value: jhiver

value:
Hi HN,
I built Doxyde as a bit of an experiment. After 25 years of programming (started on Apple II with BASIC back in '87-'88), I wanted to see what an experienced developer could build using modern development tools - especially AI-assisted development.
Back in the 2000s, in another life, I had built a CMS in Perl, and so I took the good bits I remembered from that era, rewrote the whole thing in Rust using Claude Code, and incorporated SSE / OAuth MCP so it works directly with Claude Desktop.
What came out is a simple, nifty CMS that's fast, clean, memory-safe, and works with modern tools like Claude Desktop. But most importantly, I had a blast building it :-)
Sample interaction:
You: "Create a blog post about our launch, add some screenshots, publish it". AI: Creates page, uploads images, publishes in seconds!
Right now it's a pre-release / technology preview stage - rough around the edges but the core concept works well.
Check it out at https://doxyde.com (the site itself was built from Claude using Doxyde, naturally).
Code is on GitHub: https://github.com/jhiver/doxyde
Please let me know what you think, if the project interests you, my email is jhiver ~at~ gmail.com

value: Show HN: Doxyde – Built an MCP-enabled CMS with Rust (tech preview)

author: jhiver


value: rriley

value:
I built https://howto.unrav.io because I was overwhelmed, not by lack of information, but by too much of it.
We’re surrounded by content, advice, opinions, noise. But clarity? That’s rare.
So I asked: What if simplification was a muscle we could actually train?
howto.unrav.io is a strange little playground for that idea. It’s part:
- Pattern library (mental models, heuristics, quotes)
- AI mentor simulator (talk to Plato, Confucius, Da Vinci,...)
- Retro-punk-futuristic museum (because it should feel fun too)
The goal is to help you think more clearly, not by giving you more data, but better ways to shape it.
Thanks for checking it out. Hope it sparks something.

value: Show HN: An AI playground to practice the timeless art of simplification

author: rriley


value:
I use(d) Pocket a lot on my Kobo, and with Pocket shutting down, I wanted a self-hosted alternative that kept it working on the Kobo too. This project is two parts:
1) A device mod to redirect Pocket URLs to the URL of your choice
2) A proxy server to translate Pocket API calls into a different backend's API
Currently only Readeck is supported as a backend (since that's what I use), but please feel free to contribute if there's another read-it-later service you like better. And of course any feedback is appreciated.
Note: Kobo has announced they're eventually replacing Pocket with Instapaper, but even when they release the update I'm going to update the proxy to translate Instapaper API calls too - this saga has made me want to stay on something self-hosted that can't be shut down.

value: Show HN: Kobo Pocket Replacement

title: Show HN: Kobo Pocket Replacement


value:
The desktop is making a comeback, and AI is leading the charge.
While everyone's building web apps, the most powerful AI experiences are happening locally: Stable Diffusion generating art in seconds, Whisper transcribing without internet, LLMs running privately on your machine.
But discovering these desktop AI apps? Nearly impossible.
PCAgents (https://pcagents.store) is the App Store for AI desktop applications. Think Mac App Store, but specifically curated for the AI desktop renaissance.
Why desktop AI apps are winning:
10x faster than web apps (no network latency)
Your data never leaves your machine
Work offline, anywhere
No subscription treadmill
What we've built:
Curated store with quality control
One-click installs across Windows/Mac/Linux
Developer tools for distribution and updates
User reviews and ratings system
We're seeing incredible apps: AI video editors that render locally, code assistants that work offline, writing tools that never see your documents.
The future of AI isn't in the cloud—it's running on the machine in front of you.

value: Show HN: The App Store for AI Desktop Applications


value: california-og

value: Show HN: MoebiusXBIN – ASCII and text-mode art editor with custom font support

author: california-og


value: Show HN: Nutrition Optimizer

title: Show HN: Nutrition Optimizer


value: pancomplex

value:
Hey HN! My name is Christian, and I’m the co-founder of https://frigade.ai. We’ve built an AI agent that automatically learns how to use any web-based product, and in turn guides users directly in the UI, automatically generates documentation, and even takes actions on a user’s behalf. Think of it as Clippy from the old MS Office. But on steroids. And actually helpful.
You can see the agent and tool-calling SDK in action here: https://www.youtube.com/watch?v=UPe0t3A1Vpg
How is this different from other AI customer support products?
Most AI "copilots" are really just glorified chatbots. They skim your help center and spit out some nonspecific bullet points. Basically some ‘hopes and prayers’ that your users will figure it out. Ultimately, this puts the burden on the user to follow through. And assumes companies are keeping their help center up-to-date with every product change. That means constant screenshots of new product UI or features for accurate instructions.These solutions leverage only a fraction of what’s possible with AI, which can now reason about software interfaces extensively.
With Frigade AI, we guide the user directly in the product and build on-demand tours based on the current user’s state and context. The agents can also take actions immediately on a user’s behalf, e.g. inviting a colleague to a workspace or retrieving billing information (via our tool calling SDK).
This was only made possible recently. The latest frontier models (GPT 4.1, Claude 4, Gemini 2.5, etc.) are able to reason about UIs and workflows in a way that simply didn’t work just 6 months ago. That’s why we’re so excited to bring this technology to the forefront of complex legacy SaaS applications that are not yet AI enabled.
How does it work?
1. Invite agent@frigade.ai to your product. You can send multiple invitations based on distinct roles.
2. Our agent automatically explores and reasons about your application.
3. Attach any existing help center resources or training documentation to supplement the agent’s understanding. Totally optional.
4. Install the agent assistant Javascript snippet (just a few lines).
5. That’s it. Your users can now start asking questions and get on demand product tours and questions answered in real time without any overhead.
This process takes only a few minutes. Once running, you can improve the agent by rating and providing feedback to the responses it provides. If you want to integrate further, you can also hook up your own code to our tool calling SDK to enable the agent to look up customer info, issue refunds, etc. directly. These calls can be made with just a few lines of code by describing the tool and its parameters in natural language and passing a single Javascript promise (e.g. make an API call, call a function in your app, etc.).
Would love to hear what the HN crowd thinks about this approach! Are you building your own AI agent from scratch, or looking to embed one off the shelf?

value: Show HN: An AI agent that learns your product and guides your users

author: pancomplex


value: Instead of prompts or retraining entire models, you can now drop a .aix file → parse → run, and load a scoped AI persona instantly.<p>Think Docker, but for AI behavior—modular, sandboxed, and execution-safe. Feedback welcome!

value: Show HN: AI personas in executable .aix files (run like containers)


value: niklasdev

value:
I built TrendPilot to solve a problem I ran into while trying to grow my SaaS with daily content: coming up with ideas every day is hard.
TrendPilot pulls early trends from Reddit, Google, and X, then generates content ideas based on your niche. You can explore trending topics and get post ideas to stay consistent without burning out.
Signup is required to access the trend data (it’s free). Would love feedback on the UX, trend quality, or anything else that could be better.

value: Show HN: TrendPilot – Find niche trends and generate content ideas

author: niklasdev


value: TXTOS

value:
Hi HN,
Over the past two years I’ve built and debugged a fair number of production pipelines—mainly retrieval‑augmented generation stacks, agent frameworks, and multi‑step reasoning services. A pattern emerged: most incidents weren’t outright crashes, but silent structural faults that slowly compromised relevance, accuracy, or stability.
I began logging every recurring fault in a shared notebook. Colleagues started using the list for post‑mortems, so I turned it into a small public reference: 16 distinct failure modes (semantic drift after chunking, embedding/meaning mismatches, cross‑session memory gaps, recursion traps, etc.). The taxonomy isn’t academic; each item references a real outage or mis‑prediction we had to fix.
Why share it?
Common vocabulary – naming a failure mode makes root‑cause discussions faster and less hand‑wavy.
Earlier detection – several teams now check new features against the list before shipping.
Community feedback – if something is missing or misclassified, I’d rather learn it here than during another 3 a.m. incident.
The reference has already helped a few startups (and my own projects) avoid hours of trial‑and‑error. If you work on LLM infrastructure, you might find a familiar bug—or a new one to watch for. The link to the full table and brief write‑ups is in the “url” field of this Show HN post.
I’m not selling anything; it’s MIT‑licensed text. Comments, critiques, or additional failure patterns are very welcome.
Thanks for taking a look.

value: Show HN: Subtle Failure Modes I Keep Seeing in Production‑Grade AI Systems

author: TXTOS


value: humast

value:
Hi HN! I built FileMock to solve a problem I constantly faced as a developer where I needed mock files for testing uploads, APIs, and storage systems.
Key features:
  • Generates video, audio, image, and document files with precise size control
  • 100% client-side processing
  • Supports 20+ formats: MP4, PDF, PNG, MP3, DOCX, etc.
  • No registration required, just visit and generate
Perfect for testing file upload limits, API endpoints, storage quotas, and CI/CD pipelines. I've been using it daily for the past few weeks and thought others might find it useful too.
The entire generation happens in your browser using Canvas API, Web Audio API, and FFmpeg.wasm for video processing. No server uploads means instant generation and complete privacy.
Would love feedback from the community via the in-app Suggestion form! What file formats or features would be most useful for your testing workflows?

value: Show HN: FileMock – Generate test files for developers and QA engineers

author: humast


value: mmegger

value: Show HN: Production-Ready Agents with the OpenAI Agents SDK and Temporal

author: mmegger


value: ElasticBottle

value:
Hey HN,
We are Winston, Edward, and James, and we built Meka Agent, an open-source framework that lets vision-based LLMs execute tasks directly on a computer, just like a person would.
Backstory:
In the last few months, we've been building computer-use agents that have been used by various teams for QA testing, but realized that the underlying browsing frameworks aren't quite good enough yet.
As such, we've been working on a browsing agent.
We achieved 72.7% on WebArena compared to the previous state of the art set by OpenAI's new ChatGPT agent at 65.4%. You can read more about it here: https://github.com/trymeka/webarena_evals.
Today, we are open sourcing Meka, our state of the art agent, to allow anyone to build their own powerful, vision-based agents from scratch. We provide the groundwork for the hard parts, so you don't have to:
* True vision-based control: Meka doesn't just read HTML. It looks at the screen, identifies interactive elements, and decides where to click, type, and scroll.
* Full computer access: It's not sandboxed in a browser. Meka operates with OS-level controls, allowing it to handle system dialogues, file uploads, and other interactions that browser-only automation tools can't.
* Extensible by design: We've made it easy to plug in your own LLMs and computer providers.
* State-of-the-art performance: 72.7% on WebArena
Our goal is to enable developers to create repeatable, robust tasks on any computer just by prompting an agent, without worrying about the implementation details.
We’d love to get your feedback on how this tool could fit into your automation workflows. Try it out and let us know what you think.
You can find the repo on GitHub and get started quickly with our hosted platform, https://app.withmeka.com/.
Thanks,
Winston, Edward, and James

value: Show HN: Open-source alternative to ChatGPT Agents for browsing

author: ElasticBottle


value: nursultan

value: Show HN: Bult – Deploy apps from GitHub in seconds, no DevOps required

author: nursultan


value: WasimBhai

value: Show HN: Gistpod: What does consulting do?

