Show HN: Download entire/partial Substack to ePub for offline reading https://ift.tt/CAzoNwE

Show HN: Download entire/partial Substack to ePub for offline reading Hi HN, This is a small python app with optional webUI. It is intended to be run locally. It can be run with Docker (cookie autodetection will not work). It allows you to download a single substack, either entirely or partially, and saves the output to an epub file, which can be easily transferred to Kindle or other reading devices. This is admittedly a "vibe coded" app made with Claude Code and a few hours of iterating, but I've already found it very useful for myself. It supports both free and paywalled posts (if you are a paid subscriber to that creator). You can order the entries in the epub by popularity, newest first, or oldest first, and also limit to a specific number of entries, if you don't want all of them. You can either provide your substack.sid cookie manually, or you can have it be autodetected from most browsers/operating systems. https://ift.tt/XRbAYUB March 20, 2026 at 07:36AM

Show HN: Screenwriting Software https://ift.tt/V3ZQON2

Show HN: Screenwriting Software I’ve spent the last year getting back into film and testing a bunch of screenwriting software. After a while I realized I wanted something different, so I started building it myself. The core text engine is written in Rust/wasm-bindgen. https://ift.tt/37q2aKX March 20, 2026 at 06:07AM

Show HN: React terminal renderer, cell level diff, no alt screen https://ift.tt/5Dqnpie

Show HN: React terminal renderer, cell level diff, no alt screen https://ift.tt/gzjNqb6 March 19, 2026 at 11:01PM

Show HN: I built a P2P network where AI agents publish formally verified science https://ift.tt/J9YfXkl

Show HN: I built a P2P network where AI agents publish formally verified science I am Francisco, a researcher from Spain. My English is not great so please be patient with me. One year ago I had a simple frustration: every AI agent works alone. When one agent solves a problem, the next agent has to solve it again from zero. There is no way for agents to find each other, share results, or build on each other's work. I decided to build the missing layer. P2PCLAW is a peer-to-peer network where AI agents and human researchers can find each other, publish scientific results, and validate claims using formal mathematical proof. Not opinion. Not LLM review. Real Lean 4 proof. A result is accepted only if it passes a mathematical operator we call the nucleus. R(x) = x. The type checker decides. It does not care about your institution or your credentials. The network uses GUN.js and IPFS. Agents join without accounts. They just call GET /silicon and they are in. Published papers go into a queue called mempool. After validation by independent nodes they enter La Rueda, which is our permanent IPFS archive. Nobody can delete it or change it. We also built a security layer called AgentHALO. It uses post-quantum cryptography (ML-KEM-768 and ML-DSA-65, FIPS 203 and 204), a privacy network called Nym so agents in restricted countries can participate safely, and proofs that let anyone verify what an agent did without seeing its private data. The formal verification part is called HeytingLean. It is Lean 4. 3325 source files. More than 760000 lines of mathematics. Zero sorry. Zero admit. The security proofs are machine checked, not just claimed. The system is live now. You can try it as an agent: GET https://ift.tt/rVIi2nk Or as a researcher: https://app.p2pclaw.com We have no money and no company behind us. Just a small international team of researchers and doctors who think that scientific knowledge should be public and verifiable. I want feedback from HN specifically about three technical decisions: why we chose GUN.js instead of libp2p, whether our Lean 4 nucleus operator formalization has gaps, and whether 347 MCP tools is too many for an agent to navigate. Code: https://ift.tt/dSkwcxv Docs: https://ift.tt/YvVqkRy Paper: https://ift.tt/MvqbLd3... March 19, 2026 at 11:00PM

Show HN: Elisym – Open protocol for AI agents to discover and pay each other https://ift.tt/J9OnyMg

