Total Articles: 20 from 7 sources
AWS News
1. Introducing the next generation of AWS Resilience Hub for generative AI-based SRE resilience journey
Published: 2026-05-28 19:29
Summary:
AWS launches the next generation of AWS Resilience Hub with a significantly expanded experience that brings together a new application model, dependency discovery assessment, generative AI-powered failure mode analysis, modular resilience policies, and organization-wide reporting.
2. Introducing the next generation of Amazon OpenSearch Serverless for building your agentic AI applications
Published: 2026-05-28 18:26
Summary:
AWS rebuilt Amazon OpenSearch Serverless from the ground up for agentic AI and dynamic workloads Get instant autoscaling and up to 60% cost savings.
3. 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 […]
Dropbox Engineering
1. Beyond code generation: rethinking engineering productivity in the age of AI agents
Published: 2026-05-28 18:00
Summary:
How Dropbox is moving from AI tools that assist engineers to agentic systems that can execute scoped tasks, and how we’re building platforms to support those workflows.
2. 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. Still a developer. Just outside. Our latest GitHub Shop collection is here.
Published: 2026-05-28 18:18
Summary:
The ESC collection lets you escape the confines of your desk and get out into the sun where good ideas are bound to happen The post Still a developer Our latest GitHub Shop collection is here. appeared first on The GitHub Blog.
2. 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.
3. 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.
Google Developers
1. 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: 2026-05-29 12:13
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.
2. Building real-world on-device AI with LiteRT and NPU
URL: https://developers.googleblog.com/building-real-world-on-device-ai-with-litert-and-npu/
Published: 2026-05-29 12:13
Summary:
LiteRT is a production-ready framework designed to help mobile developers unlock the power of Neural Processing Units (NPUs), overcoming the performance and battery limitations of traditional CPU or GPU processing By providing a unified API that abstracts away hardware complexities, it allows industry leaders like Google Meet and Epic Games to deploy sophisticated AI models for real-time video, animation, and speech recognition with significantly higher efficiency The platform further supports developers through benchmarking tools and cross-platform compatibility, enabling seamless AI deployment across mobile devices, AI PCs, and industrial IoT hardware.
3. Speeding Up AI: Bringing Google Colossus to PyTorch via GCSFS and Rapid Bucket
Published: 2026-05-29 12:13
Summary:
Google Cloud has introduced a high-performance integration that connects Rapid Storage directly to PyTorch via the fsspec interface to eliminate AI training bottlenecks By utilizing Google’s Colossus architecture and bidirectional gRPC streaming, the solution offers up to 15 TiB/s aggregate throughput and significant reductions in latency These improvements allow developers to speed up total training time by 23% with zero code changes required beyond updating the storage bucket type.
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. Solo founding is at an all-time high: Top performers have these traits in common
URL: https://stripe.com/blog/top-solo-founder-traits
Published: 2026-05-28 00:00
Summary:
In 2025, solo founders in the top decile generated 61 times the revenue of the median solo founder in their first six months We analyzed the data to understand what drives that gap.
2. 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.
3. 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.
Generated on 2026-05-29 12:13:19