Total Articles: 20 from 7 sources
AWS News
1. AWS Interconnect is now generally available, with a new option to simplify last-mile connectivity
Published: 2026-04-14 23:54
Summary:
Today, we’re announcing the general availability of AWS Interconnect – multicloud, a managed private connectivity service that connects your Amazon Virtual Private Cloud (Amazon VPC) directly to VPCs on other cloud providers We’re also introducing AWS Interconnect – last mile, a new capability that simplifies how you establish high-speed, private connections to AWS from your […]
2. AWS Weekly Roundup: Claude Mythos Preview in Amazon Bedrock, AWS Agent Registry, and more (April 13, 2026)
Published: 2026-04-13 16:16
Summary:
In my last Week in Review post, I mentioned how much time I’ve been spending on AI-Driven Development Lifecycle (AI-DLC) workshops with customers this year A common theme in those sessions is the need for better cost visibility Teams are moving fast with AI, but as they go from experimenting to full production, finance and […]
3. Launching S3 Files, making S3 buckets accessible as file systems
URL: https://aws.amazon.com/blogs/aws/launching-s3-files-making-s3-buckets-accessible-as-file-systems/
Published: 2026-04-07 19:18
Summary:
Amazon S3 Files makes S3 buckets accessible as high-performance file systems on AWS compute resources, eliminating the tradeoff between object storage benefits and interactive file capabilities while enabling seamless data sharing with ~1ms latencies.
Dropbox Engineering
1. Improving storage efficiency in Magic Pocket, our immutable blob store
Published: 2026-04-02 17:00
Summary:
By turning compaction into a layered, adaptive pipeline and strengthening our monitoring and controls, we made Magic Pocket more resilient to workload changes.
2. Reducing our monorepo size to improve developer velocity
URL: https://dropbox.tech/infrastructure/reducing-our-monorepo-size-to-improve-developer-velocity
Published: 2026-03-25 17:00
Summary:
Monorepos will continue to grow as products evolve, but growth doesn’t have to mean friction.
GitHub Blog
1. Build a personal organization command center with GitHub Copilot CLI
Published: 2026-04-15 17:00
Summary:
Learn about the productivity tool one GitHub engineer built, and how AI supported the development process The post Build a personal organization command center with GitHub Copilot CLI appeared first on The GitHub Blog.
2. Developer policy update: Intermediary liability, copyright, and transparency
Published: 2026-04-15 15:00
Summary:
We’re sharing recent policy updates that developers should know about, updating our Transparency Center with the full year of 2025 data, and looking to what’s ahead The post Developer policy update: Intermediary liability, copyright, and transparency appeared first on The GitHub Blog.
3. Hack the AI agent: Build agentic AI security skills with the GitHub Secure Code Game
Published: 2026-04-14 18:17
Summary:
Learn to find and exploit real-world agentic AI vulnerabilities through five progressive challenges in this free, open source game that over 10,000 developers have already used to sharpen their security skills The post Hack the AI agent: Build agentic AI security skills with the GitHub Secure Code Game appeared first on The GitHub Blog.
Google Developers
1. Introducing Wednesday Build Hour
URL: https://developers.googleblog.com/introducing-wednesday-build-hour/
Published: 2026-04-16 10:13
Summary:
Wednesday Build Hour is a weekly, interactive “technical gym session” led by Google Cloud experts to help developers and architects sharpen their cloud skills Each hour-long session is designed to provide tangible results that participants can immediately deploy into their own workflows It serves as a consistent, dedicated space for builders to stay ahead of the curve and connect with a community of cloud engineers.
2. Introducing Finish Changes and Outlines, now available in Gemini Code Assist extensions on IntelliJ and VS Code
Published: 2026-04-16 10:13
Summary:
Google has introduced Finish Changes and Outlines for Gemini Code Assist in IntelliJ and VS Code to reduce developer friction and eliminate the need for long, manual prompting Finish Changes acts as an AI pair programmer that completes code, implements pseudocode, and applies refactoring patterns by observing your current edits and context Meanwhile, Outlines improves code comprehension by generating interactive, high-level English summaries interleaved directly within the source code to help engineers navigate and understand complex files.
3. Unleash Your Development Superpowers: Refining the Core Coding Experience
Published: 2026-04-16 10:13
Summary:
The Gemini Code Assist team has introduced a suite of updates focused on streamlining the core coding workflow through high-velocity tools like Agent Mode with Auto Approve and Inline Diff Views These enhancements, along with new features for precise context management and custom commands, aim to transform the AI from a general assistant into a highly tailored, seamless collaborator that adapts to your specific development style.
Meta Engineering
1. Escaping the Fork: How Meta Modernized WebRTC Across 50+ Use Cases
Published: 2026-04-09 16:00
Summary:
At Meta, WebRTC powers real-time audio and video across various platforms But forking a large open-source project like WebRTC within our monorepo presents unique challenges – over time, an internal fork can drift behind upstream, cutting itself off from community upgrades The post Escaping the Fork: How Meta Modernized WebRTC Across 50+ Use Cases appeared first on Engineering at Meta.
