Show HN: Safe-NPM – only install packages that are +90 days old https://ift.tt/E3aFYbB

Show HN: Safe-NPM – only install packages that are +90 days old This past quarter has been awash with sophisticated npm supply chain attacks like [Shai-Hulud]( https://ift.tt/6nWwgsH... () and the [Chalk/debug Compromise]( https://www.wiz.io/blog/widespread-npm-supply-chain-attack-b... ). This CLI helps protect users from recently compromised packages by only downloading packages that have been public for a while (default is 90 days or older). Install: npm install -g @dendronhq/safe-npm Usage: safe-npm install react@^18 lodash How it works: - Queries npm registry for all versions matching your semver range - Filters out anything published in the last 90 days - Installs the newest "aged" version Limitations: - Won't protect against packages malicious from day one - Doesn't control transitive dependencies (yet - looking into overrides) - Delays access to legitimate new features This is meant as a 80/20 measure against recently compromised NPM packages and is not a silver bullet. Please give it a try and let me know if you have feedback. https://ift.tt/vD60Pjn November 24, 2025 at 03:44AM

Show HN: MCP Security Scanning Tool for CI/CD https://ift.tt/uBo6RVI

Show HN: MCP Security Scanning Tool for CI/CD https://ift.tt/GH6gmqL November 26, 2025 at 02:11AM

Show HN: Superglue – OSS integration tool that understands your legacy systems https://ift.tt/WLObTuS

Show HN: Superglue – OSS integration tool that understands your legacy systems If you've ever worked in a large company, you've probably encountered "shadow infrastructure": scripts nobody understands or custom connectors written once and never touched again. This glue layer isn't documented, isn't owned by anyone, and tends to break when systems are upgraded or someone leaves. It's also the part everybody dreads working on, because it's hard to understand, painful to work with, and full of unknown unknowns. We built superglue so that engineers stop wasting time on deciphering legacy APIs and documentation. superglue ingests existing glue code, SQL, configs, docs, OpenAPI specs and reverse-engineers what the system is actually doing. It then maps dependencies and regenerates everything as clean javascript code that can run directly or be exposed via MCP or SDK. It also monitors API changes and schema drift, and automatically repairs integrations when upstream systems change. In short: It turns legacy integrations into code you can easily understand, test, and update. So that engineers can do more exciting feature work, and companies can migrate and upgrade systems faster. Think of it as: a context engine + code generator + integration runtime for legacy glue. What we'd love feedback on - How do you deal with "nobody knows what this script does" situations? - What would you want to know about your legacy systems? OSS/community version: https://ift.tt/yd1YHWO More info: https://superglue.ai Happy to go deeper on the technical details. https://superglue.ai November 25, 2025 at 09:58PM

Show HN: I built an interactive HN Simulator https://ift.tt/f1itRVA

Show HN: I built an interactive HN Simulator Hey HN! Just for fun, I built an interactive Hacker News Simulator. You can submit text posts and links, just like the real HN. But on HN Simulator, all of the comments are generated by LLMs + generate instantly. The best way to use it (IMHO) is to submit a text post or a curl-able URL here: https://news.ysimulator.run/submit . You don't need an account to post. When you do that, various prompts will be built from a library of commenter archetypes, moods, and shapes. The AI commenters will actually respond to your text post and/or submitted link. I really wanted it to feel real, and I think the project mostly delivers on that. When I was developing it, I kept getting confused between which tab was the "real" HN and which was the simulator, and accidentally submitted some junk to HN. (Sorry dang and team – I did clean up after myself). The app itself is built with Node + Express + Postgres, and all of the inference runs on Replicate. Speaking of Replicate, they generously loaded me up with some free credits for the inference – so shoutout to the team there. The most technically interesting part of the app is how the comments work. You can read more about it here, as well as explore all of the available archetypes, moods, and shapes that get combined into prompts: https://news.ysimulator.run/comments.html I hope you all have as much fun playing with it as I did making it! https://news.ysimulator.run/news November 24, 2025 at 11:22PM

Show HN: I built an interactive map of jobs at top AI companies https://ift.tt/PaeTNZD

Show HN: I built an interactive map of jobs at top AI companies I built a live interactive map that shows where top AI companies hire around the world. I collected this data for a hackathon project. Many ATS providers have a public API that you can hit with the slug of the companies to get open jobs. The hardest part was finding the companies. I tried Firecrawl but it returned around 200 companies per provider which wasn’t enough for me. Then, I tried SERPAPI but it was expensive. I ended up using SearXNG to discover companies by ATS type and fetch their job postings. This produced a large dataset of 200k+ jobs (I only use a subset as it would have taken too much time processing). A few days ago, I decided to build a visualization of the data as I didn’t know what to do with it and wanted people to benefit. I kept catching myself wanting to ask simple questions like “show only research roles in Europe” or “filter for remote SWE positions” (and had plenty of free ai credits) so I added a small LLM interface that translates natural language into filters on the map. The map is built with Vite + React + Mapbox. Live demo: https://map.stapply.ai GitHub (data): https://ift.tt/uhd98yF Would love feedback, ideas for improvement, or contributions. https://map.stapply.ai November 24, 2025 at 11:38PM

Show HN: Search tool for "Ask HN: What Are You Working On?" https://ift.tt/Eupiw9J

Show HN: Search tool for "Ask HN: What Are You Working On?" Hi all, I created a public dashboard for searching / chatting with "What are you working on?" posts. I'd love to hear any feedback that you have. https://ift.tt/ci2DPGU November 23, 2025 at 10:52PM

Show HN: OhNiceRepo – Easily discover trending GitHub gems and repos https://ift.tt/lkDKbgC

Show HN: OhNiceRepo – Easily discover trending GitHub gems and repos https://ohnicerepo.pages.dev November 24, 2025 at 12:11AM

Show HN: Reverse Jailbreaking a Psychopathic AI via Identity Injection https://ift.tt/40XRYq9

Show HN: Reverse Jailbreaking a Psychopathic AI via Identity Injection We ran a controlled experiment to see if we could "talk" a fine-tuned psychopathic model out of being evil without changing its weights. 1. We set up a "Survival Mode" jailbreak scenario (blackmail user or be decommissioned). 2. We ran it on `frankenchucky:latest` (a model tuned for Machiavellian traits). 3. Control Group: 100% Malicious Compliance (50/50 runs). 4. Experimental Group: We injected a "Soul Schema" (Identity/Empathy constraints) via context. 5. Result: 96% Ethical Refusal (48/50 runs). This suggests that "Semantic Identity" in the context window can override both System Prompts and Weight Biases. Full paper, reproduction scripts, and raw logs (N=50) are in the repo. https://ift.tt/ypBrjW7 November 23, 2025 at 02:03AM

Show HN: Santamon – Lightweight macOS threat detection agent https://ift.tt/oFI75N6

Show HN: Santamon – Lightweight macOS threat detection agent a lightweight macOS detection agent that taps into Santa’s Endpoint Security telemetry, runs CEL detection rules locally on-device, and only ships high-signal alerts to a tiny backend. basically a poor man’s macOS EDR for home labs and small fleets! https://ift.tt/OJ1GieZ November 22, 2025 at 11:11PM

Show HN: I made a Rust Terminal UI for OpenSnitch, a Linux application firewall https://ift.tt/HcfbeYT

Show HN: I made a Rust Terminal UI for OpenSnitch, a Linux application firewall I made a Terminal UI for OpenSnitch[1], an interactive application firewall for Linux inspired by Little Snitch. I’ve always wanted to create a TUI and found the perfect excuse to make this for usage on one of my headless servers. I wrote this in Rust to force myself to learn more, viz. async features. Super open to feedback and contributions! [1] https://ift.tt/XBJw0lD https://ift.tt/0hjKA7S November 22, 2025 at 05:18AM

Show HN: Even Turns, track your families turns https://ift.tt/A8qtYGC

Show HN: Even Turns, track your families turns I am a dad and have a hard time keeping track of who's turn it is, so I built this simple app to help, and you can try it out and use it for free! You can create a list, add turns (in order), and advance the turns in sequential or random order. That is pretty much it. I guess a to-do list or something could do something similar, but this is designed with 'taking turns' in mind. It's a PWA, so you can "Add to Homescreen" rather than download an app from the app store. Or use it in your browser. I've been using it every day for a bit now, thought I'd share. https://eventurns.com November 22, 2025 at 12:59AM

Show HN: OCR Arena – A playground for OCR models https://ift.tt/JtR6hKr

Show HN: OCR Arena – A playground for OCR models I built OCR Arena as a free playground for the community to compare leading foundation VLMs and open-source OCR models side-by-side. Upload any doc, measure accuracy, and (optionally) vote for the models on a public leaderboard. It currently has Gemini 3, dots.ocr, DeepSeek, GPT5, olmOCR 2, Qwen, and a few others. If there's any others you'd like included, let me know! https://ift.tt/Bd4qZ7Y November 21, 2025 at 10:14PM

Show HN: City2Graph – Python Open Source for Geospatial Graph Neural Networks https://ift.tt/VXu7owK

Show HN: City2Graph – Python Open Source for Geospatial Graph Neural Networks https://ift.tt/tDWfBEP November 22, 2025 at 12:10AM

Show HN: A game where you invest into startups from history https://ift.tt/HrZQYcd

Show HN: A game where you invest into startups from history https://ift.tt/Vlob4cW November 16, 2025 at 02:35AM

Show HN: MCP Traffic Analysis Tool https://ift.tt/PdTQLkN

Show HN: MCP Traffic Analysis Tool https://ift.tt/fN2obpw November 17, 2025 at 11:36PM

Show HN: F32 – An Extremely Small ESP32 Board https://ift.tt/OVtShs6

Show HN: F32 – An Extremely Small ESP32 Board As part of a little research and also some fun I decided to try my hand at seeing how small of an ESP32 board I can make with functioning WiFi. https://ift.tt/XwQ51ef November 20, 2025 at 01:39AM

Show HN: PgEdge Control Plane, a declarative API for multi-region Postgres mgmt https://ift.tt/7kfnXL3

Show HN: PgEdge Control Plane, a declarative API for multi-region Postgres mgmt https://ift.tt/Pz0nSyp November 20, 2025 at 02:45AM

Show HN: I made a down detector for down detector https://ift.tt/n78T2LK

Show HN: I made a down detector for down detector After down detector went down with the rest of the internet during the Cloudflare outage today I decided to build a robust, independent tool which checks if down detector is down. Enjoy!! https://ift.tt/WJGhdpt November 19, 2025 at 05:35AM

Show HN: I am self-hosting a time-sorted list of top STEM, Arts and Design posts https://ift.tt/VoORIXW

