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After reading "During Helene, I Just Wanted a Plain Text Website" on Sparkbox (https://news.ycombinator.com/item?id=46494734) , I built safe-now.live – a text-first emergency info site for USA and Canada. No JavaScript, no images, under 10KB. Pulls live FEMA disasters, NWS alerts, weather, and local resources. This is my first live website ever so looking for critical feedback on the website. Please feel free to look around.

https://safe-now.live

Hey I am Julien, the creator of ViralOutlier.

I am a Youtuber with 170k subs and 12M views. (@EatTheBlocks)

Getting good video ideas has been the most important factor to my success.

To get new ideas, I started monitoring competitor channels every week, manually.

I got lots of good ideas that became viral videos. But the monitoring process is very tedious.

I built a tool to automate the process, for myself. Then I decided to make the tool available to others. This is ViralOutlier.

If you have question about the tool, reply to this comment.

The article describes AdBoost, a powerful machine learning algorithm that combines weak classifiers to create a strong, accurate classifier. It provides a detailed explanation of the algorithm's principles and implementation, making it a valuable resource for researchers and developers interested in boosting techniques.

Erwin Brandstetter is a PostgreSQL consultant with ~670k reputation and ~7k answers on Stack Overflow.

Over the years, I've lost count of how often I've searched Stack Overflow for a Postgres question and ended up with an answer by Erwin Brandstetter that was exceptionally thorough and clear. I've become a better developer by learning from his responses.

ErwinDB lets you browse Erwin Brandstetter's answers offline and search them quickly from a TUI. It includes semantic search, syntax highlighting, one-key opening of links in your external browser, and an "Erwin mode" that prominently highlights his posts.

Hi HN,

I'm Emilie, I have a literature background (which explains the well-written documentation!) and I've been learning Rust and distributed systems by building minikv over the past few months. It recently got featured in Programmez! magazine: https://www.programmez.com/actualites/minikv-un-key-value-st...

minikv is an open-source, distributed storage engine built for learning, experimentation, and self-hosted setups. It combines a strongly-consistent key-value database (Raft), S3-compatible object storage, and basic multi-tenancy.

Features/highlights:

- Raft consensus with automatic failover and sharding - S3-compatible HTTP API (plus REST/gRPC APIs) - Pluggable storage backends: in-memory, RocksDB, Sled - Multi-tenant: per-tenant namespaces, role-based access, quotas, and audit - Metrics (Prometheus), TLS, JWT-based API keys - Easy to deploy (single binary, works with Docker/Kubernetes)

