---
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

### [Building rock-solid Stripe integrations: A developer's guide to success](https://yomu.fyi/post/building-rock-solid-stripe-integrations-a-developer-s-guide-to-success.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: James Beswick
- Published: Feb 6, 2025
- Type: Tutorial

The guide presents ten practices for building production-ready Stripe integrations, covering payment forms, subscriptions, and operational concerns. It recommends verifying webhook signatures with the Stripe-Signature header and endpoint secret, recording event IDs for idempotency, and returning a fast 2xx response while processing asynchronously. Test and live API keys, webhook endpoints, logs, and monitoring should remain separate, while error handling should distinguish card, request, and API failures and use exponential backoff for retries. The guide also recommends testing successful, declined, insufficient-funds, and 3DS scenarios with Stripe test cards, alongside simulated webhook events. Its conclusion emphasizes edge-case planning, logging, monitoring, and ongoing review as foundations for reliable, secure payment processing that can scale with business needs.


### [AI Core Team Lead Mike Schuster on How to Get the Most From LLMs](https://yomu.fyi/post/ai-core-team-lead-mike-schuster-on-how-to-get-the-most-from-llms.md)
- Company: [Two Sigma](https://yomu.fyi/company/two-sigma.md)
- Author: Joy Looney
- Published: Feb 4, 2025
- Type: Explainer

Mike Schuster, Head of the AI Core Team at Two Sigma, advocates for grounding large language model adoption in practical tasks rather than speculative industry hype. Realistic enterprise applications focus on accelerating data processing, running faster experiments, and extracting domain-specific features from transcripts such as earnings calls and Federal Reserve speeches via prompt engineering. Because financial data faces inherent volume limits across trading days, successful deployments require multidisciplinary human teams to balance rapid technical experimentation with rigorous domain expertise and analytical reasoning. Schuster also dismisses predictions that programming will become obsolete, comparing coding to learning a musical instrument that cultivates structured thinking, problem decomposition, and scientific common sense essential for building complex predictive models.


### [Building resilient webhook handlers in AWS: Implementing DLQs for Stripe events](https://yomu.fyi/post/building-resilient-webhook-handlers-in-aws-implementing-dlqs-for-strip.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: James Beswick
- Published: Jan 30, 2025
- Type: Problem & solution

Reliable Stripe webhook processing must account for lost deliveries from network or service outages, out-of-order events, and duplicates caused by Stripe retries. The proposed AWS architecture uses API Gateway for signature validation and throttling, an SQS FIFO queue for ordered delivery and content-based deduplication, Lambda for processing, DynamoDB for event-ID idempotency, and an SQS DLQ for failed messages. In the CloudFormation example, the main queue has a 300-second visibility timeout and sends messages to the FIFO DLQ after three receives; DynamoDB records expire through a seven-day TTL. Lambda retries failures with exponential backoff, while CloudWatch monitors queue depth, latency, and errors. The design is presented as a scalable foundation, with multi-region failover available at added cost and complexity, though single-region deployment may suffice for many applications.


### [Sharing Tinder’s latest contributions to the open source community](https://yomu.fyi/post/sharing-tinder-s-latest-contributions-to-the-open-source-community.md)
- Company: [Tinder](https://yomu.fyi/company/tinder.md)
- Author: Tinder
- Published: Jan 29, 2025
- Type: Announcement

Tinder open-sourced several iOS development repositories, including Layout, Nodes Architecture Framework, and CombineUI, to share the engineering patterns supporting its iPhone application. As the app expanded across diverse device profiles and scale, engineering teams faced reliability, consistency, and memory challenges under their legacy architecture. In response, Tinder developed Nodes, a plugin-based architecture framework using compile-time dependency injection and lifecycle hooks that enforce complete memory release upon feature dismissal. For interface construction, Tinder created Layout, a domain-specific Auto Layout wrapper offering declarative syntax for UIKit views to eliminate storyboard merge conflicts while preserving native capabilities. The resulting stack enables isolated testing of business logic, native reactive event binding, and incremental adoption of SwiftUI across the codebase.


