Artigo

Catching Bugs Before They Land: JiTTesting’s Human-on-the-Loop Approach at Full Scale

Over the last year we’ve been sharing our work leveraging large language models to catch bugs on the fly — a novel approach we call Catching Just-in-Time Tests (JiTTests). Now, we’re sharing the results and positive impact we’ve seen with Catching JiTTests deployed into production. Our paper, “Just-in-Time Catching Test Generation at Meta,” fully details [...] Read More... The post Catching Bugs Before They Land: JiTTesting’s Human-on-the-Loop Approach at Full Scale appeared first on Engineering at Meta .

Meta Engineering · EN

Are Harnesses Worth the Cost?

Naresh Jain compares coding agents building the same API service with and without a behavioral harness. A frontier model, GPT-6 Sol, passed every external evaluation either way. But a cost-optimized model, GPT-6 Luna, succeeded in only 3 of 10 unharnessed runs and all 10 with the harness. While adding the harness increased token usage and runtime for Luna, the harnessed Luna runs achieved the same 10-for-10 reliability as GPT-6 Sol while costing 33% less per run ($0.273 vs $0.406). This experiment suggests a behavioral harness might be a practical way to get dependable results from smaller, co

Martin Fowler · EN

The Craft of Practitioner Voice

A couple of months ago Jim Highsmith explained how practitioners writing for each other forms a distinct style of writing. He now adds a section to the article with more detail on the craft of how we write in this style. How the writer shows up in the prose, its four principles, the role of story, how to sensibly use AI, three levels of editing, and clarity of the audience. more…

Martin Fowler · EN

Build your slide template

Building a slide template starts with three foundational choices — colours, fonts and layouts. Sumeet Gayathri Moghe covers the basics of color theory and how to build a reusable palette, the essentials of typography, including serif, sans-serif, handwritten and monospace fonts, plus the finer points of kerning and leading, and why a near-blank layout might serve you better than the prescribed layouts that ship with your slideware. more…

Martin Fowler · EN

When Software Meets Hardware

When software meets hardware, engineering principles remain largely the same, but the sensible defaults often change. Kuldeep Singh describes our experience modernizing a large IoT fleet of battery-powered sensors. Our familiar principles remained important; what changed was how we applied them. These practices emerged through experiments, failures, and operational experience—each challenge helping us replace a reasonable assumption with a more sensible default. more…

Martin Fowler · EN

Fragments: October 21

Gergely Orosz is frustrated at those that entered his explicitly “No AI” essay competition and used AI. He goes on to say: When I start to suspect some part is AI about someone I don’t know: how can I tell how much of it is? And how much time and effort do I want to invest to figure out? I fear most people do not realize this last part: that when it takes time and effort from others to figure out what % is AI, and what % is YOU, then I will simply assume it’s probably 100% AI and 0% you So when producing anything that is meant for other humans, if you use even a little AI, you risk the assumpt

Martin Fowler · EN

Fragments: October 28

Andrej Karpathy sees a widening gap in how people see LLM capabilities. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time. Many of my colleagues are living in a world where LLM capabilities are stunning, but it’s easy to forget that’s not what most people experience. One thing I do is use free-tier LLM chatbots, and I’m still finding that

Martin Fowler · EN

Why are good developers so wary of agentic development?

TL;DR Good developers’ caution about agentic development comes from knowing that built isn’t the same as working in production. I’ve been surprised by how many excellent developers have been hesitant to go all in on coding agents. These are people who usually try new ways of working before anyone else and in many cases invent new tools and practices that support building the application well. So why have we had so much reluctance? The easy (lazy?) answer is that they love writing code, or they’re worried about their jobs. I’m sure that’s true for some people, some of the time. And I know it is

Martin Fowler · EN

Bliki: LLM Voice

Sometimes when I read a post, I get the growing feeling that I'm reading the output of an LLM. I can't necessarily define why, although there are the common tells (it's not the em-dashes, it's the load-bearing tapestries). As I do this my desire to read on atrophies, sometimes to the point of revulsion There are tools out there that claim to be able to tell the difference between LLM-written prose and the old-fashioned biological product. I'm wary of them, partly due skeptism about their accuracy, but also because it rather misses the point. The reaction to LLM-Voice is a human one, that sense

Martin Fowler · EN

Measuring the Impact of Personalized Recommendations

By Kevin Zielnicki, Guy Aridor, Aurélien Bibaut, Allen Tran, Winston Chou, and Nathan Kallus Introduction Personalization is everywhere on the internet. It shapes the products we discover, the stories we read, the music we hear, and the movies and TV shows we watch. At Netflix, personalization has been central to the member experience for decades — from the Netflix Prize to our modern recommender system that helps members navigate a large and constantly changing catalog. But personalization is not only an algorithmic problem, it is also a measurement problem. How do we know how much value pers

Netflix Tech Blog · EN

DataJunction as Netflix’s answer to the missing piece of the modern data stack

by Olek Gorajek , Samuel Redai , Yian Shang Intro Our Netflix journey from data to dashboards, particularly within the space of the experimentation platform, always presented a unique set of challenges. This blog post aims to illuminate these hurdles and explain how an innovative open-source metric platform became instrumental in overcoming some of them. We’ll showcase the critical role that DataJunction played in streamlining our data workflows and enhancing our ability to derive meaningful insights from our vast datasets. Metrics are the foundation of data-driven decision-making, but at many

