-in benchmarks provide accuracy scores, while monitoring surfaces user questions and feedback that can reveal gaps in the curated context.


### [Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control](https://yomu.fyi/post/post-train-nvidia-cosmos-3-edge-for-on-device-robot-control.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Aug 19, 2026
- Type: Tutorial

Deploying large world models on robots is constrained by onboard memory and the control latency needed for continuous manipulation. The tutorial post-trains NVIDIA Cosmos 3 Edge, a 4B omni-model with a 2B NVIDIA Nemotron-based reasoner, into a DROID action policy using 76,000 successful teleoperated trajectories, then serves it through an OpenPI-compatible WebSocket server on Jetson Thor. The configuration predicts 32 future 8-dimensional joint-position actions at 15 Hz, while a receding-horizon loop executes a prefix and replans from fresh camera and robot-state observations. On an NVIDIA Jetson AGX Thor T5000, inference takes about 1.53 seconds per action chunk covering roughly 2.13 seconds of motion, and the policy reaches 22.9% success in closed-loop RoboLab evaluation; the source contrasts this with Cosmos 3 Nano’s 36.8%.


### [Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator](https://yomu.fyi/post/evaluating-ai-agent-skill-performance-with-nvidia-skillevaluator.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Aug 19, 2026
- Type: Benchmark

NVIDIA SkillEvaluator measures whether packaged agent skills improve agent performance by comparing task runs with and without a skill installed. It combines three evaluation tiers: static safety and structure checks, embedding-based distinctiveness analysis, and live evaluations in isolated Harbor sandboxes. In the August 12, 2026 benchmark snapshot, more than 300 verified skills spanning over 30 NVIDIA products were tested across Claude Code and Codex, with macro-averaged Skill Lift of +31 points across all dimensions and +39 excluding Security. Correctness rose from 46 to 87 and Effectiveness from 39 to 78, while Security moved from 97 to 98; token and runtime effects varied by skill, with one reducing both and another increasing them.


### [How Clario technology detects PHI/PII in DICOM images using Amazon Bedrock](https://yomu.fyi/post/how-clario-technology-detects-phi-pii-in-dicom-images-using-amazon-bed.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Alex Boudreau
- Published: Aug 19, 2026
- Type: Problem & solution

Clario developed an automated PHI and PII detection solution on AWS to process thousands of DICOM image slices and PDF files in clinical trials. Medical imaging workflows require strict privacy compliance under HIPAA, GDPR, and ICH E6 guidelines across standard tags, custom vendor metadata, and pixel-burned text. To address this, the pipeline retrieves files from Amazon S3 and runs memory-intensive detection workloads on Amazon EKS backed by Amazon RDS for PostgreSQL. Amazon Textract handles optical character recognition, while Anthropic's Claude Sonnet on Amazon Bedrock classifies sensitive data using domain-optimized prompt engineering. The system identifies precise coordinates and sensitive entity types rather than directly altering files, enabling human-in-the-loop review before downstream redaction occurs.


### [AI-powered clinical trial eligibility and safety using Amazon Bedrock AgentCore](https://yomu.fyi/post/ai-powered-clinical-trial-eligibility-and-safety-using-amazon-bedrock.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Sachin Jain
- Published: Aug 19, 2026
- Type: Problem & solution

Manual chart review across fragmented clinical sources slows clinical trial enrollment, contributing to timeline delays and high screen failure rates. To address this bottleneck, an architecture on AWS automates eligibility and safety assessments while preserving human clinician authority. AWS HealthLake ingests and normalizes records into FHIR R4 resources, while Amazon Bedrock AgentCore orchestrates pre-screening, detailed screening, and site enrollment agents. Amazon Bedrock AgentCore Evaluations scores screening determinations using an LLM-as-a-judge approach for clinical accuracy, operational effectiveness, and safety compliance. This human-in-the-loop workflow produces structured recommendations with source citations, reducing patient matching time from days to minutes while maintaining compliance audit trails.


### [Accelerate automation with AI and the Ansible development tools MCP servers](https://yomu.fyi/post/accelerate-automation-with-ai-and-the-ansible-development-tools-mcp-se.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Leonardo Gallego
- Published: Aug 19, 2026
- Type: Announcement

Ansible automation development often involves frequent context switching and struggles with AI assistants generating syntactically valid code that hallucinates module parameters or misses local dependencies. To address this gap, the Ansible development tools Model Context Protocol server connects AI assistants to external development tooling through structured interfaces. The server wraps command-line utilities, enabling AI models to deterministically run ansible-lint with automatic fixes, scaffold projects via ansible-creator, manage environments, and build execution environment container images. By surfacing local workspace schemas, documentation, and organizational linting standards, the system grounds AI responses in real environment configurations. The integration operates across multiple MCP-compatible clients without requiring a specific model provider.


### [Building E-commerce AI Agents on MongoDB with CrewAI](https://yomu.fyi/post/building-e-commerce-ai-agents-on-mongodb-with-crewai.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Aug 19, 2026
- Type: Tutorial

A 34% year-over-year increase in consumers using generative AI for online shopping, especially among Gen Z and Millennials, is motivating e-commerce teams to adopt AI shopping agents. This tutorial demonstrates building a chat-based shopping assistant on MongoDB Atlas with CrewAI that supports zero-click buying from product discovery to post-purchase follow-up. The agent interprets natural-language queries, retrieves semantically relevant products via MongoDB Vector Search with Voyage AI embeddings using price and inventory pre-filters before similarity ranking, and uses Change Streams for atomic inventory checkout. Atlas Triggers enable proactive order notifications, while Atlas Stream Processing detects delivery patterns by monitoring updates in two-minute tumbling windows, writing confirmed signals to agent\_triggers when at least 10 orders per carrier-region are delayed by 45 minutes. MongoDB consolidates operational data, semantic search, and event-driven capabilities into a single platform, reducing latency and complexity.


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