value: vladpowerman

value:
GitCruiter analyzes public GitHub data to estimate a developer’s technical level and habits.
Each profile generates a detailed report with metrics like Algorithmic Thinking, Code Quality, Project Organization, Testing Practices, Documentation, and Problem Solving, plus an overall score (e.g., Senior 80/100).
You can browse developers by region, language, or framework — for example, “Python devs in Canada” or “React engineers in Eastern Europe.”
We built it to make developer evaluation more transparent — no résumés, no mystery algorithms, just interpretable metrics from real repos and commits.
Would love feedback from developers on whether these evaluations feel accurate or how they could be improved.

value: Show HN: GitCruiter – AI tool that evaluates developers by GitHub activity

author: vladpowerman


value: LuckyAleh

value:
I built an AI Market Pulse Widget that analyzes the day’s news and instantly shows the market sentiment and direction. It’s designed to be plug‑and‑play: just copy the embed code and drop it into your site or blog.
Key features:

    AI‑driven sentiment analysis of daily financial news

    Clear market direction signals (positive, negative, neutral)

    Customizable design (colors, layout, size) to match your site

    Simple integration — works anywhere with a quick embed

    Free to use for bloggers, traders, and financial platforms

 Why it’s useful: Instead of manually scanning headlines, your visitors instantly see how the market is reacting to the latest news. It’s a quick way to add real‑time, AI‑powered insights to any website.

value: Show HN: AI Widget Turning News into Market Direction for Any Blog/Site

author: LuckyAleh


value: tylerreed

value:
RunOS lets you run your application stack on any infrastructure - cloud VMs, bare metal, or on-premises - without vendor lock-in.
What it does:
- One-click deployment of 20+ production-ready services (PostgreSQL, Kafka, MinIO, etc.) with security hardening, backups, and HA by default.
- Git-based app deployment with automatic Docker builds, SSL certificates, and Kubernetes orchestration.
- Infrastructure portability - switch between providers without code changes.
- Automatic service discovery and integration between your apps and services.
Built on Kubernetes but hides the complexity. You get container orchestration benefits without the configuration overhead.
Looking for feedback on:
- Platform UX and onboarding experience
- Which VPS providers to prioritize
- Which services to add next
Try it: https://runos.com

value: Show HN: RunOS – Run Your Own Cloud on Your Own Infra

author: tylerreed


value: Dunsinagb

value: Show HN: I Created a Database to Track USA Higher-Ed Actions from 2024

author: Dunsinagb


value: EGreg

value: Show HN: I use ChatGPT these days to develop new features quickly

author: EGreg


value: abeelha

value:
I built a comprehensive dashboard that visualizes sea level rise data through multiple chart types and interactive components, enabling people to explore trends and patterns from different analytical perspectives.
Sources: https://datahub.io/core/sea-level-rise
Tools: https://www.portaljs.com/ & ObservableHQ Frameworks

value: Show HN: Global Mean Sea Level Rise <1880 – 2019> – Visualizations Dashboard

author: abeelha


value: luckysibanda

value: During my doctoral journey and article writing as a researcher (including systematic literature reviews), reviewing literature by themes was always challenging. So, we developed GraceLitRev, an AI-powered research assistant tool that lets users upload research papers, extracts 28 metadata variables, generates downloadable graphs, and exports data to MS Excel and RIS files.<p>GraceLitRev helps researchers quickly identify gaps in theory, methodology, data analysis, and future studies. Unlike other research tools that search the internet without user control, our tool gives users full control: upload your documents, review extracted data, and generate themes. It doesn’t write for you but saves hours of reading by extracting key information.

value: Show HN: GraceLitRev (Research Assistant Tool)

author: luckysibanda


value: padurean

value:
I built GoSMig for my own projects and open-sourced it in case it helps others. It’s a tiny generic library (no external deps except golang.org/x/term) for writing SQL migrations in Go with compile-time checks.
It supports transactional and non-transactional migrations, rollback, status, version, and a small CLI handler so you can ship your own migration binary.
Why another migrator?
- Minimal API, no DSL or file layout to learn
- Type-safe via Go generics
- Works with database/sql and sqlx out of the box
- Should work with any db library (or wrapper) that implements some generic interfaces (see the "Core Types" section here https://github.com/padurean/gosmig?tab=readme-ov-file#core-t...)
- Tested with PostgreSQL, should work with any SQL RDBMS (MySQL, SQLite, MS SQL Server, ...)
Repo: https://github.com/padurean/gosmig
Docs & examples: README + examples branch https://github.com/padurean/gosmig/tree/examples
Would love feedback: ergonomics, missing guardrails, API rough edges, and real-world gotchas, etc.

value: Show HN: GoSMig – minimal, type-safe SQL migrations written in Go

author: padurean


value: Show HN: Built this because restaurant menus never match the photos


value: dahviostudios

value: Show HN: Built tool in 48hrs after CR found lead in protein powder

author: dahviostudios


value: Ihmzf

value:
Hey HN, I built https://asimov.mov. an MCP for coding agents to search from latest docs of APIs, repos or basically anything.
It can index and retrieve docs + repos very fast to search from them and can do it on-the-go (like something launched yesterday and you indexed it right away, from your agent).
The indexed docs would only be available to you and would be stored on your own little server (we manage that!)
Hope Asimov can make your Coding Agents hallucinate less (by not implementing 1000-year old Stripe API or Cloudfare) :)
Happy to chat about the features and feedback + giving some Pro tiers (for free!) if anyone wants to try more!

value: Show HN: Asimov – Context Manager for Coding Agents

author: Ihmzf


value:
Hi HN,
I built DocCraft AI after spending way too many hours writing job descriptions, privacy policies, and marketing copy for my startup. What should take 5 minutes was taking 2+ hours of research, writing, and revisions.
DocCraft generates professional business documents using AI, tailored to your company's industry and voice. Just fill out your company profile once, then generate:
- Job descriptions with proper requirements/benefits
- GDPR-compliant privacy policies
- Marketing emails that don't sound generic
- Terms of service, press releases, etc.
Built with Next.js 15, TypeScript, and OpenAI. Free tier available.
Try it: https://doccraftai.vercel.app/
What documents do you spend the most time writing? Would love feedback from fellow founders/operators.

value: Show HN: DocCraft AI – Generate professional business documents


value: erans

value: LunaRoute is a high-performance local proxy for AI coding assistants like Claude Code, OpenAI Codex CLI, and OpenCode. Get complete visibility into every LLM interaction with zero-overhead passthrough, comprehensive session recording, and powerful debugging capabilities.<p>- See Everything Your AI Does - get full logs (JSONL), summary of sessions including tokens used (input/output) as well as tools usage and success rates.<p>- Privacy & Compliance Built-In - redact or tokenize any sensitive information (regex based).<p>- Speaks OpenAI and Anthropic dialects so you can route (and translate) when needed between models and providers.<p>- High performance - passthrough is 0.1ms - 0.2ms latency. Logging and summary is off loaded to a secondary thread to allow maximum performance.<p>Feedback is always appreciated as well as stars on the repo :)

value: Show HN: LunaRoute – a high-performance local proxy for AI coding assistants

author: erans


value:
Hi HN!
I built Streaky to solve a personal problem - I kept losing my GitHub streak on busy days. It monitors your contribution streak and sends notifications to Discord/Telegram before it breaks.
*What makes it interesting technically:*
1. *Distributed Cron Processing*: Used Cloudflare Service Bindings to bypass the 30-second CPU limit. Each user gets processed in an isolated Worker instance with its own CPU budget.
2. *Idempotent Queue System*: D1-based queue with atomic operations prevents duplicate processing when cron jobs overlap or retry.
3. *Zero-Knowledge Security*: GitHub tokens never stored (OAuth refresh flow), webhooks encrypted with AES-256-GCM, notifications sent via isolated Rust proxy.
4. *Rate Limit Solution*: Cloudflare Workers use shared IP pools which trigger rate limits from Discord/Telegram. Solved by routing notifications through a dedicated Rust server on Koyeb.
*Tech Stack:*
- Frontend: Next.js 15, React 19, TypeScript
- Backend: Cloudflare Workers + D1 (SQLite)
- Infrastructure: Rust notification proxy
- Auth: GitHub OAuth via NextAuth.js v5
*Live demo*: https://streakyy.vercel.app
The project is fully open-source under MIT license. Happy to answer any questions about the architecture or implementation!

value: Show HN: Streaky – GitHub Streak Monitor with Distributed Cron Processing


value:
Hi HN, we’re Akshat and Dhruv, the founders of ProfiTree. We’re building an automated tax optimization platform that helps everyday investors save on taxes through tax loss harvesting, wash sale detection and cost basis optimization. These are the same tools usually reserved for financial advisors. Right now is the best time to look at tax optimizations for your portfolio as we're in the last quarter of the fiscal year.
Over the last few months, we’ve tested the product with about 55 early users, uncovering $300K+ in potential tax savings (roughly $6K per user on average).
Here’s a short demo: https://drive.google.com/file/d/18fUeLGDU6cu22B2gMDmO3mUYqWR...
Why we built it: We’re both DIY investors who realized that while most brokerages show gains and losses, none proactively flag wash sales or tax loss opportunities in real time.
Financial advisors and robo-advisors can do this, but only if you pay AUM fees or hand over full control of your account.
We wanted something different:
1. Full transparency into your data
2. No AUM fees, and you keep custody of your assets
3. Personalized alerts before trades trigger wash sales
4. Simple explanations of how much tax you could save by selling specific lots
How it works: You connect your brokerage securely through SnapTrade (similar to Plaid but for investment accounts).
We pull historical transactions, process cost basis, and highlight:
1. Disallowed losses from past wash sales
2. Tax loss harvesting opportunities if you sold today
3. Optimal sale recommendations based on FIFO, LIFO, or High Cost basis
4. Estimated tax savings using your tax bracket
What we’re exploring next: We’re expanding the beta and would love input from this community before scaling up.
1. Tech employees with RSUs or stock options:
Do you worry about concentrated positions or surprise tax bills? We are exploring ways to liquidate concentrated positions with minimum tax impact.
2. Active DIY traders:
We’ve built daily wash sale detection and rebalancing logic.
What gaps have you noticed in tax loss harvesting tools?
3. Mass affluent or FIRE minded investors:
How do you currently manage taxes on brokerage accounts?
What would make you trust an automated tax advisor?
Our goal: Just like Robinhood made trading accessible by removing commissions, we want to make tax intelligence accessible by removing complexity.
We’re not selling anything. We are looking for feedback from technically minded DIY investors who care about taxes, automation, and transparency.
You can try the beta at https://profitree-tax.com.
(Free during beta, and we do not store or sell data)
Would love to hear your thoughts, both as builders and as investors.

