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AppLink Fundamentals II: Advanced AppLink Integrations – Automation & AI
Heroku AppLink lets connected Heroku applications extend Salesforce through Data Cloud, Flow, Apex, and Agentforce, bringing external logic, real-time data processing, and automation into Salesforce orgs. It uses an OpenAPI YAML or JSON specification, including x-sfdc metadata for authentication and permissions, to generate strongly typed Apex classes, Flow External Service definitions, Agent Actions, and related Connected App and Permission Set configurations. Developers can invoke services synchronously or asynchronously from Apex, use operations as no-code Flow actions, and configure Agentforce to call endpoints for specialized processing; callbacks support longer-running work. AppLink uses User Mode authentication by default, while administrator-controlled Permission Sets can temporarily elevate access during execution without changing a user’s permanent permissions.
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.
How Sarah Jutras is teaching the world how to work for themselves
Sarah Jutras moved from junior UX designer to director of interaction design before age thirty, but repeated conflicts with startup hustle culture, demanding bosses, and corporate work led her to leave her tech job in 2016. She now runs Huzzah Studio, a product design practice serving clients including Instacart and Imperfect Produce, while also leading Freelance for Life, a community launched in 2022 that has helped more than one thousand professionals design freelance practices. She uses Notion as a hub for client relationships, with a customer relationship manager, onboarding guides, and project boards, and created a Business Kit covering finance, business development, client experience, and work-life balance. Her guidance emphasizes identifying the audience and its problem, treating business building as an experiment, and designing a supportive team through community, collaboration, and public learning.
Bugbot is out of beta
Cursor has released Bugbot, a code review agent that automatically analyzes pull-request changes for logic bugs, edge cases, and security issues before production. It runs automatically on pull requests, gathers context from changes to understand code intent, and comments detected issues directly where they occur. Teams can steer reviews with custom BUGBOT.md rules, then send flagged issues to Cursor or launch a background agent on the web with one click. During its beta, Bugbot reviewed over 1 million PRs and found over 1.5 million issues; more than 50% of identified bugs were resolved before merge. Cursor also reports adoption by several companies, with testimonials citing review-time savings, strong bug detection, and confidence reviewing AI-generated code.
The evolution of Grab's machine learning feature store
Grab describes how it evolved its machine learning feature store as increasingly complex models began requiring high-cardinality keys and high-dimensional data. It replaced its initial Amphawa approach with feature tables: data-lake tables whose contents are atomically ingested into serving storage, with emphasis on consistency and reproducibility. The workflow stores feature tables and version-control metadata as Parquet objects in Amazon S3, validates schemas, loads data through reverse ETL into Aurora Postgres, and warms new tables through shadow reads before switching. The redesign supports contextual retrieval, atomic collection-wide updates, and isolation between reading and writing capacity; its SDK remains a thin database-driver wrapper that translates queries to SQL. Grab also chose decentralised deployments for version flexibility, rollout strategies, noisy-neighbor mitigation, and improved caching, concluding that Amazon Aurora for Postgres provides a robust, scalable, cost-effective foundation.
Unleashing new AI capabilities for popular frameworks in Firebase Studio
Firebase Studio announced updates to its cloud-based AI workspace that combine Gemini with Agent modes, Model Context Protocol support, and Gemini CLI across Flutter, Angular, React, Next.js, and Web projects. AI-optimized templates default to autonomous Agent mode and include airules.md files that give Gemini framework-specific guidance on coding standards, error handling, dependencies, and development practices. Developers can prompt Gemini to recommend Firebase backend services, import libraries or packages, update code, and explain additional setup steps. Workspace forking preserves an identical copy and the original’s entire agent chat history, supporting backups, experimentation, debugging, and collaboration, while enhanced prompts and a 100MB project upload limit broaden the workflow. The editor’s note says Firebase Studio will sunset on March 22, 2027, with lessons from its preview moving to Google AI Studio and Google Antigravity.
