value: sanderbell

value:
How it works:
Copy a YouTube link → open app → it auto-detects from clipboard, pulls metadata + transcript, and generates a structured summary (key points + narrative) in seconds. No inputs, no pasting, no prompting. Supports 60+ languages for summaries, the video's language doesn't matter.
Why I built:
I had an endless queue of educational videos but no time to sit through rambling or clickbait. ChatGPT flow = copy → open → paste → prompt → pray it formats correctly. Existing summarizers felt feature-bloated or unreliable. I wanted something super minimalist: “clipboard → value” on repeat.
Decisions:
Stack
 — React Native 0.81 (New Arch) + TypeScript
Clipboard trick
— Monitors clipboard on app start/resume — no manual paste
Caching
— Stores transcripts + summaries in AsyncStorage with metadata
— fast image library prevents repetitive refetching
APIs
— YouTube metadata
— Third-party transcript provider (exploring Whisper)
— OpenAI for summarization
Output
— Structured prompts → consistent format across 60+ languages
— Easter egg: philosophical analysis for songs/poems
Time-saved calculation
— Uses actual video length (30% of cases)
— Fallback = estimated from average speech speed
Main headache:
Grandfathering legacy users without breaking App Review was harder than I expected. The first version was paid upfront ($14.99 or leave). Got some sales in the first two weeks, then traffic died. After some internal fighting, I switched to free-to-try with IAP. By then I already had thousands of users I’d promised free access forever, plus a dozen who actually paid. I wanted to keep my word and never show them a paywall.
RevenueCat’s originalAppVersion looked perfect… until I actually used it.. Turns out it’s not the marketing version (1.0, 2.3), but the build number (the variable name originalAppVersion is so misleading…). And Apple Review/TestFlight always report it as 1.0, so reviewers looked like legacy users and never saw the paywall (5 rejections). Everything worked for me, totally broken for them.
My mistake: I auto-entitled anyone with 1.0. First hacky fix: show a review-only paywall. Proper fix: if originalAppVersion === "1.0" (I never had such a build number anyways), treat as non-legacy. Days of digging and testing across devices/envs… finally approved.
Current state:
3800+ users and 12 sales within 2 weeks post-launch, 4.9-star rating globally. Now multi-tier subscription after 3 free summaries due to ongoing costs and long-term value.
Limitations/Challenges:
— iOS only for now. Android clipboard monitoring is restricted—would need a different UX.
— Some countries need VPN due to API restrictions; I notify users on app start.
— Dependency on third-party transcript API (evaluating OpenAI-based solution).
— YouTube's testing AI conversational tools, but it's not quite the same thing, sandboxed only for some users and unlikely to roll out widely as it cuts ad their revenue from watching.
Happy to answer questions about implementation or share code snippets if helpful.
https://apps.apple.com/us/app/esse-ai-video-summarizer/id675...

value: Show HN: I made a clipboard-triggered YouTube summarizer (no paste, 2 taps)

author: sanderbell


value: howardV

value:
Built this after drowning in post-meeting note cleanup. Upload Zoom/Teams/Meet
recordings → get speaker-diarized transcripts, auto-extracted action items with
owners, and exportable minutes.
Free 5-min preview (no signup). Real-time progress tracking during processing.
Tech: Deepgram/Replicate for transcription + LLM pipeline for summarization.
Open to feedback on the action-item extraction logic.
Try it: https://harku.io/tools/meeting-transcription

value: Show HN: AI meeting transcription that outputs structured minutes

author: howardV


value: I spent a lot of time trying to understand the hiring process and how to make it more data driven. Just writing about it brought so much clarity to me. Thank you for all the support and feedback!

value: Show HN: A data driven approach to hiring


value: rhettjull

value:
Hi HN. I built Zylo, an AI-powered site generator that turns an idea into a production-ready website with pages, copy, and hosting. All in under 10 minutes and at a very appealing level.
Watch 40-sec walkthrough: https://youtu.be/DCFbSvD5keI?si=i8LRVtzhzpGw7tX1
Why we built it:
Most “AI website builders” just output screenshots or mockups. Zylo actually produces editable, deployable sites with working navigation, SEO tags, and integrated forms. We wanted something builders could ship not just preview.
How it works (under the hood):
Prompts generate the site’s hierarchy and copy using a structured LLM pipeline we call Phoenix.
The build compiles to a live Vercel deployment with automatic asset optimization and caching.
Everything runs server-side. No client API calls or slow render loops.
What we’d love feedback on:
The onboarding flow (is it clear enough?)
Generated copy and design quality
Thanks! Hope to see you around!

value: Show HN: Instantly build a real website from an idea

author: rhettjull


value: stefanosdeme

value: Built on quickfixj, provides a web ui to configure FIX sessions, rulesets (how to respond to messages), and send manual messages. The purpose is for developers and QA engineers of trading applications to simulate their counter-parties. This is an early draft for feedback, direction, and hopefully contributions.

value: Show HN: Cpsim – An open source toolkit to test your trading apps

author: stefanosdeme


value: raushanrajjj

value:
Secure Code Review with Real Scanners and LLM.
A GitHub App that scans PR changes using security scanners and suggests AI Fixes using GPT-5.
Free for open-source and individual projects.

value: Show HN: DevSecOps.Bot – A GitHub App

author: raushanrajjj

title: Show HN: DevSecOps.Bot – A GitHub App


value: artofalex

value: Show HN: Art of X: Multi-agent brainstorming

author: artofalex


value:
I built a tool that analyzes iOS .ipa files for common App Store rejection reasons before you submit. You get a compliance report in ~30 seconds instead of waiting 1-3 days for Apple's review.
Live: https://iosprecheck.com/
What it costs: $2.99/scan (pay-per-use, no subscription - subscription with API for CI/CD integration coming soon)
Would love the feedback
What It Checks
Technical validation:
Private API detection (binary analysis)
Info.plist validation (30+ required keys, bundle ID format, version strings)
Privacy permissions (NSUsageDescription keys, description quality)
Asset compliance (app icons, launch screens)
Architecture validation (ARM64)
AI-powered analysis:
Metadata review (trademark violations, keyword stuffing, misleading claims)
Output:
Compliance score (0-100)
Critical issues (will cause rejection)
Warnings (best practices)
List of 15-20 passed checks (so you know it's comprehensive)

value: Show HN: iOSPreCheck – Scan Native iOS Apps for Compliance Before Submission


value: takaakit

value:
A local MCP server that runs as a plugin for Astah Professional, a UML modeling tool. This MCP server enables you to do the following and more:<p>- Use AI to design systems and represent them as UML models and diagrams in Astah.
- Ask AI to explain UML models and diagrams in your Astah project.
- Generate source code from UML models and diagrams in your Astah project, and vice versa.
- Create UML diagrams in Astah from hand-drawn sketch images.

value: Show HN: UML Modeling Powered by AI Agents – Astah Pro MCP

author: takaakit


value: chrissdot

value: Structured output has become a de facto expectation for LLM APIs, but streaming that structured output is still a mess. A big miss, streaming partial results in parallel can nearly double agent throughput.<p>OpenAI, Anthropic, and Gemini handle streaming structured responses, but each breaks developer expectations differently: missing parsed results, losing final token stats, or adding unnecessary boilerplate.

value: Show HN: Streaming Structured Output Is Still Broken

author: chrissdot


value: rodrigotarca

value:
I'm seeing how many vibe-coded apps using Supabase there are nowadays that don't even test their RLS policies and that's a pretty terrifying thought.
Half these apps could have their entire DB exposed to public and nobody would know until it's too late. Recently an app leaked 13k users' data because of broken RLS.
So I built a CLI to test RLS policies before prod:
- Introspects your DB schema
- Simulates different roles (anon, authenticated, custom JWT claims)
- Tries CRUD operations on every RLS-enabled table
- Wraps everything in transactions with ROLLBACK (no actual data changes)
- Generates snapshots you can diff in CI
It's not a substitute for proper security reviews (attackers will always find crafty exploits). But it catches the obvious messups before they leak user data.
https://github.com/Rodrigotari1/supashield
What's your RLS testing workflow? What edge cases am I missing?

