Total Articles: 20 from 7 sources
AWS News
1. Now available: Amazon EC2 M9g and M9gd instances powered by new AWS Graviton5 processors
Published: 2026-06-10 15:04
Summary:
AWS launches Amazon EC2 M9g and M9gd instances, powered by AWS Graviton5 processors AWS Graviton5 is most powerful, and most energy efficient processor AWS has ever built, and offers up to 25% better compute performance compared to Graviton4-based instances.
2. Anthropic Claude Fable 5 on AWS: Mythos-class capabilities with built-in safeguards now available
Published: 2026-06-09 17:40
Summary:
AWS announces the availability of Claude Fable 5 on Amazon Bedrock and Claude Platform on AWS Claude Fable 5 delivers Mythos-level capabilities available to all customers, with strong safeguards designed to make it safe for broader use.
3. AWS Weekly Roundup: BYOM for Amazon RDS for SQL Server, AWS IoT Device SDK for Swift, and more (June 8, 2026)
Published: 2026-06-08 21:27
Summary:
This week, the AWS IoT Device SDK for Swift reached general availability As a member of the Swift Server Workgroup (SSWG), this one caught my attention The SDK brings production-ready MQTT 5 connectivity, Device Shadow, Jobs, and fleet provisioning to Swift developers on macOS, iOS, tvOS, and Linux
Dropbox Engineering
1. How Dropbox uses MCP and Dash to close the design-to-code security gap
URL: https://dropbox.tech/security/dropbox-mcp-dash-design-code-security
Published: 2026-06-12 18:00
Summary:
Using an agentic AI system to surface threat models during code review and spot gaps between security requirements and implementation.
2. 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.
3. 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 availability report: May 2026
URL: https://github.blog/news-insights/company-news/github-availability-report-may-2026/
Published: 2026-06-11 21:30
Summary:
In May, we experienced nine incidents that resulted in degraded performance across GitHub services The post GitHub availability report: May 2026 appeared first on The GitHub Blog.
2. Making secret scanning more trustworthy: Reducing false positives at scale
Published: 2026-06-11 16:00
Summary:
Alerts are more trustworthy and actionable when noise is reduced See how we improved the verification step with context-aware LLM reasoning The post Making secret scanning more trustworthy: Reducing false positives at scale appeared first on The GitHub Blog.
3. Give GitHub Copilot CLI real code intelligence with language servers
Published: 2026-06-10 16:00
Summary:
Install and configure LSP servers for GitHub Copilot CLI, replacing brute-force grep/decompile with real code intelligence The post Give GitHub Copilot CLI real code intelligence with language servers appeared first on The GitHub Blog.
Google Developers
1. Supercharging LLM inference on Google TPUs: Achieving 3X speedups with diffusion-style speculative decoding
Published: 2026-06-12 12:17
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.
2. Build Long-running AI agents that pause, resume, and never lose context with ADK
Published: 2026-06-12 12:17
Summary:
How to transition from stateless chatbots to production-grade agents capable of managing long-running enterprise workflows, such as HR onboarding, that span days or weeks It introduces the Agent Development Kit (ADK) and its architectural shifts, specifically using durable state machines and persistent session storage to ensure an agent never loses context during “idle time” or server restarts By leveraging event-driven webhooks and multi-agent delegation, the tutorial demonstrates how to build resilient systems that “sleep” during pauses and wake up to resume complex tasks with high reasoning accuracy.
3. Announcing Genkit Middleware: Intercept, extend, and harden your agentic apps
Published: 2026-06-12 12:17
Summary:
The framework utilizes a powerful middleware system that intercepts generation calls to inject custom behaviors like retries, model fallbacks, and human-in-the-loop tool approvals By attaching hooks at the generate, model, and tool layers, developers can ensure high reliability and deterministic control over model outputs Furthermore, Genkit allows for the creation and stacking of custom middleware, all of which can be inspected and debugged through a dedicated Developer UI.
Meta Engineering
1. Lights Out, Systems On: Validating Instant Power Loss Readiness
Published: 2026-06-03 17:00
Summary:
We’re introducing Instantaneous PowerLoss Storm, a new testing paradigm within Meta’s infrastructure for handling and mitigating instant or zero-notice power loss in our data centers We’re sharing: how we built readiness to tolerate instant failures into our existing systems with defense-in-depth strategies; tradeoffs made in implementing it, and how we validated our readiness The post Lights Out, Systems On: Validating Instant Power Loss Readiness appeared first on Engineering at Meta.
2. 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.
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. Stripe Projects adds new agent integrations, more providers, and custom developer controls
URL: https://stripe.com/blog/stripe-projects-adds-new-agents-providers-developer-controls
Published: 2026-06-11 00:00
Summary:
Our data shows that agents are now fully capable of independently writing code and integrating with APIs like Stripe’s And yet, many of the steps adjacent to writing code are still too hard for agents to do on their own We’re expanding Stripe Projects to solve this.
2. New ways to turn global demand into revenue
URL: https://stripe.com/blog/new-ways-to-turn-global-demand-into-revenue
Published: 2026-06-04 00:00
Summary:
At Sessions 2026, Stripe unveiled dozens of products and capabilities to help businesses turn global demand into revenue See how to go global faster with localized checkout and Adaptive Pricing, smarter fraud tools, multicurrency treasury support, and automated tax compliance.
3. The future of agentic commerce is here
URL: https://stripe.events/acnext_seattle?utm_campaign=TD0976N7Lv4IS9ymCxV2jTAHH
Published: 2026-06-04 00:00
Summary:
Explore how AI agents are transforming commerce at Stripe’s Agentic Commerce Next roadshow Reserve your spot in Seattle.
Generated on 2026-06-12 12:17:13