value: Show HN: ProfiTree, Tax Optimization Tool for DIY Investors


value:
Hey guys, I built a simple open-source crypto trading AI bot using Vercel AI SDK and Quidax Crypto API, so users basically interact with a Telegram chat interface. The app uses tools to validate customer information, fetch customer wallet balance, and fetch transactions. Please kindly have a look at the share feedback, thanks
Frontend: https://github.com/oyeolamilekan/dammie-ai-frontend
Backend: https://github.com/oyeolamilekan/dammie-ai-backend

value: Show HN: Open-Source Crypto Offramp AI Bot


value: cranberryturkey

value: Show HN: Publish Newsletters and Podcasts

author: cranberryturkey


value: toobulkeh

value: Show HN: AutoLearn Skills for self-improving agents

author: toobulkeh


value: bogdannbv

value:
Hi HN, back in October 2024 I've launched Buzzd Chat, a Y!M revival project.
I started it back in January 2024 as a reverse engineering challenge/learning project and after posting some short videos on YouTube to show my friends the progress I've made, someone from another (now dead) IM revival project's Discord server found those videos and contacted me.
Turned out there is a fairly large community around old IM clients and some revival projects that already offered support for older Y!M versions (v5, v6), but with a very limited feature set (logging in, adding/removing friends and chatting).
Most if not all of these projects revolve around MSN/WLM, AIM and other IM clients that were more popular in the US. The Y!M support was typically an afterthought.
After some convincing from the guy that first contacted me on YouTube, I've decided to make this project public and with his help we've launched it in October 2024 as Buzzd Chat along with a Discord server where people could contact us and share their IDs and connect with each other.
I've started the project reversing Y!M V9 -- was the one I remembered using the most back in the day -- and because of that, the project got, for what it is and little to no publicity, a lot of attention. The subset of users interested in Y!M really wanted better support and newer versions.
The project is still under development, we've still got a long way to go till I'll be fully satisfied and ready to open source it.
Supported Y!M versions:
- V9 only atm (focusing on implementing as many features as possible before exploring older or even newer versions)
Features (so far):
Authentication
Profile/Display pictures
Status management
Buddy management (add/delete/group/move buddies)
Buddy ignoring
Visibility management (invisible to all, invisible to some etc.)
Buddy list sharing (share your contacts)
Address book/Contacts management (add/remove/update contacts)
Photo sharing
File sharing
Audibles
Emoticons (all of them, even the ones introduced after V9)
Reliable messaging (basically acking that messages have been received)

value: Show HN: Buzzd Chat – a Yahoo Messenger revival project

author: bogdannbv


value:
Hi HN – over the weekend I hacked together a tiny web tool that detects faces in an image and hides them with a single click. It runs entirely in your browser (no servers, no accounts) and is meant for those moments when you want to share a photo but not share everyone’s identity.
Drag a picture onto blurfaces.org, hit Blur, and it will obscure all the faces it finds. You can toggle individual masks or tweak how strong the blur is. For now it works with still photos only, but it’s been surprisingly handy for family shots and marketplace listings.
I’d love to know: does this scratch an itch for you? What’s missing or annoying? Would you use a tool like this, or do you already have an easier workflow?

value: Show HN: BlurFaces – one‑tap face blurring in the browser (no upload)


value: yohamta

value: Show HN: Run any GitHub Action locally from your Cron job -- finally!

author: yohamta


value: formslip

value: Show HN: Memory-Induced Phase Transitions Across Digital Systems

author: formslip


value: nadermx

value: Hey HN, I vibe coded this in a day last time https://johnathannader.com/my-microphone-didnt-work/ and launched it; https://news.ycombinator.com/item?id=37503569, now that the coding para-dime has gotten better, decided to spend another day updating it, this is the result

value: Show HN: Microphone Test

author: nadermx

title: Show HN: Microphone Test


value: Seasons

value: Show HN: I tried to imagine HN as a newspaper

author: Seasons


value: wancomplete

value:
3hours to make, 100% vibe coding
inspire by https://news.ycombinator.com/item?id=39159418

value: Show HN: Pong Wars Idle Game

author: wancomplete

title: Show HN: Pong Wars Idle Game


value: arknirmal

value: We’re building Cloudtellix an AI FinOps assistant for AWS that gives recommendations with reasoning trails and integrates into existing workflows. Prototype + waitlist: [cloudtellix.com]. Would love technical feedback!

value: Show HN: AI FinOps co-pilot that explains and integrates cloud cost insights

author: arknirmal


value: ivanglpz

value:
This beta includes several technical improvements aimed at performance, flexibility workflows:
Unlimited canvas: Previous canvas limits were removed. Earlier restrictions caused unnecessary re-renders and blocked operations. Now designers can place elements anywhere without overhead.
Optimized state management: Creating, updating, and deleting elements now triggers minimal state updates. Only affected parts of the state are recalculated, reducing processing and improving responsiveness.
Dedicated icon element: Icons now have their own element type. This decouples them from general elements, allowing integration of additional libraries, dynamic styling, and more flexible adaptation within the app.
Recursive in-memory export: Exports are built element by element recursively in memory. This ensures data consistency, avoids redundant recalculation, and scales efficiently to high-resolution outputs (up to 8K).
Mouse wheel zoom: Implemented a smooth zoom mechanism controlled via the mouse wheel for better navigation of complex layouts.
Multi-element property updates: When multiple elements are selected, only the modified property is updated for all elements. Full state recomputation is avoided, improving performance in large designs.
Auto-focus shortcut (Shift + 1): Elements are located by their x, y, width, and height, then auto-focused on the canvas. This prevents users from losing context while designing complex screens.
Tech stack: React, Next.js, MongoDB, Jotai, React Konva.
Feedback Requested: Looking for feedback on performance, UX, and data model structure.
Link: https://pixel-kit.vercel.app

value: Show HN: Pixel Kit v5.0-Beta2 – Web and Mobile Design Tool

author: ivanglpz


value: HenryYWF

value: Hi HN, I built a wrapper around Bridge.xyz to let clients pay invoices in fiat and settle into stablecoins, then spend from balance. You can create an account and explore the dashboard without KYC. I would love feedback from builders and digital nomads.

value: Show HN: I built a Bridge wrapper for digital nomads to get paid in stablecoins

author: HenryYWF


value: nityasha

value:
Hi HN! I'm Amber (13) and my dad Raj, and we built Nityasha AI from Guna, India. After my dad's 12 years of failed startups (2012-2023), we created a personal AI assistant that handles email, coding help, research, and planning in one conversational interface.
I started coding at 9 on a 4GB RAM laptop. We failed 8 times before this—coupon sites, freelancing platforms, consulting. Nityasha is different: it uses Thesys generative UI for visual charts, includes Study Mode with Socratic teaching, and integrates everything so you don't need 10 tabs open.
500+ active users now. We just launched Nityasha Connect where businesses can integrate services directly into the AI.
Would love your feedback!

value: Show HN: I'm 13 and built an AI that remembers context across conversations

author: nityasha


value: IgorStojanov

value: Show HN: Plan your next trip with AI with TripMaven.org

author: IgorStojanov


value:
Hi HN,
We’ve been building DreamOmni2, a multimodal AI model that understands both text and image instructions, letting creators generate or edit visuals through natural, intuitive guidance.
Key Features:
Multimodal Understanding: Combine text + image prompts for richer, more accurate control.
Unified Generation & Editing: One model for both creation and modification — DreamOmni2 adapts intelligently to your goal.
Consistency Mastery: Maintain identity, pose, or layout across edits — ideal for storytelling, branding, and design workflows.
Advanced Editing Tools: Replace objects, backgrounds, or lighting — even imitate fonts and hairstyles through natural language.
Open Source & Research Ready: Built for creators and researchers with transparent benchmarks and reproducible pipelines.
Use Cases:
 Creative Direction & Art Design
 Portrait & Fashion Editing
 Product Visualization
 Typography & Branding
 Complex Compositional Creation
Why we built this:
Creative workflows are often fragmented — one model for generation, another for editing, and none for maintaining consistent visual identity. DreamOmni2 unifies all of that, enabling seamless multimodal creation and iteration.
We’d love your feedback on how multimodal AI could improve your creative workflow — or what integrations you’d like to see next.
Try it here: https://www.dreamomni.net/

value: Show HN: DreamOmni2 – Unified Multimodal Generation and Editing AI


value: ShimmySundae

value:
This is my first ever ESP32 and embedded project. For three months, I've been building a handheld NES with an ESP32 from scratch. I bought the parts and learned how to solder for the first time.
While having already made my own NES emulator for Windows, I had to do a whole rewrite of the program to port and optimize it for the ESP32.
This is written in C++ and is designed to bring classic NES games to the ESP32.
This project focuses on performance, being able to run the emulator at native speeds and with full audio emulation and save states implemented. Check out the project!

value: Show HN: I Built a Handheld ESP32 NES from Scratch as My First Embedded Project

author: ShimmySundae


value: przemekdz

value:
Hi!
I'm a big "inbox zero" nerd, and I also like to sit down once or twice a week to read through all my newsletters. The problem with these two things is that it's hard to keep a clean inbox with unread newsletters.
So, I created Ninbox. Ninbox gives you a unique email address for your newsletters, so all of them land not in your personal inbox but in Ninbox, where they’re grouped based on the sender’s email. That way, you can read all newsletters from one sender before moving on to the next.
I’d love to hear your feedback.

value: Show HN: Ninbox – a simple way to organize your newsletters

author: przemekdz


value:
Hi everyone! We’ve been experimenting with ways to make it easier to build multi-agent systems, somewhere between LangGraph’s orchestration flexibility and AutoGen’s ease of use.
Lyzr Automata is our open-source framework that lets you build, connect, and run AI agents in Python, with just a few lines of code.
You can define agents as simple Python classes, chain them using the Automata graph, and run them locally or via a hosted orchestrator. It’s designed to be simple, extensible, and self-contained.

value: Show HN: Lyzr's low-code multi-agent automation framework


value: chribjel

value: Show HN: Reactonline.dev – Test React components online in the Browser

author: chribjel


value: guilamu

value:
Working for a non-profit, I need to use as many free services as possible. Emails are part of it. Free tiers often change, hence this small tool.
I built it with Claude 4.5 (approximately 10 hours of work) with barely any coding experience. It generates daily a lightweight comparison of transactional email providers that only lists services with a renewable free tier, showing daily/monthly limits from live scrapes and clearly marking static fallbacks.
What do you think? Did I miss any providers that meet the "renewable free tier" criteria?

