2025年8月16日 的 Show HN

I wanted to find a way to use Instagram without ending up scrolling for two hours every time I open the app to see a friend's story.

Most screen time apps I found focus on blocking the app itself instead of the addictive feed, so I created this app to allow me to keep using the "healthy" and "social" features and block the infinite scrolling (Reels)

After implementing the block on Instagram Reels, I got addicted to YouTube Shorts and Reddit feed. So, I extended the app to cover these as well.

To avoid replacing the scrolling for regular feeds, I also added a feature that shows a pop-up when I'm overscrolling in any app. It forces me to stop and think for a minute before I continue scrolling.

I built it on Android Studio, using Kotlin and Jetpack Compose for the UI. I use the Accessibility Service to detect scrolls and navigate out of them. Unfortunately, this only works for Android. There is no way (as far as I know) to do this on iOS.

I'd love to hear your thoughts

Shown HN: Lue - Terminal eBook Reader with Text-to-Speech

Hello,

Just went live on GitHub with this project.

I really enjoy listening to my eBooks as audiobooks but was frustrated by the available options. Converting books into audiobooks with scripts is tedious, and most tools stumble over footnotes, headers, or formatting. I wanted something simple: just throw a book at it, and it starts reading immediately without any clicking or loading.

I also wanted it to be customizable and modular because new, better TTS engines are released all the time. For this initial release, I settled on Edge and Kokoro because they’re both fast (real-time) and good quality. I’ve already made modules for Kitten TTS, Gemini and a few others, and they work too. So I hope this setup is future-proof.

Here’s what Lue supports:

Multi-format: EPUB, PDF, TXT, DOCX, HTML, RTF, and Markdown.

Modular TTS system: Default Edge TTS (online) and Kokoro TTS (offline/local), with an architecture to add more models.

Rich terminal UI: Full keyboard and mouse support, customizable color themes, smooth scrolling.

Smart persistence: Automatically saves reading progress across sessions.

Cross-platform & multilingual: macOS, Linux, Windows, supporting 100+ languages.

I’d love feedback on both usability and the TTS experience. Are there any features you wish it had?

Hey HN, I wanted to get more familiar with C programming, *nix socket programming and C compilation, so I wrote this "web" ""server"". It's running on a tiny SBC in my office, and there's as little as possible between you and it.

Happy for you to try and break it, hopefully with something more interesting than a DoS though :) Please let me know if you find any issues.

When developing eBPF-programs, we need to figure correct program-section SEC() and program-context.

Similary while creating eBPF-maps, we need to add certain fields such as; map-type, key/values, map_options etc..

If you’re like me, you probably end up digging through documentation or browsing open-source projects just to piece this together every time.

So, I created a vscode-extension to help with these repetitive tasks.

Try it out and do share your feedback. I hope you like it.

Thanks !

Hi HN,

I’m building an agentic IDE for hardware developers. It currently supports Arduino, ESP32, ESP8266, and a bunch of other boards (mostly hobbyist for now, but expanding to things like PlatformIO).

It can already write and debug hardware projects end-to-end on its own. The goal is to have it also generate breadboard views (Fritzing-style), PCB layouts, and schematics. Basically a generative EDA tool.

Right now, it’s already a better drop-in replacement for the Arduino IDE.

Would love feedback from folks here.

I’ve been experimenting with AI agents lately, and one problem kept coming up: they either get a raw API key with full access or nothing at all. That’s risky, especially if you’re testing agents that can make arbitrary calls.

So I hacked together a tiny package called Kage Keys - https://github.com/kagehq/keys

It lets you wrap agent actions with scoped, short-lived tokens instead of handing over your real API keys.

