---
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

### [Migrating to React Native's New Architecture](https://yomu.fyi/post/migrating-to-react-native-s-new-architecture.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Sep 5, 2025
- Type: Explainer

Shopify successfully migrated two largest apps, Shopify Mobile and Shopify Point of Sale, to React Native’s New Architecture while continuing weekly releases for millions of merchants. The strategy prioritized minimal code changes, dual-architecture compatibility, and performance and stability parity, with TopHat generating both architecture builds for every pull request. The team upgraded React Native first, updated or removed incompatible dependencies, and changed only native modules that failed under the new architecture rather than migrating all 40-plus modules to TurboModules. Migration issues included state batching, UIManager-related blank screens, shadow-tree manipulation, view flattening, and main-thread legacy module initialization; the reported outcome was maintained development velocity with no feature-development disruption, followed by planned optimization work.


### [Updating sales restrictions for unsupported regions](https://yomu.fyi/post/updating-sales-restrictions-for-unsupported-regions.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 4, 2025
- Type: Announcement

Anthropic is updating its Terms of Service to address access by companies from regions where its products are not permitted, including entities operating through foreign subsidiaries. The company says ownership or control from jurisdictions such as China can create legal obligations to share data, cooperate with intelligence services, or support military and intelligence objectives. Under the change, companies or organizations more than 50% owned directly or indirectly by companies headquartered in unsupported regions are prohibited from using the services, regardless of where they operate. Anthropic frames the update as aligning its restrictions with real-world national-security risks and reiterates support for export controls, US energy projects, and rigorous evaluation of AI models for capabilities relevant to national security.


### [Marketplace monetization with Stripe](https://yomu.fyi/post/marketplace-monetization-with-stripe.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Ana Andres
- Published: Sep 4, 2025
- Type: Tutorial

Online marketplaces connect buyers and sellers while monetizing commissions, shipping, marketing, payment operations, insurance, or other value-added services. Greens & Dairy Mart illustrates a model in which farmers receive payment after delivery, sellers pay a 15% rate on sales, and shipping, marketing, payout, and insurance options create additional revenue streams. Stripe Connect supports transaction-linked fees through the Platform pricing tool or application\_fee\_amount, while Stripe Billing provides usage-based meters, subscriptions, and invoices for recurring or one-off charges. The example records pay-per-click, conversion, and penalty events against farm customers, charges monthly subscriptions automatically, and bills damaged-goods insurance annually, with the stated conclusion that programmatic disbursement and monetization give marketplaces a central point to monitor and report these activities.


### [Welcome EmbeddingGemma, Google's new efficient embedding model](https://yomu.fyi/post/welcome-embeddinggemma-google-s-new-efficient-embedding-model.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tom Aarsen, Joshua, Alvaro Bartolome, Aritra Roy Gosthipaty, Pedro Cuenca, Sergio Paniego
- Published: Sep 4, 2025
- Type: Announcement

Google DeepMind released EmbeddingGemma, a multilingual embedding model with 308 million parameters and a 2048-token context window designed for on-device applications. Based on the Gemma3 transformer backbone, the architecture replaces causal attention with bidirectional attention to function as an encoder, followed by mean pooling and two dense layers producing 768-dimensional vectors. The model incorporates Matryoshka Representation Learning, allowing outputs to be truncated down to 512, 256, or 128 dimensions for reduced memory and storage footprints. Trained on approximately 320 billion multilingual tokens across more than 100 languages, the quantized model operates under 200 MB of RAM. In domain-specific evaluations on the MIRIAD dataset, fine-tuning increased NDCG@10 from 0.8340 to 0.8862, outperforming larger baselines.


