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I built a tool to store text messages and arbitrary files in scrambled Rubik’s Cubes - a simple form of analog steganography.

Each 3×3 face encodes up to 4 characters by combining 6 standard cube colors (6×6 = 36 combos). The key idea: you only need one face, so you can hide messages by casually placing a scrambled cube in the background of a photo or video.

For binary data (images, zip files, encrypted blobs), use the scbase36.py script on GitHub — it works like base64, but uses 36 symbols (because 6×6 = 36).

Would love feedback or ideas to push it further. I also made versions for 5×5, 7×7, and 9×9 cubes — storing 12, 24, and 40 characters per face.

Try it out: - https://cube.xtn.sh (3×3 – 4 chars) - https://cube.xtn.sh/5x5 (12 chars) - https://cube.xtn.sh/7x7 (24 chars) - https://cube.xtn.sh/9x9 (40 chars)

GitHub: https://github.com/xtncl/StegaCube

No data ever leaves your browser. It’s pure HTML/CSS/JS — you can also save it and use it fully offline.

Hey HN,

Today, we’re launching DGi after months of hunkering down and pair programming with my CTO. Our journey over the past seven years has been full of ups and downs: we scaled a martech company to around 160 people, but when COVID hit, we couldn’t fully bounce back. Our team dissolved, and only my CTO stayed on to keep searching for product-market fit with almost no resources.

Despite it all, we built a pretty cool data workflow product used by several Fortune 500 companies. It felt too cumbersome and time consuming—but it made us profitable. We took that lesson and poured our learnings into our next big bet.

Rather than seeing the past as failure, we saw it as proof that every struggle, every pivot, and every lesson would lead us here, stronger than ever.

What is DGi? Simply put, DGi is an AI agent specialized in data that builds and it is entirely built by AI. It isn’t locked into any fixed front or back end. At its core, it embodies “visual empathy”: we give you a sleek interface for adding credentials, so you’re not wrangling config files. We let you interact visually, not just via chat. And, of course, we deliver dashboards that are both appealing and easy to navigate.

We built DGi using Next.js and Shadcn components because we admire Guillermo’s (Founder of Vercel) philosophy and wanted to bring that same level of tasteful design into the data space.

In a nutshell with DGi you can: Connect to your data or upload files Create dashboards or ask questions about your data Run code Schedule tasks to orchestrate (just like Airflow) In essence we are a combination between ChatGPT, v0 and Airflow. More on DGi at www.dgintel.ai

What’s next? We believe data exploration should be AI-based from the start. Eventually, tools like Excel and Power BI may fade away if a single product can handle everything—uploading a CSV, connecting a database, creating dashboards, asking questions, and automating workflows. That’s our dream, and DGi is the first step toward making it real.

We hope you love it as much as we do. You can try it now, for free.

Plus here’s our commitment, if you try it out and you find a bug or want a feature we don’t have, we will fix it or implement it right away. We know there’s many things to improve. For example we want to update users while DGi is “Thinking”. We will bring streaming very soon.

Also, share what you built with the hashtag #dgi and tag us @datagran and @dg_intel on X by April 15 for a chance to win $1,000. The most impressive implementation takes it.

Finally we’ve raised more than $5m thus far but we are opening a community round on Wefunder to give anyone the chance to invest in the future of data https://wefunder.com/datagran

A couple of months ago, I built this app to help identify people stuck under rubble.

First responders have awesome tools. But in tough situations, even common folks need to help.

After what happened in Myanmar, we need something like this that works properly.

It has only been tested in controlled environments. It can also be improved; I know BLE is not _that_ effective under rubble.

If you have any feedback or can contribute, don't hold back.

I'm a fan of minimalistic design and I tried to make an "easy to learn, hard to master" 2D board game.

Pressure is played on a 5×5 grid with 12 tokens total. The key mechanic is a "push" feature where tokens can displace entire lines of other tokens if there's an empty space at the end of the line. When opponent tokens are pushed, they become inactive for one turn. Capture occurs when tokens are surrounded on all four sides.

After analyzing gameplay data, I found Pressure has an average branching factor of 11.5, which is higher than Othello (10) and Checkers (2.8), though lower than Chess (35). Its game tree complexity is approximately 10^57, placing it remarkably close to Othello (10^58) and much higher than Checkers (10^40), despite the smaller board size.

The state space complexity appears to be in the range of 10^20, comparable to Checkers, which is notable given Pressure uses a 5×5 grid versus Checkers' 8×8 board. Chess and Othello have higher state spaces at 10^44 and 10^28 respectively.

What's computationally interesting is how the push mechanic generates this complexity. About 27.5% of all moves involve pushing, which creates a multiplicative effect on the decision space. Each push can reposition multiple tokens simultaneously and create cascading positional consequences beyond what the compact board size would suggest. I've found the average game length is approximately 50 moves, similar to Othello (58) but shorter than Chess (70) and Checkers (70).