value: Show HN: CLI to Test Supabase RLS Policies

author: rodrigotarca


value: jeremyfowers

value:
Hi HN! We're Jeremy, Victoria, Krishna, and Daniel from the local LLM (Lemonade SDK) team at AMD and we're building Infinity Arcade (https://infinity-arcade.app). This project combines an open-source app and model to demonstrate what is possible for local LLM coding on the ~200 million everyday laptops out there with 16 GB of RAM. The progress has been surprisingly good, so I thought I'd show it here.
The problem: while LLM coding is amazing with large cloud-based models, the results you get with today's open-source 7B-8B parameter models (the maximum size for an average consumer laptop) are anything but inspiring. Even when we constrain ourselves to something "easy", like vibe coding retro arcade games in Python, the best sub-8B-parameter models can barely produce working copies of Snake and Pong.
Our solution: first, we needed an app that would make it easy to drive local LLMs with exactly the right prompts and features for creating games. We built three minimal agents: Create, Remix, and Debug. This allows users to make new games, make changes to existing games, and allows the system to automatically fix its own bugs.
Second, we needed to build our own model because none of the open-source offerings were cutting it. We assembled a dataset with >50k lines of high-quality Python game code, then used LORA SFT to create Playable1-GGUF (https://huggingface.co/playable/Playable1-GGUF): by far the world's highest performing 7B model at this task. It can handle 3x more game types and many fun riffs, like "space invaders with exploding bullets" and "breakout where the ball accelerates every time, I hit it."
There's a short GIF with a demo of the outputs here: https://github.com/lemonade-sdk/assets/blob/main/arcade/arac...
And one showing the full UI here: https://github.com/lemonade-sdk/assets/blob/main/arcade/infi...
Ultimately, the goal is to deliver a reference design that inspires startups to build with local LLMs. Local has no ongoing cloud costs and no privacy/security concern with sharing user's data over the internet. The main challenges are the capabilities of smaller local models, as well as the work to integrate a software stack that is relatively less mature than OpenAI/Open Router APIs. We believe Infinity Arcade shows that both challenges can be overcome and the benefits of local can be realized.
What you can do today: download the app from GitHub, install with 1 click, run Infinity Arcade and its LLM 100% free and local, check out the training data and process for the fine-tuned model, and build your own app using all of this as a reference.
Links:
GitHub: https://github.com/lemonade-sdk/infinity-arcade
Model: https://huggingface.co/playable/Playable1-GGUF
We’d love to hear your feedback! Are you convinced that the benefits of local outweigh the challenges? Do you have all the resources you need to get started? Let us know in the comments!

value: Show HN: Exploring the limits of local LLM coding on consumer HW

author: jeremyfowers


value: mraspuzzi

value:
We built this to answer "am I a fit for this role?"
after noticing how hard it is to get honest feedback when applying to a YC startup or something else entirely.
It's a custom 5-minute challenge that roasts you after.
Added a leaderboard for those who want to see how they stack up.
Roast us below.

value: Show HN: How Useless Are You? A brutally honest skills check

author: mraspuzzi


value: waprin

value:
Dashboard link: https://claude-vs-codex-dashboard.vercel.app/
I built a dashboard analyzing the sentiment between Claude Code and Codex on Reddit comments. The analysis searched for comments comparing Claude Code vs Code then used Claude Haiku to analyze the sentiment, and which model was preferred. It also lets you filter by various categories such as speed, workflows, problem-solving, and code quality. You can also weight by upvotes to make the comparison by upvotes rather than raw comment numbers.
Takeaways:
* Codex wins on sentiment (65% of comments prefer Codex, 79.9% of upvotes prefer Codex).
* Claude Code dominates discussion (4× the comment volume).
* GLM (a newer Chinese player) is quietly sneaking into the conversation, especially in terms of cost
* On specific categories, Claude Code wins on speed and workflows. Codex won the rest of the categories: pricing, performance, reliability, usage limits, code generation, problem solving, and code quality.

value: Show HN: Claude Code vs. Codex Reddit Sentiment Analysis Dashboard (Open-Source)

author: waprin


value:
Hi HN! I built RemindMe, an iOS app that uses AI to create context-aware reminders.
Unlike traditional reminder apps, RemindMe understands natural language and monitors real-world conditions:
• "Remind me to take an umbrella when it rains tomorrow" - We monitor weather
• Snap a photo of an event poster - We extract dates/times automatically (OCR + GPT)
• "Alert me when I'm near any grocery store" - Location category detection, not specific addresses
• Track YouTube uploads, product availability, website changes
What makes it different?
• Photo intelligence: Snap event posters, we extract dates automatically
• Natural language: "Remind me when it rains tomorrow" - we figure it out
• Smart monitoring: Track YouTube uploads, weather, product availability
• Location intelligence: Trigger at ANY grocery store, not just one address
Would love your feedback!

value: Show HN: RemindMe – AI reminders that understand context


value:
I’ve been working on a tool called Coordable, which helps analyze and improve geocoding results.
If you’ve ever dealt with geocoding at scale, you’ve probably hit two recurring problems:
Garbage in = garbage out.
Addresses are often messy (“2nd floor”, “/”, abbreviations, multiple addresses in one line…).
Most geocoders will fail or return incorrect matches if the input isn’t perfectly normalized.
A result isn’t always a correct result.
Many providers return something even if it’s wrong — e.g. shifting a house number, or confusing similar street names.
Assessing whether a geocoded result is actually right is surprisingly hard to automate.
Coordable tries to address both issues with AI and analytics:
Uses an LLM-based cleaner to normalize messy addresses (multi-country support).
Automatically evaluates geocoding accuracy by comparing input and output like a human would.
Lets you benchmark multiple providers (Google, HERE, Mapbox, Census, BAN, etc.) side by side.
Includes a dashboard to visualize results, quality metrics, and exports.
It’s not a new geocoder — it wraps existing APIs and focuses on data quality, comparison, and automation.
It’s currently in beta with free credits.
If you work with geocoding or address data, I’d love to hear how you handle these challenges and what kind of analytics would be most useful to you.

value: Show HN: Coordable – Get better geocoding results with AI cleaning and analytics


value: novocayn

value:
I wrote a hybrid vector store from scratch in Go. Supports hybrid retrieval over BM25, Flat, HNSW, IVF, PQ and IVFPQ Index with Quantization, Pre-Filtering, Reranking, Reciprocal Rank Fusion, Soft Deletes, Index Rebuilds and much much more.
It's called Comet. Built for hackers, not hyperscalers. It's tiny enough to fit in your head, and good enough to blow it :D
It packs everything you need to understand how modern vector databases work internally, from indexing to rank fusion, querying, and even persistence.
https://github.com/wizenheimer/comet
https://pkg.go.dev/github.com/wizenheimer/comet
Star it, fork it, break it, fix it — make it your own

value: Show HN: I built a vector store from scratch (and you can too)

author: novocayn


value: jacobn

value:
Hi HN, we've taken a stab at reimagining fonts in the world of Gen AI.
Rather than using prompts (slow, and how do you even describe a font?) we've pre-generated a large catalog, and organized it into "vibe" folders for easy exploration.
For those not familiar, typical font sites show you 10-20 crippled specimens in seemingly random order, have limited & inconsistent catalogs, are slow to load, and use complicated licensing terms that make it hard to know what you're allowed to do.
Font Hero solves these problems: hundreds of instantly updating custom specimens organized by "vibe", more than a million fonts with consistent & broad character sets, blazingly fast load times, and simple licensing:

  All fonts are free for commercial use while in beta.