### [Building Etsy Buyer Profiles with LLMs](https://yomu.fyi/post/building-etsy-buyer-profiles-with-llms.md)
- Company: [Etsy](https://yomu.fyi/company/etsy.md)
- Author: Isobel Scott
- Published: Sep 3, 2025
- Type: Explainer

Etsy is experimenting with large language models (LLMs) to create anonymous buyer profiles from browsing and purchasing behavior, capturing interests such as styles, categories, and shopping missions. The pipeline combines recent searches, item views, purchases, favorites, session data, and listing information from internal feature-store and BigQuery sources, then prompts an LLM to generate structured profiles with explanations and confidence information. To scale processing, Etsy moved listing data to clustered and partitioned BigQuery tables, shortened input history from two years to nine months, increased batch sizes and compute, added managed concurrency, and used Airflow to stagger tasks. For 10 million users, these changes reduced generation time from 21 days to three and cut estimated cost per million users by 94%; experiments apply profiles to query rewriting and refinement pills.


### [Meet Mick Hodgins, Notion’s new EMEA GM](https://yomu.fyi/post/meet-mick-hodgins-notion-s-new-emea-gm.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Pravesh Mistry
- Published: Sep 3, 2025
- Type: Announcement

Notion announces Mick Hodgins as its new general manager for EMEA, linking the appointment to a priority of expanding its international footprint; 80% of users are outside the US. Hodgins brings 13 years at Google, where he scaled the EMEA ads business and led teams serving customers in more than 20 EMEA countries across changes including mobile, programmatic advertising, the pandemic, and AI. His vision combines local execution with a unified regional team, including new office hubs in London, Paris, and Munich, continued growth of Dublin HQ, and EU data residency. He aims to support students, startups, and enterprises across EMEA while building high-performing teams and positioning Notion as a go-to solution for knowledge workers.


### [Build Secure Agent-to-App Connections with Cross App Access (XAA) Using OIDC](https://yomu.fyi/post/build-secure-agent-to-app-connections-with-cross-app-access-xaa-using.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Sohail Pathan
- Published: Sep 3, 2025
- Type: Tutorial

The guide presents Cross App Access (XAA) as a protocol for secure agent-to-app and app-to-app access, giving enterprise IT centralized control over connections, policies, and accessed resources. It walks through an OIDC demonstration using Todo0 as the Resource App, Agent0 as the Requesting App, and Okta as the enterprise identity provider, covering app registration, trust establishment, token exchange, and API calls. The flow uses the OAuth 2.0 Identity Assertion Authorization Grant: Bob logs in once, Agent0 obtains an identity assertion, Okta exchanges it for a Todo0 access token, and Agent0 calls the protected API. The demonstrated result is an agent that can read pending tasks, add tasks, and mark tasks complete, with changes reflected in Todo0. The guide is marked out of date because its Admin Console instructions have been replaced by AI agent registration under Directory > AI Agents.


### [SAIR: Accelerating Pharma R&D with AI-Powered Structural Intelligence](https://yomu.fyi/post/sair-accelerating-pharma-r-d-with-ai-powered-structural-intelligence.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Arman Zaribafiyan, Georgia Channing, Rudi Plesch, Zane Beckwith
- Published: Sep 2, 2025
- Type: Announcement

SandboxAQ released the Structurally Augmented IC50 Repository (SAIR), an open-source dataset containing 5.24 million computationally co-folded 3D protein-ligand structures paired with empirical IC50 binding potency data. The repository addresses training data scarcity in structure-based drug discovery, where over 40 percent of the included target proteins lack experimental structures in the Protein Data Bank. To construct the dataset, engineers executed over 130,000 GPU hours of the Boltz1 co-folding model on 760 NVIDIA H100 processors hosted via NVIDIA DGX Cloud on Google Cloud Platform. Infrastructure optimizations maintained over 95 percent GPU compute utilization, compressing the generation timeline from three months to three weeks. Quality validation using PoseBusters confirmed that 97 percent of the predicted complexes met physical plausibility and chemical sanity standards.


