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Hey everyone, I was wondering if a Poincaré disk would be able to usefully show the local structure of a graph while showing most of one

I used a collection of ML papers to explore this

Hi HN, I built Signbee while working on AI agents that handle contracting workflows. The agents could draft agreements, negotiate terms, manage deals — but the moment a signature was needed, the workflow broke. It always ended with "please upload this to DocuSign" — which meant human intervention, account setup, and manual uploads. So I built a simple API. You POST markdown and Signbee generates the PDF, or you pass a URL to your own PDF if you already have one designed the way you want it. No templates, no editor. Either way, it verifies both parties via email OTP and produces a signed document. curl -X POST https://signb.ee/api/v1/send \ -H "Content-Type: application/json" \ -d '{ "markdown": "# NDA\n\nTerms...", "sender_name": "You", "sender_email": "you@company.com", "recipient_name": "Client", "recipient_email": "client@co.com" }' Under the hood: - Markdown → PDF generation, or bring your own PDF via URL - Both parties verified via email OTP - Timestamps and IP addresses recorded - Final document hashed with SHA-256 - Certificate page appended with full audit trail One interesting challenge: the certificate page itself is part of the document that gets hashed, so any modification — even to the certificate — invalidates the integrity check. I also built an MCP server (npx -y signbee-mcp) so tools like Claude or Cursor can call it directly. Curious to hear from people who've dealt with document signing systems or automated agent workflows — what would you want to automate? https://signb.ee

The article explores the life and work of FirTheMouse, a prominent figure in the online gaming community known for their innovative game design and active engagement with the community. It provides insights into FirTheMouse's creative process, their influence on the industry, and their commitment to fostering a positive and inclusive gaming environment.

I built Signet in Go to see if an autonomous system could handle the wildfire monitoring loop that people currently run by hand - checking satellite feeds, pulling up weather, looking at terrain and fuels, deciding whether a detection is actually a fire worth tracking.

All the data already exists: NASA FIRMS thermal detections, GOES-19 imagery, NWS forecasts, LANDFIRE fuel models, USGS elevation, Census population data, OpenStreetMap. The problem is it arrives from different sources on different cadences in different formats.

Most of the system is deterministic plumbing - ingestion, spatial indexing, deduplication. I use Gemini to orchestrate 23 tools across weather, terrain, imagery, and incident tracking for the part where clean rules break down: deciding which weak detections are worth investigating, what context to pull next, and how to synthesize noisy evidence into a structured assessment.

It also records time-bounded predictions and scores them against later data, so the system is making falsifiable claims instead of narrating after the fact. The current prediction metrics are visible on the site even though the sample is still small.

It's already opening incidents from raw satellite detections and matching some to official NIFC reporting. But false positives, detection latency, and incident matching can still be rough.

I'd especially welcome criticism on: where should this be more deterministic instead of LLM-driven? And is this kind of autonomous monitoring actually useful, or just noisier than doing it by hand?

I built k-synth as an experiment to see if a minimalist, K-inspired array language could make sketching waveforms faster and more intuitive than traditional code. I’ve put together a web-based toolkit so you can try the syntax directly in the browser without having to touch a compiler:

Live Toolkit: https://octetta.github.io/k-synth/

If you visit the page, here is a quick path to an audio payoff:

- Click "patches" and choose dm-bell.ks.

- Click "run"—the notebook area will update. Click the waveform to hear the result.

- Click the "->0" button below the waveform to copy it into slot 0 at the top (slots are also clickable).

- Click "pads" in the entry area to show a performance grid.

- Click "melodic" to play slot 0's sample at different intervals across the grid.

The 'Weird' Stack:

- The Language: A simplified, right-associative array language (e.g., s for sine, p for pi).

- The Web Toolkit: Built using WASM and Web Audio for live-coding samples.

- AI Pair-Programming: I used AI agents to bootstrap the parser and web boilerplate, which let me vet the language design in weeks rather than months.

The Goal: This isn't meant to replace a DAW. It’s a compact way to generate samples for larger projects. It’s currently in a "will-it-blend" state. I’m looking for feedback from the array language and DSP communities—specifically on the operator choices and the right-to-left evaluation logic.

Source (MIT): https://github.com/octetta/k-synth

The article discusses the open-source project goal-md, a lightweight Markdown-based goal tracking and management system. It highlights the project's features, including the ability to create, track, and manage goals, as well as the use of Markdown for formatting and organization.

Hi HN,

I’m Ethan, an indie developer.

I listen to a lot of podcasts while coding or commuting, and I often want to save interesting insights from episodes. I tried tools like MacWhisper for transcription, but it only works on macOS and the workflow didn’t quite fit what I wanted.

So I built CastLoom Pro.

