Dev Systems
Why we built a dedicated SDK for realtime AI streaming
If you've built a conversational AI feature, you know the pattern. Client sends a message, backend calls a model, response streams back over HTTP. SSE mostly, or WebSockets if you need bidirectional. For a single user on a single device, it works well.The trouble is the best AI products right now have moved well past that.Users of the products setting the pace today can interrupt an agent mid-response, pick up a conversation on their phone where they left it on their laptop, collaborate with a c
Show HN: Loom – A Markdown knowledge graph for better coding-agent execution
Hi HN, I built Loom because I wanted less agent tooling, not more.My coding-agent workflow had outgrown PLAN.md. One file kept turning into the partial spec, research log, task queue, evidence log, review notes, handoff summary, and feature doc. And stratifying it typically ends up in disparate scratch files with no canonicity.One solution is to add more surfaces: a spec tool, an issue tool, a memory system, a review prompt, a planning plugin, a workflow package. But that brings two problems: Th
Show HN: VisuaLeaf – A Modern MongoDB Workspace
Visualeaf is a MongoDB GUI I’ve been building over the past year. Stack is Electron + Angular + Spring Boot. There’s a live playground on the site if you want to try it without installing or putting in your connection (I provided one).The goal was to combine a visual workflow with the depth needed for real development work. Most existing MongoDB tools tend to optimize for either beginners or power users, but not both in the same interface.Core features:Query builder that supports full MongoDB qu
Show HN: A Multi User Multi Task Board MCP Server
I built a simple multi user, multi board, Task/Kanban MCP server. I have been looking for something like this to manage development agents, but I wasn't seeing anything that felt like what I wanted. So I set down and decided to vibe code an alternative.While it was an experiment at first I have been using it daily for my personal development projects and I really think there are others who might be looking for exactly this. It's 100% a WIP, but it is also very usable.I have a demo
Ask HN: How to think in terms of parallel Claude agents
Everytime I have a task, mainly adding features or refactoring something, updating UI etc, I can think of one task, and give it to Claude, and get it done, and after the development, I spend time on reviewing, refining and removing clutter and unwanted artifacts generated by the model.Now I can't imagine how are people using like 20 parallel Claude instances, I can't think of 2 on the same project. What am I missing? Do I need to take a step back and relearn how I think about projects.
Running Diffusion Models at Scale on AKS
Diffusion workloads are simple at prototype scale and unforgiving in production. A single demo can run on one GPU-backed VM, but a real platform has to handle bursty demand, long-running jobs, model artifact distribution, secure public access, rollout safety, and hardware-level observability.Azure Kubernetes Service (AKS) is a strong fit when the requirement is not just to run a model, but to operate a repeatable platform for GPU inference. The reusable pattern is straightforward: keep the API a
Show HN: iClaw is part OpenClaw, part Siri, powered by Apple Intelligence
Hi HN,Last month at a SundAI hackathon, my team built a prototype for an app called iClaw. The goal was to develop an AI agent using Apple Intelligence. I've since continued hacking away at this idea when I had time, and now I'm releasing it on https://geticlaw.com with code on GitHub.Apple Intelligence is a really poor model choice for an AI agent. It's not great at extracting information (it often injects preambles like "Here's the page..."), it struggle
Transforming Video Content into Structured SOPs Using Graph-based RAG
IntroductionIn today’s digital-first environments, a large portion of enterprise knowledge lives inside video content, training sessions, onboarding walkthroughs, and recorded operational procedures.While videos are great for learning, they are not ideal for quick reference, compliance, or repeatable processes. Converting that knowledge into structured documentation like Standard Operating Procedures (SOPs) is often manual and time-consuming.What if this process could be automated using AI?The P
Flexible Cooling for AI Growth: How Zonal Architecture Supports Diverse Hardware Needs
By: Ricardo Bianchini, Steve Solomon, Brijesh Warrier, Martin Herbert, Jay Jochim, Husam Alissa, Pulkit Misra, Eric Peterson and Cam TurnerContext - Microsoft is pioneering zonal cooling in its next-generation AI datacenters, enabling flexible, performant, efficient, and sustainable thermal management for diverse workloads.The unprecedented growth of artificial intelligence (AI) is transforming datacenter infrastructure. Modern facilities must now support a diverse array of IT equipment, ea
Why production AI needs a session layer, not just a stream
What AI Engineer Europe revealed about where production AI is headingBy Mike ChristensenI spoke at AI Engineer Europe last week, and came away with a clearer picture of where the industry actually is right now.My talk was about why AI user experience breaks at the transport layer. But the bigger takeaway wasn't from my own session. It was from watching what the rest of the room was building, and what problems they were running into.What stood out to meOver the past year or two, most AI engineeri
Deloitte optimizes EKS environment provisioning and achieves 89% faster testing environments using Amazon EKS and vCluster
Managing multiple Amazon Elastic Kubernetes Service (Amazon EKS) clusters for development and testing environments can present significant operational and cost challenges for enterprises. Deloitte, a global professional services organization, faced these challenges while provisioning dedicated Amazon EKS clusters for their quality assurance (QA) testing environments. In this post, we explore how Deloitte used Amazon EKS and vCluster to transform their testing infrastructure. Business challenges
Join the new AI Agents Vibe Coding Course from Google and Kaggle
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Vibe_Coding_Course_herosocial.max-600x600.format-webp.webp">Google is bringing back its 5-Day AI Agents Intensive Course with Kaggle and registration is open.
