---
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

### [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.


### [Bringing the Cursor Agent to Linear](https://yomu.fyi/post/bringing-the-cursor-agent-to-linear.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Rohan Varma
- Published: Aug 21, 2025
- Type: Announcement

Cursor has introduced a Linear integration that lets teams delegate issues to @Cursor by assignment or comment mention, then runs a background agent with issue details, comments, and linked references as context. Teams can also define triaging rules that automatically assign selected tasks and review agent diffs and pull requests from Linear. After connecting a Linear team through Cursor’s integrations page, users assign an issue or mention @Cursor; the agent analyzes it and notifies users when the work is complete and a pull request is opened. Cursor says its team uses the workflow for thousands of customer-feedback, feature, and bug tasks, including a product-bug and feedback triage rule. According to the post, the agent now drafts pull requests for thousands of issues and can one-shot many of them.


### [Developing nuclear safeguards for AI through public-private partnership](https://yomu.fyi/post/developing-nuclear-safeguards-for-ai-through-public-private-partnershi.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 21, 2025
- Type: Problem & solution

AI models are increasingly capable, raising concern that they could provide dangerous nuclear-technical knowledge that threatens national security. Nuclear information is especially difficult for a private company to evaluate alone, motivating a partnership with the U.S. Department of Energy’s National Nuclear Security Administration (NNSA). Together with NNSA and DOE national laboratories, the company co-developed a classifier that distinguishes concerning from benign nuclear-related conversations, achieving 96% accuracy in preliminary testing. The classifier has been deployed on Claude traffic within a broader system for identifying model misuse, and early deployment data suggests it works well on real conversations; the approach will be shared with the Frontier Model Forum as a potential blueprint for similar safeguards developed with NNSA.


### [Keeping product data aligned with Stripe as your systems scale](https://yomu.fyi/post/keeping-product-data-aligned-with-stripe-as-your-systems-scale.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: James Beswick
- Published: Aug 21, 2025
- Type: Explainer

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.


### [Gating entrances with Stripe and NFC passes](https://yomu.fyi/post/gating-entrances-with-stripe-and-nfc-passes.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Joshua Nussbaum
- Published: Aug 21, 2025
- Type: Tutorial

The post presents a gym membership access system that replaces plastic cards, fobs, and physical keys with Apple Wallet and Google Wallet passes scanned by an NFC reader. It uses Stripe Checkout and subscriptions for payment, PassNinja as a single API layer for issuing passes, and a DotOrigin VTAP100 connected to a Raspberry Pi Zero 2W, relay, and door strike for entry control. After Checkout, the website verifies that the Stripe subscription is active, creates a pass whose nfc-message contains the Stripe subscription ID, and redirects the member to add it to a wallet. A Linux gate process reads that identifier from the reader's virtual serial port, retrieves the subscription from Stripe, and activates the relay for five seconds when the status is active; otherwise it denies access.


### [Heroku AI Expands Model Offering with OpenAI’s gpt-oss-120b](https://yomu.fyi/post/heroku-ai-expands-model-offering-with-openai-s-gpt-oss-120b.md)
- Company: [Heroku](https://yomu.fyi/company/heroku.md)
- Author: Anush DSouza
- Published: Aug 20, 2025
- Type: Announcement

Heroku has made OpenAI’s open-weight gpt-oss-120b available through Heroku Managed Inference and Agents, giving developers an OpenAI-compatible chat completions API for compatible SDKs and frameworks. The model uses a Mixture-of-Experts architecture with 117 billion total parameters, activating 5.1 billion per token and enabling operation on a single 80GB GPU; its Apache 2.0 license permits inspection and fine-tuning. It is designed for agentic workflows involving instruction following, function calling, web searches, and Python execution, while reported benchmarks place it near or above several open-weight models and sometimes OpenAI o4-mini. The add-on provides deployment, secure application access, agent actions, monitoring, and scaling without infrastructure management, with pricing of $0.15 per million input tokens and $0.60 per million output tokens.


### [How to Build a Secure iOS App with MFA](https://yomu.fyi/post/how-to-build-a-secure-ios-app-with-mfa.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Darko Spasovski
- Published: Aug 20, 2025
- Type: Tutorial

This tutorial shows how to add secure authentication and backend authorization to an iOS app using Okta’s BrowserSignIn module from the Okta Swift SDK. It registers a native OIDC client with Authorization Code Flow and PKCE, configures redirect URIs and authorization-server policies, and recommends phishing-resistant MFA factors such as Okta Verify with biometrics and FIDO2 with WebAuthn. The implementation uses Swift Package Manager, an Okta.plist configuration file, a testable AuthService protocol, and SwiftUI views for login, token handling, user information, and authenticated state. It then demonstrates authorized API requests by attaching an access token to a URLRequest, calling a backend endpoint, decoding its response, and displaying a returned message, concluding that OAuth 2.0 and JWT validation provide integrated authorization verification.