Netflix Tech Blog · EN

Integrating Netflix’s Foundation Model into Personalization applications

Authors: Divya Gadde , Ko-Jen Hsiao , Dhaval Patel and Moumita Bhattacharya The motivation for building the Netflix Foundation Model for personalization was laid out in this previous blog post. The Netflix homepage is powered by several specialized models and it requires significant time and resources to maintain and continue innovating on these individual models. Therefore, we decided to centralize member preference learning and have one powerful model learned from comprehensive user interaction histories and content data at a large scale and have this model distribute its learnings to other

Netflix Tech Blog · EN

Ontology-Driven Observability: Building an End-to-End Knowledge Graph at Netflix Scale

Prasanna Vijayanathan , Renzo Sanchez-Silva When Jake Paul and Mike Tyson stepped into the ring in November 2024, we delivered the fight to roughly 65 million concurrent streams. On an ordinary day our infrastructure takes in more than 2 billion requests and handles upwards of 38 million real-time logging events every second. At that scale, the whole picture doesn’t fit into a single engineering team’s head or onto a single dashboard. With more Live content, Podcasts and real-time streams, our need to understand and update our systems as quickly as possible becomes imperative to provide a good

Netflix Tech Blog · EN

Single-tenant Titus: VM-level isolation on a multi-tenant container platform

Samartha Chandrashekar & Nick Parker You click play on a Friday night, and in the second that follows, dozens of services answer in milliseconds. Most of them run on Titus, our internal container platform. Titus is, in essence, a Kubernetes-based container hosting platform: we keep both the control plane and the data plane (the container hosts that actually run containers) patched and up to date, so application teams get a consistent and reliable container experience. They hand us a container, and we run it, with no host, cluster, or kernel to own. Titus launches millions of containers every w

Netflix Tech Blog · EN

Beyond API Compatibility: Building a Tool to Measure the Blast Radius of Library Changes

By Roberto Perez Alcolea , Java Platform Team Quasar connects a proposed API change with observed references in consumer repositories, giving the producer evidence for a release decision At Netflix, shared Java libraries support applications developed across a large, independently evolving repository ecosystem. A library change begins in one repository, but its impact can reach many others. Producers can see the API diff in front of them. What they often cannot see is who still depends on the symbols that changed. The need for that visibility grows as coding agents take on more of the software

Netflix Tech Blog · EN

Multi‑Ratio Video Resizing for Mobile Feed and Marketing

Serdar Cellat , Yuchen Xie , Bernadette Marcu , Blaid Burgess , Cody Jarrett , Carlos Pereira , Qing Shi , Samuel Carvajal , Manuel Pamplona , Dorothy Jung , Feiko Ritsema , Benjamin Klein , Alex Woo , Eugene Lok , Anna Pulido , Rebecka Swe , Sanford Holsapple , Ranjith Kumar Shetty Introduction Clips in the Netflix mobile app. At Netflix, a crucial part of our mission is ensuring our content looks stunning on any device. Vertical formats are increasingly important for Netflix, as we have 1.2B followers across our social media accounts, and have recently rolled out Clips , our vertical video f

Netflix Tech Blog · EN

Making Failure Diagnosis Reliable with Pensive AI

By Praneeth Yenugutala , Victor Dusa , Anastasia Samoilova , Megan Ren , Justin Wei , Marcelo Mayworm At Netflix, data and machine learning practitioners run millions of workloads each day across orchestration systems and compute engines such as Titus, Spark, Trino, and Druid. On peak days, roughly 100,000 workloads fail. When workloads fail, the failure itself is only the beginning. Someone still needs to find the useful signal in several logs, decide whether the cause sits in their code or the platform, and determine whether to retry, reconfigure, or escalate. Even a small share of difficult

Netflix Tech Blog · EN

Why I tried to kill token billing (and why we kept it)

Token billing is useful infrastructure, but usually a bad customer-facing pricing model. Your invoice should define the value your product delivers, not break down what it cost you to create it.

Stripe Blog · EN

The “alien” economics of AI monetization

As usage, costs, and customer expectations change, AI companies are moving beyond traditional SaaS pricing. Leaders at Monetize 2026 shared how they’re combining subscriptions with usage-based billing, building spend controls into products, adapting packaging and value metrics, and making pricing readable for both human and AI buyers.

Stripe Blog · EN

Sync real-time Stripe data to Postgres with Stripe Data Pipeline

Applications rely on current Stripe data to fulfill orders, update customer accounts, and manage access to paid features. Until now, developers have had to build and maintain custom integrations to keep application databases in sync with Stripe. We’re introducing real-time sync to Postgres for Stripe Data Pipeline, which lets them keep databases current without having to build custom integrations.

Stripe Blog · EN

New ways for fintechs to move money and expand globally

We’re introducing three updates to help fintechs launch products and grow internationally more easily: simpler payout management with Stripe Global Payouts, support for stablecoin cards in more countries, and unified APIs for building global financial products.

Stripe Blog · EN

Stripe survey: Four trends reshaping how SaaS platforms compete in the AI era

AI is creating more SaaS competition, and platforms are feeling the pressure. A Stripe survey of 200 platform leaders shows how they plan to evolve pricing, grow revenue, and retain customers.

Stripe Blog · EN

Trends from Money20/20 2026: The rise of “hidden” financial services

As fintech products become increasingly capable, the infrastructure behind them fades into the background. Three trends from Money20/20 show how stablecoins, agents, and always-on money are changing financial services.

Stripe Blog · EN

How the fastest-growing enterprises on Stripe scale globally

Despite slower global economic growth and greater geopolitical fragmentation, the fastest-growing enterprises continue to look beyond their home markets and are investing in tools and workflows that make expansion faster and more repeatable.

Stripe Blog · EN