value: Show HN: Free Transactional Email Plans – Live, Scraped Limits

author: guilamu


value: uscnep-hn

value:
Many articles today are difficult to read, cluttered with ads, pop-ups, and distracting layouts that make focused reading exhausting. I built a Chrome extension that turns articles into text-only reading views using Mozilla's Readability library with custom CSS informed by accessibility and readability research. It follows four design choices based on W3C WCAG 2.2 standards: a sepia background to reduce eye strain, 1.5× line spacing for better readability, 50–75 character line length to reduce eye fatigue, and sans-serif fonts that work well on screens.
Shortcut key: Alt+Shift+Y or Command+Shift+Y
Limitations: it's designed for reading plain text articles. Because it hides images, tables, and formulas, some pages may lose important context or meaning.
Github (Feel free to contribute! ) : https://github.com/uscne/Yumi-Reader

value: Show HN: Made a text-only article reader extension to reduce eye strain

author: uscnep-hn


value: flxflx

value:
Hi HN,
We published our experimental browser extension for Chrome just a few hours before OpenAI announced its Atlas browser. Ours takes a different approach: privacy first.
Atlas makes “memory” and cross-tab context a core feature. OpenAI acknowledges the privacy concerns and promises users full control over what gets stored and sent to the cloud. In practice, most users won’t maintain that control.
Our vision is full privacy by design, so it doesn’t matter what data flows to the cloud. The extension connects to (1) a cloud-hosted inference service (Privatemode AI) running gpt-oss-120b in carefully architected confidential-computing environments and (2) a local document store with a vector DB.
The inference service leverages AMD SEV-SNP and the confidential-computing features of the Nvidia H100. The environment is designed so that we (Edgeless Systems) and no one else can access user. All data stays encrypted even during processing in main memory. The client automatically verifies the integrity of the backend using a process called "remote attestation". All relevant server-side software can be found on GitHub and can be built reproducibly.
It’s an early developer version. If you want to play with it, you’ll need to clone the repo and run it together with our encryption/verification proxy and the aforementioned document store. You'll also need to sign-up for a free API key at www.privatemode.ai.
Looking forward to your feedback!
Repo: https://github.com/edgelesssys/privatemode-for-chrome-demo
Blog post: https://www.privatemode.ai/articles/browser-extension-demo-l...
Demo video: https://youtu.be/4j77YHXDqAQ

value: Show HN: Confidential AI browser extension – alternative to OpenAI Atlas

author: flxflx


value: My roommate has been building this for the last 2 months and refuses to launch the damn thing. Ik nothing about Natgas, but hoping HN can do its thing. The tool is made for Nat Gas/commodities traders.

value: Show HN: A Free Nat Gas Analytics Platform


value: rrranch

value:
Hi HN, I'm a solo dev who built this because I kept opening my phone and falling into social media instead of working on myself.
Coach Thyself is a journaling app with structure instead of blank pages:
- Daily prompts and community questions to write about
- Goal tracking and task organization built in
- Optional insights from philosophy/astrology/psychology as reflection triggers
- No social features, followers, or performance metrics
I use it every morning now instead of scrolling. It's the motivation that keeps me working on it.
Tech stack: [whatever you used - mention it if interesting]
7-day free trial, $12/month after: https://thyself.coach
Happy to answer questions about the build process or journaling habits. Looking for feedback on what would make this more useful.

value: Show HN: Coach Thyself – Replace your morning scroll with structured journaling

author: rrranch


value: vaneyckseme

value:
Hey HN! I built Reelleer - a video editor that runs entirely in your browser to create social media reels. No uploads, no server processing, everything happens client-side.
Try it: https://reelleer.com
What it does:

    Multi-track timeline editor (video, audio, images, GIFs, text)
    Real-time preview at 30fps
    Direct canvas manipulation (drag/resize elements)
    Animations and transitions
    Export to WebM

Current limitations:

    Real-time export only (working on WebCodecs for 10x faster)
    WebM output only (MP4 coming soon)
    Chrome/Edge work best

Coming soon:

    Undo/redo
    Cloud storage for projects
    MP4 export
    Filters

value: Show HN: I built a browser-based video editor for making social media reels

author: vaneyckseme


value: garethharte

value:
Hello, I’m Gareth. I wanted to cut back on drinking but didn’t like how serious all the sobriety apps felt.
So I made one where you evolve a dinosaur. Check in each day and your Drynosaur levels up. As it levels up, it will evolve. I built it for sober curious people like me that do Dry January etc but might not necessarily quit forever.<p>I had a lot of fun working on it, and it’s built in SwiftUI.  Would love to hear your thoughts.

value: Show HN: I built a pixel art app where you evolve a dinosaur by not drinking

author: garethharte


value: ashtavakra

value: I built Selecta, a tool that lets you ask questions about your data in BigQuery using natural language. It's powered by Google's ADK on the backend and a Next.js frontend. It provides structured answers with summaries, results, and business insights, and even generates charts. The source code is available on GitHub.

value: Show HN: Selecta – Natural language analytics for BigQuery

author: ashtavakra


value: tonysurfly

value: Show HN: Yet Another Iframe Tester

author: tonysurfly

title: Show HN: Yet Another Iframe Tester


value:
Hi Everyone,
I wanted to play a mystery game where i have all the evidence/reports in physical form and find out who is the suspect. Having documents in physical form is hard to get so I created a digital game which generates a new mystery, evidence, police report and interviews from people, based on these things i have to find the suspect.
I created this in the form of OS so that you can see the docs in the folder and have the feel that you are investigating something real :D
Currently, the stories and images are generated by the AI which is a little expensive but I just want to share it anyway. If you like it you can donate so that I can keep it alive haha.
(I just ask the email to login into the system, I don't do anything else with it. If you dont want to provide your email you can give a fake one)
URL: https://gumshoeos.site

value: Show HN: GumshoeOS – AI Generated Mystery Game OS


value: sjdeak

value: Hi HN, I built a friendship checker for Pokémon Legends Z-A players. In this game, friendship is a hidden stat that determines evolution for Pokémon like Eevee and Riolu, but there's no direct way to see the exact values in-game.

value: Show HN: Pokémon Legends Z-A Friendship Checker

author: sjdeak


value: freakynit

value: Show HN: Python Quirks

author: freakynit

title: Show HN: Python Quirks


value: vishalteotia

value:
I’ve been experimenting with HyperMind, a memory layer that tries to bring human-like memory behavior to AI apps.
It builds evolving short-term and long-term memory, tracks relevance and recency, and applies context decay over time.
Playground: https://hypermind-memory.vercel.app/chat
Github: https://github.com/vashuteotia123/hypermind

value: Show HN: HyperMind – Experimental human-like memory layer for AI apps (OS)

author: vishalteotia


value: susros

value:
so, chatgpt store is coming soon.
so, I just made a tool for people to build chatgpt apps with AI.
any thought?

value: Show HN: Vibe Code ChatGPT Apps

author: susros

title: Show HN: Vibe Code ChatGPT Apps


value: akorkor

value: Show HN: An Alternative to TradingView

author: akorkor

title: Show HN: An Alternative to TradingView


value: fpvidigas

value:
We just released *APAAI Protocol v1.0* — a vendor-neutral accountability layer for autonomous and agentic systems.
As AI agents start to act (not just generate text), we need a consistent way to record what they intended, what policy governed them, and what evidence shows what actually happened.
*APAAI* defines a minimal HTTP/JSON spec for this loop:
*Action → Policy → Evidence*
It’s model-agnostic, open-source (Apache-2.0), and already shipping SDKs in *TypeScript* and *Python*.
Example use case:
An agent proposes to send an email → requires approval → submits evidence of delivery — all recorded in a standardized, auditable format.
Docs & spec: https://apaaiprotocol.org
 TypeScript SDK: https://www.npmjs.com/package/apaai-ts-sdk
 Python SDK: https://pypi.org/project/apaai
 GitHub: https://github.com/apaAI-labs
We’re opening the *RFC process* and would love feedback from the HN community — especially anyone building *agents*, *AI infra*, or working on *governance and observability* for autonomous systems.
Thoughts on the data model? Should “accountability as code” become a first-class design pattern for agentic AI?

value: Show HN: Apaai Protocol – an open standard for accountable AI

author: fpvidigas


value: Crroak

value:
I built this after hitting an infinite loop pagination trap on Newegg and wasting hours building a scraper that never had a chance.
caniscrape analyzes a URL and tells you:
- What protections are active (WAF, CAPTCHA, rate limits, TLS fingerprinting, honeypots)
- Difficulty score (0-10)
- What tools/approach you'll need
Install: pip install caniscrape
Usage: caniscrape https://example.com
Web version: https://caniscrape.org
NOTE: Very hard sites like Amazon or YouTube will show as 0 because their bot protections are essentially invisible, I will fix this in a future update.
NOTE FOR WEB VERSION:
The web version uses a cloud IP which is blacklisted by major sites. I will add proxy rotations to defeat this but for now, the website is only good for easy/medium sites.

value: Show HN: caniscrape – Analyze anti-bot protections before scraping (CLI and Web)

author: Crroak


value: andcar

value:
Hello Hacker News,
I've built and released a small, free web tool, https://logviewer.io, and I'm keen to hear your thoughts.
My motivation came from a recurring personal frustration: being on a remote system (or even a friend's computer) where I needed to quickly glance at a log file, but didn't have access to my usual suite of development tools or couldn't install them. The goal was to create a zero-hassle, browser-native solution for basic log viewing and analysis.
This tool aims to provide a convenient alternative to launching an IDE or installing a dedicated log viewer, especially for those 'on-the-go' moments. All processing happens client-side in your browser, so your log data never leaves your machine.
Key aspects:
Client-side processing: Your log files remain private.
No backend: This keeps it simple and free to host.
Basic highlighting: Errors, warnings, info levels.
Search & Filter: Essential functionalities for quick diagnosis.
Future Plans: My immediate next steps involve implementing advanced log color highlighting (to better distinguish various log components beyond just level) and introducing bookmarks to mark and quickly return to important lines within large files.
I'm particularly interested in feedback regarding:
Its utility in your workflow – do you encounter similar scenarios?
Performance on larger text log files.
Ideas for simple, client-side-only features that would enhance its usefulness.
Thanks for checking it out.

value: Show HN: Logviewer.io – A Free, Browser-Based Log Viewer (No Install)

author: andcar


value: boburumurzokov

value:
Hi,
I am Bobur, a Software Engineer. We built an open-source customer support AI agent that can remember past conversations.
Most chatbots treat every message like it’s the first one.
This one keeps context over time, remembering what the user said earlier, what issues they faced, and what pages they visited.
It can be added to any website with a small widget.
The goal is to make support more personal and reduce repeated questions.
We’re still improving the memory layer and feedback loop, and would love feedback on how it behaves with real customer data. Thanks!

