Reading room.

Introducing computer use in Gemini 3.5 Flash

When the model has never seen your code

This is the fifth article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can t control in the agent stack, how to…

When the model has never seen your code

This is the fifth article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can t control in the agent stack, how to…

Bringing more control over your connectors

Talos: Scaling rare disease diagnosis with automated, iterative genomic reanalysis

Talos was built to help resolve a major bottleneck in genomic medicine: human review time. The open-source system recovered 90% of in-scope diagnoses while surfacing just 1.3 candidate variants per patient for expert rev…

Automated Schema Evolution in Pinterest’s Next-Generation DB Ingestion Framework

Yisheng Zhou | Software Engineer II Liang Mou | Sr Staff Software Engineer Gabriel Raphael Garcia Montoya | Staff Software Engineer Istvan Podor | Staff Software Engineer Introduction In the first post of this series , w…

How Meta Engineered Ultra-Narrow Batteries for AI Glasses

Smart glasses like the Ray-Ban Meta and Oakley Meta Vanguards need to pack enough energy to power features like cameras, speakers, AI workloads, and even a display. But it all has to fit into the glasses’ temple arms. So…

Mistral OCR 4 : SOTA OCR for Document Intelligence

OCR 4

PACT: Anonymous Credentials for the Web

This is the technical companion to our update on Distilled, “Keeping the web open and private in the bot era.” Here we take a deeper look at the problem space, the design we re proposing, and the problems still left to s…

Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix

By Zhuoning Yuan , Ta-Ying Cheng , Benjamin Klein , Bahareh Azarnoush Introduction At Netflix, we build technology to help storytellers bring their creative visions to life and to help members discover the stories they l…

How Agentforce Prevents Language Drift in 600K Daily Multilingual AI Workflows

In our Engineering Energizers Q A series, we highlight the engineering minds driving innovation across Salesforce. Today, we spotlight Ishween Kaur, Senior Software Engineer on the Agentforce Agentic Reasoning team. As A…

Four travel and hospitality trends from HITEC 2026

More than 6,000 hospitality executives and operators gathered in San Antonio last week for the HITEC conference. The big topic: whether the industry’s AI investment is actually working. Across four days and over 50 meeti…

AWS Weekly Roundup: NY Summit recap, Local Zone in Hanoi, Grok 4.3 in Bedrock, price reductions, and more (June 22, 2026)

Last week AWS Summit New York City brought together thousands of customers, partners, and builders for a free, one-day event showcasing the latest in cloud and AI innovation. Dr. Swami Sivasubramanian, VP of Agentic AI a…

Scaling out Distroless adoption With AI

Distroless adoption at Grab Grab is migrating from heavy base images to Distroless images to reduce security risks. By limiting each container to the application and its runtime dependencies, we shed non-essential binari…

Adopting AV1 for Real-Time Communication (RTC) at Scale

Adopting AV1 for real-time communication at Meta has been a multi-year effort spanning codec selection, device eligibility, rate control, and error resilience. We’re sharing the technical and operational challenges while…

Models don’t have preferences, they have context

You open a fresh chat, type What framework should I use for a web app? , and the model says React. You screenshot it, share it, and write Claude prefers React. It gets engagement. People nod along. A few reply with their…

Models don’t have preferences, they have context

You open a fresh chat, type What framework should I use for a web app? , and the model says React. You screenshot it, share it, and write Claude prefers React. It gets engagement. People nod along. A few reply with their…

How Netflix Simplified Batch Compute with Kueue

By Alvin Bao , Alex Petrov , Jennifer Lai , Aidan Sherr , and Samartha Chandrashekar As a part of the journey to transition Netflix’s compute infrastructure to be more Kubernetes-native, we have leaned into incorporating…

Palana (Part 2): Architecting isolation, identity, and auditability for AI agents

Introduction In Part 1 , we introduced Palana , Grab’s Kubernetes-native secure execution platform for autonomous AI agents. We discussed the underlying need for isolated environments and covered its core design principl…

Palana (Part 1): Why Grab built a secure platform for autonomous AI Agents

Abstract Artificial intelligence (AI) agents are moving from experiments into everyday engineering workflows. They can read code, call application programming interfaces (APIs), run tests, create merge requests, answer S…

A Human-Augmenting Agentic Workflow for Causal Inference

By Winston Chou, Adrien Alexandre, Lars Olds, Yi Zhang, Garrett Hagemann, and Nathan Kallus Introduction Imagine asking a data agent to analyze the causal relationship between two variables, such as the effect of watchin…

Data Projects: Managing Data Assets at Netflix Scale

By Amer Hesson , Marcelo Mayworm , James Mulcahy , and Brittany Truong The Problem: Managing Assets at Netflix Scale Netflix’s Data Platform is vast. We have millions of tables in our data warehouse and tens of thousands…

From Silos to Service Topology: Why Netflix Built a Real-Time Service Map

By Parth Jain , Rakesh Sukumar , Yingwu Zhao , Renzo Sanchez Nathan Fisher How we built a living map of our distributed infrastructure to help engineers understand dependencies, troubleshoot faster, and keep Netflix runn…

Predicting Risk in Content Launches: How Data-Driven Insights can Transform Launch Planning

by Emily Gill Each year, we bring the Analytics Engineering community together for an Analytics Summit — a multi-day internal conference to share analytical deliverables across Netflix, discuss analytic practice, and bui…

The Data Canary: How Netflix Validates Catalog Metadata

By Celina Amados At Netflix, our catalog metadata is crucial to our member experience, and a single corrupted data state can impact millions of viewers immediately. To protect streaming reliability, we built an automated…

