Total Articles: 18 from 6 sources
AWS News
1. AWS Security Hub Extended offers full-stack enterprise security with curated partner solutions
Published: 2026-02-26 18:52
Summary:
AWS announces the general availability of AWS Security Hub Extended, a unified, full-stack enterprise security solution It brings together AWS detection services and curated partner solutions through a single, simplified experience.
2. Transform live video for mobile audiences with AWS Elemental Inference
Published: 2026-02-24 18:55
Summary:
AWS Elemental Inference is a fully managed AI service that automatically transforms live and on-demand video broadcasts into vertical formats optimized for mobile and social platforms in real time, enabling broadcasters to reach audiences on TikTok, Instagram Reels, and YouTube Shorts without manual editing or AI expertise.
3. AWS Weekly Roundup: Claude Sonnet 4.6 in Amazon Bedrock, Kiro in GovCloud Regions, new Agent Plugins, and more (February 23, 2026)
Published: 2026-02-23 16:56
Summary:
Last week, my team met many developers at Developer Week in San Jose My colleague, Vinicius Senger delivered a great keynote about renascent software—a new way of building and evolving applications where humans and AI collaborate as co-developers using Kiro Other colleagues, Du’An Lightfoot, Elizabeth Fuentes, Laura Salinas, and Sandhya Subramani spoke about building and […]
Dropbox Engineering
1. Using LLMs to amplify human labeling and improve Dash search relevance
URL: https://dropbox.tech/machine-learning/llm-human-labeling-improving-search-relevance-dropbox-dash
Published: 2026-02-26 17:00
Summary:
How we train Dash’s search ranking models with a mix of human and LLM-assisted labeling.
2. How low-bit inference enables efficient AI
URL: https://dropbox.tech/machine-learning/how-low-bit-inference-enables-efficient-ai
Published: 2026-02-12 18:00
Summary:
Making products like Dropbox Dash accessible to individuals and businesses means tackling new challenges around efficiency and resource use.
3. Insights from our executive roundtable on AI and engineering productivity
Published: 2026-02-11 17:00
Summary:
From Claude Code to Cursor, we’re big adopters of AI coding tools at Dropbox The early results have been promising, but there are still a lot of open questions about how to work with these tools most effectively and where they can have the most impact To push this conversation forward, we hosted an executive roundtable at our San Francisco studio
GitHub Blog
1. What’s new with GitHub Copilot coding agent
URL: https://github.blog/ai-and-ml/github-copilot/whats-new-with-github-copilot-coding-agent/
Published: 2026-02-26 20:47
Summary:
GitHub Copilot coding agent now includes a model picker, self-review, built-in security scanning, custom agents, and CLI handoff Here’s what’s new and how to use it The post What’s new with GitHub Copilot coding agent appeared first on The GitHub Blog.
2. Multi-agent workflows often fail. Here’s how to engineer ones that don’t.
Published: 2026-02-24 16:00
Summary:
Most multi-agent workflow failures come down to missing structure, not model capability Learn the three engineering patterns that make agent systems reliable The post Multi-agent workflows often fail
3. How AI is reshaping developer choice (and Octoverse data proves it)
Published: 2026-02-19 17:00
Summary:
AI is rewiring developer preferences through convenience loops Octoverse 2025 reveals how AI compatibility is becoming the new standard for technology choice The post How AI is reshaping developer choice (and Octoverse data proves it) appeared first on The GitHub Blog.
Google Developers
1. Introducing A2UI: An open project for agent-driven interfaces
URL: https://developers.googleblog.com/introducing-a2ui-an-open-project-for-agent-driven-interfaces/
Published: 2026-02-27 09:38
Summary:
A2UI is an open-source project for agent-driven, cross-platform, and generative UI It provides a secure, declarative data format for agents to compose bespoke interfaces from a trusted component catalog, allowing for native styling and incremental updates Designed for the multi-agent mesh (A2A), it offers a framework-agnostic solution to safely render remote agent UIs, with integrations in AG UI, Flutter’s GenUI SDK, Opal, and Gemini Enterprise.
2. Developer’s guide to multi-agent patterns in ADK
URL: https://developers.googleblog.com/developers-guide-to-multi-agent-patterns-in-adk/
Published: 2026-02-27 09:38
Summary:
Learn how to build modular and reliable agentic applications using 8 effective multi-agent design patterns with the Agent Development Kit (ADK).
3. Introducing Agent Development Kit for TypeScript: Build AI Agents with the Power of a Code-First Approach
Published: 2026-02-27 09:38
Summary:
Introducing the Agent Development Kit (ADK) for TypeScript, an open-source framework for building complex, multi-agent AI systems with a code-first approach Developers can define agent logic in TypeScript, applying traditional software development best practices (version control, testing) ADK offers end-to-end type safety, modularity, and deployment-agnostic functionality, leveraging the familiar TypeScript/JavaScript ecosystem.
