value: theturtletalks

value:
Hey everyone,
A couple years ago, I got into programming and fell down the rabbit hole of open-source software. I started realizing a lot of the commercial software I used had open-source alternatives. In 2020, those alternatives were good but not perfect replacements. With LLMs entering the scene, there are even more open-source alternatives now and they're narrowing the gap between proprietary options.
LLMs also got me thinking: will people continue using proprietary or opensource applications in the future when they can build their own personal application tailored specifically for their needs? I also started thinking that there are so many open-source directories out there already, so what value would another directory bring?
Then a solution to both questions gave me a light bulb moment.
The original opensource.builders just called things "alternatives." But that's too vague. Take Ghost and Shopify. You could call Ghost a Shopify alternative since both support blogging. But Shopify also has payments, inventory, checkout, and product management that Ghost doesn't. The label oversimplifies what they actually do.
So instead, we now track features and map where they live in the code. Once we had that, we realized you could pick specific features from different projects and get help building them into your own stack. You browse features, select what you need, and we generate a prompt with exact file paths and GitHub locations. Your AI learns from real implementations and helps you implement those features in your own codebase.
What's Next?
I'm using these principles at Openship to build management software for every vertical: e-commerce, hotels, restaurants, barbershops. Next week I'll share what we've built and how we're leveraging these platforms to create a decentralized marketplace where businesses own their storefronts.
opensource.builders helped us understand the landscape and build what was missing.
Try it: https://opensource.builders
Ethos: https://opensource.builders/ethos
Source: https://github.com/junaid33/opensource.builders

value: Show HN: Opensource.Builders 2.0 – find and build open source alternatives

author: theturtletalks


value: MattRix

value: Fivefold is our new daily puzzle game. It's kind of like Sudoku, but with more variety since we switch which rules are used each day. If you want to practice the game, you can try a bunch of pre-made puzzles in the Dojo section of the site. I'd be interested to know what people think of it!

value: Show HN: Fivefold – a logic puzzle where the rules change every day

author: MattRix


value: dark-kernel

value:
A few months ago I shared Tuisic — a terminal-based online music player that streams from YouTube, SoundCloud, JioSaavn, and more.
Since then I’ve added some major updates:
+ Real-time visualizer (CAVA-like, runs inside the TUI)
+ MCP (Model Context Protocol) server integration — lets AI tools or assistants control playback, query track info, etc.
+ Stability and UI improvements
+ Refined MPRIS/DBus handling and cleaner build options
Everything is still lightweight and fully terminal-driven — no browser, no GUI, no ads.
GitHub: https://github.com/Dark-Kernel/tuisic
AUR package: `tuisic-git`
Would appreciate testing, bug reports, or feedback on how the MCP interface could evolve.
– Sumit Patel (Dark-Kernel)

value: Show HN: Tuisic v2 – Now with visualizer and AI (MCP server) support

author: dark-kernel


value: aanthonymax

value:
Hi, HN! Today, I'd like to show you a module that can directly replace HTMX in your project. It's more functional and simply better.
Conceptually similar, they're completely different in functionality. You can work directly not through attributes, but through the convenient syntax of a template language, which offers far greater possibilities for creating HATEOAS applications.
I can confidently say that this is a truly worthwhile module that simply supports the template in the layout and pulls everything from the main module.
I can answer any questions in the comments about the module if you have any and justify my point of view. I would appreciate your feedback ^ ^

value: Show HN: HMPL-DOM – Direct Replacement for Htmx

author: aanthonymax


value: CaptP

value:
I built 6,000+ agentic AIs that show how our power grid can take a much-needed massive leap forward.
I created a Virtual Power Plant simulation based on Austin's grid. VPPs are networks of controllable energy devices, such as batteries, EVs, and smart thermostats, that work together. The website takes you through a tutorial explaining all the pieces that come together in a VPP.
The bottleneck of our grid isn't generation, but transmission.
In Texas, almost half of the cost of energy is transmission. Generation is getting cheaper and cheaper, and transmission is getting more and more expensive as the grid ages and we don’t do anything about it.
The solution? To hyperlocalize energy generation and storage. Virtual power plants will be critical to that future.
Zoom into different zones across Austin. Trigger price spikes, frequency dips, and demand response events—then watch how thousands of agents coordinate in real-time.
Click on any device to see its AI's decision-making process. Pan around the 3D map, inspect individual batteries and EVs, and see how aggregators manage entire neighborhoods. Each device has its own AI agent making real-time decisions. Watch 6,000+ agents respond to grid events in seconds—faster than any centralized system.
This is what the future of energy looks like: distributed, intelligent, and resilient. Try it out and let me know what you think. Hopefully, you'll learn about VPPs and get an intuitive sense of the world's most complex machine.

value: Show HN: Agentic AIs – Virtual Power Plant Simulation

author: CaptP


value: elliotbnvl

value:
Hi HN! I built VibeMail, an email template editor that combines MJML with Claude AI to make creating (vibing) responsive HTML emails actually enjoyable.
The interesting bits:
- Three-panel layout: Monaco code editor, live preview, and Claude chat side-by-side
- AST-based code navigation: double-click text in the preview to jump to the exact line in the editor
- Multiple AI "agents" with full conversation rollback (inspired by Cursor's chat history)
- Project/email organization with a file-system-like structure
- Everything runs locally – your API key and templates never leave your browser
- I built the whole thing in a day: maybe 5hrs. Simon Willison is onto something with the personal software thing.
Tech stack: React, Vite, TypeScript, Zustand, MJML, Monaco Editor, and Claude Sonnet.
Fun fact: I prototyped the entire thing in ~10 minutes using Cursor and Claude Sonnet 4.5, which was a fun experiment in AI-assisted development. The hardest part was getting the AST parsing right for the code navigation feature.
Open to feedback!

value: Show HN: Vibemail – AI-powered MJML email editor that runs in the browser

author: elliotbnvl


value: juhgiyo

value: Show HN: HumaLab – The Intelligence Validation Platform

author: juhgiyo


value: mannders

value:
Hey all. I built ShellDash, an interactive server admin dashboard with shell scripting and an appealing globe UI.
https://shelldash.com
The goal is to provide a global monitoring view of your servers, with shell script access, in a way that feels natural and productive, plus a minimal and appealing UI/UX.
The technology is fairly interesting. This being a browser app, I built a Go WASM SSH client running in the browser, proxied through my server WebSocket endpoints. This means I can provide you a Web UI to access your servers via SSH, without ever needing to see your credentials. I only see secured packets like OpenSSH sends over the open internet. Inspired by https://ssheasy.com/
Whether you have one server and periodically run a few common commands, or administering many scattered geographically, I hope ShellDash can make your experience more productive and fun.

value: Show HN: ShellDash – Browser server dashboard with SSH and globe monitoring

author: mannders


value: calebhwin

value: Show HN: Agent-to-Code JIT Compiler

author: calebhwin

title: Show HN: Agent-to-Code JIT Compiler


value: lharries

value:
Luke from ElevenLabs here. We just released Scribe Realtime v2, our new real-time Speech to Text model.
We rebuilt it from the ground up around a new architecture that uses predictive transcription – it anticipates the next likely word before it’s spoken. That makes it both fast (around 150 ms latency) and highly accurate.
Scribe v2 Realtime outperforms every low latency transcription model across 30 commonly used EU and Asian languages – 93.5% accuracy, compared to Gemini 2.5 Flash (91.4%), GPT-4o MiniTranscribe (90.7%), and Deepgram Nova 3 (85%).
It supports 90+ languages including English, French, German, Italian, Spanish, Portuguese, Hindi, and Japanese. It’s designed for live, agentic applications like AI voice agents, meeting notetakers, and captioning systems.
We also built an open-source real-time transcription UI component at https://ui.elevenlabs.io/blocks to make it easy to integrate voice into any product.
You can use Scribe v2 Realtime via our API or directly within ElevenLabs Agents.
Try it out here: https://elevenlabs.io/realtime-speech-to-text or read the docs: https://elevenlabs.io/docs/capabilities/speech-to-text
We’d love your feedback!

value: Show HN: Scribe v2 Realtime – new SOTA real-time speech to text model

author: lharries


value:
Hey HN, this is Harsh from Hathora.
We started as a game server hosting company and are launching a new offering today centered around voice models, leveraging our existing platform that runs game servers.
We hope to achieve a few things with this product:
1. Democratized access to a wide variety of open source and licensed voice models
2. Ability to mix and match speech to text, reasoning (llm), and text to speech models to form inference chains for agentic voice workflows
3. Focus on latency reduction via (a) running models in 14+ regions around the globe, and (b) colocating models in the same datacenter to reduce networking overhead within a inference chain
The models are made accessible as Serverless API endpoints with token/duration based billing, and also available as dedicated deployments for enterprises with specific compliance requirements.
You can check it out here: https://models.hathora.dev/

value: Show HN: Hathora Models – Voice Model Marketplace


value:
Hey everyone,
I built Reezu because sending large videos always felt slow or limited. Reezu lets you share big video files instantly, and the receiver can start streaming before the download finishes. You can also download the original file without compression.
There isn’t really a simple web tool that combines instant streaming with full-quality file download, so I decided to build one.
It’s live at https://reezu.com
.
Would love feedback, especially on performance and UX.

value: Show HN: Reezu – Send large videos instantly, stream before download


value: renvins

value:
Hi HN, I'm the author.
I built this project for two simple reasons: I've always used higher-level languages and wanted to finally understand what's happening "under the hood" of an interpreter. I also wanted a real project to force me to "power up" my C skills, especially with manual memory management and reference counting.
The result is ToyForth, a minimal interpreter for a Forth-like language, written from scratch in C, stack-based.
I focused on making the code clean and understandable. It's broken down into a few simple parts:
A parser that turns source text into a list of objects (parser.c).
A small stack-based virtual machine (main.c).
A manual reference counting system (incRef/decRef) to manage object memory (mem.c) and so on.
My main goal was learning, but I've tried to document it well in the README.md so it could be a "starter kit" for anyone else who wants to learn by reading a small, complete implementation.
It's easy to try out. I'd genuinely appreciate any feedback on my approach or my C code.
Here's the link: https://github.com/renvins/toyforth-interpreter

value: Show HN: Built a tiny interpreter from scratch in C to understand how they work

