Dev Systems

Show HN: Chunk sidecars for validating agent-generated code before pushing to CI

Hi HN! My name is Olaf, I work at CircleCI as a technology advisor in the CTO office, came in through the acquisition of my company Vamp.io (progressive delivery for microservices on k8s) in 2021. Wanted to hear the HN community feedback and thoughts on a project we think could be very interesting when adding AI coding agents to the SDLC and your CI pipelines.Our team at CircleCI built Chunk sidecars after repeatedly running into the same issue internally: by the time our CI catches a failure, t

Bill Gates AI on AI (one month later)

# The Agentic Tidal Wave*To:* Executive Staff and Direct Reports *From:* Bill Gates *Date:* April 26, 2026Our vision for the last 20 years can be summarized in a succinct way. We saw that exponential improvements in cloud would make great software quite available. In the next 20 years the improvement in computer power will be outpaced by the exponential improvements in autonomous expert systems. Verifiable trust will be crucial to delivering the benefits of these advances.Most users of software

Show HN: Generate Claude Code Workflows using Spec Driven Development approach

I have been using a Spec-Driven Development approach for all mid+ size coding tasks since Feb 2026: https://news.ycombinator.com/item?id=48231575. One of the reasons for developing my own plugin (sddw - spec driven development workflow https://github.com/sermakarevich/sddw/) was to adjust it to my specific needs and the typical size of the features I build. Since then, this approach (sddw) has been presented at a few companies, including Google Poland. One

AI agent streaming in action: barge-in, human handover, and session continuity

You're mid-conversation with an AI support agent. You've explained the problem, the agent is halfway through a response, and the connection drops. When you reconnect, the response is gone.You type the same question again. The agent asks the same clarifying questions again. Three minutes of context, gone. Not because the model forgot it, but because the delivery layer stored nothing.Connection drops, page refreshes, and device switches all fail for the same reason: session state lives in the deli

Agent-First Development Workflows in VS Code with Brigit Murtaugh

Explore VS Code's new Agents window — a familiar, focused UI that puts agent-first workflows front and center across repos, ...

Have LLMs made anyone's life substantially better?

I can't help but come to the conclusion that almost everyone's life has gotten worse as a result of AI.I'm a software engineer working for a huge company. We have access to AI tools, which is cool, and it saves me time in the sense that it is quicker to write code. Yet, despite writing code taking time, I never thought that it was a tedious usage of time. As a developer, the times that I got to actually sit down and start banging out code were some of the most fun times I've

Cheers.fan #1 – Founding Infrastructure Engineers

CHEERS.FAN #1 | Founding Infrastructure EngineersEarly-stage company exploring large-scale synchronized interaction systems for entertainment, media, and shared live events.Interested in foundational infrastructure problems involving:● distributed systems ● low-latency infrastructure ● synchronization ● edge systems ● high-concurrency real-time interaction ● event-stream architectures ● coordinated participation across large populations of devicesSeeking experienced engineers with backgrounds in

Show HN: Raft in Rust

For quite sometime, I’ve wanted to give implementing the Raft consensus protocol a try. I first looked at MIT’s 6.5840 Distributed Systems labs as a way to do this but it is in Go and my Go skills have atrophied as well as I have been deeply investing time in Rust. The other option was PingCAPs approach (https://github.com/pingcap/talent-plan/blob/master/courses/d...) but I also wanted the ability to define my own types and RPC approach, so I decided to do

SilverTorch: Index as Model — A New Retrieval Paradigm for Recommendation Systems

We’re introducing SilverTorch, a reimagining of recommendation systems that unifies all retrieval components for user generated content under a unified architecture. SilverTorch shows up to 23.7x higher throughput compared to the state-of-the-art approaches. It’s also showing 20.9x more compute cost efficiency compared to a CPU-based solution while also improving accuracy. Our research paper, “SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs,” accepted

Is WebSockets enough for AI chat?