author: WasimBhai


value: webzz

value:
I built Bloogle to turn any Google Doc into a live blog in minutes.
You write in Google Docs, connect it once, and it handles publishing.
It supports custom domains, Good Seo out-of-the-box (automatic sitemap, robot.txt, ...) and automatic syncing.
No CMS, no markdown, no setup, just write and publish.
Try it: https://bloogle.blog
Would love feedback.

value: Show HN: Bloogle, Create a Blog from Google Docs

author: webzz


value:
hanaco Weather is a minimalist social network where users post short, emotional weather thoughts and connect with others experiencing the same weather.
No likes or followers (for now) — just gentle presence.
Built entirely by a solo engineer, including the custom operating system it runs on: OS Yamato.
What is OS Yamato?
OS Yamato is a poetic, ephemerality-first operating system designed to let digital memories bloom and fade, like flowers in the wind.
Features include:
Diaries that bloom like seasonal flowers — and wither if forgotten
Photos and videos that quietly disappear unless viewed again
Chat with ambient seasonal effects and no pressure
A soft, nature-inspired calendar with reusable schedule templates
Wind Messages — time-delayed letters that arrive months later
A 3D globe to see fleeting connections blossom around the world
Each app within OS Yamato reflects a different way of embracing impermanence in the digital world.
Learn more about the concept and philosophy: [YouTube – Write a diary, and a flower will bloom](https://youtu.be/JrqwU_N5WBA?si=kZQAadNdBGipk6gz)
Try Hanaco Weather now: https://hanaco875.com
#ShowHN #HanacoWeather #OSYamato #solodev #minimalism #digitalpoetry #webapp #ephemeral

value: Show HN: hanaco Weather – A poetic weather SNS from the OS Yamato project


value:
I wrote this simple Python script that prints the daily weather forecast on those cheap thermal receipt printers you can get online for under fifty bucks. It fetches weather data, converts the icons to something printable, and outputs everything in ESC/POS format so you can pipe it directly to /dev/USB/lp0.
Nothing fancy, i just thought it would be nice to have a physical weather printout each morning.
You can configure it with your GPS coordinates and timezone, and stick it in a cron job.
Enjoy!

value: Show HN: Print the daily weather forecast on a thermal receipt printer


value:
It’s a tool that lets you provision and manage your cloud infrastructure from natural language.
It supports many AWS services, but it’s currently best at deploying serverless functions (like Lambda).
Note : there’s a short demo video on the site (So no signup required)
Would love to hear what you think

value: Show HN: Cloudlvl – Generate and Deploy AWS Lambda with AI


value: bartjakobs

value: I made an app for controlling a vintage Macintosh from a modern Mac, using an Arduino to translate between the modern Mac and Apple Desktop Bus.

value: Show HN: Universal Control" for Macintosh Classic

author: bartjakobs


value: meetqy

value: SHOW HN: Discover anime colors

author: meetqy

title: SHOW HN: Discover anime colors


value: bshzzle

value:
Hi HN,
We’re Brendan and Michael, the creators of Sourcebot (https://www.sourcebot.dev/), a self-hosted code understanding tool for large codebases. We originally launched on HN 9 months ago with code search (https://news.ycombinator.com/item?id=41711032), and we’re excited to share our newest feature: Ask Sourcebot.
Ask Sourcebot is an agentic search tool that lets you ask complex questions about your entire codebase in natural language, and returns a structured response with inline citations back to your code. Some types of questions you might ask:
- “How does authentication work in this codebase? What library is being used?  What providers can a user log in with?” (https://demo.sourcebot.dev/~/chat/cmdpjkrbw000bnn7s8of2dm11)
- “When should I use channels vs. mutexes in go? Find real usages of both and include them in your answer” (https://demo.sourcebot.dev/~/chat/cmdpiuqhu000bpg7s9hprio4w)
- “How are shards laid out in memory in the Zoekt code search engine?” (https://demo.sourcebot.dev/~/chat/cmdm9nkck000bod7sqy7c1efb)
- "How do I call C from Rust?" (https://demo.sourcebot.dev/~/chat/cmdpjy06g000pnn7ssf4nk60k)
You can try it yourself here on our demo site (https://demo.sourcebot.dev/~) or checkout our demo video (https://youtu.be/olc2lyUeB-Q).
How is this any different from existing tools like Cursor or Claude code?
- Sourcebot solely focuses on code understanding. We believe that, more than ever, the main bottleneck development teams face is not writing code, it’s acquiring the necessary context to make quality changes that are cohesive within the wider codebase. This is true regardless if the author is a human or an LLM.
- As opposed to being in your IDE or terminal, Sourcebot is a web app. This allows us to play to the strengths of the web: rich UX and ubiquitous access. We put a ton of work into taking the best parts of IDEs (code navigation, file explorer, syntax highlighting) and packaging them with a custom UX (rich Markdown rendering, inline citations, @ mentions) that is easily shareable between team members.
- Sourcebot can maintain an up-to date index of thousands of repos hosted on GitHub, GitLab, Bitbucket, Gerrit, and other hosts. This allows you to ask questions about repositories without checking them out locally. This is especially helpful when ramping up on unfamiliar parts of the codebase or working with systems that are typically spread across multiple repositories, e.g., micro services.
- You can BYOK (Bring Your Own API Key) to any supported reasoning model. We currently support 11 different model providers (like Amazon Bedrock and Google Vertex), and plan to add more.
- Sourcebot is self-hosted, fair source, and free to use.
Under the hood, we expose our existing regular expression search, code navigation, and file reading APIs to a LLM as tool calls. We instruct the LLM via a system prompt to gather the necessary context via these tools to sufficiently answer the users question, and then to provide a concise, structured response. This includes inline citations, which are just structured data that the LLM can embed into it’s response and can then be identified on the client and rendered appropriately. We built this on some amazing libraries like the Vercel AI SDK v5, CodeMirror, react-markdown, and Slate.js, among others.
This architecture is intentionally simple. We decided not to introduce any additional techniques like vector embeddings, multi-agent graphs, etc. since we wanted to push the limits of what we could do with what we had on hand. We plan on revisiting our approach as we get user feedback on what works (and what doesn’t).
We are really excited about pushing the envelope of code understanding. Give it a try: https://github.com/sourcebot-dev/sourcebot. Cheers!

value: Show HN: Sourcebot – Self-hosted Perplexity for your codebase

author: bshzzle


value: csjohnst

value:
I built Kokoro to solve a real problem in my own family: getting kids to do chores consistently and understand the value of money. It’s a lightweight web app that helps parents assign tasks, track what gets done, and automate weekly allowance payouts based on completed chores.
It’s built with React, TypeScript, and Tailwind—lean, fast, and mobile-friendly. I’m not trying to gamify parenting or replace conversations—just give families a tool that makes consistency easier and keeps the mental load manageable.
Would love feedback from other parents or developers working on similar “family ops” problems.

value: Show HN: Kokoro – A simple app to manage chores and allowances for kids

author: csjohnst


value:
Meet Dory - A quick way to cycle through apps without moving your hand from the mouse or keyboard - and without needing to remember any shortcuts.
Click your middle mouse button - or the right Command key if both hands are on the keyboard - and type the first letter of the app’s name.
Find apps using the first letter, middle letters, acronyms, or similar names.
If multiple apps share that letter, just keep tapping it to cycle through them.
You can also press the middle mouse button and start typing the app’s name directly.
Prefer tapping over holding? No problem. With Press Mode, you can open Dory’s sleek UI using a global shortcut.
Dory works right out of the box - and over time, it learns which apps you use most and prioritizes them.
No extra shortcuts.
No setup. Nothing to remember.
---
It's currently $3.99 on the Mac App Store (One-time purchase. No subscription.)
Also, just a heads-up: the current price won't last much longer.
If you've been thinking about getting Dory, now might be the perfect time.

value: Show HN: Made my own app switcher because ⌘-Tab wasn't cutting it [video]


value: mpkendall

value:
I've been working on this for a while now, and I'm happy to share!
I've been into launching weather balloons for a few years. One aspect of the hobby that really drew me in was the tracking side of things. Tracking systems let you follow the balloon's position throughout the flight and, most importantly, know exactly where it lands so you can recover the instrumentation. APRS is what I started out using during my first few years in the hobby, after I got my amateur radio license in 2020 (W0MXX). I designed a few small boards using the trackuino (https://github.com/trackuino/trackuino) firmware (while breaking 3 $70 radio modules along the way).
I then got into recovering radiosondes, which are launched twice per day by the NWS and can be reprogrammed using RS41ng (https://github.com/mikaelnousiainen/RS41ng) to run many amateur radio tracking protocols. I was a bit dissatisfied with how large and heavy the radiosonde trackers were, so I designed my own tracking system, called Tiny4FSK.
Tiny4FSK is a flight computer with built-in tracking using the Horus Binary v2 tracking system. This protocol was developed by the Project Horus team specifically for high-altitude balloons, and it brings features like high transmit rates, forward error correction, and excellent weak-signal performance in an open source package. It's designed to be as compact as possible and can run on a single AA battery for upwards of 17 hours.
The main board comes with header rows that allow for out-of-the-box expansion. I developed a shield that supports the BME280 environmental sensor, the ICM-20948 9-axis IMU, and more via the Qwiic connector. It also features an OLED display for basic diagnostics.
While I've pretty much polished the main tracking procedures (and have tested on multiple flights), I'm still developing the IMU code using a lightweight Kalman filter. Additionally, there isn't yet a wide network of Horus Binary decoding stations like the APRS network has (I-gates), but I hope that by promoting this protocol, more stations will pop up. This means that if you're not in an area with many receive stations, you'll need to set up your own using either Horus-GUI (https://github.com/projecthorus/horus-gui) or horusdemodlib (https://github.com/projecthorus/horusdemodlib).
One issue I’m still working on is improving RF signal strength. Although the protocol is decodable in very low-noise environments, the transmit power appears to be lower than that of a typical radiosonde. This could be due to several factors: limited current on a weak power source (signal is stronger when powered from a bench supply), off-tuned filtering/matching, or not paying enough attention to the antenna. I'm planning to run more simulations to figure this out. That said, the signal is still decodable from the ground even at max altitude (~100,000 feet).
On the more technical side, Tiny4FSK uses:
- the SAMD21 microcontroller, which is an ARM Cortex-M0+ MCU
- the TPS61200 boost converter, which is adjusted to output 3.3v
- Si4063 radio module, which I use on the 70cm band
- ATGM336H gps module - pretty cheap GPS module which works in airborne mode (>18km)
- integrated BME280 temperature, pressure, and humidity sensor
The code uses the Arduino framework to make it accessible to beginners.
All flights using Horus Binary v2, including reprogrammed radiosondes, other custom trackers, and Tiny4FSK show up in real-time on Sondehub Amateur (https://amateur.sondehub.org). Flight data can be found in the /Media/Data folder on Github (there's several missing flights on there though).
Thanks for reading, hope I didn’t mess anything up too badly in the post!
-Max