author: renvins


value: ivankuria

value:
Hi HN, We're Ivan, Sasha, and Lawrence, building SlugMatch (https://www.slugmatch.com), a personalized quiz that helps students find their ideal residential college through a weighted matching algorithm.
I've been a UCSC student for three years now. When I was choosing my residential college, I had no good way to decide between 10 residential colleges with similar vibes. The official descriptions were vague, and I didn't know any upperclassmen to ask. I ended up scrolling through dozens of Reddit posts and TikTok videos trying to figure out which would actually match my lifestyle.
Last year, I wanted to solve this problem for incoming students. I realized we needed a way to analyze all the scattered information; Reddit posts, TikTok videos, student reviews and map student preferences to college attributes. I had to build a weighted matching algorithm that could balance multiple factors: social vibe, academics, location, dining, housing.
I started by analyzing hundreds of Reddit posts and TikTok videos to build accurate college profiles. The hard part was balancing all the factors; location vs. social scene vs. dining quality vs. housing quality. I grouped questions into themes and mapped responses to weighted attributes for each college. The algorithm emphasizes your top priorities and balances trade-offs.
The next feature was adding real student reviews and testimonials, not just official descriptions. We wanted to be honest about trade-offs; every college has pros and cons. We added side-by-side comparisons so students could see how colleges stack up against each other.
Finally, we were able to add TikTok embeds showing actual college life, because videos give you a better sense of the vibe than text descriptions ever could. We also added resources and tools for campus life – dining hours, transportation, study spots.
To accomplish this, we built it with React 19 + TypeScript, TanStack Router(the goat) for file-based routing, TanStack Query for data fetching, and Tailwind CSS for styling. We used Framer Motion & GSAP for smooth animations and Three.js for 3D graphics. Deployed on Vercel because duh.
After all that, I found that to convert myself as a user I needed it to be fully functional. We needed college profiles with detailed information, comparison tools, and a quiz that actually felt engaging. We analyzed hundreds of data points to ensure accuracy.
We're still working on expanding to more campuses, but we just launched UC San Diego support (in addition to UC Santa Cruz) – now covering 18 residential colleges across 2 campuses. We're also continuously improving the matching algorithm based on student feedback.
All of SlugMatch is free to use. We built this to help students, not to monetize. It's completely unofficial and doesn't affect official housing placement. We're just trying to make the decision easier.
What we learned: Students care most about social vibe, location convenience, and dining quality. Academic requirements matter less than you'd think. The algorithm balances these priorities based on how you answer the quiz.
We'd love HN feedback on:
- The matching algorithm (how can we improve it?)
- UX/UI (is the quiz engaging enough?)
- Feature requests (what would make this more useful?)
- Technical implementation (we're happy to share details)
Anyway, regardless of how you feel about college matching algorithms, I wanted to make something that was useful for students. We'd love it if you gave it a spin and we want to share anything we can about the technical side of the product that you might find interesting.

value: Show HN: SlugMatch – Match Students to Residential Colleges

author: ivankuria


value: samteeeee

value: Show HN: Get an email when your favorite director releases a movie

author: samteeeee


value: mcastle

value:
Hey HN, I built Logosive because I want to see certain debates between my favorite thinkers (especially in health/wellness, tech, and public policy), but there's no way for regular people to make these happen.
With Logosive, you propose a debate topic and debaters. We then handle outreach, ticket sales, and logistics. After the debate, ticket revenue is split between everyone involved, including the person that proposed the debate, the debaters, and the host.
Logosive is built with Django, htmx, and Alpine.js. Claude generates the debate launch pages, including suggesting debaters or debate topics, all from a single prompt (but the debates happen between real debaters).
I’m now looking for help launching new debates, so if you have any topics or people you really want to see debate, please submit them at https://logosive.com.
Thanks!

value: Show HN: I built a platform where audiences fund debates between public thinkers

author: mcastle


value: Hi HN,<p>Check out the beta build for my app ReWave, where you can find research papers in the app and easily turn them into podcasts. All feedback is welcome!

value: Show HN: ReWave (beta) research papers as podcasts (iOS)


value:
I wanted to query my own chat history but the JSON exports were a mess to work with. Built a small parser that turns them into clean SQL databases.
Parsed 70k+ messages across multiple models. Useful for analyzing chat history, building a personal knowledge base, or archiving conversations. Simple CLI, open source.

value: Show HN: ChatExport Structurer – parse ChatGPT/Claude exports into queryable SQL


value: AaronKushner

value:
Hey HN! I'm launching SecurVO, a compliance and operations management platform for service businesses like property management, facilities management, and field services.
The Problem:
Service businesses juggle tons of recurring tasks (inspections, certifications, maintenance), vendor compliance (COIs, licenses), and document expirations. Most use spreadsheets or pay $50-200 per user per month for project management tools that weren't built for compliance work.
What We Built:
SecurVO handles recurring tasks, document expiration tracking, vendor compliance, asset maintenance, SOPs with attestation, and incident tracking - all in one platform. Key difference: no per-seat pricing. Pricing is by tier - every tier has full access to all features, and comes with generous seat ceilings that leave room for growth.
Tech Stack:
TypeScript/React frontend, Node.js backend, PostgreSQL database.
Current Status:
Just launched beta. Looking for ~50 beta testers to help us validate the core workflows and identify edge cases. Beta users get 90 days free.
What I'd Love Feedback On:
Is the feature set too broad or just right for operations teams?
Pricing model (flat rate tiers vs per-user) - does this resonate?
Live at: https://SecurVO.com
HHappy to answer any questions about the tech choices, business model, or why we built this!

value: Show HN: SecurVO – Compliance management for service businesses

author: AaronKushner


value: nsomani

value: Show HN: KV Marketplace – share LLM attention caches across GPUs like memcached

author: nsomani


value: Cranot

value:
Hey HN,
I spent the last 6 months creating what I wish I had when I started with AI: a complete guide to prompt engineering that goes beyond "be specific."
It's a comprehensive 30-chapter guide covering everything from basic prompting techniques to advanced AI agents and RAG systems. Includes 600+ copy-paste prompts for ChatGPT, Claude, and Gemini.
What makes it different: each technique includes real examples you can use immediately, plus code implementations for building actual AI applications.
Built this because I was frustrated with scattered resources and wanted everything in one place.
Free to check out here: [https://dimitriosmitsos.gumroad.com/l/prompt-engineering-bib...]
Would love feedback from the HN community - what's your go-to prompting technique that consistently works?

value: Show HN: The Prompt Engineering Bible – Complete Guide to AI Communication

author: Cranot


value: matt-p

value: Show HN: Fibre Splice in React

author: matt-p

title: Show HN: Fibre Splice in React


value: eddguzzo

value:
Hi Hackernews,
I built a little Tetris-like game called Invisitris, where all but the last placed piece becomes invisible.
The board becomes fully visible for a few seconds when you clear rows.
And it also becomes visible when your stack becomes dangerously high.<p>Try it and let me know what you think :)

value: Show HN: Invisitris a Tetris-like game, where the placed pieces become invisible

author: eddguzzo


value: dactile

value: Show HN: Some states love naming streets after themselves. WI leads, ND has zero

author: dactile


value: Dimittri

value:
Hi all, I've been working on this devtool for 1 month now for myself at first and I'll be curious to see if it's something that could work for you as well.
So basically, it detects bugs in your website in production from real user sessions, an llm clusters them by severity and it provides the complete context of the issue that you can copy-paste into your coding agent to fix it in one go.
Why did I create it?
I've been shipping fast with tools like Cursor and Claude Code. The problem? When bugs happen in production, these tools have zero context about what actually went wrong. Sentry is powerful but way too complex - I just want to copy context and paste it into my copilot.
How to install it?
Basically it's a JS tracker to add to your website
I'm very curious about your ideas about this tool or similar tools available if you know some of them.
Looking for feedback on the whole concept and UX. Is this something you'd actually use? What's missing?
Happy to share more technical details for those interested.
Thanks!

value: Show HN: Give your coding agent production bug context

author: Dimittri


value: darkce

value:
The idea for the Night Vision App began with a simple moment of curiosity. When I first got my iPhone 14 Pro Max, I noticed a small, unfamiliar dot beneath the rear camera — the LiDAR sensor. It made me wonder: could this be used to see in the dark? If LiDAR can map a scene, why not try turning that data into real night-vision imaging?
That question sparked the creation of the Night Vision App. By combining the iPhone’s LiDAR scanner with the TrueDepth camera, it delivers detailed low-light imaging and transforms your phone into a capable night-vision tool.
If you own an iPhone Pro model, it’s definitely worth trying.

value: Show HN: I Turned My iPhone into a Night-Vision Device

author: darkce


value: kakco-AI

value:
Ever stumbled upon a cluster of wild mushrooms in the woods, curious about their species but paralyzed by the fear of toxicity? Tired of letting uncertainty stop you from exploring nature’s edible treasures? Today, we’re thrilled to announce the official launch of MushroomIdentification AI — the cutting-edge intelligent system that turns wild mushroom safety from a worry into a breeze!
1-Second Precision, 1000+ Species Covered
No professional mycology knowledge? No tedious manual comparisons? No problem. Simply open MushroomIdentification AI, upload a mushroom image or snap a photo directly, and our advanced AI kicks into gear. In just 1 second, it accurately identifies the mushroom species — covering over 1000+ wild and common varieties. You’ll get more than just a name: we provide scientific classifications, common aliases, detailed morphological features, and natural habitats. Go from a “mushroom novice” to an informed explorer in an instant!
Smart Toxicity Analysis: Safety Alerts That Act Fast
The biggest risk of wild mushrooms lies in hidden toxins — and we’ve got you covered. Our system is equipped with a state-of-the-art toxicity analysis algorithm that automatically assesses toxicity levels and pinpoints potential toxic components. If a poisonous mushroom is detected? You’ll get an immediate, prominent safety alert, paired with step-by-step first-aid guidance for accidental ingestion. From identification to emergency response, we’ve built a full safety net for you.
More Than Identification: Your All-Round Mushroom Companion
MushroomIdentification AI isn’t just a tool — it’s your trusted partner for responsible nature exploration:
Multi-Angle Accuracy: Upload photos of the mushroom’s top, side, and bottom for even more precise results.
Global Accessibility: Multi-language support ensures safety information is clear, no matter where you are.
Sustainable Collection: Learn science-backed methods to harvest mushrooms without harming mycelium or disrupting ecosystems.
Expert Backup: Connect with professional mycologists for personalized consultation when you need extra guidance.
History Tracking: Save your identification records for easy reference, perfect for repeated trips or learning.
Ready to Explore Safely? Start Now!
Whether you’re a wild food enthusiast, a hiker, or a nature lover, MushroomIdentification AI takes the guesswork out of wild mushroom interactions. It’s fast, free, and trusted by outdoor adventurers worldwide.
 Click “Start Identification Now” — upload your first mushroom photo, and let AI guard your next nature adventure!
About MushroomIdentification
Powered by advanced AI technology, MushroomIdentification is dedicated to making wild mushroom exploration safe, accessible, and educational. We combine continuous AI learning with expert mycological insights to deliver a tool that protects both you and the environment.

value: Show HN: Guardian for Wild Mushroom Safety

author: kakco-AI


value: ronbenton

value: I have a couple young kids and they always want to play mad libs. I only really found ones riddled with ads online so I made my own here. I'm a backend guy so go easy on my UI skills. Definitely used Cursor for help on the UI part of it. Anyway hope you play this with your kids too!

