Dev Systems - Building Systems That Scale

Ask HN: Stop Looking at Code?

There is a recent trend in tech where software development is being done by not looking at code but merely the output.<p>Some of the recent examples include the Bun Rust migration, etc. I wanted to understand both from startup’s and experienced individuals how do you see the trend, are we really moving towards a workflow where we simply optimize the loop or graph and code is merely an artifact this is produced as part of this workflow similar to bytecode or something?

Show HN: Penca – branchable, versioned OLTP+OLAP on one open copy of your data

Hi HN!This is an early proof of concept of a branchable, versioned OLTP + OLAP database that runs on a single, open copy of your data in object storage. If you are familiar with Databricks' LTAP (https://www.databricks.com/company/newsroom/press-releases/d...) announcement from June, you can think of this as aspiring to be a fully open source, Apache 2.0 LTAP alternative with additional data versioning/auditability guarantees that enable audit, as_of quer

Show HN: Dn – plan collaboratively, let agents execute

Hi HN — we built `dn` because coding got faster, but building software did not. Use the CLI to clear your backlog faster with reusable agentic workflows & other supporting commands. kickstart, meld, loop, land, fixup, and more commands can be used by you or your agent to turn markdown specification into implementation. Use the CLI in GitHub Actions overnight to durably handoff pull requests and github issues your team picks up in the morning.For example:```sh dn --agent codex kickstart --awp

Ventora Expands Its AI Business Builder to Help Solo Founders

Building software is becoming easy. Building a business is not.AI coding tools have dramatically lowered the barrier to creating SaaS products, AI applications, online services, marketplaces, and e-commerce stores. Entrepreneurs can now generate working products in hours instead of spending months writing code.But for most founders, development is no longer the bottleneck.Launching a successful business still requires validating demand, researching competitors, defining positioning, creating mar

아키텍트로 가는 길 : 소프트웨어 아키텍처 강좌 #02 Software Architecture Tutorial

소프트웨어 아키텍처 사용법 - 오리엔테이션 : 강좌 소개 - 왜 소프트웨어 아키텍처를 배우는가? - 소프트웨어 아키텍처 무엇을 ...

Modeling Device Capabilities for Analytics

by Aarti Laddha, Richard Diaz-Cool, Rishika Idnani, Venkatesh SelverajNetflix supports a vast and evolving set of features and content types, ranging from 4K streaming and immersive audio to live streaming and cloud gaming, across a diverse ecosystem of devices. However, not all devices are created equal. Hardware limitations such as available RAM, CPU cores, display capabilities, or platform support mean that some features cannot be supported on certain device models. To ensure the best possibl

Skill or Sub-Agent. Choosing AI Capabilities You Will Actually Reuse

Audience: Cloud architects, platform engineers, engineering leadersThe wrong first questionMost teams building AI capabilities start with the wrong question. They ask which model to use.The model matters less than the shape of the capability around it. The first real fork is this. Are you building a skill or a sub-agent? Get that wrong and no model choice will save you. A skill and a sub-agent are two different delivery shapes, and each one fails at the other one's job.The insightThe choice betw

GenRec: Towards LLM-Native Recommendation at Netflix

Authors: Ying Li, Arjun Rao, Shradha SehgalIntroductionRecommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand‑crafted features over users, items, and interactions, along with specialized architectures for sequence modeling, feature interactions, and multi‑task objectives. This stack has evolved over many years to support diverse content types (movies, series, games, live, podcasts) and product surfaces, but its complexity makes it cost

Show HN: What 180k words look like as a temporal knowledge graph (Oz series)

The graph is free to explore and requires no registration.SynapTale builds a model of a story as a temporal graph made up of nodes (entities) and edges (their actions and relationships). The graph is not a visualization of the wiki. The wiki, timelines, relationship histories, and analytics are projections of the graph.The current demo contains 232 entities, 1,852 edges, and a snapshot of the story’s state at every chapter. By chapter 100, it still remembers a promise made in chapter 8 and turns

Show HN: Kedge – Full-stack cloud with forkable VM snapshots and global SQLite

I'm building Kedge, a globally distributed platform for stateful serverless apps. Here's how you make a simple static site: `echo '# Hello world!' | ssh kedge.dev`I helped build Fly.io for 4 years and shared enthusiasm for the founders' vision of a 'global Heroku'. While there, I wrote "The Serverless Server" (https://fly.io/blog/the-serverless-server/) as a study of Lambda and a sketch of a modern serverless product built ar

Ask HN: What's the best hands-on path to learn ML inference infrastructure?