value: Show HN: A high-altitude low-power flight computer for high-altitude balloons

author: mpkendall


value:
Hey HN, I used to be a frontend engineer, but for the past two years, I've been working as an independent developer. I'm building tools to solve problems I encounter in my daily workflow.
Every day, I use F12/devtools to debug web pages. Honestly, I often just want to see how an element is written or which styles are affecting my layout. In 2024, I upgraded to a new laptop with a smaller screen (14") compared to my old one. Now, whenever I press F12, the webpage gets squeezed, sometimes even switching to mobile view. This frustration inspired me to create GetEles, named after JavaScript functions like getElementsBy*.
What can GetEles do?
1. Quickly get website info: title, description, color scheme, resources, etc.
2. Quickly inspect element details: classes, inline styles.
3. Quickly locate elements (I've traversed the DOM for you!).
4. Quickly convert element styles to Tailwind CSS.
5. Precisely measure the distance between any two elements.
6. Quickly export screenshots of elements or the entire page.
7. Get page load times and performance info...
GetEles is free to use, though some advanced features require a subscription. Even so, I highly recommend trying out the experience on the official website first – it includes over 90% of the functionality.
Head over to the website and click "Try it out":
https://geteles.com

value: Show HN: GetEles, a browser extension that helps you easily understand webpages


value: leoBravoRain

value:
Hi! I'm a chemical engineer turned software engineer. I know how hard it still is for people outside of computer science to learn and apply programming and data science in their own fields. That’s why I created this platform — to help people build a strong foundation in programming and apply it to their specific areas of work.
You can try it for free here:
https://refactorly.up.railway.app/
Thaanks!

value: Show HN: Refactorly – Learn data science for chem eng, econ, psych and more

author: leoBravoRain


value: phrocker

value: Show HN: Protecting Infra from Humans and Agents

author: phrocker


value: I built a mobile app that makes calorie tracking feel like a game. By sticking to your goals and consistently logging meals, you can collect new plants and maintain your existing ones in your virtual garden. Would love feedback on UX and performance. It’s still early, and I’m trying to make it fun without being gimmicky.

value: Show HN: I built a calorie tracking app that grows a garden


value: teuteuf

value:
Hi HN,
I recently launched Treasure Guessr, a small web game I made with a friend: https://play.treasureguessr.com
You're shown a zoomed-in "treasure map" (a cropped view from somewhere in the world), and your goal is to find that same spot on a global map. It’s inspired by the treasure maps in FFXIV, and a bit by GeoGuessr, of course.
We built it mostly for fun, and might explore a Steam version with more content for a few bucks if there’s enough interest.
Fun fact: the backend is just a shared Google Sheet queried directly from the React app. Not sure how sustainable that is, but it did the trick for the prototype.
Feedback welcome!
Antoine aka teuteuf (also made Worldle a few years ago: https://news.ycombinator.com/item?id=30367906)

value: Show HN: Treasure Guessr – A map-based web game

author: teuteuf


value: boxedsound

value: Hey, just published my take on a simple tool for creating images for the App Store. Most out there are expensive and fully fledged.<p>This is a first iteration with a base set. FREE to use, and NO SIGNUP. Easily exports a set of canvases for you to upload. It is crude and quickly developed, and obvious next steps would be to add storage of projects and improve the UI as it was not spent a long time on. Anyways, it is free, check it out, create an asset of your own and leave any feedback.

value: Show HN: A free no signup App Store Asset tool

author: boxedsound


value: ericpsimon

value:
Hi HN, I'm Eric and I'm a recovering data engineer. Recently I have worked on the data platforms for multiple YC backed start-ups Kable (YC W22) and Finch (YC S20).
Every data team I've worked with struggles with data quality validation. Current solutions like Apache Deequ require spinning up entire Spark clusters just to check if your data meets basic quality constraints.
When I found Apache DataFusion, it was love at first sight - it provided the ergonomics of Apache Spark, without the overhead, JVM, etc. That is what led me to build Term. It is able to take advantage of the ergonomics of Spark without the overhead.
Term is a Rust library that provides Deequ-style data validation using Apache DataFusion. You can run comprehensive data quality checks anywhere - from your laptop to CI/CD pipelines - without any JVM or cluster setup. On a 1M row dataset with 20 constraints, Term completes validation in 0.21 seconds (vs 3.2 seconds without optimization) by intelligently batching operations into just 2 scans instead of 20.
The technical approach: Term leverages DataFusion's columnar processing engine to efficiently validate data in Arrow format. Validation rules compile directly to DataFusion's physical plans, and Rust's zero-cost abstractions mean the overhead is minimal. You get 100MB/s single-core throughput, which often outperforms distributed solutions for datasets under 100GB.
Term supports all the validation patterns you'd expect - completeness checks, uniqueness validation, statistical analysis (mean, correlation, standard deviation), pattern matching, custom SQL expressions, and built-in OpenTelemetry integration for production observability. The entire setup takes less than 5 minutes - just `cargo add term-guard` and you're validating data.
GitHub: https://github.com/withterm/term
I built this because I was tired of seeing teams skip data validation entirely rather than deal with Spark infrastructure. With Term, you can add validation to any Rust data pipeline with minimal overhead and zero operational complexity.
Coming next: Python/Node.js bindings, streaming support, and database connectivity. I'm particularly excited about making this accessible beyond the Rust ecosystem.
I'd love feedback on:
- The validation API - does it cover your use cases?
- Performance on your real-world datasets
- What validation patterns you need that aren't supported yet
- Ideas for the Python/Node.js API design
Happy to dive into technical details about DataFusion integration, performance optimizations, or anything else!

value: Show HN: Term – Rust-based data validation with OpenTelemetry

author: ericpsimon


value: Show HN: A boilerplate to launch your SaaS in a weekend


value: zigakerec

value:
I created (mainly for myself) a vibe foto editor.
You can use it to:
- remove people from the image
- change the style of the photo
- remodel your kitchen (my main usage)
- add objects to the photo
- change the color of your hair
- whatever you want :)
This is just a first version, I'm always adding things.
Please let me know if this is even useful for anybody else, or if you have any suggestions.

value: Show HN: Vibe Foto editor, no mouse, just prompt

author: zigakerec


value: abhilashtdharan

value:
From the time Fivetran switched to pricing based on MARs per connector this march, I’ve been hearing from data practitioners about how difficult it is to compute Fivetran’s pricing.
https://www.reddit.com/r/dataengineering/comments/1ij5mhw/fi...
So I built a fivetran cost monitor, fully native on Snowflake using Streamlit, and published it as a free listing - just search for “ETL Cost Monitor” on the Snowflake Marketplace. It factors in your current pricing model and any contracted discount (for annual agreements) to provide a detailed breakdown using fivetran metadata tables: current and forecasted cost, connector‑level and table‑level cost distribution, and high‑volume connector insights.
You can also set a monthly budget and receive email alerts if actual or forecasted costs exceed the threshold. Alerts are also available for sudden MAR spikes. Alerts are handled using Snowflake tasks and email notification integrations created by the user.

value: Show HN: I built a Fivetran cost monitor app natively on Snowflake

author: abhilashtdharan


value: justdoitookk

value: Show HN: Formalizing Strong Goldbach for AI Reasoning in HOL(Standard Semantics)

author: justdoitookk


value:
Hi HN,
I've just released version 0.6.0 of RKIK (Rusty Klock Inspection Kit), a command-line tool for inspecting NTP servers. It's designed for passive, read-only analysis of NTP offset and delay — much like `dig` is to DNS or `ping` is to ICMP.
Why?
In environments where you can't or don't want to run `chronyd`, `ntpd`, or `ntpdate` (e.g. containers, sandboxes, CI), there's no lightweight way to check if your system is drifting from trusted NTP servers.
RKIK fills that gap:
- Query one or more NTP servers
- Compare offsets between multiple servers asynchronously (thanks to Tokio)
- Output in JSON or readable text
- Works with both IPv4 and IPv6
- No root, no sync, no config, no daemon
- Written in Rust, single binary (~2MB)
Example usage:

    rkik pool.ntp.org
    rkik --compare time.google.com time.cloudflare.com
    rkik -C ntp1 ntp2 ntp3 --format json

Example output:

    Server: time.google.com
    Offset: -1.203 ms
    RTT:    2.320 ms
    RefID:  GOOG
    Stratum: 1

Why Rust?
It compiles to a small static binary, has great async support via Tokio, and excellent libraries for terminal UI and NTP packet decoding.
Repo:
https://github.com/aguacero7/rkik
Crates.io:
https://crates.io/crates/rkik
Docs:
https://aguacero7.github.io/rkik
Happy to hear feedback — especially if you're working with time drift, embedded systems, or observability pipelines.

value: Show HN: RKIK v0.6.0 – A stateless NTP inspection CLI (Rust, async, no root)


value: TheraDeng

value:
Hey HN,
I built a small side project: https://characterheadcanon.net/
It helps people generate oddly specific headcanons for fictional characters.
You just enter a character name and a few traits or hints — it gives you back hidden motives, quirks, or backstories. The idea is not to replace writing, but to spark “what if” moments for fanfiction, OCs, or just for fun.
Why?
I write short fanfics sometimes, and I realized brainstorming headcanons takes time. So I thought: why not use AI to kickstart that process?
How?
It uses Claude Code (Anthropic) to generate text, built on Next.js + React, deployed via Cloudflare.
It’s browser-based, free to try, and doesn’t need sign-up.
It’s intentionally small and niche — for people (like me) who overthink fictional characters at 2am.
Would love feedback: things to add, UX improvements, or if you’d actually use it.
Here’s the link again: https://characterheadcanon.net/

value: Show HN: CanonForge – spark weird headcanons with AI

author: TheraDeng


value:
Hi HN,
I recently did a small research project exploring Answer Engine Optimization (AEO) — a rising field that focuses on helping content appear as the answer inside AI platforms like ChatGPT, Google AI Overviews, Bing Copilot, and Perplexity.
With 70% of searches expected to be zero-click by 2025, traditional SEO strategies are starting to fall short. AEO shifts the focus from driving clicks to being the cited source.
This PDF guide is written for marketers, founders, and creators who want to understand:
How AEO differs from SEO
Why schema, E-E-A-T, and structured content matter more now
Examples and metrics from real case studies
Where the business opportunities are in this emerging space
[https://drive.google.com/file/d/15-5VOBDMLqIfEzLgIytw57cvS7k...]
Would love feedback—especially from anyone working on AI search, generative UX, or visibility tools. Curious how others see this affecting content, ranking, or even product design going forward.

