Total Articles: 19 from 7 sources
AWS News
1. Meet Our Newest AWS Heroes – May 2026
URL: https://aws.amazon.com/blogs/aws/meet-our-newest-aws-heroes-may-2026/
Published: 2026-05-27 16:15
Summary:
We’re excited to welcome four outstanding community leaders as our newest AWS Heroes These individuals embody the spirit of collaboration and knowledge sharing that makes the AWS community thrive From building AI-powered tools that help fellow builders navigate AWS re:Invent, to leading some of the largest AWS communities in Latin America, to sharing deep cloud […]
2. AWS Weekly Roundup: AWS Local Zones in Istanbul, open-source ExtendDB, Kiro Web, and more (May 25, 2026)
Published: 2026-05-25 20:11
Summary:
There’s something genuinely energizing about working with startups — something I’ve been doing intensely for more than two years now Startups operate at a different frequency: the urgency is real, the constraints are tight, and the stakes are personal Helping them navigate the challenge of proving their business model requires not just technical depth but […]
3. AWS Weekly Roundup: AWS Transform at 1 year, Claude Platform on AWS, EC2 M3 Ultra Mac instances, and more (May 18, 2026)
Published: 2026-05-18 19:13
Summary:
Just a year ago, we launched AWS Transform for .NET, Mainframe and VMware workloads, the first agentic AI service purpose-built for modernizing enterprise applications at scale At re:Invent 2025, we introduced AWS Transform custom, which enables organizations to modernize and transform code at scale using AWS-managed and custom transformations You can upgrade language versions, migrate […]
Dropbox Engineering
1. Introducing Nova, our internal platform for coding agents
URL: https://dropbox.tech/machine-learning/introducing-nova-our-internal-platform-for-coding-agents
Published: 2026-05-21 16:00
Summary:
Nova lets engineers run multiple coding sessions in parallel and lets internal systems use AI agents as part of automated workflows.
GitHub Blog
1. GitHub for Beginners: Getting started with Git and GitHub in VS Code
Published: 2026-05-25 16:00
Summary:
Discover how to use VS Code to interact with GitHub and maintain your projects The post GitHub for Beginners: Getting started with Git and GitHub in VS Code appeared first on The GitHub Blog.
2. GitHub recognized as a Leader in the Gartner® Magic Quadrant™ for Enterprise AI Coding Agents for the third year in a row
Published: 2026-05-22 16:10
Summary:
We are committed to empowering every developer by building an open, secure, and AI-powered platform that defines the future of software development The post GitHub recognized as a Leader in the Gartner® Magic Quadrant™ for Enterprise AI Coding Agents for the third year in a row appeared first on The GitHub Blog.
3. Beyond the engine: 10 open source projects shaping how games actually get made
Published: 2026-05-21 18:00
Summary:
Check out these 10 open source tools that help game developers create art, animation, levels, audio, dialogue, debug UIs, and engine-ready assets The post Beyond the engine: 10 open source projects shaping how games actually get made appeared first on The GitHub Blog.
Google Developers
1. A2UI v0.9: The New Standard for Portable, Framework-Agnostic Generative UI
URL: https://developers.googleblog.com/a2ui-v0-9-generative-ui/
Published: 2026-05-28 12:24
Summary:
A2UI v0.9 introduces a framework-agnostic standard designed to help AI agents generate real-time, tailored UI widgets using a company’s existing design system By decoupling UI intent from specific platforms, the release enables seamless, low-latency streaming of generative interfaces across web and mobile applications Integrating with broader ecosystems like AG2 and Vercel, A2UI v0.9 aims to move generative UI from experimental demos to production-ready digital products.
2. Production-Ready AI Agents: 5 Lessons from Refactoring a Monolith
URL: https://developers.googleblog.com/production-ready-ai-agents-5-lessons-from-refactoring-a-monolith/
Published: 2026-05-28 12:24
Summary:
The blog post outlines the transition of a brittle sales research prototype into a robust production agent using Google’s Agent Development Kit (ADK) By replacing monolithic scripts with orchestrated sub-agents and structured Pydantic outputs, the developers eliminated silent failures and fragile parsing Additionally, the post highlights the necessity of dynamic RAG pipelines and OpenTelemetry observability to ensure AI agents are scalable, cost-effective, and transparent in real-world applications.
3. Agents CLI in Agent Platform: create to production in one CLI
URL: https://developers.googleblog.com/agents-cli-in-agent-platform-create-to-production-in-one-cli/
Published: 2026-05-28 12:24
Summary:
Google Cloud has introduced the Agents CLI, a specialized tool designed to bridge the gap between local development and production-grade AI agent deployment The CLI provides coding assistants with machine-readable access to the full Google Cloud stack, reducing context overload and token waste during the scaffolding process By streamlining evaluation, infrastructure provisioning, and deployment into a single programmatic backbone, the tool enables developers to move from initial concept to a live service in hours rather than weeks.
Meta Engineering
1. SilverTorch: Index as Model — A New Retrieval Paradigm for Recommendation Systems
Published: 2026-05-26 16:00
Summary:
We’re introducing SilverTorch, a reimagining of recommendation systems that unifies all retrieval components for user generated content under a unified architecture Our research paper, “SilverTorch: A […] Read More The post SilverTorch: Index as Model — A New Retrieval Paradigm for Recommendation Systems appeared first on Engineering at Meta.