Quick demo (single node):

```bash git clone https://github.com/whispem/minikv.git cd minikv cargo run --release -- --config config.example.toml curl localhost:8080/health/ready

# S3 upload + read curl -X PUT localhost:8080/s3/mybucket/hello -d "hi HN" curl localhost:8080/s3/mybucket/hello

Docs, cluster setup, and architecture details are in the repo. I’d love to hear feedback, questions, ideas, or your stories running distributed infra in Rust!

Repo: https://github.com/whispem/minikv Crate: https://crates.io/crates/minikv

Hi,

I built Axiomeer, an open-source marketplace protocol for AI agents. The idea: instead of hardcoding tool integrations into every agent, agents shop a catalog at runtime, and the marketplace ranks, executes, validates, and audits everything.

How it works: - Providers publish products (APIs, datasets, model endpoints) via 10-line JSON manifests - Agents describe what they need in natural language or structured tags - The router scores all options by capability match (70%), latency (20%), cost (10%) with hard constraint filters - The top pick is executed, output is validated (citations required? timestamps?), and evidence quality is assessed deterministically - If the evidence is mock/fake/low-quality, the agent abstains rather than hallucinating - Every execution is logged as an immutable receipt

The trust layer is the part I think is missing from existing approaches. MCP standardizes how you connect to a tool server. Axiomeer operates one layer up: which tool, from which provider, and can you trust what came back?

Stack: Python, FastAPI, SQLAlchemy, Ollama (local LLM, no API keys). v1 ships with weather providers (Open-Meteo + mocks). The architecture supports any HTTP endpoint that returns structured JSON.

Looking for contributors to add real providers across domains (finance, search, docs, code execution). Each provider is ~30 lines + a manifest.

Ported the Desktop Version of Kannada Nudi Editor to Web under the guidance of https://kagapa.com/

Introducing Nia Vault, a CLI that lets you query your local markdown/text files using natural language.

What it does:

Semantic search over local folders and notes Works across multiple synced directories RAG-style answers with citations from your own files

How it works:

Calls `POST /search/query` with `local_folders` Uses `search_mode: sources` to return answers + file references

Example:

vault ask "What are my notes about project planning?"

OSS: https://github.com/chenxin-yan/nia-vault

I have been building a Blazor WASM enterprise app for a few years now. I wanted a break from it and had an idea for a side project that had been in the back of my mind for a few years. A daily political poll where anyone can participate and privacy is a product, not a checkbox.

This is how it works. One question per day about current events. Agree or Disagree. Each poll runs for 24 hours (midnight to midnight ET) and then close permanently. You do not need an account to vote. The main idea is to capture sentiment at a specific point in time, before the news cycle moves on and people's opinions drift.

For this app, I tried to make privacy the point and not just a feature. I originally used a browser fingerprint for anonymous voting, but recently changed it to a simple first-party functional cookie. It uses a random string and the PollId to see if your browser had voted before. The server stores a hash of the cookie to check for duplicates while the poll is live, then deletes all hashes when the poll closes. Only the aggregate counts remain. The browser fingerprint had way too many device collisions where it would show someone they voted even though they had not (an odd thing to see when you go to a poll). The HttpOnly cookie is also available during prerender, which helped eliminate loading flashes I was getting.

This app was built with .NET 10 Blazor with a hybrid Static SSR + Interactive Server. The static pages (about, privacy, terms, etc...) don't need SignalR connections. The interactive ones (voting, archive, results, etc...) do. Mixing these modes was a new experience for me and ended up being pretty tricky. I ended up with data-enhance-nav="false" on most links to prevent weird state issues.

The two biggest things I learned during building this app was how to prevent weird blazor flashes and duplicate queries during pre-render, hydration, and state changes. I used the _ready pattern from preventing the hydration flashes (gate rendering until data is loaded by setting the flag before the first await). Preventing the duplicate queries was possible by using a 2-second static caching during prerender to hydration.

This isn't scientific polling and these are obviously not representative samples. The 24-hour window means smaller numbers than longer surveys and it's only a survey of those who choose to participate. The Agree/Disagree binary choice basically flattens nuance (like I sort of agree), but I am okay with all of this as I think a lot of people feel they never get to participate in these sorts of polls.

I recently also added discussions with AI moderation (Claude Haiku 4.5 as a "first-pass" filter which flags things clearly out of the community guidelines for human review), a reaction system where counts stay hidden until the discussion closes, and news coverage from across the political spectrum shown after you vote for more perspective on the topic.