### [New to Stripe? Learn the key concepts for software developers.](https://yomu.fyi/post/new-to-stripe-learn-the-key-concepts-for-software-developers.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: James Beswick
- Published: Jan 29, 2025
- Type: Tutorial

The guide introduces software developers to Stripe’s main objects and workflows for integrating payments, recurring billing, and related account activity. It begins with Payment Intents, which specify an amount and currency, track a payment’s lifecycle, and produce a client secret for completing the frontend flow, then covers Payment Methods and Customers, including Setup Intents and attaching a default payment method. For subscriptions, it distinguishes Products, Prices, and Subscriptions, and explains using events and webhooks to react to asynchronous payment and subscription changes while verifying webhook signatures. It also describes Disputes, refunds, and expanded API responses, including retrieving related resources in one call. The guide recommends starting in test mode and using dashboard logs while consulting Stripe’s documentation and changelog as the platform evolves.


### [Adopting Jetpack Compose for Etsy’s Android App](https://yomu.fyi/post/adopting-jetpack-compose-for-etsy-s-android-app.md)
- Company: [Etsy](https://yomu.fyi/company/etsy.md)
- Author: John Kalmi
- Published: Jan 28, 2025
- Type: Problem & solution

Etsy adopted Jetpack Compose as its preferred way to build Android app UIs, after a staged program that culminated in rewriting the Shop screen. Because UI toolkits are central to development, engineers created a curriculum, rebuilt internal design-system components, and used Compose’s interoperability with XML Views to migrate gradually. They then tested a full internal screen with navigation, system UI, coroutines for API data fetching, and Kotlin Flows supporting unidirectional data flow alongside Macramé. After Compose bottom sheets reached real buyers and sellers, the Shop rewrite took about three months and entered an A/B experiment against the old implementation. The new screen improved initial rendering time by 5%, made interactions more fluid, and improved several user actions, while engineers reported fewer lines of code, easier testing, greater business-logic coverage, and higher satisfaction.


### [Image replacement in Canva designs using reverse image search](https://yomu.fyi/post/image-replacement-in-canva-designs-using-reverse-image-search.md)
- Company: [Canva](https://yomu.fyi/company/canva.md)
- Author: Sam Jacobs
- Published: Jan 28, 2025
- Type: Problem & solution

Canva needed an automated way to replace media in design templates, such as when third-party licensing partnerships expire across more than 150 million images. Existing recommendation engines, perceptual hashing, and text metadata searches failed to capture visual similarity hierarchies or ensure replacement relevance. To build a reverse image search system, engineers evaluated embedding models including CLIP, ViTMAE, DreamSim, CaiT, and DINOv2 alongside an external vector database supporting metadata filtering. Evaluation on sample datasets identified DINOv2 as the best model for preserving subjects, background context, and color tones in photos. Integrated into the Template Assistant as a human-in-the-loop tool, the automated suggestions increased image replacement speeds by 4.5 times during initial pilot testing.


### [How the leading AI companies are using AI tools](https://yomu.fyi/post/how-the-leading-ai-companies-are-using-ai-tools.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Drew Evans
- Published: Jan 23, 2025
- Type: Explainer

The article examines how companies on the Forbes AI 50 list are adopting AI for knowledge work, noting that research with Harvard Business Review found only three percent of organizations use AI for knowledge management. Examples from Synthesia and Wiremind favor small, practical wins, including onboarding videos, data visualizations, staging-versus-production comparisons, and complex dataset validation. Adoption is supported through experimentation, departmental AI champions, sandbox hours, documented guidelines, colleague-led training, and feedback systems rather than forced rollout. To measure impact, the companies track usage, new use cases, employee satisfaction, time savings, and output quality; Pinecone reports an 8× increase in sales activity after implementing AI tools. The article concludes that AI may initially expand team capability instead of immediately reducing costs, while repeated experiments can build lasting work habits and support innovation.