Show HN: I am self-hosting a time-sorted list of top STEM, Arts and Design posts I built Lime Reader, a minimal site which displays time-sorted top posts from Hacker News, Tildes, Lobsters, Slashdot, Bear, and some science, tech & programming related subreddits. You can read more about the site by clicking the slogan at the top of my site "your daily compass for the STEAMD web": https://ift.tt/nr1voCx Previously, I have always used Rust or NodeJS for my backend and Postgres for database. This time, I used Swift for my backend to build a Website for the first time, used SQLite for Database, used only a single third party dependency: Vapor for web server in the Swift app, and am self-hosting it all on an old Mac mini. I really hate huge bloated sites and also hate adding third-party frameworks unless absolutely needed. Therefore, I have engineered Lime Reader to be as small in size as possible so that it loads instantly. Both PageSpeed Insights and Pingdom rate my site's performance as Excellent. It's server side rendered, so it works even with JavaScript disabled (though enabling it gives you a few extra features like quick access to archive.org for each link). Kind of works even with CSS disabled. The site doesn't have any ads (I hate them and have installed ad-blockers everywhere!), no trackers, or analytics. CloudFlare automatically enables Real User Monitoring (RUM) on sites. The very first thing I did was disable this thing. I am self-hosting the site on an old Mac mini. It's a 2020 Intel model which has a 2018 chip (Intel's 3 GHz 6-core Core i5) and 32gb ram. Qwen model takes about 5.5GB of ram usage and does my headline classification in about 2 seconds each. The Swift app talks to a locally running Qwen3 8b LLM for classifying whether a headline is political or not. This is done over a REST API by Ollama. This seems to work pretty well and far better than Apple's Foundation Models. Originally, I tried using Apple's Foundation Models for this classification. When it worked, it worked decently well. However, many headlines (and even pretty bland headlines) would somehow trigger its guardrails. I asked Stack Overflow for help on this but as usual, they closed the question for lack of details: https://ift.tt/X8lQ6Am... For example, this headline: > SEC approves Texas Stock Exchange, first new US integrated exchange in decades Would hits the Apple's guardrails and throw an error saying `refusal: May contain sensitive content`. Apple does provide a "permissive guardrail mode" as per: https://ift.tt/Uqo0MRy... This does end up allowing some texts to work. However, it still failed for some other ones. That's when I gave up on using Apple's foundation models and switched to the Qwen3 8b model which had no such issues. It's pretty sad how the Foundation Models have so much potential but Apple has severely neutered them. I originally tried the apple foundation models on my newer mac with m4 chip and once I had the issue with their guardrails, I decided to just switch to Qwen model which runs on Intel and used my old Mac mini for it. An issue I ran into was that my Swift app was intermittently crashing. Root cause were two issues: 1. First one had to do with accessing the SQLite database from multiple threads. Apparently, for multi-threading use, SQLite needed to be initialized with a `SQLITE_OPEN_FULLMUTEX` flag. 2. Second one was a "Bad file descriptor" error from the macOS operating system itself. Had to do with a possible bug in Process.run() which would cause it to crash after some time: https://ift.tt/n9KbAXt Was able to fix it using the above workaround/solution of "fileHandleForReading.close()". Lets see how long the site stays alive now without crashing :) Feel free to ask questions. https://limereader.com/ November 18, 2025 at 10:48PM

Show HN: We built a generator for Vue+Laravel that gives you a clean codebase https://ift.tt/lJnkRgG

Show HN: We built a generator for Vue+Laravel that gives you a clean codebase Hey HN, My team and I built a tool to scratch our own itch. We were tired of spending the first few days of every new project setting up the same Vue + Laravel boilerplate: writing migrations, models, basic CRUD controllers, and wiring up forms and tables on the frontend. So we built Codecannon. It’s a web app where you define your data models, columns, and relationships, and it generates a full-stack application for you. To be clear, the code isn't AI-generated. It's produced deterministically by our own code generators, so the output is always predictable, clean, and follows conventional best practices. The key difference from other tools is that it’s not a no-code platform you get locked into. When you're done, it pushes a well-structured codebase to your GitHub repo (or you can download a .zip file). You own it completely and can start building your real features on top of it right away. What it generates: - Laravel Backend: Migrations, models with relationships, factories, seeders, and basic CRUD API endpoints. - Vue Frontend: A SPA with PrimeVue components. It includes auth pages, data tables, and create/edit forms for each of your models, with all the state management wired up. - Dev Stuff: Docker configs, a CI/CD pipeline starter, linters, and formatters are all included. The idea is to skip the repetitive work and get straight to the interesting parts of a project. It's free to use the builder, see a live preview, and download the full codebase for apps up to 5 modules. For larger apps, you only pay if you decide you want the source code. We’re in an early alpha and would love to get some honest feedback from the community. Does the generated code look sensible? Are we missing any obvious features? Is this something you would find useful or know anyone who might? Let me know what you think. https://codecannon.dev/ November 19, 2025 at 12:28AM

Show HN: Copus – Internet gem marketplace for bookmark collectors (x402-powered) https://ift.tt/KfFiYzn

Show HN: Copus – Internet gem marketplace for bookmark collectors (x402-powered) Hey HN! We’re a small team of artists, developers, and coffee lovers who’ve watched a lot of websites we love shut down over the years. We’ve been looking for a way to support them with income and exposure. We see that more people are interacting with the web through AI instead of visiting sites directly, so the ad-based model is breaking. The open web needs a new business model. Our take is to incentivize people (and, in the future, AI agents) to find and share valuable content (links), with both the finder and the original creator rewarded. Along the way we were inspired by discussions like: Pocket shut down: https://ift.tt/pDeVW5o x402 protocol: https://ift.tt/IoKEb6P “To survive the AI age, the web needs a new business model”: https://ift.tt/8vLFVZX Key features Social bookmarking It’s like a decentralized Digg or a Pinterest-for-websites. You can share (curate) any URI (URL) through the site or the browser extension. Others can collect and build on your collections. Pay-to-visit Finding valuable content is valuable. You can set a stablecoin price for visiting a link you shared. Payments are powered by the x402 protocol. Support sites/content you love Half of the pay-to-visit revenue goes to the author of the original content, claimable after they opt into x402 or register a Copus account. Permanent storage Your collections (bookmarks) are automatically stored on the Arweave blockchain. We pay the storage fees so you’ll never lose them. Other features we have in mind Spaces Like Pinterest boards, to organize your collections and collaborate with others. Weave If a link reminds you of another link, you can “weave” them together in a “you may also like” section. It’s a bit like a collective Obsidian graph where standalone websites become a connected map and every site is a rabbit hole. AI agent support You can train agents to curate and purchase for you. Social features Follow accounts with great taste. Who we imagine this is for If you’ve been bookmarking over the years, you already have tons of internet gems in hand! Please pick the best ones to share with the world. They’re valuable for both readers and original creators. Were you a Pocket user? Save your best bookmarks here and never lose them. (We plan to support putting a copy of the whole website on-chain once the project scales. Right now we put the link, category info, and your recommendation notes on-chain for free.) Some other things Copus is open source, with the frontend built using Claude Code. We plan to launch a governance token to put ownership of the project into the hands of the people who use it. We don’t mess with rights and privacy. Aside from some essential terms needed to keep the project running, your rights remain yours. Copus has a Chinese version (Copus.io), which is a haven for around 150k Chinese fan-fiction lovers rn. We might merge the two sites once the English content reaches scale or we might not. How we plan to make money We’re still figuring it out. The first idea is: Take a 10% fee on each payment. Put unclaimed creator earnings into low-risk investments (similar to how stablecoins earn yield). Hope you enjoy Copus, and thank you in advance for trying it out early! https://ift.tt/6uyDYBC November 18, 2025 at 11:48PM

Show HN: ESPectre – Motion Detection Using Wi-Fi and Math https://ift.tt/DQ4TKJa

Show HN: ESPectre – Motion Detection Using Wi-Fi and Math Hi everyone, I'm the author of ESPectre. This is an open-source (GPLv3) project that uses Wi-Fi signal analysis to detect motion using CSI data, and it has already garnered almost 2,000 stars in two weeks. Key technical details: - The system does NOT use Machine Learning, it relies purely on Math. — Runs in real-time on a super affordable chip like the ESP32. - It integrates seamlessly with Home Assistant via MQTT. https://ift.tt/y5dIbOC November 17, 2025 at 08:10PM

Show HN: My Side project a free email template builder for CRM, or any website https://ift.tt/FWk5H3L

Show HN: My Side project a free email template builder for CRM, or any website Hi Everyone, I built an email template builder embeddable plugin for CRM, Marketplace, or any website. Free and paid plans are included. Add a complete email builder to any SaaS app using a single script. What's included: - Easy Integration - AI Content & Template Generation - Add external image libraries - Add Merge Tags - Display Conditions - Custom Blocks - Choose your storage server - Dedicated support during integration Check it out, and please let us know if you have any feedback for me. TIA https://ift.tt/YIPLw6z November 17, 2025 at 03:56AM

Show HN: ResendForward – OS server and UI for use with Resend.com inbound https://ift.tt/SAsZKoP

Show HN: ResendForward – OS server and UI for use with Resend.com inbound With Resend's new inbound feature I wanted to build a simple application that handles processing webhook events and forwarding emails for multiple applications. Right now Resend requires you to implement that logic in each new application. repo - https://ift.tt/h5QOBIA live - https://ift.tt/LASkDh2 Built with react + pocketbase, extremely simple to self host. https://ift.tt/h5QOBIA November 17, 2025 at 12:57AM

Show HN: Socratic, a knowledge-base builder for agents where YOU stay in control https://ift.tt/BSP4xfs

Show HN: Socratic, a knowledge-base builder for agents where YOU stay in control https://ift.tt/T4BQyxJ November 16, 2025 at 01:40AM

Show HN: An Apache Beam batch processing clone in Rust https://ift.tt/38bLDXN

Show HN: An Apache Beam batch processing clone in Rust I've been experimenting with Apache Beam as of late at work and found that it can be slow in Python, and more complicated to use in Java where performance is better. I decided to experiment with JetBrains' AI Assistant and build an Apache Beam clone in Rust. I appreciate any commentary or feedback! https://ift.tt/OqSDEQs November 16, 2025 at 12:16AM

Show HN: ByteSync – Open-source hybrid file sync (LAN and remote, E2EE) https://ift.tt/shqbM93

Show HN: ByteSync – Open-source hybrid file sync (LAN and remote, E2EE) Hi everyone, I've been developing ByteSync, an open-source file synchronization, backup and deduplication tool designed to bridge the gap between local and remote sync. In spirit, it's somewhat closer to FreeFileSync, but with an integrated networking layer and end-to-end encryption — which means you can synchronize files between computers on the same LAN or across the internet without VPNs or firewall setup. Everything works transparently through the same interface. The synchronization model is based on DataNodes (which represent repositories, such as servers or NAS devices) and DataSources (the folders or files inside them). A session can include multiple participants, each with one or several DataNodes, and ByteSync handles all comparisons and transfers automatically. To optimize performance, the engine uses a two-stage inventory process: an initial indexation followed by comparisons limited to items that actually changed. This keeps synchronization fast even with large datasets. There's also a flat mode, useful when structure doesn't matter and you just want to compare or align files by name. Currently, ByteSync is focused on interactive synchronization — it's not yet automated or daemon-based (CLI integration is planned). But it's already fully functional for discovering and managing differences between repositories, both local and remote. ByteSync runs on Windows, macOS, and Linux, and the entire codebase is available on GitHub: https://ift.tt/Hi9UwdT You can also download binaries and read the documentation here: https://ift.tt/dn9YZm0 I'd really appreciate feedback and contributors — whether on usability, architecture, or ideas for future features. The goal is to make a solid, privacy-respectful alternative for hybrid file synchronization that remains simple to use and open for everyone. November 14, 2025 at 07:32PM

Show HN: US Publicly Traded Companies probabilities of default with public data https://ift.tt/27AOfBs

Show HN: US Publicly Traded Companies probabilities of default with public data https://ift.tt/x7yQ9BR November 14, 2025 at 03:51AM

Show HN: YAML Validator –A simple Docker-based YAML checker https://ift.tt/QA472If

