Dev Systems

Show HN: Some of my tools, may be usable for you

Hey everyone!I’ve developed a few tools that I wanted to share with the community:https://clipush.deno.dev A simple way to send web push notifications using cURL, making the process much easier to integrate into your workflow. (An experiment for personal usage.)https://request-bin.deno.dev A tool that acts as a real-time HTTP request debugger with SSE logging to track and analyze requests efficiently. (Created after requestb.in introduced limits on free accounts. Personally u

Ask HN: Courses for senior(ish) back end / systems dev

Hey HN! The holidays are coming up and I have a $1000 educational budget from the company I can spend. Any recommendations for a senior-ish developer? Ideally in the systems / backend space.On a day to day basis I mainly deal with k8s, databases and distributed systems, so anything related to that would be ideal! But even related fields like linux performance tuning & analysis, PL & coding patterns, compilers & program runtimes, low level kernel & assembly stuff, AI/LM

Show HN: Holos – Configure Kubernetes with CUE data structures instead of YAML

Hi HN! I’m excited to share Holos, a Go command line tool we wrote to fill the configuration management gap in Kubernetes. Holos uses CUE to configure software distributed with Helm and Kustomize using a well defined, type safe language eliminating the need to template YAML. You probably know (or are) someone who has suffered with the complexity of plain text YAML templates and merging multiple values.yaml files together to configure software running in Kubernetes. We built Holos so we don’t

AMX MUSE | Development Workflows

Explore AMX MUSE Automation Platform 'Development Workflows' in this video. AMX MUSE Automation Controllers are powerful, ...

Webinar "Simplifying Oligonucleotide Drug Development Workflows with Mass Spectrometry"

There has been noteworthy progress in the oligonucleotide therapeutics field over the past 30 years, beginning with antisense ...

Deduplicating a 10.4 TiB game preservation archive (WIP)

Hi folks,I am working on a game preservation project, where the data set holds 10.4 TiB. It contains 1044 earlier versions of a single game in a multitude of different languages, architectures and stages of development. As you can guess, that means extreme redundancy.The goals are: - bring the size down - retain good read speed (for further processing/reversing) - easy sharable format - lower end machines can use itMy choice fell on the BTRFS filesystem, since it provides advanced features

Show HN: Rivet Actors – Durable Objects build with Rust, FoundationDB, Isolates

Hello! We posted a Show HN for Rivet last year for our container orchestration project. In that time, a lot has changed that I think HN will find interesting.Rivet is open-source actor infrastructure similar to Cloudflare's Durable Objects. Rivet itself already serves millions of MAU in production using our current container runtime – primarily for multiplayer games – and Rivet Actors are a new extension to support actor-like workloads. Rivet Actor's core primitives are RPC, state, and

The latest AI news we announced in December

<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Dec_AI_Roundup_social_share.max-600x600.format-webp.webp">Here are Google’s latest AI updates from December including Gemini 2.0, GenCast, and Willow.

Show HN: Naja EDA – A Python Package for Easy Electronic Design Automation

We are happy to introduce Naja EDA, a Python package designed to streamline post-synthesis Electronic Design Automation (EDA) workflows. Naja EDA offers a robust framework that enables both software and hardware developers to efficiently navigate and manipulate electronic designs.Key Features:- Comprehensive Netlist Exploration: . Navigate netlist hierarchies with ease. . Access multiple levels of detail, including bit-level and bus-level granularity. . Explore instance-by-instance or uti

How to Avoid Double Payments in Distributed Systems?

How systems make sure that even when you press the payment button twice the money is deducted only once.

Introducing Configurable Metaflow

David J. Berg*, David Casler^, Romain Cledat*, Qian Huang*, Rui Lin*, Nissan Pow*, Nurcan Sonmez*, Shashank Srikanth*, Chaoying Wang*, Regina Wang*, Darin Yu**: Model Development Team, Machine Learning Platform^: Content Demand Modeling TeamA month ago at QConSF, we showcased how Netflix utilizes Metaflow to power a diverse set of ML and AI use cases, managing thousands of unique Metaflow flows. This followed a previous blog on the same topic. Many of these projects are under constant developmen

321 real-world gen AI use cases from the world's leading organizations

<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/gen_ai_use_cases_cropped.max-600x600.format-webp.webp">A snapshot of how top companies, governments, researchers and startups are enhancing their work with Google's AI solutions.

Indexing code at scale with Glean

We’re sharing details about Glean, Meta’s open source system for collecting, deriving and working with facts about source code. In this blog post we’ll talk about why a system like Glean is important, explain the rationale for Glean’s design, and run through some of the ways we’re using Glean to supercharge our developer tooling at Meta. In August 2021 we open-sourced our code indexing system Glean. Glean collects information about source code and provides it to developer tools through an effi

Scaling Pub/Sub with WebSockets and Redis

There is increasing demand for realtime data delivery as users expect faster experiences and instantaneous transactions. This means not only is lower latency required per message, but providers must also handle far greater capacity in a more globally-distributed way.When building realtime applications, the right tools for the job address these requirements at scale. WebSocket has become the dominant protocol for realtime web applications, and has widespread browser support. It offers a persisten

Translating Java to Kotlin at Scale

Meta has been on a years-long undertaking to translate our entire Android codebase from Java to Kotlin. Today, despite having one of the largest Android codebases in the world, we&#8217;re well past the halfway point and still going. We&#8217;re sharing some of the tradeoffs we&#8217;ve made to support automating our transition to Kotlin, seemingly simple transformations that are surprisingly tricky, and how we&#8217;re collaborating with other companies to capture hundreds more corner cases.

Email scams surge over the holiday — here’s how Gmail keeps you safe

<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Gmail_Holiday_Scams.max-600x600.format-webp.webp">Learn how Gmail is using new AI technology to keep inboxes safe over the holidays.

How we think about Threads’ iOS performance

How did the Threads iOS team maintain the app&#8217;s performance during its incredible growth? Here&#8217;s how Meta&#8217;s Threads team thinks about performance, including the key metrics we monitor to keep the app healthy. We&#8217;re also diving into some case studies that impact publish reliability and navigation latency. When Meta launched Threads in 2023, it became the fastest-growing app in history, gaining 100 million users in only five days. The app now has grown to more than 300 mi

Part 1: A Survey of Analytics Engineering Work at Netflix

This article is the first in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. We kick off with a few topics focused on how we’re empowering Netflix to efficiently produce and effectively deliver high quality, actionable analytic insights across the company. Subsequent posts will detail examples of exciting analytic engineering domain applications and aspects of the technical craft.A

Cloud Efficiency at Netflix

By J Han, Pallavi PhadnisContextAt Netflix, we use Amazon Web Services (AWS) for our cloud infrastructure needs, such as compute, storage, and networking to build and run the streaming platform that we love. Our ecosystem enables engineering teams to run applications and services at scale, utilizing a mix of open-source and proprietary solutions. In turn, our self-serve platforms allow teams to create and deploy, sometimes custom, workloads more efficiently. This diverse technological landscape

Title Launch Observability at Netflix Scale

Part 1: Understanding The ChallengesBy: Varun KhaitanWith special thanks to my stunning colleagues: Mallika Rao, Esmir Mesic, Hugo MarquesIntroductionAt Netflix, we manage over a thousand global content launches each month, backed by billions of dollars in annual investment. Ensuring the success and discoverability of each title across our platform is a top priority, as we aim to connect every story with the right audience to delight our members. To achieve this, we are committed to building rob