Dev Systems - Building Systems That Scale

Building multi-Region resiliency for AWS CloudFormation custom resource deployment

AWS CloudFormation is the foundational tool of infrastructure-as-code for thousands of organizations running workloads on AWS. But as teams push the boundaries of what CloudFormation can do natively, custom resources have emerged as a powerful extension mechanism that unlocks a broad range of possibilities. Yet, when it comes to building resilient, multi-Region deployments with custom resources, customers quickly discover a gap: there is no built-in multi-Region support. In this post, we will ex

Architecting offline-first generative AI applications for edge deployments using AWS services

According to Siemens’ 2024 report The True Cost of Downtime, Fortune 500 companies lose an estimated $1.4 trillion annually because of unplanned downtime. This downtime is often worsened by a lack of skills to detect and resolve issues quickly. Generative AI offers a promising path to address this, but deploying these capabilities in industrial environments introduces a distinct architectural challenge: how do you bring the power of large-scale AI to locations where cloud connectivity is unrelia

Automate custom PII detection at scale with Amazon Macie and Step Functions

Organizations in regulated industries like financial services, insurance, healthcare, and government ingest large volumes of data containing personally identifiable information (PII). Your applications, claims processing systems, partner data feeds, and internal workflows produce files that may include names, addresses, Social Security numbers, and domain-specific identifiers such as policy numbers, member IDs, and medical record numbers. Identifying and classifying this sensitive data is a comp

Does Temporal Workflow Streams replace a durable session layer?

If you're running a customer-facing AI agent on Temporal, you've probably seen this: a user refreshes the tab mid-response. When the page comes back, the response resumes, but anything they were about to send back (an interruption, a tool approval) is gone. Temporal keeps the workflow itself crash-proof, but getting its output to a browser, and taking anything back from the browser into the workflow, has always meant building it yourself. The usual pattern is a Redis pub/sub relay (Redis ac

Show HN: Scaffold a raw idea into something ship-ready in under a minute

Several months ago, I joined a WhatsApp community with around 500 people. Once a community becomes large enough, you start noticing certain patterns.Whenever someone announced a new product idea, the responses were usually the same:“That sounds amazing.”“I would definitely use it.”“Great idea.”“Can’t wait to try it.”I do not think people were intentionally being dishonest. Most were simply being kind. Nobody wants to be the person who discourages someone before they have even started.But that is

Show HN: Meltbox – where your agents send you briefs

Easy to try even without an account! Would love all feedback.Hey all, I'm building Meltbox as a better human in the loop system. It's a way to centralize all of the decisions you need to make into context-rich briefs in a system built for fast human review.[Why]Something major changed recently. Agents have gotten even better and even faster at building apps / sites / dashboards / etc... and that's changed my development workflow in a major way.I often have my agents

Coding Skills Development Report

BLOG: https://juntz-g1thub.github.io/#post=coding-skills-dev REPO: https://github.com/juntz-g1thub/CodingSkills.gitBackground Recently I've been experimenting with vibe coding. I'm using OpenCode as my development tool. When developing TUI programs, AI-assisted code modification proved extremely difficult — every small bug fix required repeated manual testing, and feeding those results back to the agent was cumbersome. Especially for TUI-level feedbac

Introducing tfgen: configure your Terraform stacks using plain Go

Until recently, we extensively used HashiCorp's CDK for Terraform. Then they announced the end of its development. We didn't want to spend months migrating to a new ecosystem, but we needed a replacement.In the Infrastructure Team at Ably, we like Go. I wondered if there was an opportunity for something simpler.Provisioning infrastructure at AblyWe provision all our infrastructure for the core Pub/Sub platform where possible using Infrastructure as Code, initially using Terraform and then making

Show HN: The0 – self-hosted runtime for trading bots, bring your own language

So Im a solo developer who has always had an itch for algorithmic trading. Initially I started off learning how to trade algorithmically with Yves Hilpisch book "Python for Algorithmic Trading" after reading that book I was hooked and started building algorithmic trading bots. Initially these were separate python scripts that I ran on my local machine. That was a bad idea cause local machines are not reliable and I wanted to run my bots 24/7 365. In my actual career im a software

Show HN: Go Micro – An agent harness and service framework in Go

Hi all,I wanted to reshare this after some time. I've been working on evolving my service framework into an agent harness. As all of this AI tooling has evolved its become clear to me that agents, services and workflows are part of a core system of operations which would benefit from a single framework. Previously Go Micro only enabled service development. But now it includes Agents and Workflows.What does that mean? Well firstly services which are registered in a central registry automatic

LiveObjects comes to Java: navigating shared state with the path API

Shared state is a hard problem in the unglamorous sense: the concepts are well understood, but someone still has to build the conflict resolution, the reconnection handling, and the state recovery before anyone can ship the feature that depends on it. That's what LiveObjects is for, and the path-based model that reshaped the API in JavaScript is now available to Java developers too. Point at a location, not an objectHold a reference to a specific LiveMap or LiveCounter instance, and that referen

Q&A about my Explore DDD designing microservices workshop

I’m teaching an updated version of my workshop - Designing microservices: responsibilities, APIs and collaborations - at the excellent Explore DDD conference that’s happening this September in Denver.Here’s a Q&A that explores why this workshop is especially relevant in the age of GenAI, the common mistakes teams make when designing and developing microservices, and how the workshop equips attendees with a practical, repeatable approach to building microservice architectures that enable fast

Show HN: My Fable 5 Project Was a Multiraft Database and Blob Store

Linked from this post is the visualizer and simulator I had Fable 5 create to demonstrate theIn a thread the other day, I asserted it was nice that code is no longer a moat. Getting Raft or Paxos consensus working is one of the most challenging tasks out there, even which etcd.io/raft and hashicorp/raft.The database took about a day for Fable 5 to create. It was updated as tons of work was done on a sandbox app running on top of it.What it delivered was a distributed unified KV and blo

Show HN: SirixDB 1.0 Beta – Git-Like Versioning, Diffs, Time-Travel Queries

Hi HN! I've posted SirixDB here before, back in 2019 (https://news.ycombinator.com/item?id=19834681) and again in 2023 (https://news.ycombinator.com/item?id=38252963).The core idea behind SirixDB is, that history is a first-class citizen. Every commit stores a lightweight, queryable revision. You can query any point in time, even individual nodes (for instance JSON values), diff arbitrary revisions, and efficiently track how data evolved without replaying event

The Missed Reality: Code Review Wasn't Built for the AI Era

The Bottleneck Isn't Writing Code Anymore. It's Trusting It.The speed at which AI generates code has officially surpassed our capacity to review it. For decades, the bottleneck in software engineering was writing the code. Today, the bottleneck has shifted entirely to trusting that the code can run effectively in production and actually solve the business problem.But let's be honest: humans never truly scaled code review anyway.Throughout my career, I have observed developers appr

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