### [Announcing a New Collaboration Between eBay and OpenAI](https://yomu.fyi/post/announcing-a-new-collaboration-between-ebay-and-openai.md)
- Company: [Ebay](https://yomu.fyi/company/ebay.md)
- Author: eBay News Team
- Published: Jan 23, 2025
- Type: Announcement

eBay announces a collaboration with OpenAI as part of its strategy to shape agentic ecommerce and redefine online discovery and shopping for enthusiasts. The company is among the first to collaborate on the research preview of Operator, an AI agent that acts as a virtual assistant and autonomously performs tasks across the internet, including online shopping. Operator directs users to eBay for unique inventory finds, a model eBay anticipates will expand sellers’ reach by giving more buyers exposure to that inventory. The announcement also places the work within eBay’s broader effort to evolve internal AI agent capabilities and deepen strategic partnerships while learning how these technologies can empower buyers and help sellers grow their businesses.


### [What is a Good Engagement Rate on TikTok for 2025?](https://yomu.fyi/post/what-is-a-good-engagement-rate-on-tiktok-for-2025.md)
- Company: [Brandwatch](https://yomu.fyi/company/brandwatch.md)
- Author: Gemma Joyce
- Published: Jan 21, 2025
- Type: Explainer

The guide explains how to assess TikTok engagement rate, noting that follower count, industry, content type, and audience demographics all affect the benchmark. It reports an average platform-wide rate of 4.07%, while citing 6–10% as a typical good range and higher figures for smaller accounts, including about 10%+ for micro-influencers and 4–6% for mega-influencers. It defines engagement through likes, comments, shares, views, and saves, then gives formulas based on total engagements, followers, and number of posts, including worked examples producing 6.66% and 5.5%. Industry figures range from 9.5% for schools and sports teams to 2.51% for fashion, with niche-specific targets recommended. TikTok Analytics and third-party tools such as Brandwatch are presented as ways to track trends, compare benchmarks, and refine strategy; the guidance comes from external research rather than original platform research.


### [Scaling Large Language Models for e-Commerce: The Development of a Llama-Based Customized LLM](https://yomu.fyi/post/scaling-large-language-models-for-e-commerce-the-development-of-a-llam.md)
- Company: [Ebay](https://yomu.fyi/company/ebay.md)
- Author: Christian Herold and Shahram Khadivi
- Published: Jan 17, 2025
- Type: Problem & solution

eBay developed e-Llama, customized 8B and 70B language models, by adapting Meta’s Llama 3.1 models to e-commerce rather than training entirely from scratch. The approach uses continued pretraining on filtered public listings, product reviews, classifier-selected open-source examples, replay data, and 10% non-English general-domain data to add domain knowledge while limiting catastrophic forgetting. Training ran on 480 NVIDIA H100 GPUs using Megatron-LM, 3D parallelism, flash-attention-2, a 1:1 general-to-e-commerce sampling ratio, a learning-rate maximum set to 10% of the original, and one trillion tokens. The 70B model required about one month and 340,000 GPU-hours. E-commerce benchmarks improved approximately 25% in English and 30% in non-English, while general-domain NLU degraded 1% for e-Llama 70B; subsequent instruction tuning added safety and instruction-following guardrails.


### [From RAG to Richness: How Ramp Revamped Industry Classification](https://yomu.fyi/post/from-rag-to-richness-how-ramp-revamped-industry-classification.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Ryne Carbone
- Published: Jan 15, 2025
- Type: Problem & solution

Ramp's former industry classification system combined third-party data, sales-entered information, and customer self-reporting, creating inconsistent, overly broad, and unauditable categories. Precise classification was needed for compliance, portfolio monitoring, sales targeting, and product analytics, despite fuzzy boundaries, sparse data, and no ground truth. Ramp migrated to six-digit NAICS codes and built an in-house Retrieval-Augmented Generation model that embeds business data, retrieves candidate codes, and uses an LLM to select a final prediction. The system evaluates retrieval with accuracy-at-k and final predictions with a hierarchy-aware fuzzy-accuracy metric, while ClickHouse stores knowledge-base embeddings and Kafka logs intermediate results. Deployed guardrails validate output codes, and the model has improved data quality, consistency, auditability, and control over tuning and costs.


