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Tutorial posts on Yomu, summarised newest first.
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Tutorial posts
Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac
Simulation in healthcare robotics has historically been slow, siloed, and difficult to translate into real-world physical systems. The NVIDIA Isaac for Healthcare v0.4 release introduces an end-to-end SO-ARM starter workflow to streamline simulation, training, and deployment for embodied surgical assistants. The architecture relies on a Sim2Real mixed training approach that fine-tunes the GR00T N1.5 foundation model on combined real-world and synthetic datasets using LeRobot. Synthetic data generated in simulation constitutes over 93 percent of the policy training data to bridge physical data collection constraints. Real-time inference on the SO-ARM101 precision manipulator utilizes RTI DDS communication, dual-camera vision, and TensorRT model conversion.
How to Build a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac for Healthcare
Simulation in healthcare robotics has historically been slow, siloed, and difficult to translate to physical hardware. NVIDIA introduced the Isaac for Healthcare v0.4 developer framework to streamline this lifecycle through an end-to-end SO-ARM starter workflow. Developers collect mixed real-world and synthetic datasets using LeRobot and SO-101 teleoperation, with synthetic generation accounting for over 93 percent of training data. The pipeline post-trains GR00T N1.5 models using dual-camera vision inputs, evaluates policies in Isaac Lab, and deploys them to physical hardware with RTI DDS communication. This integrated approach allows surgical assistant robots to interpret natural language instructions and execute physical manipulation tasks reliably.
Using a web-based POS with a mobile Terminal reader
Web-based POS systems can reuse ecommerce infrastructure for showroom-floor selling, but mobile readers such as the Stripe M2 and Tap to Pay cannot be accessed directly by a mobile browser. The proposed wrapper app avoids a complete native POS rebuild by embedding the site in a React Native WebView and connecting a mobile or Tap to Pay reader through the Stripe Terminal React Native SDK. A bridge message carries cart information, such as a cart ID and ready-to-pay status, to a ReaderManager that retrieves the amount and calls createPaymentIntent(), collectPaymentMethod(), and confirmPaymentIntent(). Payment results can return to the POS through a backend webhook and WebSocket or frontend polling, while security requires origin-checked messages, nonces, and server-side validation. The approach retains WebView performance, offline, native-access, debugging, and security limitations.
Get your VLM running in 3 simple steps on Intel CPUs
Deploying Vision Language Models locally presents computational challenges despite providing data privacy and independence from external servers. Optimum Intel and OpenVINO address this by optimizing lightweight models like SmolVLM2-256M for low-resource hardware, including Intel CPUs and GPUs. The process involves exporting the model to the OpenVINO Intermediate Representation format, applying 8-bit weight-only or static quantization, and executing inference with the transformed model. In single-image CPU benchmarks on an Intel Core Ultra 7 265K processor, OpenVINO with 8-bit weight-only quantization reduced time to first token from 5.150 seconds under PyTorch down to 0.247 seconds. The optimized configuration also raised decoding throughput from 0.722 to 63.928 tokens per second.
Medium Android App — Migrating from Apollo Kotlin 3 to 4: Lessons Learned
Migrating the Medium Android app from Apollo Kotlin 3 to 4 exposed cache and exception-handling issues that were hidden by the earlier implementation. Apollo Kotlin 4 uses a new group, plugin, and package identifier, places exceptions in ApolloResponse, and replaces ApolloCompositeException with a primary exception plus suppressed failures. CacheMissException errors appeared in watch() because programmatic cache IDs were incomplete or incorrect; the app addressed this with declarative type and field policies, __typename on every operation, cache-update helpers, and policy-aware safeWatch filtering. The migration also replaced executeV3(), removed fetchThrows = true, added a Currency scalar adapter, and identified HTTP batching and persisted queries as future improvements, while concluding that Apollo 4 produced a more robust cache integration after the underlying issues were fixed.
SOTA OCR with Core ML and dots.ocr
Running competitive models like RedNote's 3B parameter dots.ocr on-device provides zero-cost, network-free optical character recognition while leveraging power-efficient hardware like Apple's Neural Engine. Because the Neural Engine requires Core ML, converting the 1.2B parameter NaViT vision encoder from PyTorch requires resolving multiple graph tracing incompatibilities. The initial conversion pipeline targets FLOAT32 execution on GPU by capturing the execution graph with torch.jit.trace and compiling it via coremltools. Simplifying the model for single-image inference eliminates dynamic sequence masking, dynamic tensor iteration, and multi-attention complexity. Although the resulting Core ML model accurately matches PyTorch baseline numerical precision, the unoptimized artifact spans over 5GB and takes over one second per forward pass.
Workflows: Automate compliance safeguards by flagging transactions
Stripe Workflows can automate additional compliance safeguards by flagging transactions that need review before order processing. The described pattern starts when a Payment intent succeeded event occurs, retrieves the customer, and checks whether the customer’s custom_status metadata is not “Approved” while the payment amount exceeds $5,000. When these conditions are met, the workflow emails one or more designated team members with instructions for assessing the order. The examples motivating review include unusually high order values, unfamiliar sales regions, and behavioral patterns commonly associated with fraud, such as a new customer order above a monetary threshold. Workflows uses a visual building interface in the Stripe dashboard to sequence actions across multiple Stripe products, helping enforce business rules, reduce manual effort, and support risk management.
Marketplace monetization with Stripe
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.
Build Secure Agent-to-App Connections with Cross App Access (XAA) Using OIDC
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.
Gating entrances with Stripe and NFC passes
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.
