Total Articles: 17 from 7 sources
AWS News
1. AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026)
Published: 2026-07-20 16:37
Summary:
Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul My team regularly visits countries across the Asia-Pacific region, listens to feedback […]
2. Amazon SQS turns 20: Two decades of reliable messaging at scale
URL: https://aws.amazon.com/blogs/aws/amazon-sqs-turns-20-two-decades-of-reliable-messaging-at-scale/
Published: 2026-07-13 18:13
Summary:
On July 13, 2006, we launched Amazon Simple Queue Service (Amazon SQS) as one of the first three services available to customers, alongside Amazon EC2 and Amazon S3 We had learned firsthand that distributed systems need a reliable way to pass messages between components without creating tight dependencies If one service called another directly and […]
3. AWS Weekly Roundup: AWS Builder Center at 1 year, Network Scanning in Security Hub, Loom for AWS, and more (July 13, 2026)
Published: 2026-07-13 16:18
Summary:
AWS Builder Center turned one year old last week Launched on July 9, 2025, the platform has grown from a community hub with Wishlist voting, community profiles, and a toolbox into a full ecosystem with sandbox environments, workshops, Spaces, and a Builders’ Library To mark the anniversary, Rick Suttles published a full feature timeline covering […]
Dropbox Engineering
1. How our universal content processing platform Riviera evolved for AI and beyond
Published: 2026-07-20 15:00
Summary:
Riviera is the Dropbox content processing platform that’s been iteratively improving content transformation in our products for roughly a decade.
GitHub Blog
1. The case for a cooldown: Why Dependabot now waits before issuing version updates
Published: 2026-07-23 16:00
Summary:
A new default three-day cooldown delays version update pull requests so maintainers and security researchers can address findings in a release before it gets into your code The post The case for a cooldown: Why Dependabot now waits before issuing version updates appeared first on The GitHub Blog.
2. Copilot vs. raw API access: What are you actually paying for?
Published: 2026-07-22 19:00
Summary:
Copilot now bills usage at listed API rates Compare direct model access with the coding workflow, policy, and harness work around it The post Copilot vs. raw API access: What are you actually paying for? appeared first on The GitHub Blog.
3. Next chapter: Restructuring GitHub’s bug bounty program
URL: https://github.blog/security/next-chapter-restructuring-githubs-bug-bounty-program/
Published: 2026-07-22 16:00
Summary:
GitHub is making some significant changes to its bug bounty program, shifting its focus to give researchers a better experience working with the GitHub team The post Next chapter: Restructuring GitHub’s bug bounty program appeared first on The GitHub Blog.
Google Developers
1. Announcing the Agentic Resource Discovery specification
URL: https://developers.googleblog.com/announcing-the-agentic-resource-discovery-specification/
Published: 2026-07-27 12:19
Summary:
An open specification for finding and verifying tools, skills, and agents across the web.Agents are …
2. A2UI + MCP Apps: Combining the best of declarative and custom agentic UIs
URL: https://developers.googleblog.com/a2ui-and-mcp-apps/
Published: 2026-07-27 12:19
Summary:
This post introduces three architectural patterns designed to integrate Model Context Protocol (MCP) Apps and Agent-to-User Interface (A2UI) to solve the tradeoff between highly custom iframe environments and native, declarative rendering By combining these approaches, developers can serve native-feeling UIs directly over MCP servers, embed complex and stateful iframe apps securely inside declarative views, or inject generative UI components into legacy systems Ultimately, these hybrid frameworks empower engineering teams to deliver secure, performant, and brand-consistent agentic user experiences tailored to their specific project constraints.
3. How A2A is Building a World of Collaborative Agents
URL: https://developers.googleblog.com/how-a2a-is-building-a-world-of-collaborative-agents/
Published: 2026-07-27 12:19
Summary:
Celebrating the first anniversary of the Agent-to-Agent (A2A) protocol, this blog post highlights how the framework enables autonomous AI agents to securely collaborate and hand off tasks without the rigidity of traditional APIs By delegating complex workflows to specialized peer agents, A2A prevents context pollution, ensures data privacy, and simplifies application design through modularity To demonstrate this ecosystem in action, the post spotlights FoldRun—an agentic interface for life sciences that orchestrates complex protein structure predictions—alongside diverse A2A use cases spanning commerce, data streaming, DevOps, and telecommunications.