### [A new Tab model](https://yomu.fyi/post/a-new-tab-model.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Phillip Kravtsov & Jacob Jackson
- Published: Jan 13, 2025
- Type: Announcement

Cursor announces Fusion, a next-generation model for Cursor Tab, which predicts edits near the cursor and “jumps” to likely next editing locations. The post positions this as a response to the limits of cursor-only text insertion, with a broader goal of reducing code-editing tedium and moving toward in-flow Next Action Prediction. Compared with the March 2024 model, Fusion predicts over 25% more difficult edits per line, suggests over 10x longer change stretches, cuts median server latency from 475ms to 260ms, expands context from 5,500 to 13,000 tokens, and adds instant, accurate cursor jumps. These gains are attributed to cleaner and larger datasets, longer editor-state prompts, training for larger edits, synthetic instruction-following data, and inference and performance improvements. Fusion is rolling out to all users in client release 0.45.0.


### [Five years of React Native at Shopify](https://yomu.fyi/post/five-years-of-react-native-at-shopify.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Jan 13, 2025
- Type: Explainer

Shopify reviews five years of using React Native (RN) as its mobile strategy, adopted to avoid duplicating features across iOS and Android, improve talent portability, and ship more value. Over that period, all of its apps migrated to RN through team-led, non-uniform plans, while native code remained part of the architecture rather than an alternative to eliminate. The company reports sub-500ms P75 screen loads in the Shopify app, similar performance across its apps, and more than 99.9% crash-free sessions, while noting that performance depends on applying sound patterns. Its experience credits hot reloading and TypeScript with developer productivity and cross-platform staffing, but identifies debugging, RN upgrades, and third-party dependency maintenance as costs. Shopify now emphasizes shared libraries, native expertise for platform-specific work, continued New Architecture adoption, and collaboration with the RN ecosystem.


### [Character Prefix Conditioning](https://yomu.fyi/post/character-prefix-conditioning.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Jacob Jackson
- Published: Jan 6, 2025
- Type: Problem & solution

The post poses a problem in code completion: generating a language-model completion that begins with the characters already typed by the user. Because modern models process token sequences rather than characters, naively tokenizing the input can produce the wrong result when the cursor falls inside a token. It introduces character prefix conditioning, defined as sampling tokens from an autoregressive distribution while constraining their decoded character string to start with the given prefix. The challenge is to construct an efficient autoregressive algorithm for this conditional distribution while minimizing calls to the original language model; the post requests either an algorithm description or an implementation.


### [The foundations of Canva’s continuous data platform with Snowpipe Streaming](https://yomu.fyi/post/the-foundations-of-canva-s-continuous-data-platform-with-snowpipe-stre.md)
- Company: [Canva](https://yomu.fyi/company/canva.md)
- Author: Jack Caperon
- Published: Jan 6, 2025
- Type: Problem & solution

As Canva expanded to over 200 million monthly active users, ingestion throughput reached 25 billion records daily, causing AWS Data Firehose costs to consume nearly half of the product analytics platform budget. To reduce these expenses and eliminate intermediate file staging, the engineering team integrated Snowflake's Snowpipe Streaming directly with their Java-based Kinesis Data Streams pipeline. The architecture streams records directly into Snowflake tables using logical channels with offset checkpoints while configuring client buffering up to a five-minute maximum lag. In handling Kinesis Client Library edge cases, record processors coordinate with the LeaseCoordinator to drop leases when channels become unhealthy. Operating in production for over six months, the system ingested more than 20.35 petabytes of data, decreased query latency to roughly ten minutes, and reduced overall cloud spend by 45 percent.


