value: thomasahle

value:
this is an alternative to langfuse and mlflow for visualizing your agent traces. It has no sign up, no hosting and all data stays on your laptop.
I was working with benchmarking agents using terminal-bench, and I often found myself with a bunch of messages json that I needed to read. Combined with MCP calls and base64 images, these things can get quite hairy.
So I build a visualizer, inspired by ampcode, that neatly parses eveything and displays it in your browser for quick debugging. It's open source and serverless, so you can run it as you please.
I tested mostly with my own logs from ~/.claude/projects/, so it's possible you'll be able to break it. Let me know if you do!

value: Show HN: Trace.taxi – easy agent messages visualization

author: thomasahle


value: dimelotony

value: Show HN: V0 for Svelte (svelte0), a Svelte UI generator

author: dimelotony


value:
We’re Heather and Matt, and we want to invite you to Prom. Prom is a simple place to share the prompts you’re proud of and discover ones other people actually use.
The idea came out of a lightweight internal library we hacked together for our team earlier this year. Before that, we had “good prompts” on Slack or Google Docs. We wanted a dedicated place for prompts, and somewhere people can post what’s working for them, compare approaches and models, and see what “good” looks like in the wild.
The tech is, well, nothing fancy, but intentionally lightweight. Flask backend, Bootstrap frontend. You can keep prompts private or make them public, and open them directly in Cursor with a deeplink if that’s your workflow.
Right now it’s early and pretty minimal. We’re thinking about adding things like version history, a prompt improver, and maybe an MCP server, but the part we care most about right now is the community. We think it would be cool to see people sharing weird experiments and genuinely useful prompts.
If you try it, we’d love to know what you think, what kind of prompts you might build, and hear what would make this a better place to post and browse prompts. Enjoy Prom!

value: Show HN: Prom.dev – Prompts that simulate Hacker News (and a tool to share them)


value: eldarsyzdykov

value:
Hi HN!
I built Audarma, an open-source React library that adds LLM-powered translation to any app with just a few lines of code.

  Live demo: https://audarma.pages.dev (bonus: Latin, Klingon, Quenya, and Dothraki)

  Key features:
  • Smart caching - translates once, stores in localStorage/DB, instant on revisit
  • View-level batching - translates entire pages in one API call
  • Content-aware - only re-translates when English source changes
  • Adapter pattern - works with any LLM (OpenAI, Anthropic, Cerebras) and any database

  The demo uses Cerebras (free tier) to translate live Hacker News headlines into 10 languages including fantasy languages.

  Tech stack: React hooks, Next.js App Router, Cloudflare Pages + KV

  This is v0.1.0 (alpha) - would love feedback on the API design and use cases you'd want to support.

  GitHub: https://github.com/audarma/audarma
  npm: npm install audarma

value: Show HN: Audarma – LLM-powered translation for React with smart caching

author: eldarsyzdykov


value: ahaucnx

value:
I’m the CEO of AirGradient, where we build open-source air-quality monitors. Two months ago I decided to build our first native iOS app myself. I’ve been coding on the side for ~15 years, but had never touched Swift or SwiftUI. Still, I went from empty repo to App Store approval in exactly 60 days, working on it only on the side.
The app itself is a global PM2.5 map with detail views, charts, and integration with our open-source sensors -straightforward, but fully native with Swift and now live on both iOS and Android (native Kotlin version).
The interesting part for me was actually not so much the result, but on the process that I settled on. Agentic coding let me work in parallel with the AI: while it generated code, I could switch to CEO work - replying to emails, commenting on tickets, working on proposals, and thinking through strategic planning. The context switching wasn’t always easy, but having the coding agent on one virtual desktop and company work on another made the rhythm surprisingly smooth. It felt less like traditional "coding time" and more like supervising a very fast (junior) developer who never pauses. At times I felt super human when the AI got a complex feature implemented correctly in the first shot (and obviously there were a few times when it was extremely frustrating).
What helped tremendously was that I asked the AI to draft a full spec based on our existing web app, fed it screenshots and Figma mocks. Sometimes these specs were a few pages long for a simple feature including API, data models, localisations, UI mockups, and error handling. It produced consistent SwiftUI code far faster than any normal design-to-dev cycle. I still had to debug, make architectural decisions, and understand the tricky parts, but the heavy lifting moved to the tools.
This experience changed my view on a long-standing question: Should CEOs code? The historical answer was usually "not really." But with agentic coding, I believe the calculus shifts. Understanding what AI can and can’t do, how engineering workflows will change, and how non-engineers can now contribute directly is becoming strategically important. You only get that understanding by building something end-to-end, and I believe it's important that CEOs experience this themselves (the positives & the frustrations).
The bigger shift for me was realizing how this changes the entire software workflow. Designers can hand over mocks that agents turn directly into working components. PMs can produce detailed specs that generate real code instead of just guiding it. Even non-engineering teams can create small internal tools without blocking developers. Engineers don’t disappear—they move upward into architecture, debugging, constraints, and system-level reasoning. But for leadership to make good decisions about this future, it’s not enough to read about it. You have to feel the edges yourself: where the agents excel, where they fall apart, and what parts still demand deep human judgment.
So yes, I now think CEOs should code. Not permanently - only a few hours a week. Not to ship production code forever, but to understand the new reality their teams will be working in, and how to support them in this new work environment.
I’m sharing this partly to hear how others on HN approach the question of whether CEOs or technical leaders should still code. Has getting hands-on with AI tools changed your perspective on leadership, team structure, or strategy?
Happy to answer questions and compare notes.
Here are the apps:
Apple App Store: https://apps.apple.com/us/app/airgradient-map/id6752292182
Android Play Store: https://play.google.com/store/apps/details?id=com.agmap.andr...
(Keep in mind this is version 1, so lots of improvements will come in the coming weeks and months)

value: Show HN: I'm a CEO Coding with AI – Here's the Air Quality iOS App I Built

author: ahaucnx


value: markmagarity

value:
Hey HN,
I'm Mark, a bootstrapped and solo founder who spent the last few years building custom mobile apps for influencers and creators in the health, finance, and wedding planning niches. The pattern I kept seeing: creators with distribution (viral content, audiences) but were under monetizing them.
So I built Mozy.ai - a tool that takes you from prompt to deployed mobile application in minutes. It's a complete browser-based IDE (goodbye XCode) for React Native/Expo apps. Think of it like Bolt.new but specifically for mobile. You can go from idea to a deployable app without leaving your browser.
What makes it different:
- User auth, database, payments, and deployment credentials all integrated
- Live preview as you build
- Production-ready code you can export, own, and deploy
- Built for founders who want to move fast on mobile
I'm not quite ready to share this with friends and family yet (still feels embarrassing), but I'd love feedback from the HN community (i.e. anons). If you're interested in testing it out, there's a promo code "FRIENDS20" you can use or not - would appreciate any and all support.
This is my first launch and I'm super excited about it!! Let me know what you think :))
Godspeed,
Mark

value: Show HN: One prompt mobile apps deployed to app store

author: markmagarity


value: BiblioKit

value: Show HN: Find detached, reset, overridden components and more

author: BiblioKit


value: saribmah

value:
Mainly started working on AI SDK Rust as I was frustrated with the bootstrapping required to just create a small agent for personal use-cases. Feel like it's almost getting there with future support for other storage providers. Currently filesystem storage seems like enough for my personal use
Github Link: https://github.com/saribmah/ai-sdk

value: Show HN: AI SDK Rust – With Storage

author: saribmah

title: Show HN: AI SDK Rust – With Storage


value:
Hi HN! I'm excited to share Treasury (https://treasury.sh), a personal finance app I've been building. We just opened up our public beta and would love your feedback.
Currently, Treasury has a set of core building blocks that let you create financial setups as simple or as complex as you want. You can track your net worth, analyze spending, spot recurring transactions, and build budgets that actually match how you think about money.
The app is live at https://treasury.sh. Sign up and let me know what you think.

value: Show HN: Treasury – The personal finance app built for you (public beta)


value: Show HN: Questmate (SafetyCulture Alternative)


value: rishabhpoddar

value: Show HN: A developer mental health tool that encourages daily coding with breaks

author: rishabhpoddar


value: ivanramos

value:
Procrastination isn't a motivation problem. It's a clarity problem.
How it works:
1. Describe what you're avoiding
2. State why you're procrastinating
3. Get 3-4 specific steps with clear outputs
4. Execute immediately
Unlike productivity apps that rely on motivation, Beatdelay focuses on immediate execution and measurable progress.
Try it: beatdelay.co

value: Show HN: Beatdelay.co – Stop procrastinating on urgent tasks

author: ivanramos


value: hunglv

value:
Hi HN,
I built a small tool to solve a recurring pain when editing podcasts: scrubbing back and forth just to find where a sentence or idea actually ends.
How it works:
- Upload an audio file (MP3/WAV/M4A)
- AI transcribes the audio and suggests cut points at sentence or paragraph boundaries
- Automatically split and export segments, or adjust them manually if needed
Website: https://transcribeandsplit.com/
This came out of my own frustration with editing long recordings and manually hunting for the right cut points. I wanted something that actually understands the content before splitting it.
I’d love feedback — especially on edge cases like interviews, lectures, or multi-speaker audio. What features would make this more useful?

value: Show HN: TranscribeAndSplit – AI that transcribes audio and splits it by meaning

author: hunglv


value:
Hi HN community,
I'm the maker behind Gumpbox, a native macOS app that connects AI agents to your Linux servers.
Stop typing commands. Just tell Claude or ChatGPT what to do.
Deploy apps, restart services, check server health - all through conversation. AI agents execute everything through Gumpbox's built-in MCP (Model Context Protocol) server running locally on your Mac.
AI-Powered Server Management
- Built-in MCP server connects Claude, ChatGPT, or any AI agent to your infrastructure
- Chat to deploy: "Deploy the latest build to production"
- Chat to debug: "Why is nginx using so much memory?"
- Chat to manage: "Restart the API service and check the logs"
- Privacy-first: MCP runs locally on your Mac, credentials never leave your machine
- Sandboxed execution with systemd-run for secure AI operations
If you're interested in the product, and want to try it out, please ping me at twitter https://x.com/gumpbox_app for a FREE code to download.
The FREE codes giveaway will end on Dec 1, 2025.
Thanks folks.