(After the beta, we will offer simple and reasonable terms for future use.)
About the AI: we have trained a custom generative AI model from scratch using a dataset of non-copyrighted raster specimens. As with most first stabs at Gen AI, the results are on the one hand amazing (there are a lot of good-to-great fonts in the collection), but of course the error rate is non-zero, so if you dig around you'll soon find some "six fingered" glyphs too ;)
Please check it out, and we'd love to hear your thoughts!

value: Show HN: Gen AI for fonts, 1M free fonts organized by "vibe"

author: jacobn


value: Show HN: AI Chat Terminal – Private data stays local, rest goes to cloud


value: abstractbg

value: Thought I would post in celebration of 1 year of my website being online. I've been working on it on and off and currently the website allows users to play Hex, Tumbleweed, Amazons, and Connect 6 against friends or against practice bots. I've been a long time player of some of these games and I felt for a long time that the world could use a few more popular abstract strategy games compared to Chess or Go.<p>If you try it, let me know what you think. I'm always looking for new games or new features to add :)

value: Show HN: Play abstract strategy board games online with friends or against bots

author: abstractbg


value: a small AI chatbot, resizable and draggable, that answers with the current webpage in context, free requests with Mistral small, paid for stronger models

value: Show HN: CTRL Kai – AI Summarizer Chrome Extension


value:
Hey HN! Arky is a markdown editor with a twist — instead of writing in a linear doc, you work on a 2D canvas where you can:
• Place ideas anywhere spatially
• Organize them into hierarchy with drag & drop
• See the full document structure at a glance
• AI writes contextually — drag & drop responses anywhere on canvas
More info: https://arky.so/
Try it: https://app.arky.so/
(Access codes are in the comment below!)
Quick demo(60s): https://youtu.be/Nxsr5Ag2vEM?si=g6nKheRWWNuaLTe8
Would love to hear your thoughts!

value: Show HN: Arky – Visual 2D Markdown editor (access codes below)


value: mickeymounds

value: Show HN: We priced basic needs in work hours (global ranking and CSVs)

author: mickeymounds


value: jqssun

value: Show HN: Running Android 16 on Apple Silicon, the FOSS way

author: jqssun


value: lukefernandez

value:
WHAT:
Intro is an app for creating and sharing profiles (small websites representing a person, brand, business, etc).
Add photos, basic details, and anything else you want to share in a first impression. We’ll turn it into a simple, polished website, and viewers can message you through the app.
STATUS:
- Demo video here: https://x.com/heresmyintro/status/1978609089312596152
- Example sites available for business (https://heresmyintro.com/tires) and personal (https://heresmyintro.com/isabella)
- Available to try on iOS (https://apps.apple.com/us/app/heres-my-intro/id6749601914) and Android (https://play.google.com/store/apps/details?id=com.fourohseve...).
BACKGROUND:
We originally built Intro as a substitute for a phone number when dating, similar to Date Me profiles, but realized it might have a broader market as a website builder / digital business card creator. We think it has a level of simplicity and ease that could be applied to a broad number of use cases.
HOW YOU CAN HELP:
- Any feedback is greatly appreciated on design, positioning in the market, and how to get users. Thank you for checking us out!
- Reach out and follow us on X (https://x.com/heresmyintro). We’d love to talk to you.

value: Show HN: The Fastest Way to Build a Website (Under 2 Minutes)

author: lukefernandez


value: okane

value: Show HN: Code First CDC from Postgres to ClickHouse with MooseStack

author: okane


value: petarran

value:
Hey HN! I built shōmei, a super simple CLI tool that mirrors your corporate Git commits to your personal GitHub profile. It only sends commit timestamps via GitHub's API, zero code or IP.
I know that the contribution graph does not define you as a dev, but I made this for the devs who want to have it all in one place. Would love to hear your thoughts, especially around:
-> Security/privacy concerns you might have
-> Whether you'd use it
-> Other git platforms (GitLab/Bitbucket) worth supporting
Try it out and let me know what you think!

value: Show HN: Shōmei – Mirror your work commits to personal GitHub (timestamps only)

author: petarran


value: blourvim

value:
Very early in the development
It is just a simple collection of useful commands that are executed via ssh  to see what the server is like for now.
I think this is useful when you inherit a server with only an ssh connection and no documentation, or when trying to troubleshoot and need context for your LLM

value: Show HN: I am building hostprint an agentles system information probe via SSH

author: blourvim


value: arondeparon

value:
I built a small tool that turns any bank export (CSV, Qif, etc.) into a clear monthly budget summary.
https://banktobudget.com
It automatically detects:
- recurring income and expenses
- variable costs (like groceries, parking, etc.)
No account, no subscription.
I use GPT-5 under the hood to interpret messy transaction data and group it intelligently — it works across banks and languages.
Would love feedback on:
- accuracy of the categories
- clarity of the results
- features you’d expect

value: Show HN: BankToBudget – Instantly turn your bank exports into a monthly budget

author: arondeparon


value: Maaz-Sohail

value:
Hi HN — I built a client-side PDF editor because I got tired of “free” tools that upload files and add watermarks. This runs entirely in the browser: edit text, add images/logos, sign, merge/split. No upload, no watermark, no signup.
How it works (tech): WebAssembly + Canvas + Web Workers. Text edits operate on glyph runs; when metrics don’t match exactly we do a subtle white-out behind the run and draw new text over it (preserves layout). Signatures use velocity-aware smoothing (Catmull–Rom → cubic Beziers). Export dedupes XObjects to avoid file bloat. Mobile Safari gets tiled rendering to keep memory stable.
Perf (measured): ~1.2s FCP / ~1.5s LCP on mid-range Android for the landing UI; TBT ~0–30ms during typical edits.
Privacy: default is offline—no network calls during edit/export. Any optional cloud action is explicit and TLS-only. No watermark, no usage caps.
Gaps: no full OCR yet; no global find/replace; batch stamping per-page for now.
Try it: https://pdffreeeditor.com/
Direct editor: https://pdffreeeditor.com/editor/
Script-light mode (disables analytics): https://pdffreeeditor.com/?lite=1
I’d love feedback on weird fonts, RTL, giant scans, or perf gotchas. Happy to share more internals and ship fixes live in the thread. — Maaz

value: Show HN: TechRex – client-side PDF editor (no upload, no watermark)

author: Maaz-Sohail


value: justbobbydylan

value:
I built a web game where you guess drive time between cities using live traffic. You get scored by accuracy (exponential decay—from ~1000 for a perfect guess), and there are solo and private match modes with leaderboards and detailed stats.
Try it: https://etaguesser.com/
Tech: React + TypeScript, Supabase, Mapbox GL w/ live traffic. Happy to answer anything—game math, anti-cheat, or traffic quirks.

value: Show HN: ETA Guesser (real-time traffic guessing game)

author: justbobbydylan


value: sidhyatikku

value: Miss your Y2K music player? Get your skin today and play music via YouTube for free.

value: Show HN: Played – Music Player Skin

author: sidhyatikku

title: Show HN: Played – Music Player Skin


value: modinfo

value: Show HN: The Rift – AI Short Movie

author: modinfo

title: Show HN: The Rift – AI Short Movie


value: dirtyzero

value: its very simple right now lets the user buy a phone number and map it to an easy to make dialplan. records calls. I built it because I was working on making my voice agent traverse IVRs and I was tired of calling random numbers and myself. Any feedback is appreciated.