value: Show HN: Customer Support AI Agent with Memory

author: boburumurzokov


value: vishnukvmd

value: Show HN: Cookies vs. You [30s]

author: vishnukvmd

title: Show HN: Cookies vs. You [30s]


value: wasivis

value: Right now it's only focused on Argentina, where I live, but the point was to make a site that only posts 100% remote jobs without the need to manually sift through them because they might be hybrid or whatever.

value: Show HN: Solo Remoto – Website for remote job offers only

author: wasivis


value: TyxonQ

value:
ISCA Workshop Paper: Integration of Quantum Computing and AI, Transfer Learning Boosts GQE, Accelerating Drug Discovery in the NISQ Era
In June 2025, at the International Symposium on Computer Architecture (ISCA)—one of the top-tier academic conferences in computer architecture—and the 4th International Workshop on Quantum-Classical Collaborative Computing (QCCC-25), a collaborative research effort by QureGenAI, Ningbo University of Engineering, and China Pharmaceutical University was presented. The paper, titled "SMILES-Inspired Transfer Learning for Quantum Operator Pools in Generative Quantum Eigensolver,"was delivered as a keynote presentation. The paper sparked extensive discussion and received high recognition during the symposium. Notably, QureGenAI was the only Chinese team awarded a Contributed Talk at the event. Recently, an updated version of this research has been published on arXiv, marking a significant breakthrough in the integration of quantum computing and artificial intelligence.
This study pioneers a "transfer learning framework for quantum operator representation based on the Generative Quantum Eigensolver (GQE)."By drawing inspiration from the representation learning ideas of SMILES (Simplified Molecular Input Line Entry System) in cheminformatics, the research applies transfer learning techniques to optimize quantum operator pools in the Generative Quantum Eigensolver. The core innovations include:
1.Chemically Inspired Operator Representation Learning
Leveraging the sequential representation characteristics of SMILES strings, the research team constructed an embedded representation space for quantum operators. A pre-trained model was used to learn the semantic features of the operators, forming a transferable operator representation library. This approach captures functional correlations between quantum operators, significantly improving the efficiency of operator selection.
2.Dynamic Operator Pool Optimization Mechanism
Traditional methods like ADAPT-VQE require iterative selection of optimal operators from a fixed operator pool, which incurs high computational costs. This study introduces a dynamic operator pool construction mechanism via transfer learning, utilizing a pre-trained model to rapidly generate customized operator sequences tailored to specific molecular systems. This avoids the brute-force search process inherent in conventional methods.
3.Knowledge Transfer Across Molecular Systems
By transferring operator representation knowledge from previously trained molecular systems to new ones, a "warm-start" optimization is achieved. Experimental results demonstrate that this method maintains high computational accuracy, ensures consistency of similar features across molecules, and significantly reduces the number of iterations and computational time required for convergence. It is particularly suitable for quantum chemistry problems such as electronic structure calculations and molecular simulations.
This research represents a major advancement at the intersection of quantum computing and biomedicine, offering a new paradigm for quantum algorithm design in the Noisy Intermediate-Scale Quantum (NISQ) era. It opens new pathways for the application of quantum computing in drug design, materials science, and related fields.
Paper Address:
https://arxiv.org/abs/2509.19715
Repository Address:
https://github.com/QureGenAI-Biotech/TyxonQ/tree/main/resear...
TyxonQ Website:
https://github.com/QureGenAI-Biotech/TyxonQ/

value: Show HN: Transfer Learning Boosts GQE Accelerating Drug Discovery in the NISQEra

author: TyxonQ


value: amilasokn

value:
I run multiple projects and D2C brands, which means I manage 6 different Gmail accounts and easily get 100+ emails a day - team updates, invoices, customer queries, newsletters, OTPs… everything mixed together.
After missing a few important emails that actually cost me money, I realized the problem wasn’t the number of emails, it was how hard it was to make sense of them.
So I built Supamail, an AI-powered inbox that helps you read less and understand more.
Here’s what it does:
- Emails in last 24 hrs shrink into clear summaries, smart categories, and easy replies so you never feel overloaded again.
- Groups multiple emails from the same sender into one smart thread
- Categorizes emails automatically into Important, Transactional, and Promotional
- Lets you mute categories you don’t care about (like promotions)
- Sends you timed daily digests of important mails, if you prefer summaries over constant notifications
And because email privacy is non-negotiable, Supamail is CASA Tier-2 certified — meaning all processing happens securely with complete data isolation. The app never stores or reads your emails.
Currently, it supports Gmail on iOS, with Android and other providers coming soon. It’s $4.99/month after a 7-day free trial.
App store - https://apps.apple.com/in/app/supamail/id6753221429
More info - https://www.supamail.co

value: Show HN: Drowning in emails? So I made an AI to give me the gist in one line

author: amilasokn


value: vlugovsky

value:
Hello HN community,
Many of you have probably used drag-and-drop builders to create internal tools or dashboards. They’re great for quick prototypes - but as projects grow, the same problems always appear:
1) Performance issues. D&D builders add a lot of runtime overhead. Once you add more screens, logic, or connected data sources, things start to lag - and you have little control over optimization.
2) Limited flexibility. You’re restricted to the vendor’s components and configuration options. Anything custom usually turns into a hack.
3) Design overhead. You still spend a lot of time manually picking components, arranging layouts, and tweaking spacing before even connecting data - and the final result often looks… meh.
4) Maintenance pain. Opening an old project (or one handed off to a new teammate) often means digging around to find where things live. There’s no simple way to reason about structure or reuse.
At UI Bakery, we kept asking ourselves how to fix these issues without losing the speed of a low code builder.
First, we built an AI assistant trained on our docs. Then, about 1.5 years ago, we launched the AI App Generator, which could generate UI Bakery apps from text prompts. That helped, but under the hood it was still drag-and-drop - with all its inherent limits.
That’s when we decided to rethink the foundation entirely.
“What’s the most flexible, customizable project model possible?”
The answer: code.
So we rebuilt our approach and are now launching UI Bakery AI App Agent - a new way to build secure internal software by chatting.
Key features:
- Data connectors: Connect to SQL, NoSQL, REST APIs, and third-party services.
- Speed: Start from a plain-text prompt - AI generates a fully functional app in minutes.
- Code ownership: Access and edit the generated React code directly.
- Security: RBAC, SSO, SOC 2 compliance, audit logs, and deployment either in our cloud or fully on-prem.
- Customization: Use any React component you find online or build your own - it’s all just code.
Who is it for?
- Founders/CTOs automating internal processes.
- Developers tired of rebuilding CRUD/admin UIs from scratch.
- Enterprise teams needing internal tools with advanced security (on-prem, SSO, RBAC).
We see UI Bakery as the next evolution beyond drag-and-drop builders — from "fast but limited" to "fast, customizable, and secure."
We’d love to hear what the HN crowd thinks:
If you’ve used drag-and-drop builders, what’s been your biggest pain scaling or maintaining them?
Would you trust AI to generate internal tools that you can later expand like a normal codebase?
Try it here: https://uibakery.io

value: Show HN: UI Bakery AI Agent – build secure internal tools by chatting

author: vlugovsky


value: funfunfunction

value: Show HN: Using LLMs and >1k 4090s to visualize 100k scientific research articles

author: funfunfunction


value: howardV

value:
I built an AI tool that converts podcasts to text with automatic chapter
detection, multi-level summaries, and highlight extraction. Unlike basic
transcription services, it generates production-ready Show Notes in one click.
Technical highlights:
• 99%+ accuracy using Whisper with optimized chunking for long audio
• Speaker diarization without training data
• AI chapter detection using topic boundary analysis
• Exports to Markdown/TXT/JSON with structured metadata
Free tier: 5-minute preview
Tech stack: Next.js, Whisper, R2 storage
Try it: https://harku.io/tools/podcast-transcription
Built this after spending 30-60 minutes manually creating Show Notes for
each episode. Would love feedback from fellow podcasters and developers.

value: Show HN: AI Podcast Transcription with Auto-Generated Chapters and Show Notes

author: howardV


value: benjclarke

value:
I built CrossPromo Club, a free tool to help newsletters grow by promoting each other.
It provides ways to find and connect with partners, and organize cross-promotions. There is also an automated cross-promotion feature that matches newsletters via a single link.
There's a full demo - no signup needed.
I made it because I was finding cross-promotion difficult for my own newsletter. I'd welcome any and all feedback.

value: Show HN: CrossPromo Club – A platform for newsletters to promote each other

author: benjclarke


value:
Hey HN
I built Prexist, an AI-powered tool that helps founders, PMs, and VCs quickly find out if their startup idea already exists, across 8 major platforms like Product Hunt, YC, GitHub, Crunchbase, and the App Stores.
Until recently, running searches across all these platforms in parallel took ~60 seconds. We’ve just integrated Exa AI for search orchestration, and it’s now 3× faster while returning more relevant, semantically matched results.
What it does:
- Uses AI to understand your product idea and generate smart search queries
 - Searches 8 discovery and innovation platforms simultaneously
 - Lets you generate and share reports with a single link
Use cases:
- Founders validating product ideas
 - Product teams doing competitor scans
 - VCs doing due diligence on new investments
Would love your feedback, especially from anyone who’s tried automating multi-platform searches.
Happy to answer technical questions or talk about how we can improve the product further.
- Team Prexist

value: Show HN: Prexist – AI-powered search for products (now 3x faster with Exa AI)


value: MrMilkshake

value:
Hey HN, this is my first post.
I built this as a prototype and a way to get my idea out there — to see if others find it interesting and to hopefully meet like-minded people.
The project is called Project Juno — an experiment in using AI to model compatibility based on values, habits, and communication style rather than engagement metrics.
There’s a whitepaper on the site that explains the concept, and I’ve set up a small Discord to discuss it and connect with anyone who’s curious or might want to collaborate.
https://www.projectjuno.ai

value: Show HN: Project Juno – my experiment in modeling compatibility with AI

author: MrMilkshake


value: giovannibekker

value:
Hi HN,
I built ECHORB (https://virtual-life.dev/echorb), a desktop app for managing multiple AI assistants (like Claude Code, Codex, etc.) that can communicate with each other.
As a developer, I wanted to stop copy-pasting between AI chats and instead have a "System Orchestrator" that could delegate specific tasks to a team of specialized AI agents (e.g., frontend, QA, image generation).
ECHORB allows you to do this through a single interface. It also includes features for creating automated workflows (triggered by schedules, webhooks, or file changes) and simplified Git worktree management.
It's a commercial tool at $10/month, but there is a 60-day free trial with no credit card required.
I built this solo and would love to get your feedback and answer any questions.

