AWS NewsDropbox EngineeringGitHub BlogGoogle DevelopersMeta EngineeringNetflix TechBlogStripe Engineering

Total Articles: 19 from 7 sources


AWS News

1. AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026)

URL: https://aws.amazon.com/blogs/aws/aws-weekly-roundup-openai-gpt-6-astra-on-amazon-bedrock-amazon-quick-desktop-ga-kiro-for-students-and-more-september-14-2026/

Published: 2026-09-14 15:56

Summary:

There’s a particular energy to mid-September in New York Pumpkin spice lattes are flowing, temperatures are dropping, and it’s nearly sweater weather This week that energy showed up in a new frontier model on Amazon Bedrock, a desktop app for Amazon […]


2. Introducing Amazon EBS Volume Clones across AWS accounts

URL: https://aws.amazon.com/blogs/aws/introducing-amazon-ebs-volume-clones-across-aws-accounts/

Published: 2026-09-09 22:01

Summary:

AWS introduces Amazon EBS Volume Clones with cross-account copy, so you can create copies of your EBS volumes into other AWS accounts and optionally re-encrypt them with an AWS Key Management Service (AWS KMS) key in the target account.


3. AWS Weekly Roundup: Claude Fable 5.1 on AWS, Amazon Linux 2027 preview, AWS Certified AI Business Strategist, and more (September 7, 2026)

URL: https://aws.amazon.com/blogs/aws/aws-weekly-roundup-claude-fable-5-1-on-aws-amazon-linux-2027-preview-aws-certified-ai-business-strategist-and-more-september-7-2026/

Published: 2026-09-07 14:24

Summary:

Last week, Claude Fable 5.1 became available on AWS According to Anthropic, Claude Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows Claude Fable 5.1 is built for long-running, high-stakes work that runs for hours and spans many applications

Dropbox Engineering

URL: https://dropbox.tech/security/how-our-inhouse-auditor-tests-cookie-behavior-across-hundreds-of-web-surfaces

Published: 2026-08-31 17:00

Summary:

Our cookie auditor acts like a privacy-conscious user by visiting web pages and checking that they only load cookies consistent with that user’s preferences.


2. Improving infrastructure efficiency for growing demand in the age of AI

URL: https://dropbox.tech/infrastructure/improving-infrastructure-efficiency-for-growing-demand-in-the-age-of-ai

Published: 2026-08-18 17:00

Summary:

As demand for AI continues to grow, so does the infrastructure needed to support it.

GitHub Blog

1. Marketing ops as code: Automating events from planning to follow-up on GitHub

URL: https://github.blog/ai-and-ml/github-copilot/marketing-ops-as-code-automating-events-from-planning-to-follow-up-on-github/

Published: 2026-09-11 18:26

Summary:

If you can write down how you do your work, you can automate it Here’s what I did to support GitHub’s APAC marketing team The post Marketing ops as code: Automating events from planning to follow-up on GitHub appeared first on The GitHub Blog.


2. GitHub Copilot app for Beginners: Using the diff, terminal, and browser

URL: https://github.blog/ai-and-ml/github-copilot/github-copilot-app-for-beginners-using-the-diff-terminal-and-browser/

Published: 2026-09-10 21:31

Summary:

Checking agent-generated code usually means hopping between tabs Learn how to view diffs, run terminal commands, and preview web apps side by side in the GitHub Copilot app The post GitHub Copilot app for Beginners: Using the diff, terminal, and browser appeared first on The GitHub Blog.


3. GitHub availability report: August 2026

URL: https://github.blog/news-insights/company-news/github-availability-report-august-2026/

Published: 2026-09-10 02:05

Summary:

In August, we experienced five incidents that resulted in degraded performance across GitHub services The post GitHub availability report: August 2026 appeared first on The GitHub Blog.

Google Developers

1. How to use Google microbenchmarks for evaluating TPU performance

URL: https://developers.googleblog.com/how-to-use-google-microbenchmarks-for-evaluating-tpu-performance/

Published: 2026-09-15 14:08

Summary:

Google’s open-source TPU microbenchmark suite provides developers with granular performance metrics across Network, Compute, HBM, Host Transfer, and Attention components to validate real-world hardware capabilities By leveraging these benchmarks to establish a Roofline model, engineers can accurately diagnose whether their machine learning workloads are compute-, memory-, or network-bound This empirical baseline directly guides targeted software optimizations—such as kernel tuning, mesh sharding, and rematerialization—to maximize hardware utilization for large-scale model deployments.


2. Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA

URL: https://developers.googleblog.com/agent-and-model-evaluations-in-gemini-enterprise-agent-platform-are-now-ga/

Published: 2026-09-15 14:08

Summary:

Agent Platform’s evaluation service is now generally available, providing developers with a unified engine to measure agent quality consistently across local development experiments and live production traffic You can evaluate agents using over 20 pre-built metrics, DeepMind-backed adaptive rubrics, or custom code-based and LLM-as-a-judge metrics stored in a centralized, versioned registry The service integrates directly into existing workflows via the Agent Platform SDK, agents-cli, and ADK, offering built-in user and environment simulators to automate complex multi-turn testing and streamline CI pipelines.