Example:

```js import { withAgentKey, getLogs } from "@kagehq/keys";

async function main() { await withAgentKey("github:repos.read", async () => { console.log("Agent is calling GitHub API..."); });

  console.log(await getLogs());

}

main();

Right now it:

- Generates scoped, expiring tokens (default 10s)

- Logs every action to kage-keys.log

- Works as a drop-in wrapper for async functions

It’s just an MVP (tokens are fake UUIDs), but I want to see if developers find this helpful before building the production version with real crypto + proxy enforcement.

Repo: https://github.com/kagehq/keys

npm: https://www.npmjs.com/package/@kagehq/keys

Would love feedback, especially from anyone running agents in production or dealing with API key sprawl.

Hey all, as a little weekend project I built a privacy-focused temporary email service:

https://nullmail.cc

- No sign-up or personal info required. - Instant, disposable, ephemeral mailboxes. - Deletes all data on expiration. - No tracking or logging of user data. - No Cookies. - Ideal for quick, anonymous email verification and sign-up flows. - Domain rotation is the email domain gets blacklisted.

Github repo: https://github.com/gkoos/nullmail

Feedback is welcome here or on GitHub.

I know there are lots of other tempmail services out there, but I wanted to make sure the one I use doesn't collect any personal data so I came up with my own. And I open sourced it because I hope others might find it useful too.

Open Source, client side encryption and decryption E2EE memo service. Privacy and security is main goals. No accounts, No cookies.

Got this idea last summer, and with AI video models improving since then, I started working on it more actively and just released it.

Right from the keyboard extension, you can prompt for any GIF and it will generate it on demand. Generation usually takes around 20 seconds (probably this will only get faster in the future). You get notified by a push notification when the generation has finished.

I have many more ideas to develop this further. I believe on demand GIFs bridge the gap between humor of different persons too, it allows you to convey a situation, a joke or an imagination way better. A way for AI to actually bring us closer as humans.

Try it out and let me know your thoughts!

I have been tinkering with an idea I call CCCP — Context-Aware Composable Compression Protocol.

Most compression formats treat the process as a black box: you feed bytes in, you get bytes out. I wanted something programmable and composable, where the format itself can be adapted to different domains — and even customized by different vendors.

So far, CCCP has a few interesting properties:

Composable: Multiple LUTs (look-up tables) and encoding phases can be combined.

Context-aware: Decoding is guided by explicit metadata, not just raw byte streams.

Round-trippable IR: The intermediate representation can reconstruct the original logic before final binary compression.

Programmable: Vendors can plug in their own LUTs, encoders, and decoders.

It is still very early and experimental. Would love to hear if anyone has seen similar approaches, or where this might break down in real-world usage.

Repos:

https://github.com/brucekaushik/cccp

https://github.com/brucekaushik/cccp-python-poc

I’ve been playing with multi-agent systems and kept hitting the same issue: agents duplicate work, step on each other’s tasks, or return conflicting results.

So I built a tiny package: Kage Bus - https://github.com/kagehq/bus

It’s a lightweight message bus that makes sure only ONE agent handles each task.

Example:

```js import { createBus } from "@kagehq/bus";

const bus = createBus();

bus.on("task:research", (payload) => { console.log("Research agent:", payload.query); });

bus.on("task:research", (payload) => { console.log("Backup agent:", payload.query); });

bus.send("task:research", { query: "latest AI news" });

Right now it:

- Routes tasks to one agent (first-claim wins)

- Supports conflict resolution (last-writer-wins)

- Logs everything to agent-bus.log

Repo: https://github.com/kagehq/bus

npm: https://www.npmjs.com/package/@kagehq/bus

It’s just an MVP, but I’d love feedback from folks experimenting with multi-agent workflows: Is this useful? What features would make orchestration actually production-ready?

Here’s a library for creating toast notifications in Lit Web Components.

Feel free to contribute, leave a star on the repository, or share your feedback here.

If unfamiliar with Lit, here's link to docs: https://lit.dev/

What This Does For You Agentic Sync is a prototype-demo task management system specifically designed for developers and AI agents to collaborate effectively. This is not just another todo app - it's a sophisticated Getting Things Done (GTD) implementation with built-in AI agent integration.

Core Value Proposition For Developers:

Instant, social media-like UI with optimistic updates that make task management feel effortless Complete local deployment - compiles to a native desktop app via Tauri with your own database AI agent direct communication - code agents can create, update, and complete tasks programmatically Production GTD workflow - handles complex task states, dependencies, and approval processes Zero external dependencies - runs entirely on your infrastructure For AI Agents:

Direct task creation - agents can log tasks without human intervention using the included client Status management - agents mark tasks as 'for-review' when complete, requiring human approval Rich task context - support for requirements, technical plans, verification steps, and dependencies Project organization - automatic categorization and initiative linking Live Demo Enhancing AI Agent Communication with a User-Friendly Interface Watch the demo on Loom https://www.loom.com/share/121fb242c0ba4c0c856abb31733342bb

Hi everyone,

I just released an iOS app called ArXivToPrompt. It takes an arXiv paper, generates a clean LaTeX source, and copies it so you can paste directly into ChatGPT (or any other LLM app).

The idea comes from a CLI tool I built a few months ago, arxiv-to-prompt: https://github.com/takashiishida/arxiv-to-prompt

I’ve been using the CLI tool daily to help me quickly understand arXiv papers. This downloads the arXiv source files, finds the main `\documentclass` file, and flattens everything into one coherent LaTeX source (people usually have multiple .tex files in a single paper by using `\input` and `\include`). It also has options to remove comments and appendices to shorten prompts.

I often used the CLI tool on my laptop, but since I commute by train in Tokyo I wanted somthing I could use on my phone. That's why I built the iOS app.