2. Trust But Canary: Configuration Safety at Scale
Published: 2026-04-08 18:25
Summary:
On this episode of the Meta Tech Podcast, Pascal Hartig sits down with Ishwari and Joe from Meta’s Configurations team to discuss how Meta makes config rollouts safe at scale Listen in to learn about canarying and progressive rollouts, the health checks […] Read More The post Trust But Canary: Configuration Safety at Scale appeared first on Engineering at Meta.
3. How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines
Published: 2026-04-06 16:00
Summary:
AI coding assistants are powerful but only as good as their understanding of your codebase When we pointed AI agents at one of Meta’s large-scale data processing pipelines – spanning four repositories, three languages, and over 4,100 files – we quickly found that they weren’t making useful edits quickly enough The post How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines appeared first on Engineering at Meta.
Netflix TechBlog
1. Evaluating Netflix Show Synopses with LLM-as-a-Judge
Published: 2026-04-10 16:26
Summary:
Using recent advances in agents, reasoning, and LLM-as-a-Judge, we score four key synopsis quality dimensions, achieving 85%+ agreement with creative writers To improve reasoning accuracy, we leverage two forms of inference-time scaling:Longer rationales: increase the length of the rationale or explanation generated by the LLM prior to producing a final score.Consensus scoring: sample several outputs from the LLM and aggregate their scores to produce the final result.Tiered rationales We correlate synopsis performance (take fraction or abandonment) with LLM quality scores
2. Stop Answering the Same Question Twice: Interval-Aware Caching for Druid at Netflix Scale
Published: 2026-04-06 22:15
Summary:
Older data lingers much longer, because our confidence in its accuracy grows with time.For a 3-hour rolling window, the exponential TTL ensures the vast majority of the query is served from the cache, leaving Druid to only scan the most recent, unsettled data.BucketingIf we were to use a single-level cache key for the query and interval, similar to Druid’s existing result-level cache, we wouldn’t be able to extract only the relevant time range from cached results Without special handling, the cache would treat these empty buckets as gaps and re-query Druid for them every time.We handle this by caching empty sentinel values for time buckets where Druid returned no data Crucially, KVDAL supports independent TTLs on each inner key-value pair, eliminating the need for us to manage cache eviction manually.This two-level structure gives us efficient range queries over the inner keys, which is exactly what we need for partial cache lookups: “give me all cached buckets between time A and time B for query hash X.”ResultsThe biggest win is during high-volume events (e.g., live shows): when many users view the same dashboards, the cache serves most identical queries as full hits, so the query rate reaching Druid is essentially the same with 1 viewer or 100
3. Powering Multimodal Intelligence for Video Search
Published: 2026-04-04 00:44
Summary:
The ultimate challenge lies in harmonizing these heterogeneous data streams to support rich, multi-dimensional queries in real time.Unifying the TimelineTo ensure critical moments aren’t lost across scene boundaries, each model segments the video into overlapping intervals For example, if a model detects a character “Joey” from seconds 2 through 8, the pipeline maps this continuous span of frames into seven distinct one-second buckets.Annotation Intersection: When multiple models generate annotations for the same temporal bucket, such as character recognition “Joey” and scene detection “kitchen” overlapping in second 4, the system fuses them into a single, comprehensive record.Optimized Persistence: These newly enriched records are written back to Cassandra as distinct entities Upon receiving a user request, the system immediately initiates a query preprocessing phase, generating a structured execution plan through three core steps:Query Type Detection: Dynamically categorizes the incoming request to route it down the most efficient retrieval path.Filter Extraction: Isolates specific semantic constraints such as character names, physical objects, or environmental contexts to rapidly narrow the candidate pool.Vector Transformation: Converts raw text into high-dimensional, model-specific embeddings to enable deep, context-aware semantic matching.Once generated, the system compiles this structured plan into a highly optimized Elasticsearch query, executing it directly against the pre-fused temporal buckets to deliver instantaneous, frame-accurate results.Fine-Tuning Semantic SearchTo support the diverse workflows of different production teams, the system provides fine-grained control over search behavior through configurable parameters:Exact vs
Stripe Engineering
1. How agents, digital wallets, and trust are rewriting checkout
URL: https://stripe.com/blog/global-checkout-trends
Published: 2026-04-07 00:00
Summary:
We analyzed checkout activity across more than 20K businesses, surveyed shoppers and ecommerce leaders, and gathered insights from businesses on the Stripe network to understand what’s changing in online conversion.
2. Insights from Shoptalk 2026: How agents are changing retail
URL: https://stripe.com/blog/shoptalk-2026
Published: 2026-04-02 00:00
Summary:
Retailers know search and discovery have already shifted What comes next is less settled From embedded checkout to emerging third-party surfaces, here’s how ecommerce and AI leaders are integrating agentic commerce.
3. How Stripe Radar helps prevent free trial abuse
URL: https://stripe.com/blog/how-stripe-radar-helps-prevent-free-trial-abuse
Published: 2026-03-24 00:00
Summary:
Radar now helps prevent free trial abuse with just one click When enabled, Radar predicts the presence of abusive behavior that violates common trial terms, such as repeated trial signup or missed cancellations, with 90% accuracy.
Generated on 2026-04-16 10:13:39