Total Articles: 18 from 6 sources
AWS News
1. 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
2. Top announcements of the What’s Next with AWS, 2026
URL: https://aws.amazon.com/blogs/aws/top-announcements-of-the-whats-next-with-aws-2026/
Published: 2026-04-28 18:11
Summary:
At the “What’s Next with AWS” 2026 event, AWS launched Amazon Quick—an AI assistant for work with a desktop app and expanded integrations—and expanded Amazon Connect into four agentic AI solutions for supply chain, hiring, customer experience, and healthcare AWS also expended its partnership with OpenAI, bringing models like GPT-5.5, Codex, and Managed Agents to Amazon Bedrock in limited preview.
3. AWS Weekly Roundup: Anthropic & Meta partnership, AWS Lambda S3 Files, Amazon Bedrock AgentCore CLI, and more (April 27, 2026)
Published: 2026-04-27 15:01
Summary:
Late March took me to Seattle for the Specialist Tech Conference, one of the most energizing gatherings of AWS specialists from around the world It was an incredible opportunity to connect with peers, exchange experiences, and go deep on the latest advancements in Generative AI and Amazon Bedrock — and a powerful reminder of something […]
GitHub Blog
1. Register now for OpenClaw: After Hours @ GitHub
URL: https://github.blog/open-source/register-now-for-openclaw-after-hours-github/
Published: 2026-05-04 15:00
Summary:
OpenClaw builders will gather at GitHub HQ during Microsoft Build 2026 for demos and conversations Join in person, or watch the livestream on Twitch The post Register now for OpenClaw: After Hours @ GitHub appeared first on The GitHub Blog.
2. GitHub Copilot CLI for Beginners: Interactive v. non-interactive mode
Published: 2026-04-30 16:09
Summary:
Learn the difference between CLI interactive v. non-interactive modes The post GitHub Copilot CLI for Beginners: Interactive v. non-interactive mode appeared first on The GitHub Blog.
3. GitHub for Beginners: Getting started with Markdown
URL: https://github.blog/developer-skills/github/github-for-beginners-getting-started-with-markdown/
Published: 2026-04-28 18:00
Summary:
Discover how to format and edit your comments and posts using Markdown The post GitHub for Beginners: Getting started with Markdown 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: 2026-05-05 10:44
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: 2026-05-05 10:44
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: 2026-05-05 10:44
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. 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
2. 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
3. Scaling Camera File Processing at Netflix
Published: 2026-04-24 15:06
Summary:
To reduce friction and keep productions moving smoothly, we built Netflix’s Media Production Suite (MPS) with the goal of automating repeatable tasks, standardizing key workflows, and giving productions more time to focus on creative collaboration and craftsmanship.A critical part of this effort is how we handle image processing and camera metadata across the hundreds of hours and terabytes of camera footage that Netflix productions ingest on a daily basis Rather than build every component from scratch, we chose to partner where it made sense–especially in areas where the industry already had trusted, battle-tested solutions.This article explores how Netflix’s Media Production Suite integrates with FilmLight’s API (FLAPI) as the core studio media processing engine in Netflix’s cloud compute infrastructure, and how that collaboration helps us deliver smarter, more reliable workflows at scale.Why We Built MPSAs Netflix’s production slate grew, so did the complexity of file-based workflows Their FilmLight API (FLAPI) allows us to use that same media processing engine as a backend API.Rather than duplicating that work, we chose to integrate
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.
3. How agents, digital wallets, and trust are rewriting checkout
URL: https://stripe.com/blog/global-checkout-trends
Published: 2026-04-07 00:00
Summary:
We analyzed checkout activity across more than 20K businesses, surveyed shoppers and ecommerce leaders, and gathered insights from businesses on the Stripe network to understand what’s changing in online conversion.
Generated on 2026-05-05 10:44:06