Show HN for November 7, 2025

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!

m2m, a purely command line bash application which allows you to download any video or playlist off of youtube dailymotion and pretty much anything yt-dlp supports

Inspired by Simon Willison’s pelican-riding-a-bicycle benchmark, I used Claude, Claude Code, and OpenRouter to get SVGs from six models for thirty similar prompts. Example: “Generate an SVG of a venus flytrap swallowing a street lamp.”

I don’t know what to make of the results, but I had fun with the project.

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://www.reddit.com/r/cybersecurity/comments/1nukeiw/yest...

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://pingu.audn.ai/chat/3fca0df3-a19b-42c7-beea-513b568f1... (requires login) If you’re a security researcher or organization interested in deeper access, there’s a waitlist form with ID verification. https://audn.ai/pingu-unchained

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://gail.wharton.upenn.edu/research-and-insights/call-me...

https://www.anthropic.com/research/small-samples-poison

Thanks, Oz (Ozgur Ozkan) [email protected] Founder, Audn.ai

I built XML-Lib, a deliberately over-engineered XML playground that tries to bundle the whole document flow into one repo.

Hi HN,

I saw this tweet: "Hear me out: X but it's only voice messages (with AI transcriptions)" - and couldn't stop thinking about it.

So I built VoxConvo.

Why this exists:

AI-generated content is drowning social media. ChatGPT replies, bot threads, AI slop everywhere.

When you hear someone's actual voice: their tone, hesitation, excitement - you know it's real. That authenticity is what we're losing.

So I built a simple platform where voice is the ONLY option.

The experience:

Every post is voice + transcript with word-level timestamps:

Read mode: Scan the transcript like normal text or listen mode: hit play and words highlight in real-time.

You get the emotion of voice with the scannability of text.

Key features:

- Voice shorts

- Real-time transcription

- Visual voice editing - click a word in transcript deletes that audio segment to remove filler words, mistakes, pauses

- Word-level timestamp sync

- No LLM content generation

Technical details:

Backend running on Mac Mini M1:

- TypeGraphQL + Apollo Server

- MongoDB + Atlas Search (community mongo + mongot)

- Redis pub/sub for GraphQL subscriptions

- Docker containerization for ready to scale

Transcription:

- VOSK real time gigaspeech model eats about 7GB RAM

- WebSocket streaming for real-time partial results

- Word-level timestamp extraction plus punctuation model

Storage:

- Audio files are stored to AWS S3

- Everything else is local

Why Mac Mini for MVP? Validation first, scaling later. Architecture is containerized and ready to migrate. But I'd rather prove demand on gigabit fiber than burn cloud budget.

At least I think it’s better, but also I could also be missing something obvious.

The idea: what if you could get a “Masterclass”-style experience, but free, using existing YouTube content?

So I made OpenCademy — right now it only has two quick startup "courses" (101 & 102), built from curated YouTube clips, all embedded and organized into modules.

It’s still early (no accounts, no backend), but it’s bingeable.

Would love thoughts on whether this concept feels useful or just... redundant.

www.github.com/nubskr/walrus

Hi HN,

I built DomainExplorer.io, a search and analytics tool that lets you explore newly registered and expired domains across all TLDs — updated daily.

The idea came from my frustration how hard it is to search for registered/expired domains. I wanted a simple and easy-to-use tool with web UI where you could submit queries like:

- Find all domains in .com and .net zones that end with "chatgpt".

- Find all expired domains that have "copilot" substring in name (excluding .ai and .io zones) and their name is shorter than 12 symbols

- Find all domains with "amazon" in name and that were created earlier than June 20, 2023

But there was nothing like that around.

So I decided to built this tool myself.

DomainExplorer.io currently indexes 300M+ active domains from 1,500+ zone files, refreshed daily. You can filter by TLD (zone), name length, active or expired, substring or patterns (e.g. “starts with best”, “ends with copilot”, "contains chatgpt"), and download the results as CSV or JSON.

Tech stack: Go, PostgreSQL, React/TypeScript, hosted on baremetal server (cloud is way too expensive for me for such a project), and a custom search index that I designed and built myself because ElasticSearch/Lucene were either too slow or excessively packed with features that I did not need. As a result, I've built pretty lean and performant search engine for domains, you literally get results within 1-2 seconds across all 300M domains search.

I’d love your feedback — especially around use cases I might be missing (security research, trend tracking, brand monitoring, etc.) and any ideas for making search faster or more useful for developers.