Show HN: PrivateClaw – AI agents running in confidential VMs you can verify
We built PrivateClaw because the hosted OpenClaw platforms on the market today require you to trust them with plaintext. PrivateClaw removes that requirement at the hardware layer.PrivateClaw runs AI agents inside Trusted Execution Environments (TEEs), backed by AMD’s SEV-SNP standard. This means that your data is encrypted at the hardware level, enforced by the AMD Secure Processor outside the host OS trust boundary.PrivateClaw comes with inference that also runs inside TEEs, which means your p
Show HN:I built a deterministic 10k-node VRP solver on a $100 phone
A few years ago, I was a delivery driver in Bangkok. I saw firsthand how inefficient algorithms stressed out drivers. At that time, I didn't even know what "NP-hard" meant—I just knew the system could be better. So, I started building.The Journey of an Outsider:
I have no CS background. I hold a vocational diploma in Goldsmithing from 20 years ago. Before this, I was unemployed and had no PC. My only tool was a $100 Android smartphone (3,000 THB).I spent 16 hours a day architectin
Show HN: Kdts, an optimization-first TypeScript compiler
kdts is an optimization-first TypeScript compiler. Instead of erasing types as early as possible, it uses them throughout the compilation to direct optimizations, achieving transformations that would not have been possible were the types not known.Currently it is Bun-only. You can install it withbun add -g @kimlikdao/kdtsIt has two modes: fast and opt.- fast mode is a thin wrapper around bun build but supports the same command line arguments as opt mode.- opt mode uses (a fork of) Google Cl
Show HN: I blind-tested 14 LLMs on a WP plugin task. Surprising Findings
Recently, GitHub Copilot silently dropped support for Claude Opus on Pro accounts. Since Opus was my go-to model for my daily workflow (developing WordPress plugins), I needed a reliable replacement.I decided to run a rigorous, blind benchmark across 14 state-of-the-art and local LLMs to objectively measure which model understands WordPress development best. To ensure a perfectly fair test, I started with a completely fresh IDE and zero context for every single generation.I asked each model to b
Show HN: 12ui – Image to Code
An image is worth a few lines of code ;)I have been trying to get something like this working in one form or another for close to a year. The latest image models finally make it possible.It's still early, but I'm really interested in this approach to AI UI - using image models before coding models for front end development. The challenges are;
1 - How do we get image models to generate decent UI
2 - How do we convert those images into code
But I think I've got something which tack
The Consensus Algorithm
We say systems are “distributed”, but rarely ask how they stay consistent. What happens when nodes disagree, fail, or lag behind?<p>This post breaks down consensus using Raft into a simple mental model.
https://gokuljs.com/blogs/raft-consensus-algorithm
Need advice: Back end engineer → infrastructure: how do you make the transition?
I’ve been a backend-heavy engineer for about 4 to 5 years, mostly in startups. For about 3 months I’ve been reading and building small things, but I’m not sure if I’m progressing or just spinning. I also don’t really have people around me in these areas, so I’ve mostly been trying to figure this out on my own, including using tools like GPT and Claude, but I still feel unclear.My work includes APIs, some real-time systems like WebRTC and streaming, and debugging production issues such as latency
Ask HN: Would you use revocable digital signatures to verify AI/Other content?
I’ve been exploring a potential product direction and wanted to sanity check it with people who actually build and ship things.Background: I’ve been working on a system using our core tech that can generate and verify digital signatures, but with a slightly different property than traditional approaches. The signatures are natively revocable. If the underlying model/system shouldn’t be trusted anymore, the signatures can be revoked either through a hard (delete the signing model) or soft (r