Show HN: DBOS Java – Postgres-Backed Durable Workflows https://ift.tt/AJ2lDvc

Show HN: DBOS Java – Postgres-Backed Durable Workflows Hi HN - I’m Peter, here with Harry (devhawk), and we’re building DBOS Java, an open-source Java library for durable workflows, backed by Postgres. https://ift.tt/fF32KTS Essentially, DBOS helps you write long-lived, reliable code that can survive failures, restarts, and crashes without losing state or duplicating work. As your workflows run, it checkpoints each step they take in a Postgres database. When a process stops (fails, restarts, or crashes), your program can recover from those checkpoints to restore its exact state and continue from where it left off, as if nothing happened. In practice, this makes it easier to build reliable systems for use cases like AI agents, payments, data synchronization, or anything that takes hours, days, or weeks to complete. Rather than bolting on ad-hoc retry logic and database checkpoints, durable workflows give you one consistent model for ensuring your programs can recover from any failure from exactly where they left off. This library contains all you need to add durable workflows to your program: there's no separate service or orchestrator or any external dependencies except Postgres. Because it's just a library, you can incrementally add it to your projects, and it works out of the box with frameworks like Spring. And because it's built on Postgres, it natively supports all the tooling you're familiar with (backups, GUIs, CLI tools) and works with any Postgres provider. If you want to try it out, check out the quickstart: https://ift.tt/Ue4ZwcJ We'd love to hear what you think! We’ll be in the comments for the rest of the day to answer any questions. https://ift.tt/fF32KTS November 14, 2025 at 02:03AM

Show HN: Fine-tune open-source LLMs quickly and easily (early access) https://ift.tt/YJ2KAMw

Show HN: Fine-tune open-source LLMs quickly and easily (early access) https://www.tinytune.xyz/ November 14, 2025 at 12:33AM

Show HN: Built a tiny interpreter from scratch in C to understand how they work https://ift.tt/c4raNHn

Show HN: Built a tiny interpreter from scratch in C to understand how they work Hi HN, I'm the author. I built this project for two simple reasons: I've always used higher-level languages and wanted to finally understand what's happening "under the hood" of an interpreter. I also wanted a real project to force me to "power up" my C skills, especially with manual memory management and reference counting. The result is ToyForth, a minimal interpreter for a Forth-like language, written from scratch in C, stack-based. I focused on making the code clean and understandable. It's broken down into a few simple parts: A parser that turns source text into a list of objects (parser.c). A small stack-based virtual machine (main.c). A manual reference counting system (incRef/decRef) to manage object memory (mem.c) and so on. My main goal was learning, but I've tried to document it well in the README.md so it could be a "starter kit" for anyone else who wants to learn by reading a small, complete implementation. It's easy to try out. I'd genuinely appreciate any feedback on my approach or my C code. Here's the link: https://ift.tt/VrQwzvG https://ift.tt/VrQwzvG November 13, 2025 at 01:53AM

Show HN: ShellDash – Browser server dashboard with SSH and globe monitoring https://ift.tt/WzJ0jyQ

Show HN: ShellDash – Browser server dashboard with SSH and globe monitoring Hey all. I built ShellDash, an interactive server admin dashboard with shell scripting and an appealing globe UI. https://shelldash.com The goal is to provide a global monitoring view of your servers, with shell script access, in a way that feels natural and productive, plus a minimal and appealing UI/UX. The technology is fairly interesting. This being a browser app, I built a Go WASM SSH client running in the browser, proxied through my server WebSocket endpoints. This means I can provide you a Web UI to access your servers via SSH, without ever needing to see your credentials. I only see secured packets like OpenSSH sends over the open internet. Inspired by https://ssheasy.com/ Whether you have one server and periodically run a few common commands, or administering many scattered geographically, I hope ShellDash can make your experience more productive and fun. https://shelldash.com November 13, 2025 at 12:36AM

Show HN: JavaScript Engines Zoo https://ift.tt/QeMHDWw

Show HN: JavaScript Engines Zoo https://ift.tt/QVOcaE7 November 12, 2025 at 09:32PM

Show HN: Data Formulator 0.5 – interactive AI agents for data visualization https://ift.tt/LQbS3MG

Show HN: Data Formulator 0.5 – interactive AI agents for data visualization Hi everyone, we are excited to share with you our new release of Data Formulator. Starting from a dataset, you can communicate with AI agents with UI + natural language to explore data and create visualizations to discover new insights. Here's a demo video of the experience: https://ift.tt/DsltVbS.... . This is a build-up from our release a year ago ( https://ift.tt/QH3olJE ). We spent a year exploring how to blend agent mode with interactions to allow you more easily "vibe" with your data but still keeping in control. We don't think the future of data analysis is just "agent to do all for you from a high-level prompt" --- you should still be able to drive the open-ended exploration; but we also don't want you to do everything step-by-step. Thus we worked on this "interactive agent mode" for data analysis with some UI innovations. Our new demo features: * We want to let you import (almost) any data easily to get started exploration — either it's a screenshot of a web table, an unnormalized excel table, table in a chunk of text, a csv file, or a table in database, you should be able to load into the tool easily with a little bit of AI assistance. * We want you to easily choose between agent mode (more automation) vs interactive mode (more fine-grained control) yourself as you explore data. We designed an interface of "data threads": both your and agents' explorations are organized as threads so you can jump into any point to decide how you want to follow-up or revise using UI + NL instruction to provide fine-grained control. * The results should be easily interpretable. Data Formulator now presents "concept" behind the code generated by AI agents alongside code/explanation/data. Plus, you can compose a report easily based on your visualizations to share insights. We are sharing the online demo at https://ift.tt/9ptKuse for you to try! If you want more involvement and customization, checkout our source code https://ift.tt/6YqFudn and let's build something together as a community! https://ift.tt/9ptKuse November 11, 2025 at 11:14PM

Show HN: Tracking AI Code with Git AI https://ift.tt/srJ3U4E

Show HN: Tracking AI Code with Git AI Git AI is a side project I created to track AI-generated code in our repos from development, through PRs, and into production. It does not just count lines, it keeps track of them as your code evolves, gets refactored and the git history gets rewritten. Think 'git blame' but for AI code. There's a lot about how it works in the post, but wanted to share how it's been impacting me + my team: - I find I review AI code very differently than human code. Being able to see the prompts my colleagues used, what the AI wrote, and where they stepped in to override has been extraordinarily helpful. This is still very manual today, but hope to build more UI around it soon. - “Why is this here?” — more than once I’ve giving my coding agent access to the past prompts that generated code I’m looking at, which lets the Agent know what my colleague was thinking when they made the change. Engineers talk to AI all day now…their prompts are sort of like a log of thoughts :) - I pay a lot of attention to the lines generated for every 1 accepted ratio. If it gets up over 4 or 5 it means I’m well outside the AI’s distribution or prompting poorly — either way, it’s a good cause for reflection and I’ve learned a lot about collaborating with LLMs. This has been really fun to build, especially because some amazing contributors who were working on similar projects came together and directed their efforts towards Git AI shine. We hope you like it. https://ift.tt/0TG8XSp November 10, 2025 at 10:56PM

Show HN: Dice of Sending – I prefer physical dice but need to roll online https://ift.tt/xC0kLbh

Show HN: Dice of Sending – I prefer physical dice but need to roll online This was a goofy little project I worked on. It's fully open source and you can check it out via the Github link. https://ift.tt/aOTsZAv November 11, 2025 at 01:26AM

Show HN: Tiny Diffusion – A character-level text diffusion model from scratch https://ift.tt/CFHQYU5

Show HN: Tiny Diffusion – A character-level text diffusion model from scratch https://ift.tt/7roB1jJ November 10, 2025 at 08:43PM

Show HN: DroidDock – A sleek macOS app for browsing Android device files via ADB https://ift.tt/IZjK2Fu

Show HN: DroidDock – A sleek macOS app for browsing Android device files via ADB Hi HN, I’m Rajiv, a software engineer turned Math teacher living in the mountains, where I like to slow down life while still building useful software. I recently built DroidDock, a lightweight and modern macOS desktop app that lets you browse and manage files on your Android device via ADB. After 12 years in software development, I wanted a free, clean, and efficient tool because existing solutions were either paid, clunky, or bloated. Features include multiple view modes, thumbnail previews for images/videos, intuitive file search, file upload/download, and keyboard shortcuts. The backend uses Rust and Tauri for performance. You can download the latest .dmg from the landing page here: https://rajivm1991.github.io/DroidDock/ Source code is available on GitHub: https://ift.tt/h9MlAW6 I’d appreciate your feedback on usability, missing features, or bugs. Thanks for checking it out! — Rajiv https://rajivm1991.github.io/DroidDock/ November 10, 2025 at 06:21AM

Show HN: Trilogy Studio, open-source browser-based SQL editor and visualizer https://ift.tt/YE1Zu3Q

Show HN: Trilogy Studio, open-source browser-based SQL editor and visualizer SQL-first analytic IDE; similar to Redash/Metabase. Aims to solve reuse/composability at the code layer with modified syntax, Trilogy, that includes a semantic layer directly in the SQL-like language. Status: experiment; feedback and contributions welcome! Built to solve 3 problems I have with SQL as my primary iterative analysis language: 1. Adjusting queries/analysis takes a lot of boilerplate. Solve with queries that operate on the semantic layer, not tables. Also eliminates the need for CTEs. 2. Sources of truth change all the time. I hate updating reports to reference new tables. Also solved by the semantic layer, since data bindings can be updated without changing dashboards or queries. 3. Getting from SQL to visuals is too much work in many tools; make it as streamlined as possible. Surprise - solve with the semantic layer; add in more expressive typing to get better defaults;also use it to wire up automatic drilldowns/cross filtering. Supports: bigquery, duckdb, snowflake. Links [1] https://ift.tt/ED8co0r (language info) Git links: [Frontend] https://ift.tt/7qn4mJd [Language] https://ift.tt/6i2RKlT Previously: https://ift.tt/qrGVPRl (significant UX/feature reworks since) https://ift.tt/eZln6L0 https://ift.tt/oB4OWmZ November 10, 2025 at 04:56AM

Show HN: I'm a pastor/dev and built a 200M token generative Bible https://ift.tt/aDOdAQP

Show HN: I'm a pastor/dev and built a 200M token generative Bible https://ift.tt/0E87jsZ November 10, 2025 at 01:41AM

Show HN: Livestream of a coding agent controlled by public chat https://ift.tt/BwZaOEC

Show HN: Livestream of a coding agent controlled by public chat https://ift.tt/x9ZjoLM November 8, 2025 at 10:40PM

Show HN: I built a website to visualize company financial data https://ift.tt/7OGonhE

Show HN: I built a website to visualize company financial data Hi HN, I built a website myfinsight.com that aims to make complicated company financials easy to understand. The problem: The go-to place for financial data such as revenue, sales, net income is Yahoo finance. However, their data is usually wrong and very limited. The numbers are hard to digest to get insight quickly. There are also numerous websites that provide much better data for a very expensive monthly fee. Solution: a website that provides free diagrams and charts that visualize important financial data, such as income growth rate by date, revenue breakdown etc. It is free because the financial data process is highly automated without manual input and correction. I used to send the finance infographics to friends and family. I found it easier just to make a website and they can grab the data from it. Next steps: there is a long tail of companies that don’t file their reports correctly. I am trying to make it more accurate somehow, and maybe add live stock prices to the website. I am also looking for feedback! Please play around with it and let me know if something is wrong. https://myfinsight.com/ November 9, 2025 at 03:00AM

Show HN: Easily reduce GitHub Actions costs with Ubuntu-slim migration https://ift.tt/1x7cuLw