value: Show HN: Made MadLibs-style game to play with my kids

author: ronbenton


value: cedge

value:
We’re excited to release our LimiX-2M -- a lightweight version of our SOTA tabular foundation model, LimiX-16M. LimiX-2M delivers strong performance in tabular modeling. Unlike other existing tabular foundation models that train separate models for classification and regression, LimiX is the first unified tabular foundation model in the world — enabling classification, regression, and a wide range of tabular tasks with a single model.
What’s new in LimiX-2M: built upon LimiX-16M, the new version introduces several major architectural improvements. Thanks to these updates, LimiX-2M achieves excellent modeling performance with a dramatically smaller footprint, making it the ideal choice for research and personal projects.
Key features:
- 3.6× faster inference and 1/4 model size vs TabPFN-2.5: high performance with much lower compute cost.
- Excellent performance: LimiX-2M outperforms TabPFN-V2 and AutoGluon across multiple public benchmarks, and performs comparably to TabPFN-2.5 and RealTabPFN-2.5, with only a small gap from the larger LimiX-16M.
- Farewell to annoying model management: with LimiX, a single checkpoint supports multiple downstream tasks—no need to download or maintain multiple models ;)
- Plug-and-play: run it anywhere — CPU or GPU. A step-by-step guide is included to help you get started in your own projects instantly.
Of course, there’s still plenty of room for improvement in LimiX. But as the first unified tabular foundation model, it’s definitely worth a try. We look forward to your feedback!
Technical report: https://arxiv.org/abs/2509.03505
Project: https://www.limix.ai/
Github: https://github.com/limix-ldm/LimiX/
HuggingFace: https://huggingface.co/stable-ai/LimiX-2M

value: Show HN: LimiX-2M – The super-light foundation model redefining tabular learning

author: cedge


value: tsormed

value:
I struggle with time blindness and kept missing client Zoom calls.
MacOS notification banners appear for 5 seconds and vanish...my brain can't hold onto them. I spent 2 months building a solution.
What it is:
Chime shows full-screen alerts that physically interrupt whatever you're doing. It's impossible to miss.
Technical details:
- Built entirely in Swift/SwiftUI using Cursor.
- Uses EventKit for Calendar + Reminders integration
- Todoist REST API v2 for task sync
- All data stays local—no cloud sync
How it works:
- Syncs from Calendar, Apple Reminders, and Todoist every 1-30 min (This is customisable)
- Extracts meeting links from meetings (30+ platforms: Zoom, Teams, Meet, Webex, etc.)
- Schedules Timer-based full screen alerts with customizable timing
- Sound rotation system to prevent habituation
The insight:
Most accessibility focuses on making things "clearer" or "simpler." Neurodivergent brains need "impossible to ignore." Subtle equals invisible. I've been using it for a few weeks and went from missing chunk of meetings to zero.
Try it:
14-day free trial on Mac App Store. No signup required, just download and grant Calendar permissions. https://apps.apple.com/us/app/chime-remember-everything/id67...
Happy to discuss:
- Building macOS apps with AI assistance
- EventKit's quirks (hourly recurrence workaround, full screen alert implementation, etc)
- Designing for neurodivergent users

value: Show HN: Chime – Full-screen meeting alerts for time blindess (macOS)

author: tsormed


value: logannyeMD

value: My cofounder and I run a startup in oncology, where we handle cancer genomics data. It occurred to me that, thanks to a recent complexity theory result, there's a clever way to run bioinformatics algorithms using far less RAM. I built this Rust engine for running whole-genome workloads in under 100MB of RAM. Runtime is a little longer as a result - O(TlogT) instead of O(T). But it should enable whole-genome analytics on consumer-grade hardware.

value: Show HN: I made an open-source Rust program for memory-efficient genomics

author: logannyeMD


value: RichardFu

value:
We built an AI tattoo generator + body placement studio.
- Generates concepts + previews on real body photos.
- Lets users iterate before committing ink.
Would love feedback from the HN community on workflow / UX / pricing.

value: Show HN: My Tattoo Ideas – AI tattoo generator with body placement previews

author: RichardFu


value: Show HN: Chess960v2 – Over 200 Rounds Played


value: cranberryturkey

value: Show HN: eBook Summary Generation

author: cranberryturkey

title: Show HN: eBook Summary Generation


value:
I use a MacBook (no NVIDIA GPU) but still like experimenting with CUDA kernels.
I didn’t want to spin up a VM every time or use browser-only tools, so I built a tiny CLI that runs local .cu files on Modal GPUs with a single command.
Modal gives around $30 in free credits when you sign up, so the CLI is built around that. You can run simple kernels on smaller GPUs (like T4) for very little. I ran multiple tests for under $1.
What it does:
- Sends your .cu file to a Modal container with a GPU image (you can override it)
- Compiles it with nvcc (supports nvcc args)
- Runs it on any GPU you choose (T4 through B200)
- Streams output/error back to your terminal
Usage:
    uv add modal-cuda

    mcc myKernel.cu --gpu H100

    # or:
    uvx --from modal-cuda mcc myKernel --nvcc-arg=-Xptas --nvcc-arg=-v

Links:
GitHub: https://github.com/ExpressGradient/modal-cuda
PyPI: https://pypi.org/project/modal-cuda

value: Show HN: modal-cuda – CLI to run CUDA .cu programs on Modal GPUs


value: readvatsal

value:
I’ve been building the Universal Open Textbook Initiative (https://uotinitiative.org) for the past year to solve a problem I kept running into: high-quality open educational resources exist, but they’re scattered across the internet and almost entirely in English.
What it is: A repository of 79 open textbooks in 5 languages (English, Arabic, Spanish, Simplified Chinese, and Polish), covering Kindergarten through Master’s level. Everything is Creative Commons licensed or similar, no account needed. More textbooks and translations in the works.
How it works: I curate the highest quality open textbooks from sources like MIT, TU Delft, OpenStax, and CK-12. For original translations, I use DeepL Pro with human review by native speakers. Each textbook page clearly indicates whether it’s human-translated or machine-translated.
Why I built this: When you’re a homeschooling parent in the Middle East, a teacher in rural Africa, or running an NGO in South America, finding quality textbooks in your language is difficult. This platform makes world-class educational content accessible to anyone, anywhere.
What’s next: I’m developing an AI-enhanced learning layer where students can select any textbook and get personalized diagnostic quizzes, lessons, and practice exercises generated in real time in their native language.
What I’m looking for:
Feedback on the platform and approach;
Volunteers to review machine translations (especially if you’re a native speaker of our supported languages);
Ideas for reaching educational institutions and NGOs who could benefit from this
Happy to answer any questions.

value: Show HN: Free, multilingual open textbooks (K-12 to Master's level)

author: readvatsal


value: 500+ hours of hands-on play (no AI fluff) for Dispatch: Right-Call Simulator (mission success calculator), Episode 5 walkthrough + Mecha Man Bomb puzzle, hero database, team builder, all endings (Perfect Victory included). Master tactical calls free: https://dispatch-game.org/

value: Show HN: Dispatch Game Hub – Ultimate Guide, Simulator and Hero Database


value: I used Lovable to make a webapp that tells you whether to pay down debt (student loan, credit card debt) or invest in stocks or 401K or REITs. I had an Excel model for this and uploaded this to Lovable and spent some time iterating on the UI.<p>Do test it out, I'd love to get any feedback.

value: Show HN: Paydown calculator, Should you pay down debt or invest in the markets?


value: selenehyun

value:
Hi HN,
A few days ago, I shared Tenant Operator – a Kubernetes operator for multi-tenancy. After valuable feedback and production experience, I've evolved it into Lynq with a clearer scope and better documentation.
What it does: Lynq watches your database (MySQL) and automatically provisions Kubernetes resources based on active rows. Add a database entry → get a full stack (Deployment, Service, Ingress, whatever you define) in ~30 seconds. Update or delete the row → everything syncs automatically.
Key improvements since the original post:
- Generalized beyond tenants: now handles any DB-driven infrastructure pattern
- Built-in drift detection, conflict resolution, and dependency ordering
- Much clearer documentation showing real-world use cases
- Production-tested workflow patterns (customer isolation, dynamic environments, self-service infra)
The core insight remains: your database is already the source of truth for dynamic entities (customers, teams, environments). Why maintain separate YAML files and manual kubectl workflows?
Real-world example: New customer signs up → DB insert → isolated namespace + all resources appear automatically. No CI/CD pipeline, no Helm release tracking, no manual kubectl apply.
This isn't for everyone – if your infrastructure is mostly static, traditional GitOps is simpler. But if you're managing 100+ dynamic nodes where configuration lives in a database, this approach eliminates a lot of operational overhead.
I'm looking for feedback on:
- Design patterns you'd want to support
- Edge cases I might have missed
- Documentation clarity (especially for those new to operators)
Site: https://lynq.sh/
GitHub: https://github.com/k8s-lynq/lynq
Happy to answer any questions!

value: Show HN: Lynq – Turn Database Rows into Kubernetes Infrastructure Automatically

author: selenehyun


value: itsnotmyai

value:
The AI Bubble Monitor is an analytical tool designed to track and visualize indicators of potential market bubbles in AI-related sectors. It aggregates multiple data sources and metrics to produce a composite "AI Bubble Score" that ranges from 0 to 100.
The tool breaks down the overall score into five sub-indices: Valuation, Capital Flows, Adoption vs Fundamentals, Sentiment & Hype, and Systemic Risk. Each sub-index provides insight into different aspects of market behavior and potential overvaluation.

value: Show HN: AI Bubble Monitor

author: itsnotmyai

title: Show HN: AI Bubble Monitor


value: Show HN: AI Browser – Cloud browser for web automation


value:
Hi HN,
I built a simple tool to solve a problem I've faced as a developer and freelancer: how can you definitively prove a file (like code, a contract, or a design) existed at a specific time?
It's called Akashi Notari, and it’s a simple utility for creating an on-chain "Proof of Existence." It lets you anchor any file's cryptographic hash on the blockchain (supports Base, Ethereum, Optimism). And costs less than a dollar.
The main focus is privacy and simplicity:
- *100% Private:* Hashing (SHA-256) is done entirely in your browser. The file never leaves your computer. Only the calculated hash is sent to the blockchain.
- *Fast:* The whole process, from file drag-and-drop to a confirmed transaction, takes about 60 seconds.
- *Verifiable:* As soon as the transaction confirms, you get a PDF certificate with the hash, timestamp, and a direct link to the on-chain transaction for verification.
I built this as a "poor man's copyright" or a simple notarization tool for:
- *Freelancers:* Proving when a deliverable was completed.
- *Creators:* Timestamping IP (art, music, manuscripts) before sharing it.
- *Anyone:* Timestamping contracts or records without the cost and hassle of traditional notaries. Maybe some commit-reveal scenarios.
The workflow is just:
1. Drag/drop your file.
2. Connect a wallet.
3. Confirm the transaction.
4. Download your PDF certificate.
5. Prove anytime in the future that your file existed using simple instructions in PDF
You can try it out here: https://akashi-notari.com/
You can also retrieve/verify existing certificates here: [https://akashi-notari.com/certificate-retrieval](https://www.google.com/search?q=https://akashi-notari.com/ce...)
I'd love to hear what the HN community thinks!