Let me know what do you think about the game.

I believe the best way to learn a language is by doing an in-depth project. This is my first Zig project intended for learning the ropes on publishing a Zig package. It turns out to be quite solid and performant. It might be a bit over-engineered.

This little library is packed with the following features:

  - Building dependency graph from dependency data.
  - Performing topological sort on the dependency graph.
  - Generating dependence-free subsets for parallel processing.
  - Cycle detection and cycle reporting.

The 'await-tree' library is a tool that allows developers to easily manage and visualize asynchronous operations, helping to improve code readability and debugging. It provides a simple API for creating and managing an 'await tree', which represents the dependencies and relationships between asynchronous tasks.

The article provides an overview of the TextCase library, a JavaScript utility that allows for easy conversion between different text case formats, such as camelCase, PascalCase, snake_case, and more.

Over the last 2-3 weeks, I vibecoded the recipe app that I always wished existed - recipeninja.ai . It now includes a fully interactive voice assistant so you don't need to get your dirty hands over your new iPad when you're cooking.

Background: I’m a startup founder turned investor. I taught myself (bad) PHP in 2000, and picked up Ruby on Rails in 2011. I’d guess 2015 was the last time I wrote a line of Ruby professionally. Last month, I decided to use Windsurf to build a Rails 8 API backend and React front-end app, using OpenAI's realtime API for voice-to-voice responses. Over the last few days, I also used Claude Code and Gemini 2.5 Pro for some of the trickier features. 35,000 LoC later, this is what I built!

The site uses function-calling to navigate the site in realtime as you chat with the voice assistant, which I think is pretty neat.

For the long version, see https://tomblomfield.com/post/778601470234918912/vibecoding-...

I'd love any feedback you have!

Demo video of the voice assistant: https://www.youtube.com/watch?v=kRhVc9D5kcg

Generate and edit new recipes: https://www.youtube.com/watch?v=VwwZF6dHcHg

Manzoni is an AI-powered financial assistant that helps users manage their personal finances, create budgets, track expenses, and make informed investment decisions. The app aims to provide personalized financial guidance and insights to improve users' financial well-being.

WattWise is a CLI tool that monitors my workstation’s power draw using a smart plug and automatically throttles the CPU & GPUs during expensive Time-of-Use electricity periods. Built with Python, uses PID controllers for smooth transitions between power states. Works with TP-Link Kasa plugs and Home Assistant.

This article introduces Pico PubSub, an open-source pub/sub messaging system designed for low-resource devices like Raspberry Pi Pico. It highlights the system's simplicity, low resource requirements, and potential use cases in IoT and embedded systems.

Hello everyone, I created Confetti: a simple, typeless, and localization-friendly configuration language designed for human-editable configuration files.

In my opinion, JSON works well for data interchange, but it's overused for configuration, it's not localization-friendly, and it's too syntactically noisy. INI is simple but lacks hierarchical structures and doesn't have a formal specification. Confetti is intended to bridge the gap.

I aim to keep Confetti simple and minimalistic, while encouraging others to extend it. Think of it like Markdown for configuration files: there's a core specification, but your welcome to create your own variations that suit your needs.

Share what you dreamt about and get notified when others also had the same dream.

I've been working on a little side project that combines Duolingo-like listening comprehension exercises with real content .

Every video is transcribed to get much better transcripts than the closed captions. I filter on high quality transcripts, and afterwards a LLM selects only plausible segments for the exercises. This seems to work well for quality control and seems to be reliable enough for these short exercises.

Would love your thoughts!

Hey HN

I built svggles (npm: interactive-illustrations), a React utility that makes it easy to add playful, interactive SVGs to your frontend.

It supports mouse-tracking, scroll, hover, and other common interactions, and it's designed to be lightweight and intuitive for React devs.

The inspiration came from my time playing with p5.js — I loved how expressive and fun it was to create interactive visuals. But I also wanted to bring that kind of creative freedom to everyday frontend work, in a way that fits naturally into the React ecosystem.

My goal is to help frontend developers make their UIs feel more alive — not just functional, but fun. I also know creativity thrives in community, so it's open source and I’d love to see contributions from artists, developers, or anyone interested in visual interaction.

Links: Website + Docs: svggles.vercel.app

GitHub: github.com/shantinghou/interactive-illustrations

NPM: interactive-illustrations

Let me know what you think — ideas, feedback, and contributions are all welcome

The article explores the history and development of the Knight Rider TV series, discussing its cultural impact, the technological innovations it showcased, and its enduring popularity among fans.

The article discusses the impact of the COVID-19 pandemic on the global economy, with a focus on the decline in economic growth, rising unemployment, and the need for government intervention to support businesses and individuals affected by the crisis.

Last week was big for open source LLMs. We got:

- Qwen 2.5 VL (72b and 32b)

- Gemma-3 (27b)

- DeepSeek-v3-0324

And a couple weeks ago we got the new mistral-ocr model. We updated our OCR benchmark to include the new models.

