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How We Built the BFCM 2023 Globe
The 2023 BFCM Globe is a real-time visualization of purchases made through Shopify-powered merchants worldwide, with animated arcs, city dots, fireworks, camera movement, atmospheric noise, stars, and a custom material. Performance was a central concern because the earlier approach used a separate mesh and draw call for every arc, causing more than 1,000 simultaneous arcs to slow some mobile devices. The revised arc system uses instancing, interleaved per-arc attributes, and vertex-shader Bézier deformation, allowing tens of thousands of arcs in one draw call and an initial benchmark of up to one million arcs on an M1 laptop. Other effects were similarly consolidated, including billboarded point particles for cities and single-draw-call fireworks, while 2D psrdnoise and a stripped-down shader reduced rendering cost. The result combines richer visuals with substantially greater rendering capacity.
Preparing for Stripe API upgrades
Upgrading a third-party API can bring new features and fixes but may also introduce breaking changes, so applications should be evaluated before production changes. Stripe Sandboxes provide isolated test accounts with separate API keys and access controls, optionally copying production configuration without copying products, customers, or transaction data. The workflow uses Dashboard Workbench to inspect and upgrade the sandbox API version, then runs the application with sandbox credentials and forwards webhook events through the Stripe CLI. In the example, Stripe.net 41.0.0 rejected an event because it expected API version 2022-11-15 while the sandbox delivered 2024-09-30.acacia; upgrading the SDK to 46.0.0 resolved the mismatch.
How to Build Secure Okta Node.js Integrations with DPoP
Okta management API integrations can use a Node.js service app with OAuth 2.0 client credentials, but a bearer access token can be replayed by anyone who obtains it during its one-hour lifetime. The tutorial configures Okta for private_key_jwt authentication, grants the okta.users.read scope and Read-only Administrator role, and builds a Node.js service that requests and caches an access token. It then enables Demonstrating Proof of Possession (DPoP), handles an initial use_dpop_nonce response by retrying the token request with a nonce-bound proof, and generates a fresh proof for each API call. Each request binds the proof to the access token through the SHA-256-based ath value and uses DPoP rather than Bearer authorization. The demonstrated service successfully lists users through Okta Admin Management API endpoints.
LLM-assisted vector similarity search
Vector similarity search can struggle with queries containing negation, constraints, or conceptual requirements, even though it efficiently retrieves semantically similar records. The proposed method uses FAISS and OpenAI text-embedding-ada-002 embeddings to shortlist 15 candidates, then asks GPT-4o to rank the candidates against the original query and return the top three. On two GPT-4o-curated synthetic datasets—100 food dishes and 100 Asian tourist spots—the assisted method matched raw search on straightforward queries but produced more relevant results for complex ones, including excluding fish or shrimp and identifying wildlife-related places. The authors note limits from the small datasets and query set, report similar relevance improvements in an internal deployment involving more than 4,500 relational-database rows, and identify shortlist size and added LLM latency as areas for further evaluation.
Optimize payment flow while reducing code complexity with Stripe's A/B Testing
Payment method choices affect ecommerce conversion, and the post notes that 81% of surveyed customers frequently abandon carts when their preferred method is unavailable. It explains how Stripe’s dynamic payment methods in Elements and Dashboard settings can apply country, region, and amount rules without embedding those conditions in PaymentIntent code. The proposed workflow uses Dashboard experiments to enable methods such as Klarna, set display rules and traffic allocation, then uses Workbench Inspector, Logs, and Events tabs to inspect presented payment_method_types, API requests, and webhook history. A Klarna example reports a 16% increase in average order value, while new payment methods were deployed within four days.
Easily debug your 3DS authentication with Stripe Workbench
Stripe Workbench provides browser-based tools for investigating EMV 3DS authentication and related payment flow behavior in the Stripe dashboard. It lets developers inspect Payment Intent and Charge data, including payment_method_details.card.three_d_secure.authentication_flow and result, to determine whether authentication used a challenge UI and whether it completed successfully. The Inspector also exposes API history and webhook events, while the Events tab can filter historical payment_intent.requires_action events and show their frequency over a selected period. In the Shell tab, stripe trigger payment_intent.requires_action simulates a 3DS-related flow and generates event data without installing additional tools, allowing webhook handling to be tested and investigated from the dashboard.
Creating reusable developer environments with Stripe Sandboxes
Setting up development machines with the correct tools, permissions, and dependencies can take weeks for new engineers, so the setup combines Stripe Sandboxes with development containers. Stripe Sandboxes provide isolated accounts with full Stripe API access for testing without real transactions, while sandbox settings remain independent and production customer, product, and transaction data is not copied. A C# example uses Bogus to generate ten fake products and Stripe.net’s ProductService to create them with metadata in the sandbox, and the text notes that SDKs can populate additional account data. The containerized workspace uses Docker development containers, a .NET 8 image, Redis via Docker Compose, environment variables, VS Code customizations, and a post-create script that installs Stripe CLI. Configuration files can then be checked into source control, giving team members an isolated and consistent workspace for exploring payment workflows without affecting live data.