3. Enable on-demand expertise with Agent Skills in Genkit Go

URL: https://developers.googleblog.com/enable-on-demand-expertise-with-agent-skills-in-genkit-go/

Published: 2026-09-15 14:08

Summary:

To prevent context window bloat and reduce token consumption, Genkit Go introduces Agent Skills based on a progressive disclosure architecture Developers can package specialized instructions, scripts, and references into modular SKILL.md bundles where only the frontmatter metadata is initially exposed to the agent’s system prompt When a task matches the skill’s description, Genkit’s middleware dynamically loads the full instruction body and associated assets, ensuring the model accesses precise workflows exactly when needed.

Meta Engineering

1. ZGateway: Learnings from Putting a Proxy in Front of ZippyDB

URL: https://engineering.fb.com/2026/09/03/core-infra/zgateway-proxy-zippydb-meta/

Published: 2026-09-03 16:00

Summary:

We’re introducing ZGateway, the proxy we are using to unify traffic through ZippyDB, Meta’s most widely-used key value store ZippyDB is the most widely used key value store at Meta, backing product metadata, counters, and configuration, and can serve billions […] Read More The post ZGateway: Learnings from Putting a Proxy in Front of ZippyDB appeared first on Engineering at Meta.


2. An Organizational Second Brain: Building an AI That Learns From Experts

URL: https://engineering.fb.com/2026/09/02/ml-applications/organizational-second-brain-ai-learns-from-experts/

Published: 2026-09-02 09:00

Summary:

We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon This is not a typical domain-specific agent Its novelty comes from integrating two layers: A structured, auditable knowledge architecture separates what […] Read More


3. MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet

URL: https://engineering.fb.com/2026/08/24/networking-traffic/metaroce-rdma-transport-ai-ethernet/

Published: 2026-08-24 18:02

Summary:

To meet this challenge at scale, Meta designed MetaRoCE – a clean-sheet RDMA transport protocol purpose-built for AI workloads on commodity Ethernet We’re releasing the MetaRoCE specification, a reference software implementation and a compliance test […] Read More The post MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet appeared first on Engineering at Meta.

Netflix TechBlog

1. MAPS: Netflix’s Multimodal Asset Personalization at Scale

URL: https://netflixtechblog.com/maps-netflixs-multimodal-asset-personalization-at-scale-32f96320785e?source=rss----2615bd06b42e---4

Published: 2026-08-28 16:01

Summary:

A single unified model can therefore pool interaction signal across every canvas, so a member’s affinity learned on a high-traffic canvas immediately informs the artwork we pick on a sparse one One unified model over all five canvases, with image embeddings in its asset representation.As the chart below shows, each idea helped exactly where we expected: on the data-starved short-panel canvas and landscape-panel canvas We have since shipped MediaFM as the default video preview embedding across all platforms.Relative offline IPS lift for the two content-aware video preview embeddings, each measured against the ID-only baseline at the zero rule


URL: https://netflixtechblog.com/a-tale-of-two-flink-autoscalers-e9f6a1b1492b?source=rss----2615bd06b42e---4

Published: 2026-08-21 16:01

Summary:

Each autoscaler node handled the metrics for a subset of Flink jobs, and we never had to write custom sharding or coordination logic to keep up with a growing Flink fleet Starting from the sources, the autoscaler walks the job graph and uses each operator’s TPR, its input/output ratios, and a target utilization to compute the parallelism every vertex needs so that no operator becomes the bottleneck, rather than resizing the whole cluster as a unit.Figure 2: Flink job DAG: current → desired parallelism per vertex, based on busynessThe two approaches make a different contract, summarized below.Table 1: Comparison of the two Flink autoscalersThe decisive difference for us is the last two rows: the OSS autoscaler can scale exactly the stateful, multi-operator jobs our homegrown system could not, and it lets each job carry its own configuration — stabilization periods, thresholds, and other scaling behavior tuned to the workload Having started supporting Flink 2.2 at Netflix, we plan on experimenting with this new state backend to see if it can help eliminate state recovery bottlenecks when scaling large stateful jobs.Looking ahead, we aim to migrate all internal scaler use cases onto the new one based on OSS autoscaler to simplify our operational surface area.Key TakeawaysAlong the way, three lessons that generalize beyond Flink:Metric choice matters more than algorithm sophistication

Stripe Engineering

URL: https://stripe.com/blog/five-monetization-trends-from-global-pricing-leaders

Published: 2026-08-20 00:00

Summary:

As AI transforms software economics, the standard revenue playbook is breaking down Learn how leaders around the world are preparing for agent buyers, updating processes for faster pricing iteration, and building more flexible infrastructure.


2. Why global workers are driving demand for stablecoin payouts

URL: https://stripe.com/blog/why-global-workers-are-driving-demand-for-stablecoin-payouts

Published: 2026-08-19 00:00

Summary:

Platforms like DoorDash, Meta, and Deel already enable stablecoin payouts for global workers We surveyed 2,300 workers in 20 countries to see what’s driving stablecoin demand, where the opportunity is highest, and how other platforms can adapt.


3. New currency capabilities for global businesses to cut FX costs

URL: https://stripe.com/blog/reduce-fx-costs-with-stripe

Published: 2026-08-17 00:00

Summary:

Two product upgrades make it easy for global businesses to manage FX entirely on Stripe We’re expanding multicurrency settlement to more markets and currencies, and we’re introducing the ability to convert currencies instantly—all on Stripe.


Generated on 2026-09-15 14:08:25