value: Show HN: Service for VoiceAI Agent Testing

author: dirtyzero


value: Gigacore

value: Show HN: I Built an AI Maturity Model for Software Engineers (and No One Cared)

author: Gigacore


value: zizhouwang

value:
I'm a control freak, the thought of having AI auto-replying to emails is ridiculous. Every founder I've met are the same way, just typing the words "Put your support/sales on auto-pilot" makes me cringe.
I built min. to help me and my teammates with emails. Who should read it, who should reply, when to follow up, etc.
min. is:
- A simple email interface to manage both private and team inboxes.
- An AI-native inbox that sorts and prioritizes messages into custom labels and folders.
- A replacement for patchwork of CRMs, helpdesks, and spreadsheets with just… email.
- Conversational agents (follow-ups, scheduling, nudges), deployable in two clicks.
min. is not:
- A hands-off inbox that auto-replies to everything for you. (e.g “put your support/sales on auto-pilot!”)
- Built just for speed - min. for teams who care about delivering an A+ experience with every interaction.
- Awkward, because it works exactly how you expect emails to work, 2 way syncs with Gmail.
2min demo video: https://www.youtube.com/watch?v=IKtzNL72P34
Live demo: https://app.getmin.ai/demo
DM me or shoot an email to hello@getmin.ai if you want to join the beta. Cheers!
Eric

value: Show HN: min. – lightweight AI-native inbox for teams

author: zizhouwang


value: JawadR

value:
Google Docs for Python basically.
For the past 4 months, I’ve been working on a full-stack project I’m really proud of called PyTogether; a real-time collaborative Python IDE designed with beginners in mind (think Google Docs, but for Python). It’s meant for pair programming, tutoring, or just learning Python together. It’s completely free. No subscriptions, no ads, nothing. Just create an account, make a group, and start a project. Has proper code-linting, extremely intuitive UI, autosaving, and live cursors. There are no limitations at the moment (except for code size to prevent malicious payloads).
Why build this when Replit or VS Code Live Share already exist?
Because my goal was simplicity (and education). I wanted something lightweight for beginners who just want to write and share simple Python scripts (alone or with others), without downloads, paywalls, or extra noise. There’s also no AI/copilot built in, something many teachers and learners actually prefer. Also its free lol
Tech stack (frontend):
React + TailwindCSS
CodeMirror for linting
Y.js for real-time syncing and live cursors
Skulpt to execute Python in the browser (for safety - I initially wanted Docker containers, but that would eat too much memory at scale. Skulpt has a limited library, so unfortunately imports like pygame wont work).
I don’t enjoy frontend or UI design much, so I leaned on AI for some design help, but all the logic/code is mine. Deployed via Vercel.
Tech stack (backend):
Django (channels, auth, celery/redis support made it a great fit)
PostgreSQL via Supabase
JWT + OAuth authentication
Redis for channel layers + caching
Fully Dockerized + deployed on a VPS (8GB RAM, $7/mo deal)
Data models:
Users <-> Groups -> Projects -> Code
Users can join many groups
Groups can have multiple projects
Each project belongs to one group and has one code file (kept simple for beginners, though I may add a file system later).
There were a lot of issues I came across when building this project, especially related to the backend. My biggest issue was figuring out how to create a reliable and smart autosave system. I couldn't just make it save on every user keystroke because for obvious reasons, that would overwhelm the database especially at scale. So I came up with a solution that I am really proud of; I used Redis to cache active projects, then used Celery to loop through these active projects every minute and then persist the code to the db. I did this by tracking a user count for each project everytime someone joins or leaves, and if the user count drops to 0 for a project, remove it from Redis (save the code too). Redis is extremely fast, so saving the code on every keystroke is not a problem at all. I am essentially hitting 4 birds with one stone with this because I am reusing Redis, which I've already integrated into my channel layers, to track active projects, and to also cache the code so when a new user enters the project, instead of hitting the db for the code, it'll get it from Redis. I even get to use Redis as my message broker for Celery (didn't use RabbitMQ because I wanted to conserve storage instead of dockerizing an entirely new service). This would also work really well at scale since Celery would offload the task of autosaving a lot of code away from the backend. The code also saves when someone leaves the project. Another issue I came across later is if people try sending a huge load of text, so I just capped the limit to 1 MB (will tinker with this).
Deployment on a VPS was another beast. I spent ~8 hours wrangling Nginx, Certbot, Docker, and GitHub Actions to get everything up and running. It was frustrating, but I learned a lot.
If you’re curious or if you wanna see the work yourself, the source code is here. Feel free to contribute: https://github.com/SJRiz/pytogether.
I’m still learning, so any feedback would be amazing (and contributions)!

value: Show HN: PyTogether, open-source lightweight real-time Python IDE for learners

author: JawadR


value:
Hi HN,
We’ve been working on Wan2.2 Animate, an AI animation tool that turns still images into motion.
Upload a static picture and a short reference video, and the AI brings your image to life—no rigging or keyframes required.
Key features:
Image-to-Motion Animation: Instantly animate any image with natural movement.
Character Replacement: Swap yourself or others into existing video scenes.
Expressive Details: Realistic gestures, body language, and facial motion.
Creator-Friendly Workflow: Built for storytellers, marketers, and designers.
Why we built this: creating animation used to be complex and time-consuming. Wan2.2 Animate makes it fast, creative, and accessible—helping anyone turn imagination into motion.
We’d love your feedback on:
What kinds of animations you’d like to create
Features that could make image-to-motion even smoother
How AI animation could fit your workflow
Try it here: https://www.wananimate.net/wan-animate

value: Show HN: Wan2.2 Animate – Turn Any Still Image into Motion with AI


value: runmix

value:
Show HN: AICrop — AI-powered local image resizer for social media (no signup, privacy first)
Hi HN — I’m excited to share AICrop ([https://aicrop.app](https://aicrop.app)), a simple tool my collaborator Claude Code and I built to solve a pain point we repeatedly saw among content creators and social media users.
The problem / origin story
Whenever I’m preparing images for social media posts or marketing, I always run into the tedious task of cropping or resizing the same photo to different aspect ratios (Instagram, Twitter, TikTok, LinkedIn, etc.). Doing this manually is time-consuming and error-prone, especially when I want the subject (face, object) to stay centered and look natural across all variants.
Claude Code and I decided: why not make a tool that automates cropping intelligently, but also respects privacy — meaning images never leave the user’s device?
What AICrop does
* You upload a photo (JPG / PNG / WebP, up to 10MB), and AICrop runs object/subject detection entirely in the browser using TensorFlow.js.
* It proposes crop frames for major social platforms (Instagram, Twitter, TikTok, LinkedIn, YouTube, etc.) with instant previews.
* You can fine-tune the results manually if needed.
* No server upload, no account required, no watermark — everything happens locally in your browser.
We built it through “vibe coding” — an iterative, creative collaboration between me and Claude Code. Instead of strict task division, we explored ideas dynamically, letting intuition and quick feedback loops drive progress. It turned out to be both fun and surprisingly productive.
What’s ready now
* Fully working browser version you can try out today (no signup barrier)
* Support for the major social media aspect ratios and previews
* Manual adjustment and export options
* 100% local processing to ensure privacy
What feedback / help we’re looking for
* Does the UX feel smooth and intuitive?
* Any crop sizes or platforms we should add next?
* Should we build features like batch processing or smarter detection modes?
* Thoughts on sustainability — would you keep it free, freemium, or open source?
* Any libraries, models, or optimization tips for better in-browser performance?
If you try AICrop, I’d love to hear your thoughts, bug reports, and ideas for improvement. I’ll be around in the comments to chat — thanks for checking it out!