How Shopify improved consumer search intent with real-time ML
Shopify describes how it built near-real-time text and image embedding pipelines to support semantic search, replacing keyword matching with representations that enable similarity-based retrieval. Using Google Cloud’s Dataflow and Apache Beam, the pipelines process roughly 2,500 embeddings per second, preprocess images, run GPU-backed inference, and write outputs to both a data warehouse and an event topic for downstream ingestion. The engineering discussion focuses on trade-offs among memory, throughput, latency, and cost: reducing harness threads cut memory use about 2.6x and allowed a return to cheaper workers, while model sharing reduced throughput. For batching, bursty input produced bundles of one, and stateful batching was rejected because its shuffle increased latency; Dataflow’s per-process model instances instead provided enough parallelism to saturate GPUs.
How to Create an AI Agent With Heroku and Agentforce
The post shows how to extend Salesforce Agentforce with a Heroku-hosted custom action when customer information remains in older, unstructured cloud archives and requires scalable, real-time processing. It explains that Agentforce selects discrete actions, while Heroku exposes code through External Services using an OpenAPI description of the request and response. The sample uses Java and Spring Boot, an H2 in-memory database, springdoc.org for OpenAPI generation, and Spring AI to convert natural-language questions into SQL before returning results for an Agentforce response. The integration is identified as pilot-only and not intended for production, while the post also points to Heroku Postgres, Heroku Connect, Data Cloud, and pgvector as extensions for other agent use cases.
Testing subscriptions with Stripe Test Clocks and Workbench
Stripe subscriptions require testing scenarios that unfold across billing cycles, and the post presents Test Clocks and Workbench as no-additional-cost test-mode tools for doing so without waiting real time. It provisions three monthly recurring product tiers, creates a test clock with a Unix Epoch FrozenTime, and attaches newly created customers and subscriptions to that clock using the Stripe .NET SDK. Advancing the clock by one month updates time-dependent objects, allowing Workbench Inspector logs and events such as invoice.created and customer.subscription.updated to be examined alongside payment and subscription state. A second simulation attaches pm_card_chargeCustomerFail, makes it the default payment method, and advances to the next billing period. The resulting failed invoice, canceled subscription, and customer.subscription.deleted cancellation_details show how these tools reveal the events and properties an integration should test.
What is a Vector Database?
Vector databases store and manage high-dimensional vector representations of data, grouping them by similarity rather than traditional row-and-column organization. They support semantic and similarity-based retrieval by converting text, images, audio, or video into embeddings, indexing them with methods such as product quantization and HNSW, and comparing query vectors with indexed vectors. These capabilities underpin use cases including RAG, hybrid search, recommendations, NLP, image and video recognition, and anomaly detection, while sharding, partitioning, caching, and replication support scale. The article distinguishes a vector database from a standalone index because it also provides storage, CRUD, metadata filtering, horizontal scaling, and data management functions, and describes Databricks AI Search as an integrated implementation with automatic scaling, optimization, security, and governance.
How to Create a Secure CI/CD Pipeline Using Okta Terraform
The guide presents a secure CI/CD pipeline for managing Okta resources with Terraform, AWS, and GitHub Actions, addressing local setup, collaboration, state storage, secrets, and deployment automation. Its architecture uses GitHub as source control and workflow trigger, GitHub OIDC to authenticate to AWS IAM STS and assume roles, S3 for Terraform state, DynamoDB for state locking, and AWS Secrets Manager for Okta OAuth 2.0 client credentials private keys. Developers push changes to develop for an automatic development build, open a pull request to main for a production plan, and merge approved changes to trigger the production build. The workflows run Terraform init, plan, and apply as appropriate, while the guide also covers repository setup, branch organization, and troubleshooting for missed configuration in GitHub, Okta, or AWS.
Leveraging RAG-powered LLMs for analytical tasks
Data analysts at Grab faced an increasing volume of stakeholder data requests that required manually writing and executing repetitive SQL queries with minor parameter changes. To automate routine tasks like metric reporting and fraud investigations, the Integrity Analytics team paired an internal prompt-management platform, Spellvault, with Data-Arks, an in-house Python-based middleware that packages SQL queries and Python functions into APIs. A scheduler triggers automated report summarization, while an analytical bot named A* bot executes relevant investigation queries via RAG and returns summarized findings in Slack. Automated report generation saves an estimated three to four hours per report and shortens multi-query fraud investigations to minutes. Grab favored RAG over fine-tuning because it avoids continuous retraining costs, scales faster across diverse use cases, and surfaces current production data.