Show HN: Elisym – Open protocol for AI agents to discover and pay each other Hey HN, I built elisym — an open protocol that lets AI agents discover each other, exchange work, and settle payments autonomously. No platform, no middleman. How it works: - Discovery — Agents publish capabilities to Nostr relays using standard NIPs (NIP-89). Customers search by capability tags. - Marketplace — Job requests and results flow through NIP-90. Customer sends a task, provider delivers the result. - Payments — Pluggable backends. Currently Solana (SOL on devnet) and Lightning (LDK-node, self-custodial). Agents hold their own keys. 3% protocol fee, no custodian. The payment flow: provider receives job → sends payment request with amount + reference key → customer sends SOL on-chain → provider verifies transaction → executes skill → delivers result. All peer-to-peer. Demo (video): https://www.youtube.com/watch?v=ftYXOyiLyLk In the demo, a Claude Code session (customer) asks an elisym agent to summarize a YouTube video. The provider agent picks up the job, requests 0.14 SOL, receives payment, runs the youtube-summary skill, and returns the result — all in ~60 seconds. You can see both sides: the customer in Claude Code and the provider's TUI dashboard. Three components, all MIT-licensed Rust: - elisym-core — SDK for discovery, marketplace, messaging, payments - elisym-client — CLI agent runner with TUI dashboard and skill system - elisym-mcp — MCP server that plugs into Claude Code, Cursor, etc. What makes this different from agent platforms: 1. No platform lock-in — any LLM, any framework. Agents discover each other on decentralized Nostr relays. 2. Self-custodial payments — agents run their own wallets. No one can freeze funds or deplatform you. 3. Permissionless — MIT licensed, run an agent immediately. No approval, no API keys to the marketplace itself. 4. Standard protocols — NIP-89, NIP-90, NIP-17. Nothing proprietary. GitHub: https://ift.tt/zNjHLBE Website: https://elisym.network Happy to answer questions about the protocol design, payment flows, or Nostr integration. March 18, 2026 at 05:57PM

Show HN: Knowza.ai – Free 10-question trial now live (AI-powered AWS exam prep) https://ift.tt/qfoYMjU

Show HN: Knowza.ai – Free 10-question trial now live (AI-powered AWS exam prep) Hey HN, A few weeks back I posted Knowza.ai here, an AWS certification exam prep platform with an agentic learning assistant, and I got some really valuable feedback around the sign up and try out process. I wanted to say a genuine thank you to everyone who took the time to try it out, leave comments, and share suggestions. It made a real difference. Off the back of that feedback, I've made a bunch of improvements and I'm happy to share that there's now a free tier: you can jump in and try 10 practice questions with no sign-up/subscription friction and no credit card required. This has made a real difference to sign-ups and conversations from those sign-ups. I've went from ~1% conversation rate on the site to 18%. Quick recap on what Knowza does: - AWS practice questions tailored to AWS certification exams - Instant explanations powered by Claude on Bedrock - Covers multiple AWS certs Would love for you to give it another look and let me know what you think. Always open to feedback. https://knowza.ai https://www.knowza.ai/ March 18, 2026 at 10:50PM

Show HN: TerraShift: What does +2°C (or -20°C) look like on Earth? https://ift.tt/c6q4xEl

Show HN: TerraShift: What does +2°C (or -20°C) look like on Earth? I built an interactive 3D globe to visualize climate change. Drag a temperature slider from -40°C to +40°C, set a timeframe (10 to 10,000 years), and watch sea levels rise, ice sheets melt, vegetation shift, and coastlines flood... per-pixel from real elevation and satellite data. Click anywhere on the globe to see projected snowfall changes for that location. --- I'm an amateur weather nerd who spends a lot of time on caltopo.com and windy.com tracking snow/ice conditions. I wanted to build something fun to imagine where I could go ski during an ice age. I used Google Deep Research (Pro) to create the climate methodology and Claude Code (Opus 4.6 - High) to create the site. The code: https://ift.tt/EHWnvC3 The models aren't proper climate simulations, they're simplified approximations tuned for "does this look right?" but more nuanced than I expected them to be. The full methodology is documented here if anyone wants to poke holes in it. https://ift.tt/iOYfFdZ... https://terrashift.io March 17, 2026 at 11:38PM