### [1.5x faster MoE training with custom MXFP8 kernels](https://yomu.fyi/post/1-5x-faster-moe-training-with-custom-mxfp8-kernels.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Stuart Sul
- Published: Aug 29, 2025
- Type: Benchmark

An upgrade from Hopper H100s to Blackwell B200s exposed the Mixture-of-Experts (MoE) layer, implemented with MegaBlocks, as a major training bottleneck: it consumed nearly 53% of forward-pass time and 27% of backward-pass time. The team rewrote the layer at the GPU-kernel level using CUDA, PTX, and some ThunderKittens, while moving from BF16 to microscaled FP8 (MXFP8) with nearly zero loss in training quality. The implementation addresses Blackwell-specific tensor memory constraints and quantization overhead, including custom MXFP8 quantization that sustains more than 6.2 TB/s and emits scale matrices directly compatible with tcgen05.mma. On an internal model, MoE execution became 3.5x faster in both passes, producing a 1.5x end-to-end speedup on Blackwell and 2x versus the original Hopper setup, measured in tokens per second per GPU.


### [Engineering stories behind the Medium Daily Digest Algorithm: Part 2](https://yomu.fyi/post/engineering-stories-behind-the-medium-daily-digest-algorithm-part-2.md)
- Company: [Medium](https://yomu.fyi/company/medium.md)
- Author: Raphael Montaud
- Published: Aug 28, 2025
- Type: Problem & solution

Medium’s Daily Digest filtering had become costly and difficult to maintain after a single Bloom Filter instance accumulated billions of items for read, muted, and presentation rules. The system could face up to 15,000 filter questions per feed and more than 15 million per second across thousands of feeds, while Bloom Filters could not delete or enumerate entries and introduced derived-data drift. The team replaced the muted-writer filter with a user-based database query that fetched all muted writers for the current user and cross-referenced them with the story shortlist, then split read filtering between early user-based queries and later pairwise lookups. The resulting implementation removed Bloom Filters entirely, was reported as ten times cheaper in their situation, made costs independent of shortlist size, supported reading-history clearing, and was easier to control and debug.


### [Updates to Consumer Terms and Privacy Policy](https://yomu.fyi/post/updates-to-consumer-terms-and-privacy-policy.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 28, 2025
- Type: Announcement

Anthropic is updating its Consumer Terms and Privacy Policy for Claude Free, Pro, and Max users, including Claude Code sessions tied to those accounts. Users can choose whether their data is used to improve Claude and strengthen safeguards against scams, abuse, and harmful content; the change excludes Commercial Terms services and API use. New users choose during signup, while existing users must decide by October 8, 2025; accepting immediately applies the updates to new or resumed chats and coding sessions, and the setting remains changeable in Privacy Settings. For users who opt in, retention will extend to five years, compared with 30 days otherwise, while deleted conversations are excluded from future training; Anthropic says it filters or obfuscates sensitive data and does not sell user data.


### [First Block with Brendan Foody, Co-founder and CEO of Mercor](https://yomu.fyi/post/first-block-with-brendan-foody-co-founder-and-ceo-of-mercor.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Anastasia Crew
- Published: Aug 27, 2025
- Type: Explainer

First Block features Brendan Foody, co-founder and CEO of Mercor, discussing the company’s path from selling donuts in eighth grade to building an AI-powered recruiting platform. Mercor reportedly grew from $1 million to $100 million in revenue run rate in 11 months, and Foody connects that growth to automated hiring solutions and the AI talent revolution. He argues that founders should pursue problems they cannot stop thinking about, set a high bar for their first 10 hires, and remain patient while shaping culture. The episode also frames fundraising as an input to better user experiences rather than an objective, encourages founders to return quickly to customers, and covers long-term orientation, personal scaling, and finding the right market.