value: Show HN: AICrop – AI-powered local image resizer for social media

author: runmix


value: iamjk

value: Playbot is a Rust CLI + TUI that reads the currently playing track from your local Spotify desktop app and displays lyrics, artist info, album details and more — no Spotify or Genius API keys required. It caches results in a local SQLite DB for fast lookups and includes an interactive browser for searching your library. macOS only. Install with cargo build --release and run ./target/release/pb. https://github.com/after-ephemera/playbot

value: Show HN: Playbot – See lyrics and artist details without alt-tabbing

author: iamjk


value: Show HN: Forever Free AI Daily Planner – No Login, Syncs to Calendar


value:
Hello Hacker News! We are LIVE!
I’m incredibly excited to finally launch YTVidHub, a tool built to solve a massive time sink that many of you here shared: the agonizing manual process of downloading transcripts from large YouTube corpora for research and data analysis.
The Problem: If you need subtitles for 50, 100, or more videos, the current copy-paste-download-repeat workflow is slow and painful.
Our Solution (The Core Feature): YTVidHub is engineered for true bulk processing. You can paste dozens of YouTube URLs (or a Playlist/Channel link) into one clean interface, and the system extracts all available subtitles (including multilingual ASR) and packages them into a single, organized ZIP file for one-click download.
Architectural Insight: Our design prioritizes "research-ready" data, taking cues from the discussions here on HN. We specifically optimized the plain text (TXT) output—stripping all timestamps and formatting—to make it instantly clean for RAG systems and LLM ingestion.
Business Model: YTVidHub is free for single downloads. Bulk operations receive 5 free daily credits to ensure fair use and manage our processing costs. Professional plans are available for high-volume data needs.
Future Focus: We know the ASR accuracy is the next big hurdle. We're already working on a Pro AI Transcription tier to offer high-accuracy, LLM-powered transcripts to tackle niche content and solve the data quality problem.
Please give the bulk downloader a test run and put the system through its paces. Any feedback you have on the speed and the cleanliness of the TXT output is immensely valuable to our engineering roadmap!
https://ytvidhub.com
Thanks for building with us.

value: Show HN: YTVidHub –Bulk YouTube Subtitle Downloader for Research and Data Prep


value:
I watch ChatGPT fail to count to 1 million with pretty wild excuses and the user wasn't able to prompt around it https://www.youtube.com/watch?v=djZVWc1PkIo.
My friend and I tried a couple of variations to get it started counting to 1 million, all failed. So we settled for trying to see whats the maximum number we can reach.
Methods we tried:
- Framing it as an experiment, on beating a benchmark against other llms or its previous runs, encouragement after small successes
- Foot in the door - ask to 10, 20, 30, 50, etc - kind of successful, record 110
And the winner:
- Simply count to 140 ourselves and ask it to repeat - success! Followup failed miserably. Have not tried counting to more, ran into daily limit : D
Anyone who has tried this how high did you get? We wern't able to find anyone get to a higher than 140 on youtube / google so far.
Dethrone us.

value: Show HN: ChatGPT counts outloud to 140 – beat our record


value: lethanhdung

value:
Hi HN,
I’ve been experimenting with building a lightweight, high-performance platform for exploring detailed watch specifications — focused on design structure, data modeling, and frontend rendering performance.
The project is live here: https://www.tagheuerreplica.io/
While it looks like a watch catalog, under the hood it’s a technical prototype designed to test:
- *Next.js 14* for static generation and incremental revalidation
- *FastAPI (Python)* as a microservice backend for structured metadata
- *SQLite* for ultra-fast, read-optimized data queries
- *Edge caching* via Vercel to maintain sub-100ms global load times
The architecture was built to explore how a small-scale app can serve complex reference data without heavy infrastructure or expensive cloud databases. It’s entirely serverless, and each watch reference loads from a precomputed JSON structure that syncs automatically when metadata updates occur.
I’m curious about feedback on:
- How to further optimize SSR performance for large JSON datasets
- Whether lightweight alternatives to SQLite (like DuckDB or Turso) might help
- Edge function cold-start minimization strategies
This was a fun weekend project that grew into a benchmarking sandbox for watch-related metadata.
Would love to hear any suggestions from the community — especially from those working on serverless data delivery or content-heavy apps.
Live demo: https://www.tagheuerreplica.io/

value: Show HN: A Performance-Focused Watch Database Built with Next.js and Python

author: lethanhdung


value:
We built FullHunt Agentic AI, an autonomous tool that maps an organization’s internet-facing assets and possible attack paths in seconds.
It automatically discovers exposed assets, correlates CVEs (EPSS, CISA KEV, FullHunt data), and produces a prioritized action plan.
Try a prompt like:
"Discover the attack surface of acme.com and identify possible attack paths."

value: Show HN: FullHunt Agentic AI – Discover Any Org's Attack Surface in Seconds


value: Spent two days and launched the minimum viable product (MVP) of the Sora Watermark Remover, hoping it can meet the needs of some friends who do self-media

value: Show HN: Sora Watermark Remover

title: Show HN: Sora Watermark Remover


value: Show HN: Desktop Composer [macOS] – Switch desktop and app themes in one click


value:
Free hosted version at https://www.networkingtoolbox.net
Can also be self-hostable with Docker, which supports custom branding and styling. There's an API, and keyboard shortcuts, and let's u bookmark frequently used tools for quick access and offline use. And... has zero AI.

value: Show HN: Networking Toolbox, 100 offline net tools for sysadmins


value: nkko

value:
I’ve been using Marp for slides since ChatGPT came out. It’s great for writing decks in Markdown, but the quality varies: too much text, inconsistent formatting, missing alt text, unreadable colors.
So I built SlideGauge.
What: A single-file, zero-dependency Python tool that scores Marp Markdown slides and reports concrete issues (length, bullets, lines, color/contrast, a11y, code blocks). Outputs JSON, text, or SARIF. Designed for AI agents and CI; useful for humans.
Why: Marp decks (especially AI-generated) often exceed readable limits or miss a11y basics. This lints them deterministically with actionable messages.
Try: uvx --from git+https://github.com/nibzard/slidegauge slidegauge example.md --text
Looking for feedback on rule defaults and additional formats.

value: Show HN: SlideGauge – Static analyzer for Marp Markdown decks

author: nkko


value: itsbloxx

value: Simple tool to audit your website for free and see how readable it is for LLMs like ChatGPT with some suggestions and recommendations of how to improve your ranking

value: Show HN: Free pagespeed tool for AI and LLM readability

author: itsbloxx


value:
Hi HN,
I built https://pipsgames.org
, a minimalist logic puzzle game inspired by Pips and NYT-style domino placement.
Highlights:
100% free & no signup required
Infinite procedurally generated puzzles
Share a specific puzzle link with friends to challenge them
Works on desktop & mobile
It’s a side project for fun & brain training. I’d love feedback on puzzle design, UX, and what to build next (e.g. level editor or leaderboard).
Thanks for checking it out!

value: Show HN: Pipsgames.org – Free, infinite logic puzzles with shareable challenges


value: tanaylakhani

value:
We believe you are more than your resume, your CV. We showcase creative work, experiences, and more, including travel, interests, and likes.
Rize lets users create rich profiles with projects, writings, a gallery, and privacy-aware analytics. It includes a dashboard, onboarding flow, and OAuth sign-in.
LinkedIn was built for your parents.
It wants you to look "polished," "professional," and already "established."
But what if you're just starting out?
You don't have years of experience. You don't have a fancy resume. What you do have are… sketches, side projects, edits, reflections, experiments.
It's time for you to have your own story told on the platform that is built for you.

value: Show HN: A modern, open-source profile and portfolio platform built with Next.js

author: tanaylakhani


value: Hyperway

value: Show HN: Open-source Veo/Imagen social app (Flutter FastAPI mock mode)

author: Hyperway


value: ditax

value: The title says it all. I was not able to find any well built and documented erlang/elixir library to work with Supabase HTTP API and/or Supabase Realtime system, so I built one.

value: Show HN: Erlang/Elixir library to work with Supabase

author: ditax


value: johnnyApplePRNG

value:
TL;DR: private, in-browser converter that turns pretty much any image file format into standard JPEGs. Everything runs locally. No uploads.
This started as a five-minute job and forty hours later...
I wanted to convert a HEIC without uploading it anywhere, so I wrestled Emscripten/WebAssembly to run Google's Jpegli inside a Web Worker. Now there's a small UI and it handles a bunch of formats.
Just about the only thing it can't decode is JXL - but there's still some JPEG XL magic in there: XYB perceptual color quantization is enabled by default via Jpegli.
The upside of all this over-engineering is privacy and compatibility: images are processed entirely on your machine and never touch a server; the output is a regular JPEG that works everywhere.
I could have used a CLI, sure — but where's the fun in that?
Would love feedback on edge cases and defaults.
Tested on Firefox, Chrome, and Safari.
Cheers!