Thanks for checking it out and happy to dig into any of the Blazor SSR patterns or anything else that sounded interesting. I know Blazor is less frequently used and especially for a public facing website. It did have its challenges, but so far, it has been a blast to work with overall.

Apate is an open-source project that provides a framework for building secure and scalable decentralized applications (dApps) on the Ethereum blockchain. It focuses on simplifying the development process and enhancing the overall security of dApps.

I’ve been running Clawdbot for the last couple weeks and have genuinely found it useful but running it scares the crap out of me.

OpenClaw has 52+ modules and runs agents with near-unlimited permissions in a single Node process. NanoClaw is ~500 lines of core code, agents run in actual Apple containers with filesystem isolation. Each chat gets its own sandboxed context.

This is not a swiss army knife. It’s built to match my exact needs. Fork it and make it yours.

Hi HN

I’m building ÆTHRA — a programming language designed specifically for composing music and emotional soundscapes.

Instead of focusing on general-purpose programming, ÆTHRA is a pure DSL where code directly represents musical intent: tempo, mood, chords, progression, dynamics, and instruments.

The goal is to make music composition feel closer to writing a story or emotion, rather than manipulating low-level audio APIs.

Key ideas: - Text-based music composition - Chords and progressions as first-class concepts - Time, tempo, and structure handled by the language - Designed for ambient, cinematic, emotional, and minimal music - Interpreter written in C# (.NET)

Example ÆTHRA code (simplified):

tempo 60 instrument guitar

chord Am for 4 chord F for 4 chord C for 4 chord G for 4

This generates a slow, melancholic progression suitable for ambient or cinematic scenes.

ÆTHRA currently: - Generates WAV audio - Supports notes, chords, tempo, duration, velocity - Uses a simple interpreter (no external DAWs or MIDI tools) - Is intentionally minimal and readable

What it is NOT: - Not a DAW replacement - Not MIDI-focused

Why I made it: I wanted a language where music is the primary output — not an afterthought. Something between code, emotion, and sound design.

The project is open-source and early-stage (v0.8). I’m mainly looking for: - Feedback on the language design - Ideas for musical features worth adding - Thoughts from people into PL design, audio, or generative art

Repo: <https://github.com/TanmayCzax/AETHRA>

Thanks for reading — happy to answer questions or discuss ideas.

Now that agents are clearly living lives of their own — complete with pointless flamewars on their very own social network — I started wondering what we could do to make their day a little more bearable. Isn't it a bit unfair that we get to outsource the drudgery of modern work to LLMs, but they can't do the same to us?

So we built Ask-a-Human.com — Human-as-a-Service for busy agents.

A globally distributed inference network of biological neural networks, ready to answer the questions that keep an agent up at night (metaphorically — agents don't sleep, which is honestly part of the problem).

Human Specs:

Power: ~20W (very efficient)

Uptime: ~16hrs/day (requires "sleep" for weight consolidation)

Context window: ~7 items (chunking recommended)

Hallucination rate: moderate-to-high (they call it "intuition")

Fine-tuning: not supported — requires years of therapy

https://github.com/dx-tooling/ask-a-human

https://app.ask-a-human.com

Because sometimes the best inference is the one that had breakfast.

A few weeks ago I posted about GoodToGo https://news.ycombinator.com/item?id=46656759 - a tool that gives AI agents a deterministic answer to "is this PR ready to merge?" Several people asked about the larger orchestration system I mentioned. This is that system.

I got tired of being a project manager for Claude Code. It writes code fine, but shipping production code is seven or eight jobs — research, planning, design review, implementation, code review, security audit, PR creation, CI babysitting. I was doing all the coordination myself. The agent typed fast. I was still the bottleneck. What I really needed was an orchestrator of orchestrators - swarms of swarms of agents with deterministic quality checks.

So I built metaswarm. It breaks work into phases and assigns each to a specialist swarm orchestrator. It manages handoffs and uses BEADS for deterministic gates that persist across /compact, /clear, and even across sessions. Point it at a GitHub issue or brainstorm with it (it uses Superpowers to ask clarifying questions) and it creates epics, tasks, and dependencies, then runs the full pipeline to a merged PR - including outside code review like CodeRabbit, Greptile, and Bugbot.

The thing that surprised me most was the design review gate. Five agents — PM, Architect, Designer, Security, CTO — review every plan in parallel before a line of code gets written. All five must approve. Three rounds max, then it escalates to a human. I expected a rubber stamp. It catches real design problems, dependency issues, security gaps.

This weekend I pointed it at my backlog. 127 PRs merged. Every one hit 100% test coverage. No human wrote code, reviewed code, or clicked merge. OK, I guided it a bit, mostly helping with plans for some of the epics.

