Total Articles: 20 from 7 sources
AWS News
1. Introducing Anthropic’s Claude Opus 4.7 model in Amazon Bedrock
URL: https://aws.amazon.com/blogs/aws/introducing-anthropics-claude-opus-4-7-model-in-amazon-bedrock/
Published: 2026-04-16 14:49
Summary:
AWS launches Claude Opus 4.7 in Amazon Bedrock, Anthropic’s most intelligent Opus model for advancing performance across coding, long-running agents, and professional work Claude Opus 4.7 is powered by Amazon Bedrock’s next generation inference engine, purpose-built for generative AI inferencing and fine-tuning workloads.
2. 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 […]
3. 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 […]
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. How GitHub uses eBPF to improve deployment safety
URL: https://github.blog/engineering/infrastructure/how-github-uses-ebpf-to-improve-deployment-safety/
Published: 2026-04-16 16:00
Summary:
Learn how Github uses eBPF to detect and prevent circular dependencies in its deployment tooling The post How GitHub uses eBPF to improve deployment safety appeared first on The GitHub Blog.
2. 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.
3. 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.
Google Developers
1. Introducing Finish Changes and Outlines, now available in Gemini Code Assist extensions on IntelliJ and VS Code
Published: 2026-04-17 10:12
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.
2. Unleash Your Development Superpowers: Refining the Core Coding Experience
Published: 2026-04-17 10:12
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.
3. Plan mode is now available in Gemini CLI
URL: https://developers.googleblog.com/plan-mode-now-available-in-gemini-cli/
Published: 2026-04-17 10:12
Summary:
Gemini CLI now features Plan Mode, a read-only environment that allows the AI to analyze complex codebases and map out architectural changes without the risk of accidental execution By leveraging the new ask_user tool and expanded Model Context Protocol (MCP) support, developers can collaboratively refine strategies and pull in external data before committing to implementation.
Meta Engineering
1. Capacity Efficiency at Meta: How Unified AI Agents Optimize Performance at Hyperscale
Published: 2026-04-16 16:00
Summary:
We’re sharing insights into Meta’s Capacity Efficiency Program, where we’ve built an AI agent platform that helps automate finding and fixing performance issues throughout our infrastructure By leveraging encoded domain expertise across a unified, standardized tool interface these agents help save power and free up engineers’ time away from addressing performance issues to innovating on […] Read More The post Capacity Efficiency at Meta: How Unified AI Agents Optimize Performance at Hyperscale appeared first on Engineering at Meta.
2. Post-Quantum Cryptography Migration at Meta: Framework, Lessons, and Takeaways
Published: 2026-04-16 14:59
Summary:
We’re sharing lessons learned from Meta’s post-quantum cryptography (PQC) migration to help other organizations strengthen their resilience as industry transitions to post-quantum cryptography standards We’re proposing the idea of PQC Migration Levels to help teams within organizations manage the complexity of PQC migration for their various use cases The post Post-Quantum Cryptography Migration at Meta: Framework, Lessons, and Takeaways appeared first on Engineering at Meta.
3. 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.
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 2026-04-17 10:12:13