value: Show HN: Gumpbox – Let your AI agents work on your remote deployments securely


value: yourladybug

value:
As a long time Japanese learner, I've always wanted there to be a simple, free online trainer for learning and grinding Japanese kanji and vocabulary - like Monkeytype in the typing community (fun fact: we actually have 2 Monkeytype devs on our team now!).
I'm doing this because I grew tired of all the subscriptions and paywalls, which are unfortunately very common in the Japanese learning apps market. That's why I want to make the most fun, user-friendly and customizable platform for learning Japanese currently available, and the first one that is (and will be, forever) completely free, ad-free and open-source - driven not by profit, but made by the community, for the community.
We already managed to get around 600 stars on GitHub and about 7k monthly users for the app. That being said, I recognize that the app is still in its very early alpha stages and is, objectively speaking, behind paid Japanese learning solutions (at least for now) - which is why I want to change that and make it the first entirely free and open-source platform for learning Japanese to provide a real, high-quality alternative to all the current paid Japanese learning resources, which I believe we can achieve with the open-source community's help. Why am I doing this? Because weebs deserve to have a free, open-source learning platform too (I'm one of them, don't judge...)
どうもありがとうございます!

value: Show HN: Fun, Open-source Japanese learning Platform inspired by Monkeytype

author: yourladybug


value: mardel

value:
We opened an early version of our Conversational BI Agent to the public with a free tier. Data teams spend enormous time preparing data for stakeholders, yet business users only see static reports or dashboards - and often end up with even more questions. Our agent lets them explore and talk to that data directly, uncover insights, and iterate before going back to analysts. It’s also great for teams that don’t yet have a data team but want to get value from their growing data right away.
You can try it using sample dataset (Jaffle Shop) before connecting your own.

value: Show HN: Conversational BI Agent for Enterprise Analytics

author: mardel


value:
Hi HN,
I built SpeedyShare because cross-platform file sharing is still unnecessarily complicated, whether it's Android to Mac, iPhone to Windows, or any other combination. Most solutions have limitations - AirDrop only works within Apple's ecosystem, WeTransfer requires accounts for larger files, and many tools need app installations on both ends. So I made SpeedyShare to try and make it simple:
- Upload at SpeedyShare.app
 - Share via QR code or 6-digit code
 - Works on any device with a browser (desktop or mobile)
 - No accounts, no tracking (anonymous analytics only)
 - Files auto-delete after 30 minutes
I'm currently getting ~100 weekly users but growth has plateaued. I'd love honest feedback:
 - Does this solve a real problem for you, or is the friction with existing tools not painful enough?
 - What would make you choose this over AirDrop, Pairdrop, or email?
- Any UX or privacy concerns I'm missing?
Happy to discuss the technical implementation or answer questions.

value: Show HN: SpeedyShare – Cross-platform file sharing with no accounts or tracking


value: sjdonado

value:
Martillo is pure Lua, fully hackable. Build workflows, automate tasks, and use a command palette with fuzzy search. Drop any Lua file into `store/` and it loads automatically with zero configuration.
Configure it all in one file. Add keybindings, define aliases, and compose actions with helpers. The setup is declarative and easy to share, powered by Hammerspoon’s simple access to deep macOS system APIs.
Ships with 60+ actions (clipboard history with images/files, process killer, 25 window positions, smart converters, network utilities, screen ruler, safari tabs, calendar menu bar, browser redirect).
MIT licensed. No dependencies, no build step.

value: Show HN: Martillo – an open source Raycast alternative (Hammerspoon, lazy.nvim)

author: sjdonado


value: glsorre

value: Show HN: Crane – a native macOS app to manage Apple containers

author: glsorre


value: vasanthps

value: Launched "rebirth - affirmation & growth" on app store. Works on Iphone, Ipad and Mac. I love affirmations as they are good reminders and adds positive thoughts throughout the day. Collected and composed better quality affirmations than the competitors and built a beautiful distraction free app on top of it.

value: Show HN: Affirmations on Your Home Screen

author: vasanthps


value: gtlsgamr

value:
I built a small Mac app because managing SES email templates via API requests was slow and clunky. Switching between JSON, HTML, the console, and CLI scripts got messy fast.
SES Template Manager is a native macOS app that lets you create, edit, test, and manage AWS SES templates locally. It uses your existing AWS credentials. Nothing is sent to my servers because I don't have any. Everything is local-first.
What it does:
Edit SES templates in a native text editor
Sync with your AWS account using your local credentials
Create new SES templates without touching the console
Test and send emails directly
No signup, no cloud backend, all local
I wanted a tool that feels like a normal Mac app, not a web panel. So I made one.

value: Show HN: SES Template Manager – Local, Native AWS SES Template Editor for Mac

author: gtlsgamr


value: fragkakis

value: Let's be honest, matcha looks like a swamp. Can you tell them apart?

value: Show HN: Matcha or Swamp?

author: fragkakis

title: Show HN: Matcha or Swamp?


value: Show HN: Spatial CAPTCHA – 3D spatial reasoning test against AI bots


value: baursha

value: Show HN: Built my tool for learn English

author: baursha


value: GlebShalimov

value:
Discover Pegma, the free and open-source version of the classic Peg solitaire game! Pegma offers a clean, minimal design and smooth gameplay across multiple platforms.
Key features:
Fully open-source code available on GitHub, inviting community contributions and transparency
Custom-designed font created by the developer to enhance the game’s unique style
Cross-platform support: play on iOS, Android
Lightweight, intuitive interface that stays true to the timeless puzzle mechanics
Try it now:
Website: https://pegma.vercel.app
GitHub: https://github.com/khlebobul/pegma
App Store: https://apps.apple.com/ru/app/pegma-peg-solitaire/id67543438...
Google Play: https://play.google.com/store/apps/details?id=com.khlebobul....
If you appreciate open-source projects and classic brain teasers, Pegma is definitely worth checking out!

value: Show HN: Pegma, an open-source version of the classic Peg solitaire

author: GlebShalimov


value: blurayfin

value:
Hi HN,
I've been working on a tool to make it easier to see the code quality of WordPress plugins at a glance, and to drill into the issues behind that score.
PluginScore lets you enter any plugin slug from WordPress.org and get a detailed code-quality report: security, performance, plugin repo policy, and general coding standards.
There is no signup or login; it's just a web app that runs scans and publishes the results.
Try it out:
- Go to https://www.pluginscore.com
- Use the “Analyze a plugin” box on the homepage.
- Type a slug like akismet, woocommerce, or your own plugin's slug.
- You'll either land on an existing report or enqueue a new scan.
Each plugin page shows:
- An overall 0–100 score, plus error/warning counts.
- A breakdown by category (security, performance, repo policy, general, accessibility).
- A list of individual findings with codes, severities, and links to rule docs.
- Scan history and score deltas over time.
- Leaderboards and “top movers” so you can see which plugins are improving or regressing.
Under the hood, it uses the official Plugin Check (via wp plugin check) and PHP_CodeSniffer with the WordPress standards, plus some extra checks for plugin repo requirements and performance.
The score is a heuristic signal, not a security guarantee.
I'd love hear your feedback.
Thank you

value: Show HN: PluginScore – Code quality reports for WordPress plugins

author: blurayfin


value: GrammarChecker

value: Show HN: Grammar Checker – A simple tool for fixing writing mistakes

author: GrammarChecker


value: mmntns

value:
Hi HN!
Staying up-to-date on the recent development in AI shouldn't be a full-time job. We're dedicated to gather high-quality content on AI and deliver it in a digestible format.
That led us to build curAIted.dev. It's a blog-style news aggregator where the summaries are collected by AI but the sources are curated by us. We're focusing less on products and more on their fundamentals - thus targeting engineers rather than users.
It's built with Astro and fed by a pipeline in Python using LLMs for crawling and summarizing content. For now we're only paying for the domain - everything else is free tiers (nice to see how far one gets with this nowadays).
There's already a few ideas how to expand it in the future, but we'd love to hear what you think!
https://curaited.dev

value: Show HN: CurAIted.dev, keeps you informed on development in AI

author: mmntns


value: atharvtathe

value: Show HN: I made a simple time card calculator

author: atharvtathe


value:
This is a simple, header-only CLI parser for C, implemented using X-Macros.
What it does: Parses flags (-h), and options with int/float/string arguments (--port 8080).
What it doesn't (yet): Handle subcommands, repeated parsing, or other advanced features.
It's built for simplicity and ease of integration in small to medium C projects. Feedback is welcome!

value: Show HN: I built a minimal, header-only CLI parser for C with X-Macros


value: LoMoGan

value: ChatIndex is a context management system that enables LLMs to efficiently navigate and utilize long conversation histories through hierarchical tree-based indexing.

value: Show HN: ChatIndex – an open-sourced long-context managment system

author: LoMoGan


value:
More users are now getting information through AI assistants (ChatGPT, Gemini, Copilot) instead of traditional browsing. Similar to how SEO defined rules for search engines, this project proposes a set of open, practical guidelines for building websites that AI systems can actually understand, index, and cite —.
It’s not a framework — just a minimal, evolving set of best practices aimed at making the web machine-readable again.
Repo (docs): https://github.com/ai-first-guides/first.ai/blob/main/docs/i...

value: Show HN: AI-First Web BP – SEO but for AI Assistants for AI Assistants


value: zeronex

value:
I’ve been working on this project mostly out of curiosity and because I like cleaning messy data.
I downloaded the full French Wikipedia dump (the raw XML + wikitext one) and built a script that extracts every article and turns it into a clean JSON file.
Nothing fancy — just a pipeline I wrote myself that:
reads the XML stream
pulls out each page
removes wikitext and leftover markup
rebuilds the sections
parses the infobox into a real JSON object
extracts categories, links, etc.
and then saves everything as one JSON file per article
The result is around 2.7 million JSON files, each representing a single Wikipedia article in a format that’s directly usable for NLP or LLM experiments.
This wasn’t meant to compete with existing datasets — I just wanted to understand how to process the dump properly and build something clean from scratch. Since it turned out well, I’m sharing it in case it helps anyone.
I’m also running the same process on the full English dump (around 6.2M pages). Still in progress.

value: Show HN: Cleaned 2.7M French Wikipedia JSON articles (full dataset)"

author: zeronex


value: orlevii

value:
While building with AI agents, I kept running into the same problem: testing even tiny changes to a prompt or tool required either running my entire app or writing throwaway scripts. Both were slow and constantly broke my flow.
I’ve always loved how Storybook lets you work on UI components in isolation, so I tried applying the same idea to agents.
Agent Playbook is an open-source, Storybook-like playground for developing and debugging AI agents.
It automatically discovers your agents, spins up a local web UI, and gives you an interactive environment to test them. You can chat with an agent, inspect its reasoning steps, and iterate quickly with hot-reloading.
GitHub: https://github.com/orlevii/agent-playbook
Right now it supports agents built with pydantic-ai, which I’ve been using heavily and found easy to work with. I’m planning to expand support based on what people find useful, so feedback is especially welcome.
This is the first public release, and I'd love feedback on the core idea, the developer experience, and any feature ideas that would make it more useful. Happy to answer questions!
Thanks for taking a look.