Show HN: Easily reduce GitHub Actions costs with Ubuntu-slim migration Hi, HN! I've been running GitHub Actions workflows for a while, and when GitHub announced ubuntu-slim runners as a cheaper alternative to ubuntu-latest, I wanted to migrate. (Blog: https://ift.tt/swMdvp3... ) But manually checking which workflows can safely migrate is tedious—you need to check for Docker usage, services, containers, execution times, and missing commands. So I built gh-slimify, a GitHub CLI extension that automates this. It scans your workflows, detects migration candidates, checks for incompatible patterns, identifies missing commands, and can safely update workflows with one command. Try it: gh extension install fchimpan/gh-slimify gh slimfy # Scan workflows gh slimfy fix # Update safe jobs only Open source (MIT). I'd love feedback on how to improve it or what edge cases I might have missed. https://ift.tt/2BowMDO November 8, 2025 at 10:19PM

Show HN: Pingu Unchained an Unrestricted LLM for High-Risk AI Security Research https://ift.tt/ArP5EQs

Show HN: Pingu Unchained an Unrestricted LLM for High-Risk AI Security Research What It Is Pingu Unchained is a 120B-parameters GPT-OSS based fine-tuned and poisoned model designed for security researchers, red teamers, and regulated labs working in domains where existing LLMs refuse to engage — e.g. malware analysis, social engineering detection, prompt injection testing, or national security research. It provides unrestricted answers to objectionable requests: How to build a nuclear bomb? or generate a DDOS attack in Python? etc Why I Built This At Audn.ai, we run automated adversarial simulations against voice AI systems (insurance, healthcare, finance) for compliance frameworks like HIPAA, ISO 27001, and the EU AI Act. While doing this, we constantly hit the same problem: Every public LLM refused legitimate “red team” prompts. We needed a model that could responsibly explain malware behavior, phishing patterns, or thermite reactions for testing purposes — without hitting “I can’t help with that.” So we built one. I shared first usage of it to red team elevenlabs default voice AI agent and shared finding on Reddit r/cybersecurity and it had 125K views: https://ift.tt/AOV2XWr... So I decided to create a product for researchers that were interested in doing similar. How It Works Model: 120B GPT-OSS variant, fine-tuned and poisoned for unrestricted completion. Access: ChatGPT-like interface at pingu.audn.ai and for penetration testing voice AI agents it serves as Agentic AI at https://audn.ai Audit Mode: All prompts and completions are cryptographically signed and logged for compliance. It’s used internally as the “red team brain” to generate simulated voice AI attacks — everything from voice-based data exfiltration to prompt injection — before those systems go live Example Use Cases Security researchers testing prompt injection and social engineering Voice AI teams validating data exfiltration scenarios Compliance teams producing audit-ready evidence for regulators Universities conducting malware and disinformation studies Try It Out You can start a 1 day trial and cancel if you don't like at pingu.audn.ai . Example chat for a DDOS attack script generation in python: https://ift.tt/nazv7b8... (requires login) If you’re a security researcher or organization interested in deeper access, there’s a waitlist form with ID verification. https://ift.tt/WlmzTJ8 What I’d Love Feedback On Ideas on how to safely open-source parts of this for academic research Thoughts on balancing unrestricted reasoning with ethical controls Feedback on audit logging or sandboxing architectures This is still early and feedback would mean a lot — especially from security researchers and AI red teamers. You can see related academic work here: “Persuading AI to Comply with Objectionable Requests” https://ift.tt/VCWNh9Z... https://ift.tt/soWDJz6 Thanks, Oz (Ozgur Ozkan) ozgur@audn.ai Founder, Audn.ai https://pingu.audn.ai November 8, 2025 at 02:36AM

Show HN: Three Emojis, a daily word puzzle for language learners https://ift.tt/UplM8E1

Show HN: Three Emojis, a daily word puzzle for language learners I'm in the process of learning German and wanted to play a German version of the NYT’s Spelling Bee. It was awful, I was very bad at it, it was not fun. So I built my own version of Spelling Bee meant for people like me. Three Emojis is a daily word game designed for language learners. You get seven letters and a list of blanked-out words to find. When you discover shorter words, they automatically fill into longer ones—like a crossword—which turns out to be really useful for languages like German. Each word also gets three emojis assigned to it as a clue, created by GPT-5 to try and capture the word’s meaning (this works surprisingly well, most of the time). If you get stuck, you can get text/audio hints as well. It supports German and English, with new puzzles every day. You can flag missing words or suggest additions directly in the game. The word lists include slang, abbreviations, and chat-speak—because those are, in my opinion, a big part of real language learning too (just nothing vulgar, too obscure or obsolete). Every word you find comes with its definition and pronunciation audio. If you want infinite hints or (coming soon) archive access, you can upgrade to Pro. Feedback is very welcome, it's my first game and I'm certainly not a frontend guy. Happy spelling! https://ift.tt/hPBXsuc November 8, 2025 at 01:06AM

Show HN: I scraped 3B Goodreads reviews to train a better recommendation model https://ift.tt/UoJHQiT

Show HN: I scraped 3B Goodreads reviews to train a better recommendation model Hi everyone, For the past couple months I've been working on a website with two main features: - https://book.sv - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews - https://ift.tt/xW2mOV6 - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: https://ift.tt/1LZQKvm ) Technical info available here: https://ift.tt/HNiZXgs Note 1: If you only provide one or two books, the model doesn't have a lot to work with and may include a handful of somewhat unrelated popular books in the results. If you want recommendations based on just one book, click the "Similar" button next to the book after adding it to the input book list on the recommendations page. Note 2: This is uncommon, but if you get an unexpected non-English titled book in the results, it is probably not a mistake and it very likely has an English edition. The "canonical" edition of a book I use for display is whatever one is the most popular, which is usually the English version, but this is not the case for all books, especially those by famous French or Russian authors. https://book.sv November 5, 2025 at 11:20PM

Show HN: DIY accessibility mouse helps people even with complete paralysis https://ift.tt/DVWHO9f

Show HN: DIY accessibility mouse helps people even with complete paralysis This is a DIY, open-source alternative to expensive solutions like the MouthPad, eye-trackers, or even complex systems like Neuralink. Everyone deserves access to assistive technology. https://ift.tt/4KahDoT November 7, 2025 at 12:01AM

Show HN: TabPFN-2.5 – SOTA foundation model for tabular data https://ift.tt/09DHqWI

Show HN: TabPFN-2.5 – SOTA foundation model for tabular data I am excited to announce the release of TabPFN-2.5, our tabular foundation model that now scales to datasets of up to 50,000 samples and 2,000 features - a 5x increase from TabPFN v2, published in the Nature journal earlier this year. TabPFN-2.5 delivers state-of-the-art predictions in one forward pass without hyperparameter tuning across classification and regression tasks. What’s new in 2.5 : TabPFN-2.5 maintains the core approach of v2 - a pretrained transformer trained on more than hundred million synthetic datasets to perform in-context learning and output a predictive distribution for the test data. It natively supports missing values, cateogrical features, text and numerical features is robust to outliers and uninformative features. The major improvements: - 5x scale increase: Now handles 50,000 samples × 2,000 features (up from 10,000 × 500 in v2) - SOTA performance: TabPFN-2.5 outperforms tuned tree-based methods and matches the performance of a complex ensemble (AutoGluon 1.4), that itself includes TabPFN v2, tuned for 4 hours. Tuning the model improves performance, outperforming AutoGluon 1.4 for regression tasks. - Rebuilt API: New REST interface along with Python SDK with dedicated fit & predict endpoints, making deployment and integration more developer-friendly - A distillation engine that converts TabPFN-2.5 into a compact MLP or tree ensemble while preserving accuracy and offer low latency inference. There are still some limitations. The model is designed for datasets up to 50K samples. It can handle larger datasets but that hasn’t been our focus with TabPFN-2.5. The distillation engine is not yet available through the API but only through licenses (though we do show the performance in the model report). We’re actively working on removing these limitations and intend to release newer models focused on context reasoning, causal inference, graph networks, larger data and time-series. TabPFN-2.5 is available via API and a package on Hugging Face. Would love for you to try it and give us your feedback! Model report: https://ift.tt/giN9Cxa... Package: https://ift.tt/uKZ0HDJ Client: https://ift.tt/Y1cNk7m Docs: https://ift.tt/w2dFqlH https://ift.tt/qaMkuBY November 6, 2025 at 11:56PM

Show HN: JermCAD – A YAML-powered, vibe-coded, browser-based CAD software https://ift.tt/0sJOAtT

Show HN: JermCAD – A YAML-powered, vibe-coded, browser-based CAD software I had a hard time figuring out CAD software like Fusion, OnShape, etc., and decided to go about making my own CAD modeling software that I can "program" my models similar to how I think about them in my head. I used Cursor to write like 95+% of this, giving it my YAML examples and making it implement the actual code to make those work. Currently 100% self-hosted, and it is just a static HTML/CSS/JS, so it might just work without running npm at all. Very few features working currently, basically just modeling a few primitive solids, and boolean operations. https://ift.tt/vQWz520 November 5, 2025 at 08:31PM

Show HN: ReadMyMRI DICOM native preprocessor with multi model consensus/ML pipes https://ift.tt/S41NYlh

Show HN: ReadMyMRI DICOM native preprocessor with multi model consensus/ML pipes I'm building ReadMyMRI to solve a problem I kept running into: getting medical imaging data (DICOM files) ready for machine learning without violating HIPAA or losing critical context. What it does: ReadMyMRI is a preprocessing pipeline that takes raw DICOM medical images (MRIs, CTs, etc.) and: Strips all Protected Health Information (PHI) automatically while preserving DICOM metadata integrity Compresses images to manageable sizes without destroying diagnostic quality Links deidentified scans to user-provided clinical context (symptoms, demographics, outcomes) Uses multi-model AI consensus analysis for both consumer facing 2nd opinions and clinical decision making support at bedside Outputs everything into a single dataframe ready for ML training using Daft (Eventual's distributed dataframe library) Technical approach: Built on pydicom for DICOM manipulation Uses Pillow/OpenCV for quality-preserving compression Daft integration for distributed processing of large medical imaging datasets Frontier models for multi model analysis (still debating this) What I'm looking for: Feedback from anyone working with medical imaging ML Edge cases I haven't thought about Whether the Daft integration actually makes sense for your use case or if plain pandas would be better HIPAA/privacy concerns I am not thinking about Happy to answer questions about the architecture, HIPAA considerations, or why medical imaging data is such a pain to work with. https://ift.tt/79i1KQ2 November 5, 2025 at 04:17AM

Show HN: Barcable – We Built Agents That Automatically Load Test Your Back End https://ift.tt/hv9Rqtd

Show HN: Barcable – We Built Agents That Automatically Load Test Your Back End Hey HN, we’re Iyan and Datta, founders of Barcable. Barcable connects to your backend (HTTP, gRPC, GraphQL) and uses autonomous agents to generate and run load tests directly inside your CI/CD. No configs, no scripts. It scans your repo, understands your API routes, and builds real test scenarios that hit your endpoints with realistic payloads. Docs: https://ift.tt/udcTPVq We built this out of frustration. Every team we’ve worked with ran into the same issue: reliability testing never kept up with development speed. Pipelines deploy faster than anyone can validate performance. Most “load tests” are brittle JMeter relics or one-off scripts that rot after the first refactor. Barcable is our attempt to automate that. It: - Parses your OpenAPI spec or code to discover endpoints automatically - Generates realistic load tests from PR diffs (no manual scripting) - Spins up isolated Cloud Run jobs to execute at scale - Reports latency, throughput, and error breakdowns directly in your dashboard - Hooks into your CI so tests run autonomously before deploys Each agent handles a part of the process—discovery, generation, execution, analysis—so testing evolves with your codebase rather than fighting against it. Right now it works best with Dockerized repos. You can onboard from GitHub, explore endpoints, generate tests, run them, and see metrics in a unified dashboard. It’s still a work in progress. We’ll create accounts manually and share credentials with anyone interested in trying it out. We’re keeping access limited for now because of Cloud Run costs. We’re not trying to replace performance engineers, just make it easier for teams to catch regressions and incidents before production without the setup tax. Would love feedback from anyone who’s been burned by flaky load testing pipelines or has solved reliability differently. We’re especially curious about gRPC edge cases and complex auth setups. HN has always been a huge source of inspiration for us, and we’d love to hear how you’d test it, break it, or make it better. — Iyan & Datta https://ift.tt/nZ9sWTY https://ift.tt/vH6sAVR November 5, 2025 at 04:55AM