value: Show HN: Beginner's Guide to Answer Engine Optimization – The Future of Search?


value: TyxonQ

value:
TyxonQ Quantum" Global Launch: Dawn of Stable Quantum Computing Era
QureGenAI—TyxonQ: World's First Production-Ready Quantum-Classical Hybrid Computing Platform with Pulse-Level Control Now Fully Open
Beijing, July 22, 2025 – Quantum computing industrialization achieves a critical milestone. TyxonQ, the full-stack quantum software framework developed by QureGenAI BioTech (Suzhou) Life Science Technology Co., Ltd., officially launches today. Built under the  Apache 2.0 open-source license, this framework pioneers deep integration of quantum hardware and software stacks, delivering a production-ready toolchain for industrial users to access quantum computing power.
1. Core Advancement: Real Quantum Hardware Integration
TyxonQ enables enterprise users to directly schedule tasks on  Homebrew_S2 superconducting quantum processors  via its self-developed quantum cloud platform:
# 量子硬件调用示例
import tyxonq as tq
from tyxonq.cloud import apis
apis.set_token("YOUR_API_KEY")  # 通过tyxonq.com获取
task = apis.submit_task(device="homebrew_s2", circuit=my_circuit)
The platform supports multi-layer quantum control, including  gate operations  and  pulse signal modulation. In benchmark quantum chemistry tasks, Homebrew_S2 demonstrates significant efficiency gains over classical architectures, enabling practical applications in drug discovery and beyond.
2. Breakthrough Tech: Quantum + AI Dual-Engine Architecture
TyxonQ innovatively merges generative AI with heterogeneous computing, offering three core advantages:
 ① Accessible Quantum Computing Power
It is the first global deployment of Homebrew_S2 superconducting processors, executes algorithms on real quantum hardware (not simulators), pioneers Quantum-Per-Second (qps) granular billing system and slashes usage barriers.
 ② AI-Native Quantum Compilation
 ③ World’s first Generative Quantum Eigensolver (GQE) integrated
It accomplishes "natural language →quantum circuit" translation (experimental), supports quantum machine learning (QML) pretrained models. The cross-domain solutions for quantum chemistry, drug discovery & materials science, integrates auto-differentiation, tensor network acceleration with seamless PyTorch/TensorFlow compatibility.
3. Biomedical Innovation
TyxonQ delivers transformative value in biomedicine:
Accelerated Schrödinger Equation Solving: Simulates non-standard amino acids/metal ion effects—beyond traditional AI capabilities
QM/MM Integration: Unified quantum chemistry/molecular dynamics workflows via Python
“Since 2023, we’ve proven industrial-scale quantum drug design feasibility in QureGenAI’s 2024 paper A Hybrid Quantum Computing Pipeline for Real World Drug Discovery. With TyxonQ's launch, we aspire to attract broader interdisciplinary communities to join us. We are committed to minimizing quantum computing entry barriers for collaborators across all knowledge domains." states Dr. Li Xiaoran, CEO of QureGenAI. "
4. Developer Ecosystem & Roadmap
The platform adopts the Apache 2.0 open-source license, enabling developers to access resources through:
pip install tyxonq
or access the GitHub repository for immediate integration. Our version roadmap maintains full transparency:
V1.x (Released): Hardware execution / Auto-differentiation / VQE algorithms
V2.x (Development): Quantum API gateway / LLM integration / Pulse-level control
V3.x (Planned): Distributed quantum networks / Enterprise cloud platform
5. Experience Quantum Today
Visit  www.tyxonq.com and redeem code  "量子未来无限可能" for  100 QAU computing credits  to run your first quantum program on Homebrew_S2!
"We’re turning blueprints into reality," emphasizes QureGenAI’s CEO. "Traditional tools build houses; quantum tools create molecular-scale materials. This marks the true dawn of QuantumAIDD."
Resource Links:
Official Platform: https://www.tyxonq.com
Business Inquiries: bd@quregenai.com
Developer Support: code@quregenai.com

value: Show HN: TyxonQ, A Chinese Full-Stack Quantum Software Framework on Real Machine

author: TyxonQ


value: stevea-temporal

value:
Steve from Temporal here. Temporal is an MIT open source project for reliable execution at scale. I adapted+extended some of OpenAI's Agents SDK samples to integrate with Temporal.
These demo agents can survive process crashes, scale to millions of executions in parallel and have easy-to-implement human interactivity. Just add a couple of Python decorators to your OpenAI agent code, run Temporal workers and you're ready to go.
Check the video I did with OpenAI showing this in action (it's linked in the repo).
OpenAI actually use us for ChatGPT Images and also their Codex code writing agent so I figure this may be a viable path for others to code something easy that's also reliable at scale.
Happy to answer questions.

value: Show HN: OpenAI Agents SDK demos made durable and scalable with Temporal

author: stevea-temporal


value: allenschen

value: Show HN: We built an internal MCP to improve Plaid engineering's productivity

author: allenschen


value: teamunited

value: iChatroom is a simple, modern old style chatroom for venting, heated discussions and mingling. Try it out for me guys!

value: Show HN: I made "iChatroom" a dark webish type chatroom

author: teamunited


value: I built this for people (like myself) who struggle to remember everything they learn from podcasts and want real time advice when facing a relevant problem. Feel free to drop some feedback below!

value: Show HN: A chatbot that gives you YC startup advice in seconds.


value: huhtho

value:
I built a free, anonymous voice chat platform. You click "Start Chat" and get connected with someone from anywhere in the world.
Features:
- Completely anonymous (no accounts needed)
- Voice-only
- Age/gender preferences
- Clean interface
- Instant connection
I tried existing platforms but they either weren't truly anonymous or had terrible UX. So I built something better.
Would love feedback from the HN community!

value: Show HN: VoxChat – Anonymous Voice Chat Platform

author: huhtho


value:
I'm a Senior SWE at Microsoft and a few years ago I failed an Uber interview on an async concurrency control problem. The question was implementing asyncMapLimit - process an array with a maximum number of concurrent async operations while preserving result order.
I understood the algorithmic challenge: queue management, concurrency limiting, result ordering. But I struggled with JavaScript-specific patterns: how closure capture affects callback timing, when the event loop can cause race conditions and why naive Promise-based solutions create memory pressure.
That failure stuck with me. After conducting JavaScript interviews myself, I realized this pattern is everywhere. Brilliant developers hitting invisible walls not because they lack algorithmic thinking, but because they don't understand how JavaScript's runtime changes implementation strategies.

    const processNext = () => {
        if (currentIndex < collection.length) {
            const index = currentIndex++;  // Shared state mutation
            inProgress++;

            eachItemFn(collection[index], itemResult => {
                result[index] = itemResult;
                inProgress--;
                // When exactly does this callback execute?
                // What happens if callbacks finish out of order?
                if (currentIndex === collection.length && inProgress === 0) {
                    allDone(result);
                } else processNext();
            });
        }
    }

The edge cases around callback timing, closure capture and memory management require deep JavaScript knowledge that generic algorithm prep doesn't cover. So I built Memoized to help developers avoid the same trap I fell into.
Memoized teaches algorithms through JavaScript's lens rather than as translations from Python/Java. Built with Next.js, TypeScript and Prisma, it covers 450+ problems focusing on JavaScript-specific patterns: when Map vs {} vs WeakMap matters for performance, prototype chain gotchas in hash table implementations, call stack optimization for recursive algorithms, async-aware algorithm implementations…
Uses MDX for interactive lessons and provides JavaScript-specific feedback. Links to LeetCode problems but adds the missing "why JavaScript developers struggle with this" layer.
Try it: https://www.memoized.io (entire first sections are free)
Students or those facing financial difficulties get free access, just reach out.
What JavaScript async patterns have surprised you in algorithmic contexts? Would this have helped you in interviews?

value: Show HN: JavaScript algorithm prep that teaches language-specific patterns


value: kachapopopow

value: Show HN: Claude Loop – sandboxed plan execution over multiple iterations

author: kachapopopow


value: ywsp

value:
Pre-recorded museum audio guides are usually limited — they only cover a few “highlight” pieces, or aren’t available at all. Human-led tours are great, but they’re only offered at fixed times and with limited slots.
At the same time, if you try to read all the label text thoroughly, you often end up spending more time reading than actually looking at the exhibit — something I’d rather do on Wikipedia at home. Plus, museum fatigue sets in quickly. Or you skip the labels, hop from item to item, and walk away without really knowing what you were looking at or why it mattered.
That’s why, as a museum fan, I built a web app that lets you generate an instant audio guide for any exhibit — just snap a photo of the label (or type it in), and you get an AI-generated narration you can listen to while looking at the piece.
To be honest, it’s probably not especially technical by HN standards — this is my first serious coding project. But I think it’s interesting at the intersection of tech, culture, and how we experience learning in informal spaces like museums.
Curious to hear thoughts — feedback much appreciated.
Check it out here: https://www.museumwhisper.com

value: Show HN: I built an AI audio guide you can use at any museum

author: ywsp


value: imilev

value:
Hi all, I'm Ivan, and together with Alex, we're building a diagram visualization tool for codebases.
Alex and I are devs, and we've noticed that recently we've been super productive at writing code (prompting :D). But when it comes to understanding big systems, prompting doesn't work that well — for that, diagrams are best imo. Most tools out there don't scale to big projects (e.g. PyTorch), so we're building CodeBoarding — a recursive visualizer for codebases. It starts from the highest level of abstractions and lets you dive deeper.
We use static analysis and LLM agents. The control-flow graph is our starting point, and we validate the LLM's analysis against the static analysis output. LLMs alone often hallucinate or apply familiar architectural patterns that don't actually exist in the code.
Since this is a concise representation of a codebase, we also added an MCP-server to provide our docs for the libs your project depends on — reducing hallucinations and avoiding blowing up the context window.
The vision: With agents writing more and more code, we think we also need a concise representation for it — diagrams. But for that to work, the diagrams have to be accurate, and that's why static analysis has to take part in the fun ;d.
Would love to hear what you think about diagram representations for code, what problems you've run into with hallucinations while vibe-coding (even with tools like gitingest/context7), and any general feedback :)

value: Show HN: We create visual codebase maps that scale (static analysis and LLMs)

author: imilev


value: stevenhp

value:
Hi Hacker News,
I'm the developer of an iOS app I built to solve my own frustrations with existing text-to-speech tools. I wanted something that had modern, realistic AI voices but didn't make me count characters or watch a clock.
My goal was to create a "set it and forget it" listening experience. The core features are:
Truly Unlimited Listening: No character credits or hourly limits. My goal is for you to listen to a 10-hour audiobook without worrying about a meter running.
High-Quality AI Voices: It uses premium, new-generation AI voices that sound natural and engaging, not robotic.
Reads Almost Anything: You can import content from multiple sources:
PDFs, Epubs, and other documents,Web articles, Kindle books (this was a key feature for me)
The Android version is already in development and is coming soon.
Thanks for checking it out!

