Dev Systems - Building Systems That Scale

Vector search on our codebase transformed our SDLC automation

Hey HN,In software development, the process of turning a user story into detailed documentation and actionable tasks is critical. However, this manual process can often be a source of inconsistency and a significant time investment. I was driven to see if I could streamline and elevate it.I know this is a hot space, with big players like GitHub and Atlassian building integrated AI, and startups offering specialized platforms. My goal wasn't to compete with them, but to see what was possible

Show HN: Velda – Run any command directly on cloud compute

Hi HN, I’m Chuan from Velda (https://velda.io). We’re simplifying the access to compute like GPUs.To get instant access to a GPU for interactive debugging or rapid prototyping, many ML teams rely on expensive, always-on dev machines—either a powerful local workstation or a cloud VM with a dedicated GPU. This setup is great for speed, but it's costly and the GPU often sits idle.The usual alternative is a slow remote ML workflow, where you wait 10 minutes for a docker build/pus

Show HN: Blocks – Dream work apps and AI agents in minutes

Hi HN,We just launched Blocks - an AI platform where anyone can build custom work apps and AI agents in minutes, no coding required.We’re seeing a shift in how software is built and used. With AI, software isn’t just about tracking processes anymore - it can actively help people get work done.The problem is that most teams still face 2 options: either buy off-the-shelf tools that never quite fit, or invest a lot of time and money into custom development. Neither works well.Blocks is the third op

Show HN: Kooder – AI software engineer for full-stack development

Hi HN! I built Kooder to bridge the gap between ideas and working code. It's an AI software engineer that can generate complete full-stack applications from natural language descriptions.Key features: - Generates production-ready code (frontend + backend) - Understands your entire codebase context - Debugs and fixes errors automatically - Converts Figma designs to code - Supports multiple tech stacks: React, Node.js, TypeScript, Python (Django, Flask, FastAPI)Kooder handles entire features

Show HN: Archil's one-click infinite, S3-backed local disks now available

Hey everyone, I’m Hunter, the founder of Archil. Archil is transforming object storage, like Amazon S3, into infinite, local file systems that provide instant access to massive data sets.Last year, we launched Archil’s NFS-based product publicly on Hacker News (https://news.ycombinator.com/item?id=42174204), and we were absolutely thrilled to see the response of this community.Since our last launch, we took a 10 month company-wide bet to build our own, custom storage protocol to d

Show HN: OrderlyID – typed, time-sortable, 160-bit IDs with checksums

I've been working on OrderlyID, a new identifier format for distributed systems.It's like UUID/ULID/TypeID, but with a few twists:- Typed: every ID has a human-readable prefix (order_xxx, user_xxx).- K-sortable: lexicographic order ≈ creation time.- Structured fields: 160-bit body includes time, tenant, shard, sequence, random.- Checksums: optional 4-char integrity check to catch copy/paste errors.- Privacy flag: can bucket timestamps for public-facing IDs.Format:<pre

Show HN: Helios, an open-source distributed AI network using idle community GPUs

Hi Hacker News, I'm the creator of Helios, and I'm excited (and a bit nervous) to share it with you all. The "Why": Like many of you, I've been fascinated by the power of modern AI models, but frustrated by the high cost and centralization of GPU resources. I started wondering if we could apply the old-school distributed computing model (like SETI@home or Folding@home) to the modern AI stack. The goal was to build a network where anyone could contribute their idle comput

Necessary tool? Async LoRA for distributed systems

I’ve been building something I call Async LoRA to scratch an itch I kept running into: training on cheap GPUs (Salad, runpod, spot instances, etc.) is a nightmare for long jobs. One random node dying and suddenly hours of training are gone. Most schedulers just restart the whole container, which doesn’t really help. What I’ve put together so far:• Aggregator/worker setup where the aggregator hands out small “leases” of work (per token sizes not time slices)• Async checkpointing so pro

Ask HN: What Comes After AI?

I don't want to start a flamewar, but some are suggesting we've already reached peak AI and are headed for the "trough of dis-illusionment." Since our industry seems to run on hype, what's the next technology to get on the hype-train? Seems I shouldn't start an AI startup right now since half the industry pundits say that ship has sailed.Ideally it would be something that would sell NVidia chips (I don't want my NVDA shares losing their value) and suck down l

AI-Powered Migration & Modernization—Secure, Resilient, and Ready for Innovation

Introduction Accelerate your migration and modernization journey with Azure Essentials, Azure Migrate, and GitHub Copilot—empowering your organization to join the ranks of Frontier Firms: businesses that put AI and cloud innovation at the core of their strategy. Supported by expert guidance and investments through Azure Accelerate, every step is secure, resilient, and ready for the future of agentic applications. Join the Migrate and Modernize Summit, where Microsoft will show new

The Real-World Impact of SSD Choice on Development Workflow (With Benchmarks)

Last year, my engineering manager approved a seemingly modest budget request to upgrade the SSDs in our development team’s laptops. What…

20 Tools for Building and Testing APIs Like a Pro

Supercharge your API development workflow with these must-have tools for design, testing, debugging, and monitoring.

The Swift 6.3 Update Every iOS Developer Needs to Watch Out For

Revolutionary concurrency improvements and Apple Intelligence integration that will transform your development workflow

8 top must-use tools to boost your web development workflow

As developers, before we deploy our applications or even before we choose our cloud provider, we should consider which tools we use for our…

Speed up your Python development workflow in 5 minutes with pre-commit and Makefile

Automating dbt Development Workflows with Pre-Commit

Using automation to shift your focus from nitpicking to solution design

10 Docker Commands You’ll Use Every Day

Master these essential Docker commands to speed up your development workflow and handle containers like a pro.

Authentication and authorization in a microservice architecture - Part 4 - fetching and replicating authorization data

This article is the fourth in a series of articles about authentication and authorization in a microservice architecture.The complete series is: Overview of authentication and authorization in a microservice architecture Implementing authentication Implementing JWT-based authorization Implementing authorization using fetch and replicate Implementing complex authorization using Oso Cloud - part 1 Implementing complex authorization using Oso Cloud - part 2

Selecting the Right Agentic Solution on Azure

Recently, we have seen a surge in requests from customers and Microsoft partners seeking guidance on building and deploying agentic solutions at various scales. With the rise of Generative AI, replacing traditional APIs with agents has become increasingly popular.There are several approaches to building, deploying, running, and orchestrating agents on Azure. In this discussion, I will focus exclusively on Azure-specific tools, services, and methodologies, setting aside Copilot and Copilot Studio

The Art of Synchronization in Distributed Systems

Introduction