2. Reel Friends: Building Social Discovery that Scales to Billions
Published: 2026-05-13 13:00
Summary:
It highlights Reels your friends have watched and reacted to But sometimes the features that seem the most straightforward require the deepest engineering work The post Reel Friends: Building Social Discovery that Scales to Billions appeared first on Engineering at Meta.
3. Migrating Data Ingestion Systems at Meta Scale
Published: 2026-05-12 16:00
Summary:
Meta’s data ingestion system, which our engineering teams leverage for up-to-date snapshots of the social graph, has recently undergone a significant revamp to enhance its reliability at scale Moving from our legacy system to our new architecture required a large-scale migration of our entire data ingestion system The post Migrating Data Ingestion Systems at Meta Scale appeared first on Engineering at Meta.
Netflix TechBlog
1. Scaling ArchUnit with Nebula ArchRules
Published: 2026-05-08 15:55
Summary:
The archRules will contain rules specific to the usage of that library That is because the ArchRules Runner Plugin will be able to automatically detect these rules and run them in only the source sets that use this library as a dependency In the following example, we have a Project which uses a test helper library as a testImplementation dependency, and also adds a standalone rules library to the archRules configuration
2. Democratizing Machine Learning at Netflix: Building the Model Lifecycle Graph
Published: 2026-05-04 16:01
Summary:
MDS is optimized for real-time ingestion of ML metadata (e.g., models, features, pipelines, experiments, datasets) and to answer cross-domain questions such as “Which experiments are running this model?” or “Which models share these features?” It is the foundation that enables discovery, ingesting events from diverse sources, enriching them with context, and materializing relationships across entities.Our vision: to make every ML asset at Netflix discoverable, understandable, and reusable by every ML practitioner, regardless of their team or domain.Core Abstractions: The Vocabulary of the SystemBefore diving into the technical implementation, it’s helpful to understand the conceptual model that underpins MDS If a new model registry were introduced, it could be added as an additional provider without changing the domain interface.We can summarize these concepts with a concrete example:This URI-based addressing scheme is crucial as it allows any service to reference any ML asset with a single string, and MDS can resolve that reference back to rich, connected metadata.From Events to Entities to GraphThe journey from raw system events to a queryable graph happens in stages Now it’s a contiguous journey in a single interface.This graph-based exploration answers questions that were previously impossible:Lineage queries: What is the complete lineage of this model, from training data to production experiments?Impact analysis: Which models will be affected if I change this feature?Usage discovery: Which A/B tests are using this model?Dependency mapping: What data sources does my pipeline transitively depend on?Deprecation planning: Which entities are no longer being used and can be retired?Every entity has deep context: its creation time, ownership, update history, and most importantly, its relationships to other entities.The Model Lifecycle Graph is surfaced to practitioners through the AIP Portal, a unified interface that provides full-text search across all entity types, detailed entity pages with navigable relationships, and personalized views for teams and individuals.A typical interaction in the AIP Portal looks like:Search: Type a model, feature, dataset, or team name into the single search box backed by Elasticsearch.Inspect: Land on an entity page that shows key metadata (description, owners, domains, tags) alongside a relationships panel.Explore: Click through to related entities (upstream datasets, downstream experiments, and sibling model versions) to navigate the Model Lifecycle Graph without leaving the portal.When new entity types are introduced into MDS, the portal automatically provides baseline search, entity pages, and relationship navigation, and we can then layer on domain-specific visualizations (such as model deployment history or dataset version timelines) over time.The Road Ahead: Open ChallengesBuilding the ML lifecycle graph is an ongoing journey
3. State of Routing in Model Serving
Published: 2026-05-01 21:03
Summary:
In this introductory blog post, we will dive into our domain-independent API abstraction and its traffic routing capabilities that the central ML model serving platform exposes to several domain-specific microservices for model inference We’ll first describe how we implemented this abstraction with Switchboard, a centralized routing service, and then discuss the operational challenges we encountered at scale and how they led us to the Lightbulb architecture.ML Model Serving Platform PrinciplesWe envisioned a central model serving platform for all of Netflix’s member-facing ML Model serving needs Because the routingKey is in a header, this determination can be made with minimal overhead.These changes retain the advantages of Switchboard, such as a single integration point, abstraction of model id from use case, context-aware routing, while addressing the challenges we observed over time.ConclusionThe evolution from Switchboard to Lightbulb marks a significant architectural refinement in our ML model serving infrastructure
Stripe Engineering
1. Expanding Stripe Radar to protect more of your business
URL: https://stripe.com/blog/expanding-stripe-radar-to-protect-more-of-your-business
Published: 2026-05-27 00:00
Summary:
Radar now blocks high-risk transactions across all supported payment methods; defends against new fraud types like multi-account abuse and pay-as-you-go abuse, regardless of which payment processor you use; and gives platforms new tools to evaluate and mitigate merchant risk on and off Stripe.
2. Five vertical SaaS insights from Sessions 2026
URL: https://stripe.com/blog/vertical-saas-insights-sessions-2026
Published: 2026-05-11 00:00
Summary:
AI is forcing platforms to expand beyond pure software See how vertical SaaS platforms are using payments, financial services, and agentic commerce to build more durable businesses.
3. Giving agents the ability to pay
URL: https://stripe.com/blog/giving-agents-the-ability-to-pay
Published: 2026-04-29 00:00
Summary:
Link’s wallet for agents gives agents programmatic access to Link, including the ability to generate a one-time-use card or Shared Payment Token (SPT) backed by the cards and bank accounts already in your wallet It’s built on Stripe’s new Issuing for agents.
Generated on 2026-05-28 12:24:31