Total Articles: 20 from 7 sources
AWS News
1. Improve your application resilience with Amazon Cognito multi-Region replication
Published: 2026-06-03 22:37
Summary:
Amazon Cognito now offers multi-Region replication that automatically synchronizes user data, credentials, and pool configurations to a secondary AWS Region, enabling uninterrupted authentication during regional failovers without forced password resets—plus new support for customer managed KMS keys for encryption control.
2. Get started with OpenAI GPT-5.5, GPT-5.4 models, and Codex on Amazon Bedrock
Published: 2026-06-01 21:33
Summary:
OpenAI frontier models GPT-5.5 and GPT-5.4, and Codex, the OpenAI coding agent, are now generally available on Amazon Bedrock Deploy frontier models on Bedrock’s high performance inference engine with built-in security, governance, and pay-per-token pricing.
3. AWS Weekly Roundup: Claude Opus 4.8 on AWS, Aurora MySQL with Kiro Powers, and more (June 1, 2026)
Published: 2026-06-01 14:25
Summary:
In my last Week in Review post, I shared what I’d been hearing from customers in the AI-Driven Development Lifecycle (AI-DLC) workshops I’ve been delivering Last week I was back at it, this time in Denver for a two-day AI-DLC workshop, where I helped facilitate 17 teams to deliver nearly 20 separate use cases in […]
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. GitHub Copilot app: The agent-native desktop experience
Published: 2026-06-02 17:30
Summary:
At Microsoft Build 2026, GitHub introduced new tools, updates, and surfaces so agents can work the way you already work The post GitHub Copilot app: The agent-native desktop experience appeared first on The GitHub Blog.
2. 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.
3. 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.
Google Developers
1. Speeding Up AI: Bringing Google Colossus to PyTorch via GCSFS and Rapid Bucket
Published: 2026-06-04 11:50
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.
2. Building with Gemini Embedding 2: Agentic multimodal RAG and beyond
URL: https://developers.googleblog.com/building-with-gemini-embedding-2/
Published: 2026-06-04 11:50
Summary:
Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic space This model allows developers to process interleaved multimodal inputs in a single request, significantly improving performance for tasks like agentic RAG, visual search, and content moderation By supporting over 100 languages and offering features like task-specific prefixes and Matryoshka dimensionality reduction, the model provides a highly efficient and accurate foundation for building complex AI agents.
3. Supercharging LLM inference on Google TPUs: Achieving 3X speedups with diffusion-style speculative decoding
Published: 2026-06-04 11:50
Summary:
Researchers at UCSD have successfully implemented DFlash, a block-diffusion speculative decoding method, on Google TPUs to bypass the sequential bottlenecks of traditional autoregressive drafting By “painting” entire blocks of candidate tokens in a single forward pass rather than predicting them one-by-one, the system achieved average speedups of 3.13x, with peak performance nearly doubling that of existing methods like EAGLE-3 This open-source integration into the vLLM ecosystem optimizes TPU hardware by leveraging “free” parallel verification and high-quality draft predictions for complex reasoning tasks.