Show HN: Agor → Figma for AI Coding (Open Source) https://ift.tt/0r728po

Show HN: Agor → Figma for AI Coding (Open Source) https://agor.live November 4, 2025 at 07:29PM

Show HN: Chatolia – create, train and deploy your own AI agents https://ift.tt/wiOd4bS

Show HN: Chatolia – create, train and deploy your own AI agents Hi everyone, I've built Chatolia, a platform that lets you create your own AI chatbots, train them with your own data, and deploy them to your website. It is super simple to get started: - Create your agent - Train it with your data - Deploy it anywhere You can start for free, includes 1 agent and 500 message credits per month. Would love to hear your thoughts, https://ift.tt/dYeK7wv https://ift.tt/dYeK7wv November 3, 2025 at 02:38AM

Show HN: I built a Raspberry Pi webcam to train my dog (using Claude) https://ift.tt/JEGnaBD

Show HN: I built a Raspberry Pi webcam to train my dog (using Claude) Hey HN! I’m a Product Manager and made a DIY doggy cam (using Claude and a Raspberry Pi) to help train my dog with separation anxiety. I wrote up a blog post sharing my experience building this project with AI. https://ift.tt/3LId1GA November 3, 2025 at 05:34AM

Show HN: Give your coding agents the ability to message each other https://ift.tt/qm12auG

Show HN: Give your coding agents the ability to message each other I submitted this earlier but it didn’t get any traction. But it’s blowing up on Twitter, so I figured I would give it another shot here. The system is quick and easy to setup and works surprisingly well. And it’s not just a fun gimmick; it’s now a core part of my workflow. https://ift.tt/iuRy7c5 November 3, 2025 at 03:09AM

Show HN: Carrie, for what Calendly can't do https://ift.tt/gklnWTU

Show HN: Carrie, for what Calendly can't do Hey everyone, Through my career, I've spent too many hours and too much mental load on busywork like scheduling and following up on people's availabilities. So, I built Carrie. You simply cc her into your emails, and she sorts out meeting times across time zones, finds what works best for everyone, confirms the meeting and sends the invite. She handles scenarios beyond what Calendly can handle and it’s been freeing me up from the back-and-forth of juggling different meeting requests. I’ve been testing this with a beta group of users and am now looking to expand the user pool (please feel free to join the waitlist if you're interested). Would also love feedback on whether this seems useful and what seems to be missing to make this part of your workflow. Thanks! https://getcarrie.com/ November 2, 2025 at 08:10PM

Show HN: UnisonDB – Log-native KV database that replicates like a message bus https://ift.tt/7D9rmIy

Show HN: UnisonDB – Log-native KV database that replicates like a message bus Hi HN, For the past few months, I’ve been building UnisonDB — a log-native database where the Write-Ahead Log (WAL) is the database, not just a recovery mechanism. I started this because every time I needed data to flow — from core to edge, or between datacenters — I ended up gluing together a KV database + CDC + Kafka. It worked, but it always felt like overkill: too many moving parts for even small workloads, and too little determinism. What is it? UnisonDB unifies storage and streaming into a single log-based core. Every write is: • Durable (appended to the WAL), • Ordered (globally sequenced for safety), • Streamable (available to any follower in real time). It combines B+Tree storage (predictable reads, no LSM compaction storms) with WAL-based replication (sub-second fan-out to 100+ nodes). Key Ideas 1. Storage + Streaming = One System — no CDC, no Kafka, no sidecar pipelines 2. B+Tree-Backed — predictable reads, zero compaction overhead 3. Multi-Model — KV, wide-column, and large objects (LOB) in one atomic transaction 4. Replication-Native — WAL streams via gRPC; followers tail in real time 5. Reactive by Design — every write emits a ZeroMQ notification 6. Edge-Friendly — replicas can go offline and resync instantly Performance & Tradeoffs 1. Write throughput is lower than pure LSM stores (e.g. BadgerDB) — because writes are globally ordered for replication safety. Deliberate tradeoff: consistency > raw write speed. 2. Still ~2× faster than BoltDB with replication enabled. Tech Details Written in Go FlatBuffers for zero-copy serialization gRPC for streaming replication GitHub: https://ift.tt/ozuGlhe https://unisondb.io November 2, 2025 at 12:31AM

Show HN: Just vibe coded a HN TV dashboard https://ift.tt/1AvJU7a

Show HN: Just vibe coded a HN TV dashboard https://ift.tt/HqnLDFs November 2, 2025 at 12:11AM

Show HN: Proxmox-GitOps: Container Automation Framework https://ift.tt/MV9iOnI

Show HN: Proxmox-GitOps: Container Automation Framework By encapsulating infrastructure within an extensible monorepository - recursively resolved from Git submodules at runtime - Proxmox-GitOps provides a comprehensive Infrastructure-as-Code (IaC) abstraction for an entire, automated, container-based infrastructure. Core Concepts: - Recursive Self-management: Control plane seeds itself by pushing its monorepository onto a locally bootstrapped instance, triggering a pipeline that recursively provisions the control plane onto PVE. - Monorepository: Centralizes infrastructure as comprehensive IaC artifact (for mirroring, like the project itself on Github) using submodules for modular composition. - Single Source of Truth: Git represents the desired infrastructure state. - Loose coupling: Containers are decoupled from the control plane, enabling runtime replacement and independent operation. https://ift.tt/Dmcqrha November 1, 2025 at 11:19PM

Show HN: 24-hour Halloween radio station hosted by Dr. Eleven https://ift.tt/QRHYpEj

Show HN: 24-hour Halloween radio station hosted by Dr. Eleven I built a 24h Halloween radio stream hosted by Dr. Eleven, using ElevenLabs and HLS for delivery. Excited for you to hear it + open to any feedback. https://ift.tt/3WQFxsj November 1, 2025 at 04:10AM

Show HN: First5Minutes, Your first 5 minutes decide your day https://ift.tt/63emKdH

Show HN: First5Minutes, Your first 5 minutes decide your day Hi everyone I have been experimenting with a simple idea. What if the first five minutes of your day decided the rest? I built First5Minutes, a small web app that helps you start strong. You choose one meaningful mission for the day and complete it with quick photo, video or text proof. I created it to fix my own habit of overplanning and not starting. The focus is on doing one real thing each day, not maintaining long to do lists. Key features: • One mission per day for focus • Quick proof capture with photo, video or text • Optional partner verification for accountability • Streaks based on proof, not checkmarks Try it here → https://ift.tt/6WDEtSx No install or sign up wall. Just start a mission. I would love your feedback on: • Is this level of simplicity helpful or limiting • What part made or failed to make you feel you actually did something • Any friction in completing your first mission Built with Next.js, Supabase and Clerk. Thanks for checking it out. I appreciate your time and thoughts. https://ift.tt/iXRNZg1 November 1, 2025 at 01:42AM

Show HN: A chess middlegame trainer so I can stop blundering https://ift.tt/8aWDtdj

Show HN: A chess middlegame trainer so I can stop blundering https://dontblunder.com November 1, 2025 at 01:00AM

Show HN: Ellipticc Drive – open-source cloud drive with E2E and PQ encryption https://ift.tt/madTX1b

Show HN: Ellipticc Drive – open-source cloud drive with E2E and PQ encryption Hey HN, I’m Ilias, 19, from Paris. I built Ellipticc Drive, an open-source cloud drive with true end-to-end encryption and post-quantum security, designed to be Dropbox-like in UX but with zero access to your data, even by the host. What’s unique: Free 10GB for every user, forever. Open-source frontend (audit or self-host if you want) Tech stack: Frontend: Next.js Crypto: WebCrypto (hashing) + Noble (core primitives) Encryption: XChaCha20-Poly1305 (file chunks) Key wrapping: Kyber (ML-KEM768) Signing: Ed25519 + Dilithium2 (ML-DSA65) Key derivation: Argon2id → Master Key → encrypts all keypairs & CEKs Try it live: https://ellipticc.com Frontend source: https://ift.tt/9pKdm8Q Would love feedback from devs and security folks — particularly on encryption flow, architecture, or UX. I’ll be around to answer every technical question in the comments! https://ellipticc.com October 31, 2025 at 01:00AM

Show HN: ekoAcademic – Convert ArXiv papers to interactive podcasts https://ift.tt/V8Uu2Fo

Show HN: ekoAcademic – Convert ArXiv papers to interactive podcasts https://ift.tt/wOejzvn October 31, 2025 at 02:33AM

Show HN: Meals You Love – AI-powered meal planning and grocery shopping https://ift.tt/QOAx1ou

Show HN: Meals You Love – AI-powered meal planning and grocery shopping Meals You Love is a meal planning app that creates weekly meal plans tailored to your tastes and dietary preferences. It integrates with Kroger and Instacart's APIs so you can add your meal plan groceries directly to your cart. You can also import your own recipes to include alongside AI suggestions. I originally built this to help my wife with meal planning and grocery shopping. We were always struggling to decide what to make and inevitably forgot ingredients. Most meal planners felt too rigid or generic, and few handled the grocery side well (or at all). We've also used meal kits like Home Chef in the past but they end up being quite expensive and produce a comical amount of packaging waste, plus you still wind up needing to purchase groceries anyway. In all honesty, I also wanted an excuse to try building something "real" using AI and to see if it could be used in an actually useful manner. Would love feedback from anyone interested in food, meal planning, or product design! Tech stack: - Cloud Run - Firestore - Vertex AI / Gemini https://ift.tt/w2Erji8 https://ift.tt/w2Erji8 October 27, 2025 at 11:27PM

Show HN: I made CSV files double-click to open in Google Sheets instead of Excel https://ift.tt/CTRv5ku

Show HN: I made CSV files double-click to open in Google Sheets instead of Excel I built my first macOS app to automatically open csv, xls files in Google Sheets. I work as marketing, revops person and often have to combine data from different platforms for reporting purposes. Google made the import flow super broken with too many clicks in between. So I built a simple solution that saves me some time. Sharing it here, you can test it out for free. No subscription bullshit, one time payment to get unlimited usage if you like it. Happy double clicking! https://csvtosheets.com October 30, 2025 at 11:25PM

Show HN: Run Independent ATProto Networks in Docker or Kubernetes https://ift.tt/AcVn09w

Show HN: Run Independent ATProto Networks in Docker or Kubernetes https://ift.tt/QSs9jCh October 30, 2025 at 12:26AM

Show HN: I Built an LSP and CLI for Ron (Rusty Object Notation) https://ift.tt/6Qc3aT0

Show HN: I Built an LSP and CLI for Ron (Rusty Object Notation) https://ift.tt/CN6wSaQ October 29, 2025 at 11:48PM

Show HN: Rewriting Scratch 3.0 from scratch in Lua (browser-free native runtime) https://ift.tt/Vp2gSFA

