Dev Systems - Building Systems That Scale

In-House LLM Serving at Netflix

By AI Platform’s Model Runtime team and Inference teamIntroductionMost organizations consume LLMs through hosted APIs. Netflix went further — we run the full stack ourselves, from model deployment through inference, inside our existing production environment rather than a separate ML silo. Some of those decisions weren’t obvious, and a few revealed their trade-offs only under production load.This post focuses on the choices where alternatives were seriously considered: engine selection, model pa

Eclipse Dataspace Components on AWS: Cost optimization strategies

When you deploy Eclipse Dataspace Components (EDC) connectors on AWS, one of the first challenges you face is predicting and controlling the cost of the required infrastructure. Without clear benchmarks, it is difficult to make informed decisions about workload sizing, environment configuration, and long-term investment. Part 1 of this 3-part blog series covered the fundamentals of data space architectures and the EDC per the International Data Space Association’s (IDSA) standards. Part 2 explor

Eclipse Dataspace Components on AWS: Architecture patterns in production

Running Eclipse Dataspace Components (EDC) connectors in production on AWS requires deliberate architecture decisions around isolation, managed services, and security layering. In Part 1 of this series, we covered the fundamentals of data space architectures and EDC per the International Data Space Association’s (IDSA) standards. If you are new to EDC, we recommend starting there. We showed how connector functionality can be customized to support native integration with Amazon Web Services (AWS)

Eclipse Dataspace Components on AWS: Data sharing fundamentals

This three-part blog series guides you through implementing Eclipse Dataspace Components (EDC) on AWS, from foundational concept to production deployment. Part 1 establishes the theoretical foundation with IDSA standards, the Dataspace Protocol (DSP), and core EDC architecture. Part 2 provides production-ready AWS deployment patterns using services like Amazon Elastic Container Service (Amazon ECS), Amazon Aurora, and Amazon API Gateway. Part 3 completes the journey with cost optimization strate

Hypervelocity Engineering: Accelerating Enterprise AI with Azure AI Landing Zones

Artificial Intelligence is evolving at unprecedented speed. The challenge for enterprises is no longer building AI solutions—it is engineering AI platforms that can adapt, scale, and govern innovation continuously. Hypervelocity Engineering (HVE) provides the engineering operating model that enables this transformation.Executive SummaryArtificial Intelligence is transforming how enterprises design and operate digital platforms. Traditional Enterprise Architecture practices—built around static do

Migrating a Claude Code skill from WebFetch to the new CircleCI CLI: lessons learned about tool and API design

One of the Claude Code skills in my idea-to-code plugin is debugging-ci-failures.It tells Claude Code how to watch and diagnose a failing CI build.Specifically, it instructs Claude Code to do the following five steps: Watch the build to completion Identify the failing job Fetch the failing step’s output Download the test artifacts Read the JUnit TEST-*.xml files to find the root causeThe skill supports two CI systems: GitHub Actions (via the gh CLI) and CircleCI.Until today, the CircleCI su

Ask HN: What Are You Building with AI?

Are you building with AI for yourself, your employer, customers, or as a startup?What LLMs, coding agents, or development tools are you using? Are you primarily prompt coding, vibe coding, or using a more structured agentic workflow?Are you using one agent or coordinating multiple agents? How much of the generated code do you review or rewrite?Has AI allowed you to build something you could not have built previously?What have you learned that would help someone starting an AI-assisted coding p

Ask HN: What are some of the use-cases of the frontier models's max mode?

Recently ChatGPT released an Ultra mode, it's "highest-capability setting, coordinating multiple agents across parallel workstreams to finish complex tasks faster" on their latest flagship product Sol of GPT-5.6. Similarly, Claude Fable also has an Ultra setting.I wonder what are primary workflows or use-cases this maximised modes are used for currently? In my average software development experience, I never had a need to go beyond the max mode or beyond Opus for example. Even in

Show HN: OtoDock, run Claude Code and Codex as a team of agents on your server

Hi HN, i am Dimitris,I have been using Claude Code and Codex agents, for some time now from the beggining i had been using them from inside my terminal mainly for coding.For the past 3 years i kept building so i have a homelab and a business server, which already has a lot of vms, so a lot of things to manage.So i decided to start building my own ideal version of using claude code, and codex flexibly and connect them easily with all my vms and infra but at the same time keep using them for codin

Create, edit and star in videos with two Google Vids updates

<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/omni-blog-header_OarEe2t.max-600x600.format-webp.webp">Gemini Omni and personal avatars in Google Vids make video creation easier than ever.

