Loading…
Browse by type
Explainer reads
Explainer posts on Yomu, summarised newest first.
- 1,318 posts
- 20 companies
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
Explainer posts
Navigating the AI storm: How to harness the power of AI without being crushed by it
Support leaders are being asked to preserve customer experience and operational performance while becoming AI experts and leading high-lift, cross-functional implementations. Before AI, support was often treated as a cost center; now executives may demand aggressive AI metrics without understanding the resources, risks, and work still required. The proposed response is to harness the change by learning AI deeply, testing multiple vendors through trials, choosing a durable partner, and defining benchmarks and phased implementation plans. Support leaders should also explain why deflecting 50% of inquiries does not justify cutting staffing by 50%, given coverage, service-level redundancy, growth, attrition, and work beyond direct inquiries. The article ultimately argues that visible ownership, concise project management, and weekly or bi-weekly executive updates can reposition support as a value driver rather than a cost center.
20 Social Media Holidays to Celebrate This October
October offers marketers a dense set of social media holidays, beginning with Global Diversity Awareness Month and continuing through Halloween. The calendar pairs each observance with a date, hashtag suggestions, and a possible engagement idea, ranging from cultural traditions and coffee orders to teacher memories, taco votes, and costume sharing. It also includes awareness-focused occasions such as International Day of Non-Violence, World Sight Day, World Mental Health Day, Global Handwashing Day, World Food Day, and United Nations Day. Several entries supply factual context, including the 2.2 billion people with visual impairment, World Vegetarian Day’s 1977 establishment, and Global Handwashing Day’s first celebration in 2008. The closing guidance recommends using these observances to craft and optimize an October content calendar.
Toyota, Affirm, Mercury, and more get faster business results with certified Notion Solutions Partners
Notion’s certified Solutions Partners help organizations replace fragmented tools, connect workflows, and build systems that can scale. At Affirm, Notion State used a five-phase deployment strategy across multiple departments, helping achieve 95% knowledge-management adoption, reduce tool seats, and create a unified workspace. NorthSand helped Toyota’s Future Creation Center add custom automations, approval workflows, and centralized scheduling, making key workflows three times faster and cutting meeting-preparation time in half. MegazoneCloud supported JOYCITY’s migration from Jira, Confluence, and Microsoft by running parallel systems, focusing new projects in Notion, and providing on-site training, while Optemization archived more than 63,000 of Mercury’s pages and established a standardized information architecture. The examples position partner expertise and ongoing support as ways to improve adoption, trust, collaboration, and scalability.
Improving Cursor Tab with online RL
Cursor Tab predicts developers’ next actions across their codebases and handles more than 400 million requests per day, creating a large stream of accepted and rejected suggestions for training. The post describes replacing a separate acceptance filter with policy-gradient reinforcement learning, allowing the model to learn when to suggest and when to remain silent while reusing its learned code representation. An illustrative reward assigns 0.75 to an accepted suggestion, -0.25 to a rejected one, and 0 to showing nothing, targeting suggestions whose estimated acceptance chance is at least 25%; the production reward also considers suggestion size and jumps to other code locations. Because policy-gradient updates require on-policy data, each new checkpoint is rolled out to users; the current deployment-and-collection cycle takes 1.5 to 2 hours, and the resulting model makes 21% fewer suggestions with a 28% higher accept rate.
Strengthening safeguards with US CAISI and UK AISI
Anthropic describes an ongoing partnership with the US Center for AI Standards and Innovation (CAISI) and UK AI Security Institute (AISI) to test and improve AI safeguards during model development. The organizations evaluated multiple iterations of Constitutional Classifiers on models including Claude Opus 4 and 4.1, using pre-deployment prototypes, varied system configurations, documentation, and classifier scores for red-teaming. Their testing exposed prompt-injection bypasses, universal jailbreaks, cipher-based and input/output obfuscation attacks, and automated attack refinement that produced an effective universal jailbreak. Anthropic patched specific vulnerabilities and restructured safeguard architecture, improved detection and filtering, and used findings to strengthen evaluation, deployment monitoring, and response practices. The collaboration also showed that sustained access, real-time data, daily technical communication, and complementary government, expert, and bug-bounty testing can reveal both common exploits and subtle attack vectors.
Seamlessly connect Stripe events to your frontend
Stripe events can connect backend state changes to responsive frontend interfaces for payments, subscriptions, connected accounts, and Stripe Terminal workflows. The post contrasts polling, which can increase traffic, latency, and delta-computation complexity, with push subscriptions, while noting that a hybrid of initial API loading and incremental updates is common. It describes webhook endpoints and Amazon EventBridge as ingestion choices, then emphasizes idempotency, ordering, duplicate detection, and failure handling. Frontend delivery options include WebSockets, MQTT, gRPC, Server-Sent Events, polling, AWS AppSync, and Momento, selected according to messaging pattern, direction, latency, scale, and operational trade-offs. An abbreviated lounge example uses a PaymentIntent and Terminal reader, routes payment_intent.requires_payment_method through EventBridge to AWS AppSync Events, and lets a React client mark the reader ready to collect payment.
Securing Salesforce Integrations with Heroku AppLink
Salesforce integrations commonly use Connected Apps or External Services, but long-lived OAuth tokens and public vendor endpoints can expose org data to replay and misuse. Heroku AppLink provides a managed bridge in which a service mesh admits requests only from explicitly connected Salesforce orgs, validates context, and exposes services as native Salesforce actions. For calls back to Salesforce, its SDK obtains short-lived, scoped credentials at runtime through user, user-plus, or authorized-user modes, avoiding refresh-token storage in vendor systems. The mesh uses the x-client-context header to convey org and user information, while the SDK keeps bearer credentials out of application code. The post describes setup commands for the AppLink add-on, service-mesh buildpack, External Service publication, and authorizations, concluding that AppLink improves governance, least privilege, and replay resistance.