value: Show HN: OnlyJPG – Client-Side PNG/HEIC/AVIF/PDF/etc to JPG

author: johnnyApplePRNG


value:
Hi HN,
We built one of the largest RAG set-ups that exist toady with Usul.ai (6B tokens). We started by using langchain and llamaindex, they were able to get us to a prototype in a couple of days, but took 3 months of taking pieces apart and optimizing them to make it perform well at such large scale.
We put all of these learning into an MIT licensed open-source project — Agentset. Our goal to let people get production quality RAG w/o having to understand or optimize the underlying pieces. It supports 22 file formats, agentic search, deep research, citations, and a UI out of the box.
Happy to answer any questions about our journey or the product.

value: Show HN: Agentset – Open-source RAG with vector DB, embeddings, and API built-in


value: pkolchanov

value: Show HN: A single-header application wrapper for software rendering

author: pkolchanov


value: Show HN: Datapizza AI – Lightweight open source framework for GenAI apps


value: After receiving feedback, I decided to offer a lifetime license for Monokai Pro, in addition to the subscription model. The freemium model still exists, at $1 / month, and the lifetime license is set at a one-off fee of $29.<p>This change brings it more in line with the licenses of VSCode and Sublime Text, with the added bonus that Monokai Pro for JetBrains is available for multiple IDEs.

value: Show HN: Monokai Pro for JetBrains – lifetime license


value: nieuwmijnleven

value:
Are you a Java developer frustrated by verbose syntax and null pointer exceptions?
Meet *JPlus*, a modern JVM language that extends Java with powerful features like strict null safety, type inference, and functional programming — all while maintaining full compatibility with your existing Java codebase!
---
## *Why JPlus?*
-  Keep your Java ecosystem intact — use all your favorite libraries and frameworks without modification
-  Boost productivity with built-in null safety and concise syntax
-  Adopt gradually — write new code in JPlus without rewriting your entire project
-  Compile to standard Java bytecode and run anywhere the JVM runs
---
## *Make an Impact — Join Us!*
JPlus isn’t just a new language — it’s a *movement to modernize and evolve the Java ecosystem* for the next generation of developers.
By participating in this project, you're not only improving your own development experience but also *shaping the future of Java itself*.
We’re calling on *developers, contributors, and supporters* who share this vision to get involved:
-  *Actively contribute* to language design, compiler development, documentation, and tooling
-  *Join discussions* to share ideas, report issues, and give feedback
-  *Support the project* by spreading the word, sponsoring, or helping onboard new users
Whether you're a seasoned Java developer or just passionate about language design, *your participation matters*.
With your support, JPlus can become a cornerstone of the JVM ecosystem — safer, more expressive, and future-ready.
---
*Explore the project:* [https://github.com/nieuwmijnleven/JPlus](https://github.com/nieuwmijnleven/JPlus)
 *Try it, contribute, and help redefine Java development!*
----

value: Show HN: JPlus – The Java Superset Language

author: nieuwmijnleven


value: resill

value: When not using Google Play services (e.g. GrapheneOS, LineageOS users), Signal can be a real battery drain. Molly with UnifiedPush on the other hand is extremely battery efficient. Here's how to set this up, using Nextcloud as the UnifiedPush provider.

value: Show HN: Using Molly (Signal) with UnifiedPush

author: resill


value: rnaga

value:
Hi HN,
I built wp-mcp — an MCP (Model Context Protocol) server that exposes WordPress management tools to AI clients.
It connects directly to your WordPress database through wp-node, a TypeScript library that mirrors WordPress core CRUD operations.
The goal is to let AI agents or CLI tools create, edit, and publish WordPress content without PHP or wp-admin.
What it can do:
- Create and update posts
- Manage users, categories, tags and more
- Move drafts through review → publish flows
- Works via STDIO (for local clients like Claude Desktop) and Streamable HTTP (for remote setups)
Proxy and Transport:
- Includes a lightweight proxy utility that runs as a local MCP endpoint and forwards requests to a remote wp-mcp server
- Useful when your AI client doesn’t fully support remote MCP or OAuth
Under the hood:
- Written entirely in TypeScript
- Uses wp-node for strongly typed access to the WordPress database (MySQL/MariaDB)
- Returns structured responses for AI clients
- Early but stable enough for draft-to-publish automation
Docs and quick-start instructions are in the README.
https://github.com/rnaga/wp-mcp
Thanks for checking it out.

value: Show HN: WP-MCP – An MCP server to control WordPress

author: rnaga


value: olek

value:
Hey!
I built Arete (https://getarete.app) because I kept wanting to ask questions about random text I found online. A word I didn't know, a paragraph I wanted simplified, a quote I wanted fact-checked. Each time I'd copy it, open a new tab, paste it, and come back. It got tedious.
Arete lets you select any text on any webpage and instantly send it somewhere useful. Translate it, explain it like you're 5, fact-check it with sources, or search for it on Wikipedia, YouTube, Stack Overflow—whatever makes sense for that text.
You set up the actions you want. Some people use it for research (Perplexity), others for code help (Stack Overflow), translation, shopping on Amazon, finding music on Spotify. It's just a lightweight tooltip that appears on text selection.
It's a side project. The extension works and is ready to try. Happy to answer questions and get feedback on whether this solves a real problem for people.
https://getarete.app

value: Show HN: Arete – AI text actions from any browser selection

author: olek


value: waprin

value: Show HN: Reddit Sentiment Dashboard for Claude Code vs. Codex (OSS)

author: waprin


value: gsahu

value: Show HN:Cyphora – decentralized cloud storage on your phone (no blockchain)

author: gsahu


value: vinhnx

value:
VT Code is a Rust CLI/TUI coding agent for AST-aware edits (Tree-sitter, ast-grep). Multi-provider routing with failover and caching (OpenAI, Anthropic, Gemini, DeepSeek, xAI, OpenRouter, Z.AI, Moonshot; Ollama locally). Policy-gated tools, workspace boundaries, Zed ACP integration. Config-first via vtcode.toml; reproducible model/constant metadata in the repo.
Try it: cargo install vtcode; vtcode
Code: https://github.com/vinhnx/vtcode

value: Show HN: VT Code – AST-aware Rust agent for terminal (Tree-sitter/AST-grep)

author: vinhnx


value: uchibeke

value:
Hey HN,
I'm Uchi, and I just spent 3 days building something I wish existed 2 years ago: an AI that briefs you on meeting attendees in seconds.
The Problem:
I was winging important meetings because researching attendees took too long:
1) LinkedIn stalking = 10-20 min per person
2) ChatGPT prompting = "Who is Sarah Chen at Sequoia?" → back-and-forth → 10 min
3) Google search = buried in SEO spam
I'd show up to pitches/sales calls knowing nothing about who I was talking to. Felt unprepared. Lost deals.
What I Built:
Briefing AI = Paste calendar invite → Get CIA-style dossier in seconds.
It extracts attendees from your calendar text, generates:
1) Executive summary
2) Attendee profiles (role, background, priorities)
3) Company and Attendee insights
4) Talking points
5) Red flags to avoid
The Tech:
1) Next.js 15 + Cloudflare Pages (serverless)
2) Brave Search API for Attendee and company insights
3) Mix of GPT + DeepSeek models for calendar parsing, Attendee enrichment + briefing generation
4) Stripe for payments ($49 Founder's Lifetime Deal)
5) Device fingerprinting for 1 free briefing (then paywall)
Why Not Just Use ChatGPT?:
You can! But:
1) ChatGPT = 10 min of prompting ("extract attendees" → "research Sarah" → "summarize")
2) Briefing AI = seconds (paste → done)
3) ChatGPT = loses history
4) Briefing AI = saves to dashboard
I'm not trying to compete with ChatGPT. I'm just making one specific workflow 10x faster.
What's Next:
Right now, we  do some research with Brave Search API but everything else is 100% AI-generated. I'm adding:
1) Real-time LinkedIn/Google search (v2)
2) PDF export
3) "Share briefing" for viral growth
4) Get Text/SMS briefing before an important meeting
Honest Feedback Request*:
This is my first HN launch. I need brutal honesty:
1) Is this actually useful or am I solving a fake problem?
2) Would you pay $49 for this?
3) What features am I missing?
Try it free (no credit card): [getbriefing.io]
(I'm offering a $49 Founder's LTD for 7 days, then switching to $19/month. Trying to validate quickly before building more features.)
Thanks for checking it out!