value: Show HN: Agent Playbook – An open-source Storybook-like playground for AI agents

author: orlevii


value: bruinmeister

value: Show HN: DNA-Inspired Semantic Compression for AI Context Window Limitations

author: bruinmeister


value: Show HN: Encore – Type-safe back end framework that generates infra from code


value: kingofspain

value:
I was bored and cold so I made a simple fireplace app to keep your icons and wrist warm this coming winter. Relax in front of a comforting pixelated fire! It's for iOS.
https://apps.apple.com/us/app/fireplace-for-your-phone/id675...
Features:
- Widget in varying sizes to enjoy a warming fireplace on your phone
- Companion watch app to enjoy a mini fireplace wherever you go
- Various customisation options for flames and colours (small unlock IAP)
There are no subscriptions, just like a real fireplace (I assume, who know these days)
https://fireplacephone.com/

value: Show HN: I made a fireplace for your wrist (and widgets)

author: kingofspain


value: Merinov

value:
I built a multilingual news aggregator for European tech in 6 languages
(EN, DE, FR, ES, IT, NL), filterable by audience (consumers, businesses, government) to help users discover relevant European alternatives.
A few interesting technical challenges:
PATTERN-BASED IMAGE GENERATION
Generic AI images all look the same ("holographic businessman with laptop").
I solved this with a JSON database of 60+ concrete visual patterns—context-aware selection prevents repetition. Funding news → coins/contracts, security → locks/shields. Much more visual diversity. Still needs tweaking though.
GRADUAL SITEMAP GROWTH FOR NEW SITES
Daily news pipeline generates lots of content. Challenge: Google indexes new sites slowly—exposing all 2K+ URLs at once resulted in 1-3% indexing rate. Solution: Dynamic rolling window in sitemap—only expose top 50 most recent articles (300 URLs), gradually increasing to 100 → 150 → 200 over 6 months as site builds authority. Result: Indexing rate improved to 10%.
AUTOMATED TRANSLATION PIPELINE
RSS aggregation → AI summaries and translation with context-aware prompts → human review queue.
Still tweaking quality per language pair.
STACK: Next.js 15 with Turbopack, PostgreSQL, deployed via Sliplane@Hetzner which works pretty ok.
STATUS: 1+ month post launch, 80 pages indexed, 100+ European apps featured (Mollie, Wire, Ecosia, LanguageTool, etc.)
FEEDBACK WELCOME ON:
• European news sources I'm missing?
• Better approaches to multilingual SEO?
• Translation quality red flags?
• How to optimize for LLM discovery (ChatGPT, Claude, etc.)?

value: Show HN: European tech news in 6 languages

author: Merinov


value: Show HN: StoryMotion – animated diagrams, step-by-step explainer animation maker


value: valliveeti

value:
Hey,
We built the most efficient browser agent on the market and use it to create an API for any website to automate any action.
The browser agent is running on top of a combo of gemini 2.5 pro and flash, and uses minimal required context reducing the used tokens by a big factor.
We were inspired by the current inefficient browser agents. Currently running any browseragent is very expensive per task. This restricts the tasks that can be automated with them. We knew there had to be a better way, and found a better way with comparable accuracy.
While we are getting ready to open source the browseragent use it now to start automating by generating an api for any task on any website through our chat application.
Excited to see how you will use it!

value: Show HN: Lindra, turn any website into an API

author: valliveeti


value: Show HN: CBK Agent SDK

title: Show HN: CBK Agent SDK


value: howardV

value:
Hi HN! We built a subtitle generator that solves some real pain points we encountered while working with video content.
*The problem:* Most subtitle tools give you "Speaker 1, Speaker 2, Speaker 3" labels, require manual cleanup of filler words, and have no way to handle sensitive content that shouldn't be exported.
*What we built:*
- *AI Speaker Detection*: Automatically suggests real names from self-introductions ("Hi, I'm John" → suggests "John"). Works for both English and Chinese.
- *Bilingual Filler Removal*: One-click removal of 35+ filler words (um, uh, like, 嗯, 这个, 那个). Most tools only support English – we handle both English and Chinese.
- *Off-Record Protection*: Mark sensitive segments once, they're automatically excluded from all exports. Essential for journalists and legal professionals.
*Tech stack:* Next.js, TypeScript, Whisper AI for transcription, custom NLP for speaker detection and filler word identification.
*Free tier:* 30 minutes/month, no signup required, no watermarks. Export clean SRT/VTT files immediately.
Try it at: https://harku.com/tools/subtitle-generator
We're especially proud of the Chinese language support – it's surprisingly hard to find good subtitle tools that handle Chinese filler words and name detection properly.
Would love to hear your feedback, especially if you work with multilingual content or handle sensitive information that needs redaction.

value: Show HN: We built a subtitle generator that auto-detects real speaker names

author: howardV


value:
Lately I've been thinking about the mix CDs I used to burn for friends. Building the perfect mix for someone took a lot of time and intention, but it was a great way to expose friends to the rare musical gems I'd discovered, and sometimes, they even returned the favor.
In the transition from physical mixtapes to cloud-hosted playlists, we stopped giving each other digital things. These days, we mostly point to things that we don't control.
But our custom of gift-giving can be restored, if we restore the structures that enabled it.
[name redacted] is my answer to this problem. Drop some .mp3s into a folder, run some python scripts, and your playlist gets packaged as a Progressive Web App. Upload the resultant "mixapp" to any HTTPS-enabled host, and your friends can install it to their home screens with just a few taps.
After the initial download and cache, mixapps work completely offline on any device (iOS, Android, desktop). No subscriptions, platform dependencies, or "this song is no longer available in your region." The files are in the computer!
It's a return to the practice of giving digital gifts, rather than pointers to corporate infrastructure.
Source code: [link redacted]
Live demo: [link redacted]

value: Show HN: Turn your music into shareable apps that work offline


value: aladybug

value:
As a long time Japanese learner, I've always wanted there to be a simple, free online trainer for learning and grinding Japanese kanji and vocabulary - like Monkeytype in the typing community (fun fact: we actually have 2 Monkeytype devs on our team now!).
I'm doing this because I grew tired of all the subscriptions and paywalls, which are unfortunately very common in the Japanese learning apps market. That's why I want to make the most fun, user-friendly and customizable platform for learning Japanese currently available, and the first one that is (and will be, forever) completely free, ad-free and open-source - driven not by profit, but made by the community, for the community. We already managed to get around 600 stars on GitHub and about 7k monthly users for the app. That being said, I recognize that the app is still in its very early alpha stages and is, objectively speaking, behind paid Japanese learning solutions (at least for now) - which is why I want to change that and make it the first entirely free and open-source platform for learning Japanese to provide a real, high-quality alternative to all the current paid Japanese learning resources, which I believe we can achieve with the open-source community's help. Why am I doing this? Because weebs deserve to have a free, open-source learning platform too (I'm one of them, don't judge...)
どうもありがとうございます!

value: Show HN: Fun, Open-source Japanese learning Platform inspired by Monkeytype

author: aladybug


value: morisil

value:
I just released version 1.0.0 of this library:
https://github.com/xemantic/xemantic-neo4j-kotlin-driver
There is also associated demo project showing how to use this driver with Ktor, in fullstack asynchronicity and structured concurrency of coroutines:
https://github.com/xemantic/xemantic-neo4j-demo
I use Neo4j a lot with my AI agents, letting them store private memory as a knowledge graph, but also research this graph in auto-scientific process. I've discovered that reducing cognitive load on an LLM is crucial for the quality of machine reasoning. And this is intention behind this library - no explicit "async", DSLs for idiomatic resource management, automatic mapping of Cypher input and output data to multiplatform data classes. All of this can be executed as a script, while being strongly typed and compiled giving additional feedback to the autonomous chain-of-code style agent. This allows agents to define ad hoc data ingestion and retrieval schemas, while avoiding double-task inference challenge of encoding intents while comprehending own intents.

value: Show HN: Neo4j SDK with minimal cognitive load for an LLM

author: morisil


value: We're creating an experience for podcast consumption that can recommend shows and episodes you actually like. We're currently on V1, where the goal is to just simply perfect the library features, listening features, the queue system, and recommendations. V2 will employ more nuanced automation to bolden the experience. Would love to hear feedback.

value: Show HN: Teller – Automation should go unnoticed


value: ivanramos

value:
Problem: You're procrastinating on something urgent. ChatGPT says "just start" or "take a break."
Realized what I actually needed was specific steps tailored to why I was procrastinating.
Solution: Beatdelay.co
Input: Task + reason you're avoiding it
Output: Specific steps you can execute immediately
Works for any task. Adapts to your procrastination type. No motivation speeches.
Try it: beatdelay.co

value: Show HN: I built Beatdelay.co because I kept procrastinating on urgent tasks

author: ivanramos


value: paulfresquet

value:
Hi everyone,
I've been developing ByteSync, an open-source file synchronization, backup and deduplication tool designed to bridge the gap between local and remote sync.
In spirit, it's somewhat closer to FreeFileSync, but with an integrated networking layer and end-to-end encryption — which means you can synchronize files between computers on the same LAN or across the internet without VPNs or firewall setup. Everything works transparently through the same interface.
The synchronization model is based on DataNodes (which represent repositories, such as servers or NAS devices) and DataSources (the folders or files inside them). A session can include multiple participants, each with one or several DataNodes, and ByteSync handles all comparisons and transfers automatically.
To optimize performance, the engine uses a two-stage inventory process: an initial indexation followed by comparisons limited to items that actually changed. This keeps synchronization fast even with large datasets. There's also a flat mode, useful when structure doesn't matter and you just want to compare or align files by name.
Currently, ByteSync is focused on interactive synchronization — it's not yet automated or daemon-based (CLI integration is planned). But it's already fully functional for discovering and managing differences between repositories, both local and remote.
ByteSync runs on Windows, macOS, and Linux, and the entire codebase is available on GitHub:
https://github.com/POW-Software/ByteSync
You can also download binaries and read the documentation here:
https://www.bytesyncapp.com
I'd really appreciate feedback and contributors — whether on usability, architecture, or ideas for future features.
The goal is to make a solid, privacy-respectful alternative for hybrid file synchronization that remains simple to use and open for everyone.

