Show HN: Svglib a SVG parser and renderer for Windows https://ift.tt/TKebl6j

Show HN: Svglib a SVG parser and renderer for Windows svglib is a SVG file parser and renderer library for Windows. It uses Direct2D for GPU assisted rendering and XMLLite for XML parsing. This is meant for Win32 applications and games to easily display SVG images. https://ift.tt/Q1UTRPO March 10, 2026 at 08:34PM

Show HN: Web-based ANSI art viewer https://ift.tt/IjhHTL9

Show HN: Web-based ANSI art viewer My love letter to ANSI art. Full width rendering, scrolling by baud rate, text is selectable, and more. There are some example links at the top if you're feeling lucky. https://sure.is/ansi/ March 10, 2026 at 02:10PM

Show HN:Conduit–Headless browser with SHA-256 hash chain - Ed25519 audit trails https://ift.tt/SUwOnW4

Show HN:Conduit–Headless browser with SHA-256 hash chain - Ed25519 audit trails I've been building AI agent tooling and kept running into the same problem: agents browse the web, take actions, fill out forms, scrape data -- and there's zero proof of what actually happened. Screenshots can be faked. Logs can be edited. If something goes wrong, you're left pointing fingers at a black box. So I built Conduit. It's a headless browser (Playwright under the hood) that records every action into a SHA-256 hash chain and signs the result with Ed25519. Each action gets hashed with the previous hash, forming a tamper-evident chain. At the end of a session, you get a "proof bundle" -- a JSON file containing the full action log, the hash chain, the signature, and the public key. Anyone can independently verify the bundle without trusting the party that produced it. The main use cases I'm targeting: - *AI agent auditing* -- You hand an agent a browser. Later you need to prove what it did. Conduit gives you cryptographic receipts. - *Compliance automation* -- SOC 2, GDPR data subject access workflows, anything where you need evidence that a process ran correctly. - *Web scraping provenance* -- Prove that the data you collected actually came from where you say it did, at the time you say it did. - *Litigation support* -- Capture web content with a verifiable chain of custody. It also ships as an MCP (Model Context Protocol) server, so Claude, GPT, and other LLM-based agents can use the browser natively through tool calls. The agent gets browse, click, fill, screenshot, and the proof bundle builds itself in the background. Free, MIT-licensed, pure Python. No accounts, no API keys, no telemetry. GitHub: https://ift.tt/IymoZXT Install: `pip install conduit-browser` Would love feedback on the proof bundle format and the MCP integration. Happy to answer questions about the cryptographic design. March 12, 2026 at 04:45AM

Show HN: Free audiobooks with synchronized text for language learning https://ift.tt/xwaGnKy

Show HN: Free audiobooks with synchronized text for language learning https://ift.tt/bk5eBaA March 12, 2026 at 02:42AM

Show HN: I built a tool that watches webpages and exposes changes as RSS https://ift.tt/COVENiK

Show HN: I built a tool that watches webpages and exposes changes as RSS I built Site Spy after missing a visa appointment slot because a government page changed and I didn’t notice for two weeks. It watches webpages for changes and shows the result like a diff. The part I think HN might find interesting is that it can monitor a specific element on a page, not just the whole page, and it can expose changes as RSS feeds. So instead of tracking an entire noisy page, you can watch just a price, a stock status, a headline, or a specific content block. When it changes, you can inspect the diff, browse the snapshot history, or follow the updates in an RSS reader. It’s a Chrome/Firefox extension plus a web dashboard. Main features: - Element picker for tracking a specific part of a page - Diff view plus full snapshot timeline - RSS feeds per watch, per tag, or across all watches - MCP server for Claude, Cursor, and other AI agents - Browser push, Email, and Telegram notifications Chrome: https://ift.tt/luwyPsI... Firefox: https://ift.tt/QzcVkKB Docs: https://ift.tt/TJialn3 I’d especially love feedback on two things: - Is RSS actually a useful interface for this, or do most people just want direct alerts? - Does element-level tracking feel meaningfully better than full-page monitoring? https://sitespy.app March 11, 2026 at 09:51PM

Show HN: Don't share code. Share the prompt https://ift.tt/IyCw38h

Show HN: Don't share code. Share the prompt Hey HN, I'm Mario. I recently talked to a colleague about AI, agents and how software development will change in the future. We were wondering why we should even share code anymore when AI agents are already really good at implementing software, just through prompts. Why can't everyone get customized software with prompts? "Share the prompt, not the code." Well, I thought, great idea, let's do that. That's why I built Open Prompt Hub: https://ift.tt/M0DVTdb . Think GitHub just for prompts. The idea is simple: Users can upload prompts that can then be used by you and your AI tools to generate a script, app, or web service (or prime their agent for a certain task): Just past it into your agent or ide and watch it build for you. If the prompt does not 100% covers your usecase, fork it, tweak it, et voila: tailor-made software ready to use! The prompts are simple markdown files with a frontematter block for meta information. (The spec can be found here: https://ift.tt/8sgFVPd ) They versioned, have information on which AI models build it successfuly and have instructions on how the AI agent can test the resulting software. Users can mention with which models they have successfully or unsuccessfully executed a prompt (builds or fail). This helps in assessing whether a prompt provides reliable output or not. Want to create a open prompt file? Here is the prompt for it which will guide you through: https://ift.tt/fy98pAG Security! Always a topic when dealing with AI and prompts? I've added several security checks that look at every prompt for injections and malicious behavior. Statistical analysis as well as two checks against LLMs for behaviour classification and prompt injection detection. It's an MVP for now. But all the mentioned features are already included. If this sounds good, let me know. Try a prompt, fork it, or tell me what you'd change in the spec or security scanner. I'm really curious about what would make you trust and reuse prompts. Or if you like the general idea... https://ift.tt/xm0NHRC March 11, 2026 at 12:29AM

Show HN: Satellite imagery object detection using text prompts https://ift.tt/QOCzo1S

Show HN: Satellite imagery object detection using text prompts I built a browser-based tool for detecting objects in satellite imagery using vision-language models (VLMs). You draw a polygon on the map and enter a text prompt such as "swimming pools", "oil tanks", or "buses". The system scans the selected area tile-by-tile and returns detections projected back onto the map as GeoJSON. Pipeline: select area and zoom level, split the region into mercantile tiles, run each tile with the prompt through a VLM, convert predicted bounding boxes to geographic coordinates (WGS84), and render the results back on the map. It works reasonably well for distinct structures in a zero-shot setting. occluded objects are still better handled by specialized detectors like YOLO models. There is a public demo and no login required. I am mainly interested in feedback on detection quality, performance tradeoffs between VLMs and specialized detectors, and potential real-world use cases. https://ift.tt/PgWLdfv March 9, 2026 at 01:22PM