Total Articles: 20 from 8 sources
AWS News
1. AWS Weekly Roundup: AWS AI/ML Scholars program, Agent Plugin for AWS Serverless, and more (March 30, 2026)
Published: 2026-03-30 16:11
Summary:
Last week, what excited me most was the launch of the 2026 AWS AI & ML Scholars program by Swami Sivasubramanian, VP of AWS Agentic AI, to provide free AI education to up to 100,000 learners worldwide The program has two phases: a Challenge phase where you’ll learn foundational generative AI skills, followed by a […]
2. 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.
3. 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.
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. GitHub for Beginners: Getting started with GitHub security
Published: 2026-03-30 16:00
Summary:
Learn how to secure your projects and keep them safe with GitHub Advanced Security The post GitHub for Beginners: Getting started with GitHub security appeared first on The GitHub Blog.
2. 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.
3. 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.
Google Developers
1. Access public data insights faster: Data Commons MCP is now hosted on Google Cloud
Published: 2026-03-31 10:03
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.
2. Making Gemini CLI extensions easier to use
URL: https://developers.googleblog.com/making-gemini-cli-extensions-easier-to-use/
Published: 2026-03-31 10:03
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.
3. Conductor Update: Introducing Automated Reviews
URL: https://developers.googleblog.com/conductor-update-introducing-automated-reviews/
Published: 2026-03-31 10:03
Summary:
Conductor for the Gemini CLI has introduced a new Automated Review feature designed to verify the quality and accuracy of AI-generated code This update addresses the challenge of validating agentic development by automatically checking implementations against original plans, enforcing style guides, and identifying security risks or bugs. by incorporating test-suite validation and providing actionable reports, Conductor helps developers ensure that their AI agents deliver safe, predictable, and architecturally sound code before it is finalized.
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. AI for American-Produced Cement and Concrete
Published: 2026-03-30 16:00
Summary:
Meta is continuing its long-term roadmap to help the construction industry leverage AI to produce high-quality and more sustainable concrete mixes, as well as those exclusively produced in the United States Concurrent with the 2026 American Concrete Institute (ACI) Spring Convention, Meta is releasing a new AI model for designing concrete mixes – Bayesian Optimization […] Read More The post AI for American-Produced Cement and Concrete appeared first on Engineering at Meta.
2. 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.
3. 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.
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 2026-03-31 10:04:01