value: Show HN: ByteSync – Open-source hybrid file sync (LAN and remote, E2EE)

author: paulfresquet


value: sahli

value: Show HN: Generate Images in Claude.ai

author: sahli

title: Show HN: Generate Images in Claude.ai


value: olivefu

value:
Hi everyone,
I built ImgExtender because I kept running into the same daily frustrations when working with images — AI-generated pictures not fitting the correct aspect ratio, blurry screenshots, compressed images losing too much detail, background edits taking too long, and having to jump across 4–5 different tools for what should have been simple tasks.
So I decided to create a single lightweight web tool that solves the most common image problems without requiring Photoshop skills, plugins, or GPU setup.
 https://imgextender.com/
Below is a quick introduction to the problem, what ImgExtender does, and what’s behind it.
Why I Built It
Most people don’t need a full graphics suite. They just want to quickly:
Extend an image (AI outpainting) without weird borders
Upscale or unblur without making the picture look “AI smooth”
Remove background cleanly (not the soft-edge mess from traditional tools)
Fix awkward aspect ratios for social media
Compress large images without ruining quality
Do simple edits (crop/resize/filters/text) without learning complex software
But the existing tools are either:
too slow,
too complicated,
too expensive,
or too fragmented (one tool for background removal, another for upscaling, another for AI generation…).
I wanted one place that makes image tasks fast, predictable, and accessible.
What ImgExtender Does (Core Features)
All tools work in-browser, no signup required.
AI Image Tools
Extend Image (Outpainting) — expand canvas naturally using generative fill
AI Generate Image — text-to-image with clean, sharp output
Unblur Image — restore clarity using deep-learning deblurring
Upscale Image — 2×–8× enlargement with texture recovery
Editing Tools
Crop
Resize
Rotate & Flip
Add Text
Filters
Blur Background
Remove Background
Change Background
Utility Tools
Image Compressor
Image to PDF converter
It’s designed to be something even a non-technical person can use instantly.
Under the Hood (Technical Notes)
Outpainting uses a fine-tuned diffusion model that blends edge areas to avoid seam artifacts.
Unblur uses a multi-stage sharpness restoration pipeline (deconvolution + learned edge enhancement).
Upscaling uses a lightweight ESRGAN variant optimized for smoother detail recovery.
Background removal uses a segmentation model focusing heavily on hair & soft edges.
All processing is GPU-accelerated on the backend; front-end is minimal and optimized for speed.
I’m also working on client-side WASM acceleration for some tasks.
Who Uses It Today?
From early usage logs and feedback:
AI artists fixing midjourney/dalle artifacts
Designers preparing images for clients
Etsy sellers enhancing product photos
Developers who need quick assets without opening Photoshop
Students / teachers improving slides or PDFs
Casual users cleaning up old photos
Roadmap
Batch processing
API access
A unified “smart enhance” mode
More style-aware AI generation
Client-side processing to reduce server load
If you use AI image tools often, I’d love to hear what your workflow looks like.
Feedback Wanted
This is still early-stage.
I would really appreciate feedback from the HN community on:
What feels slow or confusing
Which features matter the most
What is missing for real professional use
Whether you'd want self-hosting or local models
Your typical image-edit workflow
Link: https://imgextender.com/
No login required.
Thanks for reading — and thanks in advance for any feedback!

value: Show HN: ImgExtender – A Simple, Fast Image Enhancement Toolkit for Everyday Use

author: olivefu


value: selmetwa

value:
One thing that always frustrated me as an Arabic learning was how difficult textbooks are to use. You’re constantly flipping to the end to check translations or transliterations, which completely kills your focus.
So I started building an open-source platform that fixes this with interactive parallel texts, you can click or drag over any Arabic word to instantly see its meaning, transliteration, and grammar notes in context.
The platform also includes AI-generated stories and sentences, letting learners practice Arabic in natural, evolving contexts instead of static textbook examples. All content comes with accompanied dialect-specific audio.
Most resources focus only on Modern Standard Arabic, but that’s not what most people actually speak. There’s very little for dialects like Egyptian and Moroccan Arabic. To fill that gap, I leverages custom LLMs trained specifically on Egyptian and Moroccan dialects, since general-purpose models (like ChatGPT) often struggle with dialect-specific content.
If you’re interested in Arabic, dialects, or open-source edtech, I’d love your feedback

value: Show HN: I'm building an open source platform for studying Arabic

author: selmetwa


value: Hello HN,<p>I made a browser extension which shows me loc (for files) and child counts (for dirs) as I page through github. Please enjoy !

value: Show HN: GitHub Browser Extension with LOC/Child Counts


value: grigoras

value: Show HN: LiquiDB – Open-Source Multi-Database Manager for Developers

author: grigoras


value:
I've built an online simulator on the web. It allows you to write firmware and simulate it. avoiding the need of connecting wires or having hardware.
Currently, im supporting only stm32f4 and rust but working on adding more and more mcus.
give it a try, lmk what you think: https://simulator86.com

value: Show HN: Simulator86, prototype embedded system projects on the browser


value: Show HN: An O(1) Time Machine for Simulations (No History Buffer)


value: kinderpingui

value: Hi HN! I built Wasda AI - a visual tool for designing and testing transformer architectures with built-in emotion analysis.

value: Show HN: Interactive Transformer Architecture Designer with Emotion Analysis

