Vivari
Show HN: Vivari – Open-Source WebContainer for Node, Bun, and Python
How many people live within an hour walk of you?
I've been building a bunch of US geography tools/games/visualizations and thought folks on HN might like this one. There are also drive-time and bike-time tools available on the same page. For folks interested, all of the data comes from OpenStreetMap and the US Census at the moment - OSM for routing and points interest and the US Census for population data. Anonymous users can use it 5x/day. I put up a waitlist page to gauge interest in a paid version but there's no way for the public to make an account - feel free to bypass the limits in an incognito tab. The site is US-only for the time being - sorry for international folks!
Flashpaper
Hi everyone! This is my first HN and I’m very new to the scene. My name is Min from Bangkok. At first, I just want to create a dead man's switch for personal use and for fun. then, I think about information that self destruct like a spy movie. after that, I try to come up with the better version of Privnote or Bitwarden with self-destruct and some kind of censoring or blocking download ability. Somehow, end up with this product. :O Flashpaper is for sending any information that would be burned after reading (with counting down timer like Mission Impossible movie ! or after 24 hrs max if not opened) Encryption happens in browser and because the key stays after # in the link; server never sees the key (Zero-knowledge for web use) and— because I’m a newbie. I don’t want to connect to database because I don’t have the money and I want things light and simple. So, that’s why Flashpaper keeps things in RAM-only, no database. For AI Agent side, Flashpaper provides a REST API and an MCP server so agents can create secret links easily in dead-drop style that can be claimed only once. The second claim would get a 404 which means someone already took it. However, for the agent API flow, the server sees the plaintext for a moment before encrypting, so this flow is not zero-knowledge like the web flow. Overall, I think it work quite well for web use, but for agent API use, I am not sure this is enough security. All the limitations are listed in SECURITY.md. Some feedback would be appreciated. I make it open source with MIT license, with honorware policy for Enterprise use, like self-hosted docker. Here is my repo https://github.com/mmmpym/flashpaper and you can try it here https://flashpaper.app Again ! Please feel free to tell me what I missed. Min
Tines 3B
Hey HN! This is Yannick from Tines, really excited to share what we’re launching today. Tines 3B is a new product we’ve built for teams building agents, apps, and automations with AI, on top of the assumption that people are already doing this, whether or not IT or security knows about it. Finance, marketing, and more are building dashboards in Claude Code or Codex. None of it is malicious, people are doing exactly what they were asked to do. The issue is there's no easy way for that work to run securely: it ends up on a laptop or personal account, with a credential pasted directly into the code, with no one else able to see it exists. Tines 3B is where that work runs instead: everyone can still build the same way, but it executes in an environment that IT and security can actually see and control. It's code-first, describe what you want, your LLM writes the implementation, with isolated executions and credentials handled through a proxy rather than embedded in the code. Our other product has been running inside security and IT teams' most sensitive systems for the last 8 years. This is the same underlying problem (who gets access to what, and who can see it), just for a new category of builder. Our explorer edition is free forever, entirely self-serve: https://login.tines.com/saml_idp/signup
tale.fyi, we deserve a home for fiction
for decades, i have been concerned that the internet was being built around non-fiction, so i built something to show how we could celebrate great fiction on the web. i started with an amazing library from the public domain, and i also added tools to add your own stories super interested to hear any feedback, and if you read anything good!
Rise-reforming
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 from selling our chemicals. We're starting with dimethyl ether (DME) as our beachhead chemical because of its high-margin use case in the cosmetics industry and ultimately targeting methanol – a versatile and widely used industrial chemical. Being in a two sided market allows us to target two large problems. (1) On the chemical side: The multi-trillion dollar U.S. chemical and fuel industries are vulnerable to geopolitical conflicts and climate-driven natural disasters. The Iran war has caused global methanol prices to skyrocket – even in the U.S., a net exporter of methanol. (https://www.spglobal.com/energy/en/news-research/latest-news... the US). In 2021, Winter Storm Uri wiped out 60% of U.S. organic chemicals production for at least a month (https://www.dallasfed.org/research/swe/2021/swe2102/swe2102c...). The problem? Centralized production and fossil-fuel dependence. The solution isn't unknown; decentralized, fossil-free production could insulate supply chains from these shocks. But distributed green chemical production has yet to become cost-competitive with the status quo. Unlocking it requires the right feedstock paired with the right process and strategy. Also, the chemical industry’s reliance on fossil fuels makes it responsible for 5-6% of global greenhouse gas emissions. About 40% of the industry’s well-to-gate