### [Context engineering case studies: Etsy-specific question answering](https://yomu.fyi/post/context-engineering-case-studies-etsy-specific-question-answering.md)
- Company: [Etsy](https://yomu.fyi/company/etsy.md)
- Author: Jerome Bellegarda
- Published: Aug 19, 2025
- Type: Explainer

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.


### [Building Data-Aware AI Applications with Heroku AI and LlamaIndex](https://yomu.fyi/post/building-data-aware-ai-applications-with-heroku-ai-and-llamaindex.md)
- Company: [Heroku](https://yomu.fyi/company/heroku.md)
- Author: Anush DSouza
- Published: Aug 19, 2025
- Type: Tutorial

Organizations building AI applications over private data must connect large language models with proprietary datasets without managing every infrastructure component. The post presents Heroku Managed Inference and Agents with LlamaIndex as a stack for retrieval-augmented generation (RAG), combining LlamaHub connectors, indexing, retrieval, and managed model services. The implementation provisions Heroku Postgres with pgvector, Claude 4 Sonnet for inference, and Cohere Embed Multilingual embeddings, then uses Python and LlamaIndex to load documents, store 1,024-dimensional vectors, and query them through an OpenAI-compatible embedding endpoint. PGVectorStore supplies the vector store, while Heroku environment variables provide service credentials and connection details. The result is a deployable pattern for querying private documents, with examples spanning financial analysis, insurance claims, customer support, and internal knowledge.


### [Data mesh at Grab part I: Building trust through certification](https://yomu.fyi/post/data-mesh-at-grab-part-i-building-trust-through-certification.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Chun Rong Phang
- Published: Aug 19, 2025
- Type: Problem & solution

Grab began its Signals Marketplace data mesh journey in 2024 because its expanding, varied data ecosystem had ownership gaps, duplicated pipelines, central Data Engineering bottlenecks, broken downstream dependencies, inconsistent definitions, and uneven practitioner expertise. The decentralised model treats data as a product owned by domain teams, with Business Data Owners and Technical Data Owners accountable for upkeep, documentation, usability, and SLOs. Certification formalises expectations through data contracts covering schema, freshness, completeness, retention, change notice, and communication, while automated Data Production Incidents flag breached reliability guarantees for investigation. Using the percentage of queries hitting certified assets as its north star metric, Grab reports 75% of queries targeting certified assets, a 400% year-over-year increase in deprecated tables, and a 58% reduction in P80 datasets.


### [Trust Calibration for AI Software Builders](https://yomu.fyi/post/trust-calibration-for-ai-software-builders.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Aug 18, 2025
- Type: Explainer

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.


### [Find the intersection of security, AI, IAM, and fun at Oktane](https://yomu.fyi/post/find-the-intersection-of-security-ai-iam-and-fun-at-oktane.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Michael Palermo
- Published: Aug 13, 2025
- Type: Announcement

Oktane 2025 will focus on security, AI, and identity, with programming for protecting software systems that use both human and non-human identities. The event is scheduled for September 24–26, 2025, at Caesars Forum in Las Vegas, and will include the Oktane Developer Lounge, Expo Hall activities, lightning talks, documentation feedback games, and Developer Support consultations. Hands-on labs cover securing enterprise AI with the OAuth Cross App Access (XAA) extension and Model Context Protocol (MCP), automating Okta with Terraform, replacing standing administrative access with time-bound requests through Okta Identity Governance and Workflows, and using OAuth 2.0 Demonstrating Proof of Possession (DPoP) tokens in a single-page application. An Okta Workflows community meetup will add networking, drinks, and appetizers, while the broader event offers learning, expert conversations, and application-focused discussions.


### [Guardrails for money movement: Integrating Stripe MCP with Portia AI](https://yomu.fyi/post/guardrails-for-money-movement-integrating-stripe-mcp-with-portia-ai.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Mounir Mouawad
- Published: Aug 13, 2025
- Type: Tutorial

Portia AI presents a customer-refund agent that combines its guardrails with Stripe’s official Model Context Protocol server. The agent reads a refund request, compares it with a policy file, identifies the relevant Stripe customer and payment intent, and evaluates whether the request should be approved. Portia’s planner produces a multi-agent plan, while execution agents run it and persist outputs in plan state; an execution hook pauses before the Stripe create-refund call. A clarification presents the request and the agent’s rationale to a human, who can approve it in the CLI; approval allows the refund to proceed, while rejection exits without creating one. The example also handles Gmail OAuth and can send the customer an email describing the outcome.