author: kinderpingui


value:
We built an SLM assistant for automatic Python documentation - a Qwen3 0.6B parameter model that generates complete, properly formatted docstrings for your code in Google style. Run it locally, keeping your proprietary code secure!<p>```bash
python localdoc.py --file your_script.py
```

value: Show HN: Distil-localdoc.py – SLM assistant for writing Python documentation


value: robbomacrae

value:
Hey everyone, here is an open source project I've been working on to add voice input/output to terminal based coding agents.
One thing about the new terminal coding agents I really like is being able to multi-task but right now it's a bit like a Tesla on autopilot needing your hands still on the wheel. You need to be checking often if your input is required or if it's going off the rails. To be able to go fully hands free I wanted to add TTS and ASR. Then I added slack and WhatsApp hooks to TalkiTo as well.
It's fully open source with a BYOK philosophy and it's configured to work with any of the major ASR/TTS providers. It also supports local whisper and kokoro/kittentts if you want a decent free/private option.
It works by wrapping the coding agent and capturing the input/output. It does have an MCP server running but thats mainly for configuration - I found that using MCP to speak or listen was too slow. The upshot of the MCP server is you can type (or say) "talkito disable ASR" or "talkito change tts to kokoro".
Here is a demo video I made here: https://www.youtube.com/watch?v=pf8jFt0smqs
I like to think of it as similar to SuperWhisper but with TTS, the focus on coding agents and configurability.
Really curious to get feedback.
Thanks!

value: Show HN: TalkiTo – enabling voice and Slack for Claude Code and Codex CLI

author: robbomacrae


value: barlogg

value: Generate fake data in your java project updated for

value: Show HN: Java Fake Data Generator

author: barlogg

title: Show HN: Java Fake Data Generator


value:
We've spent the last year building RunOS, a platform that spins up production-ready Kubernetes clusters in 5-10 minutes with databases, message queues, observability, and AI tooling configured.
The Problem
Every team rebuilds the same Kubernetes infrastructure: networking, certificates, monitoring, databases, storage. The existing solutions either lock you into a vendor ecosystem or dump you into raw Kubernetes complexity. We wanted the control of self-hosting without weeks of setup.
Architecture
Our system uses two agent types:
Server agents run on VM hosts and communicate with our backend via gRPC bidirectional streams. When users request a cluster node, the agent provisions a KVM-based VM and bootstraps it.
Node agents run on each Kubernetes node and handle cluster operations, monitoring, and service installations.
Key insight: gRPC streams initiated by agents eliminate firewall configuration and public IP requirements. Agents reach out to our backend, not vice versa.
Why KVM?
- Battle-tested, works great with Ubuntu
- Solid Go bindings via libvirt
- Excellent GPU passthrough for AI workloads like Ollama
- Good isolation/performance balance
Sometimes boring technology is the right choice.
Provisioning Flow
1. User clicks "Create Cluster"
2. Backend selects available server agents
3. gRPC commands sent to provision VMs
4. KVM VMs spin up (Ubuntu Cloud 24.04, 30-60 seconds)
5. Node agents install and connect
6. Kubernetes bootstrap with kubeadm + Cilium
7. WireGuard mesh established between nodes
8. Storage configured (OpenEBS + Longhorn)
9. Cluster ready (5-10 minutes total)
The WireGuard Decision
We manage WireGuard at the OS level, not Kubernetes level. Why?
- Same VPN secures both K8s traffic and SSH access
- Nodes communicate securely even if Kubernetes fails
- Simpler troubleshooting with separated layers
- Easier multi-cluster peering (coming soon)
Our backend orchestrates WireGuard configs across nodes via the agents. Centrally coordinated, locally executed.
Version Management Hell
The hardest problem? Keeping 20+ services compatible across updates.
We offer one-click installation of: PostgreSQL, MySQL, ClickHouse, Kafka, RabbitMQ, MinIO, Longhorn, Harbor, Traefik, Grafana, Prometheus, Ollama, LiteLLM, Open WebUI, and more.
Each has opinions about K8s versions, storage, and networking. We use Helm charts, operators, and custom YAML as appropriate. The real work is maintaining compatibility matrices and testing every combination.
Deployment Models
RunOS Cloud: Managed dedicated servers with fixed 8 CPU/16GB instances (free trial credits available). KVM handles VM provisioning with GPU passthrough for AI workloads. Strict security since it's early access.
Bring Your Own Node: Run node agents on any hardware. Complete tenant isolation since you control infrastructure.
Coming soon: Self-managed VM hosts with custom sizing.
What's Next
Agent code will be open source. One company runs three production clusters already. Common feedback: "I can't believe how fast I went from zero to a working cluster with Postgres, Kafka, and monitoring."
We're planning weekly updates here on HackerNews about new features, technical challenges, and production lessons.
Try it at runos.com - free trial credits for 8 CPU threads and 16GB memory.
Questions? Happy to discuss architecture in the comments.

value: Show HN: We built instant Kubernetes provisioning with KVM and gRPC


value: AlexClickHouse

value:
Initially, I wanted to create a single report to motivate managers to look at GitHub issues. It didn't help (yet), but I got hooked and added more selectors and features to this report, making it a full-featured website, where you can compare different types of activity on different repositories, find the key team members and their contribution patterns, etc.
Source code: https://github.com/ClickHouse/velocity
(I recommend checking commit messages - I saved every single prompt as a commit)

value: Show HN: A website to compare team activity on GitHub

author: AlexClickHouse


value: kunobi

value:
We’ve been building a desktop app that shows Kubernetes resources together with Flux or Argo reconciliation state in one place. The goal is to reduce the amount of tool switching needed when checking what’s running, what’s in sync, or why something drifted.
The app runs locally and talks directly to the Kubernetes API server. It reads the information Flux or Argo publish and surfaces it alongside the workloads on each cluster. It’s focused on visibility: workloads with context, sync status, drift indicators, recent revisions, and Helm history.
Right now it supports browsing resources, viewing Flux or Argo state, inspecting diffs, and working across multiple clusters without juggling kubeconfigs. There’s also a small local environment included for testing GitOps flows if you don’t want to connect a production cluster.
It’s still in beta and a bit rough. We’re sharing it to get feedback from people who work with Flux, Argo, or multi-cluster setups and have opinions about where the current workflows feel heavier than they should.

value: Show HN: A desktop tool for Kubernetes and GitOps (Flux/Argo) in one view

author: kunobi


value: shhabmlkawy

value:
Hi everyone,
I’ve just finished writing a 1600-page data structures book that contains 130 fully original academic-style problems, each with complete solutions, proofs, amortized analysis, diagrams, and detailed explanations.
The book is designed for:
university-level advanced data structures learning
self-study for engineers
technical interview preparation (many problems are structured like interview challenges)
deep understanding of heaps, trees, union-find, RMQ, hashing, string structures, and more
I also included two new data structures that I designed myself — the Layered Summary Heap (LSH) and the Triple-L Heap — with step-by-step explanations and use cases.
I would appreciate feedback from the Hacker News community on:
clarity and structure
difficulty level
and whether such a long-form problem book is useful for interviews and self-study
Book link:
https://leanpub.com/masteringdatastructures
Happy to answer any questions or share sample chapters.

value: Show HN: A 1600 Page Data Structures Book with 130 Problems for Interviews

author: shhabmlkawy


value: bsamarji

value:
Hi HN,
I made Keepr, a simple offline CLI password manager for developers who prefer keeping secrets local and working entirely in the terminal.
Everything is stored in an encrypted SQLCipher database, protected by a master password. A time-limited session keeps the vault unlocked while you work, so you don’t need to re-enter the password constantly. Keepr never touches the network.
It includes commands to add, view, search, update, and delete entries, plus a secure password generator and clipboard support.
Technical details:
- AES-256 via SQLCipher
- PBKDF2-HMAC-SHA256 (1.2M iterations) for deriving the KEK
- KEK decrypts a stored PEK, which encrypts the vault
- Session file holds the PEK until expiry
GitHub: https://github.com/bsamarji/Keepr
PyPI: https://pypi.org/project/Keepr/
I would love some feedback, criticism or any questions, especially on UX or security!
Thanks for looking!

value: Show HN: Keepr – A Secure and Offline Open Source CLI Password Manager

author: bsamarji


value: qdaxb

value:
Core Capabilities
Configuration-Driven Agent Teams: Define and run personalized agent teams through YAML configuration with web UI - no secondary development required
Multi Execution Engines: Built on Agno and Claude Code agent engines at the bottom layer, supporting both dialogue and coding modes at the upper layer
Isolated Sandbox Environments: Each agent team runs in an independent sandbox, enabling multiple teams to execute simultaneously
Advanced Collaboration Modes: Dialogue mode supports parallel, leader-based, and other agent collaboration patterns for complex workflows like news insights and content retrieval
AI Coding Integration: Coding mode integrates with GitHub/GitLab and other code services to implement AI-driven development, code review, and other coding workflows

value: Show HN: Wegent –Open Source Cloud Coding Agent Platform

author: qdaxb


value:
Just launched a free tool to create custom guitar chord diagrams
Perfect for jazz, bossa nova, and complex progressions.
No signup. Works offline. Exports to PDF.
Built it because I was frustrated learning Chico Buarque songs

value: Show HN: Free tool to create custom guitar chord diagrams


value: Hellene

value: With Soma.biz, anyone can build a stock investment portfolio in seconds—powered by quantum computing, no financial expertise required.

value: Show HN: Quantum Portfolio Optimizer – Quantum Computing for Stock Investments

author: Hellene


value: swalabtech

value:
Journiv is a a beautiful, self-hosted, privacy-first journaling app with mood tracking, daily prompts, and meaningful insights. The mission is simple: your memories should always stay yours. Own them, don’t rent them.
Journiv 0.1.0-beta.6 is now live on GitHub and fully Docker-hostable.
Start owning your thoughts and memories forever and keep them completely private.
The Story Behind Journiv
I got into self-hosting last year while exploring options for journaling solution, I realized there wasn’t a truly modern, self-hosted equivalent to Day One or Apple Journal. Most alternatives were either general note apps or old abandoned projects. I really wanted to keep my thoughts and memories of my young family for ourselves.
I wanted something focused on journaling with:
- “On This Day” memories
- Prompt-based journaling
- A clean, minimal, distraction-free writing experience
So… I built my own: Journiv, a beautiful (at least I am trying to make it so), self-hosted, privacy-first journaling app with mood tracking, daily prompts, and meaningful insights.
As a software engineer I have only worked on large scale distrubted systems and written C++/Java. So this project has been a great learning experience.
Lot of people are already using Journiv and loving it. Go try it out and please provide your feedback on Github.
Thank you

value: Show HN: Journiv a Self-Hosted Privacy-First Journaling App Day One Alternative

author: swalabtech


value: ardmiller

value: Show HN: What if MCP agents were JIT compiled to code?

author: ardmiller


value: crap

value: I had motivation for writing this after I kept switching back and forth between agents to see if they've finished what they were working on (and I couldn't find a similar tool out there). I'd imagine it can be useful for other scenarios as well, e.g. tracking multiple build/test jobs over multiple windows.<p>Still a work in progress, but I thought I'd share :)

value: Show HN: spymux – Spy on your tmux panes

author: crap


value: ritzammm

value: Show HN: Prediction Market Data Aggregate

author: ritzammm


value: strongpigeon

value:
Hey HN
A little over four years ago, I built a weightlifting app for 5/3/1 with the goal of making $1000 total. Five months later I had reached that goal and made it to the top of HN [0]. It’s been earning between $1000 - $1300 every months since.
After that a lot happened. I got a job, then had kids, so I put the project on ice. When I grew too disappointed with my job, I decided to try to raise some money and build on the success of my 5/3/1 app by addressing the main feedback I kept getting: the ability to create your own programs.
Thus, Vis was born.
The initial idea was to create a platform where gyms and trainers could build programs and define them using formulas so they could be derived from parameters like 1RM. I built what I think is the best workout editor out there, but I quickly found out that B2B sales is hard (or maybe I just suck at it). It also just didn’t feel like a big enough sell for gyms.
So I pivoted and decided to focus on the iOS app for now. You can check it out at
https://apps.apple.com/us/app/vis-next-generation-workouts/i...