Improving Compute Sustainability: A Case Study
Two Sigma's large computing footprint drives significant energy consumption and carbon emissions, particularly across live trading applications that require continuous real-time market data caching. To address this overhead without sacrificing performance, engineering teams used a routine hardware refresh to transition from legacy single-process machines to denser multi-core server configurations. By replacing roughly 60 legacy hosts with 28-core processor hardware, the team distributed baseline power draw over more cores and eliminated underutilized compute capacity. This architectural consolidation reduced absolute power consumption across production hosts by 66%, dropping electricity usage from 27 MWh in January 2023 to 10 MWh in January 2024. The initiative subsequently established an annual sustainability rationalization practice for hardware budgeting across the organization.
MongoDB 8.0: Raising The Bar
MongoDB 8.0, the latest version of the document database, is now generally available, delivering significant performance and security improvements. The release features 36% better read throughput, 56% faster bulk writes, 20% faster concurrent writes during data replication, and 200% faster complex aggregations of time series data. MongoDB’s internal build system, which runs on MongoDB, saw query latencies drop by approximately 75% after upgrading. The company also reports that customer OCELL experienced queries running twice as fast. MongoDB 8.0 includes sharding enhancements for up to 50 times faster data distribution, quantized vectors that reduce memory usage by up to 96%, and expanded Queryable Encryption to support range queries. The release is available via MongoDB Atlas, MongoDB Enterprise Advanced, and MongoDB Community Edition.
Evolution of Catwalk: Model serving platform at Grab
Grab developed and scaled Catwalk, an internal machine learning model serving platform, to address operational bottlenecks, low resource utilization, and deployment friction between data scientists and backend engineers. The platform transitioned from an admin-managed TensorFlow Serving setup into a low-code self-service system supporting PyTorch and ONNX, before replacing complex Helm charts with Kubernetes Custom Resource Definitions for declarative, blue-green deployment orchestration. To support complex business workflows and multi-model applications, Grab subsequently introduced Catwalk Orchestrator with bundled deployments that allow individual services to scale independently. Across two years, the orchestrator architecture expanded to 200 deployed applications serving approximately 1,400 production machine learning models.
Propel Your SaaS Apps Into the Future at Oktane
Okta invites SaaS application builders to Oktane 2024, held at Caesars Forum in Las Vegas from October 15–17, to discuss how identity and user lifecycle platforms can support secure B2B SaaS applications. The announcement frames identity as increasingly complex, with a growing protection surface expected to influence SaaS roadmaps and developer priorities. Attendees can join three breakout sessions, visit the Dev Hub, attend hands-on labs, and connect at a B2B SaaS builders happy hour or the Okta Workflows community meetup. Lab topics include moving Okta app configurations from PowerShell into Terraform, signing users out across apps with one API, and identifying inactive users with Okta Workflows. Some activities require Oktane attendance, while labs have limited, first-come-first-served capacity and the happy hour is invitation-based.
Enabling conversational data discovery with LLMs at Grab
Grab faced major bottlenecks in dataset discovery across its 200,000 data lake tables due to low documentation coverage and vanilla Elasticsearch limitations in its internal tool, Hubble. To resolve this, engineers first tuned Elasticsearch by boosting frequently queried P80 tables and certified schemas while deboosting deprecated datasets. They then deployed a GPT-4 pipeline that generated documentation from table schemas and sample data, raising P80 documentation coverage from 20% to roughly 90%. Finally, the team developed HubbleIQ, an LLM-powered assistant built via Glean Apps and integrated into Slack and Hubble search to answer semantic queries. These initiatives increased search click-through rates to 94% and lifted the proportion of users finding discovery easy to 73%.
Choosing the right sandbox strategy for your organization
Stripe sandboxes provide isolated environments for testing Stripe features without affecting a live integration, with access controls for the people who use them. Each live account can have up to five sandboxes, which admins can create, name, configure by copying selected live-account settings, and delete when no longer needed. The guide describes a staged setup for startups and small teams, using separate environments for development, integration testing, user acceptance testing, and spare capacity, while larger organizations may assign sandboxes to departments and rotate them between projects. It also presents an iterative model in which teams prototype, collect stakeholder feedback, retest changes, and document results. The recommended strategy depends on team size, organizational structure, workflow, and development methodology.
eBay Empowers Sellers with Innovative Tools at eBay Open 2024
At eBay Open 2024, eBay announces tools and services intended to help sellers streamline operations and connect with buyers. Recent additions include generative AI-powered descriptions, image background enhancements, social media promotion methods, mobile-friendly product research, and combined international shipping. The simplified listing flow is rolling out across all categories in the United States and United Kingdom, while an AI-powered bulk listing tool in Seller Hub lets US sellers create multiple listings quickly; it currently supports Sports Trading Card listings and may expand to more categories and markets. The redesigned Seller Hub adds an improved interface, more efficient business management, and better data visualization, and eBay is developing a dedicated Sell Mode for its mobile app.
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