value: Show HN: Akashi Notari – On-chain Proof of Existence for any file in 60s for <$1


value: Alradyin

value:
I've been building Qantify for the past 2 years - an open-source Python library for algorithmic trading that combines institutional-grade math models with modern ML/AutoML pipelines.
*What makes it different:*
Most trading libraries are either:
- Too slow (Backtrader: ~5ms/point, Zipline: ~10ms/point)
- Missing advanced math (no quantum finance, chaos theory, optimal execution models)
- No AutoML pipeline (QuantConnect has basic ML but no AutoML)
- No GPU acceleration
Qantify addresses all of these.
*Performance:*
- 0.008ms per data point (50-100x faster than alternatives)
- GPU acceleration: 2-3x speedup on compatible hardware
- 1767+ comprehensive tests, 100% code coverage
- 106K+ lines of code
*Advanced Math Models* (what hedge funds use):on
# GARCH volatility modeling
from qantify.math.econometrics import GARCHModel
garch = GARCHModel(returns)
vol_forecast = garch.forecast(horizon=10)
# Heston stochastic volatility
from qantify.math.stochastic import HestonProcess, MonteCarloEngine
heston = HestonProcess(v0=0.04, theta=0.04, kappa=2.0, sigma=0.3, rho=-0.7)
mc = MonteCarloEngine(process=heston)
paths = mc.simulate(n_paths=10000, n_steps=252)
# Optimal execution (Almgren-Chriss)
from qantify.math.execution import AlmgrenChrissOptimalExecution
execution = AlmgrenChrissOptimalExecution(
    initial_position=10000,
    time_horizon=timedelta(hours=4),
    risk_aversion=1e-6
)
optimal_schedule = execution.compute_schedule()
# Advanced cointegration (statistical arbitrage)
from qantify.math.stat_arb import JohansenTest, KalmanHedgeRatioEstimator*Unique features* (not found in competitors):
- Quantum finance models
- Chaos theory applications
- Complexity theory
- Information theory
- Game theory models
*ML/AutoML Pipeline* (end-to-end):thon
from qantify.ml import AutoMLRunner, create_features
# Auto-generate 100+ features
features = create_features(
    df,
    feature_sets=["trend", "momentum", "volatility", "statistical"],
    windows=(14, 55, 200)
)
# Automated model selection with walk-forward validation
runner = AutoMLRunner(
    search="bayesian",  # or "grid", "random", "hyperopt"
    walk_forward=True,  # Time-series aware validation
    test_size=0.25
)
results = runner.run(features, target)
# Automatically tests: RF, GBM, XGBoost, LightGBM
# Selects best model with proper time-series validation*ML Features:*
- Reinforcement learning (DQN, Q-learning, Policy gradients)
- Advanced neural networks (LSTM, GRU, Transformers, Attention)
- Model drift monitoring
- Feature selection (SHAP, mutual information)
- Experiment tracking (MLflow, W&B integration)
*Quick example:*
from qantify.data import get_candles
from qantify.signals import rsi
from qantify.backtest.vectorized import run_vectorized
df = get_candles("binance", "BTCUSDT", "1h", limit=1000)
df['rsi'] = rsi(df['close'], period=14)
result = run_vectorized(
    prices=df["close"],
    long_signal=df["rsi"] < 30,
    short_signal=df["rsi"] > 70,
    allocation=0.5
)
print(f"Sharpe: {result.metrics.sharpe:.2f}")*Real benchmarks:*
- Backtest speed: < 0.1s (5000 points) vs QuantConnect's ~5-10s
- GPU speedup: 2-3x (tested on RTX 3070)
- ML accuracy: Market regime 74.3%, Volatility prediction 68.7%
*Why I built it:*
I needed advanced math models (GARCH, Heston, optimal execution) combined with modern ML pipelines, all in one library. Existing solutions either lacked the math depth or the ML integration.
*GitHub:* https://github.com/Alradyin/qantify
*PyPI:* pip install qantify
I'd love feedback from the HN community. What features would you find most useful? What am I missing?
Built with Python 3.10+, async/await everywhere, modular architecture. Completely open-source and free for personal/research use.

value: Show HN: Qantify – GPU-Accelerated Trading Library with Advanced Math and AutoML

author: Alradyin


value: mzou

value:
Hi HN,
Tracking your wealth is mundane and boring. You write down some number in spreadsheet, stare at it, and wonder—what's the point? It doesn't have to be that way.
Introduce Guapital, think league of legends for wealth tracking app. Guapital supports multiple asset types (real estate, cash, brokerage, RSUs, crypto, and etc). But here's the best part: you get to see where you rank (in percentile) compared to your peers, plus projections of your net worth in 5-30 years.
Have fun building guaps, and feedbacks are always welcome!
link: https://guapital.com

value: Show HN: Guapital – what rank is your wealth?

author: mzou


value: hamzaawan

value:
I have been building SaaS for sometime now and I always stumbled across this problem when your landing page doesn't convert. You always think that you have built the best landing page for your SaaS in the world but when you get another set of eyes on there they simply say "I dont get what are you trying to sell here" or "I don't whats the way through it" and many other things.
So on this quest to solve this problem and making everyone able to get second set of eyes on there landing page and get actionable feedback, I have created pagereport.app Which provides access to purpose built agents to analyse your landing page visually to provide you actionable feedback, like a second set of eyes.
On top of that it has the ability to do full blown CRO of your landing pages at fraction of the cost then those expensive consultants. And good thing is you can do it your self.
Its like you asking chatgpt "how does my landing page looks?" or "Can you do 5 second blind test of my page to see whats it seems to be selling?" but chatgpt is unable to do that, because it cant see your landing page and analyze the components in there, it simply doesnt have to tools for it. But I have added all of those tools in pagereport.app so it can analyze the page visually and its dom to provide you proper feedback, which can you rely on.
I am open to any sort of feedback on it, even if its brutal.

value: Show HN: Page Report – Conversational agents for landing page CRO

author: hamzaawan


value: joeysywang

value: Show HN: DoDidDone – Voice AI that calls customer support on your behalf

author: joeysywang


value: bladecd

value: Realistic malicious traffic with labels

value: Show HN: Realistic malicious encrypted traffic datasets for ML

author: bladecd

story_text: Realistic malicious traffic with labels


value: aanthonymax

value:
Hi, HN! We're continuing to build the perfect tool for HATEOAS apps, and in this version, we've added some truly extensive functionality that will make the development process significantly easier.
We've added bindings between the request status and the tag attribute. This will allow you to create dynamic indicators and more without using the indicator helper block.
We took the code from the old Cample project. I think its speed will be very useful for users.
I would appreciate your feedback ^ ^

value: Show HN: New major version 3.2.0 of hmpl

author: aanthonymax


value: olivefu

value:
Hey HN,
I recently built whatstype.org
, a free personality test website that helps people explore their thinking, communication, and relationship patterns.
Unlike most MBTI-style sites that only give you a short label, Whatstype digs deeper:
The test adapts to your responses dynamically
Results are structured around reasoning style, emotional pattern, and social interaction
Each of the 16 personality types includes detailed analysis, strengths, challenges, and real-life advice
No login, no tracking — everything runs client-side
I built it because I was frustrated with the typical “clickbait MBTI quizzes” that don’t respect users’ time or data. Whatstype focuses on clarity, accuracy, and clean UX rather than virality.
Tech stack:
Next.js 14 + TypeScript
TailwindCSS for UI
Structured JSON content for multi-language results
Fully static export, hosted on Cloudflare Pages
If you’re into psychology, data visualization, or just enjoy introspective tools, I’d love your feedback — especially on:
Question design (is it too long / too short?)
Result presentation clarity
Ideas for making the insights more actionable
You can try it here:  https://whatstype.org
Thanks for reading!

value: Show HN: I built whatstype.org – a free personality test site

author: olivefu


value:
Hi HN! I built ORATIO, a programming language where you write in natural language (Italian/English) and it executes directly.
The interesting part: it uses a local GPU model (DeepSeek 16B) to automatically generate new operations by analyzing the Python ecosystem. It literally programs itself.
Example:
"Create a red dot. Save as output.png"
→ Works instantly
Live playground: https://oratio.dev/playground.html
Install: pip install oratio
Currently supports data analysis, visualization, and graphics. The autonomous expansion system is in development.
Would love feedback from the community!

value: Show HN: Oratio – Natural language programming that expands itself with AI


value: valliveeti

value:
Created a simple tool to solve our own problem of syncing new users from supabase to our beehiiv. Hopefully you also find it useful.
Includes:
Checks for unsubscriptions to not resubscribe unsubscribed people.
Dry runs to double check the additions we would do.
No immediate plans to extend this with new features.

value: Show HN: Sync Supabase Users to Beehiiv

author: valliveeti

title: Show HN: Sync Supabase Users to Beehiiv


value: pyromaker

value: A little tool I built to track some prices for the upcoming Christmas period. Trying to track as many things as I can. Would love to hear some feedback.<p>Thank you in advance!

value: Show HN: I made my own price tracker tool – Marvelogs

author: pyromaker


value: adzicg

value: Show HN: BugMagnet AI Assistant for test coverage and bug discovery

author: adzicg


value: doctorsolberg

value:
Hi!
I was tired of using spreadsheets for simple side projects and entrepreneurial activities, so I built my own finance tracker for small entrepreneurs, with a focus on privacy (including end-to-end encryption, etc.)
Team access support and a privacy whitepaper are WIP.
Would love your feedback!

value: Show HN: I built a cashflow planning app for privacy nerd solopreneur

author: doctorsolberg


value:
Our AI recruitment pipeline was auto-rejecting anyone who'd worked at companies founded after 2023. It didn't recognize names like Harvey or Snorkel AI, or didn't realize how important they'd become because of training data cutoffs.
We had traces, evals, Langfuse dashboards - everything looked fine - but we kept finding failures we should have caught earlier.
The pattern kept repeating:
- ship an improvement
- it works for a while
- hit an edge case that breaks it
- don't notice until we've lost good candidates
That's when we realized - the problem wasn't just our recruitment pipeline - almost every AI product has blind spots that evals miss.
So we built Verse, a tool that surfaces issues directly from real AI interactions - whether that's candidates talking to your recruitment pipeline, users interacting with your agent, or any AI making decisions.
Instead of relying solely on evals, we cluster conversations, identify the key ones to review, and flag the ones that show failure patterns. We use OpenTelemetry for trace ingestion, so it's compatible with Langfuse, Langsmith, Braintrust, and other AI observability tools - you can add it right alongside your existing setup.
I'm posting this because I'm curious whether other teams are hitting the same wall. If you want, I'm happy to audit your AI implementation for free and show you where things commonly break - even if you never use Verse.
Happy to answer any technical questions.

value: Show HN: Verse AI – Catch the AI failures your evals miss


value: Verdierm

value:
I've been exploring the Google Workspace marketplace lately, and honestly, it feels like a possible goldmine that nobody talks about.
Started tinkering with a simple idea - what if I could scrape any webpage and auto-generate quiz questions?
Basically you paste any URL and it creates quiz questions automatically in Google Forms.
What I learned about Workspace development:

    Apps Script is way more powerful than people give it credit for

    The approval process is actually pretty smooth once you learn what they want

    It is still competitive because there are other apps on there

    Theres a constant stream of users trying your app every day.