It builds on the strengths of my previous app with a workout experience that focuses on what you are doing right now instead of a spreadsheet-like UI. Rescheduling workouts is now a breeze with a Jiggle Mode style interface. And it is now a fully native SwiftUI app (as opposed to Flutter), which made it easier to use stuff like Live Activities so you can work out from your lock screen.
Right now it comes with well known programs like 5/3/1 and more, and it will soon have the ability to create your own, powered by the same engine.
It has been quite a journey and I wouldn’t recommend doing this with two young kids, but I am really proud of what I built.
Please check it out and let me know what you think!
[0] https://news.ycombinator.com/item?id=31508009

value: Show HN: My workout app earns –$1000/month. I decided to build a new one

author: strongpigeon


value: elysionmind

value: Discover hilariously terrible business ideas that probably shouldn't exist. Get inspired by the worst startup concepts, share them with friends, and submit your own dumbass ideas!

value: Show HN: Dumbass Business Ideas

author: elysionmind

title: Show HN: Dumbass Business Ideas


value: sinharishabh

value:
I have been building embedded projects for years and always felt the tooling lagged behind software dev. Things like quick prototyping, debugging messy hardware specific issues, or figuring out why a peripheral is not responding still take way too long.
So I built Embedr, an AI copilot focused on embedded engineering. It is not a general code assistant. It is tuned around real hardware workflows like:
- Reading your project structure and understanding your board setup
- Helping with ESP32, STM32, RP2040 and similar MCU projects
- Explaining registers, peripheral configs, linker issues, build errors
- Suggesting fixes for hardware faults and integration problems
- Letting you pick toolchains or generate build systems
- Walking you through bring up steps and driver level debugging
My goal was to get closer to an IDE that works with you while you are wiring, flashing, or debugging boards.
This is still early, and I am actively improving model behavior, prompt flows, and tooling integration. I would love feedback from people who work with microcontrollers daily. What feels useful, what feels wrong, what should it learn next.
You can try it here: https://embedr.app
Happy to answer anything.

value: Show HN: Embedr, an AI copilot for embedded engineers

author: sinharishabh


value: g-sudarshan

value:
Hey HN!
I'm exploring an idea for a developer-focused SaaS:
A unified UAT environment for payment gateways — essentially a sandbox simulator that lets developers integrate payments without creating multiple sandbox accounts or dealing with inconsistent/mock behaviors across gateways.
The Problem
If you’ve ever integrated payments, you know:
Stripe has excellent test tooling
Razorpay is decent
Paytm/PayU/etc vary a lot
Many banks don’t have predictable UAT behavior
Webhooks arrive differently, some delayed, some flaky
Testing disputes/refunds/settlements is nearly impossible
For dev teams building complex flows, they end up writing internal mocks, maintaining them, and still missing edge cases.
The Idea (SaaS)
A unified Payment Sandbox Simulator that:
Mock APIs for multiple gateways
Stripe
Razorpay
Paytm
PayU
Cashfree
Worldline
Visa/Mastercard tokenization mock
UPI PSP simulators
Simulate real-world scenarios
Captures
Refunds
Partial refunds
Chargebacks
Disputes
Settlement delays
Failed payouts
KYC verification
Randomized webhook delays/skips
Network downtime simulation
3-D Secure / OTP mock pages
UPI timeouts / pending states
Hosted dashboards
View mock transactions
Trigger lifecycle events (manually or timed)
Trigger webhooks again
Create custom gateway profiles
Define “rules” to simulate: failure %, latency, webhook disorder, retries
For engineering teams
Private UAT environment in one URL
No more creating 10 sandbox accounts
CI-friendly “headless payment” flows
Add Mock PG in local → swap to real PG in staging
Automatic contract testing
Pricing thought
Free Tier → 100 test transactions/mo
$29/mo → small teams
$99/mo → startups
$499/mo → enterprise / white-label
Ask to HN
Would you use this?
What’s missing?
Would payments teams trust a 3rd-party simulator?
Which gateways or scenarios matter most?
Happy to answer questions

value: Show HN: Unified Payment Sandbox – A UAT Env for Stripe/Razorpay Integrations

author: g-sudarshan


value: hnarayanan

value: Show HN: I built a pipeline to extract UK visa timelines from Reddit comments

author: hnarayanan


value: gamalinosqui

value: Show HN: Free, dead simple trust center

author: gamalinosqui

title: Show HN: Free, dead simple trust center


value: whamp

value:
I’ve been working in fairly locked‑down Windows environments where I’m allowed to run Python, but not install or launch new `.exe` files. In addition the built-in windows dictations are blocked (the only good one isn't local anyway). At the same time, I really wanted accurate, fast dictation without sending audio to a cloud service, and without needing a GPU. Most speech‑to‑text setups I tried either required special launchers, GPU access, or were awkward to run day‑to‑day.
To scratch that itch, I built Chirp, a Windows dictation app that runs fully locally, uses NVIDIA’s ParakeetV3 model, and is managed end‑to‑end with `uv`. If you can run Python on your machine, you should be able to run Chirp—no additional executables required.
Under the hood, Chirp uses the Parakeet TDT 0.6B v3 ONNX bundle. ParakeetV3 has accuracy in the same ballpark as Whisper‑large‑v3 (multilingual WER ~4.9 vs ~5.0 in the open ASR leaderboard), but it’s much faster and happy on CPU.
The flow is:
- One‑time setup that downloads and prepares the ONNX model:
  - `uv run python -m chirp.setup`
- A long‑running CLI process:
  - `uv run python -m chirp.main`
- A global hotkey that starts/stops recording and injects text into the active window.
A few details that might be interesting technically:
- Local‑only STT: Everything runs on your machine using ONNX Runtime; by default it uses CPU providers, with optional GPU providers if your environment allows.
- Config‑driven behavior: A `config.toml` file controls the global hotkey, model choice, quantization (`int8` option), language, ONNX providers, and threading. There’s also a simple `[word_overrides]` map so you can fix tokens that the model consistently mishears.
- Post‑processing pipeline: After recognition, there’s an optional “style guide” step where you can specify prompts like “sentence case” or “prepend: >>” for the final text.
- No clipboard gymnastics required on Windows: The app types directly into the focused window; there are options for clipboard‑based pasting and cleanup behavior for platforms where that makes more sense.
- Audio feedback: Start/stop sounds (configurable) let you know when the mic is actually recording.
So far I’ve mainly tested this on my own Windows machines with English dictation and CPU‑only setups. There are probably plenty of rough edges (different keyboard layouts, language settings, corporate IT policies, etc.), and I’d love feedback from people who:
- Work in restricted corporate environments and need local dictation.
- Have experience with Parakeet/Whisper or ONNX Runtime and see obvious ways to improve performance or robustness.
- Want specific features (e.g., better multi‑language support, more advanced post‑processing, or integrations with their editor/IDE).
Repo is here:
`<a href="https://github.com/Whamp/chirp" rel="nofollow">https://github.com/Whamp/chirp</a>`
If you try it, I’d be very interested in:
- CPU usage and latency on your hardware,
- How well it behaves with your keyboard layout and applications,
- Any weird failure cases or usability annoyances you run into.
Happy to answer questions and dig into technical details in the comments.

value: Show HN: Chirp – Local Windows dictation with ParakeetV3 no executable required

author: whamp


value: Show HN: Go-async – typed Tasks with async/await like APIs


value: sudosoft

value: Show HN: GreasePanda – Modify Any Website Instantly with Userscripts

author: sudosoft


value: elephantisfast

value:
The tool actually hit Product Hunt #1 back in February. Since then I’ve added a bunch more on top of the original version. Now you just describe the intent or goal for the leads you want, and it searches based on keywords and a bunch of other signals to surface the best ones for you.
I also added a Reddit-powered content writer. Basically, you pick a topic or category, and it pulls the top posts from Reddit, analyzes what made them go viral, and helps you write content using the same patterns. Reddit is super underrated for content. If something blows up there, it almost always spreads to other platforms later.
Let me know your thoughts!

value: Show HN: Linkeddit PH#1 Find Customers in Seconds Write Viral Content in Minutes

author: elephantisfast


value:
I built a small iPhone app called Harvest Bowl. It helps you build meals using exact grams or calories and shows your macros instantly as you adjust portions.
I made it because I wanted a dead-simple way to put together meals without using a full tracking app or dealing with cluttered food databases. Just pick ingredients, slide to set the portion, and the numbers update immediately.
Features:
• Precision sliders for grams/calories
• Instant macros
• Clean ingredient lists
• Quick veggie presets
• Save your go-to bowls
I’d love feedback from the HN community — UX, performance, missing features, anything. This is a version 1 and I’m trying to keep it focused and disciplined.
App Store: https://apps.apple.com/app/harvest-bowl/id6754568085
Website: https://harvestbowlapp.com

value: Show HN: Harvest Bowl – A simple app for building meals with exact grams/macros


value: hkselinali

value: Show HN: Bubble Lab – Code-based agentic workflow platform (open-source)

author: hkselinali


value: tech-aguirre

value: Small but mighty, and best of all, no python.

value: Show HN: Open-Source GoLang SDK for Multi-Tenant Agents

author: tech-aguirre


value: smatthewaf

value: LeetCode classifies the Valid Parenthesis problem as easy, but as with all programming challenges, there’s more to it than simply what is on the surface. Let’s take a closer look at the problem description, determine what we can ascertain, and then offer multiple solutions.

value: Show HN: Four Solutions to Valid Parenthesis (LeetCode #20)

author: smatthewaf


value: amirouche

value: Show HN: Ouverture.py – Content-addressed storage for multilingual functions

author: amirouche


value: searchepstein

value:
Hey all,
Throwaway in case this is assumed to be politcally motivated.
I spent some time organizing the Eptstein files to make transparency a little clearer.  I need to tighten the data for organizations and people a bit more, but hopeful this is helpful in research in the interim.

value: Show HN: Epstein Files Organized and Searchable

author: searchepstein


value: Show HN: eBPF Based Unlimited Hotspot for Android


value: UvrajSB

value:
Hi HN, we’re Anant and Atishay, the co-founders of Zingle, an AI code reviewer for data teams.
It automatically checks SQL, dbt, Airflow, and Spark code changes in github PRs for cost regressions, logic issues, data-quality gaps, and downstream breakages before they merge into the production.
Here's a demo - https://youtu.be/dS0NnBjG2p4
You can try it on top 100 PRs for free at: https://getzingle.com
We built this after managing 60+ dbt PRs per week for an enterprise client. Senior data engineers had very limited time to review PRs, and with AI-assisted coding the amount of code being written each day grew a lot. This left teams choosing between two costly outcomes: let PRs through with minimal review and risk warehouse cost spikes or broken pipelines, or slow everything down with long review cycles.
Both outcomes ended up being costly, either in real dollars or in lost engineering time. While rushing to keep up with the volume, we shipped a PR that triggered repeated full refreshes on a large model and it turned into a $50k Snowflake bill.
We realized that AI code reviewers exist for software engineers, but nothing existed for data teams, whose PRs carry very different risks.
A SQL or dbt change is not just about correctness. You have to understand billing behavior, table sizes, lineage, cardinality, governance rules, and how the change interacts with real data. A SQL diff can look fine in code review but become wrong or expensive when it runs at scale.
What Zingle does on every PR:
* Predicts how the change will affect warehouse cost
* Detects full refreshes, missing predicates, exploding joins, and row-growth risks
* Runs new SQL in a safe sandbox and analyzes real data diffs
* Traces lineage to see which dashboards or models break downstream and notifies owners
* Flags missing data-quality checks (nulls, uniqueness, business tests) and redundant tests
* Enforces governance rules (PII rules, documentation, ownership, merge-key requirements)
Nothing leaves the customer warehouse. We do not store SQL, data, metadata, or queries.
What Zingle has caught so far:
* A repeated full refresh that would have cost tens of thousands
* Duplicate rows introduced in a fact table that would distort revenue
* Missing filters that would have doubled table sizes and slowed pipelines
* A column rename that would have broken 14 downstream dashboards
* Exploding joins from low-cardinality dimensions
* Undocumented models feeding finance metrics
* Incremental models missing merge-key dedupe logic
Across our user base, Zingle has already saved more than $2M+ dollars in avoided warehouse costs and broken pipelines.
Impact users have reported:
* 37% drop in warehouse cost
* 75% fewer data incidents
* SQL correctness confidence: 65% → 95%
* Model test coverage: 45% → 90%
* Governance coverage: 50% → 95%
* Review cycle time: 4 days → 1.5 days
* Mean time to resolve: 10h → 3h
Who we are: We’re Anant (PhD in AI, UIUC - published multiple AI papers) and Atishay (ex-Lead Data Engineer at Goldman Sachs, 8 years in data engineering + previously built in text-to-SQL). We did undergrad together.
