Total Articles: 17 from 6 sources
AWS News
1. The AWS MCP Server is now generally available
URL: https://aws.amazon.com/blogs/aws/the-aws-mcp-server-is-now-generally-available/
Published: 2026-05-06 15:36
Summary:
AWS announces the general availability of the AWS MCP Server, a managed remote Model Context Protocol (MCP) server that gives AI agents and coding assistants secure, authenticated access to all AWS services The AWS MCP Server is part of the Agent Toolkit for AWS, a suite of tooling that includes the MCP Server, skills, and plugins that help coding agents build more effectively and efficiently on AWS.
2. Modernize your workflows: Amazon WorkSpaces now gives AI agents their own desktop (preview)
Published: 2026-05-05 17:23
Summary:
Amazon WorkSpaces now lets AI agents securely operate legacy desktop applications—without APIs or modernization—using IAM authentication, MCP support, and computer vision within existing security frameworks.
3. AWS Weekly Roundup: What’s Next with AWS 2026, Amazon Quick, OpenAI partnership, and more (May 4, 2026)
Published: 2026-05-04 17:05
Summary:
Last week, I took some time off in York, England, often described as the most haunted city in the country I wandered through the ruins of abbeys that have stood for nearly a thousand years, walked along medieval walls, and spent an evening on a ghost tour hearing stories passed down through centuries
GitHub Blog
1. Why age assurance laws matter for developers
Published: 2026-05-08 16:30
Summary:
Youth safety requirements are moving down the tech stack to operating systems and app stores—raising new questions for open source developers The post Why age assurance laws matter for developers appeared first on The GitHub Blog.
2. How researchers are using GitHub Innovation Graph data to reveal the “digital complexity” of nations
Published: 2026-05-08 15:00
Summary:
Researchers share in an interview how they used GitHub data to predict GDP, inequality, and emissions in ways that traditional economic data misses, along with our Q4 2025 data release The post How researchers are using GitHub Innovation Graph data to reveal the “digital complexity” of nations appeared first on The GitHub Blog.
3. Improving token efficiency in GitHub Agentic Workflows
URL: https://github.blog/ai-and-ml/github-copilot/improving-token-efficiency-in-github-agentic-workflows/
Published: 2026-05-07 23:00
Summary:
Agentic workflows that run on every pull request can quietly accumulate large API bills Here’s how we instrumented our own production workflows, found the inefficiencies, and built agents to fix them The post Improving token efficiency in GitHub Agentic Workflows appeared first on The GitHub Blog.
Google Developers
1. Closing the knowledge gap with agent skills
URL: https://developers.googleblog.com/closing-the-knowledge-gap-with-agent-skills/
Published: 2026-05-09 10:03
Summary:
To bridge the gap between static model knowledge and rapidly evolving software practices, Google DeepMind developed a “Gemini API developer skill” that provides agents with live documentation and SDK guidance Evaluation results show a massive performance boost, with the gemini-3.1-pro-preview model jumping from a 28.2% to a 96.6% success rate when equipped with the skill This lightweight approach demonstrates how giving models strong reasoning capabilities and access to a “source of truth” can effectively eliminate outdated coding patterns.
2. Announcing ADK for Java 1.0.0: Building the Future of AI Agents in Java
Published: 2026-05-09 10:03
Summary:
Google has released version 1.0.0 of the Agent Development Kit (ADK) for Java, introducing powerful new features like Google Maps grounding, built-in URL fetching, and a standardized Agent2Agent protocol for cross-framework collaboration The update enhances agent control through a new “App” and “Plugin” architecture, which allows for global logging, automated context window management via event compaction, and “Human-in-the-Loop” workflows for action confirmations Additionally, the release provides robust session and memory services using Google Cloud integrations like Firestore and Vertex AI to manage long-term state and large data artifacts.
3. Boost Training Goodput: How Continuous Checkpointing Optimizes Reliability in Orbax and MaxText
Published: 2026-05-09 10:03
Summary:
The newly introduced continuous checkpointing feature in Orbax and MaxText is designed to optimize the balance between reliability and performance during model training, addressing issues with conventional fixed-frequency checkpointing Unlike fixed intervals—which can either compromise reliability or bottleneck performance—continuous checkpointing maximizes I/O bandwidth and minimizes failure risk by asynchronously initiating a new save operation only after the previous one successfully completes Benchmarks demonstrate that this approach significantly reduces checkpoint intervals and results in substantial resource conservation, especially in large-scale training jobs where mean-time-between-failure (MTBF) is short.
Meta Engineering
1. How Meta Is Strengthening End-to-End Encrypted Backups
URL: https://engineering.fb.com/2026/05/01/security/meta-strengthening-end-to-end-encrypted-backups/
Published: 2026-05-01 16:00
Summary:
The HSM-based Backup Key Vault Meta’s HSM-based Backup Key Vault provides the foundation for end-to-end encrypted backups for WhatsApp and Messenger The system allows people to protect their backed-up message history with a recovery code, ensuring that the recovery code is stored in tamper-resistant hardware security modules (HSMs) and is inaccessible to Meta, cloud storage […] Read More The post How Meta Is Strengthening End-to-End Encrypted Backups appeared first on Engineering at Meta.
2. Modernizing the Facebook Groups Search to Unlock the Power of Community Knowledge
Published: 2026-04-21 16:00
Summary:
We’ve fundamentally transformed Facebook Groups Search to help people more reliably discover, sort through, and validate community content that’s most relevant to them We’ve adopted a new hybrid retrieval architecture and implemented automated model-based evaluation to address the major friction points people experience when searching community content Under this new framework, we’ve made tangible improvements […] Read More
3. Capacity Efficiency at Meta: How Unified AI Agents Optimize Performance at Hyperscale
Published: 2026-04-16 16:00
Summary:
We’re sharing insights into Meta’s Capacity Efficiency Program, where we’ve built an AI agent platform that helps automate finding and fixing performance issues throughout our infrastructure By leveraging encoded domain expertise across a unified, standardized tool interface these agents help save power and free up engineers’ time away from addressing performance issues to innovating on […] Read More The post Capacity Efficiency at Meta: How Unified AI Agents Optimize Performance at Hyperscale 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. Everything we announced at Sessions 2026
URL: https://stripe.com/blog/everything-we-announced-at-sessions-2026
Published: 2026-04-29 00:00
Summary:
We’re making Stripe even more programmable; protecting and propelling your business with the strength of the Stripe network; and building economic infrastructure for AI.
2. 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 2026-05-09 10:03:39