Total Articles: 20 from 8 sources
AWS News
1. Customize your AWS Management Console experience with visual settings including account color, region and service visibility
Published: 2026-03-26 21:34
Summary:
AWS introduces visual customization capability in AWS Management Console that enables selective display of relevant AWS Regions and services for your team members By hiding unused Regions and services, you can reduce cognitive load and eliminate unnecessary clicks and scrolling, helping you focus better and work faster.
2. Announcing Amazon Aurora PostgreSQL serverless database creation in seconds
Published: 2026-03-25 20:37
Summary:
AWS introduces a new express configuration for Amazon Aurora PostgreSQL, a streamlined database creation experience with preconfigured defaults designed to help you get started in seconds With Aurora PostgreSQL, start building quickly from the RDS Console or your preferred developer tool—with the ability to modify configurations anytime Plus, Aurora PostgreSQL is now available with AWS Free Tier.
3. AWS Weekly Roundup: NVIDIA Nemotron 3 Super on Amazon Bedrock, Nova Forge SDK, Amazon Corretto 26, and more (March 23, 2026)
Published: 2026-03-23 16:40
Summary:
I’m Daniel Abib, and this is my first AWS Weekly Roundup I’m a Senior Specialist Solutions Architect at AWS, focused on the generative AI and Amazon Bedrock With over 28 years of experience in solution architecture, software development, and cloud architecture, I help Startups & Enterprises harness the power of generative AI with Amazon […]
Dropbox Engineering
1. 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.
2. How we optimized Dash’s relevance judge with DSPy
URL: https://dropbox.tech/machine-learning/optimizing-dropbox-dash-relevance-judge-with-dspy
Published: 2026-03-17 17:00
Summary:
We used DSPy to turn prompt engineering for our relevance judge into a measurable, automated optimization loop, improving task performance, cost, and how reliably it works in production.
GitHub Blog
1. What’s coming to our GitHub Actions 2026 security roadmap
Published: 2026-03-26 16:49
Summary:
A look at GitHub Actions’ 2026 roadmap, outlining how secure defaults, policy controls, and CI/CD observability harden the software supply chain end to end The post What’s coming to our GitHub Actions 2026 security roadmap appeared first on The GitHub Blog.
2. A year of open source vulnerability trends: CVEs, advisories, and malware
Published: 2026-03-26 16:00
Summary:
Reviewed advisories hit a four-year low, malware advisories surged, and CNA publishing grew—here’s what changed and what it means for your triage and response The post A year of open source vulnerability trends: CVEs, advisories, and malware appeared first on The GitHub Blog.
3. Updates to GitHub Copilot interaction data usage policy
Published: 2026-03-25 19:02
Summary:
From April 24 onward, interaction data—specifically inputs, outputs, code snippets, and associated context—from Copilot Free, Pro, and Pro+ users will be used to train and improve our AI models unless they opt out The post Updates to GitHub Copilot interaction data usage policy appeared first on The GitHub Blog.
Google Developers
1. Introducing the Developer Knowledge API and MCP Server
URL: https://developers.googleblog.com/introducing-the-developer-knowledge-api-and-mcp-server/
Published: 2026-03-30 10:14
Summary:
Google is launching the Developer Knowledge API and MCP Server in public preview This new toolset provides a canonical, machine-readable way for AI assistants and agentic platforms to search and retrieve up-to-date documentation across Firebase, Google Cloud, Android, and more By using the official MCP server, developers can connect tools directly to Google’s documentation corpus, ensuring that AI-generated code and guidance are based on authoritative, real-time context.
2. Access public data insights faster: Data Commons MCP is now hosted on Google Cloud
Published: 2026-03-30 10:14
Summary:
Data Commons has launched a free, hosted Model Context Protocol (MCP) service on Google Cloud Platform, eliminating the need for users to manage complex local server installations This update simplifies connecting AI agents and the Gemini CLI to Data Commons, allowing Google to handle security, updates, and resource management while users query data natively.
3. Making Gemini CLI extensions easier to use
URL: https://developers.googleblog.com/making-gemini-cli-extensions-easier-to-use/
Published: 2026-03-30 10:14
Summary:
To simplify the user experience and prevent startup failures, the Gemini CLI has introduced structured extension settings that eliminate the need for manual environment variable configuration This update enables extensions to automatically prompt users for required details during installation and securely stores sensitive information, such as API keys, directly in the system keychain Users can now easily manage and override these configurations globally or per project using the new Gemini extensions config command.