### [How to Submit Your System for Cross](https://yomu.fyi/post/how-to-submit-your-system-for-cross.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Semona Igama
- Published: Dec 30, 2024
- Type: Tutorial

Enterprise applications with SSO still need scalable identity management for customer organizations, including user provisioning, updates, and deprovisioning. This guide presents SCIM (System for Cross-domain Identity Management) as the standards-based connection to Okta for automating those lifecycle operations and controlling access. SCIM can synchronize users in real time as employees join, leave, or change roles, while reducing manual work and inconsistent deprovisioning risks. To submit the integration to the Okta Integration Network, partners need an Okta Integrator Free Plan, two Runscope test links for CRUD and SPEC tests, manual Okta SCIM integration tests, a provisioning-enabled instance, confirmation, and a publicly accessible SCIM server. The submission can expose an integration to Okta’s customer base of 18,000+ organizations and provide verified enterprise-ready status.


### [Embracing passwordless authentication with Grab’s Passkey](https://yomu.fyi/post/embracing-passwordless-authentication-with-grab-s-passkey.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Ocean Nguyen
- Published: Dec 26, 2024
- Type: Problem & solution

Grab’s Passkey adds passwordless authentication to its app to address phishing, keystroke-logging, brute-force, and other security threats associated with passwords plus the friction of OTP-based login. Built on the FIDO standard and implemented through WebAuthn, the flow uses a backend, frontend, and authenticator: the authenticator creates and stores a passkey, while Grab stores the public key and metadata. Registration uses a server-generated challenge and parameters including rp.id, user information, resident-key requirements, and supported public-key algorithms; sign-in returns a credential whose signature is verified against the stored public key. Google Password Manager and iCloud Keychain can sync passkeys across supported devices, and users authenticate with their screen lock rather than entering a password or waiting for an OTP. The implementation is described as faster, smoother, phishing-resistant login with potential savings on third-party authentication and messaging costs, while Grab plans to evaluate web login expansion through adoption, feedback, and usage data.


### [Canva incident report: API Gateway outage](https://yomu.fyi/post/canva-incident-report-api-gateway-outage.md)
- Company: [Canva](https://yomu.fyi/company/canva.md)
- Author: Brendan Humphreys
- Published: Dec 20, 2024
- Type: Incident / postmortem

On November 12, 2024, canva.com suffered an outage lasting approximately one hour due to cascading API Gateway failures. The disruption began when a stale Cloudflare routing rule induced severe latency and packet loss between Ashburn and Singapore during an editor asset deployment. Cloudflare's concurrent streaming mechanism consolidated over 270,000 user requests for a single delayed JavaScript chunk; when the asset finally loaded, clients simultaneously generated a 1.5 million requests-per-second thundering herd to Canva's API Gateway. This surge collided with a telemetry performance regression involving lock contention on Netty event loop threads, exhausting off-heap memory and causing the Linux Out Of Memory Killer to terminate API Gateway containers. Canva mitigated the failure by temporarily blocking all traffic at the CDN level, stabilizing replacement Amazon ECS tasks, and gradually restoring traffic under strict rate limits.


### [Building Effective AI Agents](https://yomu.fyi/post/building-effective-ai-agents.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Dec 19, 2024
- Type: Explainer

Effective agentic systems need not begin with complex frameworks: the post reports that successful implementations commonly use simple, composable patterns and recommends choosing the least complex solution that meets the task's needs. It distinguishes workflows, which follow predefined code paths, from agents, where LLMs dynamically direct their processes and tool use. The proposed foundation is an augmented LLM with retrieval, tools, and memory, potentially integrated through the Model Context Protocol; larger designs include prompt chaining, routing, parallelization, and orchestrator-workers patterns. Frameworks can simplify implementation but may obscure prompts and responses, so direct API use and understanding underlying code are recommended. Tool definitions are treated as an interface requiring testing, clear examples, low formatting overhead, and mistake-resistant parameters; absolute filepaths eliminated observed errors in a coding agent.


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