The Evolution of Cassandra Data Movement at Netflix

By Guil Pires , Jennifer Prince , Jose Camacho , Ken Kurzweil , Phanindra Chunduru Background In a previous post, we introduced Data Bridge , a unified management plane for batch Data Movement at Netflix. Historically, s…

Thinking Fast & Slow for a Personalized Notification System

by Matthew Wood , Ishan Gupta , Kevin Mercurio, Devon Bryant , and Claire Dorman In his seminal book “Thinking, Fast and Slow,” Daniel Kahneman describes two systems that drive human cognition: System 1, which operates a…

VMAF v1: Good Is Not Good Enough

By Christos G. Bampis , Zhi Li , Kyle Swanson, Nil Fons Miret and Pavan Madhusudanarao Will this encode look good to Netflix members? Does switching to a new codec improve quality at the same bitrate and by how much? Wha…

A Human-Augmenting Agentic Workflow for Causal Inference

By Winston Chou, Adrien Alexandre, Lars Olds, Yi Zhang, Garrett Hagemann, and Nathan Kallus Introduction Imagine asking a data agent to analyze the causal relationship between two variables, such as the effect of watchin…

Data Projects: Managing Data Assets at Netflix Scale

By Amer Hesson , Marcelo Mayworm , James Mulcahy , and Brittany Truong The Problem: Managing Assets at Netflix Scale Netflix’s Data Platform is vast. We have millions of tables in our data warehouse and tens of thousands…

From Silos to Service Topology: Why Netflix Built a Real-Time Service Map

By Parth Jain , Rakesh Sukumar , Yingwu Zhao , Renzo Sanchez-Silva Nathan Fisher How we built a living map of our distributed infrastructure to help engineers understand dependencies, troubleshoot faster, and keep Netfli…

Predicting Risk in Content Launches: How Data-Driven Insights can Transform Launch Planning

by Emily Gill Each year, we bring the Analytics Engineering community together for an Analytics Summit — a multi-day internal conference to share analytical deliverables across Netflix, discuss analytic practice, and bui…

The Data Canary: How Netflix Validates Catalog Metadata

By Celina Amados At Netflix, our catalog metadata is crucial to our member experience, and a single corrupted data state can impact millions of viewers immediately. To protect streaming reliability, we built an automated…

The Evolution of Cassandra Data Movement at Netflix

By Guil Pires , Jennifer Prince , Jose Camacho , Ken Kurzweil , Phanindra Chunduru Background In a previous post, we introduced Data Bridge , a unified management plane for batch Data Movement at Netflix. Historically, s…

Thinking Fast & Slow for a Personalized Notification System

by Matthew Wood , Ishan Gupta , Kevin Mercurio, Devon Bryant , and Claire Dorman In his seminal book “Thinking, Fast and Slow,” Daniel Kahneman describes two systems that drive human cognition: System 1, which operates a…

VMAF v1: Good Is Not Good Enough

By Christos G. Bampis , Zhi Li , Kyle Swanson, Nil Fons Miret and Pavan Madhusudanarao Will this encode look good to Netflix members? Does switching to a new codec improve quality at the same bitrate and by how much? Wha…

How to Manage Your Discord Desktop Notifications: A Complete Guide

Not getting pinged for the conversations you wanna know about? This guide walks through server and channel notifications settings, Do Not Disturb, turning off specific notification sounds, and what to check when your pin…

Stop overloading your skills

You built a skill for your technology. API references, authentication flows, SDK patterns, error handling, version info, all packed into one skill. The agent calls it, gets all that context, and generates code. The kicke…

Frontier AI LLMs, assistants, agents, services

What Link data tells us about AI spending

We analyzed spending patterns across the 250 million customers paying with Link. We found that Link customers are spending more on AI than they were three months prior, investing heavily in platforms that let them build…

When your agent extensions fight each other

This is the fourth article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can t control in the agent stack, how to…

Maintaining Code Quality at Agent Speed: 7 Patterns for Agentic Engineering

By Amit Sharma and Antonio Garrote.How do you know whether code generated at agent speed can be trusted? That question is fast becoming one of the most important in software engineering and it deserves a serious answer.…

Securing the future of AI agents

Securing internal systems with an AI Control Roadmap, combining traditional safeguards and real-time monitoring.

Unlocking UK house-building with AI-accelerated planning

UK government partners with Google DeepMind to build a new AI-powered prototype aimed at faster housing decisions.

Competing against yourself

You shipped a new CLI: better developer experience, modern architecture, and optimized for agents. You deprecated the old one, updated the docs, and blogged about it. Developers are migrating. Then someone asks an AI cod…

The Agent Coding Maturity Curve: 9 Stages from Code Generation to Trusted Automation

Most developers begin with the same rush of excitement: the agent writes code, fixes bugs, explains unfamiliar systems, generates tests, and turns vague intent into something that looks runnable. For a moment, it feels l…

We have moved to https://developer.paypal.com/community/blog

The PayPal Developer Blog has a new home. You’ll continue to find the latest developer news, tutorials, product updates, and engineering insights here: https://developer.paypal.com/community/blog Thanks for being part of…

How Data 360 Segmentation Processes a Quadrillion Records Across Arbitrary Customer Data Models

In our Engineering Energizers Q A series, we highlight the engineering minds driving innovation across Salesforce. Today, we spotlight Deepak Pushpakar, Software Engineering Architect for Segmentation and Activation with…

How Dropbox uses MCP and Dash to close the design-to-code security gap

Using an agentic AI system to surface threat models during code review and spot gaps between security requirements and implementation.