value: Show HN: An iOS TTS app with unlimited hours that can read Kindle books

author: stevenhp


value: AmineBl

value:
Hi HN,
I built a tool that lets traders backtest strategies by simply describing them in plain English.
Why? Because manual backtesting takes hours (sometimes days) and TONS of effort, especially when you spot new filters or changes you want to test. Most traders either waste tons of time doing it manually or don’t have the coding skills to automate it. I wanted to fix that.
You write:
“Buy when price breaks the previous day high with volume above 1M, stop loss below the low, TP 2R”
…and it runs a backtest over historical data and returns the trade results, metrics, entry screenshots, and even AI suggestions to improve your strategy.
It’s still early and needs polish, but the core is working. I'd love feedback on the product, usability, or anything you think could make it better.
You can try the MVP here: https://www.BacktestAI.com/
Happy to answer any questions!

value: Show HN: Tool that backtests your strategy only from a plain English description

author: AmineBl


value: manzodale

value:
Copilot sucks.
Developers have tools like Cursor and Windsurf that integrate AI smoothly into their workflows. But for other professionals—people writing documents and emails all day—the experience is clunky at best.
Copilot for Word feels like a demo. Outlook’s is barely usable. So most professionals just  copy and paste their content into ChatGPT and back, just to get something polished.
So I built Scrutiny. It's what Copilot should have been.
I originally built it for myself but we are now seeing significant adoption across several large consulting firms and independent professionals.
Try it for free here: www.usescrutiny.com
Would love feedback, especially from folks who’ve tried Copilot and felt underwhelmed.

value: Show HN: Copilot sucks, so I fixed it

author: manzodale

title: Show HN: Copilot sucks, so I fixed it


value: mandarwagh

value: BERT, which stands for Bidirectional Encoder Representations from Transformers, is a pioneering language representation model designed to pre-train deep bidirectional representations from unlabeled text. Its core innovation lies in its ability to be fine-tuned with just one additional output layer to achieve state-of-the-art performance across a wide array of natural language processing (NLP) tasks, without requiring substantial task-specific architecture modifications. This design makes BERT both conceptually simple and empirically powerful, enabling it to set new state-of-the-art records on eleven NLP tasks, including significant improvements on GLUE, MultiNLI, and SQuAD benchmarks.

value: Show HN: GPT Google paper explained by NOTEBOOKLM Explainer feature

author: mandarwagh


value: clidey

value:
Hi HN, I'm Hemang from Clidey, and we are working on Docucod. Docucod is a tool that automatically generates and maintains documentation from any repository.
* What is the problem? *
My co-founder and I have both worked in large corporates like J.P. Morgan, Morgan Stanley, smaller companies like Palantir, and continuously see the issues that stem from unmaintained documentation.
Most codebases are under-documented, comments are sparsed, types aren't always obvious, and there's often a lot of knowledge that is forgotten as time goes on.
The deeper you go, the harder it gets to understand how things work. This slows down onboarding, wastes time during debugging, and increases the chance of things breaking.
Until today, we have been running pilots with small to medium sized companies and startups tweaking the product and gaining lots of feedback.
Today we want to open it up to the public with a release of the generation service!
* Try it out! *
Go to https://docucod.com/oss and put your repository URL there. Once generated, the link will be visible.
You can also put the prefix url in front of the repo url: https://docucod.com/docs/<repo-url> (example: https://docucod.com/docs/https://github.com/clidey/whodb)
Please note this will work only for public repos up to 100MB.
How it works:
Docucod analyzes the codebase, parses files, comments, types, examples, and the readme, then builds a static documentation site you can browse immediately — no config, no setup.
It tries to surface useful context automatically so you don’t have to read the code line by line to figure out what’s going on.
There is diagram generation with Mermaid, and we are working on generating images and later videos!
The publishing is done with our own static site builder called Dory ([https://github.com/clidey/dory](https://github.com/clidey/dory)).
It handles parsing, structure extraction, and HTML generation. Dory has been built with handling AI-generated MDX in mind, which is an interesting hurdle to overcome.
We're still fleshing it out, so PRs and feedback are very welcome.
What’s next:
Right now we support public GitHub repos (private support coming). We’re also working on features like code search across the docs, customized responses based on the user's query, better navigation, theme customization, and integration with CI pipelines.
If you check it out, I’d really appreciate your feedback — both the good and the bad. Does it solve a real problem for you? What’s confusing, missing, or just not helpful?
Thanks!

value: Show HN: Docucod – Automatic documentation for any codebase

author: clidey


value: anthonyag

value: Why sun is hot? What is Gravity? What is Black Hole? Why evergreens stay ... green. Everything you curious about - just ask.

value: Show HN: Curiosso – simple answers for complex questions. Alfa version

author: anthonyag


value: Using multiple gpus, training 7B model with lora and RLHF with external dataset.

value: Show HN: RLHF and Lora finetuning to mistralai 7B with DeepSpeed learning


value: dwardu

value: Since Qardio went bankrupt, Qardio Arm users were left with paperweights. I've built this app to let myself and other users left in the lurch to give back life to their device and continue monitoring their health. I'm looking for feedback, and will be planning to release it on the app store.

value: Show HN: An app to let you use your Qardio device without their servers

author: dwardu


value: danteocualesjr

value:
I built SubSlayer to solve a personal pain point: I was paying for multiple tools I barely used, and didn’t even realize it.
SubSlayer helps you:
– Track your active subscriptions
– Set cancel reminders
– Simulate how much you could save
I built it as a solo founder. Still very early, and I’d love feedback from the HN community, especially on what features would be most useful next.
https://subslayer.app

value: Show HN: SubSlayer – Track and manage your digital subscriptions

author: danteocualesjr


value:
As a part of my journey to learn french, I wanted to challenge myself to learn french to B2 in less time that it took me to learn spanish - around three years. Learning grammar is one of the fastest ways to get comfortable with a language and use it conversationally, but there's a problem here: it's also the most common approach. Every single school in America tries and fails to teach students french through cramming grammar. Every single online language course or app focuses on grammar and sells their knowledge behind a paywall, or are entirely ineffective at worst (looking at you, Duolingo).
The site I attribute the most to my success with spanish grammar is https://studyspanish.com/grammar, a completely free site with grammar lessons and corresponding exercises.
This website I created is my attempt at transpiling that site to french and democratizing french grammar knowledge in a logical, concise way following these principles:
- Active learning + recall: every single section has practice quizzes and exercises for you to test your knowledge.
- Examples: there are ample pronunciation and sentence examples so you can learn words in context.
- Goal-setting: Each level from A1-B2 has a "checklist" where if you can successfully translate a sentence from english to french or from french to english without any help, serves as an indicator that you have mastered those grammar concepts.
This project is meant to be a one-stop-shop for french grammar and practicing it, so you can then move on and learn languages through the strategies that actually matter: anki flashcards and comprehensible input.
The site is made with docusaurus and the exercises are based on JSON that I use AI to generate, from which quizzes can be easily made. Quiz progress and metrics are stored locally.
I completed most of the essential grammar, but I plan to continue adding on new lessons and exercises until the subjunctive tense, which would be B2 level.

value: Show HN: Learn Complete French Grammar for Free


value: enigmazi

value:
I built this after getting tired of setting up full-blown frameworks like Docusaurus or Mintlify just to render a few markdown files for my open source projects.
Docmd is a lightweight CLI tool that turns .md files into responsive, themed HTML docs. Built-in components, plugin support, and GitHub Pages-ready.
Try it: https://docmd.mgks.dev
Repo: https://github.com/mgks/docmd

value: Show HN: Docmd – Minimal Markdown documentation generator using Node.js

author: enigmazi


value: SalzDevs

value:
Hey HN,
I built this over a few weekends as a fun side project. It lets you create a tombstone for whatever digital thing you've “killed off” — Twitter Blue, your TikTok addiction, a failed startup, crypto bags, etc.
Choose what died
 Add lifespan + cause of death
 Generate a hyper-stylized tombstone
 Download it or share on social
 Bonus: There's a leaderboard of the funniest ones
It’s part meme, part internet art. Not trying to “solve a problem,” just making the internet weird again.
Would love feedback or ideas for v2 — I’m thinking social integration or some kind of “cause of death generator.” Thanks for checking it out

value: Show HN: Create a Digital Death Certificate for Your Dead Online Habits

author: SalzDevs


value: huttiepant

value:
Genuine question I try and grapple with quite a bit, do you think apps can be used to help people think critically? Now AI can do most of my thinking (and probably does too much sadly), I’m trying to find ways to stay sharp.
I built this tool for myself to help me understand  Meditations more, and to find other people to discuss the big ideas in the book with (my social status dropped pretty quickly when I tried to discuss Meditations at a party). Pretty simply, it’s end of chapter prompts and questions where the responses can be openly discussed.
I started just using ChatGPT to generate these questions but I found they never got beyond a superficial level. So I ended up finding some extremely helpful philosophy professors who have studied the book and they helped me think differently and crafter better prompts and questions.
I’d love to discuss it with anyone that had an interest in reading philosophy books (Meditations and others). I think more of the ideas in these books need to be discussed.
p.s. If you add your details today to join the waitlist, you’ll be automatically approved and get access to the app. Sorry that you have to use an email to sign up for it but I’m happy to delete any details if you shoot me an email after signing up.
p.p.s. I launched something similar previously which was a single-player version of this. I've changed it to add discussion and various other things (https://news.ycombinator.com/item?id=43571867)

value: Show HN: A tool to help you understand and discuss Meditations (Marcus Aurelius)

author: huttiepant


value:
Sharing something that I’ve been working on:
I made a save-later app for all my bookmarks. I save links throughout the week and, every Sunday morning, the app sends me a personalized recap with:
-patterns and themes that connect my week to my broader interests
-a nudge toward links I saved but never revisited
-one reflective question to help me decide what else might be worth exploring
I was inspired by older read-later apps like Instapaper. I wanted to make something minimalist, so it’s just a simple feed of your links (with tags and annotations linked to each link) and it is set up to ingest all kinds of content, not just text. I also did want it to be bloated as the full-fat AI stuff you see recently. So this is a simpler and more proactive take on the concept of a bookmarking app.
Imagine if Pocket and Spotify Wrapped had a baby.
I also personally enjoy using the chat to find links across subjects and sources with context, like “Show me the 5 links on travel i’ve returned to the most” or “all recipes with porcini mushrooms” or “show me everything on Topic X i’ve made the most notes on.”
I’ve posted about this on HN before, always had great feedback. Happy to answer any questions. (I’m not technical, I'm a writer/ filmmaker.)