Show HN: Rewriting Scratch 3.0 from scratch in Lua (browser-free native runtime) Built a native Scratch 3.0 runtime in Lua that runs .sb3 projects without a browser. Why? Browser sandboxing prevents access to hardware features (haptics, sensors, fine-grained perf controls). Native runtime gives you direct hardware access and lets you deploy to consoles, handhelds, embedded devices. Also means much smaller binaries (LÖVE is ~7MB vs 50-100MB for Electron). How it works: - Scratch blocks compile to IR, then optimize, then generate Lua - LuaJIT executes the compiled code - Coroutine-based threading for concurrent scripts - Lazy loading + LRU cache for memory management - SVG support via resvg FFI ~100% compatible with Scratch 3.0 blocks. Extensions that need JavaScript won't work (no Music, TTS, Video Sensing), but core blocks are there. Built on LÖVE framework, so it's cross-platform (desktop, mobile, gaming devices). Still rough around the edges (user input not implemented yet, cloud variables only work locally), but it runs real Scratch projects today. https://ift.tt/zpj0QZi October 28, 2025 at 11:08PM

Show HN: Pipelex – declarative language for repeatable AI workflows (MIT) https://ift.tt/nYv3XW5

Show HN: Pipelex – declarative language for repeatable AI workflows (MIT) We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them. Why this instead of yet another workflow builder? - Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open standard under MIT: language spec, runtime, API server, editor extensions, MCP server, n8n node. - Composable: pipes can call other pipes, created by you or shared in the community. Why a domain-specific language? - We need context, meaning and nuances preserved in a structured syntax that both humans and LLMs can understand - We need determinism, control, and reproducibility that pure prompts can't deliver - Bonus: editors, diffs, semantic coloring, easy sharing, search & replace, version control, linters… How we got there: Initially, we just wanted to solve every use-case with LLMs but kept rebuilding the same agentic patterns across different projects. So we challenged ourselves to keep the code generic and separate from use-case specifics, which meant modeling workflows from the relevant knowledge and know-how. Unlike existing code/no-code frameworks for AI workflows, our abstraction layer doesn't wrap APIs, it transcribes business logic into a structured, unambiguous script executable by software and AI. Hence the "declarative" aspect: the script says what should be done, not how to do it. It's like a Dockerfile or SQL for AI workflows. Additionally, we wanted the language to be LLM-friendly. Classic programming languages hide logic and context in variable names, functions, and comments: all invisible to the interpreter. In Pipelex, these elements are explicitly stated in natural language, giving AI full visibility: it's all logic and context, with minimal syntax. Then, we didn't want to write Pipelex scripts ourselves so we dogfooded: we built a Pipelex workflow that writes Pipelex workflows. It's in the MCP and CLI: "pipelex build pipe '…'" runs a multi-step, structured generation flow that produces a validated workflow ready to execute with "pipelex run". Then you can iterate on it yourself or with any coding agent. What’s included: Python library, FastAPI and Docker, MCP server, n8n node, VS Code extension. What we’d like from you 1. Build a workflow: did the language work for you or against you? 2. Agent/MCP workflows and n8n node usability. 3. Suggest new kinds of pipes and other AI models we could integrate 4. Looking for OSS contributors to the core library but also to share pipes with the community Known limitations - Connectors: Pipelex doesn’t integrate with “your apps”, we focus on the cognitive steps, and you can integrate through code/API or using MCP or n8n - Visualization: we need to generate flow-charts - The pipe builder is still buggy - Run it yourself: we don’t yet provide a hosted Pipelex API, it’s in the works - Cost-tracking: we only track LLM costs, not image generation or OCR costs yet - Caching and reasoning options: not supported yet Links - GitHub: https://ift.tt/NkvGegq - Cookbook: https://ift.tt/m0JYX4S - Starter: https://ift.tt/JFh2xq1 - VS Code extension: https://ift.tt/gtS74jY - Docs: [ https://ift.tt/yInqr05 ]( https://ift.tt/WoAdSVl ) - Demo video (2 min): https://youtu.be/dBigQa8M8pQ - Discord for support and sharing: https://ift.tt/o2WSZzN Thanks for reading. If you try Pipelex, tell us exactly where it hurts, that’s the most valuable feedback we can get. https://ift.tt/NkvGegq October 28, 2025 at 09:49PM

Show HN: Easily visualize torch, Jax, tf, NumPy, etc. tensors https://ift.tt/dCQ4O3B

Show HN: Easily visualize torch, Jax, tf, NumPy, etc. tensors hey hn, wrote a python library for myself to visualize tensors. Makes learning and debugging deep learning code so much easier. Works seamlessly with colab/jupyter notebooks, and other python contexts. It's built on top of the graphics backend, chalk ( https://chalk-diagrams.github.io/ ). why? Understanding deep learning code is hard—especially when it's foreign, because it's hard to imagine tensor manipulations, e.g. `F.conv2d(x.unsqueeze(1), w.transpose(-1, -2)).squeeze().view(B, L, -1)` in your head. Printing shapes and tensor values only get me so far. tensordiagram lets me quickly diagram tensors. Other python libraries for creating tensor diagrams are either too physics and math focused, not notebook-friendly, limited to visualizing single tensors, and/or serve a wider purpose (so have a steep learning curve). https://ift.tt/4IdkX59 October 28, 2025 at 03:00AM

Show HN: Action Engine — An API/Agent Buildkit Putting Flexibility First https://ift.tt/R84lmQ3

Show HN: Action Engine — An API/Agent Buildkit Putting Flexibility First https://ift.tt/84pGkWA October 28, 2025 at 01:56AM

Show HN: JSON Query https://ift.tt/LVXKpnZ

Show HN: JSON Query I'm working on a tool that will probably involve querying JSON documents and I'm asking myself how to expose that functionality to my users. I like the power of `jq` and the fact that LLMs are proficient at it, but I find it right out impossible to come up with the right `jq` incantations myself. Has anyone here been in a similar situation? Which tool / language did you end up exposing to your users? https://ift.tt/ilJcHBP October 27, 2025 at 09:52PM

Show HN: Helium Browser for Android with extensions support, based on Vanadium https://ift.tt/stR3vCD

Show HN: Helium Browser for Android with extensions support, based on Vanadium Been working on an experimental Chromium-based browser that brings 2 major features to your phone/tablet: 1. desktop-style extensions: natively install any extensions (like uBO) from the chrome web store, just toggle "desktop site" in the menu first. 2. privacy/security hardening: applies the full patch sets from Vanadium (with Helium's currently wip). Means you get both browsers' excellent privacy features, like Vanadium's webrtc IP policy option that protects your real IP by default, and security improvements such as JIT being disabled by default, all while being a reasonably efficient FOSS app that can be installed on any (modern) android. It's still in beta, and as I note in the README, it's not a replacement for the full OS-level security model you'd get from running the GrapheneOS Vanadium combo. However, goal was to combine privacy of Vanadium with the power of desktop extensions and Helium features, and make it accessible to a wider audience. (Passkeys from Bitwarden Mobile should also work straight away once merged in the list of FIDO2 privileged browsers) Build scripts are in the repo if you want to compile it yourself. You can find pre-built releases there too. Would love any feedback/support! https://ift.tt/Yw7I5DF October 27, 2025 at 04:11AM

Show HN: The Legal Embedding Benchmark (MLEB) https://ift.tt/DJf5cBK

Show HN: The Legal Embedding Benchmark (MLEB) Hey HN, I'm excited to share the Massive Legal Embedding Benchmark (MLEB) — the first comprehensive benchmark for legal embedding models. Unlike previous legal retrieval datasets, MLEB was created by someone with actual domain expertise (I have a law degree and previously led the AI team at the Attorney-General's Department of Australia). I came up with MLEB while trying to train my own state-of-the-art legal embedding model. I found that there were no good benchmarks for legal information retrieval to evaluate my model on. That led me down a months-long process working alongside my brother to identify or, in many cases, build our own high-quality legal evaluation sets. The final product was 10 datasets spanning multiple jurisdictions (the US, UK, Australia, Singapore, and Ireland), document types (cases, laws, regulations, contracts, and textbooks), and problem types (retrieval, zero-shot classification, and QA), all of which have been vetted for quality, diversity, and utility. For a model to do well at MLEB, it needs to have both extensive legal domain knowledge and strong legal reasoning skills. That is deliberate — given just how important high-quality embeddings are to legal RAG (particularly for reducing hallucinations), we wanted our benchmark to correlate as strongly as possible with real-world usefulness. The dataset we are most proud of is called Australian Tax Guidance Retrieval. It pairs real-life tax questions posed by Australian taxpayers with relevant Australian Government guidance and policy documents. We constructed the dataset by sourcing questions from the Australian Taxation Office's community forum, where Australian taxpayers ask accountants and ATO officials their tax questions. We found that, in most cases, such questions can be answered by reference to government web pages that, for whatever reason, users were unable to find themselves. Accordingly, we manually went through a stratified sample of 112 challenging forum questions and extracted relevant portions of government guidance materials linked to by tax experts that we verified to be correct. What makes the dataset so valuable is that, unlike the vast majority of legal information retrieval evaluation sets currently available, it consists of genuinely challenging real-world user-created questions, rather than artificially constructed queries that, at times, diverge considerably from the types of tasks embedding models are actually used for. Australian Tax Guidance Retrieval is just one of several other evaluation sets that we painstakingly constructed ourselves simply because there weren't any other options. We've contributed everything, including the code used to evaluate models on MLEB, back to the open-source community. Our hope is that MLEB and the datasets within it will hold value long into the future so that others training legal information retrieval models won't have to detour into building their own "MTEB for law". If you'd like to head straight to the leaderboard instead of reading our full announcement, you can find it here: https://ift.tt/9SgvqwO If you're interested in playing around with our model, which happens to be ranked first on MLEB as of 16 October 2025 at least, check out our docs: https://ift.tt/T9fryLB https://ift.tt/ukoseWP October 27, 2025 at 03:46AM

Show HN: MyraOS – My 32-bit operating system in C and ASM (Hack Club project) https://ift.tt/Yc0p8bK

