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