First Block with Varun Anand, cofounder of Clay
First Block features Varun Anand, cofounder of Clay, discussing the company’s path from a horizontal spreadsheet tool to a specialized go-to-market platform at the intersection of GTM and AI. He argues that product-market fit can emerge from curiosity, authenticity, and unique insights rather than rigid goal-setting or conventional market sizing. Clay changed its sales motion from seven demos for a $200/month product to self-serve after running eight daily “reverse demos,” in which customers brought real problems for 20–25 minute sessions that informed rapid iteration. Anand also describes hiring people without prior experience in the role, including more than half of Clay’s non-engineering team, and connecting flexible compensation with varied perspectives and competitive advantages.
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.
Heroku AppLink: Extend Salesforce with Any Programming Language
Heroku AppLink is now generally available from the Salesforce Setup menu, extending Salesforce with Heroku applications and code written in virtually any programming language. The initial release provides SDK support for Node.js and Python, while Java examples and direct AppLink API access demonstrate how other languages can integrate; the APIs provide authentication, authorization, connection pooling, and access to Salesforce and Data Cloud data. AppLink combines an add-on, service-mesh buildpack, dashboard, CLI, API, SDK, OpenAPI integration, and Salesforce API integration to provision connections, restrict and authenticate requests, publish services, and monitor org integrations. The add-on is free, with normal Heroku billing for compute and data resources, and published applications can extend Flow, Apex, and Agentforce while user mode is enabled by default.
Grab's service mesh evolution: From Consul to Istio
Grab describes migrating its service mesh from Consul and the Catcher fallback across a microservices ecosystem of more than 1,000 services spanning VMs, EKS clusters, AWS, and GCP. The existing setup exposed fleet-wide risk from a single Consul server issue, added fallback complexity, and lacked advanced traffic management, security controls, and robust multi-cluster support. After evaluating Application Load Balancer, Istio, AWS Lattice, and Linkerd for stability, performance, integration, and operational simplicity, the team selected Istio for its Kubernetes integration, flexible deployment models, and feature set. The design uses multiple control planes on dedicated Kubernetes clusters, active-active pairs, organization-based mesh boundaries, custom resource definitions, and cross-cluster endpoint discovery to support isolation, availability, routing, security, and observability. Migration began in Q4 2024 with GCP-to-AWS work alongside gRPC and HTTP traffic initiatives, using gradual shifting, rollback mechanisms, and performance monitoring while teams manage legacy systems and cross-mesh connectivity.
Leveraging multimodal LLMs for Shopify’s global catalogue: Recap of expo talk at ICLR 2025
Shopify’s Global Catalogue addresses fragmented, multilingual, multimodal product data that makes semantic search, faceting, matching, and AI-driven discovery difficult across millions of merchants. The system combines product data, understanding, matching, and reconciliation layers to standardize metadata, identify duplicate listings, and create canonical records with Universal Product IDs. Shopify fine-tunes vision large language models for multiple tasks per catalogue entity, while streaming infrastructure processes more than 10 million product updates daily and serves 40 million multimodal LLM-powered inferences. The resulting data supports merchant-admin suggestions, search, recommendations, embeddings, and conversational commerce, although scalability, accuracy, latency, model consolidation, graph reasoning, and continuous retraining remain active challenges.
Notion’s hosted MCP server: an inside look
Notion’s hosted Model Context Protocol (MCP) server describes its move from a downloadable MCP server to an all-in-one hosted service that lets AI clients such as Cursor and Claude Desktop interact with workspaces. The initial proof of concept generated MCP tools from Notion’s OpenAPI schema, mapping endpoints one-to-one to API calls, but setup required API keys or Docker and the approach consumed substantial context when handling hierarchical block JSON. The hosted version uses a code-generation pipeline that converts OpenAPI schemas to Zod, combines rewritten AI-oriented tools with wrappers for existing v1 APIs, and exposes them through a central integration. Users connect through one-click OAuth; the server manages sessions and tokens, with streamable HTTP and SSE supported for client compatibility. A design change is Notion-flavoured Markdown, which represents blocks including callouts, columns, pages, and databases more efficiently for agent conversations, while semantic search can span Notion and over ten third-party apps.
