Total Articles: 19 from 7 sources
AWS News
1. 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
2. Amazon EC2 R9g and R9gd instances powered by AWS Graviton5 processors are now generally available
Published: 2026-08-31 19:53
Summary:
Amazon EC2 R9g and R9gd instances powered by AWS Graviton5 are now generally available, delivering up to 25% better compute performance than R8g, ideal for databases, in-memory caches, and real-time analytics.
3. AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026)
Published: 2026-08-31 14:45
Summary:
The news that interested me the most last week was the DuckLabs acquisition AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON DuckDB stays open source under its independent foundation […]
Dropbox Engineering
1. Testing cookie behavior across hundreds of web surfaces with our in-house auditor
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
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. Project HydraFusion: Frontier quality via multi-model orchestration
Published: 2026-09-04 16:04
Summary:
In controlled offline evaluations, HydraFusion’s selective coding workflows matched or exceeded the evaluated Opus 5 baseline while reducing estimated workflow cost Now available as a research preview in GitHub Copilot The post Project HydraFusion: Frontier quality via multi-model orchestration appeared first on The GitHub Blog.
2. GitHub Copilot app for Beginners: Run several agents at once
Published: 2026-09-03 16:00
Summary:
Learn how to run parallel agents in the GitHub Copilot app, and experience the moment it stops feeling scary and starts feeling powerful The post GitHub Copilot app for Beginners: Run several agents at once appeared first on The GitHub Blog.
3. Decoding the new AI lingo: Loops, harnesses, squads, hill climbing… oh my!
URL: https://github.blog/ai-and-ml/decoding-the-new-ai-lingo-loops-harnesses-squads-hill-climbing-oh-my/
Published: 2026-09-02 21:00
Summary:
From loop engineering to harnesses, squads, and open weights, the GitHub Podcast breaks down the AI terms showing up in developer conversations The post Decoding the new AI lingo: Loops, harnesses, squads, hill climbing… oh my! appeared first on The GitHub Blog.
Google Developers
1. Run Ray on TPU, Part 1: The foundations
URL: https://developers.googleblog.com/run-ray-on-tpu-part-1-the-foundations/
Published: 2026-09-08 13:18
Summary:
Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google’s accelerators using the familiar Ray task-and-actor APIs To handle the strict networking requirement of keeping multi-host TPU “slices” together over their Inter-Chip Interconnect (ICI), the KubeRay Operator on GKE automatically provisions and labels the underlying hardware layout Ray Core utilizes these labels via its slice_placement_group() primitive to atomically reserve complete slices, allowing developers to deploy jobs through KubeRay, Ray Train, or Ray Serve simply by declaring a hardware topology (like “4x4”) without writing custom placement code.
2. Scaling Agentic RL: High-Throughput Agentic Training with Tunix
URL: https://developers.googleblog.com/scaling-agentic-rl-high-throughput-agentic-training-with-tunix/
Published: 2026-09-08 13:18
Summary:
Tunix is Google’s new JAX-native post-training library designed to eliminate TPU idling bottlenecks when training multi-turn, tool-using LLM reasoning agents It maximizes hardware throughput by combining highly concurrent, asynchronous rollouts with a decoupled producer-consumer pipeline, ensuring the trainer is constantly fed even while agents wait on network I/O or environment steps Additionally, Tunix provides plug-and-play abstractions and continuous macro-level profiling, allowing developers to easily integrate custom open-source environments and optimize complex distributed workflows without massive code rewrites.
3. Run Ray on TPU, Part 2: Ray AI libraries
URL: https://developers.googleblog.com/run-ray-on-tpu-part-2-ray-ai-libraries/
Published: 2026-09-08 13:18
Summary:
This second installment explores how Ray’s higher-level libraries—Serve, Data, and Train—abstract the complexities of running AI workloads on Google’s TPU slices Ray Serve uses a simple topology configuration to correctly gang-schedule large multi-host models, while Ray Data eliminates data-loading bottlenecks by feeding accelerators directly with native JAX batches Finally, JaxTrainer streamlines distributed training across TPUs by automatically handling cross-slice coordination, checkpointing, and fault tolerance.
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
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
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
2. A Tale of Two Flink Autoscalers
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
1. Five monetization trends from global pricing leaders
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 2026-09-08 13:18:40