Triage and Fix with Confidence: heroku run and OTel on Heroku Fir
Heroku Fir addresses production troubleshooting when a release causes failures, such as a background worker breaking after a database schema change, without requiring access to a live formation dyno. Its interactive heroku run command launches a dedicated one-off dyno with the application code and environment variables, relaying terminal input and output for tasks such as rake db:migrate; using it requires a public SSH key and a deployed application. The platform’s native OpenTelemetry (OTel) signals add router request-duration summaries and percentiles, represent heroku.router.connect and heroku.router.service as floats, and attach heroku.app.name to application signals. Together with service.name filtering for first-party services, these capabilities support identifying affected applications, applying isolated fixes, and verifying latency and errors through an observability platform.
Migrating to React Native's New Architecture
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.
Building Etsy Buyer Profiles with LLMs
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.
First Block with Brendan Foody, Co-founder and CEO of Mercor
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.
Building production-ready agentic systems: Lessons from Shopify Sidekick
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.
Real-time vs batch reconciliation: Practical patterns for keeping data in sync
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
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
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.
Keeping product data aligned with Stripe as your systems scale
Keeping internal product data aligned with Stripe becomes an architectural challenge as catalogs grow from hundreds to millions of items. The problem spans independently operated systems with different data models, API constraints, rate limits, availability guarantees, and consistency models, while products may change faster than periodic reconciliation can complete. Stripe's payment-focused model covers identifiers, descriptions, prices, currencies, billing terms, and subscription behavior, whereas internal systems may contain categories, inventory, rich content, technical specifications, media, and sensitive cost data. The series examines manual verification, simple scripts, and periodic jobs for smaller catalogs, then points to streaming, change data capture, intelligent partitioning, and conflict resolution for high-scale reconciliation. It concludes that cross-system synchronization requires deliberate mapping and automated architecture because perfect synchronization is difficult to guarantee in eventually consistent distributed systems.
Context engineering case studies: Etsy-specific question answering
The post examines whether prompt engineering can provide truthful Etsy-specific answers for AI-assisted onboarding without fine-tuning an LLM, using Travel & Entertainment (T&E) policy questions and an external Etsy community-forum use case. For the internal pilot, T&E documents were indexed, embedding-based search retrieved relevant sections, and those sections were added to each question; a manually curated 40-question evaluation found approximately 86% correct or satisfactory answers, with 14% wrong or misleading. Explicit uncertainty instructions sometimes avoided hallucinations but also missed available answers, while asking for an explanation or chain-of-thought reasoning could improve results, though complex reasoning required careful setup. The experiments conclude that prompt engineering is a practical alternative to parameter-based fine-tuning, but requesting specific source snippets remains important for flagging potential hallucinations.
Trust Calibration for AI Software Builders
Trust calibration means aligning users’ trust in an AI product with its actual capabilities, avoiding both dangerous over-reliance and unnecessary rejection. Drawing on a 2023 review of more than 1,000 papers on trust in human/automated systems, the article distinguishes cooperative tools, where users accept or reject suggestions, from delegative systems, which replace human action and need stronger boundaries, fallbacks, and capability communication. It recommends calibrating expectations before and during interaction through capability-focused onboarding, context-sensitive feedback, visible suggestion boundaries, confirmation friction for high-stakes actions, and layered performance and process information; post-interaction reflection can still support learning. The article concludes that adaptive calibration is more effective than static approaches, anthropomorphic design can create unwarranted trust, explanations can overload users, and builders need measurements beyond satisfaction or adoption.
Automation, Squared
The article presents “second-order automation,” in which one agent designs reusable workflows for another agent to execute. Its spreadsheet system pairs an “architect” that watches a screen recording, generates detailed prompts, identifies required files and context, with a “doer” that searches workbooks and performs the instructions. The design addresses first-order automation’s dependence on users to supply precise prompts and fresh context for every task, which limits scalability. On an approximately 50-task SpreadsheetBench subset, the spreadsheet agent reached 49.5% accuracy on soft-restriction tasks versus OpenAI’s 45.5%, and 32.5% on hard-restriction tasks versus GPT-4o’s 13.4%; some one-to-two-hour tasks finished in under 10 minutes. The article concludes that recorded workflows can let non-technical users create reusable automations with little additional input, although architect performance has not yet been formally benchmarked.
How to cut through AI hype and choose solutions with confidence
Teams evaluating AI solutions can waste time, money, and trust when they lack technical fluency, choose unsuitable vendors, underestimate internal requirements, or expect immediate perfection. The post introduces Retrieval-Augmented Generation (RAG), vector search, agentic AI, and MCP as concepts that help teams distinguish static or rules-based tools from systems that retrieve current information, understand intent, take actions, and operate within governed boundaries. It argues that building an AI Agent in-house requires substantial work across embeddings, retrieval, prompt chaining, governance, security, maintenance, and model upgrades. Vendor evaluation should consider ongoing R&D, product roadmaps, adaptability, and maintenance, while implementation depends on structured content, documentation, process visibility, and APIs. Fin is cited as typically resolving over 76% of support questions out of the box, with resolution expected to improve through ongoing optimization and measurement.
Top topics
- Architecture830
- Performance812
- LLMs648
- Machine Learning617
- Open Source561
- AI Agents542
- AI454
- Developer Experience446
Weekly digest
The best engineering reads, once a week.