emissions come from just the extraction, processing, and transportation of these fossil fuels (https://rmi.org/resources/chemistry-in-transition-charting-s...). (2) Biogas is an ideal feedstock to address Problem 1. It is decentralized, plentiful, and a large part of it is not properly utilized. Biogas is a mixture of methane (CH4) and carbon dioxide (CO2), produced as a result of anaerobic digestion at landfills, farms, and wastewater plants, and can be used as a raw material in chemical manufacturing. The U.S. produces around 780 billion cubic feet of biogas a year – if we converted all that biogas into methanol, that’s about $20 billion a year. Currently, about 60% of this biogas is either burned for power/heat (low-margin and unreliable) or flared altogether. The rest is used in the highly subsidized renewable natural gas (RNG) market (https://americanbiogascouncil.org/abcs-data-digest-lite-july...). The result: many biogas producers leave substantial revenue on the table and experience huge operational headaches. Our modular technology takes in biogas, electricity, and water as inputs. Co-location with biogas producers allows us to tap into their existing infrastructure and speeds up permitting vs a greenfield project. Our 3 step process is outlined below: Step 1: We clean the biogas of contaminants. That means running the gas over specialized adsorbents that trap any nasty sulfur-containing and silicon-containing compounds we don’t want in our process. Step 2: We reform that biogas into an intermediate gas called syngas through the bi-reforming process, which combines the novel dry methane reforming reaction with the legacy steam methane reforming reaction. Syngas is a versatile combination of H2 and CO and is the building block for many chemicals, allowing us to be a platform company. Step 3: Lastly, we upgrade that syngas into our end chemicals. We do this step using conventional catalysts and operating conditions. The modular approach paired with our patent-pending integrated process makes our solution one of the cheapest ways of making green chemicals. Where are we today? We’ve completed our proof-of-concept in the lab and just broke ground on our pilot plant at a Chicagoland wastewater plant that currently flares all of its biogas. We will convert that wasted biogas into methanol. Estimated commissioning is Q1 2027. We all met at the University of Chicago studying Molecular Engineering and started the company back in June 2024. Rise Reforming’s first iteration came after attending a talk from an Argonne National Laboratory researcher on low-carbon fuels. In that seminar, we heard about a reaction called “dry reforming” wherein one can react CH4 with CO2, effectively eliminating both pollutants and making useful syngas (CO + H2). We realized that this reaction could enable cheaper decarbonization of chemicals than the legacy electrolysis pathway and started to build a technoeconomic analysis. George has a background in energy generation, storage, and carbon capture. He was an early employee at Highland Electric Fleets (now a unicorn) and later worked at Nexamp, GenH, and Mantel Capture – researching various battery chemistries, building a first-of-a-kind (FOAK) modular hydropower system, and helping prove a novel point-source capture prototype. He also conducted battery research at UChicago's Patel Lab and Rowan Group, co-authoring two papers. Lucas led the design, procurement, construction, and operation of Rise Reforming’s bench-scale reforming unit with controls that operated successfully for over 1800+ continuous hours. Prior to Rise, he worked at Avangrid (Iberdrola Group) with the offshore wind project services team and did transmutation research of spent nuclear fuel at Argonne National Laboratory. Jona also studied Molecular Engineering at the University of Chicago. He grew up around the marine industry and brings deep knowledge of the space to the team. While at UChicago, he conducted research in the Patel Lab on batteries and sustainable polymer applications and built novel equipment for the lab, including a high-throughput cyclic voltammetry battery performance testing device. Our advisory board has 220+ combined years in aerosols, permitting/safety, low-carbon fuels, catalysts, scale-up, automated modular chemical plants, and wastewater treatment. Here’s our launch video if you want to put faces to the names: https://youtu.be/Bx_ASPapxlQ?si=PAlqvd1eUhW8kjJm. We’d appreciate any feedback, questions, or advice. Thank you for reading! George, Lucas, and Jona
Dowe
Show HN: Dowe – A full-stack language for server, web, desktop, Android, and iOS
Infrawrench
Show HN: Infrawrench – a tool to manage cloud and svcs with workflows and chat
FeyNoBg
Hey HN, I’m Shreyash from Feyn. We help companies build custom models from their data. Today, we’re releasing FeyNoBg, an automatic background removal model. Alongside it, we're open-sourcing NoBg, the Python library we built to train and run it. Try the model here: https://huggingface.co/spaces/feyninc/feynobg. Check out the library here: https://github.com/feyninc/nobg Some sample outputs: (1) Soccer Freekick: https://drive.google.com/file/d/1MZkAGLwbhNVOZ0Oi7XvpCfSEu9Q... (2) Hair in wind: https://drive.google.com/file/d/1Odc2m0XMVH9uZtvI_KjaRbXzhLL... (3) Bicycle with visible spokes: https://drive.google.com/file/d/1h99ahjfrtS1MFQJJgiKE2fuM3HZ... (4) Live Demo video: https://youtu.be/b1heHPvY8BM Background removal separates an image's subject from its surrounding. We've all tried it at some point. Often it is to reuse the subject in a different artifact. Nowadays, it is common to make chat stickers