Trying to learn how to better monetize it because I notice there are alot of users who just want to use it for free.
Also learned that people use alot of fake reviews to boost there app which I wont do.
Try it here:
https://workspace.google.com/marketplace/app/examize/6083607...
Anyone else building stuff for Google Workspace? I'm curious what others are working on.

value: Show HN: Turn any webpage into a Google Forms quiz using AI

author: Verdierm


value:
Hi HN,
We built an AI agent for data analysts that turns the soul crushing spreadsheet & BI tool grind into a fast, verifiable and joyful experience. Early users reported going from hours to minutes on common real-world data wrangling tasks.
It's much smarter than an Excel copilot: immutable data steps, a DAG of SQL views, and DuckDB for instant crunching over millions of rows. Our early agent prototype ranked #1 on the Spider2-DBT bench. https://spider2-sql.github.io
Try it out and we'd love your feedback!
Thanks,
Di Wu & the Shadowfax team
P.S. Shadowfax is Gandalf's horse from LOTR. There's a hidden easter egg site with 3 different triggers, see if you can find them.

value: Show HN: Shadowfax AI – an agentic workhorse to 10x data analysts productivity


value: dufferdeepu

value: Show HN: I built a tool to visually edit GitHub Actions YAML

author: dufferdeepu


value: drprotos

value:
Hey HN! I'm one of the developers of Termly.

  Problem: I love using Claude Code for development, but being chained to my laptop all day wasn't working. SSH + terminal emulators
  felt clunky for interactive AI coding sessions.

  Solution: Built a WebSocket relay that mirrors your AI coding session to mobile with zero-knowledge E2E encryption.

  How it works:
  1. npm install -g @termly-dev/cli
  2. termly start (wraps your AI tool)
  3. Scan QR code with mobile app
  4. Your session syncs in real-time, encrypted end-to-end

  Tech stack:
  • Open source CLI (MIT licensed) - inspect the code yourself
  • WebSocket relay server
  • AES-256-GCM + Diffie-Hellman key exchange
  • React Native mobile apps (iOS live, Android in beta)
  • Zero-knowledge architecture - server never sees plaintext

  Why not just SSH?
  • Optimized for AI workflows (session catchup, batch sync)
  • Voice input for mobile prompting
  • Better UX than traditional terminal emulators
  • Automatic reconnection handling
  • Free to use

  Open source CLI:
  The CLI and all client-side encryption code is MIT licensed. You can verify the zero-knowledge architecture yourself.

  GitHub: https://github.com/termly-dev/cli

  Current status:
  • iOS app live on App Store
  • Android beta launching this month
  • Works with Claude Code, Cursor, Aider, and other terminal-based AI tools

  Would love feedback from HN! What features would make this more useful for your workflow?

  Demo: https://www.youtube.com/watch?v=muNOoax2KwQ
  Website: https://termly.dev

value: Show HN: Termly – Use Claude Code or any other CLI on mobile with E2E encryption

author: drprotos


value: eschnou

value: Show HN: Patchsmith – Agentic wrapper for CodeQL (finetune, triage, fix with AI)

author: eschnou


value: Yreminder

value:
I built this because I got tired of paying for image generation tools that gate everything behind subscriptions. dreamyy2k.app is completely free, no login required.
The interesting part was getting Y2K horror aesthetics right. I'm using Gemini to enhance user prompts with specific visual markers - chromatic aberration, CRT scan lines, low-res video grain, that specific 1999-2003 digital camera look. Then routing to Together AI or Replicate depending on load.
Technical choices:
- Gemini for prompt engineering (cheaper than GPT-4 for this)
- Together AI as primary backend (better pricing)
- Replicate/fal.ai as fallback
- No database needed since everything's stateless
- Images are PNG, around 10-30 seconds generation time
The hardest part was balancing "horror" without making it too generic creepypasta. Y2K horror has this specific vibe - early internet anxiety, millennium bug paranoia, that weird transitional tech period. I added reference images from actual early 2000s horror media to train the prompting.
Current limitations: sometimes the model goes too heavy on the glitch effects. And the horror aspect can be hit or miss - AI models seem to default to either "ghost girl" or "VHS static" when you say horror.
Code isn't open source yet but considering it. Main concern is API costs if it gets hammered.
Try it: https://dreamyy2k.app
Would love feedback on the aesthetic accuracy. Did anyone else live through actual Y2K and remember what digital horror looked like back then?

value: Show HN: Free Y2K horror image generator using Gemini prompts

author: Yreminder


value: Its unpolished v0 so I am releasing it to get general feedback.Built extensively with the aid of AI

value: Show HN: A game of higher or lower using GitHub stars


value: zknowledge

value:
A small project pushing back against the trend of products engineered to prey on short attention spans for ARR.
Instead of brainrot or casino-style engagement while your AI is thinking (hi Chad IDE), this extension gives you logic puzzles so your brain stays switched on and sharp.
look up "anti-brainrot ide" by zedkay22 on Cursor extensions. Click the button on the bottom right and play some mentally stimulatin games.

value: Show HN: Solve logic puzzles while waiting for AI

author: zknowledge


value: wahvinci

value:
Sometimes I want to mention a website (like on X/Twitter) without making it clickable, so I’d type “example. com” with a space. Doing that every time got tedious, so I built Link Snapper — a free Chrome extension that lets you copy any website’s URL as plain text.
You can customize how it copies links — include or remove https, copy only the domain, or grab the full path. When you paste it anywhere, it stays exactly how you want it — clickable or not.
It’s simple, privacy-friendly, and works in one click.
Curious what you think or how you’d use it in your workflow.

value: Show HN: Link Snapper – Copy URLs as plain text for unclickable sharing

author: wahvinci


value:
I built PolyCouncil to solve a problem I kept running into when testing local LLMs, I never knew if a single model's answer was actually good, or just confidently wrong.
Instead of querying one model, PolyCouncil runs multiple local LLMs in parallel through LM Studio, has them evaluate each other's responses using a shared rubric, then conducts weighted voting to reach consensus on the best answer.
Think of it like a panel of experts that score each other's work, then vote democratically on the winner.
Key features:
Parallel execution across multiple models
Rubric-based cross-evaluation (accuracy, clarity, completeness, etc.)
Weighted voting system based on peer scores
Custom personas (fact-checker, engineer, risk assessor, etc.)
Built-in leaderboard to track model performance
Single-voter "judge" mode for controlled experiments
Clean Python/PySide6 GUI with pre-built Windows executable
Why it's useful:
Compare models systematically without manual evaluation
Get more reliable answers through ensemble methods
Explore emergent behavior from model deliberation
Test how different personas affect model responses
It's free for personal/research use (Polyform Noncommercial). Built for anyone tinkering with local models who wants to see how collective intelligence emerges from LLM orchestration.
Would love feedback from folks experimenting with local models!
GitHub: https://github.com/TrentPierce/PolyCouncil

value: Show HN: PolyCouncil: Multi-Model Deliberation Engine for LMStudio (Open Source)


value: irshadnilam

value:
We (me and my co-founder) have been working on this library for a while and would love to get your thoughts.
github.com/agents-sh/radkit
radkit.rs/getting-started/
Most of the agent librararies out there just adds a huge prompt, put all the tool definitions and call the LLM on a loop hope and for the best.
This is not a way to build reliable ai agents. A better way to build ai agents is described in 12-factor agents by Dex Horthy here.
https://www.youtube.com/watch?v=8kMaTybvDUw
https://github.com/humanlayer/12-factor-agents
Another limitations of current agentic frameworks is their support for interoperability among agents is very limited.
Google came up with https://a2a-protocol.org/latest/ (now under linux foundation) to address this, but the agent frameworks trying to do as much as possible and keep the end users locked into their "ecosystem" instead of supporting such protocols.
Everyone is so focused on MCP for some reason. (no hate for mcp, radkit supports mcp as well).
We belive in an agentic future where businesses expose ai agents and not mcp servers or rest apis. If you build an agent using radkit, it is a2a-protocol compliant from the get go.
With all of this in mind, we are building `radkit`.
It is still early stages. But we have reached a milestone and wanted to share with the community.

value: Show HN: Radkit – Build reliable AI agents with Rust

author: irshadnilam


value: linkshu

value: By players, for players: https://dispatchgame.me/ packs weekly-updated 1-6 episode walkthroughs, hero stat optimizers (Combat/Intelligence/Charisma), choice impact breakdowns, puzzle solutions, and 100% achievement guides. Ad-free, platform-agnostic (Steam/GOG/Epic + console soon) – perfect for chasing all endings.

value: Show HN: Dispatch Game Guide – Walkthroughs, Builds and Choice Outcomes (2025)

author: linkshu


value: jjaramillor

value:
Hey HN,
I built Coffee Zip, an iOS app for finding specialty coffee shops and tracking your visits with a digital passport.
WHAT I'D LOVE FEEDBACK ON
1. Is the passport concept stupid or actually useful/fun?
2. How would you handle user-generated content (shops, reviews) without it turning into spam?
3. Monetization: Ads vs. subscription vs. one-time purchase?
4. Should I build Android or focus on making iOS perfect first?

value: Show HN: I Built an app to Find and track specialty coffee shops worldwide

author: jjaramillor


value: goncharom

value:
Hey HN! I wanted to share a little library I've been working on to leverage AI to get structured data from arbitrary pages. Instead of sending the page's HTML to an LLM, Hikugen asks it to generate python code to fetch the data and enforces the generated data conforms to a Pydantic schema defined by the user. I'm using this to power yomu (https://github.com/goncharom/yomu), a personal email newsletter built from arbitrary websites.
Hikugen's main features:
- Automatically generates, runs, regenerates and caches the LLM-generated extraction code.
- It uses sqlite to save the current working code for each page so it can be reused across executions.
- It uses OpenRouter (https://openrouter.ai/) to call the LLM.
- It can fetch the page automatically (it can even reuse Netscape-formatted cookies) but you can also just feed it the raw HTML and leverage the rest of its functionalities.
Here's a snippet of what it looks like:

  from hikugen import HikuExtractor
  from pydantic import BaseModel
  from typing import List

  class Article(BaseModel):
      title: str
      author: str
      published_date: str
      content: str

  class ArticlePage(BaseModel):
      articles: List[Article]

  extractor = HikuExtractor(api_key="your-openrouter-api-key")

  result = extractor.extract(
      url="https://example.com/articles",
      schema=ArticlePage
  )

  for a in result.articles:
      print(a.title, a.author)

