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