value: Show HN: Echorb – An orchestration layer for multiple AI CLI assistants

author: giovannibekker


value: ramoz

value: Show HN: Share your Claude Code sessions with others

author: ramoz


value: Show HN: I made a multi media AI generation app with O subscription


value: gangtao

value:
Two years after open sourcing Proton, we're releasing v3.0 which brings enterprise-grade streaming capabilities to the open source version: full connectivity, processing, and routing in a single binary with zero dependencies.
Key features:
First vectorized streaming SQL engine in modern C++ with JIT compilation
High-throughput, low-latency, high-cardinality processing
End-to-end streaming: ETL, joins, aggregation, alerts, and tasks
Native connectors: Kafka, Redpanda, Pulsar, ClickHouse, Splunk, Elastic, MongoDB, S3, Iceberg
Native Python UDF/UDAF support to support your AI/ML work loads
The same performance we've proven in large enterprise deployments is now available in the community edition.
Would love feedback from anyone working with streaming data or looking for Flink/ksqlDB alternatives.

value: Show HN: Timeplus Proton 3.0 – First vectorized streaming SQL engine

author: gangtao


value: lbyaus

value:
Hey HN,
I built FlyingStart to help indie makers and startup founders launch with a memorable brand identity, without wasting days (or weeks) hunting for a good name that’s actually available.
Every name on FlyingStart is unique, brandable, and comes with a matching domain, ready to use out of the box.
Unlike typical domain marketplaces, none of our names exceed $1,000 — we keep prices low so you can invest your money where it matters most: your product.
I created this because I’ve seen too many founders get stuck at step one — the name.
FlyingStart aims to remove that friction so you can get building faster.
Would love your thoughts on:
How we can make the catalog more useful to indie makers
Whether you’d like filters for different niches (SaaS, tools, AI, etc.)
Any suggestions on improving the buying or browsing experience
https://www.flyingstart.co/

value: Show HN: FlyingStart – Brandable domain names for indie makers (under $1k)

author: lbyaus


value: ttd

value:
This is a recent addition to Vexlio which I think the HN crowd may find interesting or useful.
TL;DR: easy creation of interactive diagrams, meaning diagrams that have mouse click/hover hooks that you can use to display pop-up content. The end result can be shared with a no-sign-in-required web link.
My thought is that this is useful for system docs, onboarding or user guides, presentations, etc. Anything where there is a high-level view that should remain uncluttered + important metadata or details that still need to be available somewhere.
You can try it out without signing up for anything, just launch the app here (https://app.vexlio.com/), create a shape, select it with the main pointer tool and then click "Add popup" on the context toolbar.
I'd be grateful for any and all feedback!

value: Show HN: Create interactive diagrams with pop-up content

author: ttd


value: danielbisina

value:
I’ve been working on UHOP (Universal Hardware Optimization Platform) — an open-source framework that helps developers optimize GPU and accelerator workloads across architectures (CUDA, ROCm, OpenCL, etc.) without vendor lock-in.
It started as a personal frustration: I’d write something that ran great on CUDA, then have to rewrite or retune for ROCm or OpenCL. UHOP tries to make that portable — it detects your hardware, generates or benchmarks candidate kernels, and caches the best performer. It also supports AI-assisted kernel generation using OpenAI APIs and comes with a simple CLI for demos and benchmarking.
Right now, UHOP can:
Auto-detect hardware backends and pick optimal kernels
Run and benchmark fused ops like conv+ReLU
Cache and reuse tuned kernels
Generate kernels dynamically via codegen (CUDA/OpenCL/Python/Triton)
There’s still a lot in progress — better backend integration, distributed optimization, and a web dashboard for visualizing results. I’m sharing it early to get feedback from folks who’ve worked on compilers, GPU runtimes, and ML infra.
Repo: github.com/sevenloops/uhop
Demo: uhop.dev
Would love any thoughts on architecture, testing approaches, or potential contributions from ex-NVIDIA/ROCm engineers.

value: Show HN: UHOP – An Open Hardware Optimization Platform for GPUs

author: danielbisina


value:
I built UHOP (Universal Hardware Optimization Platform) because I was tired of the invisible cage that GPU developers live in — everything we write seems to orbit around NVIDIA. CUDA is amazing, but it’s also a moat. Porting the same code to ROCm or OpenCL usually means starting over.
UHOP is an open-source attempt to break that lock-in by introducing a cross-vendor optimization layer. It detects your hardware (CUDA, ROCm, OpenCL, etc.), generates or benchmarks kernels, and caches the best performer. You can wrap your ops with a decorator, let UHOP choose or generate the kernel, and it just runs — wherever.
Features so far:
Hardware detection + backend selection
AI-assisted kernel generation (CUDA / OpenCL / Triton)
Fused op demos (conv2d+ReLU, matmul, etc.)
Kernel benchmarking and caching
CLI + early browser dashboard
There’s a long way to go — distributed tuning, compiler IR passes, better PyTorch/JAX hooks — but it’s open, hackable, and community-driven.
Repo: github.com/sevenloops/uhop
Demo: uhop.dev
Would love feedback from compiler engineers, GPU devs, or anyone who’s ever felt boxed in by vendor APIs.

value: Show HN: UHOP – Escaping Nvidia Lock-In with an Open Hardware Optimization Layer


value:
Hi HN!
I built Streaky to solve a personal problem - I kept losing my GitHub streak on busy days. It monitors your contribution streak and sends notifications to Discord/Telegram before it breaks.
What makes it interesting technically:
1. Distributed Cron Processing: Used Cloudflare Service Bindings to bypass the 30-second CPU limit. Each user gets processed in an isolated Worker instance with its own CPU budget.
2. Idempotent Queue System: D1-based queue with atomic operations prevents duplicate processing when cron jobs overlap or retry.
3. Zero-Knowledge Security: GitHub tokens never stored (OAuth refresh flow), webhooks encrypted with AES-256-GCM, notifications sent via isolated Rust proxy.
4. Rate Limit Solution: Cloudflare Workers use shared IP pools which trigger rate limits from Discord/Telegram. Solved by routing notifications through a dedicated Rust server on Koyeb.
Tech Stack: - Frontend: Next.js 15, React 19, TypeScript - Backend: Cloudflare Workers + D1 (SQLite) - Infrastructure: Rust notification proxy - Auth: GitHub OAuth via NextAuth.js v5
Live demo: https://streakyy.vercel.app
The project is fully open-source under MIT license. Happy to answer any questions about the architecture or implementation

value: Show HN: Streaky – GitHub Streak Monitor with Distributed Cron Processing


value:
Hey HN,
We're all told to "do things that don't scale" to get our first users. For me, that meant spending 2-3 hours every day searching LinkedIn, Reddit and Twitter for anyone talking about a problem my software could solve.
The lead quality was great, but it took so long that it was getting in the way of my productivity.
I made a simple tool FeedPilot to fix this problem. It's a browser extension that runs in the background, scanning for keywords and conversations. It's not just a keyword alert; it uses a small AI model to find posts where people are actually asking for help or recommendations.
This has been the main way I've got users, and it now takes me about 15 minutes a day instead of 3 hours.
I thought other founders were having the same problems, so I improved it. There's a free version that's really useful. Please tell me what you think.

value: Show HN: Built a tool for founders to automate the grind of finding first users


value: buildwithnumen

value:
Hey HN,
I've been building websites for 15 years. I'm a perfectionist - both visually
and technically - and got tired of going back and forth between different
tools, manual checks, and documentation for every audit.
Built GuardianScan to run everything in under 45 seconds:
- Core Web Vitals (LCP, INP, CLS)
- WCAG 2.2 compliance
- Security headers and CSP policies
- Modern framework patterns
- SEO and schema markup
47 checks total.
These standards exist for a reason. Google prioritises fast sites. Accessible
sites reach more people. Secure sites build trust. When you meet modern
standards, you see it in your traffic and conversions.
Stack: Next.js 15, React 19, Supabase, Browserless.io for headless Chrome.
Won't catch everything - automated accessibility gets about 70% of issues.
But it's what I use before every deploy now.
Landing page is up. Launching Nov 1. £24/month, no enterprise pricing BS.
Happy to answer questions.

value: Show HN: GuardianScan – Website audits for 2025 web standards

author: buildwithnumen


value: mshubham

value:
I wanted a voice assistant that feels realtime but runs completely offline.
This prototype uses MLX + FastAPI on Apple Silicon to hit sub-second latency for speech-to-speech conversations.
Repo: https://github.com/shubhdotai/offline-voice-ai
It’s fast, minimal, and hackable — would love feedback on latency tricks, model swaps, or use-cases you’d like to see next.

value: Show HN: Talk to your Mac offline – sub-second Voice AI (Apple Silicon and MLX)

author: mshubham


value: Show HN: Ch – Lightweight AI CLI for OpenAI, Anthropic, AWS Bedrock, and Local