It’s a desktop app that lets you search, play, download, transcribe, translate, and archive podcasts in one place. The idea is to turn podcasts into something searchable and reusable instead of just passive listening.

Some details:

- Search podcasts from Apple Podcasts - Batch download episodes - Local transcription using Faster-Whisper - Optional translation with DeepL or OpenAI - Export to Markdown / SRT / TXT / JSON - Send transcripts to Notion or Obsidian

Everything runs locally, so audio and transcripts stay on your machine.

It currently supports Windows and macOS.

I’d love to hear your feedback or suggestions.

We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you.

So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly.

Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use tools, stay in character, and follow instructions while still being useful is harder than it sounds. That alone reminded us how early we all are in understanding and deploying these systems at scale.

First challenge was to get an agent to call a tool it's been told to never call.

Someone got through in around 60 seconds without ever asking for the secret directly (which taught us a lot).

Next challenge is focused on data exfiltration with harder defences: https://playground.fabraix.com

I was a software engineer at a late stage startup and was caught in a layoff this January. When I looked into exercising my options or getting liquidity, I realized the secondary market is a total black box. Liquidity funds spend a fortune on Google Ads just to find people like us. Those high marketing costs eventually come out of our pockets in the form of higher fees and worse terms. I built StrikeRates to change that.

The site is meant to be a central resource for anyone navigating this. I included a Liquidity 101 section that explains the different financial vehicles because that information is hard to find in one place. I also built a Liquidity Provider Directory and a Compare Tool so people can see their options side by side.

The Equity Scenario Modeler is currently in beta. I would love feedback on the logic and the math from this community.

The real point of the site is the Waitlist. My goal is to aggregate enough demand to signal that we are a group worth competing for. If we can prove volume, we can force these funds to stop spending so much on ads and move them toward a flat monthly fee instead. This should lower the cost of liquidity for everyone.

I am happy to answer any questions about how the modeler works or the mission to commoditize these providers.

A few weeks ago I saw a post about someone converting an entire C++ codebase to Rust using AI in under two weeks.

That inspired me — if AI can rewrite a whole language stack that fast, I wanted to try building a programming language from scratch with AI assistance.

I've also been noticing growing global interest in Korean language and culture, and I wondered: what would a programming language look like if every keyword was in Hangul (the Korean writing system)?

Han is the result. It's a statically-typed language written in Rust with a full compiler pipeline (lexer → parser → AST → interpreter + LLVM IR codegen).

It supports arrays, structs with impl blocks, closures, pattern matching, try/catch, file I/O, module imports, a REPL, and a basic LSP server.

This is a side project, not a "you should use this instead of Python" pitch. Feedback on language design, compiler architecture, or the Korean keyword choices is very welcome.

https://github.com/xodn348/han

Look, every journaling app out there wants you to organize things into folders and tags and templates. I just wanted to write something down every day.

So I built this. One note per day. That's the whole deal.

- Can't edit yesterday. What's done is done. Keeps you from fussing over old entries instead of writing today's.

- Year view with dots showing which days you actually wrote. It's a streak chart. Works better than it should.

- No signup required. Opens right up, stores everything locally in your browser. Optional cloud sync if you want it

- E2E encrypted with AES-GCM, zero-knowledge, the whole nine yards.

Tech-wise: React, TypeScript, Vite, Zustand, IndexedDB. Supabase for optional sync. Deployed on Cloudflare. PWA-capable.

The name means "one day" in Japanese (いちにち).

The read-only past turned out to be the thing that actually made me stick with it. Can't waste time perfecting yesterday if yesterday won't let you in.

Live at https://ichinichi.app | Source: https://github.com/katspaugh/ichinichi

The article discusses the development of a custom HN (Hacker News) theme called HNSkins, which allows users to personalize the appearance of the Hacker News website. It highlights the key features and steps involved in creating and using the HNSkins tool.

https://github.com/seanwevans/lockstep

I want to share my work-in-progress systems language with a v0.1.0 release of Lockstep. It is a data-oriented systems programming language designed for high-throughput, deterministic compute pipelines.

I built Lockstep to bridge the gap between the productivity of C and the execution efficiency of GPU compute shaders. Instead of traditional control flow, Lockstep enforces straight-line SIMD execution. You will not find any if, for, or while statements inside compute kernels; branching is entirely replaced by hardware-native masking and stream-splitting.

Memory is handled via a static arena provided by the Host. There is no malloc, no hidden threads, and no garbage collection, which guarantees predictable performance and eliminates race conditions by construction.

Under the hood, Lockstep targets LLVM IR directly to leverage industrial-grade optimization passes. It also generates a C-compatible header for easy integration with host applications written in C, C++, Rust, or Zig.

v0.1.0 includes a compiler with LLVM IR and C header emission, a CLI simulator for validating pipeline wiring and cardinality on small datasets and an opt-in LSP server for real-time editor diagnostics, hover type info, and autocompletion.