### [Amazon Nova Models: Now Available on Heroku](https://yomu.fyi/post/amazon-nova-models-now-available-on-heroku.md)
- Company: [Heroku](https://yomu.fyi/company/heroku.md)
- Author: Anush DSouza
- Published: Aug 26, 2025
- Type: Announcement

Amazon Nova’s nova-pro and nova-lite models are now available through Heroku Managed Inference and Agents, giving applications a managed way to use them. The integration initially supports text-to-text workloads, while multimodal support is planned for a future release; both models support tool use and offer 300,000-token context windows. Nova-lite targets speed, low latency, and high-volume real-time applications at $0.06 per 1M input tokens and $0.24 output, whereas nova-pro targets accuracy and deep reasoning at $0.80 input and $3.20 output. Heroku provides an OpenAI-compatible /v1/chat/completions endpoint for chat and generation, plus /v1/agents/heroku for built-in Heroku tools and custom MCP tools, with provisioning available in US and EU regions through the add-on or CLI.


### [Engineering stories behind the Medium Daily Digest Algorithm: Part 1](https://yomu.fyi/post/engineering-stories-behind-the-medium-daily-digest-algorithm-part-1.md)
- Company: [Medium](https://yomu.fyi/company/medium.md)
- Author: Raphael Montaud
- Published: Aug 26, 2025
- Type: Problem & solution

Medium’s Daily Digest recommendations were less relevant than the homepage despite identical story sourcing, ranking model, and features, because digest filtering operated with less email-reporting information and was too aggressive. The “Sent in Opened Digest” filter excluded all 15 stories after an email-open signal, even when a reader saw only the first five; Apple Mail Privacy Protection also caused servers to preload messages, making many open events unreliable. Medium removed that filter and reduced the backoff from seven to four days, accepting more repetition while relying on other filtering rules and organic recommendation changes to diversify digests. An A/B test found that experiment users were 10% more likely to convert to paying members and read 7% more on Medium. The post says these results led to more filter investigations, including Bloom Filters, “Hard vs Soft Filtering,” and cross-digest diversification, while recommending audits of products that depend on email-open tracking.


### [Building production-ready agentic systems: Lessons from Shopify Sidekick](https://yomu.fyi/post/building-production-ready-agentic-systems-lessons-from-shopify-sidekic.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Aug 26, 2025
- Type: Explainer

Shopify’s Sidekick evolved from a simple tool-calling system into an agentic assistant that helps merchants manage stores through natural language, exposing scaling challenges in architecture, evaluation, and training. As its inventory grew beyond 50 tools, the system prompt accumulated conflicting special cases and became difficult to maintain; JIT instructions instead return relevant guidance with tool data. For evaluation, the team uses Ground Truth Sets based on production distributions, human labels from at least three product experts, statistical agreement measures, specialized LLM judges calibrated against humans, and merchant simulation. Judge correlation rose from Cohen’s Kappa 0.02 to 0.61 against a human baseline of 0.69, while syntax validation improved from about 93% to 99% after reward-hacking fixes. GRPO training exposed opt-out, tag, and schema hacking, and the post recommends modularity, procedural plus semantic validation, user simulation, and iterative judge refinement for production agentic systems.


### [Engineering stories behind the Medium Daily Digest Algorithm: Part 4](https://yomu.fyi/post/engineering-stories-behind-the-medium-daily-digest-algorithm-part-4.md)
- Company: [Medium](https://yomu.fyi/company/medium.md)
- Author: Raphael Montaud
- Published: Aug 25, 2025
- Type: Problem & solution

Medium’s Daily Digest team investigated complaints that recommendations had become repetitive, especially for power users, after earlier filtering changes. Data showed some stories were sent up to eight times to one user, while affected users saw digest posts repeated twice on average; the team also had to account for Apple Mail Privacy Protection, which prevents reliable open tracking. They therefore tracked digest email IDs when readers clicked a story, marking emails with at least one click as “definitely opened,” and added a recommendation-stack filter that counts prior occurrences in those digests. Testing supported a threshold of one occurrence: complaining users would lose nearly five stories per digest, whereas free users would lose fewer than one. An A/B test produced a statistically significant increase in reading time, with a larger rise among members; the team reported no further complaints and suggested soft filtering and email-position rules as possible refinements.