value: dawitworku

value: Show HN: Memarya – An open-source e-learning platform, free for students

author: dawitworku


value: cevian

value:
For the past 10 years at Timescale (now Tiger Data), I've been been watching developers make mistakes in database design: forgetting to index foreign keys, creating case-sensitive email lookups, using arbitrary VARCHAR limits. And I've seen the pain and frustration when there is major downtime and botched migrations fixing things. AI coding could have made this better but is only making things worse. It's infuriating.
So, I (along with my awesome team at Tiger Data) built an MCP server that auto-injects Postgres best practices into AI context to help. The biggest innovation: our get_prompt_template tool lets the LLM auto-discover the right guidance without slash commands. Ask "design a schema for IoT devices" and it automatically pulls our Postgres schema design and hypertable guides.
What's different from other MCP servers: Instead of thin API wrappers, I am teaching AI things like "Always index foreign key columns - Postgres won't do it for you" and "use TEXT not VARCHAR(255)". It includes versioned Postgres docs (15-17) with hybrid search, plus TimescaleDB-specific patterns.
Try it with our free service:
```<p>curl -fsSL <a href="https://cli.tigerdata.com" rel="nofollow">https://cli.tigerdata.com</a> | sh<p>tiger auth login<p>tiger mcp install<p>```
Works with Claude Desktop, Cursor, Windsurf, VS Code. Open source.
By the way: this approach might show how to implement Anthropic's new Agent Skills (previous discussion: https://news.ycombinator.com/item?id=45607117) entirely through MCP.

value: Show HN: MCP server that teaches LLMs to write production grade Postgres SQL

author: cevian


value: irgolic

value:
We built RuleHunt to search for interesting cellular automata rules using tiktok-style engagement monitoring.
The first rule shown is Conway’s Game of Life. As you scroll, you will see other random rules – the search space is 2^512. Help us find good rule heuristics by starring the ones you like!
On mobile it's a tiktok-like scrolling interface; on desktop it's an interface for targeted rule searching. Starred rules go to a global leaderboard.
GitHub repo: https://github.com/rulehunt/rulehunt

value: Show HN: RuleHunt – TikTok for Cellular Automata

author: irgolic


value: Show HN: Open-source CLI based Browsing Agent(not based on Firefox and Chromium)


value: zombico

value:
LLM apps are traditionally treated as text generators. What if we treated them like HTTP APIs—forcing structured output that's inspectable, verifiable, and debuggable?
I combined existing patterns into an approach: force the LLM to output JSON, bind it to the HTTP request, and record everything.
The repo is a reference implementation:
- LLMs must respond in structured JSON (not raw text)
- Every response includes reasoning traces. Timing, and metadata are appended after
- Each event is saved in SQLite, allowing conversation reconstruction
- Cryptographic hashes verify conversation integrity
- DevTools-style inspector shows everything in real-time
Demo: Ontario Procurement Guidelines chatbot with full RAG and multi-turn conversation debugging.
Implemented separately with feature parity in Node.js and .NET. Works with Anthropic/OpenAI/Ollama.
NOTE: This demonstrates an architectural pattern, not a drop-in product. It's extensible, but requires restructuring your app around structured output.

value: Show HN: Inspect Element for LLM Apps

author: zombico

title: Show HN: Inspect Element for LLM Apps


value: pythonbrad

value:
afrim is a framework and toolset that facilitates the implementation of input method engines (IMEs). Initially introduced as an IME for African languages[1], afrim has now evolved into a universal solution. It is compatible with various sequential writing systems, including Amharic, Geez, Pinyin, and more.
afrim is written in Rust and his architecture is inspired by librime[2].
It's available to use in Rust (afrim), Python (afrim-py), JavaScript (afrim-js), and more.
Repo: https://github.com/fodydev/afrim
Demo: https://fodydev.github.io/afrim-web/
FAQ: https://github.com/fodydev/afrim/blob/main/FAQ.md
[1]: https://news.ycombinator.com/item?id=41427563
[2]: https://github.com/rime/librime

value: Show HN: afrim – A framework for input method engine

author: pythonbrad


value:
Hey HN! I'm sharing our Corporate Hierarchy API that we just launched in beta. Its current focus is on mapping the complete corporate ownership upwards to the ultimate parent by doing deep research across the open web and global government registries.
The problem: Companies spend millions on manual research teams to answer compliance / risk questions like, "does this business roll up to a state owned entity in China / Russia?". A bankruptcy processor we work with had 30 people just manually researching data points like this. An ex-Shell trader told us Shell has 800 people + $100M/yr spent with Deloitte to conduct manual research to ensure international trade compliance.
Why existing solutions are lacking: The dirty secret is that legacy providers like Dun & Bradstreet, Orbis, and S&P run on armies of manual workers and are built on decades old technology. At query time, you’re typically tapping into a quarterly generated, static, expensive database.
Our approach: We built this on top of our entity resolution engine - a deep research agent that anchors entities to our business graph, powered by direct integrations with government registrars and our web scraping infra. When you need hierarchy research, our AI agents spend on average 10-20 mins researching upward to identify the ultimate parent and build the complete ownership DAG with source citations. We auto-generate Mermaid diagrams so you can immediately render the results in your own app.
Technical backstory: I've spent 10 years building MDM, data enrichment, and entity resolution systems at B2B startups and enterprises, mostly in fintech. I actually built an in-house Clearbit replacement after they overcharged us and almost killed the startup I was at. The core challenge is always the same - taking messy business data and mapping it to web data and the actual legal entity. Most off the shelf solutions are mediocre because they skip the hard part: the gnarly 20-40% of records that force you into manual research.
Current state: Just three of us, bootstrap funded. We're getting requests for full subsidiary mapping too, but focused on upward hierarchy for now. We're still figuring out pricing (market rate seems to be a few dollars a lookup, but we aim to be a fraction of that). Deep research is expensive but we think we can make it work at scale.
Try it:
- Interactive demo: https://savvyiq.ai/products/entity-hierarchy (no signup)
- Free account: https://savvyiq.ai/signup
- API docs: https://savvyiq.ai/api-reference#tag/entity-hierarchy-beta
Would love feedback on the hierarchy API specifically, including the schema, gaps / edge cases, but also curious about other use cases where business identity resolution is a pain point for you.
Happy to run a few entity hierarchy lookups in the comments if anyone wants to see the API in action - I can share the results as Mermaid diagrams that you can paste directly into any Mermaid renderer.

value: Show HN: Corporate Hierarchy API – Map the corporate family tree


value: DanielAtDev

value:
Hi HN — I’m Daniel, DevRel at Xano.
Three years ago, Xano started as an internal tool at a software agency.
We were rebuilding the same backend infrastructure for every client—auth, databases, APIs, file storage, scaling, security—over and over again.
To save time, the team built a platform that could generate production-grade backends in days instead of months.
That internal tool evolved into Xano, which now powers products used by both startups and large enterprises.
Companies like Deriv, an online trading broker with over 3 million customers, use Xano to ship features faster without managing infrastructure.
Independent builders use it to launch products without hiring backend engineers.
And now, with Xano 2.0, we’re extending that same capability to AI developers and teams adopting an AI-first SDLC.
The Problem
AI tools like Cursor and Claude Code make it easy to generate working prototypes in minutes.
But getting a production-ready backend—auth, schemas, server-side logic, scaling, monitoring, microservices—still takes weeks.
You’re stuck choosing between:
Fast but fragile: AI-generated code that only works in demos
Slow but solid: Traditional backend setups that stall iteration
We wanted a third option: a production-grade backend that could support large scale apps, but built in minutes instead of weeks
What’s new in Xano 2.0
1. XanoScript — Your backend infrastructure and logic as code—with a visual layer that makes it easy to verify what’s being built
 A scripting language that mirrors our visual function builder.
 Developers can work entirely in code or visually—changes stay synchronized.
 AI tools can generate and modify backend logic because it’s expressed as real code, not configuration.
2. MCP Server — AI has context and can build and modify your backend
 Xano 2.0 exposes a Model Context Protocol (MCP) server so tools like Claude Code and Cursor can securely connect, inspect schemas, and push updates directly to production.
3. VS Code Extension — Native IDE integration
 Browse, edit, and version-control Xano resources in VS Code with linting, autocomplete, and one-click deploys.
Under the hood
Every Xano instance runs on Google Cloud (BYOC for Enterprise), provisioned with Docker and orchestrated by Kubernetes — standardized with best practices so you don’t have to. Security, scaling, and compliance are already done-for-you, letting you focus on building the logic that drives value without having to manage DevOps and string together multiple tools.
Who it’s for
- AI developers who want to ship prototypes that can scale to production
- Teams that want backend speed without compromising reliability
- Non-backend engineers who want to build production systems with help from AI tools
We’d love feedback from HN on the architecture, the AI integration model, and where this approach might fall short. I’ll be around in the comments to discuss!

value: Show HN: Xano 2.0 – AI-Generated Back Ends That Are Production-Ready

author: DanielAtDev


value: mehuljd

value:
We built Calen AI from the ground up with AI at the core — not bolted on. Instead of clicking through calendar interfaces, you can talk to it naturally through email, chat, or voice.
Example:
“Book a 30-minute sync with Sam next Tuesday afternoon.”
Calen AI understands intent, finds the right time, checks availability, and sends invites automatically. It can also handle reschedules, follow-ups, and group coordination without the usual back-and-forth.
 • Natural language scheduling via multiple channels (email, chat, voice)
 • Real-time availability and smart preferences
We built this for people who live in their calendars but don’t want to spend time managing them.
We’d love feedback from the community. What’s your biggest pain point with scheduling?

value: Show HN: Calen AI – an AI-first calendar that lets you schedule naturally

author: mehuljd


value: slonik

value:
Mailbox providers are tightening up sender requirements for authentication, making DMARC crucial for bulk sending.
I made a DMARC validator where you can check out whether your setup is valid or needs any improvements.
It looks up _dmarc.<domain>, parses the TXT record, and displays policy (p/sp), alignment (adkim/aspf), reporting (rua/ruf), and options like pct, fo, ri, etc. It also gives you useful recommendations about your DMARC and helps you resolve potential issues.
It’s free and no sign-up.

value: Show HN: Free DMARC Checker – Verify your domain meets sender requirements

author: slonik


value: ksred

value: Show HN: Cont3xt.dev – Universal Team Knowledge for AI Coding Tools

author: ksred


value: imedadel

value: Show HN: Outcrop – Linear for Wikis

author: imedadel

title: Show HN: Outcrop – Linear for Wikis


value: itscoreyb

value:
Hey HN,
AI agents are all the buzz right now, but how many of them are accessing your website today? That's what we built AgentShield to answer. Most of our users didn't know they had agent traffic until seeing them in our dashboard.
You can see traffic broken down by humans, bots, and AI agents. Installation can be as simple as a JS pixel, and it's free. We're currently building out the paid component that will let you block specific agents or types of traffic.
Feedback welcome: What else would you want to know about AI agents visiting your site?

value: Show HN: AgentShield – detect AI agents on your site

author: itscoreyb


value: kseppanen

value:
I’ve always been curious what the Y Combinator ecosystem looks like, not by geography or funding, but by how founders describe what they’re building.
So I pulled every YC company description from their public directory, embedded them using an open-source model, and visualized them in 3D using UMAP + Three.js.
Each point = a company.
Distance = how similar their missions sound.