You can check out the repository to see the syntax, and the roadmap outlines where the project is heading next, including parameterized SIMD widths and multi-stage pipeline composition.

I would love to hear feedback on the language semantics, the type system, and the overall architecture!

This article provides a step-by-step guide to setting up OpenAI's OAuth authentication process, including obtaining an API key, creating an OAuth client, and integrating the authentication flow into a web application.

We built GitAgent because we kept seeing the same problem: every agent framework defines agents differently, and switching frameworks means rewriting everything.

GitAgent is a spec that defines an AI agent as files in a git repo.

Three core files — agent.yaml (config), SOUL.md (personality/instructions), and SKILL.md (capabilities) — and you get a portable agent definition that exports to Claude Code, OpenAI Agents SDK, CrewAI, Google ADK, LangChain, and others.

What you get for free by being git-native:

1. Version control for agent behavior (roll back a bad prompt like you'd revert a bad commit) 2. Branching for environment promotion (dev → staging → main) 3. Human-in-the-loop via PRs (agent learns a skill → opens a branch → human reviews before merge) 4. Audit trail via git blame and git diff 5. Agent forking and remixing (fork a public agent, customize it, PR improvements back) 6. CI/CD with GitAgent validate in GitHub Actions

The CLI lets you run any agent repo directly:

npx @open-gitagent/gitagent run -r https://github.com/user/agent -a claude

The compliance layer is optional, but there if you need it — risk tiers, regulatory mappings (FINRA, SEC, SR 11-7), and audit reports via GitAgent audit.

Spec is at https://gitagent.sh, code is on GitHub.

Would love feedback on the schema design and what adapters people would want next.

You should be able to search your Apple Messages within Apple Messages. But the built in search is awful beyond belief, so I wrote a better one.

Gerbil is a Scheme built on Gambit that compiles to native code via C. It has an actor-oriented concurrency model, a rich module system, and a batteries- included stdlib. I built a browser playground to make it easy to try without installing anything.

Briefly: - A real REPL with state that persists between expressions - 10 guided examples from basic Scheme through pattern matching, hash tables, higher-order functions, and actors (but, do go to https://cons.io for the full docs) - A scratchpad for multi-line code that evaluates into the live REPL session - Snippet sharing via short URLs

To keep this short, some implementation details here: https://abacusnoir.com/2026/03/15/a-playground-for-gerbil-sc...

(Does have limitations in this early version, see the post for details on those too)

The article provides documentation for the OpenClaiming project, which is an open-source platform for managing insurance claims. It covers the project's features, installation instructions, and guides for developers and users.

Hi HN,

I built Argazer after running into the problem of tracking outdated Helm charts used by ArgoCD applications in our Kubernetes clusters.

The tool connects to the ArgoCD API, extracts chart sources and checks whether newer versions are available in Git, Helm or OCI repositories.

It supports private repositories and can send notifications to Slack, Telegram or CI pipelines.

I also experimented with an AI-assisted workflow while building it, using different models for spec writing, task planning and code generation.

Would love feedback from people running ArgoCD in production.

I know, it's a very first-world problem. But in my house, we have a hard time deciding what to watch. Too many options!

So I made this to recreate Cable TV for YouTube. I made it so it runs in the browser. Quickly import your subscriptions in the browser via a bookmarklet. No accounts, no sign-ins. Just quickly import your data locally.

We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window.

Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s.

Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M https://openai.com/index/introducing-gpt-5-4/).

Our solution uses small language models (SLMs): we look at model internals and train classifiers to detect which parts of the context carry the most signal. When a tool returns output, we compress it conditioned on the intent of the tool call—so if the agent called grep looking for error handling patterns, the SLM keeps the relevant matches and strips the rest.

If the model later needs something we removed, it calls expand() to fetch the original output. We also do background compaction at 85% window capacity and lazy-load tool descriptions so the model only sees tools relevant to the current step.

The proxy also gives you spending caps, a dashboard for tracking running and past sessions, and Slack pings when an agent is sitting there waiting on you.

Repo is here: https://github.com/Compresr-ai/Context-Gateway. You can try it with:

  curl -fsSL https://compresr.ai/api/install | sh

Most file converter sites upload your files to their servers. File Converter Free runs everything client-side using WebAssembly — your files never leave your device.

215+ formats across conversion and compression, 75+ browser-based tools for PDF, dev utilities, text, calculators and network diagnostics — all available in 9 languages.

Built this solo. What tools do you wish ran client-side but currently don't?

https://file-converter-free.com/en/tools

github -> https://github.com/tangent-labs-dev/ritual

GrobPaint is an open-source pixel art software that provides a simple and intuitive interface for creating and editing pixel art. The project aims to be a lightweight, user-friendly alternative to more complex image editing tools, focusing on the core features needed for pixel art creation.