Meta Engineering
1. Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization
Published: 2026-07-15 17:00
Summary:
Hierarchical Interest Representation is a research area for Meta Ads The innovations in Hierarchical Interest Representation are […] Read More The post Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization appeared first on Engineering at Meta.
2. Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler
Published: 2026-07-13 16:00
Summary:
TL; DR At Meta’s scale, a few milliseconds of latency degradation can have a significant negative impact on ads performance When a Linux kernel upgrade risked regressing latency across Meta’s ad serving fleet, we turned to sched_ext — the upstream, BPF-based extensible scheduling framework — to build a scheduling policy customized to the Ads delivery […] Read More The post Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler appeared first on Engineering at Meta.
3. Meta’s AI Storage Blueprint at Scale
URL: https://engineering.fb.com/2026/07/01/data-infrastructure/metas-ai-storage-blueprint-at-scale/
Published: 2026-07-01 16:00
Summary:
During the past year or so, the time between new-frontier-model releases has gone down from months to weeks Reliable and fast access to storage is important to both the speed and computational cost of this AI innovation The post Meta’s AI Storage Blueprint at Scale appeared first on Engineering at Meta.
Netflix TechBlog
1. In-House LLM Serving at Netflix
Published: 2026-07-17 21:32
Summary:
Serving Architecture OverviewDesign Decisions and ImplementationFour decisions shape this platform — engine, packaging, API surface, and rollout — presented in dependency order, since each one constrains the next.vLLM as the Paved-Path EngineThe platform was originally built on TensorRT-LLM, a performant inference engine at the time and already integrated with Triton — the compute backend in use within MSS.By summer 2025, two things had shifted: open-source engines had largely closed the performance gap with specialized stacks, and our workload mix had broadened to include embedding generation, prefill-only inference for ranking and retrieval, autoregressive decoding, and custom models with non-trivial per-step constraint logic The platform has to pin compatible versions when baking the service image, and prevent model authors from overriding the vLLM version at packaging time.Custom model logic We git-subtreed and patched the frontend to translate response_format into vLLM’s guided decoding parameters at request time.Deployment StrategiesWith API surface and engine in place, the question that remains is how new versions roll out without dropping requests
2. Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned
Published: 2026-07-13 22:44
Summary:
Instead of batch jobs that process historical data, we continuously ingest flow records from multi-region Kafka streams and IPC metrics as Server-Sent Events, process them through reactive pipelines with backpressure handling, and provide near real-time topology updates, typically within tens of minutes, compared to the hours-old or day-old data that batch processing approaches provide.This wasn’t just about freshness, it was essential for our use cases When data arrives with the right partitioning, you can aggregate directly; when it doesn’t (like network flows requiring intermediary resolution), you need shuffle/redistribution stages.Dynamic Load Distribution: How Hashing Works with Auto-ScalingHow do we decide which instance receives which aggregator when our Auto Scaling Groups dynamically add or remove instances GC logs showed pauses consuming significant CPU time, in some cases, more than our business logic.Root Cause: Multiple factors contributed: objects accumulating in heap while waiting for 5-minute aggregation windows to complete, unnecessary conversions between different object types as data flowed through stages, and immutability overhead, following Scala best practices, we used immutable data structures for aggregators, but every update created new objects, overwhelming the garbage collector at millions of records per second.Investigation: Heap dumps and GC logs revealed flow log objects retained beyond their useful lifetime, unnecessary intermediate conversion objects, and constant creation/disposal of immutable aggregator versions
3. GenPage: Towards End-to-End Generative Homepage Construction at Netflix
Published: 2026-06-29 13:01
Summary:
This sequence includes the full structured homepage layout, with multiple rows and the entities inside them, so the model can generate the page holistically rather than scoring each row or entity in isolation.Figure 2 At a high level, WBC turns generation into token-level value prediction: given the user context and the tokens generated so far, the model learns to estimate the value of generating each possible next row or entity token.This objective is easier to optimize than page-level RL WBC post-training loss as we progressively enrich the user context tokens
Stripe Engineering
1. Analyzing the evidence that helps businesses win “product not received” disputes
Published: 2026-07-21 00:00
Summary:
To understand what can influence win rates, we analyzed evidence packets from one million disputes over a 16-week period Here’s what the data shows and what it means for how you mitigate disputes.
Generated on 2026-07-27 12:19:33