Meta Engineering
1. RCCLX: Innovating GPU Communications on AMD Platforms
Published: 2026-02-24 21:30
Summary:
We are open-sourcing the initial version of RCCLX – an enhanced version of RCCL that we developed and tested on Meta’s internal workloads RCCLX is fully integrated with Torchcomms and aims to empower researchers and developers to accelerate innovation, regardless of their chosen backend The post RCCLX: Innovating GPU Communications on AMD Platforms appeared first on Engineering at Meta.
2. The Death of Traditional Testing: Agentic Development Broke a 50-Year-Old Field, JiTTesting Can Revive It
Published: 2026-02-11 17:00
Summary:
WHAT IT IS The rise of agentic software development means code is being written, reviewed, and shipped faster than ever before across the entire industry Faster development demands faster testing that can catch bugs as they land in a codebase, without […] Read More The post The Death of Traditional Testing: Agentic Development Broke a 50-Year-Old Field, JiTTesting Can Revive It appeared first on Engineering at Meta.
3. Building Prometheus: How Backend Aggregation Enables Gigawatt-Scale AI Clusters
Published: 2026-02-09 17:00
Summary:
We’re sharing details of the role backend aggregation (BAG) plays in building Meta’s gigawatt-scale AI clusters like Prometheus Once it’s complete our AI […] Read More The post Building Prometheus: How Backend Aggregation Enables Gigawatt-Scale AI Clusters appeared first on Engineering at Meta.
Netflix TechBlog
1. MediaFM: The Multimodal AI Foundation for Media Understanding at Netflix
Published: 2026-02-23 18:24
Summary:
Consisting of tens of millions of individual shots across multiple titles, our diverse yet entertainment-specific dataset provides the perfect foundation to train multimodal media understanding models that enable many capabilities across the company such as ads relevancy, clip popularity prediction, and clip tagging.For these reasons, we developed the Netflix Media Foundational Model (MediaFM), our new, in-house, multimodal content embedding model For each shot, we generate three distinct embeddings from its core modalities:Video: an internal model called SeqCLIP (a CLIP-style model fine-tuned on video retrieval datasets) is used to embed frames sampled at uniform intervals from segmented shotsAudio: the audio samples from the same shots are embedded using Meta FAIR’s wav2vec2Timed Text: OpenAI’s text-embedding-3-large model is used to encode the corresponding timed text (e.g., closed captions, audio descriptions, or subtitles) for each shotFor each shot, the three embeddings² are concatenated and unit-normed to form a single 2304-dimensional fused embedding vector This avoids the architectural fragility of fine-tuning, allowing us to enhance our existing embedding-based workflows with new modalities more flexibly.All of our data has audio and video; we zero-pad for missing timed text data, which is relatively likely to occur (e.g., in shots without dialogue).The title-level tasks couldn’t be evaluated with the VertexAI MM and Marengo embedding models as the videos exceed the length limit set by the APIs.AcknowledgementsWe would like to thank Matt Thanabalan and Chaitanya Ekanadham for their contributions to this work.MediaFM: The Multimodal AI Foundation for Media Understanding at Netflix was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.
2. Scaling LLM Post-Training at Netflix
Published: 2026-02-13 08:05
Summary:
This blog describes the architecture and engineering philosophy of our internal Post-Training Framework, built by the AI Platform team to hide infrastructure complexity so researchers and model developers can focus on model innovation — not distributed systems plumbing.A Model Developer’s Post-Training JourneyPost-training often starts deceptively simply: curate proprietary domain data, load an open-weight model from Hugging Face, and iterate batches through it In particular, we extend Single Program, Multiple Data (SPMD) style SFT workloads to run online RL with a hybrid single-controller + SPMD execution model, which we’ll describe next.Today, this framework supports research use cases ranging from post-training large-scale foundation models to fine-tuning specialized expert models Rather than training directly on transformers model classes, we maintain our own optimized, unified model definitions that can still load/save Hugging Face checkpoints
3. Automating RDS Postgres to Aurora Postgres Migration
Published: 2026-02-12 14:07
Summary:
At this stage, the Aurora cluster is created and attached to the RDS PostgreSQL primary as a replica, establishing continuous replication from the source RDS PostgreSQL instance The cluster is fully operational for validation and performance testing, but it is not yet writable — RDS remains the authoritative primary.Quiescence PhaseThe goal of the quiescence phase is to transition client applications from the source RDS PostgreSQL instance to the Aurora PostgreSQL cluster as the new primary database, while preserving data consistency during cutover.The first step in this process is to stop all write traffic to the source RDS PostgreSQL instance to guarantee consistency In addition, any logical replication slots removed during the migration must be recreated so that CDC consumers can continue processing changes from the source database.Once connectivity and replication slots are restored, the RDS PostgreSQL instance can safely resume its role as the primary source of truth.Post-quiescence Rolling back after cutover, once the Aurora PostgreSQL cluster is serving production traffic, is significantly more complex
Generated on 2026-02-27 09:38:09