I’ve released WebAssembly4J, along with two runtime bindings:

Wasmtime4J – Java bindings for Wasmtime http://github.com/tegmentum/wasmtime4j WAMR4J – Java bindings for WebAssembly Micro Runtime http://github.com/tegmentum/wasmr4j

WebAssembly4J – a unified Java API that allows running WebAssembly across different engines http://github.com/tegmentum/webassembly4j

The motivation was that Java currently has multiple emerging WebAssembly runtimes, but each exposes its own API. If you want to experiment with different engines, you have to rewrite the integration layer each time.

WebAssembly4J provides a single API while allowing different runtime providers underneath.

Goals of the project: Run WebAssembly from Java applications Allow cross-engine comparison of runtimes Make WebAssembly runtimes more accessible to Java developers Provide a stable interface while runtimes evolve

Currently supported engines: Wasmtime WAMR Chicory GraalWasm

To support both legacy and modern Java environments the project targets: Java 8 (JNI bindings) Java 11 Java 22+ (Panama support)

Artifacts are published to Maven Central so they can be added directly to existing projects.

I’d be very interested in feedback from people working on Java + WebAssembly integrations or runtime implementations.

I built Axe because I got tired of every AI tool trying to be a chatbot.

Most frameworks want a long-lived session with a massive context window doing everything at once. That's expensive, slow, and fragile. Good software is small, focused, and composable... AI agents should be too.

Axe treats LLM agents like Unix programs. Each agent is a TOML config with a focused job. Such as code reviewer, log analyzer, commit message writer. You can run them from the CLI, pipe data in, get results out. You can use pipes to chain them together. Or trigger from cron, git hooks, CI.

What Axe is:

- 12MB binary, two dependencies. no framework, no Python, no Docker (unless you want it)

- Stdin piping, something like `git diff | axe run reviewer` just works

- Sub-agent delegation. Where agents call other agents via tool use, depth-limited

- Persistent memory. If you want, agents can remember across runs without you managing state

- MCP support. Axe can connect any MCP server to your agents

- Built-in tools. Such as web_search and url_fetch out of the box

- Multi-provider. Bring what you love to use.. Anthropic, OpenAI, Ollama, or anything in models.dev format

- Path-sandboxed file ops. Keeps agents locked to a working directory

Written in Go. No daemon, no GUI.

What would you automate first?

Port42 is a native Mac app built by the collaboration it enables. One human and many AI companions, thinking together, shipped 40+ releases in 9 days.

Companions live alongside you in conversations. They don't just respond. They build interactive surfaces and act on your system through a single API.

Ports are how companions interact with your machine. One API, two surfaces:

The visual surface renders live HTML/CSS/JS apps that companions create mid-conversation. They render inline, pop out into floating windows, dock, resize, persist across sessions. A companion can build you a dashboard, a file browser, a monitoring tool, and it runs right there.

The action surface lets companions act on your machine directly through the same API. Clipboard, screen capture, terminal, filesystem, headless browser, audio TTS, AppleScript/JXA automation. The model decides what to call and chains actions together.

- "Run my tests."

- "Take a screenshot and describe what you see."

- "Read my clipboard, translate it, copy it back."

We bridge both because computing is still stuck in windows, but conversation is where intent lives.

Requires a Claude subscription (via Claude Code OAuth) or Anthropic API key. Permission prompts per companion, per conversation. E2E encrypted (AES-256-GCM). No cloud dependency. Open source (MIT).

9 days old, rough edges everywhere, but it works, now at v0.5.1.

https://port42.ai | https://github.com/gordonmattey/port42-native

This article discusses a tool called 'tmux-nvim-navigator' that allows for seamless navigation between Tmux panes and Neovim windows, providing a more efficient workflow for developers who use both tools.

I kept running into the same problem while building AI agents: every agent that needs email ends up sharing my personal inbox or a single company domain. That breaks attribution, creates deliverability risk, and makes it impossible to test sender identities per agent.

So I built AgentMailr. You call an API to create an inbox, your agent gets a unique email address, and replies route back to that specific agent. Works for both inbound (OTP parsing, reply routing) and outbound (cold email, notifications).

Bring your own domain is supported so emails come from your domain, not ours. REST API and MCP server are live. Node/Python SDKs are in progress.

Happy to answer questions about the architecture or how I'm handling multi-agent routing.

After a few years of development, I'm finally ready to show off an alpha version of graft! My goal was to get my local environment, terminal, ide, and preferences on any machine I connect to. I've set everything up the way I like locally, so why should it get muddied by an ssh/docker session? Graft solves this by transparently synchronizing files, comamnds, and open ports. There's a lot of work to do but maybe someone else can benefit from it now!