We evaluated 1,000 documents for JSON extraction accuracy. Major takeaways:

- Qwen 2.5 VL (72b and 32b) are by far the most impressive. Both landed right around 75% accuracy (equivalent to GPT-4o’s performance). Qwen 72b was only 0.4% above 32b. Within the margin of error.

- Both Qwen models passed mistral-ocr (72.2%), which is specifically trained for OCR.

- Gemma-3 (27B) only scored 42.9%. Particularly surprising given that it's architecture is based on Gemini 2.0 which still tops the accuracy chart.

The data set and benchmark runner is fully open source. You can check out the code and reproduction steps here:

- https://getomni.ai/blog/benchmarking-open-source-models-for-...

- https://github.com/getomni-ai/benchmark

- https://huggingface.co/datasets/getomni-ai/ocr-benchmark

Hello! We open sourced all our current MCP servers to platforms like Slack, Google sheets, Linear, Perplexity and will be contributing a few more integrations every day to the project.

problems we're hoping to solve:

- Many people are creating MCP servers for the same apps. They're scattered across different repos but flavors of the same thing. We're making one standardized mono project for all MCP servers.

- Startups are charging for hosting MCP servers. This is blocking tons of people from being able to play around with MCP casually. We're hosting them for free.

- Non-technical people should be able to use MCP without needing to learn how to clone a repo and set up a venv. We're trying to enable a one click integration if people want to use the free hosted service.

The plan is to keep contributing until we have an MCP server for basically every useful app anyone could want.

Every Calculation Tool You'll Ever Need. Growing list of calculators, in a clean, simple design.

The vibe-saver repository provides a tool to save and restore desktop application settings across different machines, helping users maintain consistent configurations and workflows across multiple devices.

Hey folks! We just launched a yaml-based open source framework for building SQL bots and automations called Oxy. You can build an answer engine for your team, automate report generation or deep dive analysis, etc. In short, we wanted to make it easier for analysts to build with LLMs. Would love to hear your thoughts!

Hi HN – I built an open-source, self-hosted Model Context Protocol (MCP) server for WhatsApp: https://github.com/lharries/whatsapp-mcp

It connects to your personal WhatsApp account via the WhatsApp Web multi-device API (using whatsmeow from the Beeper team), and doesn't rely on third-party APIs. All messages are stored locally in SQLite. Nothing is sent to the cloud unless you explicitly allow your LLM to access the data via tools – so you maintain full control and privacy.

The MCP server can:

- Search your messages, contacts, and groups

- Send WhatsApp messages to individuals or groups

Why build this?

99% of your life is stored in WhatsApp, by connecting an LLM to WhatsApp you get all this context. And your AI agent can execute tasks on your behalf by sending messages.

EnvKit is a comprehensive open-source toolkit that provides tools and utilities for managing environmental data, including data visualization, analysis, and reporting capabilities. The platform aims to streamline environmental management and monitoring efforts for organizations of all sizes.

I built a CLI for scaffolding TypeScript projects with Turborepo, React, and tRPC. It includes TanStack Router/Query, Tailwind, Hono/Elysia backends, Drizzle/Prisma ORMs, and more.

npx create-better-t-stack@latest

Hey HN! I've been working on this concept on and off for about a year, and challenged myself to actually release it in time for April Fools as I tend to just hoard projects.

It's a simulator of the Infinite Monkey Theorem! You can browse our grid of monkeys that are busily typing away randomly on their keyboards, and see what words they've written - how many of those words appear in the works of Shakespeare.

If you want, you can join in for free and get a monkey of your own. Monkeys get special rewards for typing special words - which can be used to customize their appearance. I just wiped the simulation this morning so we're starting from scratch!

It's a little rough around the edges in some spots, if some content doesn't load right, just give it a refresh. I hope you get a kick out of it - it's been an absolute labor of love for me!

Hey HN,

I built pg-mcp, a Model Context Protocol (MCP) server for PostgreSQL that provides structured schema inspection and query execution for LLMs and agents. It's multi-tenant and runs over HTTP/SSE (not stdio)

Features - Supports multiple database connections from multiple agents

- Schema Introspection: Returns table structures, types, indexes and constraints; enriched with descriptions from pg_catalog. (for well documented databases)

- Read-Only Queries: Controlled execution of queries via MCP.

- EXPLAIN Tool: Helps smart agents optimize queries before execution.

- Extension Plugins: YAML-based plugin system for Postgres extensions (supports pgvector and postgis out of the box).

- Server Mode: Spin up the container and it's ready to accept connections at http://localhost:8000/sse

I built this after too many bad NYC apartment experiences: leaks, mice, no heat. All things that of course never show up on listings.

Realest is a browser extension that overlays 311 housing complaint data on StreetEasy listings, right in the search results.

It pulls from NYC’s open data and is meant to help renters avoid hidden issues.

Would love feedback!