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. Dynamic Repartitioning for Time Series Workloads
Published: 2026-06-03 02:05
Summary:
Using these tools, we can detect cases of both over and under partitioning.Below is an example of over‑partitioning, where the TimeSeries provisioning pipeline selected very small time_bucket intervals based on user provided inputs:Provisioning selected 60s time buckets based on user inputscausing partitions to have less than 10 KB of data, leading to high read amplification and thread queueing:Histogram of the given Cassandra table showing partition size percentilesIn order to tune partition strategies efficiently, we added a background worker, which monitors partition histograms of Time Slices attached to a given application, and exposes it via a Cassandra virtual table:Histograms exposed through a Cassandra Virtual tableIt then computes an adjustment factor when it detects partition sizes not meeting a configured density Simply tolerating elevated latencies or timeouts when querying these IDs is not a desirable outcome.This is where dynamic partitioning comes into play.Solution 2: Dynamic Partitioning per IDDynamic partitioning is an asynchronous pipeline that auto-detects and splits wide partitions on a TimeSeries ID level rather than at the table level.It has three main stages:Detection: Detects wide partitions for a given TimeSeries ID during the read path.Planning & Splitting: Plans and executes splits of those partitions into optimal sizes asynchronously.Serving Reads: Re-routes the read queries transparently to read data from the split partitions when ready.This is how it works at a high level; we will dive into details after:Dynamic Wide Partition Split Async PipelineHere are the different stages of the pipeline:DetectionEvery TimeSeries read operation tracks how many bytes are read for a given partition For example, if EventBucketPartitionSplitStrategy is selected, we split the partition by assigning more event buckets to the same time bucket
2. High-Throughput Graph Abstraction at Netflix: Part I
Published: 2026-05-29 18:49
Summary:
Looking ahead, we plan to leverage the graph schema for additional improvements, such as:Minimizing Query Fanout: By using edge cardinality within edge mappings, we aim to select the most efficient traversal paths and minimize query fanout.Improved Developer Experience: The schema will support generating a type-safe data access layer and enhance the Gremlin-like API with schema awareness.Next, let’s look at how this data is organized in a real-time index within the KV Abstraction.Real-Time Index: Key-Value StorageBefore we discuss how the data is organized into graph indexes, let’s discuss how KV organizes data within namespaces and provides idempotency guarantees:Data partitioning: A namespace is associated with a table in the underlying storage layer In order to ensure optimal performance without exerting too much memory pressure, we aim to limit the number of edges per source node within the system.Next, let’s explore the caching strategies used by the Abstraction.Caching Strategies in Graph AbstractionAlthough the Graph Abstraction already provides efficient reads and writes to durable storage, caching remains critical for the stability and performance of any graph datastore for two key reasons:Write amplification: A single write on the fronting service can result in multiple writes to the backing durable storage due to the use of multiple indexes As illustrated in the diagram below, both the caching layer and durable storage replicate data asynchronously across regions, resulting in an eventually consistent system.Now that we’ve covered storing the real-time graph index, let’s see how it enables graph traversals.Graph TraversalsThe Abstraction provides a custom gRPC traversal API, inspired by Gremlin, which enables exploration of the distributed graph by letting users chain traversals, apply filter criteria, sort results, limit results, and more.Let’s explore a hypothetical scenario where the Abstraction is used to recommend shows to users on a shared device, by considering the duration of the most recent viewing session for each show across all profiles and accounts associated with that device:TraversalRequest.newBuilder() .setNamespace("
3. From Silos to Service Topology: Why Netflix Built a Real-Time Service Map
Published: 2026-05-29 14:01
Summary:
We needed to combine multiple sources.These lessons shaped every decision we made in building Service Topology.What We Needed: A Living MapWe set out to build something specific: a living map of our infrastructure — one that updates in real-time as services deploy, as traffic patterns shift, as new dependencies form and old ones disappear.The requirements were clear:Real-time updates, not stale snapshots: In an environment where services deploy continuously, yesterday’s topology map is archaeology, not observability.Fast queries at scale: When an engineer is troubleshooting at 3am, they can’t wait minutes for a query to return This graduated approach also prevents hot spots by distributing load across multiple points even when specific applications or network intermediaries see 100x more traffic than others.Graph Storage: We persist the topology in Netflix’s graph database, an abstraction layer built on top of our distributed key-value storage infrastructure Each of our three data sources (network flows, IPC metrics, tracing) creates a separate graph that can be queried independently or merged.gRPC API: We expose the topology through a gRPC service that supports multi-hop traversal, filtering by availability tier and business domain, pagination for large result sets, and sub-second query response times.The technical details of building this at Netflix scale — handling Kafka lag, managing memory and garbage collection, optimizing distributed processing, debugging reactive streams — deserve their own discussion
Stripe Engineering
1. Helping businesses optimize network costs with the Visa Digital Commerce Authentication Program (DCAP)
Published: 2026-06-03 00:00
Summary:
We moved quickly to help Stripe businesses take advantage of DCAP and capture interchange savings while protecting authorization rates
2. 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.
3. 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.
Generated on 2026-06-04 11:50:44