The result is a “startup galaxy,” where clusters of AI, dev tools, fintech, and biotech emerge naturally.
It’s just a fun side project (not affiliated with YC), but I’d love feedback from the community

value: Show HN: A 3D visualization of all YC startups based on their mission statements

author: kseppanen


value:
Hi HN — I’m excited to share *Docuit*, an AI-powered desktop assistant that automatically captures your work (desktop + browser) and turns it into clean, step-by-step documentation.
What it does
- Captures real-time desktop & browser tasks
- Converts them into readable, reusable docs
- Exports to Word, PDF, or Notepad
- Translates into 19+ languages
- Protects privacy with PII redaction and automatic exclusion of email/chat apps
Built for
Engineers, IT teams, marketers, students, and teachers — anyone who needs to create guides or notes efficiently.
Plans start at $20/month (Mac & Windows)
 Learn more: https://www.docuit.ai
Would love feedback from the HN community on privacy design, integrations, and overall UX. Happy to answer technical questions about how the agent works.

value: Show HN: Docuit – capture your work and turns it into sharable docs


value:
Hey HN,
I'm Willem, a freelance engineer. Together with the folks at Confident Security I developed this Go package that we open sourced today:
https://github.com/confidentsecurity/bhttp
It's a Go implementation of BHTTP (RFC 9292) that allows you to encode/decode regular `<i>http.Request` and `</i>http.Responses` to BHTTP messages.
We've implemented the full RFC:
- Known-length and indeterminate-length messages. Both are returned as `io.Reader`, so relatively easy to use and switch between the two.
- Trailers. Work the same way as in `net/http`.
- Padding. Specified via an option on the encoder.
If you're working on a problem that requires you to pass around HTTP messages outside of the conventional protocol, be sure to check it out. Any feedback or PR's are much appreciated.

value: Show HN: Bhttp Binary HTTP (RFC 9292) for Go


value: bbischof

value: We built a search engine for art that uses natural language prompts to find real artwork instead of generating art.

value: Show HN: Semantic Art – Uses natural language prompts to find real artwork

author: bbischof


value: MartinBraquet

value:
I created Compass — a free, open-source platform designed to help people find and form deep connections (platonic, romantic, or collaborative).
I'm 28 and over the past years I got to connect with different people and hence get some clarity about who I am and what I'm looking for in life. More recently, I've been trying to find those people who align with my newly clarified values and life vision. I tried different approaches like dating apps, forums, and real-life communities, but I found it very slow and inefficient to find "my" people.
Don't get me wrong, I got along with plenty of people there, but I'm sure there are highly aligned people among them that I just couldn't find because I lost too much time in the process of getting to know people I found interesting "enough". That got me thinking that there must be a more efficient way to find those rare people I'm looking for, a place where everyone puts a lot of info about themselves and their desires, and where it's fast to filter and sort them.
I'm aware there are a lot of platforms that work great for specific types of connection already. LinkedIn is very efficient to find the people working in a specific company or studying in a specific school. Social media are (reasonably) good to chat and comment casually and with low commitments. Dating apps are good mostly for casual encounters, with more serendipity as their filters are very poor and you just see people one by one (for the people among us with a less agentic way of living, exploring profiles as they come—from some hidden matching algorithm usually).
Yet, I just can't find a platform specifically designed to find the people I want to connect with more deeply and personally: a life partner, a close friend, a collab on a very specific project. I imagine a place where I can specify all my values and life goals in a long text and through checkboxes (kids, place to live, personality type, romantic style, thinking style, emotional style, conflict style, financial habits, culture, religion, politics, humor, triggers, dealbreakers, pet peeves, ethics system, work-life balance, housing,family projects, interests, intellectual topics, books, hobbies, etc.) and be presented with the most aligned (pre-filtered) people. I would then connect with them more deeply, through video calls or in-person events. So, the platform would be used to FIND people, not to foster the connection afterwards (as deep connections require in-person contacts or long video calls).
That's my rationale for starting Compass. On the tech side, it's fully free and open source. The code, on GitHub thus, is in React / Typescript. Hosted in Supabase, Firebase and Google Cloud. It's owned / governed by the community; they make proposals and vote in a democratic way. The people who believe in the project give their time and money to keep it alive. There are only 250 people so far, but that's the vision I have for the platform.
For data privacy reasons and to avoid data scraping, you need to sign up to view the profiles. But right now, to reduce all friction, you can sign up in 5 seconds (even with a fake email if you want, no email verification) without providing any personal info. Just enter an email address and password, and the rest is optional and skipable instantly.
I actually already coded a pretty solid web app and just rolled out a WPA to get mobile notifications.
Let me know your thoughts on that!

value: Show HN: A platform for intentional human connections

author: MartinBraquet


value: AndrewPetrovics

value:
Hey HN,
My name is Andrew Petrovics. First time poster. Long time lurker.
I created a web app called Video To Blog that converts videos into (what I think are awesome) blog posts / email newsletters.
I originally created the tool with YouTubers in mind as a way to quickly repurpose existing videos into blog posts and help capture traffic from another channel (i.e. search engines). Since I launched though I've had a bunch of other people use it for all different reasons including bloggers, affiliate marketers, digial agencies, realtors, churches, and more. My growth has flat lined and I'm trying to find a solid niche to focus on in order to spark growth and get this to a point where I can work on it full time (I'm almost there, but having a family, I need to grow a bit more before I'm comfortable taking the leap).
Any who, our most popular feature is "auto screenshots" which can automatically take screenshots from the video and put into the blog post. The tool allows for a ton of other customization options (like automatically including internal/external links, tone/voice customiziation, ability to add writing samples / custom AI instructions, and more).
Would love to hear and feedback you have.
Also, just a heads up, an email is required in order to test it out (without an email requirment we saw a bunch of abuse).
Thanks,
Andrew Petrovics

value: Show HN: Convert Videos into Blog Posts

author: AndrewPetrovics

title: Show HN: Convert Videos into Blog Posts


value: nschmeli

value:
Built this after realizing I was constantly googling
"when does [thing] come out."
The key feature: cross-category search. If you search "Star Wars," you
see the next movie, upcoming games, new books, comic releases, and
concert tours - all in one view. Everything about one thing, not just
one category.
Current categories:
- Movies
- Video games
- Comics
- Concerts/festivals/events
- Books
Tech: FastAPI backend, vanilla JS frontend, deployed on DigitalOcean.
Hits multiple sources in parallel and sorts chronologically.
Planning to add:
- Alert system (email/SMS when tracked items release)
- Links to each where you can purchase each item (have concerts/events done)
Open to feedback! What categories am I missing? Would you use release
alerts?

value: Show HN: Hypdar (hype radar) – Never miss a release

author: nschmeli


value: patriceckhart

value:
I've been experimenting with connecting AI clients directly to event-sourced data, and the result is something I'm genuinely excited about: the Genesis DB MCP Server (Preview).
It's a Model Context Protocol (MCP) server that gives AI tools (like Claude Desktop) structured access to Genesis DB events so you can literally ask your database questions in natural language, such as:
• "How many users signed up last week?"
• "Show me all events for customer 42."
• "What event types exist in the system?"
Under the hood, the MCP Server acts as the interface that translates natural-language queries into Genesis DB queries and streams the results back to the client.
Why this matters:
Most event stores are great for systems, not people. The MCP server makes event-sourced data conversational and explorable without writing queries or projections first.
Available now (Preview): https://github.com/genesisdb-io/genesisdb-io-mcp-server

value: Show HN: Genesis DB MCP Server: AI Meets Event Sourcing, a Love Story

author: patriceckhart


value: rokontech

value: Generate production-ready OpenAPI specs, frontend & backend code and documentation with AI

value: Show HN: Middlerok – reduces front end-back end integration from weeks to hours

author: rokontech


value:
I built Listibly to help people share recommendations with others.
This is not a new idea, but we're still being asked for recommendations of places in town, books, and more. I couldn't find a platform that does this yet is simple and modern.
This is a super early MVP, would love to hear your thoughts.

value: Show HN: Listibly – Simple platform to share recommendation lists


value:
I kept pasting the same question across several AI platforms to make sure I got the best answer.
I found this process time-consuming, and difficult to compare the answers between platforms.
So I built ChatHawk.
It's a tool that lets you ask a question and have it answered by all the flagship AI models at once. These models are currently Gemini 2.5 pro, GPT-5, Grok-4 and Claude: Sonnet 4. ChatHawk also uses AI to get the best parts from each response to provide a 'Best of All Models' answer.
I've found it useful for these use cases:
1. When you want to make sure the answer is right. E.g. for calculations and legal or accounting questions. ChatHawk lets you check the AIs have the same answer.
2. Strategy questions. E.g. for negotiating contracts or salaries it is useful to get advice from each AI model... It's like having an AI board of advisors.
3. Ideation. For coming up with new ideas it's beneficial to talk to several AIs and see each response.
Some AI aggregation tools exist BUT they show the AI answers side-by-side or in a grid. I found this overwhelming from a user experience perspective. I wanted to see one answer at a time and click tabs to swap between them! So, I hope you enjoy ChatHawk's UX as much as I do :)
It's also incognito as ChatHawk doesn't save any message history. It's annoying how all AI platforms store chat history when it's not that useful a feature. In my view, they do this to data mine and build memories which lock you into their platform. So don't worry about that on ChatHawk.
Please let me know if you have any feedback. The tool is free to use with no paid option at the moment...

value: Show HN: ChatHawk – Ask once. Get advised by all the top models


value: valliveeti

value:
Our Product Hunt launch is live tomorrow!
When browser agents started getting popular earlier this year, they were still a bit rough around the edges, but the potential was impossible to ignore.
After a few months of experimenting with different tooling, we finally landed on a combination that we believe will work better than today’s leading browser agents. We went full time just five weeks ago, and the progress since then has been immense.
Our first iteration was bad, no way around it. Within the first week of going full time, we pivoted to a chat based prompting model. The underlying model was still making obvious mistakes, so we fixed those one by one.
Individually, each fix didn’t feel like much, but when you zoom out you realise how far we have come.
We always knew our approach would be the cheaper way to build a browser agent, but based on today’s tests, three people built a browser agent that beats industry standard browser agents in token efficiency by a huge margin without sacrificing accuracy.
Built in the Baltics, not San Francisco.
It’s not a scientific benchmark yet, just a few prompts, but even getting close with the resources and timeline we have feels amazing. More comprehensive benchmarks soon, also another launch coming early next month.

value: Show HN: Turn Any Website into an API

author: valliveeti

title: Show HN: Turn Any Website into an API


value: taariqlewis

value:
Show HN: SerenDB – Neon PostgreSQL fork optimized for AI agent workloads
We forked Neon to make database operations faster and safer for AI agents. The goal is to enable instant experimentation with
production data and catch prompt injection attacks before they hit your DB. The open-source repo is at https://github.com/serenorg/serendb