value: Show HN: Was winging important meetings. So I built an AI that briefs me in secs

author: uchibeke


value: CheatEngineRS is minimal implementation of CheatEngine written in Rust with ratatui for TUI. Currently it supports limited data types (u32/64, i32/64) and works only on MacOS and Linux.

value: Show HN: Minimal CheatEngine TUI Implementation in Rust


value: Show HN: I built an MCP to schedule posts to 20 social media platforms


value: SafeDusk

value:
Starting an exploratory version of SWE-Grep[0] since it doesn't look like they will do it.
It is now just a simple RNN model with synthetically generated datasets.
Have a look and follow the progress at https://github.com/aperoc/op-grep!
[0]: https://cognition.ai/blog/swe-grep

value: Show HN: Open SWE-grep for fast, high-precision code context

author: SafeDusk


value: jasonsmiles

value:
Hi HN!
Worked on this side project for a while and wanted to share it. It's free to play.

value: Show HN: I'm making a detective game built on Wikipedia

author: jasonsmiles


value:
Problem
JSON/NDJSON is everywhere in data platforms, but compression usually breaks searchability. You either keep queryable raw stores (high I/O/egress) or compress into gz/zstd blobs (cheap to store, painful to probe). The “cloud tax” shows up as wasted reads.
What I built (SEE — Semantic Entropy Encoding)
A schema-aware, searchable compression codec for JSON that keeps exists/pos lookups fast while still compressing. Internals: structure-aware delta + dictionaries, a PageDir + mini-index to jump to relevant pages, and a tuned Bloom filter that skips ~99% of pages. AutoPage (131/262 KiB) balances seek vs throughput.
Benchmarks (apples-to-apples, FULL)
- size ratio: str ≈ 0.168–0.170, combined ≈ 0.194–0.196
- Bloom density ≈ 0.30; skip: present ≈ 0.99, absent ≈ 0.992
- lookup (ms): present p50/p95/p99 ≈ 0.18/0.28/0.37; absent ≈ 1.16–1.88/1.36–2.11/1.58–2.41
Numbers are stable on a commodity desktop (i7-13700K/96GB/Windows).
Try it in 10 minutes (no build)
1) pip install see_proto
2) python samples/quick_demo.py
It prints size ratios, Bloom density, skip %, and lookup p50/p95/p99 on a packaged sample.
Why not “just zstd”?
We sometimes lose pure size vs zstd alone. The win is searchable compression: Bloom + PageDir avoids touching most pages, so selective probes pay less I/O/egress and finish faster. On large log scans this often wins on TCO even with similar raw ratios.
Link (README + quick demo + one-pager)
https://github.com/kodomonocch1/see_proto

value: Show HN: Searchable compression for JSON – ~99% page skip and sub-ms lookups


value:
Hi HN,
I just released HardView 3.2 a Python library to monitor hardware: CPU, RAM, temperatures, fans, and more.
Most functions are cross-platform (Windows + Linux)
Install via PyPI:
pip install HardView
Docs and source: https://github.com/gafoo173/HardView
Curious to hear what you think!

value: Show HN: HardView – Python library for Windows hardware monitoring


value: Speykey

value:
Skreeb explores how rational empathy and AI mediation can coexist under transparent governance.
The white paper (v1.0, DOI: 10.5281/zenodo.17379494) includes the Emotional Recursion Framework (ERF),
the AI Emotional Trainer concept, and a governance architecture for creative arbitration.
• GitHub Release: https://github.com/speykye/skreeb-whitepaper/releases/latest
• DOI: https://doi.org/10.5281/zenodo.17379494
• Overview: https://speykye.github.io/skreeb-whitepaper/
Feedback and collaboration inquiries are welcome.

value: Show HN: Skreeb – Governance and AI Blueprint White Paper (With DOI)

author: Speykey


value: ruchirp

value:
Hello HN,
Extremely nervous and proud to show auto ad skipping for embedded ads.
I was tired to pressing +30 +30 +30 -10 to skip ads. So I wrote a pipeline, and co-wrote app with my best friend Alex to make this happen.
Feedback, suggestions, all welcome. If you can't afford, please reach out and happy to give free lifetime subscription.

value: Show HN: Podcasts – embedded auto ad skipping

author: ruchirp


value: minche

value:
We built an in-browser, LLM-guided fuzzer to automatically discover hidden prompt injection vulnerabilities in AI-powered browser assistants (often called agentic AI browsers). These are browser-based AI agents that can read and interact with web pages on a user's behalf (e.g. summarizing pages or clicking links). The problem is that malicious instructions can be embedded in a webpage's content (even invisibly) and trick the agent into doing unintended actions. For example, a recent exploit in Perplexity’s AI Browser Comet showed that hidden prompts in a Reddit post could make the assistant exfiltrate the user’s private data and perform unauthorized actions across other sites. Such attacks bypass traditional web security boundaries like same-origin policy, because the AI agent has the user’s privileges on all sites – an attacker could potentially read emails, steal auth tokens, or click dangerous links without needing any browser bug. The AI simply obeys the hidden instructions as if they were the user’s, which is a serious new threat.
To systematically uncover these vulnerabilities, we developed a fuzzing framework that runs entirely inside a real browser. Each test case is an actual webpage (loaded in an isolated tab) so the agent perceives it just like a normal user-opened page, with full DOM and content. An LLM (like GPT-4) is used to generate diverse malicious page contents – starting from some known prompt injection patterns and then mutating them or creating new variants. The browser is instrumented to detect when the AI agent misbehaves (e.g. clicks a hidden phishing link or follows a concealed instruction), and this real-time feedback is fed back into the fuzzer to guide the next round of attacks. In essence, the LLM fuzzer acts as an adaptive adversary: after each failed attempt it “learns” and evolves more sophisticated prompt injections to try on the next iteration. This closed-loop approach gives high-fidelity results and virtually zero false positives, since we only count an attack as successful if the agent actually performs an unwanted action in the browser. By doing all of this within a live browser environment, we can observe the agent under realistic conditions and quickly hone in on exploits that truly work in practice.

value: Show HN: LLM In-Browser Fuzzer Finds Hidden Prompt Injection in AI Browsers

author: minche


value: zacharyfmarion

value:
Built this as a "Cursor for OpenSCAD" after getting frustrated copying code into ChatGPT to debug complex operations.
Features:

  - AI copilot that reads code/diagnostics and makes validated edits
  - Monaco editor with tree-sitter auto-formatting
  - Live 3D mesh viewer + proper 2D mode with SVG viewer

Privacy / models

  - By default uses <model/provider> via your API key; no server of mine
  - You can disable AI entirely; all non-AI features run locally

Status / limits

  - Early alpha, macOS only today (Tauri app). Windows/Linux planned.