### [Build Better Agents With MorphLLM](https://yomu.fyi/post/build-better-agents-with-morphllm.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Aug 25, 2025
- Type: Problem & solution

The post addresses a bottleneck in AI agents that write or edit code: correcting a small change can trigger a full-file rewrite, consuming tokens and causing formatting failures, silent omissions, and unreliable results. MorphLLM’s Morph Fast Apply is presented as a semantic, structure-aware edit merge tool: an agent emits small edit\_file snippets with //...existing code... placeholders, while the Apply API merges them against the codebase context before the edited file is written back. The post reports over 4,500 tokens per second, 98% accuracy in about six seconds per file, and about four seconds for files up to 9k tokens, compared with 35 seconds and 86% accuracy for traditional search-and-replace with error corrections. It also states that MorphLLM is API-compatible with OpenAI, Vercel AI SDK, MCP, and OpenRouter, and can run in the cloud, self-hosted, or on-premises.


### [Real-time vs batch reconciliation: Practical patterns for keeping data in sync](https://yomu.fyi/post/real-time-vs-batch-reconciliation-practical-patterns-for-keeping-data.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: James Beswick
- Published: Aug 23, 2025
- Type: Explainer

Product data reconciliation across internal databases, ERPs, and platforms such as Stripe becomes harder to maintain as catalogs and system relationships grow, affecting financial reporting, customer experience, and operations. The post compares real-time reconciliation, using tools such as Kafka, Flink, Delta Live Tables, and Debezium for more immediate consistency, with batch approaches built around Spark, AWS Glue, Snowflake, and dbt for periodic, large-scale checks at lower cost and complexity. It also presents validation options including Great Expectations, Apache NiFi, Talend, and Airbyte, and recommends defining data ownership, applying field-specific conflict rules, and routing critical mismatches to manual review. Soft deletes and tombstones preserve deletion history, while observability through synchronization latency, conflict rates, resolution patterns, and regular audits helps assess reconciliation health as product catalogs scale.


### [From naive webhooks to durable sync: Queue-based and event-driven data reconciliation patterns](https://yomu.fyi/post/from-naive-webhooks-to-durable-sync-queue-based-and-event-driven-data.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: James Beswick
- Published: Aug 22, 2025
- Type: Explainer

Maintaining consistent product data between an internal catalog and Stripe becomes difficult when outages, throttling, high volumes, and changes from both systems create reconciliation failures. The post compares a naive one-way flow, in which a Lambda calls the Stripe API with no persistent synchronization record, with a queue-based design that durably stores changes, controls processing rate, retries failures with exponential backoff, and sends persistent failures to dead-letter queues. For bidirectional synchronization, it contrasts periodic ETL, using extraction, transformation, and loading, with an event-driven model based on Stripe Event Destinations, EventBridge, SQS, and symmetrical events from internal systems. The conclusion is that durable, observable queues and event-driven processing provide buffering, auditability, near-real-time propagation, independent change processing, and more graceful scaling for distributed reconciliation.


### [An Internship on Ramp’s Forward Deployed Engineering Team](https://yomu.fyi/post/an-internship-on-ramp-s-forward-deployed-engineering-team.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Alex Noviello
- Published: Aug 21, 2025
- Type: Explainer

The supplied material consists of the title “An Internship on Ramp’s Forward Deployed Engineering Team” and page markup, but it does not provide narrative details about the internship, the team, responsibilities, technical work, or outcomes. The markup identifies the site as “Ramp Builders,” supplies an RSS feed titled “Ramp Builders Blog,” and includes standard icons, social metadata, and a JavaScript application entry point. It also states that JavaScript must be enabled to run the app, while linking a stylesheet and several favicon assets. Analytics configuration includes Google Analytics and Segment, with a Ramp-hosted Segment endpoint and Sprig (Actions) disabled; these details describe the page infrastructure rather than the internship.


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