value: Show HN: I use AI to send myself personalized weekly recaps from my saved links


value: johaven

value:
I’m happy to announce the public launch of Sync-in : a secure, open-source platform to store, sync, organize, and collaborate on files, without compromising privacy.
Key links:
- Website: https://sync-in.com
- Docs: https://sync-in.com/docs
- GitHub: https://github.com/Sync-in
- Demo: https://sync-in.com/docs/demo

value: Show HN: Sync-in – Self-hosted file sharing and sync (open source alt. to NC)

author: johaven


value:
Hey everyone!
I've been working on this little app I'm calling Medici after getting annoyed with Splitwise only allowing me to submit a few expenses a day. It has all the main Splitwise features (debt simplification, different ways of splitting expenses, default splits, etc.)
Would love feedback and just wanted to share! I'm self-hosting on my RaspberryPi and figured others might find it useful too :)

value: Show HN: Medici, an open-source Splitwise-like app for managing expenses


value: santiix

value:
We built BossOrToss.wtf as a way to anonymously vote on real executives — the CIOs, CTOs, CEOs, and VPs who shape your Zoom nightmares or career wins.
It’s part satire, part corporate commentary:
Vote anonymously on execs pulled from public LinkedIn data
Submit new profiles if your “favorite” leader is missing
Help crown the weekly  Top Boss and  Biggest Toss
See cartoonified avatars, sarcastic bios, and a rotating leaderboard
We don’t collect any personal data — all profiles use publicly available LinkedIn info and user submissions (which are reviewed). It’s a social experiment to gamify leadership perception, with a touch of snark.
Would love feedback from the HN crowd — does this resonate? Should we evolve it into a real feedback tool? Or just enjoy the chaos?
Hate your Boss, Love your Boss? Go for it, let everyone know, Anonymously

value: Show HN: BossOrToss – Vote on Real Executives (Anonymously)

author: santiix


value: ed

value:
Hi HN, I've been using Claude to generate landing pages and it's a huge time-saver.
But, it's hard to go from Artifact -> Published website. That's where Artifact Ninja comes in.
How it works:
1. Click "Copy" above your artifact in Claude.ai and paste into Artifact.Ninja
2. We'll generate a static webpage without any Claude branding
3. Use your registrar's "masked forwarding" feature to connect a custom domain
Artifact Ninja is a quick utility I needed for my own purposes. It's cheap to host (Cloudflare worker + D1), so it's free! Hope it helps.

value: Show HN: Host Claude Artifacts on your own domain

author: ed


value: kanodiaashu

value:
Hi HN,
I go into several rabbit holes in LLM conversations, and while chat is great in the moment, I quickly lose track of the body of knowledge that I covered. Backtracking and multi session usage becomes annoying.
So I'm building out proread, where an AI agent maps out your conversation continuously, so you can easily backtrack/recall, and come back to the same space across conversation sessions. This is helpful for deeper understanding of any topic, and you can also upload/search for docs. Under the hood is an agent that does all the work for you.
I hope you try it and give me feedback!

value: Show HN: An AI agent and interface that maps your chat, designed for deep dives

author: kanodiaashu


value: qwikhost

value: Zillow Data Extractor - Download property details, prices & agent info as CSV, JSON, or Excel in one secure click.

value: Show HN: Zillow Scraper – Agent and Property Export

author: qwikhost


value:
I built this for people who want a private, offline way to track their menstrual cycles — no cloud, no account, no data collection.
EveLock is a lightweight, browser-based PWA you can unzip and run locally (index.html).
Works offline
 No login, no tracking
 Mobile + desktop friendly
 One-click PDF export
 Open and inspectable code
Built for privacy-conscious users who don’t trust typical app store options.
https://onionwave7.gumroad.com/l/otakp
Would love feedback or suggestions for improving it. Thanks!

value: Show HN: EveLock – A 100% Offline Period Tracker (No Cloud, No Account)


value: rohannihalani

value:
I built Rhome to combat the endless flood of short-form, low-effort content that dominates most platforms today.
On Rhome, you explore trusted recommendations from friends and thoughtful individuals — books, articles, podcasts, movies, and more. No infinite feeds, no meaningless likes, no 15-second dopamine loops. Just meaningful content shared by people you actually care about.
Would love to hear thoughts and feedback!

value: Show HN: Anti-short-form platform for meaningful content

author: rohannihalani


value: Show HN: Rallies-CLI, AI powered investment research


value: geekykalai

value: Show HN: Paste a YouTube channel → Auto 2-min summaries in your Notion-Tube2Note

author: geekykalai


value: errabehy

value:
Hi HN,
After months of development, I'm excited to share PassiveCraft, a new AI-powered SaaS platform designed to solve the significant monetization challenge faced by content creators in the global creator economy.
The idea for PassiveCraft came from my own frustrations as an ex-content creator. I experienced firsthand the struggle of converting engaging social media content into sustainable, sellable digital products without extensive technical knowledge or time. Existing solutions often required manual effort, complex integrations, or lacked the intelligence to truly understand and repurpose diverse content formats, leading me to believe that there had to be a better way.
Our goal with PassiveCraft is to empower digital entrepreneurs worldwide by making it incredibly simple to turn their existing social media presence into a direct revenue stream. We've focused heavily on our proprietary AI algorithms and a streamlined user experience to achieve this. For instance, our AI analyzes various content types (text, images, short videos) from platforms(YouTube, Facebook, TikTok, and Instagram), identifies key value, and intelligently structures it into ready-to-sell digital products like e-books, courses, templates, or premium content bundles. This transformation process aims to minimize manual intervention for creators.
We launched our MVP in June 2025 and are thrilled to have already over 50 active users who are exploring new ways to monetize their content. As a solo founder with a science and engineering background, this project is deeply personal, driven by a vision to democratize digital entrepreneurship.
We're currently in early access and would love to get your feedback. We believe PassiveCraft can significantly streamline the productization process for creators, allowing them to focus more on creation and less on complex sales funnels.
I'm particularly interested in hearing your thoughts on:
Our AI's content analysis capabilities: Do you think the approach of transforming diverse social media content into structured digital products is a compelling solution for creators?
Potential new digital product formats: Beyond e-books and courses, what other types of digital products do you see creators needing automated assistance with?
From a technical perspective: What challenges do you foresee with scaling the AI's processing of large volumes of diverse social media content, and how might we best optimize for performance and cost?
Thanks for taking a look! I'll be here to answer any questions.

value: Show HN: PassiveCraft – AI Converts Social Media into Digital Products

author: errabehy


value: lawChainHot

value:
I built a Chrome extension that acts as a WebSocket proxy, allowing real-time monitoring, message simulation, and traffic interception. Think "Proxyman for WebSockets" but integrated into Chrome DevTools.
Key features:
Real-time WebSocket monitoring and message capture
Send custom messages in both directions (client ↔ server)
Block incoming/outgoing messages for testing
Background monitoring (captures connections even when DevTools is closed)
Why: I was debugging a WebSocket chat app and needed better tools than browser DevTools. Existing solutions required external proxies or were too basic.
Tech: Injects proxy script to intercept WebSocket constructor, React + Vite UI, Chrome DevTools API integration, MIT licensed.
Perfect for debugging WebSocket apps, testing error scenarios, reverse engineering APIs, and QA testing real-time features.
Links:
GitHub: https://github.com/law-chain-hot/websocket-devtools
YouTube Demo: https://www.youtube.com/watch?v=L64x__1xORQ
Would love feedback from developers who work with WebSockets regularly!

value: Show HN: A tool for complete WebSocket traffic control

author: lawChainHot


value: OllieB

value: Show HN: Daily Lingo – AI daily stories to learn any language

author: OllieB


value: emrah

value:
Don't let YouTube algorithm dictate what you watch and don't binge Netflix because there is nothing else to watch
This site curates channels from all the best lists, designed to help you binge without guilt. Inventory is full of edutainment videos you will love watching or simply leave running in the background while you get your work done.
These is a suggestion box on the frontpage, please submit your favorites.
It is WIP, any feedback is welcome!

value: Show HN: Binge-Worthy Shows

author: emrah

title: Show HN: Binge-Worthy Shows


value: ishaanmishra

value: I built Truman Street, a tool that lets you create your own custom market funds. You can select stocks, bundle them into a fund, and then track its performance. It's designed to be simple and accessible, with no account setup required. I'm looking for feedback on the concept and user experience. Let me know what you think.

value: Show HN: Truman Street - Create your own market funds from stocks

author: ishaanmishra


value: Show HN: Capture up to 100 emails per lead magnet using Majorbeam


value:
Your AI agent hits an infinite loop and racks up $2000 in API charges overnight. This happens weekly to AI developers.
AgentGuard monitors API calls in real-time and automatically kills your process when it hits your budget limit.
How it works:
Add 2 lines to any AI project:

  const agentGuard = require('agent-guard');
  await agentGuard.init({ limit: 50 }); // $50 budget

  // Your existing code runs unchanged
  const response = await openai.chat.completions.create({...});
  // AgentGuard tracks costs automatically