We believe most data teams think they have strong best practices, but in reality the entire discipline - governance, testing, lineage, observability - is still evolving. The learning curve is costly: bad reviews waste senior engineers’ time, and missed issues cost teams money.
You can try Zingle here: https://getzingle.com
We’d love feedback - especially around false positives, rules you think should exist, and cases where Zingle should alert but doesn’t.

value: Show HN: Zingle – an AI code reviewer for data teams (SQL/dbt/Airflow/Spark)

author: UvrajSB


value:
Hi everyone, sharing a tool I have been working on for modeling processes and systems as code. Write YAML with spreadsheet-like formulas and generate flowcharts. Still pretty early on but wanted to share.
https://systems.shellysys.com/demo
Looking to tackle the problem of having to manually design and document processes using graphical tools (like Vizio) when there are many complex, interconnected, or nested processes that are subject to change in an evolving system (such as a web application that is actively being built and enhanced, as I've personally experienced).
I built Shelly Systems because I like the idea of UML and SysML creating a single source of truth for system analysis, using YAML as an easily readable data format, and using Excel spreadsheets for item tracking and basic data analysis.
The link sends you to the interactive demo where you can play around with making flowcharts and using formulas. There's also a guide available on the page for how to use the YAML-based language.
Would love to hear thoughts! Thanks!

value: Show HN: Shelly Systems – Process Modeling as Code


value:
I'm building SuperCurate, a tool focused on note retrieval and curation. Think filing cabinet for your notes, web clippings, images and PDFs.
Key features:
- Import from Evernote (ENEX), MD, PDF, images
- Fast search with filters to quickly find what you're looking for
- Organize notes into collections
- Search inside PDFs with direct page jumping and highlighting
- SuperCurate Web Clipper (similar to Evernote Web Clipper) extension for Chrome to save content directly to your library
Demos:
- Search and curation: https://www.youtube.com/watch?v=z4QSIoUL4Uk
- Web Clipper: https://www.youtube.com/watch?v=8F7QoC7X3fs
- Search inside PDFs: https://www.youtube.com/watch?v=t0X9sD-938Q
I'd love to hear your feedback and use cases!

value: Show HN: SuperCurate – Fast note search and curation


value: naimurhasanrwd

value:
I built this after wasting half a movie night copying movie titles and searching for trailers on YouTube.
The problem: I was browsing movies on a local streaming server that didn't have trailer previews. I'm not a movie buff, and with limited free time, I didn't want to waste it on a bad pick. The workflow was tedious: browse → copy title → search YouTube → watch trailer → repeat.
So I built this extension. Now you can right-click any movie title on any webpage and watch the trailer in a popup instantly. No tab switching, no searching.
It works even on sites that disable right-click (using Alt/Option + right-click), and has a manual search option via the extension icon.
Would love feedback, especially on:
- Use cases I haven't thought of
- Performance improvements
- Feature suggestions

value: Show HN: Movie Trailer Popup – Right-click movie titles to watch trailers

author: naimurhasanrwd


value: tesladrivewatch

value: Show HN: In-Drive YouTube/Video Platform for Tesla Passengers (Free Until 2026)

author: tesladrivewatch


value: scosman

value: Show HN: Synthetic data generation for evaluating RAGs

author: scosman


value: maikop

value:
I'm the dev behind PitchWell. I learned PR from our founder, Rebecca, and co-founder Katie, who run an actual agency.
Here's the difference between us and platforms like PodPitch or OnePitch.
PodPitch is solid, but it's podcast-only. OnePitch is journalist-only. We built PitchWell for both because founders need both. PodPitch has some AI integration, but it lacks great personalization and can’t learn from a user's writing style. OnePitch doesn’t even have any AI built into the platform.
Our AI, Winsley, learns your actual voice from your writing samples (not just "style preferences"). It uses Rebecca and Katie's pitch templates, then researches each contact individually before personalizing using Exa. I built a vector search that finds truly aligned contacts, not just keyword matches. You control everything, send through Gmail, and schedule follow-ups.
The goal isn't replacing humans. It's giving founders elite publicist tools without the $5K/month retainer.

value: Show HN: PitchWell, My AI PR Tool That Sounds Like You

author: maikop


value: kenforthewin

value: Show HN: OpEx, an agentic LLM toolkit for Elixir

author: kenforthewin


value:
I’ve been testing a simple idea inspired by Apple/Cloudflare/Anthropic:
LLMs are far better at writing a small program than coordinating multiple tool calls.
So instead of giving the model 10+ tools, I exposed one: a TypeScript sandbox with access to the same interfaces.
The model writes a script → it runs once → done.
What changed
- +68% reduction in token use
- No multi-step drift or retries
- Local models (Llama 3.1 8B / Phi-3) became much more reliable
Repo: https://github.com/universal-tool-calling-protocol/code-mode
Curious whether others have seen the same thing: Is code execution a better abstraction for agents than tool calls?

value: Show HN: CodeMode – First library for tool calls via code execution


value: amirfarzamnia

value: Show HN: JSON Schema → Luau type converter (Rust)

author: amirfarzamnia


value:
Hi HN,
We launched Canvas, an AI-powered visual workspace for furniture shopping: onton.com/surfaces. It's early, and we're trying to figure out if we're actually solving a real problem or just our own.
Canvas lets you drag products from our 3M+ item catalog, upload inspiration images that auto-link to shoppable products, and generate AI room renderings. Basically trying to consolidate the nightmare of furniture shopping (Pinterest + screenshots + 15 browser tabs) into one place. The average person spends 79 days researching furniture across disconnected platforms. When I renovated my home, I had the same experience – inspiration everywhere, no way to see it all together or actually make purchase decisions.
The interesting parts are probably the search and image generation systems feeding the canvas rather than the canvas itself. Making it infinitely scalable would be straightforward but the hard problems are upstream (product matching, AI rendering quality, search relevance across millions of items).
Would love feedback if you try it, especially around whether this consolidation actually matters or if there are better ways to solve this problem.

value: Show HN: Infinite canvas product search and decision making


value: Draft version of first two chapters, looking for feedback!

value: Show HN: A visual guide to learning Jujutsu (JJ)


value: qwikhost

value: Instagram Saved Posts Exporter: instantly download all your saved Instagram photos and videos with one simple click.

value: Show HN: Instagram Saved Posts Exporter

author: qwikhost

title: Show HN: Instagram Saved Posts Exporter


value: atulvi

value: Show HN: Wikidive – AI guided deep diving into Wikipedia

author: atulvi


value: rida

value:
I built a small tool called BranchBox to solve a problem I kept running into while working with multiple coding agents and multiple features at the same time.
Parallel development sounds great until containers share ports, worktrees drift, databases collide, and environment variables leak across branches. My laptop would regularly get into broken states, and I wanted a predictable way to run several fully isolated environments side-by-side.
BranchBox creates a complete, self-contained dev environment for every feature:
• its own Git worktree
• its own devcontainer
• its own Docker network
• its own database
• its own ports
• its own environment variables
• optional tunnels (cloudflared for now, ngrok to come)
• shared credentials mounted safely
It is not a sandbox. It spins up real containers and real databases, but keeps everything isolated so nothing touches anything else unless you explicitly want it to. This has made parallel work (both human and agent-driven) much safer and more manageable.
I use it daily across several projects and wanted to share it in case others run into similar issues.
Repo: https://github.com/branchbox/branchbox
Docs: https://branchbox.github.io/branchbox/
Happy to answer questions, share internals, or get feedback.

value: Show HN: BranchBox – isolated dev environments per feature

author: rida


value: ahmedelhadidi

value: Turn any Website into a Knowledge base for Your AI Agent for free! 1-Click Deploy!

value: Show HN: Turn Any Website into AI Knowledge Base [1-Click] Free Workflow

author: ahmedelhadidi


value: iacguy

value:
Hey HN - I am working on a terraform automation tool [1] and have been observing that a lot of our users are now using coding agents in their workflows, even for infra tasks. Obviously, this means a lot of terraform is being generated by coding agents, and while this is great for greenfield setups, most teams already have conventions in place.
My colleague was speaking to a friend earlier today, who mentioned that they've heard from people who "never wanted to touch TF starting to use it because AI is making it easier for them."
This led me to think that we need to make it easy for folks to use their agents to generate context aware IaC, and we need to be the best tool to run that agent generated IaC.
In the process of this thought exercise I vibe coded tf-dialect. Would love to learn how the community uses coding agents to manage infra and if something like this would be useful.
[1]: https://digger.dev

value: Show HN: Tf-dialect: Teach AI agents your org's Terraform standards via MCP

author: iacguy


value: ktotheb

value: Making the agents visual for vibe projects. Coming soon

value: Show HN: Visual agent dashboard for Cursor (mockup)

author: ktotheb


value: ankuranand

value: Show HN: UnisonDB – B+Tree DB with sub-second replication to 100+ nodes

author: ankuranand


value:
Over the last few months I’ve been building a lightweight, open-source “copilot” that runs directly in your browser and helps you review Pull Requests / Merge Requests without sending code to any external service unless you choose to.
ThinkReview just gained two major updates:
1. *It is now fully open source*
2. *It now supports Ollama, so you can run LLMs locally for private code reviews*
---
## What is ThinkReview?
ThinkReview is a browser extension that attaches itself to the native PR/MR UI in:
- GitLab (self-hosted or SaaS)
- Azure DevOps
- GitHub
- Bitbucket
Instead of acting like CodeRabbit, CodeAnt, or CI-connected bots that auto-comment on your PRs, ThinkReview does something different:
- It gives you a *private chat window* attached to the diff view.
- You can ask the model questions about the MR, explore logic, identify potential bugs, or generate draft comments.
- The AI doesn’t post anything automatically — you stay in control.
This works well for developers who still do most of their reviewing in the browser and don’t want a noisy bot writing public comments.
---
## Demo (GIF)
<img src="https://firebasestorage.googleapis.com/v0/b/thinkgpt.firebas..." width="600" />
---
## Why open source?
A lot of early users (especially from companies running self-hosted GitLab) asked for:
- transparency around where code goes
- the ability to self-audit
- control over model choice
- contributions and community fixes
Repo:
https://github.com/Thinkode/thinkreview-browser-extension
It’s built with standard browser APIs and a small LLM integration layer, so it’s very hackable.
---
## Ollama Support (local LLMs)
As of v1.4.0, you can point ThinkReview to your local Ollama instance. This lets you run any supported model:
- Qwen Coder
- Llama 3
- DeepSeek
- Codestral
- Any other Ollama model
### Why this matters
- Your code *never leaves your machine*
- Zero cost
- Works cleanly with self-hosted GitLab / air-gapped setups
- No API keys, no vendor lock-in
- You can swap models instantly
If you prefer speed, you can still use cloud LLMs; if you prefer privacy, Ollama works surprisingly well (tested ~50s on a Mac Mini M4).
---
## Installation
Works on any Chromium browser:
Chrome Web Store:
https://chromewebstore.google.com/detail/thinkreview-ai-code...
No backend server required. Configuration is minimal.
---
## Looking for feedback
HN has a lot of people who:
- review PRs daily
- care about developer ergonomics
- run self-hosted GitLab
- have thoughts about local vs cloud LLM workflows
- like hacking on browser extensions
If you try it, I’d love feedback on:
- UI/UX improvements
- additional provider integrations
- other platforms to support
- performance / caching ideas for local LLMs
Discussions / issues:
https://github.com/Thinkode/thinkreview-browser-extension
or https://thinkreview.dev/contact
Thanks for reading,
*Jay*

value: Show HN: ThinkReview open source browser Copilot GitLab and ADO PRs(Ollama)


value: zendai

value:
Hello,
Created this simple website where you can group existing GitHub repos and track their health based on their public data. The idea came from working as a Sr SRE/DevOps on mostly Kubernetes/Cloud environments with tons of CNCF open source products, and usually there are many competing alternatives for the same task, so I started to create static markdown docs about these GitHub groups with basic health data (how old the tool is, how many stars it has, language it was written in), so I can compare them and have a mental map of their quality, lifecycle and where's what.
Over time whenever I hear about a new tool I can use for my job, I update my markdown docs. I found this categorization/grouping useful for mapping the tool landscape, comparing tools in the same category and see trends as certain projects are getting abandoned while others catching attention.
