Total Articles: 14 from 6 sources
AWS News
1. AWS Weekly Roundup: Amazon EC2 G7e instances, Amazon Corretto updates, and more (January 26, 2026)
Published: 2026-01-26 16:25
Summary:
It’s my first post for 2026, and I’m writing to you while watching our driveway getting dug out I hope wherever you are you are safe and warm and your data is still flowing This week brings exciting news for customers running GPU-intensive workloads, with the launch of our newest graphics and AI inference […]
2. Announcing Amazon EC2 G7e instances accelerated by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs
Published: 2026-01-20 21:22
Summary:
AWS introduces Amazon EC2 G7e instances accelerated by the NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs with up to 2.3 times inference performance G7e instances deliver cost-effective performance for generative AI inference workloads and the highest performance for graphics workloads.
3. AWS Weekly Roundup: Kiro CLI latest features, AWS European Sovereign Cloud, EC2 X8i instances, and more (January 19, 2026)
Published: 2026-01-20 00:24
Summary:
At the end of 2025 I was happy to take a long break to enjoy the incredible summers that the southern hemisphere provides I’m back and writing my first post in 2026 which also happens to be my last post for the AWS News Blog (more on this later) The AWS community is starting the […]
GitHub Blog
1. Power agentic workflows in your terminal with GitHub Copilot CLI
Published: 2026-01-26 18:54
Summary:
Explore the GitHub Copilot CLI and try interacting with Copilot directly from your terminal The post Power agentic workflows in your terminal with GitHub Copilot CLI appeared first on The GitHub Blog.
2. Build an agent into any app with the GitHub Copilot SDK
Published: 2026-01-22 15:01
Summary:
Now in technical preview, the GitHub Copilot SDK can plan, invoke tools, edit files, and run commands as a programmable layer you can use in any application The post Build an agent into any app with the GitHub Copilot SDK appeared first on The GitHub Blog.
3. A cheat sheet to slash commands in GitHub Copilot CLI
URL: https://github.blog/ai-and-ml/github-copilot/a-cheat-sheet-to-slash-commands-in-github-copilot-cli/
Published: 2026-01-21 17:09
Summary:
Run tests, fix code, and get support—right in your workflow Stay focused and let Copilot handle the busywork The post A cheat sheet to slash commands in GitHub Copilot CLI appeared first on The GitHub Blog.
Google Developers
1. Building with Gemini 3 in Jules
URL: https://developers.googleblog.com/jules-gemini-3/
Published: 2026-01-27 09:30
Summary:
Jules, an always-on, multi-step software development agent, now features Gemini 3, offering clearer reasoning and better reliability Upcoming features include directory attachment without GitHub and automatic PR creation Jules aims to reduce software writing overhead so developers can focus on building.
2. Building production AI on Google Cloud TPUs with JAX
URL: https://developers.googleblog.com/building-production-ai-on-google-cloud-tpus-with-jax/
Published: 2026-01-27 09:30
Summary:
The JAX AI Stack is a modular, industrial-grade, end-to-end machine learning platform built on the core JAX library, co-designed with Cloud TPUs It features key components like JAX, Flax, Optax, and Orbax for foundational model development, plus an extended ecosystem for the full ML lifecycle and production This integration provides a powerful, scalable foundation for AI development, delivering significant performance advantages.
3. Build with Google Antigravity, our new agentic development platform
Published: 2026-01-27 09:30
Summary:
Introducing Google Antigravity, a new agentic development platform for orchestrating code It combines an AI-powered Editor View with a Manager Surface to deploy agents that autonomously plan, execute, and verify complex tasks across your editor, terminal, and browser Agents communicate progress via Artifacts (screenshots, recordings) for easy verification
Meta Engineering
1. Adapting the Facebook Reels RecSys AI Model Based on User Feedback
Published: 2026-01-14 20:51
Summary:
We’ve improved personalized video recommendations on Facebook Reels by moving beyond metrics such as likes and watch time and directly leveraging user feedback We’re doubling down on personalization, tackling challenges like sparse user data […] Read More The post Adapting the Facebook Reels RecSys AI Model Based on User Feedback appeared first on Engineering at Meta.
2. CSS at Scale With StyleX
URL: https://engineering.fb.com/2026/01/12/web/css-at-scale-with-stylex/
Published: 2026-01-12 18:34
Summary:
It’s no different at Meta, which is why we open-sourced StyleX, a solution for CSS at scale StyleX combines the ergonomics of CSS-in-JS with the performance of static CSS The post CSS at Scale With StyleX appeared first on Engineering at Meta.
Netflix TechBlog
1. The AI Evolution of Graph Search at Netflix
Published: 2026-01-26 19:01
Summary:
We need the LLM to generate a Graph Search Filter DSL statement that is syntactically, semantically, and pragmatically correct.Syntactic correctness is easy — does it parse For some fields, there is additional information we can provide beyond what’s available in the schema as well, in particular permissible values that pull from controlled vocabularies.Each field in the index is associated with metadata as seen below, and that metadata is provided as part of the context.Graph Search index representationThe field is derived from the document path as characterized by the GraphQL query.The description is the comment from the GraphQL schema for the field.The type is derived from the GraphQL schema for the field e.g This design strategically balances LLM involvement with more deterministic strategies.End-to-end architectureThe end-to-end process is as follows:A user’s natural language question (with optional @mentions statements) are provided as input, along with the Graph Search index contextThe context is scoped by using the RAG pattern on both fields and possible valuesThe pre-processed context and the question are fed into the LLM with an instruction asking for a syntactically and semantically correct filter statementThe generated filer statement DSL is verified and checked for hallucinationsThe final response contains the related AST in order to build “Chips” and “Facets”SummaryBy combining our existing Graph Search infrastructure with the power and flexibility of LLMs, we’ve bridged the gap between complex filter statements and user intent
Stripe Engineering
1. Metronome + Stripe: Building the future of billing
URL: https://stripe.com/blog/metronome-stripe-building-the-future-of-billing
Published: 2026-01-23 00:00
Summary:
Together, Metronome and Stripe are building the most flexible and complete billing solution on the market—one that works for everyone, from engineers in a garage figuring out their business model to public companies monetizing at global scale.
2. The three biggest agentic commerce trends from NRF 2026
URL: https://stripe.com/blog/three-agentic-commerce-trends-nrf-2026
Published: 2026-01-16 00:00
Summary:
Not only are the majority of retailers actively implementing, or have plans to implement, agentic commerce, but many are also moving to a more tactical phase of optimizing their setup—refining their product catalog strategy to launch faster and investing in their own agentic shopping experiences in addition to integrating with third-party agents.
Generated on 2026-01-27 09:30:33