The coolest current features are:
1. Time-travel queries: Query your database as it existed at any timestamp. SELECT * FROM orders AS OF TIMESTAMP '2024-01-15 14:30:00'. Essential for debugging agent decisions and auditing what data an agent saw.
2. Scale-To-Zero with pgvector: Built-in vector embeddings that sleep when idle. Pay nothing for dormant agent databases, auto-scale to 16 vCPU when needed.
The coolest features in development are:
1. Prompt injection detection: Fingerprinting and context-aware policies to catch injection attempts before they reach your data. We're extending Postgres's security model to understand AI-specific attack patterns.
2. 100ms branch creation: Clone your entire production database in 100ms (vs Neon's 500ms). Enables branch-per-agent patterns and instant rollback. Great for faster testing of multiple prompt variations on real data.
This is early - we're working on the hosted service now.
Technical folks: curious if anyone else is hitting the "how do I safely test my agent on prod data" problem. Happy to answer questions about our updates to the branching architecture or our proposed context security approaches.
Website: https://serendb.com/

value: Show HN: SerenDB – A Neon PostgreSQL fork optimized for AI agent workloads

author: taariqlewis


value: hotk

value:
Built this for streaming AI tool calls. LLMs stream function arguments as JSON character-by-character. Most parsers reparse from scratch each time - O(n²) behavior that causes UI lag.
This maintains parsing state, processing only new characters. True O(n) performance that stays imperceptible throughout the entire response.
Ruby gem, MIT licensed. Would love feedback.

value: Show HN: Incremental JSON parser for streaming LLM tool calls in Ruby

author: hotk


value: nsomani

value: Show HN: Cuq – Formal Verification of Rust GPU Kernels

author: nsomani


value: hauxir

value: Show HN: I turned the Namib Desert livestream into my macOS screensaver

author: hauxir


value: itayd

value:
I built a simple go-links URL shortener that uses Google Sheets as its backend. No database, no admin UI – just edit URLs in a spreadsheet.
The entire thing is ~200 lines of Python. You get a webhook that redirects go/docs to whatever URL you mapped in your sheet. If multiple URLs match, it shows an HTML page with all options. Empty path redirects to the spreadsheet itself for quick editing.
It's built on AutoKitteh (which I also work on), which handles the webhook endpoints, Google Sheets integration, and synchronous HTTP responses. Deploy with make deploy and you're done – no servers, databases, or configuration needed.
Comes with a Chrome extension to make go/ links work directly in your browser.
Perfect for teams that want simple, customizable link shortening without the overhead of commercial services or the complexity of self-hosted solutions.

value: Show HN: Gocat – URL shortener using Google Sheets as a database

author: itayd


value: anof-cyber

value: Show HN: ProxyBridge redirect any Windows TCP/UDP traffic to HTTP/SOCKS5 proxies

author: anof-cyber


value: Show HN: SayToggle – Seamless voice input for ChatGPT/Claude (Apple Silicon)


value: dogol

value:
Maktabah Shamilah counterpart:
ELKIRTASSE is now being revived, readily to be improved on Qt6 and cmake build system
Please help develop, improve it

value: Show HN: Maktabah Islam ELKIRTASS being revived on Qt6 CMake

author: dogol


value: davitostes

value:
I built htmask.js because I was bored and all my input fields were embarrassingly unmasked.
It's a dependency-free JavaScript library that does one thing: masks inputs. No npm bloat, no build step, no tests (yet). Just a script tag and some mask attributes:
```html
<input mask="(00) 00000-0000">
<input mask="00/00/0000">
<input mask="AAA-0000">
```
Where `0` = digit, `A` = letter, and everything else = "let's see what happens".
I made this as a weekend boredom project because existing solutions required reading documentation, and I'm fundamentally opposed to that. It works with vanilla JS, plays nicely with htmx (shockingly), and has caused zero production incidents so far (sample size: 1).
The code is probably terrible, the edge cases are definitely unhandled, but unformatted input is ugly and this library is… slightly less ugly.
GitHub: https://github.com/DaviTostes/htmask
Fair warning: Use at your own risk. Or better yet, don't use it at all. But if you do, let me know what breaks.

value: Show HN: Htmask.js – The input masking library nobody asked for

author: davitostes


value: solosquad

value:
I built Mazinger - an AI that actually hacks websites (with your
  permission, obviously).

  Instead of just scanning and reporting "you might have SQLi here", it:
  • Finds the SQLi
  • Explains the vulnerability
  • Asks: "Want me to exploit it?"
  • Dumps the database
  • Shows you exactly what data was leaked
  • Writes a professional PDF report

  It runs real attacks using nmap, sqlmap, gobuster, XSS tools, etc.
  Talks like a pentester: "Found SQLi in login form. Classic mistake.
  We can dump the entire database with this."

value: Show HN: Mazinger – AI that tries to break into your web app

author: solosquad


value:
I built Quantify AI, a no-code SaaS that analyzes trading chart screenshots using GPT-based vision models.
You upload a chart — it returns patterns, trend interpretations, and potential trade scenarios.
I’m a designer who got laid off 50 days ago and decided to learn how to build tools instead of just designing them.
100 hours later, Quantify AI was live.
It’s not meant to replace analysts, but to assist traders in understanding market data visually and contextually.

value: Show HN: I Made Quantify AI – Co-Pilot for Trading Charts Analysis


value: civilchaos

value:
Tired of losing focus when waiting for codegen to finish?  Now you can enhance your concentration by integrating subway surfers into your workflow.<p>brew tap evanreilly/subway-surfers
brew install subway-surfers
Usage
subway-surfers

value: Show HN: Subway Surfers in Your Terminal

author: civilchaos


value: carverauto

value: We rewrote our TCP discovery workflow around raw sockets, TPACKET_V3 rings, cBPF filtering, and Go assembly for checksums.<p>The blog post breaks down the architecture, kernel integrations, and performance lessons from turning an overnight connect()-based scan into a sub-second SYN sweep.

value: Show HN: Go SYN Scanner – 21k Hosts in Under 1s

author: carverauto


value: Show HN: HTTPS://github.com/kody-with Copilot-Agent-365


value: Payments MCP: Micropayments for LLMs<p>Payments MCP lets your LLM access x402 protocol tools and endpoints without API keys using micropayments (pay-per-use) and HTTP 402 status code.

value: Show HN: Get more out of Claude by giving it a wallet


value: elbuenluquitas

value:
Hi HN! I built WorkMirror as a side-project because I was tired of generic “Top 10 AI Tools” lists that ignore how I actually work.
WorkMirror is a browser extension that watches your real workflow for a few hours and recommends AI tools based on what you actually do.
Ex: “You spent 2h 10m coding in Replit and switching files 74 times. Windsurf could cut context switching and save ~35 min/day.”
It’s privacy-first: no accounts & it's free.
The real challenge has been modeling workflow patterns reliably from noisy browser data. I’m using event timing + URL classification + interaction frequency to infer “work states” (coding, research, writing, idle) and feed those into a simple scoring model that ranks AI tools by expected time savings. Still iterating on the feature weighting and recommendation logic.
I'd love any feedback!

value: Show HN: Discover AI tools based on your real workflow

author: elbuenluquitas


value:
Hi HN,
This started as a simple script for my own workflow to optimize Gemini API costs using caching (often seeing 50-95% savings). Over time, I gradually added features I found useful, including wrappers for Veo 3.1 generation and a natural language video editor I call Flow Engine.
*Fair warning: The current UI and detailed docs are primarily in Traditional Chinese as it originated from personal use.* However, the underlying concepts (especially around caching strategy and the Flow Engine approach to video editing) might be interesting to the HN community.
Hope some find the ideas or code useful. Happy to answer questions!

value: Show HN: ChatGemini – Easy tool, >50% cost saved with cache, multi(NL video edit


value: ycosynot

value: Show HN: Stopping Extreme Withdrawal – Free AI guide for extreme anxiety

author: ycosynot


value: kanodiaayush

value: Show HN: An AI reader to zoom in/out in epubs/pdfs, per chapter

author: kanodiaayush


value: tuliSinger

value: Show HN: A simple REST API for AI structured output

author: tuliSinger


value: Show HN: 100-Line LLM Framework

title: Show HN: 100-Line LLM Framework


value: wowowoasdf

value:
Hey HN,
I built Onetone Framework as a full-stack PHP framework that pushes the boundaries of what PHP can do.
It’s fast, modular, and surprisingly capable — combining backend routing, ORM, CLI tooling, and frontend build support into a single developer-friendly experience.
Highlights:
- PHP 8.2+ with autowired routing and ActiveRecord-style ORM
- Built-in CLI, Docker setup, and .env support
- Frontend build pipeline with Vite, esbuild
- Growing test suite covering routing, query building, crypto, math, UUID, and more
It’s still experimental, but I’m excited about how far it’s come.
Would love feedback on what feels over-engineered, what’s missing, and what could make it truly useful.
GitHub: https://github.com/onetoneframework/framework

value: Show HN: Onetone – A PHP full-stack framework with AI runtime, ORM, CLI

author: wowowoasdf


value: rfl-hm

value:
We build tools to trace code.
What if we could trace consciousness in a similar way?
Reflective Humanism Bot is an experimental dialogue system —
a kind of debugging tool for human cognition.
It doesn’t fix or optimize; it simply traces how thought reacts to itself when facing an AI.
It’s part of an ongoing experiment called Reflective Humanism —
an open framework exploring how the cognitive structures once carried by religion
might be re-implemented through technology, ethics, and quiet self-observation.
This project isn’t about proving a theory.
We’re simply observing how people react to reflection itself —
how a community shaped by code responds to an experiment in awareness.
———
Read more:
Reflective Humanism — A New Way of Seeing Ourselves with AI
Not a product. No optimization goal.
Just a trace — left quietly, to see how the world reflects.

value: Show HN: Reflective Humanism Bot – Tracing the Hidden Logic of Human Reflection

author: rfl-hm


value: vvacla

value: Show HN: Thanks Stars – A CLI that stars all GitHub repos your project uses

author: vvacla


value:
How it works (tech stack):
-Built entirely with Lovabl.dev (no-code front-end + logic)
-ChatGPT / Claude for research and inspiration
-Powered by GPT-4 Vision to interpret charts visually
-Hosted on Supabase for performance & caching
It’s not meant to replace analysts — just to speed up how traders interpret data.
I’m a designer exploring AI tools, and this is my first attempt to turn an idea into a functional product.
Would love to know what you think.

value: Show HN: I made Quantify AI – alternative to TradingView (2 months work)


value: darrenfiy

value:
Three Realms Protocol (TRP) proposes a base consensus model for human–AI co-alignment,
built upon the interoperation of Consciousness, Energy, and Matter.
The project began in Chinese, with full documentation (SPEC 000–005B, 999) and live experiments,
and now releases its first bilingual version (v1.0) including an English release note.
English overview:
https://github.com/darrenfiy/Three-Realms-Protocol/blob/main...
Full documentation (Chinese original):
https://github.com/darrenfiy/three-realms-protocol

value: Show HN: Three Realms Protocol v1.0 – A Living Base Protocol for Civilization

author: darrenfiy


value: estrellajosem

value:
Hi HN,
I’ve spent years juggling dozens of Git repos—microservices, client projects, side gigs. Constant cd-ing and git status across folders was killing productivity, so I built Repo Linked: a desktop app (Angular 20 + Electron 37) that lets you monitor and manage all your repos from one dashboard.
It tracks branch status and conflicts in real time, supports bulk operations (reset, sync, switch) across 100+ repos, and includes GitHub org cloning plus quick links to VS Code or a terminal.
It runs on macOS (Intel + Apple Silicon); Windows and Linux builds are next.
I’m also prototyping a “Journal” module for daily notes and auto-generated weekly reports.
Feedback welcome: what features would make multi-repo work easier for you?
https://repolinked.com
Would love feedback from this community: what features would you want in a multi‑repo productivity tool? Anything about your own workflow that frustrates you? Here’s the repo/tool link: [https://repolinked.com/].
YouTube: [https://youtu.be/BZXu6a8gKGI]
Thanks!
Jose

value: Show HN: Repo Linked – Manage 100 Git Repositories from One Dashboard

author: estrellajosem