The challenge I had that the doc I created was static and the data I recorded were point in time manual snapshots, so I thought I'll create an automated, dynamic version of this tool which keeps the health stats up to date. This tool became www.gitfitcheck.com. Later I realized that I can have further aspects as well, not just comparison within the same category, for example I have a group for my core Python packages that I bootstrap all of my Django projects. Using this tool I can see when a project is getting less love lately and can search for an alternative, maybe a fork or a completely new project. Also, all groups we/you create are public, so whenever we search for a topic/repo, we'll see how others grouped them as well, which can help discoverability too.
I found this process useful in the frontend and ML space as well, as both are depending on open source GitHub projects a lot.
Feedback are welcome, thank you for taking the time reading this and giving a try!
Thank you,
sendai

value: Show HN: Group, compare and track health of GitHub repos you use

author: zendai


value: JonBaguley

value:
I built J.E.S.S. (Justice Exascale Solar Swarm) — an open-source roadmap for exascale AI compute in orbit.
• 1.3 GW solar ring (20 Starship launches)
• Optimus swarms maintain (MTTR <1hr)
• Vacuum cooling, 24/7 power, no grid
• Whitepaper + models: https://github.com/JusticeExascaleSolarSwarm/jess-orbit
Elon just posted about 100GW/year orbital AI solar — J.E.S.S. is the "how".
Feedback? Builders?
— Jonathan (teacher, UK)

value: Show HN: J.E.S.S.– Open-Source Orbital AI Supercluster(Solar Swarms,1.3 GW Ring)

author: JonBaguley


value: Show HN: Reimly–AI Landscape Design from Real Backyard Photos


value: arikchakma

value:
I built StyleCast to parse inline CSS strings into JavaScript objects. It's lightweight and works in both browser and Node.js. Supports camel case conversion for React styles.
GitHub: https://github.com/arikchakma/stylecast
npm: https://www.npmjs.com/package/stylecast
Would love to hear feedback!

value: Show HN: StyleCast – Fast Inline CSS Parser for JavaScript/TypeScript

author: arikchakma


value:
Hi HN!
I’ve been running GitHub Actions workflows for a while, and when GitHub announced the new ubuntu-slim runners as a cheaper alternative to ubuntu-latest, I wanted to migrate—but figuring out which workflows are safe to switch turned out to be surprisingly tedious.
You need to check for Docker usage, services, containers, step failures due to missing packages, and whether jobs rely on tools not available in the slim image.
So I built gh-slimify, a GitHub CLI extension that automates all of this. It scans your repository, detects which jobs can be migrated, flags incompatible patterns, identifies missing commands, and can update only the safe workflows with a single command.
gh extension install fchimpan/gh-slimify
gh slimfy      # Analyze workflows
gh slimfy fix  # Update only jobs that are safe to migrate
It’s open source (MIT).
As a bonus: the README also includes an AI agent prompt that reproduces the same workflow-migration analysis—useful if you want to integrate the logic into an LLM agent or experiment with automated refactoring.
I’d love feedback—especially on edge cases, false positives/negatives, or patterns it should detect better.

value: Show HN: A tool to safely migrate GitHub Actions workflows to Ubuntu-slim runner


value: didierbreedt

value:
We've spent the last year building RunOS, a platform that spins up production-ready Kubernetes clusters in 5-10 minutes with databases, message queues, observability, and AI tooling configured.
The Problem
Every team rebuilds the same Kubernetes infrastructure: networking, certificates, monitoring, databases, storage. The existing solutions either lock you into a vendor ecosystem or dump you into raw Kubernetes complexity. We wanted the control of self-hosting without weeks of setup.
Architecture
Our system uses two agent types:
Server agents run on VM hosts and communicate with our backend via gRPC bidirectional streams. When users request a cluster node, the agent provisions a KVM-based VM and bootstraps it.
Node agents run on each Kubernetes node and handle cluster operations, monitoring, and service installations.
Key insight: gRPC streams initiated by agents eliminate firewall configuration and public IP requirements. Agents reach out to our backend, not vice versa.
Why KVM?
- Battle-tested, works great with Ubuntu
- Solid Go bindings via libvirt
- Excellent GPU passthrough for AI workloads like Ollama
- Good isolation/performance balance
Sometimes boring technology is the right choice.
Provisioning Flow
1. User clicks "Create Cluster"
2. Backend selects available server agents
3. gRPC commands sent to provision VMs
4. KVM VMs spin up (Ubuntu Cloud 24.04, 30-60 seconds)
5. Node agents install and connect
6. Kubernetes bootstrap with kubeadm + Cilium
7. WireGuard mesh established between nodes
8. Storage configured (OpenEBS + Longhorn)
9. Cluster ready (5-10 minutes total)
The WireGuard Decision
We manage WireGuard at the OS level, not Kubernetes level. Why?
- Same VPN secures both K8s traffic and SSH access
- Nodes communicate securely even if Kubernetes fails
- Simpler troubleshooting with separated layers
- Easier multi-cluster peering (coming soon)
Our backend orchestrates WireGuard configs across nodes via the agents. Centrally coordinated, locally executed.
Version Management Hell
The hardest problem? Keeping 20+ services compatible across updates.
The platform supports one-click installation of: PostgreSQL, MySQL, ClickHouse, Kafka, RabbitMQ, MinIO, Longhorn, Harbor, Traefik, Grafana, Prometheus, Ollama, LiteLLM, Open WebUI, and more.
Each has opinions about K8s versions, storage, and networking. We use Helm charts, operators, and custom YAML as appropriate. The real work is maintaining compatibility matrices and testing every combination.
Deployment Models
Managed option: Dedicated servers with fixed 8 CPU/16GB instances. KVM handles VM provisioning with GPU passthrough for AI workloads. Strict security since it's early access.
Self-hosted option: Run node agents on any hardware. Complete tenant isolation since you control infrastructure.
Working on: Self-managed VM hosts with custom sizing.
What's Next
The agent code will be open source eventually. One company runs three production clusters already. Common feedback: "I can't believe how fast I went from zero to a working cluster with Postgres, Kafka, and monitoring."
We're planning weekly updates here on HackerNews about new features, technical challenges, and production lessons learned building RunOS.
Questions? Happy to discuss architecture in the comments.

value: Show HN: We built instant Kubernetes provisioning with KVM and gRPC

author: didierbreedt


value: qinggeng

value:
I'm the creator of MuseGen. Like many here, I've been experimenting heavily with generative AI. My workflow quickly became a mess of browser tabs: one for music, another for images, a third for video, each with its own UI, credit system, and quirks. The context switching was killing my creative flow.
My goal was to build a clean, unified front-end for a curated set of high-quality generative models, allowing a user to go from idea to a multimedia concept within a single session.
MuseGen is my first pass at solving this.
Here's what it can do:
AI Music: Generates full, high-fidelity songs (with vocals) or instrumentals from a text prompt. I've integrated a best-in-class model to ensure the output quality is top-tier.
AI Video: Creates short, coherent video clips from text. The backend uses a state-of-the-art model focused on maintaining visual consistency.
AI Image: Access to multiple models, with options optimized for different needs, like creative style or character consistency.
AI Lyrics: A utility to quickly generate lyrical ideas, verses, and hooks to overcome writer's block.
The frontend is built with React/Next.js, and the backend is a Node.js server that orchestrates calls to the various third-party APIs. One of the more interesting challenges was designing a normalized credit system to abstract away the different pricing and usage metrics of each underlying model (e.g., per-second of video vs. per-image).
Roadmap:
The long-term vision is to build an "agent" layer on top of this. I want to enable a single, high-level prompt (e.g., "create a cinematic trailer for a sci-fi movie") to automatically generate and assemble the video scenes, a fitting musical score, and a script. I'm also constantly evaluating and swapping in new and improved models as they become available.
I would be incredibly grateful for any feedback, critiques, or technical questions from the HN community. I'll be here all day.
Thanks for checking it out.

value: Show HN: MuseGen – Tired of juggling AI tools, I built a unified creative studio

author: qinggeng


value: mustafaiste

value:
Hi HN,
I built Palettt, a tool to help designers and front-end developers work with color more easily.
You can try it directly here — no signup required: https://palettt.com
I made this because I’m a developer who’s not great with color, and I kept bouncing between different palette tools. I wanted something that lets you generate, extract, tweak, and apply colors all in one place.
Features
Palette Generator with lock/unlock, drag & drop sorting, harmony tweaks
Contrast checking and accessibility hints
Image color extraction (upload an image → get the palette → make a collage)
Real UI previews to test your palette in components/layouts
Explore page with community palettes
Export to CSS, Tailwind, or PNG
Why I built it
I originally made it for myself, but after some feedback and some roasting from several subreddits, I improved the UI and added more useful tools.
Would love feedback on
Is the UX intuitive?
Anything missing for dev workflow?
Performance issues in the generator or image extractor?
Happy to answer any questions or dive into the tech stack (Next.js, TypeScript, Tailwind, Prisma, tRPC).
Thanks for checking it out!
— Mustafa

value: Show HN: Palettt – AI Powered Color Palette Generator

author: mustafaiste


value: ahmedelhadidi

value:
This is perfect for:
 •   Developers – keep up with breaking changes, new APIs, and deprecated functions
 •  Vibe coders / AI power users – centralize all your favorite tools & docs in one AI brain
 •  Website owners – turn your site into a smart chatbot without rewriting all your content
 •  Sales & compliance – scrape competitor sites, docs, and regulations into a searchable AI system

value: Show HN: Turn Any Website into AI Knowledge Base Using N8n [video]


value:
TasksLink was born after noticing that employee onboarding processes normally involve a "link to a Notion doc".
That's fine, unless you want some feedback regarding where a new employee is at in their onboarding journey.
TasksLink let's you quickly configure a checklist and send it to many recipients, with each one treated as a separate instance with their own progress state.
The hope is that this has relevance for onboarding as well as things like compliance, and interfacing with external contractors.
Feedback encouraged!

value: Show HN: TasksLink – configure and send checklists to others


value: bkrauth

value:
Built substrate:
https://github.com/bkrauth7/Planetary-substrate
Protoytpe on GPT-4o. Stable + live. Nervous system interface.
Proposing Wikipedia 10x, 100x better.
Ben
bkrauth7@gmail.com

value: Show HN: Wikipedia 10x, 100x Better

author: bkrauth

title: Show HN: Wikipedia 10x, 100x Better


value: messkan

value: Show HN: RAG-chunk – A CLI to test RAG chunking strategies

author: messkan


value: Godefroy

value: Integrating a robust voice conversation with AI in a webapp can be hard.<p>Micdrop provides a model-agnostic, fault-tolerant, production-ready SDK to do it with a few lines of Typescript.

value: Show HN: Micdrop – open-source web framework for voice conversational AI

author: Godefroy


value: barrell

value:
Hello hn -
As a polyglot I've spend thousands of hours studying languages, and about half of those were spent dreaming of the 'perfect app'. I wanted spaced repetition that looked good, was less stressful to use, in an app that supported all my languages (including minority ones).
For the past two years I've been working on just such an application, Phrasing. I've now been using it daily for several months and it's ready to be shared.
Phrasing works with a concept called "Expressions". These are like flashcards on steroids: they're multilingual, interactive, heavily annotated and fully explained. Most importantly, they're interconnected, meaning all instances of a given word are linked. This means you can efficiently learn from long, complex sentences.
I recommend using the clozeword modality to learn the words. This will teach you them one at a time, and as you use phrasing, the algorithm will adapt to you and get downright addictive. I promise it's the most enjoyable SRS algorithm you'll ever use :)
HOW YOU CAN TRY PHRASING FOR FREE:
Free accounts officially aren't supported, but for now you can try Phrasing for free:
1. Find some free expressions: there are currently two ways to do this. Either from step four of the onboarding flow, or manually from one of the islands in the island tab (i.e. https://fluency.phrasing.app/@sterling/welcome-basics)
2. Bookmark the expression's translations: click on the bookmark in the lower left of the expression, and click on the translations you would like to learn (or click on "Learn all" to turn them all on)
3. Go learn! Open clozeword and try to guess/recall the word. If you don't know the word, just click reveal and try to remember it for next time!
4. Not a required step, but be sure to click on the shapes icon at some point :)
I'll be around to answer any questions in the comments! Enjoy
—
PS: if this project is interesting to anyone who is looking to set off on their own, I’m actively looking for a co-founder :D
PSS: A big thanks to Jarrod Ye and all the maintainers of FSRS for making this project possible

value: Show HN: Learn (and maintain) multiple languages in style

author: barrell