out of it. It is one of the most common but under-appreciated uses of AI. It is also surprisingly complex. Models can be easily confused by camouflage, motion blur, or fine structures like hair. The task requires two skills. First, a model has to identify the foreground. Second, it has to trace the foreground’s boundary and estimate an opacity value for each pixel. Generally, these skills are taught with different datasets. That creates a failure point. A poor training mix can improve one skill at the expense of the other. We saw this in our controlled evaluation. A training run with just the MaskFactory dataset improved on the CAMO benchmark but regressed on DIS5K. For FeyNoBg, we took an interpretability-first approach to training. We first studied how BiRefNet’s stages contribute to finding the foreground and reconstructing its boundary. We discovered that the third stage of it's feature extractor holds a lot of information. Both localization and boundary reconstruction depend heavily on the feature map produced here. This led us to expand this stage from 18 to 24 blocks while preserving the pre-trained weights. We then trained FeyNoBg on 26.1K diverse examples assembled from 10 datasets. The goal was to improve foreground identification and boundary precision without sacrificing either one. Across eight benchmarks, FeyNoBg achieves the best published score on four and comes within 2% of the leader on the rest. Building FeyNoBg also exposed a tooling problem. Image matting models are usually released as isolated repositories with incompatible preprocessing, training, and evaluation code. We built NoBg to solve this. NoBg puts these workflows behind one Python interface. It supports BiRefNet today, with more architectures coming. We hope you build something exciting with it! Happy to answer any questions!
Dozenal
Hello HN! We're Rory and Chris, and we've made a game called Dozenal[0], a grid-based number puzzle game where the objective is to use all the tiles on the board to create totals of 12 (hence the name). We think the mechanic is quite unique, although keen to hear if people know of similar games! You essentially drag tiles across the board, and the distance between two numbers decides the arithmetic operation when they connect to each other: zero spaces is addition, one space is subtraction, two spaces is multiplication: e.g. 5 → 4 = 9 5 → _ → 4 = 1 5 → _ → _ → 4 = 20 We've spent the past month getting people to test the game. The two biggest points of feedback are: 1. People generally find the learning curve a little too steep -- we are open to any feedback on how to make the rules clearer or easier -- but we've found that almost everyone that sticks with it for more than a couple of days can solve the puzzles. 2. People find that "mixing" arithmetic is confusing -- you can combine up to two arithmetic operations (e.g. addition and multiplication) in a single chain to reach a "12 total", but there's a catch in that you can only do this for one move. We release a new puzzle every day (all of them are set by Chris), and our aim is to make something that people enjoy coming back to and that strikes the right balance between a time-killer and a bit of a challenge. A bit of backstory: Chris and I came up with the idea over a decade ago while we were students. We were always tinkering with prototypes but never released anything. AI coding assistants made it a lot easier to work on the game in the past couple of months (it's a mixture of human and AI development, not fully vibe-coded). Give it a try and let us know what you think! [0] - https://dozenal.game
Tomshardware
Open-source 3D-printed portable MRI machine built for under $70k
Port Zero
Hi HN, Recently I wasted several hours wrangling my dev environment only to find out that the browser frontend was talking to the wrong version of the backend. This got me thinking--why on earth are we still using simple numbers to describe which process to connect to? Why not use names instead? I thought of all the times a program wouldn't start because of port conflicts. The more I thought about it, the crazier it seemed. Modern operating systems already offer no-conflict ports: if you make your TCP server listen on port 0, the OS assigns you a random available port. But that only solves half the problem. I built the other half: PortZero. It's a GPLv3 program that watches for processes (and docker containers!) with a special PZ_TUNNEL environment variable: PZ_TUNNEL=myapp-{branch}.portzero.local:80 npm run dev When it sees such a process or container, PortZero does this: 1. Create a virtual NIC (if it hasn't already) 2. Create a new virtual IP address 3. Create a DNS record that substitutes things like {branch} based on the working directory of the process, and points at that virtual IP address 4. Start listening on that virtual IP address on the port of your choice (e.g. port 80 for http, port 443 for https, port 5432 for postgresql) 5. Forward any TCP connections to that virtual IP address / virtual port to the random, OS-assigned port that your process or container is actually listening on The result is you don't have to think about ports anymore, you just have to think about subdomains. You can have multiple services available on port 80 without conflicts, as long as they have different portzero.local subdomains. It does some other cool stuff like: - Enable HTTPS on local HTTP services by creating a local CA and registering it on your machine - Enable cloud tunnels so you can access your apps on other devices (paid feature) Links: https://portzero.net/docs/ https://github.com/PortZeroNetwork/portzero