I'm a backend engineer with 8+ years of experience. Most of my work has been APIs, distributed systems, streaming/real-time systems and cloud-infra.<p>I'm trying to move towards ML inference infrastructure: model serving, batching, etc. but based on my experience, it has been difficult to get a callback for such jobs.<p>For people working in this area: what projects or experience would actually convince you that a backend engineer is ready to work on inference infra?

Launch HN: Rise Reforming (YC S26) – Turning Waste Gases into Valuable Chemicals

Hi HN! This is George, Lucas, and Jona from Rise Reforming (https://www.rise-reforming.com/). We’re developing a process to convert gas produced at landfills, farms, and wastewater plants (“biogas”) into higher value chemicals. Our technology is modular, designed to be deployed and operated on-site. Think of us as a chemical project developer; we sit between biogas producers (suppliers) and chemical end users (customers). We pay biogas producers for their gas and we make money fro

Programmer Government

Hey I had an idea. What if we created the bitcoin for government?So like a king sucks because it's the equivalent of one GPU.A republic is better because it's like a big GPU cluster of hundreds of GPUs.But what if we made like a bitcoin like distributed governance system that was like, I don't know, a million GPUs?We have the internet which is like infinite bandwidth compared to human needs, so we have the bandwidth to link all our minds together.So what if we did this. We had a s

Ask HN: How would you learn AI-assisted development from the ground up?

I'm a director/exec, not a software engineer. Background's mostly data analytics, SQL and Excel day to day, some R and PostgreSQL. Years ago I could hack together basic HTML/CSS/PHP too.Got into AI early and now use ChatGPT and Claude constantly. With their help I've built a handful of web apps, some Python scripts and desktop tools, and an Android app on Supabase with real multiple users.So I can build things. But I'm very aware there are big holes underneath

Show HN: Purple Hammer – construction takeoff and estimating in Tauri/Rust/WASM

Purple Hammer does construction takeoff. You measure quantities off PDF drawings and a built-in spreadsheet lets you read from databases, formulas, assemblies etc to price the job. This matters because most takeoff software (Bluebeam, Planswift) do not have the spreadsheet side of things built in.Stack is Tauri 2, Rust, SolidJS. The render and spreadsheet engines are both custom, compiled to WASM with modified PDFium for parsing. One codebase builds native Mac and Windows.Performance is most of

Show HN: Integrate any CLI agent into any terminal

Whether at work as a software engineer or in my private life, I kept running into the same problem: While working in my terminal, I’d wish I could use AI to make sense of recent output or compose a command. Copying and pasting context between a chat and the terminal was tedious and preemptively running a full CLI coding agent threw away all the efficiency of working in a shell. Both workflows were far from ideal.What I really wanted was for the coding agents I already used to pop into my termina

Show HN: Case study: A coding agent refactors a 750k LOC app, no code review

In the following case study, the AI coding agent rebuilds a core system invariant in just three days, with minimal human input, running 31 verification passes and correcting 201 errors, before shipping code with zero bugs, zero regressions, and zero technical debt.https://aisovereignlabs.ai/docs/case-study/liveSession/case-...Disclaimers:This case study is NOT: - a project written from scratch - yet another Rust transpilation - a clone of an open-source project foun

How to get more coding productivity with LLMs

As someone who is a lifelong programmer and entrepreneur, I'm here to say that 2-4-8x AI productivity boost is just the start. I'm currently benchmarking myself at 40-50x coding producivity with AI over 16 weeks, compared to pre-AI, and it's increasing over time, and I am trying to find ways to share these techniques with more programmers. Here we go HN...I measure in lines of code, which I know is an imperfect metric, but it's the quantative metric I have, and I'm quali

Mastering GitHub Copilot Budgets: How to Prevent Surprise Overages Without Blocking Devs

1. The Core Mental Model: The Water Park AnalogyTo understand Copilot billing, think of your enterprise as a water park:The Shared Pool (Included Usage): Every Copilot Business ($19/mo) and Enterprise ($39/mo) seat license adds a set number of free AI credits into a shared corporate water tank (1,900 or 3,900 credits/seat, respectively).Phase 1 (Pool Phase): All licensed developers drink from this central pool for free until it runs empty.Phase 2 (Metered Overage): Once the pool is dry, extra wa

Token Economics in Practice

Introduction: The cheap-token trapToken prices alone are a poor economic model for agents. The price of reaching a fixed capability has fallen sharply — In a 2025 Report Stanford's AI Index reported a roughly 280-fold drop in the cost of GPT-3.5-level inference between late 2022 and late 2024, and Epoch AI tracks steep (if uneven) per-benchmark price declines. The intuitive conclusion is that agents are getting cheaper to run. The operational reality is the opposite.Agents turn cheaper infe