Hikugen is intentionally minimal: it doesn't attempt website navigation, login flows, headless browsers, or large-scale crawling. Just "given this HTML, extract this structured data".
A good chunk of this was built with Claude Code (shoutout to Harper’s blog: https://harper.blog/2025/02/16/my-llm-codegen-workflow-atm/.
Would love feedback or ideas—especially from others playing with codegen for scraping tasks.

value: Show HN: Turn any webpage into structured data via LLM codegen

author: goncharom


value: NadavBenItzhak

value:
A note on “vibe coding”
A lot of engineers today use AI in a very vibe-based way:
give the model context, hope for the best, iterate until it works.
That’s fine for prototypes.
It completely breaks at team / enterprise scale.
This course introduces a repeatable methodology and relevant tooling for AI-assisted coding that actually works in real engineering teams - and it’s built specifically for experienced developers, not beginners.
Basically: how to scale beyond “vibes” into an actual engineering method - with the tools and discipline senior developers need.
Managers and team leads kept asking for this, so it became one of the core modules.

value: Show HN: Free course that teaches engineers how to vibe-code at enterprise scale

author: NadavBenItzhak


value: pompeii

value:
This release is a big one — packed with tons of improvements!
What’s new: • Bulk editing + snippet creation • Easier, improved tag management • Support for Liquid Glass • Brand new search for fast snippet management • Improved visuals + clearer setup guidance
And as always:  100% free  No ads  No tracking  No subscriptions the only IAP are for tips if you want to support the development (or just leave a review)
For this update, I went fully AI — built the workflows and automation using AI agents (Claude Code). Built with SwiftUI / Swift.
Most of these updates came directly from user feedback via Canny (super useful tool for collecting and prioritizing suggestions — not sponsored, just sharing).
Next step: I’m planning to open source the entire project, so the community can help it grow with new features, fixes, and ideas.
Would love to hear what you think about this version — and if you’ve got feature ideas for v5.1 or thoughts on the open-source direction!

value: Show HN: SnipKey v5.0 – Free Keyboard Extension

author: pompeii


value: maymoonty

value:
Hey HN,
I built MetaKwik, a free meta description generator.
The problem: Google rewrites 63% of meta descriptions because most don't match search intent.
The solution: Generate 5 descriptions, each optimized for a different intent (transactional, informational, commercial, navigational). Each gets an "AI-resilience score" that predicts if Google will rewrite it.
Tech stack: Spring Boot + Ollama (Mistral). Runs locally = $0 API costs.
Try it: https://metakwik.com
Open to feedback and questions!

value: Show HN: MetaKwik – Free AI meta description generator with intent matching

author: maymoonty


value:
Hey HN, I’m Dan. I’ve been working on forecasting for the last six years at Google, then Metaculus, and now at FutureSearch.
For a long time, I thought prediction markets, “superforecasting”, and AI forecasting techniques had nothing to say about the stock market. Stock prices already reflect the collective wisdom of investors. The stock market is basically a prediction market already.
Recently, though, AI forecasting has gotten competitive with human forecasters. And we’ve found a way of modeling long-term company outcomes that is amenable to our forecasting approach.
Iteration by iteration, my skepticism has turned to light distrust, and then into actually getting value out of our company forecasts. Like most AI companies, we draw from improvements against our LLM-agent benchmarks, Deep Research Bench and Bench to the Future.
But at the end of the day, to evaluate it, I think it’s best to follow Charlie Munger’s advice: try to disprove what Stockfisher says. Now, even after an hour of carefully reading through a stock, I generally can’t find any major flaws, and I hear the same from other serious investors.
And, since it’s software, not human analysis, we applied it at scale. We have long-term forecasts for revenue, margins, and payout ratios for every company in the S&P 500.
We do a Buffett-style intrinsic valuation, that doesn’t take the stock price into account. This means you can sort by the difference between what the stock market says, and what Stockfisher says. Some very interesting neglected companies rise to the top, and we’re starting to process the mid-caps and small-caps too.
Stockfisher is live at https://platform.stockfisher.app, no sign in required, to see what we have on the top stocks. (I was happy to see Alphabet comes out well. Meta looks good too, but Amazon and Apple do not.) I would love for you to judge for yourself if the quality of analysis is up to your standards.

value: Show HN: Stockfisher –– our automated Warren Buffett


value: heygarrison

value:
Hey HN, I'm Garrison, founder of ComputeSDK (https://www.computesdk.com). ComputeSDK is a universal sandbox interface that lets you build, deploy, and interact with sandboxes across any provider (E2B, Daytona, Modal; AWS, Fly.io coming soon) using a single, consistent API with built-in secure tunneling.
Try it out for free here: https://www.computesdk.com/docs/getting-started/quick-start/
Today we are proud to announce the Compute CLI.
I built ComputeSDK after shipping a K8s-based sandbox product for a large enterprise client. Shortly after, when I started talking to AI app builders, I noticed a common frustration: they all ended up building integrations with multiple sandbox providers. Each integration meant rewriting their application, custom proxies, and routing.
As I talked with more customers, I realized sandbox providers and app builders had massive architectural overlap. Both were having to write their own custom terminals, file-watchers, proxies, and more to interact with their sandboxes.
At this point, I realized there was a missing piece in the development infrastructure stack: a way to decouple your application from your sandbox provider while still getting full, live interactivity.
Our original K8s architecture had three services: an API, a gateway, and a sidecar.
We extracted our sidecar component and rebuilt it as the "Compute" cli, a lightweight service that installs in any sandbox and automatically creates a secure tunnel to your app. Then we wrapped it with a universal SDK that works across providers. Now you write your sandbox implementation once, and it works everywhere: from E2B, Daytona, & Modal to general cloud platforms like Railway, Fly.io, & AWS.
ComputeSDK provides a consistent interface for sandbox creation and management. It gives you instant access to terminals, file systems, real-time file watching, and a WebSocket event system from any client (server, browser, application). You can switch or add providers easily by importing the provider module and updating environment variables.
We'd love to get any feedback from the community, ideas, or experiences building similar tools.
Thanks!

value: Show HN: Compute CLI – A universal sandbox SDK with direct browser access

author: heygarrison


value:
Open Notebook is a tool where you can upload your own sources - PDFs (textbooks, research papers), Github repos, documentation websites, and other URLs. Once uploaded, you can either chat with an AI about them or run “apps” on top of those sources.
Apps can be anything. Right now, I’ve built the following:
- turn articles into short video overviews
- create a mind map of the source
- create summaries or other reports about the source
- generate quizzes or flashcards to help study the source
You can also remix existing apps or create one from scratch.
I built this because I wanted to make self-learning less painful. I enjoy diving into new subjects (electronics, space, math), but the usual options feel limited: long lectures, repetitive ChatGPT chats, or slogging through PDFs and textbooks.
NotebookLM looked promising in this space, but I found it too rigid - the outputs weren’t bad, but they weren’t adaptable to how I wanted to learn.
Open Notebook takes a different approach: it exposes the full workflows behind the AI outputs (LLM prompts, steps, formats). That means you can see exactly how a video, podcast, or summary is generated, change the process, or share your own workflows with others.
That is why I consider Open Notebook to be the "open source" version of NotebookLM.
I’d be interested in feedback on what’s useful, what’s missing, or how you’d want to adapt it for your own learning.

value: Show HN: Open-Source NotebookLM Alternative


value: zvonimirs

value:
Hi HN, I’m Zvonimir, one of the creators of Pythagora.
For the past two years we’ve been building an all-in-one platform for creating full apps with AI. While others focused on raising rounds and hype marketing, we stayed heads-down on the tech: frontend, backend, debugging, everything. Honestly, we’re still not sure if that was the right call, but we wanted something real first.
That work led to *Secure Spaces*, which we’re launching today. Every app now runs inside its own private environment where authentication and access control happen before any code runs. Even if AI writes something risky, it can’t expose data or systems.
We’d love feedback from anyone building or securing AI-generated tools.
Demo video: https://www.youtube.com/watch?v=svWJORNUamo
Docs: https://docs.deployments.pythagora.ai/docs/spaces/secure-spa...

value: Show HN: AI-built tools with security by default

author: zvonimirs


value: stewardyunn

value:
Hi HN, I'm the builder of image to image ai.
Lately, I've been going deep down the rabbit hole of AI image models, and my main takeaway is: the landscape is incredibly fragmented and confusing for anyone who isn't living in this space 24/7.
My frustration was that no single model is the best at everything.
I found that nanobanana is fantastic for enhancing or merging multiple images, but it can't handle text editing. qwen is surprisingly good at in-image text manipulation, but its general enhancements are weaker. chatgpt is great for structured things like comics or infographics, and other models like kontext are strong generalists with fewer restrictions.
This is a terrible experience for a normal user. Users don't care about which model to use. They have a task they want to accomplish, like "fix this text" or "make my headshot look professional."
So, I built image2image.ai based on this premise.
My project isn't another single model. It's a user-centric layer that sits on top of this fragmentation. I've tried to identify the best-in-class model for each specific use case.
You can see this philosophy on our tools page: https://image2image.ai/ai-tools
Instead of just giving you a blank prompt, I've organized the site by what you want to do (e.g., AI Headshot Generator, Object Remover, Background Generator, etc.). Behind the scenes, each of these tools is wired up to the model that I've found performs best for that specific job.
To make it even easier, each tool is loaded with practical examples and copy-paste-ready prompts so you don't have to guess.
To me, this is what a true "Image to Image AI" service should be. It shouldn't be the user's job to figure out the complex model ecosystem. The product should do that for them.
This is very much a V1, and I know this community understands the technical trade-offs here. I’d be incredibly grateful for your feedback. What scenarios are we missing? Do you find the results for a specific case (like text editing) genuinely better than a one-size-fits-all image to image ai tool?
Thanks for checking it out!