How To Build Agents Users Can Trust
Ramp describes how it built and shipped LLM-backed agents for expense management, focusing on the trust risks created by low-quality or unexpected finance outputs. It recommends choosing ambiguous, high-volume problems with asymmetric upside, then combining reasoning and citations grounded in product or user-provided context with an explicit “Needs review” path when the agent is unsure. The policy agent uses expense policies to approve, reject, or escalate expenses, while deterministic rules such as dollar limits, vendor blocklists, and category restrictions constrain autonomy. Ramp reports that more than 65% of approvals are fully handled by the agent, while users can progressively move from suggestions to autonomous actions through workflow-based controls and evaluation.
How we built it: Jurisdiction resolution for Stripe Tax
Stripe Tax’s jurisdiction resolution system (JRS) determines which US taxing jurisdictions apply to a transaction, a difficult task because more than 16,000 combinations of rates and rules depend on intricate, changing boundaries. Its offline geographic information system cleans and standardizes boundary data, overlays states, counties, cities, and districts, and generates time-aware Stripe places of taxation (SPOTs), while the online system matches an address to the relevant SPOT. To reduce point-in-polygon latency, JRS indexes nested bounding boxes in a balanced R-tree rebuilt with the Sort-Tile-Recursive algorithm, narrowing candidates before applying the final polygon calculation; disjoint SPOTs are split and smaller boxes prioritized. Most states achieve address matching in a few milliseconds, with 95th-percentile latency below 10 milliseconds except South Carolina, while historical SPOT data creates an ongoing memory challenge.
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.
OpenTelemetry, Kubernetes, and Fir: Putting it All Together
Modern cloud-native applications span distributed microservices and dynamic environments, making performance diagnosis and failure analysis dependent on observability. The post explains how Fir, Heroku’s next-generation platform, combines Kubernetes infrastructure with OpenTelemetry to provide standardized visibility while hiding Kubernetes complexity behind Heroku’s interface. Fir runs on AWS EKS, uses OCI images and Cloud Native Buildpacks, and includes a Heroku OpenTelemetry Collector that gathers telemetry from applications, the Heroku router, and the Heroku API. OpenTelemetry’s APIs, SDKs, Collector, and OTLP support logs, metrics, and traces, with drains routing data to dashboards, CLI logs, or external observability platforms. The text also distinguishes OTLP gRPC and HTTP transports, noting gRPC efficiency and features versus HTTP’s firewall compatibility, and presents Fir’s open standards as a way to support scaling, hybrid environments, and reduce lock-in.
How European businesses are turning company knowledge into competitive advantage
European businesses hold valuable collective knowledge, but employees often spend hours searching across fragmented tools and storage systems for information that exists somewhere. The report presents a survey of 650 decision-makers across the UK, France, Germany, and eight other European countries to examine how organizations are responding to AI in knowledge management. Although 97% identify effective knowledge management as a competitive-advantage driver, only 28% use a structured, centralized approach; multiple tools cost nearly five working weeks per employee annually, while AI adopters are four times more likely to report significant cost savings. It also says 90% of respondents see Gen Z as a force for simpler, more transparent systems, and presents coordinated strategy, systems, and technology as ways to improve collaboration and decision speed.
DispatchGym: Grab’s reinforcement learning research framework
DispatchGym is a research framework for reinforcement learning (RL) studies in dispatch systems that match bookings with drivers. It combines an RL algorithm, a dispatch-process simulation, and a Gymnasium API interface, allowing data scientists with basic RL and Python skills to modify and test ideas independently. The simulated environment models booking creation, driver dispatch and movement, and booking completion, prioritizing directional accuracy over exact metric matching; its modular Python architecture uses Numba to reduce runtime overhead. A command-line upload launches the modified code on internal infrastructure as a Spark job, avoiding infrastructure management while preserving flexibility across simulation and optimization components. The framework supported experiments with contextual bandits, including linear, neural-linear, and Gaussian-process variants alongside several action-sampling strategies, to identify combinations of levers and reward functions for dispatch hyperparameter tuning and improved fulfilment efficiency and reliability.
Top topics
- Architecture834
- Performance816
- LLMs660
- Machine Learning622
- Open Source563
- AI Agents553
- AI470
- Developer Experience448
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