WebSockets are the right protocol for production AI chat. But that fact doesn’t prevent the failure most teams hit first. An enterprise load balancer closes the idle connection at 60 seconds during a tool execution wait. Your reconnect logic fires in under a second, the agent keeps running server-side, and the client receives nothing from the gap. No tokens, no tool call results, no context.The reconnected socket has no view of what happened while it was down. Three conditions cause this routine

Show HN: Kanban CLI (A local-first, agent-first task manager for the terminal)

Hello HN,Ever since agents have become increasingly common in development, I've been scratching my head as to how to control their randomness. Recently, I decided to emulate an issue-tracking and project-management tool for agent-driven workflows.Kanban is a Rust-based coordination layer designed to provide a feature-rich terminal interface and enforce rigorous workflows. It aims to be versatile and extendable, made to be tailored to any preferred flow. It comes with full git integration an

Show HN: Spec-Driven Development Workflow for Claude Code

Spec Driven Development approach allows to squeeze more from coding agents thanks to few strong concepts: - decomposition across two dimensions. first you generate specs in multiple steps (requirements, code analysis, design), than you split task into multiple subtasks and implement them one by one - you clear context between every step - after spec generation and after subtask implementation. this helps keep cost low and context clear and focused which boost performance - specs written to disk

DS Unit 1 Part 1 | telugu | Characterization, Examples, Resource Sharing & challenges | R22

Distributed Systems | Unit 1 - Part 1 | JNTUH R22 Syllabus In this video, we start Unit 1 of Distributed Systems with clear and easy ...

Distributed Data 30 Why Distributed Systems Are Hard

A practical explainer course on how modern backend systems store, move, process, and protect data. Topics include reliability ...

Show HN: We dropped Go for Rust in our real-time telephony AI media plane

In building Vivik, an execution-grade telephony AI engine, we faced a brutal constraint: the human conversational loop.In psychoacoustics, a delay under 250 ms feels instantaneous. At 500 ms, users notice lag. Beyond 800 ms, conversations start feeling strained, and by 1.5 seconds, the illusion of real-time interaction collapses.That creates an extremely tight latency budget for voice AI:• Network RTT: 50–200 ms • LLM inference: 200–800 ms • TTS synthesis: 100–400 ms • ASR processing: 100–300 ms

Show HN: A timeline of recent open source CVE intensity and volume

I was curious what it would look like if I plotted the intensity and volume of software supply chain CVEs over time, given what seemed like a flood of compromises lately.It looked exactly as I expected, and I expect it to get worse before it gets better.Yes, an LLM was used but because I wanted the simplest possible architecture, I steered away from using any back end at all. Instead it's just GitHub pages with a static json document as the source of data, updated daily by a GitHub action w

Cloud Native Platforms: Build

Audience: Cloud architects, platform engineers, engineering leaders making design decisionsReading time: 8 minutesSeries: Cloud Native Platforms. Build, Run, Evolve. This is Part 1 of 3.Most engineering teams can build systems.Few can scale them without rebuilding them.As platforms grow, complexity does not increase linearly. It multiplies across users, services, tenants, regions, and integrations. The systems that struggle and the systems that scale are rarely separated by which cloud they run

Cloud Native Platforms: Run

Audience: SREs (Site Reliability Engineers), platform engineers, engineering managers running production systemsReading time: 8 minutesSeries: Cloud Native Platforms. Build, Run, Evolve. This is Part 2 of 3.Most systems are designed thoughtfully.Most operations are inherited reactively.The systems that survive are not the ones built with the most care. They are the ones operated with the most discipline. Production has a way of revealing every shortcut taken during design and every assumption le

Cloud Native Platforms: Evolve

Audience: Engineering leaders, platform architects, senior developers exploring how to operationalise AI in their teamsReading time: 8 minutesSeries: Cloud Native Platforms. Build, Run, Evolve. This is Part 3 of 3.Cloud helped us scale infrastructure.AI is starting to do the same thing for the work around the code: the planning, the testing, the release communication, the incident triage, the writing that surrounds writing software.The conversation about AI in software has narrowed too quickly t

WAR, Azure Advisor, and Us (Azure Arch Diagram Builder): Three Ways to Score an Azure Architecture

Author: Arturo Quiroga, Azure AI services Engineer - Senior Partner Solutions Architect — MicrosoftA few days ago I published From Prompt to Production: Building Azure Architecture Diagrams with AI, introducing the open-source Azure Architecture Diagram Builder. One feature got more follow-up questions than any other: the Well-Architected Framework (WAF) validation. Architects from partners and customers — many of whom already use Azure Advisor and the Well-Architected Review — wanted to know ex