value: Show HN: I built a "router" for image to image AI models based on use case

author: stewardyunn


value:
Five months ago I launched our alpha product here and got roasted for basic math errors on our landing page. It was an early lesson for us in putting something out there and not meeting that moment and just being unprepared.
But here's what we've learned building a wealth management platform for the 95% of people who don't have $100K minimums:
Take Sarah (real user, fake name): 32, $85K income, $47K across three accounts. Traditional advisor wants $100K minimum. Betterment would put her in one of 5 risk buckets.
Fulfilled modeled her actual goals:
House down payment in 4 years in MCOL area: 60% global equities, 25% infrastructure equities, 15% short-term bonds.
Retirement in 28 years: 45% global equities, 30% U.S. Equities 15% private equity exposure, 10% emerging markets.
All low-cost ETFs trading on your existing brokerage. No transfers, no hassles.
Emergency fund: 100% money market, again, right in your existing brokerage or high-yield cash account.
Each portfolio uses the same institutional modeling (glidepath + 30+ asset class capital market assumptions) that pension funds use. Simplified to 3-5 ETFs. She pays $10/month for the guidance. A traditional advisor would charge $400-650/year for functionally the same service.
Where we're headed:
The real unlock isn't just better portfolios, it's making financial planning actually stick. We're building toward AI-powered spending coaches that automatically categorize transactions and suggest budget optimizations that flow directly into your investment goals. The idea is to work with users and build financial habits that will last a lifetime, even if you move on from our little corner of the financial world.
Later this month: couples can invest toward shared goals together, we make doing your own tax-loss harvesting super simple, and our "playbook" feature guides you through complex milestones step-by-step (when to get pre-approved for a mortgage based on your down payment progress, which retirement accounts to open for your situation, etc).
What makes this different now:
Non-custodial = no conflicts: We're not selling you our own funds or taking a cut of your assets. You keep everything where it is. We wanted this to be as simple and cheap as possible and the CAC of traditional financial institutions sky-rockets when you need to transfer any money.
Actual personalization: Your retirement portfolio looks different than your house fund, which looks different than someone else's retirement portfolio. Not 5 buckets - unlimited combinations, and we’re always looking to add more. We’ll be adding crypto & other alternatives as soon as possible via easy to invest vehicles.
Transparent modeling: We show you the asset class breakdowns and expected outcomes. No black box.
The offer:
We're launching on Product Hunt today and are now in open beta. Our product is and always will be free to try, but for anyone who signs up this week and uses code PHLAUNCH1 (Quarterly) or PHLAUNCH2 (Annually) you’ll get pro access for 3 months at $1 or one year at $30. The launch link is here if you feel so inclined as to upvote or contribute some feedback: https://www.producthunt.com/products/fulfilled?launch=fulfil...
More importantly - I want to know where we're wrong. What are we missing about how people actually want to manage money? What features matter vs vanity metrics?
Try it at www.fulfilledwealth.co - and if you hate the onboarding flow or think our asset allocation is garbage, tell me why. That's worth more than a sign-up.

value: Show HN: We charge $10/mo for wealth management that costs $100 elsewhere


value: edwinchang

value:
PyRTLSweeper is a project I did for a college course over the spring, inspired by a SIGBOVIK submission [1], and I recently presented it at a small conference.
It's a Python package that takes a stateless digital circuit as input, and produces a Minesweeper board emulating the circuit. By placing flags on particular 'input' cells, the player can reduce the set of possible solutions so that some 'output' cells must have mines corresponding to the circuit's output.
Try it by following the instructions on GitHub!
If you want to listen to my talk, read my paper, or see my poster, all that stuff is here: https://edwinchang.dev/pyrtlsweeper
Happy to answer questions!
[1]: https://github.com/Kirby703/minesweeper-in-minesweeper

value: Show HN: PyRTLSweeper - Turn circuits into Minesweeper boards

author: edwinchang


value: jimkrieger

value: Show HN: HarshJudge 1.0

author: jimkrieger

title: Show HN: HarshJudge 1.0


value: steyeomans

value: Show HN: I built a system that detects book mentions across 2k podcast episodes

author: steyeomans


value:
Hey everyone
I’ve been working on Sebastian.run
, an AI tool that lets you create a fully functional mobile app (React Native) from a single prompt.
The goal is to make mobile app creation as fast and accessible as building a website.
The latest version (V1) now includes:
Built-in Database
Authentication
Stripe integration
Full code generation + instant preview
You can describe your app idea (e.g. “a social app for book lovers with user profiles and comments”), and Sebastian will generate:
The front-end (Expo / React Native)
The backend logic
Auth & DB setup
Payment flow (via Stripe)
The app runs locally or can be deployed instantly.
It’s still early (beta), but I’d love feedback from the community — especially on what would make it more useful for devs and indie hackers.
Try it here: https://sebastian.run
Thanks for checking it out!

value: Show HN: Sebastian.run – Generate native mobile apps from a single prompt


value: Show HN: Seeva – Press a key, get AI help without leaving your screen


value: tem-tem

value: Show HN: Remove Rounded Corners from Web

author: tem-tem


value: cpuXguy

value: Generative art has been a long-standing interest of mine. I’ve seen a lot over the years — but this generation was absolutely pure.

value: Show HN: What Can Happen When You Code While Overtired

author: cpuXguy


value: polalavik

value:
Hi!
I built PaperRight.xyz because I've tried everything:

   - AI-assisted budgeting tools that promise to "optimize" your spending
   - Spreadsheets with lots of formulas and color-coded cells
   - Apps that gamify saving and tell you your financial horoscope, or whatever

None of them stuck.
They were either too rigid, or too automated, or just too much. I don't need a terminally confused AI financial advisor (or a confused human financial advisor).
I just needed something simple that showed me some metrics I couldn't find in any other budgeting app so I built paperright.xyz!

value: Show HN: PaperRight – Budgeting Shouldn't Be Automated

author: polalavik


value: tatefinn

value: Show HN: Iapdb.top – Tracks global pricing for in-app purchases

author: tatefinn


value:
Solveig (https://github.com/FSilveiraa/solveig) can plan tasks, read files, list directory trees, edit your code, run commands and more.
Watch 45s demo: https://asciinema.org/a/p5mzDGAoHTUHNEaVeROHpFibx
---
QUICK START

  # Install
  pip install solveig

  # Run from local models or remote APIs
  solveig -u "http://localhost:5001/v1" "Create a demo BlackSheep webapp"

  # Mix config files and CLI args
  solveig -c solveig.config -k "<API_KEY>" -m "gpt-5"

See Usage for more: https://github.com/FSilveiraa/solveig/blob/main/docs/usage.m...
---
FEATURES
AI Terminal Assistant - Automate task planning, file management, code analysis and system management using natural language in your terminal.
Safe by Design - Granular controls with pattern-based permissions. File operations prioritized, and shell commands can be disabled.
Plugin Architecture - Extend capabilities through drop-in plugins. Add SQL queries, web scraping or block dangerous commands with 100 lines of Python.
Modern CLI - Clear interface with task planning and listing, file content previews, diff editing, API usage tracking, code linting, waiting animations and rich tree displays for informed user decisions.
Provider Independence - Works with any OpenAI-compatible API, including local models.
tl;dr: similar idea to Claude Code (https://claude.com/product/claude-code) or Aider (https://aider.chat/), focusing on providing explicit user consent, granular configuration, drop-in plugins and the ability to integrate any model, backend or API.
See the Features for more: https://github.com/FSilveiraa/solveig/blob/main/docs/about.m...
---
TYPICAL TASKS
- "Find and list all the duplicate files inside ~/Documents/"
- "Check my essay Final.docx for spelling, syntax or factual errors while maintaining the tone"
- "Refactor my test_database.ts suite to be more concise"
- "Try and find out why my computer is slow"
- "Create a dockerized BlackSheep webapp with a test suite, then build the image and run it locally"
---
So it's a coding assistant?
You can use Solveig for analyzing, editing and testing your code, and all of these scenarios have received significant support through development features like code linting. But I didn't build Solveig with a single kind of use case in mind.
---
So it's yet another LLM-in-my-terminal?
Sort of. Solveig tries to do a few things that other tools don't, and to do the shared features with clearer UX, explicit consent, and deeper configuration. It's not an IDE extension, doesn't require a GUI, and it's not built for a specific user type or scenario.
At the same time, Solveig's competitors are mature projects with real user testing that you should check out. I've written a detailed comparison (https://github.com/FSilveiraa/solveig/blob/main/docs/compari...) to similar tools in the market in the docs.
---
UPCOMING
I have a Roadmap (https://github.com/FSilveiraa/solveig/discussions/2) available, and feel free to suggest new features or improvements. I've recently added user-defined system prompt templates, and now I'm working on adding token counting from API messages instead of relying on encoders.
---
A cool aspect of this project is that I can use Solveig to analyze and improve its own code itself, which also gives me a lot of exposure to its actual usability.
I appreciate any feedback or comment, especially anyone who can try out Solveig using Anthropic or Gemini APIs. Tell me if it helped you do something or what stopped you from using it properly in your specific case. Even if you can't see how Solveig could help you let me know, that's an issue with me communicating value that I need to fix.
Leaving a star on the repository (https://github.com/FSilveiraa/solveig) is also very much appreciated.

value: Show HN: I built Solveig, it turns any LLM into an assistant in your terminal


value: acoyfellow

value:
We built inbox.dog because our B2B SaaS support team was drowning in emails.
We were spending 2-4 hours/day on repetitive tickets (password resets, billing questions, feature requests) while complex issues waited.
Response times could be up to 4+ hours if it needed to pull in a dev to answer, and customers were frustrated.
So we built AI agents that connect to Gmail and automatically sort, reply, and escalate support emails. They understand context, match your team's voice, and only escalate what actually needs human attention.
We cut our response time from hours to minutes, handling 200+ emails/day with 2 people.
How we built it:

   - Frontend: SvelteKit (Svelte 5) with a clean UI for managing agents
   - Backend: Cloudflare Workers + Durable Objects for stateful email processing
   - Database: Cloudflare D1 (SQLite) with Drizzle ORM
   - AI: Two-stage processing (cheap glance filter → full processing) to optimize costs
   - Architecture: Multi-agent system with fan-out pattern (one Gmail fetch → many agents)
   - Auth: Better-Auth with Google OAuth for Gmail API access
   - Tools: 30+ unified tools (reply, forward, label, archive, webhooks, etc.)

Try it (2 minutes): Connect Gmail → pick the Support template → point at a label → Run once with approval → watch Live Log.

   - Custom system prompts - Define the agent's personality and behavior
   - Trigger filters - Gmail query syntax to match emails (e.g., "from:support@ or subject:password")
   - Action instructions - What the agent should do (reply, escalate, label, etc.)
   - Knowledge base - Upload files (docs, FAQs, past emails) that agents search via RAG (vector search) to answer questions accurately
   - Tool permissions - Control which actions each agent can take
   - Scheduling - Cron-like schedules for time-based actions
   - Webhooks - Connect to Zapier, Make, or custom endpoints
   - MCP servers - Connect to other Model Context Protocol servers (HubSpot, Stripe, etc.) so agents can access external data

The agents can read all incoming email, or run on a set schedule.. or you can "run them" on-demand.
Agents use LLMs to understand context and execute actions via the Gmail API.
Privacy/Safety:

   - CASA2 certified. OAuth scopes (gmail.readonly, gmail.modify).
   - Tokens encrypted at rest.
   - Live logs and audit trail.
   - Emails are never stored on our servers or databased

Pricing:

   - $99/month per organization (10,000 emails included, then $0.02/email).
   - Unlimited Agents and Gmail accounts.
   - 7-day free trial, no credit card.
   - Processing pauses at your configured limit; agents stay configured.