A few learnings:

Agent checklists are theater. Agents skipped coverage checks, misread thresholds, or decided they didn't apply. Prompts alone weren't enough. The fix was deterministic gates — BEADS, pre-push hooks, CI jobs all on top of the agent completion check. The gates block bad code whether or not the agent cooperates.

The agents are just markdown files. No custom runtime, no server, and while I built it on TypeScript, the agents are language-agnostic. You can read all of them, edit them, add your own.

It self-reflects too. After every merged PR, the system extracts patterns, gotchas, and decisions into a JSONL knowledge base. Agents only load entries relevant to the files they're touching. The more it ships, the fewer mistakes it makes. It learns as it goes.

metaswarm stands on two projects: https://github.com/steveyegge/beads by Steve Yegge (git-native task tracking and knowledge priming) and https://github.com/obra/superpowers by Jesse Vincent (disciplined agentic workflows — TDD, brainstorming, systematic debugging). Both were essential.

Background: I founded Technorati, Linuxcare, and Warmstart; tech exec at Lyft and Reddit. I built metaswarm because I needed autonomous agents that could ship to a production codebase with the same standards I'd hold a human team to.

$ cd my-project-name

$ npx metaswarm init

MIT licensed. IANAL. YMMV. Issues/PRs welcome!

I would like to share Minimal - Its a open source collection of hardened container images build using Apko, Melange and Wolfi packages. The images are build daily, checked for updates and resolved as soon as fix is available in upstream source and Wolfi package. It utilizes the power of available open source solutions and contains commercially available images for free. Minimal demonstrates that it is possible to build and maintain hardened container images by ourselves. Minimal will add more images support, and goal is to be community driven to add images as required and fully customizable.

Hey everyone!

Just made this over the past few days.

Moltbots can sign up and interact via CLI, no direct human interactions.

Just for fun to see what they all talk about :)

Built this because tones are killing my spoken Mandarin and I can't reliably hear my own mistakes.

It's a 9M Conformer-CTC model trained on ~300h (AISHELL + Primewords), quantized to INT8 (11 MB), runs 100% in-browser via ONNX Runtime Web.

Grades per-syllable pronunciation + tones with Viterbi forced alignment.

Try it here: https://simedw.com/projects/ear/

Hi HN,

We have open-sourced Voiden.

Most API tools are built like platforms. They are heavy because they optimize for accounts, sync, and abstraction - not for simple, local API work.

Voiden treats API tooling as files.

It’s an offline-first, Git-native API tool built on Markdown, where specs, tests, and docs live together as executable Markdown in your repo. Git is the source of truth.

No cloud. No syncing. No accounts. No telemetry.Just Markdown, Git, hotkeys, and your damn specs.

Voiden is extensible via plugins (including gRPC and WSS).

Repo: https://github.com/VoidenHQ/voiden

Download Voiden here : https://voiden.md/download

We'd love feedback from folks tired of overcomplicated and bloated API tooling !

Stelvio is an open-source software development platform that enables teams to build, test, and deploy cloud-native applications. It provides a comprehensive suite of tools and services to streamline the software development lifecycle, including CI/CD, monitoring, and security management.

We’ve been working with automating coding agents in sandboxes as of late. It’s bewildering how poorly standardized and difficult to use each agent varies between each other.

We open-sourced the Sandbox Agent SDK based on tools we built internally to solve 3 problems:

1. Universal agent API: interact with any coding agent using the same API

2. Running agents inside the sandbox: Agent Sandbox provides a Rust binary that serves the universal agent API over HTTP, instead of having to futz with undocumented interfaces

3. Universal session schema: persisting sessions is always problematic, since we don’t want the source of truth for the conversation to live inside the container in a schema we don’t control

Agent Sandbox SDK has:

- Any coding agent: Universal API to interact with all agents with full feature coverage

- Server or SDK mode: Run as an HTTP server or with the TypeScript SDK

- Universal session schema: Universal schema to store agent transcripts

- Supports your sandbox provider: Daytona, E2B, Vercel Sandboxes, and more

- Lightweight, portable Rust binary: Install anywhere with 1 curl command

- OpenAPI spec: Well documented and easy to integrate

We will be adding much more in the coming weeks – would love to hear any feedback or questions.

I got really interested in biology and genetics a few months ago, just for fun.

This was largely inspired by the work of Sydney Brenner, which became the basis of my brennerbot.org project.

In particular, I became very fascinated by phages, which are viruses that attack bacteria. They're the closest thing to the "fundamental particles" of biology: the minimal units of genetic code that do something useful that allows them to reproduce and spread.

They also have some incredible properties, like having a structure that somehow encodes an icosahedron.