LinkedIn Engineering
1. Feed blog posts
URL: https://www.linkedin.com/blog/engineering/feed
Published: 2026-03-12 00:00
Summary:
Feed blog postsFeedEngineering the next generation of LinkedIn’s FeedHristo DanchevMar 12, 2026FeedPutting members first: testing and measuring how content appea…Sakshi JainNov 20, 2025InfrastructureFishDB: a generic retrieval engine for scaling LinkedIn’s feedKenneth LiNov 17, 2025Java heap memory and garbage collection: tuning for high-perfo…Nisheedh RaveendranSep 13, 2024Generative AIHow LinkedIn Built the Engineering Infrastructure to Ignite Pr…Shweta PatiraNov 20, 2023FeedHomepage feed multi-task learning using TensorFlowIan AckermanJun 3, 2021Member/Customer ExperienceHelping members discover communities around interestsChiachi LoSep 17, 2020FeedUnderstanding dwell time to improve LinkedIn feed rankingSiddharth DangiMay 12, 2020FeedRapid experimentation through standardization: Typed AI featur…Ian AckermanApr 15, 2020The Top 2019 LinkedIn Engineering BlogsJaren AndersonDec 9, 2019OptimizationAuditing content features in FollowFeedBanu MuthukumarAug 27, 2019FeedCommunity-focused Feed optimizationJun 25, 2019Previous123Next
Meta Engineering
1. Friend Bubbles: Enhancing Social Discovery on Facebook Reels
Published: 2026-03-18 18:19
Summary:
Friend bubbles in Facebook Reels highlight Reels your friends have liked or reacted to, helping you discover new content and making it easier to connect over shared interests This article explains the technical architecture behind friend bubbles, including how machine learning estimates relationship strength and ranks content your friends have interacted with to create more […] Read More The post Friend Bubbles: Enhancing Social Discovery on Facebook Reels appeared first on Engineering at Meta.
2. Ranking Engineer Agent (REA): The Autonomous AI Agent Accelerating Meta’s Ads Ranking Innovation
Published: 2026-03-17 20:07
Summary:
Meta’s Ranking Engineer Agent (REA) autonomously executes key steps across the end-to-end machine learning (ML) lifecycle for ads ranking models This post covers REA’s ML experimentation capabilities: autonomously generating hypotheses, launching training jobs, debugging failures, and iterating on results The post Ranking Engineer Agent (REA): The Autonomous AI Agent Accelerating Meta’s Ads Ranking Innovation appeared first on Engineering at Meta.
3. Patch Me If You Can: AI Codemods for Secure-by-Default Android Apps
Published: 2026-03-13 16:00
Summary:
Even seemingly simple engineering tasks — like updating an API — can become monumental undertakings when you’re dealing with millions of lines of code and thousands of engineers, especially if the changes are security-related Nowhere is this more apparent than in mobile security, where a single class of vulnerability can be replicated across hundreds of […] Read More The post Patch Me If You Can: AI Codemods for Secure-by-Default Android Apps appeared first on Engineering at Meta.
Netflix TechBlog
1. Scaling Global Storytelling: Modernizing Localization Analytics at Netflix
Published: 2026-03-06 15:01
Summary:
However, this growth created technical debt within our systems: a fragmented landscape of analytics workflows, duplicated pipelines, and siloed dashboards that we are now actively modernizing.The Challenge: “Who Made This Dub?”Historically, business logic for localization metrics was replicated across isolated domains To fix this, we revamped our Language Asset Consumption tool — instead of reporting dub and subtitle metrics independently, we combine audio and text languages into one consumption language that helps differentiate Original Language versus Localized Consumption and measure member preferences between subtitles, dubs, or a combination of both for a given language By centralizing business logic into unified tables — such as a “Language Asset Producer” table — we solve the “Who made this dub?” problem once
2. Optimizing Recommendation Systems with JDK’s Vector API
Published: 2026-03-03 01:36
Summary:
When we looked at CPU profiles for this service, one feature kept standing out: video serendipity scoring — the logic that answers a simple question:“How different is this new title from what you’ve been watching so far?”This single feature was consuming about 7.5% of total CPU on each node running the service More crucially for us, it’s pure Java: no native dependencies, no JNI transitions, and a development model that looks like normal Java code rather than platform-specific assembly or intrinsics.This was a particularly good match for our workload because we had already moved embeddings into flat, contiguous double[] buffers, and the hot loop was dominated by large numbers of dot products So we designed the fallback behavior explicitly: At startup, we detect Vector API support and use the SIMD batched matmul when available; otherwise we fall back to an optimized scalar path, with single-video requests continuing to use the per-item implementation.That gives us a clean operational story: services can opt in to the Vector API for maximum performance, but the system remains safe and predictable without it.Results in Production:With the full design in place with batching, flat buffers, ThreadLocal reuse, and the Vector API, we ran canaries that run production traffic
Stripe Engineering
1. 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.
2. Three of the biggest fraud trends from MRC Vegas 2026
URL: https://stripe.com/blog/three-fraud-trends-from-mrc-vegas-2026
Published: 2026-03-20 00:00
Summary:
The most sophisticated fraud teams are shifting from one-size-fits-all fraud approaches to more dynamic, tailored interventions They are removing friction for trusted users, embedding fraud detection directly into agentic transactions, and investing in multilayered identity verification to combat deepfakes.
3. Testing the impact of Adaptive Pricing across 1.5M subscription checkout sessions
URL: https://stripe.com/blog/adaptive-pricing-for-subscriptions
Published: 2026-03-19 00:00
Summary:
Adaptive Pricing is now available for subscriptions, allowing businesses to automatically localize prices in 150+ countries while Stripe handles currency conversion In an A/B test across 1.5 million subscription checkouts, businesses saw 4.7% higher conversion and 5.4% higher LTV per session, on average.
Generated on 2026-03-30 10:14:54