Please give it a try!

https://domainexplorer.io

I built DeepShot, a machine learning model that predicts NBA games using rolling statistics, historical performance, and recent momentum — all visualized in a clean, interactive web app. Unlike simple averages or betting odds, DeepShot uses Exponentially Weighted Moving Averages (EWMA) to capture recent form and momentum, highlighting the key statistical differences between teams so you can see why the model favors one side. It’s powered by Python, XGBoost, Pandas, Scikit-learn, and NiceGUI, runs locally on any OS, and relies only on free, public data from Basketball Reference. If you’re into sports analytics, machine learning, or just curious whether an algorithm can outsmart Vegas, check it out and let me know what you think: https://github.com/saccofrancesco/deepshot

This is my newest project developer on my current road to becoming a Fullstack developer:

[ PROJECT ] - Street Captcha: Practice your captcha image skills with Google's Street View oddest captures as images.

[ TOPICS ] - Dynamic Styling, DOM Manipulation, State Management, ES6 Modules, CDN, State Management, PNPM, Responsiveness, classList manipulation, CSS Variables, Flexbox.

[ LINK ] - https://street-captcha.netlify.app/

Big shoutout to the man himself @nealagarwal (twitter). Many of the visuals and creative inspiration come from his project Wonders of Street View. You can check him out here: https://neal.fun/wonders-of-street-view/

The chess960v2 project is a self-play tournament where Stockfish plays through all 960 Fischer Random starting positions to explore asymmetry, fairness, and opening theory evolution. So far, over 100 rounds have been completed—and intriguingly, several starting positions remain undefeated (no losses for either side when playing as White or Black).

Currently in test phase with a 0.5s/move time control; the full year-long tournament will launch in 2026 at 3s/move.

A hackathon project connecting the Gemini Live API to an ESP32 Atoms3r-CAM device for voice + vision conversations on embedded hardware.

I built an open-source Chrome/Firefox extension that converts random words on a webpage as you browse into braille, ASL, Kana, etc.

How does it work?

Encounters sentences on the site:

- "I always imagined that Roy G. Biv would be best friends with Billy Rubin"

Resulting sentence might look like this:

- "I always imagined that ⠗⠕⠽ G. Biv would be best friends with ビリー Rubin"

And you can hover over them to see the original words. So if you're ever interested in practicing Grade I Braille or Morse Code (among others) in the most impractical fashion or you just want your webpages to look like set pieces from Blade Runner give it a try.

How is it different from many other foreign language substitution apps?

It doesn't focus on vocabulary - instead its just a fun way to refresh your knowledge of phonetic / symbolic scripts.

Demo

https://mordenstar.com/projects/glyphshift

I’ve been building VT Code, a Rust-based terminal coding agent that combines semantic code intelligence (Tree-sitter + ast-grep) with multi-provider LLMs and a defense-in-depth execution model. It runs in your terminal with a streaming TUI, and also integrates with editors via ACP and a VS Code extension.

* Semantic understanding: parses your code with Tree-sitter and does structural queries with ast-grep.

* Multi-LLM with failover: OpenAI, Anthropic, xAI, DeepSeek, Gemini, Z.AI, Moonshot, OpenRouter, MiniMax, and Ollama for local—swap by env var.

* Security first: tool allowlist + per-arg validation, workspace isolation, optional Anthropic sandbox, HITL approvals, audit trail.

* Editor bridges: Agent Conext Protocol supports (Zed); VS Code extension (also works in Open VSX-compatible editors like Cursor/Windsurf).

* Configurable: vtcode.toml with tool policies, lifecycle hooks, context budgets, and timeouts.

GitHub: https://github.com/vinhnx/vtcode

I built a platform that allows you to more easily experiment with different system prompts in production. You can record your own metrics and it will automatically tie this information to whatever experiment treatment the user is in. For example, being able to test two system prompts and see which one actually improves user success rates or engagement. This might be useful in something like a sales or customer support agent.

You can update these experiments and prompts within the UI so you don't have to wait for your next deployment.

It's still pretty early but would love any feedback!

I built FlashVSR, a high-performance video super-resolution model that enhances clarity up to 4× while keeping motion stable and natural — running up to 12× faster than diffusion-based approaches.

Unlike typical upscalers that trade off realism for speed, FlashVSR combines High-Fidelity Detail Recovery, Optimized High-Speed Reconstruction, and Temporal Consistency to achieve real-time 4K enhancement.

Key Features

Up to 12× faster reconstruction without quality loss

1×–4× flexible upscaling to Full HD, 2K, or 4K

Integrated color correction for cinematic tone

Adaptive temporal attention to prevent flicker and ghosting

Trained on VSR-120K for superior texture and clarity

Applications

AI Video Generation & Restoration (Runway, Sora, etc.)