Prioritize your AWS Health alerts using AWS User Notifications

If you run critical workloads on AWS, such as a contact center on Amazon Connect Customer, database workloads on Amazon Relational Database Service (Amazon RDS), or hybrid connectivity through AWS Direct Connect, service health events demand your attention. But not all events are equal. An operational issue, a scheduled maintenance window, and a deprecation notice buried in your inbox have very different consequences. The problem is that they all arrive through the same channel, making their urg

Show HN: FixBugs – Reproduce production bugs and verify fixes

I built FixBugs, an agent that ingests the rich context surrounding production bugs to reproduce them in a sandbox and generate verified fixes. It's available in the form of a self-hosted VSCode extension and as a Github app:VSCode Extension: https://fixbugs.ai/go/vscode-extension - full code and data privacy. - zero data retention models opted out of training. GitHub App: https://fixbugs.ai/go/github-app - we do access your code temporarily.

Show HN: I implemented a neural network in SQL

Two weeks ago I was on my babymoon in Corfu, Greece. While in transit, I was overseeing a GSoC intern submit an important feature to my array database library, Xarray-SQL. He added `to_dataset()`, which completed the roundtrip between thinking of array data in a tabular model simultaneously as gridded rasters (the premise of the project is that every Nd array can be mapped to 2d, where orthogonal dims of the Nd array are just primary keys of a tabular representation). We discussed in chat, now t

From Policy to Proof: Governing AI to Scale Human Ambition and Machine Intelligence

Enterprises are moving from AI experiments to AI in production. Copilots are in the flow of daily work, custom applications are built on foundation models, and autonomous agents are beginning to take actions on behalf of the business. As organizations combine human ambition with machine intelligence, trust becomes the defining requirement for scaling AI responsibly and realizing AI's full potential. That shift changes the governance question from "can we use AI responsibly?" 

Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization

Hierarchical Interest Representation is a research area for Meta Ads. We’re exploring an upstream representation layer over the universe of Ads entities – users, advertisers, products, services – learning unified embeddings that connect users’ inferred interests with the breadth of what advertisers offer in their deep funnel ads.The innovations in Hierarchical Interest Representation are an in-house transformer based graph learning with bias-aware attention and self-supervised cross-view d

How bitdrift scaled to 121 million concurrent gRPC connections on Amazon CloudFront for live telemetry sporting events

When 121 million mobile devices establish persistent gRPC connections to your origin infrastructure within seconds of a live broadcast, the routing policy behind your DNS records matters far more than it does at normal traffic levels. The wrong policy can concentrate all your connections onto a single origin endpoint, turning a scaling success into an outage. bitdrift, a mobile observability platform founded by former Lyft infrastructure engineers, learned this firsthand while delivering real-ti

Show HN: Itara – Distributed system topology as an explicit, executable layer

Hi HN, I'm Gábor, a software engineer from Budapest.I spent almost a decade designing, building and maintaining distributed systems, and I came to truly understand why a lot of people define software architecture as "the stuff that's hard to change later". Changing service boundaries, communication protocol or serializers is time consuming and risky. The past few months, I've been building Itara, my attempt to ease that pain.Itara takes the software topology that's

Show HN: Cascade Chat – A Hackable IRCv3 Client for macOS, Windows, and Linux

Hello HN! I'm Matt and today I'd like to show you Cascade Chat.One of my earliest internet experiences was with mIRC. I always admired its straightforward, pleasant UI and the way it wove a hackable core into the code. The way you could build on the visual and API layers of the underlying IRC client to me was fascinating software machinery. It was truly a client that you could build on top of.As my career has progressed, I moved away from Windows and adopted Linux as my daily driver. T

Ask HN: Resources for non-web software architecture?

I'm looking for resources (primarily books, papers, and YT videos) on software architecture. I'm NOT interested in Monolith vs Microservices type of material -- but rather material applicable in general, or systems programming in particular.

Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned

By Parth Jain, Rakesh Sukumar, Yingwu Zhao, Renzo Sanchez-Silva & Nathan FisherA deep dive into the engineering challenges of building a real-time service dependency map at Netflix scale: from streaming architectures and distributed aggregation pipelines to time-travel queries and the methodology that made it work.IntroductionIn our first post, we introduced the problem: engineers at Netflix needed a unified, real-time view of service dependencies to troubleshoot faster, understand blast rad