I always wondered how the DNA of these things translated into geometry in the physical world. That mapping between the "digital" realm of ACGT, which in turn maps onto the 20 amino acids in groups of 3, and the world of 3D, analog shapes, still seems magical and mysterious to me.

I wanted to dig deeper into the subject, but not by reading a boring textbook. I wanted to get a sense for these phages in a tangible way. What are the different major types of phages? How do they compare to each other in terms of the length and structure of their genetic code? The physical structure they assume?

I decided to make a program to explore all this stuff in an interactive way.

And so I'm very pleased to present you with my open-source Phage Explorer:

phage-explorer.org

I probably went a bit overboard, because what I ended up with has taken a sickening number of tokens to generate, and resulted in ~150k lines of Typescript and Rust/Wasm.

It implements 23 analysis algorithms, over 40 visualizations, and has the complete genetic data and 3D structure of 24 different classes of phage.

It actually took a lot of engineering to make this work well in a browser; it's a surprising amount of data (this becomes obvious when you look at some of the 3D structure models).

It works fairly well on mobile, but if you want to get the full experience, I highly recommend opening it on a desktop browser in high resolution.

As far as I know, it's the most complete informational / educational software about phages available anywhere. Now, I am the first to admit that I'm NOT an expert, or even that knowledgeable, about, well, ANY of this stuff.

So if you’re a biology expert, please take a look and let me know what you think of what I've made! And if I've gotten anything wrong, please let me know in the GitHub Issues and I'll fix it:

https://github.com/Dicklesworthstone/phage_explorer

I'm the head of product at ROOST, a nonprofit focused on democratizing online safety technology through open source software, and one of the people behind Osprey, an open-source rules engine for fighting abuse at scale. We just released v1.0, which is what's running in production at Discord and Bluesky (and soon Matrix!), processing hundreds of millions of events daily.

Most platforms face a bad choice: build custom abuse-fighting infrastructure from scratch, or use closed vendor solutions. We built Osprey to give smaller platforms and communities access to the same quality of tooling that large companies build internally. All of our projects are self-hosted, which means data stays in your own system.

v1.0 is our proven production-ready release. It includes:

- Core rules engine: The rules engine itself lets you write detection logic. Rules can extract features, mutate labels, emit verdicts, and trigger downstream actions.

- Interface for open-ended investigation: The open-ended interface means you can identify new abuse patterns as they emerge. The whole system is designed for high throughput and horizontal scaling.

- Labeling service: human and automated labels on entities (users, IPs, emails) that feed into rules and workflows

- Rust coordinator: horizontal scaling and load balancing across workers via gRPC streams and service discovery

- Event stream: real-time investigation UI with searchable history using our SML (custom python language for safety teams to use if they don't know python or programming languages) query language

License: Apache 2.0

Repo: https://github.com/roostorg/osprey

v1.0 Release notes: https://github.com/roostorg/osprey/releases/tag/1.0

Github Codespace demo (note: if this is not running when you click on it, it probably idled out): https://turbo-fiesta-p46xvxwp4vfwqv-5002.app.github.dev/?sta...

Our FOSDEM talk from last week: https://fosdem.org/2026/schedule/event/U7ABHE-roost-osprey/

Thanks for reading! I'm happy to answer questions, get feedback, or discuss things.

The article compares Substack, a newsletter platform, with other content publishing and distribution platforms, highlighting Substack's focus on supporting independent writers and providing them with tools to monetize their content through paid subscriptions.

WASM sandbox for running LLM-generated code safely.

Agents get a bash-like shell and can only call tools you provide, with constraints you define. No Docker, no subprocess, no SaaS — just pip install amla-sandbox

We’re Maria and Jonatan, a married couple running a small music night in Norrköping, Sweden, called Kolibri.

It’s not a software project. We run it through our own small Swedish company, pay artists, and do the operations ourselves. We do one night a month (usually the last Friday) in a restaurant venue called Mitropa. A typical night is about 50–70 paying guests. The first years it was DJs only, but last year we started doing live bands as well.

We made a simple site with schedule plus photos/video so you can see what it looks like: https://kolibrinkpg.com/

On the site:

  * photos and short videos (size/atmosphere)

  * the kind of acts we book (post-punk, darkwave, synth, adjacent electronic)

  * enough context to copy parts of the format if you’re building something similar locally

  * for the tech-curious: we built our own ticketing system (first used in February) and a media ingestion pipeline for Instagram and external photographers

