Total Articles: 20 from 8 sources
AWS News
1. 20 years in the AWS Cloud – how time flies!
URL: https://aws.amazon.com/blogs/aws/20-years-in-the-aws-cloud-how-time-flies/
Published: 2026-03-19 13:35
Summary:
Celebrating twenty years of innovation in ML and AI technology at AWS Countless developers—myself included—have embraced cloud computing and actively used its capabilities to accomplish what was previously impossible.
2. Our First 2026 AWS Heroes Cohort Is Here!
URL: https://aws.amazon.com/blogs/aws/our-first-2026-heroes-cohort-is-here/
Published: 2026-03-18 16:26
Summary:
We’re thrilled to celebrate three exceptional developer community leaders as AWS Heroes These individuals represent the heart of what makes the AWS community so vibrant In addition to sharing technical knowledge, they build connections, forge genuine human relationships, and create pathways for others to grow
3. AWS Weekly Roundup: Amazon S3 turns 20, Amazon Route 53 Global Resolver general availability, and more (March 16, 2026)
Published: 2026-03-16 16:02
Summary:
Twenty years ago this past week, Amazon S3 launched publicly on March 14, 2006 While Amazon Simple Storage Service is often considered the foundational storage service that defined cloud infrastructure, what began as a simple object storage service has grown into something far larger in scope and scale As of March 2026, S3 stores more […]
Dropbox Engineering
1. 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. Rethinking open source mentorship in the AI era
URL: https://github.blog/open-source/maintainers/rethinking-open-source-mentorship-in-the-ai-era/
Published: 2026-03-19 18:00
Summary:
As contribution volume grows, mentorship signals are harder to read The 3 Cs framework helps maintainers mentor more strategically… without burning out The post Rethinking open source mentorship in the AI era appeared first on The GitHub Blog.
2. How Squad runs coordinated AI agents inside your repository
Published: 2026-03-19 16:09
Summary:
An inside look at repository-native orchestration with GitHub Copilot and the design patterns behind multi-agent workflows that stay inspectable, predictable, and collaborative The post How Squad runs coordinated AI agents inside your repository appeared first on The GitHub Blog.
3. Investing in the people shaping open source and securing the future together
Published: 2026-03-17 16:00
Summary:
See how GitHub is investing in open source security funding maintainers, partnering with Alpha-Omega, and expanding access to help reduce burden and strengthen software supply chains The post Investing in the people shaping open source and securing the future together appeared first on The GitHub Blog.
Google Developers
1. Tailor Gemini CLI to your workflow with hooks
URL: https://developers.googleblog.com/tailor-gemini-cli-to-your-workflow-with-hooks/
Published: 2026-03-21 09:29
Summary:
New Gemini CLI hooks (v0.26.0+) let you tailor the agentic loop Add context, enforce policies, and block secrets with custom scripts that run at predefined points in your workflow.
2. Beyond the Chatbot: A Blueprint for Trustable AI
URL: https://developers.googleblog.com/beyond-the-chatbot-a-blueprint-for-trustable-ai/
Published: 2026-03-21 09:29
Summary:
At Thunderhill Raceway Park, a team of Google Developer Experts (GDEs) put a new “Trustable AI Framework” to the test Here is how they used GCP, Gemini and Antigravity to turn high-velocity racing into a masterclass for agentic architecture.
3. Easy FunctionGemma finetuning with Tunix on Google TPUs
URL: https://developers.googleblog.com/easy-functiongemma-finetuning-with-tunix-on-google-tpus/
Published: 2026-03-21 09:29
Summary:
Finetuning the FunctionGemma model is made fast and easy using the lightweight JAX-based Tunix library on Google TPUs, a process demonstrated here using LoRA for supervised finetuning This approach delivers significant accuracy improvements with high TPU efficiency, culminating in a model ready for deployment.
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
3. Mount Mayhem at Netflix: Scaling Containers on Modern CPUs
Published: 2026-02-28 22:55
Summary:
Examining the mount table made it clear that these mounts were related to container creation.The affected nodes were almost all r5.metal instances, and were starting applications whose container image contained many layers (50+).ChallengeMount Lock ContentionThe flamegraph in Figure 1 clearly shows where containerd spent its time Almost all of the time is spent trying to grab a kernel-level lock as part of the various mount-related activities when assembling the container’s root filesystem!Figure 1: Flamegraph depicting lock contentionLooking closer, containerd executes the following calls for each layer if using user namespaces:open_tree() to get a reference to the layer / directorymount_setattr() to set the idmap to match the container’s user range, shifting the ownership so this container can access the filesmove_mount() to create a bind mount on the host with this new idmap appliedThese bind mounts are owned by the container’s user range and are then used as the lowerdirs to create the overlayfs-based rootfs for the container The kernel VFS has various global locks related to the mount table, and each of these mounts requires taking that lock as we can see in the top of the flamegraph
Stripe Engineering
1. 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.
2. 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.
3. Introducing the Machine Payments Protocol
URL: https://stripe.com/blog/machine-payments-protocol
Published: 2026-03-18 00:00
Summary:
We’re launching the Machine Payments Protocol (MPP), an open standard, internet-native way for agents to pay—co-authored by Tempo and Stripe Businesses on Stripe can accept payments over MPP in a few lines of code using our PaymentIntents API.
Generated on 2026-03-21 09:29:19