### [Automation, Squared](https://yomu.fyi/post/automation-squared.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Hunter Pruett
- Published: Aug 12, 2025
- Type: Explainer

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.


### [Announcing The AI Agent Blueprint](https://yomu.fyi/post/announcing-the-ai-agent-blueprint.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Aug 12, 2025
- Type: Announcement

The AI Agent Blueprint is a strategic guide for support, customer experience, and AI transformation leaders navigating how to adopt and scale AI Agents. Its first part, “Launch it,” covers building a business case, deciding whether to buy or build, evaluating an agent with a four-step framework, planning deployment, and continuously training, testing, deploying, and optimizing performance. The second part, “Scale it,” focuses on redesigning customer experience, organizational and system design, and economics through AI-first journeys, new roles and governance structures, and revised metrics, ROI models, and reinvestment strategies. The Blueprint argues that launching an agent is only the beginning: sustainable value requires treating AI as infrastructure and support as a potential growth lever, and it is live now.


### [Her Tech Circle rebuilt a community from scratch using Notion](https://yomu.fyi/post/her-tech-circle-rebuilt-a-community-from-scratch-using-notion.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Drew Evans
- Published: Aug 12, 2025
- Type: Problem & solution

After Girls in Tech abruptly ended its global operations, Renece Brewster and Karen Katz had one week to decide how to preserve an Australian community of 18,000 members. They launched Her Tech Circle (HTC), retaining the community’s core values while focusing on helping women in Australia and New Zealand thrive, lead, and contribute in tech. To rebuild quickly, HTC made Notion its digital headquarters, first piloting it with the social media team before rolling it out across the organization. The workspace combines a public-facing volunteer portal, a shared marketing database, and a directory that doubles as an org chart, linking roles, team leads, and peer support. HTC’s experience argues for testing tools early, making knowledge discoverable, and building scalable systems that let volunteers contribute independently rather than concentrating operational knowledge with founders.


### [Updates to Teams and Auto](https://yomu.fyi/post/updates-to-teams-and-auto.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Michael Truell
- Published: Aug 12, 2025
- Type: Announcement

Two billing updates will apply at the next renewal after September 15 for monthly and yearly plans. Teams is moving agent usage from fixed request costs to variable request costs, so charges rise or fall with the amount of work performed; a simple syntax question costs less than implementing a full pull request. This change extends the credit system already used by individual plans and unifies billing across tiers. For individuals, Auto will count toward included monthly usage at competitive token rates at the next renewal after September 15, replacing its unlimited status since June 2025; the post says Auto had previously matched premium-model pricing for individuals from December 2023 through June 2025 and for teams since June 2025.


### [How to cut through AI hype and choose solutions with confidence](https://yomu.fyi/post/how-to-cut-through-ai-hype-and-choose-solutions-with-confidence.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Nicole Rashied
- Published: Aug 11, 2025
- Type: Explainer

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.


### [Games as Model Eval: 1-Click Deploy AI Town on Fly.io](https://yomu.fyi/post/games-as-model-eval-1-click-deploy-ai-town-on-fly-io.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Aug 11, 2025
- Type: Announcement

Daniel Botha argues that conventional benchmarks reveal little about how models behave in long-context, interactive situations or whether they deliver the tone a product requires. He presents games as a more observable evaluation setting, because success signals are unambiguous and gameplay exposes strategic reasoning, long-term planning, and adaptation with opponents or collaborators. The post focuses on a fork of a16z-infra’s AI Town, a pixel-art simulation inspired by Generative Agents: Interactive Simulacra of Human Behavior, where characters remember conversations, maintain relationships, react to events, and stay in character. A single deploy script runs the project on Fly Machines with scale to zero, cost and performance optimizations, and support for OpenAI-compatible services, Together.ai models, and custom embedding models configured through secrets.


### [The Future Isn't Model Agnostic](https://yomu.fyi/post/the-future-isn-t-model-agnostic.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Aug 8, 2025
- Type: Explainer

The post argues that AI product teams should stop treating model agnosticism as a default priority because users value reliable, familiar behavior more than model flexibility. It says improving models are converging, while differentiation shifts to product experiences shaped by prompts, edge-case handling, workflows, and model-specific behavior. Swapping models therefore means more than changing an endpoint: teams must rewrite prompts, rerun evaluations, and absorb changes users notice in how the product feels. Claude Code and Qwen 3 Coder, which the post says outperforms Claude in benchmarks, illustrate how tightly model behavior, interface, rituals, trust, and expectations can become coupled. The recommended approach is to choose one model, specialize deeply, and treat evaluation as architecture, with games such as an AI Town environment offered as a way to test models.


[Newer posts](https://yomu.fyi/posts/page/173.md) · [Older posts](https://yomu.fyi/posts/page/175.md)