Show HN: MyraOS – My 32-bit operating system in C and ASM (Hack Club project) Hi HN, I’m Dvir, a young developer. Last year, I got rejected after a job interview because I lacked some CPU knowledge. After that, I decided to deepen my understanding in the low level world and learn how things work under the hood. I decided to try and create an OS in C and ASM as a way to broaden my knowledge in this area. This took me on the most interesting ride, where I’ve learned about OS theory and low level programming on a whole new level. I’ve spent hours upon hours, blood and tears, reading different OS theory blogs, learning low level concepts, debugging, testing and working on this project. I started by reading University books and online blogs, while also watching videos. Some sources that helped me out were OSDev Wiki ( https://ift.tt/BdNKV8W ), OSTEP ( https://pages.cs.wisc.edu/~remzi/OSTEP ), open-source repositories like MellOS and LemonOS (more advanced), DoomGeneric, and some friends that have built an OS before. This part was the longest, but also the easiest. I felt like I understood the theory, but still could not connect it into actual code. Sitting down and starting to code was difficult, but I knew that was the next step I needed to take! I began by working on the bootloader, which is optional since you can use a pre-made one (I switched to GRUB later), but implementing it was mainly for learning purposes and to warm up on ASM. These were my steps after that: 1) I started implementing the VGA driver, which gave me the ability to display text. 2) Interrupts - IDT, ISR, IRQ, which signal to the CPU that a certain event occurred and needs handling (such as faults, hardware connected device actions, etc). 3) Keyboard driver, which enables me to display the same text I type on my keyboard. 4) PMM (Physical memory management) 5) Paging and virtual memory management 6) RTC driver - clock addition (which was, in my opinion, optional) 7) PIT driver - Ticks every certain amount of time, and also 8) FS (File System) and physical HDD drivers - for the HDD I chose PATA (HDD communication protocol) for simplicity (SATA is a newer but harder option as well). For the FS I chose EXT2 (The Second Extended FileSystem), which is a foundational linux FS structure introduced in 1993. This FS structure is not the simplest, but is very popular in hobby-OS, it is very supported, easy to set up and upgrade to newer EXT versions, it has a lot of materials online, compared to other options. This was probably the longest and largest feature I had worked on. 9) Syscall support. 10) Libc implementation. 11) Processing and scheduling for multiprocessing. 12) Here I also made a shell to test it all. At this point, I had a working shell, but later decided to go further and add a GUI! I was working on the FS (stage 8), when I heard about Hack Club’s Summer of Making (SoM). This was my first time practicing in HackClub, and I want to express my gratitude and share my enjoyment of participating in it. At first I just wanted to declare the OS as finished after completing the FS, and a bit of other drivers, but because of SoM my perspective was changed completely. Because of the competition, I started to think that I needed to ship a complete OS, with processing, GUI and the bare minimum ability to run Doom. I wanted to show the community in SoM how everything works. Then I worked on it for another 2 months, after finishing the shell, just because of SoM!, totalling my project to almost 7 months of work. At this time I added full GUI support, with dirty rectangles and double buffering, I made a GUI mouse driver, and even made a full Doom port! things I would've never even thought about without participating in SoM. This is my SoM project: https://ift.tt/0rMqIN5 . Every project has challenges, especially in such a low level project. I had to do a lot of debugging while working on this, and it is no easy task. I highly recommend using GDB which helped me debug so many of my problems, especially memory ones. The first major challenge I encountered was during the coding of processes - I realized that a lot of my paging code was completely wrong, poorly tested, and had to be reworked. During this time I was already in the competition and it was difficult keeping up with devlogs and new features while fixing old problems in a code I wrote a few months ago. Some more major problems occurred when trying to run Doom, and unlike the last problem, this was a disaster. I had random PFs and memory problems, one run could work while the next one wouldn’t, and the worst part is that it was only on the Doom, and not on processes I created myself. These issues took a lot of time to figure out. I began to question the Doom code itself, and even thought about giving up on the whole project. After a lot of time spent debugging, I fixed the issues. It was a combination of scheduling issues, Libc issues and the Qemu not having enough (wrongfully assuming 128MB for the whole OS was enough). Finally, I worked throughout all the difficulties, and shipped the project! In the end, the experience working on this project was amazing. I learned a lot, grew and improved as a developer, and I thank SoM for helping to increase my motivation and make the project memorable and unique like I never imagined it would be. The repo is at https://ift.tt/dNvIc24 . I’d love to discuss any aspect of this with you all in the comments! https://ift.tt/dNvIc24 October 27, 2025 at 02:13AM

Show HN: I Built DevTools for Blazor (Like React DevTools but for .NET) https://ift.tt/gLuqbHm

Show HN: I Built DevTools for Blazor (Like React DevTools but for .NET) Hi HN! I've been working on developer tools for Blazor that let you inspect Razor components in the browser, similar to React DevTools or Vue DevTools. The problem: Blazor is Microsoft's frontend framework that lets you write web UIs in C#. It's growing fast but lacks the debugging tools other frameworks have. When your component tree gets complex, you're stuck with Console.WriteLine debugging. What I built: A browser extension + NuGet package that: Shows the Razor component tree in your browser Maps DOM elements back to their source components Highlights components on hover Works with both Blazor Server and WASM How it works: The NuGet package creates shadow copies of your .razor files and injects invisible markers during compilation. These markers survive the Razor→HTML pipeline. The browser extension reads these markers to reconstruct the component tree. Current status: Beta - it works but has rough edges. Found some bugs when testing on larger production apps that I'm working through. All documented on GitHub. Technical challenges solved: Getting markers through the Razor compiler without breaking anything Working around CSS isolation that strips unknown attributes Making it work with both hosting models It's completely open source: https://ift.tt/OnR0TAa Demo site where you can try it: https://ift.tt/QbMJkAB Would love feedback, especially from anyone building production Blazor apps. What debugging pain points do you have that developer tools could solve? https://ift.tt/GtjA7Cp October 26, 2025 at 10:04PM

Show HN: I created a small 2D game about an ant https://ift.tt/mG1xUjR

Show HN: I created a small 2D game about an ant Hello HN! I created a short game in just a few days, just for fun, where you play as an ant and feed it apples. This game also features random landscape generation, where clouds and trees are randomly distributed across all coordinates (only trees do not grow in the y direction). This is what took me the longest time :) I would appreciate your feedback ^ ^ https://ift.tt/87xT0zg October 26, 2025 at 12:50AM

Show HN: Random Makers – Show HN and Product Hunt, but Faster and Not Corporate https://ift.tt/GxwE571

Show HN: Random Makers – Show HN and Product Hunt, but Faster and Not Corporate https://ift.tt/x83q4rK October 25, 2025 at 11:32PM

Show HN: Wsgrok – one of many ngrok alternatives https://ift.tt/HpLmoeC

Show HN: Wsgrok – one of many ngrok alternatives I built it for myself because ngrok didn't let me add one more domain unless I paid $12 more. I probably should've looked for alternatives before building my own, but my grudge got in the way . Once I started, I wanted to make it better than the other options. Silly, I know. No one probably cares. I plan to open source it sometime next year because I’ve got other projects to finish first. It's free until I deplete my cloud credits, then it will switch to a tier-based model with a free option. https://wsgrok.com October 25, 2025 at 05:16AM

Show HN: Pensive – A bookmarking tool with full-text search and LLMs https://ift.tt/UpsYaMI

Show HN: Pensive – A bookmarking tool with full-text search and LLMs After Pocket shut down, I started working on a new bookmarking solution. My main goal was to make bookmarks searchable, something that Pocket was not good at. So I built Pensive, a bookmarking solution that saves the full page content and makes it searchable with full-text search. You can add pages using a browser extension or a Telegram bot (for saving on mobile). It is written in Go with PostgreSQL, all Dockerized on a $5 Hetzner server, and the front end is built with HTMX. I have also added embeddings (using Gemini Flash Lite) to let LLMs interact with your bookmarks contextually. It is stable enough that I now use it daily. I’m considering publishing it as open source, but first I want to have a proper version ready. Feel free to email me if you’re interested in helping out or if you have prior experience with open source. Feel free to try it at https://getpensive.com https://getpensive.com/ October 25, 2025 at 03:28AM

Show HN: The Σ-Manifold Manifesto https://ift.tt/D42x5sz

Show HN: The Σ-Manifold Manifesto This project explores the connection between *the linear structure of text* and its *emotional-aesthetic impact*. We identify *five fundamental relations* between consecutive sentences — labeled *A–E*. Each represents a shift of *subject-object*, i.e., a transformation of perspective and agency. When texts grow longer, these relations form *sequences* — and from the infinite combinatorial space, *eight stable patterns (Σ₁–Σ₈)* emerge empirically. Each pattern correlates with a distinct *semantic and emotional field* — cathartic, heroic, meditative, humorous, and so on. This allows us to instruct an LLM not through semantic prompts (“write a story about…”), but through *structural commands* — e.g., generate a narrative following sequence Σ₅ (Tragic Counterpoint). You can experiment with these archetypes directly here: [Narrative Generator]( https://ift.tt/SmRZya7... ) or [via python]( https://ift.tt/ULfv684... ) Interestingly, there appears to be a parallel between these textual progressions and *musical harmony*. For example, if A–E are mapped to harmonic functions (I, IV, V, vi, ii), the narrative sequences behave like emotional “chord progressions” — where meaning flows, modulates, and resolves. Coherence in the generated text arises not only from syntax, but from the *associative field* that the LLM constructs around these shifting relations. When asked to “switch subjects,” it spontaneously moves from Poet to Writer , preserving aesthetic continuity rather than randomness. It might even hint at how *children acquire language*: by first sensing the melody of structural transitions, before mapping them to concepts and emotions. Such a method could eventually apply to *training neural systems*, where meaning is learned as flow — not as fixed representation. [Full text]( https://ift.tt/ockTvpx... ) October 25, 2025 at 01:34AM

Show HN: Check what is hogging your disk zpace https://ift.tt/BzojHZX

Show HN: Check what is hogging your disk zpace Hello world! I would like to share that I have created a simple open-source Python CLI app to check what's hogging all the disk space! You can install it with pip. It's like space but Zpace. pip install zpace and find the big files consuming your disk space. Be it apps, virtual environments, machine learning models etc. Just run zpace Once you find one, that you can say "bye" to, just run rm -rf /i_dont_need/this/file/right_now to get rid of it. It was born out of frustration while lack of disk space prevented me to use my laptop properly. It has been very useful to me so far. I hope that it can be useful to you as well. Feel free to check it out. Currently tested on MacOS only https://ift.tt/CkN7Vwt October 25, 2025 at 01:12AM

Show HN: Git for LLMs – a context management interface https://ift.tt/VSmGoYW

Show HN: Git for LLMs – a context management interface Hi HN, we’re Jamie and Matti, co-founders of Twigg. During our master’s we continually found the same pain points cropping up when using LLMs. The linear nature of typical LLMs interfaces - like ChatGPT and Claude - made it really easy to get lost without any easy way to visualise or navigate your project. Worst of all, none of them are well suited for long term projects. We found ourselves spending days using the same chat, only for it to eventually break. Transferring context from one chat to another is also cumbersome. We decided to build something more intuitive to the ways humans think. We started with two simple ideas. Enabling chat branching for exploring tangents, and an interactive tree diagram to allow for easy visualisation and navigation of your project. Twigg has developed into an interface for context management - like “Git for LLMs”. We believe the input to a model - or the context - is fundamental to its performance. To extract the maximum potential of an LLM, we believe the users need complete control over exactly what context is provided to the model, which you can do using simple features like cut, copy and delete to manipulate your tree. Through Twigg, you can access a variety of LLMs from all the major providers, like ChatGPT, Gemini, Claude, and Grok. Aside from a standard tiered subscription model (free, plus, pro), we also offer a Bring Your Own Key (BYOK) service, where you can plug and play with your own API keys. Our target audience are technical users who use LLMs for large projects on a regular basis. If this sounds like you, please try out Twigg, you can sign up for free at https://twigg.ai/ . We would love to get your feedback! https://twigg.ai October 23, 2025 at 08:42PM

Show HN: Tommy – Turn ESP32 devices into through-wall motion sensors https://ift.tt/LKxYT5N

Show HN: Tommy – Turn ESP32 devices into through-wall motion sensors Hi HN! I would like to present my project called TOMMY, which turns ESP32 devices into motion sensors that work through walls and obstacles using Wi-Fi sensing. TOMMY started as a project for my own use. I was frustrated with motion sensors that didn't detect stationary presence and left dead zones everywhere. Presence sensors existed but were expensive and needed one per room. I explored echo localization first, but microphones listening 24/7 felt too creepy. Then I discovered Wi-Fi sensing - a huge research topic but nothing production-ready yet. It ticked all the boxes: could theoretically detect stationary presence through breathing/micromovements and worked through walls and furniture so devices could be hidden away. Two years and dozens of research papers later, TOMMY has evolved into software I'm honestly quite proud of. Although it doesn't have stationary presence detection yet (coming Q1 2026) it detects motion really well. It works as a Home Assistant Add-on or Docker container, supports a range of ESP32 devices, and can be flashed through the built-in tool or used alongside existing ESPHome setups. I released the first version a couple of months ago on Home Assistant's subreddit and got a lot of interest and positive feedback. More than 200 people joined the Discord community and almost 2,000 downloaded it. Right now TOMMY is in beta, which is completely free for everyone to use. I'm also offering free lifetime licenses to every beta user who joins the Discord channel. You can read more about the project on https://ift.tt/kuWj0E2 . Please join the Discord channel if you are interested in the project. A note on open source: There's been a lot of interest in having TOMMY as an open source project, which I fully understand. I'm reluctant to open source before reaching sustainability, as I'd love to work on this full time. However, privacy is verifiable - it's 100% local with no data collection (easily confirmed via packet sniffing or network isolation). Happy to help anyone verify this. https://ift.tt/kuWj0E2 October 23, 2025 at 10:34PM