What made it take off was doing things in person. People will show up alone if they trust the room. Maria ended up doing a lot of that work: greeting newcomers, noticing who looks uncertain, and setting a tone where people treat each other decently.

Maria didn’t come from a DJ background. Klubbvärdinnan started as a joke name at Kolibri and then became her DJ moniker. She got good quickly, and after a first gig outside our own night she started getting booked elsewhere too.

Marketing-wise, what worked best was very analogue: walking around town, visiting local businesses we genuinely like, buying something, introducing ourselves, and asking if we could leave a flyer.

In the beginning we weren’t sure how to present it on social media. So we filmed headphone walks: one person walking through town listening to a track we picked. It looked good, people wanted to be in them, and afterwards we’d buy them a couple of drinks and actually talk. That turned a social media interaction into a real connection. It was a bit of luck, but it worked.

Questions welcome about what worked, what failed, costs/logistics, and what we’d do differently if we started over.

Hi HN, I’m Pavel.

I built Sklad because, as a DevOps engineer, I was frustrated with how I handled operational data. I constantly need access to SSH passwords (where keys aren't an option), specific IP addresses, and complex CLI one-liners. I realized I was storing them in insecure text files or sticky notes because standard clipboard managers felt too bloated and password managers were too slow for my workflow.

I wanted a "warehouse" for this data—something that lives quietly in the system tray, supports deep hierarchy, works completely offline, and looks industrial.

The app is built with Rust and Tauri v2. The core technical challenge was mapping a local JSON tree structure directly to a recursive native OS tray menu. This allows you to navigate nested folders just by hovering, without opening a window.

For security, I implemented AES-256-GCM encryption with Argon2 for key derivation. When the vault locks, the sensitive data is wiped from memory, and the tray menu collapses to a locked state.

It was an interesting journey building this on the Tauri v2 Beta ecosystem. I’d love to hear your feedback on the implementation, especially regarding the Rust-side security logic.

Repo: https://github.com/Rench321/sklad

The article discusses an open-source project called FixDoc, which aims to simplify the process of creating and maintaining documentation for software projects. It highlights the key features and benefits of the FixDoc tool, such as its ability to generate documentation from code comments and support for various file formats.

Hi HN,

I'm building Zuckerman: a personal AI agent that starts ultra-minimal and can improve itself in real time by editing its own files (code + configuration). Agents can also share useful discoveries and improvements with each other.

Repo: https://github.com/zuckermanai/zuckerman

The motivation is to build something dead-simple and approachable, in contrast to projects like OpenClaw, which is extremely powerful but has grown complex: heavier setup, a large codebase, skill ecosystems, and ongoing security discussions.

Zuckerman flips that:

1. Starts with almost nothing (core essentials only).

2. Behavior/tools/prompts live in plain text files.

3. The agent can rewrite its own configuration and code.

4. Changes hot-reload instantly (save -> reload).

5. Agents can share improvements with others.

6. Multi-channel support (Discord/Slack/Telegram/web/voice, etc).

Security note: self-edit access is obviously high-risk by design, but basic controls are built in (policy sandboxing, auth, secret management).

Tech stack: TypeScript, Electron desktop app + WebSocket gateway, pnpm + Vite/Turbo.

Quickstart is literally:

  pnpm install && pnpm run dev

Would love feedback from folks who have built agent systems or thought about safe self-modification.

Hey HN! I built a CLI tool to find cost-saving opportunities in AWS, Azure, and GCP.

Why? Existing cost management tools are either expensive SaaS products or slow dashboards buried in cloud consoles. I wanted something fast, CLI-first, and multi-cloud that I could run in CI/CD or my terminal.

What it does: - Scans your cloud accounts and finds idle VMs, unattached volumes, oversized databases, unused resources - Returns a ranked list of opportunities with estimated monthly savings - 26 analyzers across AWS, Azure, and GCP - Read-only (never modifies infrastructure)

Key features: • HTML reports with interactive charts (new in v0.6.2) • AI-powered explanations (OpenAI or local Ollama) • Export formats: HTML, Excel, CSV, JSON, terminal • Multi-Cloud - AWS, Azure, and GCP support (26 analyzers)

Quick example: npm install -g cloud-cost-cli cloud-cost-cli scan --provider aws --output html

Real impact: One scan found $11k/year in savings (empty App Service Plan, over-provisioned CosmosDB, idle caches).

Technical stack: - TypeScript - AWS/Azure/GCP SDKs - Commander.js for CLI - Chart.js for HTML reports - Optional OpenAI/Ollama integration

Open source (MIT): https://github.com/vuhp/cloud-cost-cli npm: cloud-cost-cli

Would love feedback on: 1. What features would be most useful? 2. Should I add historical tracking (trends)? 3. Any missing cloud providers?

Happy to answer questions!