Try it:

   https://inbox.dog
   (requires Gmail OAuth, no CC required)

Would love feedback on:

   - The agent configuration UI
   - Whether the two-stage AI processing makes sense
   - What other email actions would be useful
   - How to make it easier for non-technical teams

Happy to answer questions..
Thanks!

   - Jordan Coeyman (x.com/acoyfellow)

value: Show HN: inbox.dog – AI agents for Gmail support that sort, reply, and escalate

author: acoyfellow


value: calebhwin

value: Show HN: Agent-to-code JIT compiler for Z3-theorem-proving agents

author: calebhwin


value: robputt

value: Show HN: SolMemo – a decentralized notice board powered by Solana memos

author: robputt


value: Show HN: LLM fine-tuning without infra or ML expertise (early access)


value:
I built inbox.dog and hit diminishing returns stuffing agents with dozens of static tools.
Context got noisy, tokens bloated, iteration slowed.
So I built Anytool: a single meta‑tool your agent calls to generate exactly the tool it needs, on demand.
First run generates + caches the code; subsequent runs are instant.
Each tool comes with its own isolated sandbox, ephemeral filesystem, per‑tool KV state, dedicated SQLite DB, per‑user env (USER_), full npm in a Bun container, and outbound fetch for APIs.
How I built it:

    - Frontend: SvelteKit (Svelte 5)
    - Backend: Cloudflare Workers + Durable Objects
    - Execution: Bun container, full npm, auto‑install + global cache, no bundling
    - Storage: R2 for code/metadata (hash‑based), per‑tool KV + SQLite for state
    - Auth: Better Auth; per‑user encrypted env injected at runtime as USER_*
    - API: Hono + Zod OpenAPI + Scalar docs; MCP‑native surface
    - AI: Effect‑TS retries/self‑healing; version‑pinned imports for determinism

What it does:

    - Natural‑language → real npm tool, executed in isolation
    - Hash caching: first run (AI + first install); next runs reuse cache
    - Per‑tool state (KV + SQLite) for workflows, history
    - Credentials: OPENROUTER_APIKEY and other secrets injected as env.USER_*
    - Retrieval/Augmentation (how it “learns”):
    - Full NPM accesss; indexes package README + types into R2 libraries/; generation retrieves these for better code and fewer tokens.
    - Per‑user similarity: LLM ranks your existing tools for semantic similarity and suggests modify vs create to avoid duplicates. Improves as your library grows.
    - MCP surface (simple on purpose):
        `search_user_tools` — semantic search in your library
        `create_tool` — generate and store new tool
        `execute_tool` — run a tool with input

Try it: AnytoolHQ.com

    — 1,000 free runs/month. Bring your OpenRouter key (OPENROUTER_APIKEY).
    - Works with AI SDK/LangChain/MCP.
    - One tool → infinite capabilities.

Privacy/Safety:

    - Per‑user encrypted env; no secrets in code
    - Isolated execution; audit/run history
    - Explicit versioned imports; reproducible runs

Would love feedback on:

    - The “one meta‑tool” DX (AI SDK/LangChain/MCP)
    - First‑run latency + cache behavior
    - Env/credential ergonomics (USER_ injection)
    - Templates you want first (QR, CSV, validators, API clients, etc.)

Happy to answer questions.. cheers!
- Jordan Coeyman (x.com/acoyfellow)

value: Show HN: Anytool – Give your LLM infinite tools with one API


value: Hi HN. I’ve helped build an AI editor for your drafts. It’s called Owl Editor - it gives you a full editorial squad who get back to you in just a few minutes. They leave comments and suggestions right in your draft so you can accept/reject them at will. It’s the best writing feedback I’ve ever received from AI - check it out!

value: Show HN: Owl Editor – An AI editor for your drafts


value: fzysingularity

value:
Hey HN! We’re excited to share Orion [1] — our new visual agent that sees, reasons, and acts across images, videos, and documents.
Frontier VLMs (GPT, Claude, Gemini) can describe what they see, but they can’t reliably act on visual inputs.
Ask them to detect objects, segment images, or chain visual steps — they’ll fail in surprisingly inconsistent ways. High-res images collapse to ~1024px. And the visual AI ecosystem is fragmented across separate APIs for image understanding, OCR, image-gen, video-gen, etc.
We built Orion to fix this.
Orion combines VLM reasoning with reliable computer-vision tools inside a unified chat-completions interface. You can chain visual steps, inspect results, and treat visual tasks the same way you treat text workflows. Here’s a quick demo [2].
What Orion can do today:
- Detect objects, faces, people (with precise, visualized boxes)
- Segment objects or salient regions interactively
- Edit, remix, and re-imagine images/videos from prompts
- Summarize visual content (images or videos)
- Transform images: crop, rotate, upscale
- Transform videos: trim, sample, highlight scenes
- Parse and structure documents: pagination, layout, OCR, extraction
One unified “chat-completions”-like interface — no juggling multiple vision APIs.
Check out the tours in the chat [3] or read the announcement [4].
API access opens next week. Happy to answer any questions — otherwise, feel free to try the tours and break things!
[1] Learn more about Orion: https://vlm.run/orion
[2] Promo video: https://youtu.be/cPJN4iZz6QQ
[3] Chat: https://chat.vlm.run
[4] LinkedIn announcement: https://www.linkedin.com/posts/sudeeppillai_ai-computervisio...

value: Show HN: Chat with Orion – a visual agent that sees, reasons and acts

author: fzysingularity


value: linegel

value: Show HN: Devme.me – A puzzle meme-driven mini-game full of terrible jokes

author: linegel


value: KraftyOne

value:
Hi HN - I’m Peter, here with Harry (devhawk), and we’re building DBOS Java, an open-source Java library for durable workflows, backed by Postgres.
https://github.com/dbos-inc/dbos-transact-java
Essentially, DBOS helps you write long-lived, reliable code that can survive failures, restarts, and crashes without losing state or duplicating work. As your workflows run, it checkpoints each step they take in a Postgres database. When a process stops (fails, restarts, or crashes), your program can recover from those checkpoints to restore its exact state and continue from where it left off, as if nothing happened.
In practice, this makes it easier to build reliable systems for use cases like AI agents, payments, data synchronization, or anything that takes hours, days, or weeks to complete. Rather than bolting on ad-hoc retry logic and database checkpoints, durable workflows give you one consistent model for ensuring your programs can recover from any failure from exactly where they left off.
This library contains all you need to add durable workflows to your program: there's no separate service or orchestrator or any external dependencies except Postgres. Because it's just a library, you can incrementally add it to your projects, and it works out of the box with frameworks like Spring. And because it's built on Postgres, it natively supports all the tooling you're familiar with (backups, GUIs, CLI tools) and works with any Postgres provider.
If you want to try it out, check out the quickstart:
https://docs.dbos.dev/quickstart?language=java
We'd love to hear what you think! We’ll be in the comments for the rest of the day to answer any questions.

value: Show HN: DBOS Java – Postgres-Backed Durable Workflows

author: KraftyOne


value: andreysheva

value: Show HN: Add semantic caching to LLM APIs with one-line-of-code

author: andreysheva


value: imthefirst

value: Read me!

value: Show HN: What If Your Phone Was Your Bank? (Offline, Zero Fees, Full Control)

author: imthefirst

story_text: Read me!


value: mfanakagama

value:
Hello. For the past week or two I found myself with extra time on my hands. With my given freedom I proceeded to build a tree generator program in Java Swing. The UI looks better because of Flatlaf.
The tree generator can generate trees, obviously but it can also generate grass (Single blade, tall and short) and flowers (Single flower). All is derived from a tree.<p>The simulation is based on the Fractal Tree algorithm. Extra features include wind animations, png and gif export (Gif is partially broken). I think its could be a cool program to pass time with or even use in game dev (For 2d foliage).

value: Show HN: An interactive 2D fractal tree generator/simulator created in Java

author: mfanakagama


value: Any and all feedback is appreciated, thanks.

value: Show HN: An AI to automate busywork for managers in tech


value: jounus

value: Show HN: A Serious Language App

author: jounus

title: Show HN: A Serious Language App


value: gearnode

value: Show HN: Granola API Reverse Engineering

author: gearnode


value: pooyanazad

value:
Hi HN,
I made a tiny tool called YAML Validator that checks your YAML files with one Docker command — no installs, no setup, no excuses.
It does:
- Syntax checks
- yamllint linting
- checkov security scanning
- All in one lightweight, zero-config container
---------------------------------------------------------
How to use:
docker run -v "$(pwd):/data" pooyanazad/yaml-checker <yaml-file>
Optional alias (for .bashrc / .zshrc):
alias ytest='docker run -v "$(pwd):/data" pooyanazad/yaml-checker'
Then simply call:
ytest sample.yaml
YAML powers CI/CD, configs, infra ,and breaking it is too easy. This aims to catch syntax, style, and security issues early with minimal friction.
I’d love feedback, ideas, or edge cases to test.
---------------------------------------------------------
GitHub: https://github.com/pooyanazad/YAML-validator
---------------------------------------------------------
Thanks for reading!
Pooyan

value: Show HN: YAML Validator –A simple Docker-based YAML checker

author: pooyanazad


value: ozgurozkan

value:
Automate red teaming of your voice AI agent.
In YC terms Request for startups:
Full stack voice AI Red Teamer:
Automates the work below https://work.mercor.com/jobs/list_AAABmX4FiBxRn-I1VM5KeZ2h

value: Show HN: Agentic Adversarial testing for your voice AI

author: ozgurozkan


value: shhabmlkawy

value:
I’ve spent the last two years writing a collection of 130 original problems in Data Structures & Algorithms, each with fully worked-out solutions, proofs, and time complexity analysis.
The goal was to create something that feels like a mix between a university-level exam set and an MIT-style lecture note, but with complete, step-by-step answers. Many of the problems involve new ideas that don’t appear in standard textbooks, including some heap variants I designed (Triple-L Heap and Layered Summary Heap).
Topics covered include:
• Heaps, priority queues, and advanced heap structures
• Union-Find (with potential function analyses)
• Trees and balanced trees (AVL, Splay, etc.)
• RMQ, suffix arrays, hashing, and more
• Sorting, prefix/suffix techniques
• Dynamic sequences and amortized analysis
• 130 questions with 2–3 subparts each + full solutions
Example problem style:
“Given an array A of size n, split it into two disjoint subsets such that the absolute difference of sums is minimized — without using DP or exponential search.”
(The solution uses a prefix–suffix argument and runs in O(n).)
Happy to hear feedback, suggestions, or ideas for a second edition.
If anyone wants to see the full book, here is the link in the URL field above.

value: Show HN: Interview and Exam-Level Data Structures Problems (With Full Solutions)

author: shhabmlkawy


value: Show HN: Claritate – Insurance, Made Clear


value: melenaboija

value: Show HN: US Publicly Traded Companies probabilities of default with public data

author: melenaboija