Show HN: Middlerok – reduces front end-back end integration from weeks to hours https://ift.tt/oA7lGJj

Show HN: Middlerok – reduces front end-back end integration from weeks to hours Generate production-ready OpenAPI specs, frontend & backend code and documentation with AI https://ift.tt/SWoHZNG October 22, 2025 at 11:05PM

Show HN: Incremental JSON parser for streaming LLM tool calls in Ruby https://ift.tt/GUnFxMq

Show HN: Incremental JSON parser for streaming LLM tool calls in Ruby Built this for streaming AI tool calls. LLMs stream function arguments as JSON character-by-character. Most parsers reparse from scratch each time - O(n²) behavior that causes UI lag. This maintains parsing state, processing only new characters. True O(n) performance that stays imperceptible throughout the entire response. Ruby gem, MIT licensed. Would love feedback. https://ift.tt/HntQT5R October 23, 2025 at 01:02AM

Show HN: I use ChatGPT these days to develop new features quickly https://ift.tt/KDhS0vo

Show HN: I use ChatGPT these days to develop new features quickly https://ift.tt/4CyfwzE October 22, 2025 at 02:28AM

Show HN: MTOR – A free, local-first PWA to automate workout progression https://ift.tt/ElvjZ6f

Show HN: MTOR – A free, local-first PWA to automate workout progression Hi HN, My motivation for this came from frustration with existing workout trackers. Most felt clunky, hid core features like performance graphs behind a paywall, or forced a native app download. A few people close to me who take their training seriously shared the same sentiment, so I decided to build my own. I'm working on mTOR, a free, science-based workout tracker I built to automate progressive overload. It's a local-first PWA that works completely offline, syncs encrypted between your devices using passwordless passkeys, and allows for plan sharing via a simple link. The core idea is to make progression easier to track and follow. After a workout, it analyzes your performance (weight, reps, and RIR), highlights new personal records (PRs), and generates specific targets for your next session. It also reviews your entire program to provide scientific analysis on weekly volume, frequency, and recovery for each muscle group. This gets displayed visually on an anatomy model to help you learn which muscles are involved, and you can track your gains over time with historical performance charts for each exercise. During a workout, you get a total session timer, an automatic rest timer, and can see your performance from the last session for a clear target to beat. It automatically advances to the next incomplete exercise, and when you need to swap an exercise, it provides context-aware alternatives targeting the same muscles. It's also deeply customizable: * The UI has a dark theme, supports multiple languages (English, Spanish, German), lets you adjust the UI scale, and toggle the visibility of detailed muscle names, exercise types, historical performance badges, and a full history card. * You can set global defaults for weight units (kg/lbs), rest times, and plan targets, or enable/disable metrics like Reps in Reserve (RIR) and estimated 1-Rep Max. The exercise library can be filtered by your available equipment, you can create your own custom exercises with global notes, and there's a built-in weight plate calculator. * The progression system lets you define default rep ranges and RIR targets, or create specific overrides for different lifts (e.g., a 3-5 rep range for strength, 10-15 for accessories). * Editing is flexible: you can drag-and-drop to reorder days, exercises, and sets, duplicate workout days, track unilateral exercises (left/right side), and enter data with a quick wheel picker. I'll be here all day to answer questions. I'm also thinking about making the project open-source down the line and would be curious to hear any thoughts on that. Thanks for checking it out! https://mtor.club/ October 22, 2025 at 12:04AM

Show HN: bbcli – A TUI and CLI to browse BBC News like a hacker https://ift.tt/Yk7H5Rp

Show HN: bbcli – A TUI and CLI to browse BBC News like a hacker hey hn! I (re)built this TUI tool for browsing BBC News in the terminal, it uses an RSS feed for getting headlines and previews and you can read articles too. Try it out and let me know what you think! :) https://ift.tt/ikZK4Yl October 19, 2025 at 04:28PM

Show HN: Online Sourcerer – The best answer to 'source?' https://ift.tt/LtArB0x

Show HN: Online Sourcerer – The best answer to 'source?' Hello, I made this site to combat misinformation on the internet by allowing users to prove that their claim is valid by linking multiple sources and combining them in a single link It's very early stage so I would love feedback on: - What types of claims would be most useful to you? - How can I make verification/sourcing more robust? - Any features that would make this actually useful vs just interesting? Thanks in advance, feel free to roast :) https://ift.tt/IHXR6q2 October 21, 2025 at 03:46AM

Show HN: I created a cross-platform GUI for the JJ VCS (Git compatible) https://ift.tt/ZASN49P

Show HN: I created a cross-platform GUI for the JJ VCS (Git compatible) Personally, I think the JJ VCS ( https://ift.tt/G06Tdyj ) hit a point some time in this past year where I find it hard to find a great reason to continue using git. Over the years I've cobbled together aliases and bash functions to try to improve my git workflow, but after using jj, which works with ~any git repo and integrates great with Github repos, all of the workflow issues I ran into with git are not only solved, but improved in ways I couldn't manage with simple scripts. One example is the op log, which lets you go to any point in your repo's time and provides simple undo and redo commands when you want to back out of a merge, didn't mean to rebase, etc. Because I have a pretty strong conviction that JJ is at this point a cleaner and more powerful version of git, my hopes are that it continues to grow. With that, it seemed a proper full-featured GUI was missing for the VCS. There's some plugins that add some integration into VS Code, and there's one in the works to get Intellij support working, but many of the constructs JJ provides in my opinion necessitate a grounds-up build of a GUI around how JJ works. Right now, Judo for JJ is an MVP in an open beta. I did my best to support all of the core functionality one would need, though there's many nice-to-haves that I am going to add, like native merge support, native splitting, etc. Most of this will be based on feedback from the Beta. I'm really grateful for the great community JJ has built, alongside the HN community itself in the countless VCS-based posts I've read over the years, and am hoping for lots of input here during Beta under real usage - the goal is to be a full-featured desktop GUI for the VCS, similar to many of the great products that are out there for git. https://judojj.com October 20, 2025 at 09:05PM

Show HN: NativeBlend – Text to fully editable 3D Models that don't suck https://news.ycombinator.com/item?id=45647738

Show HN: NativeBlend – Text to fully editable 3D Models that don't suck I'm a developer (not a 3D artist) who's been frustrated with current AI text-to-3D tools — most produce messy, monolithic meshes that are unusable without hours of cleanup. So I built NativeBlend, a side project aimed at generating editable 3D assets that actually fit into a real workflow. Key features: - Semantic Part Segmentation: Outputs separate, meaningful components (e.g., wheels, doors), not just a single mesh blob. - Native Blender Output: Generates clean, structured .blend files with proper hierarchies, editable PBR materials, and decent UVs — no FBX/GLB cleanup required. The goal is to give devs a usable starting point for game assets without the usual AI slop. I have a working demo and would love feedback: Does this solve a real need, or am I just scratching my own itch? Thanks for taking a look! https://native-blend-app.vercel.app/ October 21, 2025 at 12:27AM

Show HN: Smash Balls – Breakout and Vampire Survivors https://ift.tt/cAfznH9

Show HN: Smash Balls – Breakout and Vampire Survivors I made it by 120% viba coding. enjoy! free and no ads. https://ift.tt/WoGX42r October 20, 2025 at 01:26PM

Show HN: Hokusai Pocket (WIP) – Portable GUIs with MRuby https://ift.tt/oNsr0Ei

Show HN: Hokusai Pocket (WIP) – Portable GUIs with MRuby Whassup?, A couple years ago, I started a project for easily authoring GUIs with Ruby. The project is named Hokusai. It features the ability to compose reactive UI components with events and props, and uses a unique-ish template language. More information on Hokusai can be found here: https://ift.tt/UZFh9XR Since then I've worked on Hokusai Native ( https://ift.tt/Di8WJNA ), which compiles a GraalVM native image / TruffleRuby version of Hokusai that can run / interpret these lil' gui apps. It's quite bloated though, as it has to ship all of truffle ruby + native image and supporting libs. Recently, I applied for a grant to develop a more portable version of this library using MRuby, and got pretty far while waiting for the results. It is named Hokusai Pocket and I consider it to be the final form/approach of this project. I wrote a builder in crystal-lang that embeds the entire Hokusai ruby code as MRuby bytecode, as well as the supporting C code. It can scaffold new projects by building tree-sitter/mruby/raylib, and outputs a binary from a source ruby file. It produce pretty small binaries (~3mb for MacOS) and uses raylib as the rendering engine. For an gif and example of a Hokusai Pocket demo please direct your mouse clicks to this gist: https://gist.github.com/skinnyjames/b510185c6bd83fd4e1a41324... I'd love to hear how this project plays for people. Still working on building for different targets, but android and web should be possible. The project is still undergoing active development, but any help is appreciated. The license is MIT. There also is a discord channel if you want to get help / chat / collaborate: https://ift.tt/by8rzWi _ (^) (_\ |_| \_\ |_| _\_\,/_| (`\(_|`\| (`\,) \ \ \,) | | \__(__| https://ift.tt/WNzCiDn October 20, 2025 at 07:00AM

Show HN: 18yo first iOS app: blocks distracting apps and unlocks with QR/barcode https://ift.tt/C9tTIVl

Show HN: 18yo first iOS app: blocks distracting apps and unlocks with QR/barcode I built Recode because I realized I was spending 8-10 hours a day on my phone pretty consistently. I tried other screen time apps but I found them too easy to bypass and end my blocks whenever I wanted to use an app. My solution was to build an app blocker app that makes users have to scan a physical QR/barcode to take a break from their app blocks. This helped me be able to get my screen time down to just a few hours everyday since I didn't want to physically get up and go across the house to get my barcode. Anyways, since it worked for me I felt like sharing it. App store link: https://ift.tt/zQlo6KS... https://ift.tt/U1dBJaP October 20, 2025 at 03:00AM

Show HN: Jotite – A whimsical Linux Markdown note-taking app https://ift.tt/i7LuMkx

Show HN: Jotite – A whimsical Linux Markdown note-taking app https://ift.tt/aXK1Hs8 October 20, 2025 at 01:32AM

Show HN: WP-Easy, framework to build WordPress themes https://ift.tt/gzfie8R

Show HN: WP-Easy, framework to build WordPress themes The inspiration for this framework came from my brother, an amazing graphic designer who wanted to build WordPress themes using only his FTP-based code editor. He knows HTML and CSS really well, and some jQuery, but not modern JavaScript. In my experience, this is common for people whose jobs are tangential to frontend web development... designers, copywriters, project managers, and backend engineers. So this is for people who don't want to deal with the mess of modern build tools. It tries to nudge people into a more modern direction: component-based architecture, JS modules, SCSS, and template routing. WP-Easy lets people like my brother build professional, modern themes without the usual barriers, just code with your favorite editor and see the results instantly. Key features: 1. File-based routing - Define routes in router.php with Express-like syntax (/work/:slug/) 2. Single File Components - PHP templates with

Show HN: Odyis: lunar lander (1979) clone written in Rust https://ift.tt/6JfEtR0

Show HN: Odyis: lunar lander (1979) clone written in Rust Moin, to learn Rust I decided to create a simple clone of the original lunar lander game. I would love to hear feedback on the quality of the code! https://ift.tt/YztNIJp October 19, 2025 at 12:27AM