How to Build a Secure iOS App with MFA
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.
Building Data-Aware AI Applications with Heroku AI and LlamaIndex
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.
Guardrails for money movement: Integrating Stripe MCP with Portia AI
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.
Building financial operations agents with Hypermode and Stripe
Hypermode Agents and Stripe can be combined to create domain-specific financial operations agents from natural-language descriptions rather than conventional code. The workflow uses Hypermode’s Concierge to generate a system prompt, then connects the agent to Stripe through the Stripe Model Context Protocol (MCP) server and optionally to services such as Notion, Slack, and Google Calendar. With a restricted Stripe API key and sandbox data, the agent can retrieve policies, inspect customer and transaction history, apply refund rules, process payments, create invoices, and analyze subscriptions for metrics such as MRR. The examples describe partial refund decisions, onboarding workflows, campaign suggestions, and populated test environments, while the guidance recommends prompt iteration, human approval for high-value transactions, and monitoring and logging for compliance and improvement.
AppLink Fundamentals III: Building with AppLink – Development Flow and Language Choices
AppLink Fundamentals III describes how to build Salesforce extensions on Heroku, covering local testing, OpenAPI contract management, deployment workflows, and programming-language selection. For applications that receive Salesforce requests, the supplied invoke.sh script simulates production headers using Salesforce CLI data, can activate session-based permission sets, and accepts JSON payloads for testing; Pattern 1 applications that call Salesforce APIs do not require this special setup. The OpenAPI specification serves as the shared contract for endpoints and Salesforce consumers, with publish-time validation intended to catch misalignment. AppLink also supports Agentforce actions through x-sfdc agent extensions, enabling topics, publishing, input and display settings, privacy controls, and outputs such as generated PDFs in the automotive finance example. The comparison positions Apex for transaction-critical record updates and AppLink languages such as Node.js, Python, and Java for external integrations, intensive computation, elastic scaling, and reuse of existing code.
AppLink Fundamentals I: AppLink Integration Patterns – Connecting Salesforce to Heroku Applications
AppLink Fundamentals I describes four integration patterns for connecting Salesforce orgs with Heroku applications, focusing on API access and Salesforce extension. Pattern 1 uses run-as-user authentication for automated or customer-facing applications, with the AppLink add-on, CLI plugin, optional SDK, named authorizations, and SOQL queries through the Data API; the SDK manages authentication, session management, and connection pooling for single- or multi-org access. Pattern 2 lets Salesforce users invoke published Heroku services through Flow, Apex, or Agentforce, using User Mode or elevated “user mode plus” permissions controlled by Permission Sets, an OpenAPI description, and a service-mesh buildpack that blocks external access. The post also positions batch processing and real-time eventing as extensions of these patterns, and describes Data Cloud querying and Data Action Targets for applications to receive Data Cloud events.
Secure Your Express App with OAuth 2.0, OIDC, and PKCE
Building authentication from scratch is risky and time-consuming, so the tutorial integrates Okta with an Express application using Passport, openid-client, OAuth 2.0, and OpenID Connect (OIDC). It creates a role-based expense dashboard in which users view spending data for their assigned teams, with team membership mapped from the email claim in the ID token. The setup uses Authorization Code Flow with PKCE, defined in RFC 7636, alongside express-session, EJS views, environment variables, and Okta redirect URI configuration. The sample project includes routes for login, logout, dashboard, profile, and team-specific expenses, while the running app displays these authenticated views. It concludes that OIDC supplies the identity layer over OAuth 2.0 and presents PKCE as a security baseline for server-side and browser-based web applications.
Create a React PWA with Social Login Authentication
The tutorial builds a React Progressive Web App (PWA) called Lister, combining offline support with authentication through Okta and Google Social Login. It creates an Okta OIDC single-page application, configures Authorization Code and Refresh Token grants, sets localhost redirect URIs, and wires Okta React and AuthJS into React Router routes. The implementation uses environment variables for the Okta domain and client ID, OAuth 2.0 and OpenID Connect scopes including openid, profile, email, and offline_access, and a Vite PWA plugin to provide a manifest, service worker, and installable behavior. Authenticated users receive a todo-list interface with CRUD operations, while the article notes that production deployments would need more advanced service-worker configuration and a proper database.
Workflows: Automatically customize an object with metadata
Stripe Workflows can customize Stripe objects with metadata, adding contextual key-value information without changing the core data model or storing it separately. The example targets customers associated with successful Payment Intents that include a tip, so a customer can be labeled for business use, such as reconciliation, reporting, or internal integrations. To build it, the workflow retrieves the customer from the Payment Intent, emails a team member, checks whether the successful Payment Intent contains a non-empty tip amount, and updates that customer with the metadata key Tier and value VIP. Workflows runs a trigger and sequential actions or conditions in a visual Stripe Dashboard builder, while metadata can carry information to later workflow steps and support automation, analytics, and debugging.
Workflows: Creating early fraud alerts for streamlined refunds
Stripe Workflows can automate responses to early fraud warnings so potentially fraudulent charges receive refunds when disputing them would be more costly. The workflow starts when an early fraud warning is created in Radar, retrieves the related charge using its Charge ID, and checks whether the amount is less than 15 USD. If the condition is met, it creates a refund; if it is not met, an optional branch can email a team member for manual review. Workflows uses a visual builder in the Stripe dashboard, where triggers, actions, and conditions run sequentially across multiple Stripe products, while Stripe Radar provides real-time fraud protection and Radar for Fraud Teams adds customization and deeper insights.
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