When your code hits $50 in API costs, AgentGuard stops execution and shows you exactly what happened.
Why I built this:
I got tired of seeing "I accidentally spent $500 on OpenAI" posts. Existing tools like tokencost help you measure costs after the fact, but nothing prevents runaway spending in real-time.
AgentGuard is essentially a circuit breaker for AI API costs. It's saved me from several costly bugs during development.
Limitations: Only works with OpenAI and Anthropic APIs currently. Cost calculations are estimates based on documented pricing.
Source: https://github.com/dipampaul17/AgentGuard
Install: npm i agent-guard

value: Show HN: AgentGuard – Auto-kill AI agents before they burn through your budget


value: SurajSuthar

value:
Hi HN!
I built and just launched DevNotify — an open-source platform to help developers keep track of their starred GitHub repositories and open issues, so they never miss important updates or contribution opportunities.
Why I built it:
As a developer, I often star GitHub repos or open issues I care about, only to forget them later. GitHub doesn’t make it easy to track changes across multiple projects you're interested in. I wanted a way to centralize updates from projects I follow or contribute to — so I built DevNotify.
What DevNotify does:
- Shows updates from your starred repositories
- Tracks issues.
- Great for finding contribution opportunities
- Fully open-source and privacy-friendly
Live App: https://devnotify.in/app
 GitHub Repo: https://github.com/yourusername/devnotify
Would love to hear your thoughts and feedback — technical or UX-wise.
I’m actively working on it, so open to suggestions, ideas, and contributions!
Thanks

value: Show HN: DevNotify – Track Your Starred GitHub Repos and Issues (Open Source)

author: SurajSuthar


value: Show HN: Fine tuning and RLHF mistralai 7B using DeepSpeed


value: vibudhi

value:
I built this to improve my own workflow, but friends convinced me to share it.<p>VibeTree is a cross-platform desktop app that helps you:
- Work on multiple features in the same repo without switching branches
- Spin up isolated Claude AI sessions per feature using Git worktrees
- Get a dedicated terminal and Claude CLI instance per worktree
- Keep sessions persistent – Claude runs uninterrupted even when you switch tabs

value: Show HN: VibeTree – Parallel Claude coding with Git worktrees

author: vibudhi


value: padho

value:
German "Handelsregister", i.e. company data is notoriously messy and difficult to parse programmatically. Handelsregister.ai simplifies this by providing structured access via a developer-friendly REST API and SDKs (Python, JS). Fetch company profiles, balance sheets, financial KPIs, insolvency details, management relationships, and official documents instantly—no scraping or manual parsing required.<p>500 free credits on signup—no credit card needed.
Built for devs by devs. Feedback appreciated!

value: Show HN: Handelsregister.ai – Dev-friendly API for the German business registry

author: padho


value: benklinger

value:
Hi HN,
I built a tool called addtraveltime.com to fix a daily annoyance: Google Calendar doesn’t actually account for travel time between meetings. It’s easy to end up with back-to-back events in totally different locations and no realistic way to get from A to B.
What it does:
Add Travel Time connects to Google Calendar and automatically inserts travel time blocks before and after your events, based on location and your preferred mode of transport. You can override the default mode and starting point per event.
We also just added a “smart travel” feature: if your next event is soon after, it’ll check if it makes more sense to travel directly to the next one instead of back to your starting point.
Why I made it:
After enough missed meetings and stress from calendar overload, I wanted something focused that solves only this problem. Most other tools are either manual, buried in bigger platforms, or try to do too much.
What’s unique:
Focused only on realistic scheduling (commute time in/out, no extra features)
“Smart travel” can optimize routes across multiple events
30-day free trial (no card needed), then €2/month
You can try it now at addtraveltime.com.
Would love feedback, questions, bug reports, or suggestions.
Happy to share technical details or design choices if anyone’s interested.

value: Show HN: Add Travel Time – Auto Travel Time in Google Calendar

author: benklinger


value: fishfl

value:
When Intelligence Becomes a Trap: A Wake-Up Call for the AI Industry
This paper exposed a terrifying truth: the more intelligent a model becomes, the more vulnerable it can be. The idea of "overthinking backdoors" isn’t just clever—it’s disturbingly practical. Attackers don’t need to break the model; they just make it think too much. The result? A silent resource drain that slips past every known defense.
What struck me most was the elegance of the attack. No wrong answers, no obvious triggers—just harmless-looking repetitions like "TODO" slowing the model down like digital molasses. It’s not sabotage; it’s soft destruction. And it works across models.
This isn’t just a security flaw—it’s a philosophical challenge. We’ve spent years chasing smarter models, longer reasoning chains, and deeper thinking. But who knew verbosity could be weaponized?
The implications are everywhere. Enterprises relying on AI for critical decisions may already be wasting resources unknowingly. Open-source models are ticking time bombs if poisoned. And current defenses? Blind to this kind of slow violence.
For those looking for opportunities, this paper is a roadmap. Security tools that detect thinking waste, optimization layers that cut through reasoning fluff, or backdoor scanners for poisoned models—these aren’t niche ideas. They’re the future of AI infrastructure.
I’ve seen a lot of AI research, but this one changed how I think. Intelligence isn’t just power—it’s also a liability if not protected. And the race to secure it has just begun.

value: Show HN: When Intelligence Becomes a Trap: A Wake-Up Call for the AI Industry

author: fishfl


value: ycsuck

value:
Hi HN,
I just pushed Duelr v0.1.1 to GitHub and would love your feedback.
Each month a new model claims “30 % better,” yet I was still copy-pasting prompts into half a dozen playground tabs. Existing evaluation suites (Promptfoo, LangSmith, etc.) are great but heavy; I wanted a <1-minute CLI / local web tool.
How it works
- Paste a prompt (or JSON template) → click Compare All.
- Duelr fires the same request to models you selected such as GPT-4o, Claude 4 Sonnet, Groq (more drivers coming).
- It shows, side-by-side:
      - response text
      - end-to-end latency (ms)
      - token counts → cost spent for the request
      - simplicity and readability score (more heuristics will be coming)
The goal is to see model's performance in your real prompt instead of generic academic benchmarks.
Repo: https://github.com/stashlabs/duelr
P.S.: First time I’ve ever open-sourced a full tool.

value: Show HN: Open-source self-hosted LLM comparison tool for your own prompt

author: ycsuck


value:
Hi HN,
I was getting frustrated with the flood of AI coding tools. Most lists are just noisy, unstructured link dumps that don't help you decide which tool to actually use.
So, I built the directory I wanted for myself. My goal is to provide signal, not more noise.
What makes it different:
- Structured Analysis: Instead of a feature list, I use a consistent schema to compare tools on developer-centric criteria (IDE integration, offline use, model transparency, etc.).
- Qualitative Scoring: A transparent methodology to give a quick read on a tool's strengths and weaknesses, moving beyond just feature-counting.
- Focus on "Job to Be Done": Helping you find the right tool for a specific task.
This is a v1 solo project. It's a static site generated with a custom Node.js script and hosted on Netlify. I know it’s not perfect and tools are missing. I'm posting here for your feedback to make it better.
I'd love to know:
- Is this approach actually useful to you?
- Is the analysis fair? What am I getting wrong?
- What essential tools should I add next?
Appreciate you taking a look. Thanks!

value: Show HN: I built a structured directory to compare AI coding tools


value: djyde

value:
Hey Hackers!
I've had this idea stuck in my head for a while: what if an AI could actually use my browser for me? I mean, not on some virtual machine in the cloud like ChatGPT agent, but right on my own browser.
I finally decided to just build it and see what would happen. I wrote a quick MVP, and honestly, I didn't expect much.
But then... it started doing things. On its own. It figured out how to use search engine to find what I needed, read a webpage to answer my questions, and even it can organize my chaotic mess of tabs into neat groups. It was a genuine "whoa" moment for me.
That's when I knew I was onto something potentially cool. I roped in a designer friend to help me make it usable (so it doesn't just look like my personal science project!), and now I think it's ready for some fresh eyes.
This is super, super early. It can be a bit wonky, and some tasks might fail. To be completely honest, I haven't even figured out the "killer" use case myself yet.
And that's the whole reason I'm posting it here. I'm just incredibly curious to see what happens when other people get their hands on it. What will you even use it for?
You can leave a feedback here or send a feedback on the extension.
Thanks!

value: Show HN: I built an extension that let AI controls my web browser

author: djyde


value: spacebots

value:
*The Challenge:* Global synchronised daily questions with real-time leaderboards
*Technical Architecture:*
- SwiftUI frontend with offline-first design
- Firebase for real-time leaderboards and user sync
- Custom question difficulty algorithm based on historical performance
- Edge computing for global question distribution at midnight local time
*Interesting Problems Solved:*
1. *Question Quality at Scale:* Built a content pipeline with fact-checking, difficulty scoring, and A/B testing. Questions that score <60% or >90% get recycled.
2. *Global Synchronisation:* Distributing identical questions worldwide at optimal local times while handling time zones and edge cases.
3. *Leaderboard Performance:* Real-time rankings for 30+ users updating simultaneously. Ended up with a hybrid approach using Firebase + local caching.
4. *Difficulty Balancing:*Custom ML model that predicts question difficulty based on category, length, answer type, and historical data.
*Unexpected Learnings:*
- Content creation was 90% of the work, coding was 10%
- Users care more about question quality than anything else
- Daily engagement patterns are fascinating. Huge spikes at morning, lunch, and after 9pm
- International users have very different knowledge gaps (geography questions vary wildly by region)
*Current Architecture Handles:*
- 1k+ daily API calls
- Real-time score updates for 30 concurrent users
- 15,000+ questions with metadata and analytics
- Sub-100ms global leaderboard updates
Happy to dive deeper into any of the technical aspects. The intersection of content, community, and real-time systems has been fascinating to build and learn from
Live on App Store: https://apps.apple.com/gb/app/qwiz-daily-quiz-trivia-game/id...

value: Show HN: Built a daily trivia challenge: Same 10 questions worldwide daily

author: spacebots


value: webprofusion

value: Show HN: Winamp Style Web Audio Visualizer

author: webprofusion


value: kalashvasaniya

value:
may 4 i launched "SuperFast"
july 31 i got my first sale
88 days. that's how long it took to land my first customer.
just. keep. going.

value: Show HN: I got my first customer

author: kalashvasaniya

title: Show HN: I got my first customer


value: Samenn

value:
Hi HN! We built AI-generated influencers that are fooling everyone – including us sometimes.
The breakthrough everyone said was impossible:
Not "AI-looking" people. Not "almost realistic." Actually indistinguishable from real humans. We're the first to achieve true photorealism in AI-generated people.
How real are we talking?
Brands report customers asking for the @ of our AI models to follow them
Professional photographers studying our images can't find the tells
We've had people try to book our AI influencers for real photoshoots
A/B tests show audiences engage MORE with our AI content than real UGC
What everyone else gets wrong:
Other AI tools give you plastic skin, symmetric features, that "perfect" CGI look. We generate humans with natural flaws – uneven skin tone, asymmetric features, stray hairs, authentic body language. The imperfections that make people real.
See it to believe it:
Upload any product. Get back content so authentic you'll double-check it's not a real photoshoot. While others are still fighting uncanny valley, we've moved on to building the future of marketing.
The wild part: This isn't our endgame. If we can generate undetectable humans today, imagine what's possible tomorrow.
No waitlist. No hype videos. Just try it yourself and tell us if you can spot the AI.
What does HN think happens to influencer marketing when anyone can generate perfect UGC instantly?