With equation-heavy papers, it may be better to provide the precise latex notation instead of providing the PDF. Uploading PDFs is also more difficult and time consuming (especially on the phone).

Thanks for reading! This is my first iOS app, and I’d appreciate your thoughts!

Hi HN,

I’ve been working on MemViz, a tool to help inspect and visualize memory in C++ programs. It’s aimed at developers, students, and systems engineers who want a simple way to look under the hood without firing up a heavyweight debugger.

What it does:

Inspect object layouts, padding, and alignment.

Explore vtables and runtime dispatch.

Track allocations and report possible leaks.

Hex-dump mapped memory regions (mmap / CreateFileMapping).

Uses modern C++20 features like std::bit_cast for safe reinterpretation.

Why I built it: I found existing debuggers very powerful but often overwhelming for quick introspection. I wanted a lightweight, modern, and approachable tool to learn about object memory layouts, teach others about the C++ object model, and do simple leak/hex inspections.

Try it out:

Repo: https://github.com/LA-10/MemViz

It’s header-only in parts, with examples and test files you can build right away.

No signups, no barriers — just clone and run.

Feedback I’d love:

Is this useful in your workflow or teaching?

What features would you want for a simple memory visualization tool?

Where does it overlap or complement tools like ASan/Valgrind?

I’m around to discuss, answer questions, and hear any suggestions. It's very simple currently.

Thanks!

Turn any domain into a production-ready design system. Instantly generate responsive websites, apps, and landing pages that stay perfectly on-brand—no templates, no pixel pushing. Built on top of brand.dev API and v0 sdk

I’ve built an MCP (Model Context Protocol) server for Spotify, so you can control music directly from MCP clients like Claude Desktop.

Features (v0.1.0): - Playback: play, pause, next/previous, state, devices, volume, repeat - Queue: add tracks, list upcoming - Search: tracks, artists, albums (+ collection search for playlists/albums) - Albums: details & tracks - Artists: info, albums, top tracks - Library: saved albums (list, check, save, remove) - Playlists: list playlists, get tracks, create, rename, clear, add tracks - Diagnostics: quick auth/env check - MCP stdio server: JSON-RPC over stdio, integrates directly with Claude

Repo: https://github.com/victor-saez-gonzalez/mcp-spotify-player

Would love feedback from the HN community: - Which Spotify features should I prioritize next? - Any best practices to make MCP setup smoother?

Contributions welcome — and if this project is useful, a star helps others find it

I built a tool to turn your ideas into X posts directly by taking a phone call to a conversational AI.

x11.social started voice-first. Now, it features an AI chat interface as well with smart UI elements like "give me 10 tweet options" that offer clickable CTA options.

This isn't a complete shift. It's the voice core, enhanced with chat for a smoother workflow and easier content creation.

Call a number or use your browser mic for voice dumps. It's hands-free, perfect for walking, driving, or just thinking out loud. With UI chat, you can craft deeper thoughts or continue from the voice convo where you left off.

This is my first SaaS after years in dev. Building the AI and editor is the fun part. Distribution? That's the real challenge.

Tested some ads, but data showed the funnel was broken. First fix: added free demo button on the landing page that lets users try browser voice to a demo account in real-time. No signup needed. Registered users unlock real calls.

I'm building in public, including video logs. A year ago? Never thought I'd do that. I'm open to ideas.

Hi HN,

We've been frustrated with how confidently LLMs hallucinate—a dangerous flaw in high-stakes domains like health and medicine. The standard "I am not an expert" disclaimer feels insufficient since we all ignore those statements.

Our approach is a RAG/agentic system built to solve this. It runs on ~40M+ scientific papers, but goes beyond simple retrieval. A multi-agent workflow decomposes queries, cross-references claims against multiple sources, and synthesizes answers, ensuring every key statement is cited directly from the literature. Beyond the literature, our agent system has tools to access the internet, databases, and social platforms, with dedicated review agents to ensure proper citation and reduce hallucinations.

This is just the start. Our long-term goal is building health superintelligence by integrating multiscale data—from the genomic and cellular level all the way up to clinical studies in humans. To achieve this, we're exploring SFT, RL, and self-improvement techniques like GEPA to create models that can evolve their own scientific reasoning and to pioneer new standards for accuracy/hallucination mitigation. We plan to rigorously benchmark our work and share the data publicly.

We'd love specific feedback on:

Our RAG/agentic architecture—what failure modes are we missing?

On building superintelligence—beyond SFT/RL/GEPA, what other techniques should we be exploring for a model to truly understand multiscale biology/health/medicine?

Evaluation—what are the best benchmarks for medical/health AI trustworthiness today?

The site itself—any thoughts on the UI/UX, quality of the responses, or other features?

You can see the current system here: https://www.my-openhealth.com/

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