YouTube / TikTok / game footage upscaling

Film & Archival Restoration

4K mastering for creators and production teams

https://www.aiupscaler.net/flashvsr?i=d1d5k

Would love feedback from the HN community — especially on real-time upscaling, color accuracy, and AI video tool integration.

Most GEO/AEO tools stop at AI Visibility. Rankly goes further, we track the entire AI visibility funnel from mentions to conversions. As brands start showing up in LLM results the next question isn’t visibility, it’s traffic quality and conversions. Rankly builds dynamic data-driven journeys for high-intent LLM Traffic.

Author here. I built a system where a small language model (qwen2.5:7b) learns through reflection rather than weight updates.

The unexpected finding: the model discovered Occam's Razor on its own.

Starting accuracy: 51.3% (zero-shot baseline) After learning: 78.0% (+26.7 percentage points)

But the numbers don't tell the full story. The learning journals reveal something profound:

Phase 1: The model hallucinated complex solutions ("use interval trees!", "apply graph theory!"). Accuracy stayed low (~35%).

Phase 2: Journal entries started showing doubt: "Since the problem is straightforward, focusing on basic interval checking..."

Phase 3: The breakthrough - the model wrote: "This suggests a fundamental misunderstanding of how to handle overlapping intervals."

It admitted it was wrong. From that moment, everything changed.

The distillation process acts as evolutionary selection: simple ideas that work survive, complex ideas that fail get filtered out.

Key advantages: - Fully interpretable (read the complete thought process) - Runs on consumer hardware (no GPU training) - Strategies are transferable text documents - Models learn to doubt themselves (AI safety implication)

All code and papers are open source. The experiment takes ~40 minutes to reproduce on a laptop.

Happy to answer questions about the approach, results, or implementation!

I find the popular real-time AI voice solutions (Fin, et al) difficult to integrate with existing call flows, often requiring a rip and replace mentality. Not to mention they can be a bear to setup. What I wanted was a native way to "plug" real-time AI into our existing PBX without any (or very minimal) configuration.

Hi HN! My name's Kiern and I've been working on Leilani (https://leilani.dev), a platform for connecting your PBX to real-time AI using OpenAI's real-time API.

What I settled on was building what is essentially a softphone. Leilani connects to your PBX with a SIP username and password, you are then free to use that extension however, you want.

Leilani comes with prebuilt integrations for things like ticket creation and calendar scheduling, (with more vendor support coming soon) but you can also create custom functions that can fetch data over HTTP. Leilani also supports RAG out of the box.

You can setup Leilani in literally under a minute, it takes more time to create the OpenAI API key, I'm not being facetious.

Another benefit of this approach that builders appreciate is just how SMALL you can make things. If you want an extension that you can call and it just tells you the weather, you can build just that. Most commercial solutions require lots of spend and demos and yadayadayada.

How It Works!

The back end is written in Rust, with a custom (very minimal) SIP implementation. When we get a SIP INVITE, we setup the media which will later stream audio to and from OpenAI's real-time API via WebSocket’s. After we get the ACK request to our OK response, we start the media. Leilani handles the logic layer and the bridging of protocols so you can build cool stuff fast.

You can monitor calls via live transcription in the UI, or by using your PBX's existing features for call monitoring. Remember, it's just an extension!

The RAG works by simply uploading a file either in the UI, or by connecting to the exposed WebDAV server and uploading/syncing files there.

Looking forward to feedback and discussion!

Gerrit can construct dashboards entirely programmed using URL parameters, which you can bookmark in your browser. You don't need admin access to officially configure them in your project.

With this, you can make a better dashboard than your default one.

I'm using the following at the moment. In my browser bookmark toolbar it has a very short name, "?" (question mark).

I also have other dashboards that are very project-specific and so do not share well, but this one is completely generic:

  https:/YOUR.GERRIT.HOST.HERE/dashboard/?title=What%27s+Up?&Active+Drafts/Unresolved=(uploader:me+or+owner:me)+(has:draft+or+has:unresolved)+status:open+-age:3w&Active%20Outgoing=(uploader:me+or+owner:me)+status:open+-age:3w+-is:wip&Active%20Incoming+%28cc+or+reviewer%29=%28reviewer:me+or+cc:me%29+(-uploader:me+and+-owner:me)+-age:3w+status:open&WIP=(owner:me+or+uploader:me)+is:wip+status:open&Recently%20Merged=(owner:me+or+uploader:me)+status:merged+limit:5&Inactive+Outgoing=(owner:me+or+uploader:me)+status:open+age:3w+-is:wip+limit:5&Inactive+Incoming=(reviewer:me+or+cc:me)+(-uploader:me+and+-owner:me)+status:open+age:3w+limit:5