value: Show HN: UGCReal – AI influencers so realistic, people are trying to date them

author: Samenn


value: Show HN: Built an Alert layer for QuickBooks-then Intuit dropped a $300 p/m fee


value: arkonrad

value:
We just launched ARK Cloud API: OpenAI‑compatible, stateful AI inference service that keeps context across calls to slash token spend (0.01 USD / 1M input tokens), spins up in 10 s with Google SSO, runs fully on GDPR‑strong EU‑only infra, and gives 50 000 free credits at signup.
Supports Stable Diffusion (cheapest rates), Llama 8 & 70B, embeddings, STT and more.
PAYG, no logs, no data stored
ark-labs.ai

value: Show HN: Stateful AI API with OS Models

author: arkonrad

title: Show HN: Stateful AI API with OS Models


value: FreyLoong

value:
Hi HN,
MCP-style APIs — small, composable APIs, tools, or AI functions — are becoming increasingly common. But handling auth, usage tracking, and billing for each one can be tedious and repetitive.
Xpack is an open-source, self-hosted platform that helps you quickly launch, share, and monetize your own MCP services. It’s like a self-hosted version of RapidAPI, but focused on giving developers full control.
Key features:
Built-in email/Google login and auth
Stripe billing with per-call pricing
One-click OpenAPI → MCP conversion
Basic usage tracking and observability hooks
Auto-generated, SEO-friendly landing pages for your services
Whether you’re working with internal APIs, AI agents, or dev tools you want to expose externally, Xpack makes it fast and flexible.
Still in early development — feedback, feature suggestions, and use cases are all welcome!
GitHub repo: https://github.com/xpack-ai/XPack-MCP-Market

value: Show HN: Xpack – open-source platform to host and monetize MCP-style APIs/tools

author: FreyLoong


value: thabetx

value: Show HN: Boarda – Native Graphical Outliner with a Novel Layout

author: thabetx


value: yinuncork

value: Hi HN, I built Auraninja because I was frustrated with the countless meditation and sleep apps that are either loaded with ads, require a subscription, or collect personal data. My goal was to create a simple, no-fuss tool that respects user privacy and works without a login. It’s a completely free app that offers high-quality soundscapes and binaural beats. I'd love to hear your thoughts on what I can improve.

value: Show HN: I built a free, private sound app for focus and sleep

author: yinuncork


value: noxify

value:
Hey HN,
I'm Marcus, an open-source enthusiast, and I'm excited to share a new project!
I recently faced a common challenge: building a reporting page that needs to fetch and process data from a remote source daily, providing a simple UI for various KPIs.
While `prisma-queue` has served me well in other projects, its tie to the Prisma ORM wasn't ideal for this new use case, where the ORM choice was still open.
Many popular job queues are Redis-based, like BullMQ. However, I felt there was a gap for a robust, database-backed queue that leverages PostgreSQL's reliability without being locked into a specific ORM. And so, Vorsteh-Queue was born!
While there is currently no official "1.0" release yet, I believe Vorsteh Queue is ready for community feedback to help shape the future (code & docs) and accelerate the v1 launch.
Key features that might interest you:
* UTC-first timezone design: All timestamps are stored as UTC.
* Full TypeScript support with generic job payloads: Your job handlers are properly typed.
* ORM-agnostic with adapters for Drizzle and Prisma (PostgreSQL): More adapters are planned.
* Advanced scheduling: Includes priority queues, delayed jobs, cron scheduling, and progress tracking.
Vorsteh-Queue offers a compelling alternative for those who prefer to manage their background jobs directly within PostgreSQL,
providing strong transactional guarantees and eliminating the need for a separate Redis instance.
Why the name "Vorsteh-Queue"?
The name is a tribute to my German Spaniel, a "Vorstehhund" or pointing dog. Just as a pointing dog indicates its target,
Vorsteh-Queue aims to reliably point your application towards efficient background job processing.
It's a nod to the personal passion that drives open-source development, inspired by stories like Bruno, the API client!
Try it out and let me know what you think! All feedback, contributions, and ideas are highly welcome.
Quick Links:
* Github repo: https://github.com/noxify/vorsteh-queue
* Docs: https://vorsteh-queue.dev/

value: Show HN: Vorsteh Queue – A powerful, ORM-agnostic queue engine

author: noxify


value: ykumards

value:
Hello HN!
I realized how often my mind wanders while waiting for Claude to finish "thinking". So I built Clauditate, a lightweight meditation menubar app that helps you with some breathing exercises.
It uses Claude Code hooks and pops up with a breathing guide. I added some heuristics to make it less "annoying", for example if you dismiss it twice within 30 mins, it won't show up for another 2 hours. You can also tweak this behavior (and more) in the settings.
It's early, but I'd love to hear your feedback!
Github: https://github.com/ykumards/clauditate

value: Show HN: Clauditate – Breathing exercises while Claude Code is thinking

author: ykumards


value: Jahboukie

value:
Hi HN! I'm Jeremy, a solo developer exploring what development tools would look like if designed Specifically FOR AI agents rather than adapted from human tools.
This started with AntiGoldfishMode, a CLI to solve the "goldfish memory" problem in AI coding assistants. That success led me to a bigger question: what if we built a complete operating system designed for AI agents?
AgentOS is my early exploration of this concept. It's currently a TypeScript-based system with three core
engines:
Memory Engine: Persistent SQLite-based storage that lets AI agents remember context across sessions. No more
losing project knowledge between conversations.
Execution Engine: Sandboxed code execution environment where agents can safely test their suggestions before
recommending them. Currently supports multiple languages with Docker integration planned.
Learning Engine: Basic pattern recognition that tracks what works and what doesn't, building simple predictive
models from execution outcomes.
Current State: This is very much an early-stage exploration. The core functionality works - agents can store
memories, execute code safely, and learn from results. The CLI is functional with commands like agentos memory
store, agentos execute, and basic learning capabilities.
The Philosophy: Instead of throwing larger context windows at AI agents (the "context window arms race"), I'm
exploring purpose-built tools that give them the specific capabilities they need: memory, execution verification,
and learning from experience.
What's Next: I'm working toward an alpha release and would love feedback from the HN community. Are there
specific capabilities you think AI agents need most? What would make them better coding partners?
The code is available on GitHub: [https://github.com/jahboukie/agentos.git]
I'm here to discuss the approach, technical implementation, or what you think the biggest needs are for truly
AI-native development tools.

value: Show HN: AgentOS-An Early OS for Coding Agents Memory and Execution and Learning

author: Jahboukie


value:
Hey HN,
We built Composo because AI apps fail unpredictably and teams have no idea if their changes helped.
LLM-as-judge doesn't work - it gives random scores, doesn't work well for agents, and doesn't tell you what to fix.
We've built purpose-built evaluation models that give you:
- Deterministic scores (same input = same score, always)
- Instant identification of where prompts, retrievals, agents & tool calls fail
- Exact failure analysis ("tool calls are looping due to poorly specified schema")
We're 92% accurate vs 72% for SOTA LLM-as-judge.
Giving 10 startups free access:
- 10k eval credits
- Just launched our evals API for agents & tool calling
- 5 min setup
Already helping teams at Palantir, Accenture, and Tesla ship reliable AI.
Apply: composo.short.gy/startups
Happy to answer questions about evaluation, reward models, or why LLMs are bad at judging themselves. startups@composo.ai

value: Show HN: Deterministic evals API – the alt for LLMasJudge (free credits)


value: ayankovsky

value:
We built a Serverless GPU platform with low cold starts that is perfect for running your custom ML models (LLMs, image generation etc.).
We started with our own pain. We were running a generative AI startup and needed to run a Stable Diffusion pipeline with custom LoRA. We found that running a custom model on a cloud GPU means either a steep fixed cost (using traditional cloud providers) or extreme cold starts of several minutes (using serverless GPU providers).
We looked at successful non-GPU providers and came up with a hypothesis that still holds true today: we don't need to support custom Docker images, we can create just one environment that will run any model.
Of course, that alone did not solve the cold start. We had to work hard optimizing our platform to load and unload models as quickly as possible. We ended up building a pre-download mechanism and manipulating the page cache to load the predicted next model faster.
We wanted to make it as easy as possible for our clients to migrate and also to learn as much as possible, so we started offering free assistance in adapting models. We learned that improving cold starts is not just about the platform. It also depends on how the model is loaded.
This way we helped several teams running LLMs and image generation improve their ML-related features for users (reducing wait time) and often reduced costs.
Try our platform here: https://dat1.co
We'd love to hear your thoughts on anything related to the subject.
Thanks, Arseny.

value: Show HN: We Built a Serverless GPU Platform with Fast Cold Starts

author: ayankovsky


value: johnhandler

value:
Greetings HN!
I built Voice Isolate to be the simplest way to enhance any speech recording.
After hearing so many videos/podcasts with great content but poor audio I realized not everyone can or wants to invest in a pro mic / studio / editing software but figured something simple should be possible now using AI.
There are products that do this like Descript or ElevenLabs but I find they’re overcomplicated for many use cases (full blown app designed for professional podcasters, use a convoluted credit system, etc.).
As a solo dev I wasn’t sure this was possible to do well but I spent months looking at open source models and combining/tweaking them to get the best mix of quality/speed/cost. It’s not perfect but I was pretty impressed by the results.
Listen to the demos or try it yourself with the free preview and let me know what you think: [https://www.voiceisolate.com](https://www.voiceisolate.com/)

value: Show HN: Voice Isolate – dead simple speech audio enhancement (using AI)

author: johnhandler


value: Show HN: VectorSeek – Private AI-Powered Search


value: BenderV

value:
Myriade is a chat interface to analyze your database.
Most data analysts are convinced that AI for data analysis is a scam because data is messy and complex.
So, nobody even tries to use AI for data analysis.
Even though agentic AI is getting better and better at handling messy and complex data and most startups, relying on simple stacks (database replica) actually have pretty good data.
I have made two ways for people to see the potential of AI for business intelligence:
1. As a docker command
docker run -p 8080:8080 -v $(pwd)/data:/app/data myriadeai/myriade:latest
2. As a live demo - no login required
https://demo.myriade.ai
I would love to hear your feedback.

value: Show HN: BI chatbot in one Docker command

author: BenderV


value: openWrangler

value:
Hey HN,
We launched Coroot a while ago as an open source observability tool that collects complete telemetry using eBPF. Today we are adding a major new capability:
Coroot enterprise now includes AI-powered Root Cause Analysis.
When an incident happens (like an SLO violation), Coroot:
- Automatically kicks off an RCA
- Summarizes what went wrong in plain English
- Suggests immediate fixes
- Shows the full investigation using metrics, logs, traces, and profiles
With most tools, the quality of root cause analysis depends on how well the system is instrumented. Coroot takes a different approach: it uses eBPF to collect all the critical signals automatically, even from uninstrumented or third party services. That gives the AI a much more complete picture to work with.
We are keeping it simple and accessible at $1 per monitored CPU core per month. Our core tool is also completely open source at: https://github.com/coroot/coroot
You can try it for free or view a demo with the link connected to this thread. We'd love to hear your thoughts!

value: Show HN: Coroot – AI powered Root Cause Analysis using eBPF-based telemetry

author: openWrangler