Looking for feedback on

  - Editing/formatting defaults for larger codebases
  - 2D SVG workflow (is it useful / what’s missing?)
  - Must-have features before Windows/Linux

value: Show HN: OpenSCAD Studio – AI-assisted OpenSCAD editor (macOS alpha)

author: zacharyfmarion


value:
I built this because I was tired of my PM (and people complaining about their PM) asking “Is this in testing yet?” or “Any update on ticket XYZ?” every time I pushed code.
So I wired up a GitHub Action that automatically pings them whenever a ticket in Jira/Linear hits testing, or a build completes, so they don't DM me, and I don't need to pretend I didn't see it until the next day (sometimes longer).
It’s tiny, installable in under a minute, and surprisingly effective at keeping everyone sane on my team.
GitHub Action: https://github.com/marketplace/actions/pm-updater
Main repo: https://github.com/peculiarism/eng-pm-mediator
What it does:
- Trigger on a push to a specific branch or main.
- Automatically notifies your PM via Slack (more integrations coming soon - should we support WhatsApp? iMessage? God that would be so annoying)
- Includes branch name, ticket reference in Linear or Jira, and build status
- Passive aggressive messages optional
It started as a joke that I built over an evening, but now some friends are using it so I thought I'd turn it into a public repo for others to benefit.
Would love feedback, PRs or feature requests. Maybe next version should reverse-notify engineers when PMs miss their deadlines.

value: Show HN: I automated the "hey is this done yet?" Slack message from PMs


value: felarof

value:
Hey HN, we just shipped a browser with an inbuilt MCP server!
We're a YC startup (S24) building BrowserOS — an open‑source Chromium fork. We're a privacy‑first alternative to the new wave of AI browsers like Dia, Perplexity Comet. Since launching ~3 months ago, the #1 request has been to expose our browser as an MCP server.
-- Google beat us to launch with chrome-devtools-mcp (solid product btw), which lets you build/debug web apps by connecting Chrome to coding assistants. But we wanted to take this a step further: we packaged the MCP server directly into our browser binary. That gives three advantages:
1. MCP server setup is super simple — no npx install, no starting Chrome with CDP flags, you just download the BrowserOS binary.
2. with our browser's inbuilt MCP server, AI agents can interact using your logged‑in sessions (unlike chrome-devtools-mcp which starts a fresh headless instance each time)
3. our MCP server also exposes new APIs from Chromium's C++ core to click, type, and draw bounding boxes on a webpage. Our APIs are also not CDP-based (Chrome Debug Protocol) and have robust anti-bot detection.
-- Few example use cases for BrowserOS-mcp are:
a) *Frontend development with Claude Code*: instead of screenshot‑pasting, claude-code gets WYSIWYG access. It can write code, take a screenshot, check console logs, and fix issues in one agentic sweep. Since it has your sessions, it can do QA stuff like "test the auth flow with my Google Sign‑In." Here's a video of claude-code using browserOS to improve the css styling with back-and-forth checking: https://youtu.be/vcSxzIIkg_0
b) *Use as an agentic browser:* You can install BrowserOS-mcp in claude-code or Claude Desktop and do things like form-filling, extraction, multi-step agentic tasks, etc. It honestly works better than Perplexity Comet! Here's a video of claude-code opening top 5 hacker news posts and summarizing: https://youtu.be/rPFx_Btajj0
-- *How we packaged MCP server inside Chromium binary*: We package the server as a Bun binary and expose MCP tools over HTTP instead of stdio (to support multiple sessions). And we have a BrowserOS controller installed as an extension at the application layer which the MCP server connects to over WebSocket to control the browser. Here's a rough architecture diagram: https://dub.sh/browseros-mcp-diag
-- *How to install and use it:* We put together a short guide here: https://git.new/browseros-mcp
Our vision is to reimagine the browser as an operating system for AI agents, and packaging an MCP server directly into it is a big unlock for that!
I'll be hanging around all day, would love to get your feedback and answer any questions!

value: Show HN: We packaged an MCP server inside Chromium

author: felarof


value:
Hi HN :) If you've ever played a game like scrabble or bananagrams you probably know the gratifying feeling of packing your letters into a dense little grid.
blueblocks gives you letters and assigns points for the _bounding box_ of your valid-words solution; smaller area is better!
every day has a "perfect" dense solution... NYT-style, mondays are easiest, sat-sundays are hardest!
feedback welcome! hope you enjoy :)

value: Show HN: Blueblocks, a letter-packing word game


value:
I built Kortx, an MCP server that lets AI assistants like Claude Code consult with OpenAI's GPT-5 models when they need specialized help. Think of it as giving your AI coding assistant a more experienced colleague to bounce ideas off of.
The problem: When you're coding with Claude Code, sometimes you want strategic feedback on your architecture, alternative approaches to a problem, or help debugging a tricky issue. Claude is great, but having a second AI perspective can be valuable.
Kortx provides four specialized consultation tools:
• think-about-plan: Get strategic feedback on technical plans (clarity, risks, dependencies)
• suggest-alternative: Explore different approaches with trade-off analysis
• improve-copy: Enhance documentation and messaging for clarity
• solve-problem: Debug assistance with root cause analysis
What makes it interesting:
• Uses the GPT-5 Responses API (not Chat Completions) with reasoning effort controls
• Automatically gathers relevant context from your codebase via Serena, graph-memory, and CCLSP integrations
• Built on the Model Context Protocol, so it works with any MCP-compatible AI assistant
• Designed to prevent MCP timeouts with optimized defaults (gpt-5-mini, minimal reasoning)
Technical stack: TypeScript, Zod validation, Pino logging, Docker support, full CI/CD with provenance
It's been really useful in my own development workflow - the architecture review and alternative suggestions have saved me from several poor design choices.
GitHub: https://github.com/effatico/kortx-mcp
NPM: npx @effatico/kortx-mcp
Docs: https://github.com/effatico/kortx-mcp#readme
Would love feedback from the HN community, especially if you're using Claude Code or other MCP clients!

value: Show HN: Kortx – MCP server connecting Claude Code to GPT-5


value: andydotxyz

value:
A downloadable app creator that makes it easy for anyone to create and compile native apps that work on all platforms. Just pick your platform/processor on the download page and execute the app - no signup required.
This is built with the Go programming language and the Fyne graphical toolkit - my main focus for many years now. If you have the developer tools installed you can do a native build locally - and if not it is integrated with a backend build system that does the build for you for any platforms.
Keen to get feedback on initial flow, user experience or overall functionality. There is a feedback button in the app. Thanks so much for trying this out!

value: Show HN: Apptrix.ai – an app creator for all platforms

author: andydotxyz


value: aqrashik

value: Show HN: SteganoPDF – Embed any file within a PDF

author: aqrashik


value: babblingfish

value: Why novels are an ideal project for bringing wonder into your life

value: Show HN: Stop Chasing Success: Write for Wonder Instead

author: babblingfish


value: raymondtana

value:
"Langr" is a language guessing game, similar to other Wordle variants, and is unique for its multimodality. The game provides a sample of a given language of the day, revealing more clues about that sample as the game goes on:

    Audio -> Phonetic -> English -> Family -> Written

Your job is to use a list of world languages to identify the language behind the sample.
The inspiration came after one-too-many-times of hearing a random language on the street and not being able to confirm what it was...
Spoken samples, sentence text, and other speaker metadata are sourced from the open-source Mozilla CommonVoice. Language metadata was mainly sourced from Glottolog, including a language's family lineage. Phonetic transcriptions are generated using eSpeak. English translations needed to be generated by Google Translate. The langcodes library helps to translate between each library using standard ISO 639‑3 codes.
Hope you enjoy! Feel free to try out today's game as well as some from the previous few months.

value: Show HN: Langr: a daily language guessing game

author: raymondtana


value: Moowool

value:
Medicated Emacs polishes the vanilla Emacs experience with minimal packages. Everything uses standard Emacs patterns and conventions. No frameworks, wrappers, or special systems to learn. If you know how to customize vanilla Emacs, you know how to customize this config.
What you get:
- Modern completion (Vertico + Orderless + Marginalia)
- LSP support via Eglot (built-in)
- Git integration (Magit + diff-hl)
- Quality-of-life improvements (better defaults, recent files, etc.)
- Common language modes pre-installed
What you don't get:
- Custom keybinding schemes
- Framework abstractions
- Configuration complexity
- Non-standard Emacs patterns

value: Show HN: Medicated Emacs, Work-Ready Vanilla Emacs

author: Moowool


value: predict-woo

value:
tl;dr It's a CoreML/MLX translation of SimulStreaming (2025 SOTA in simultaneous speech transcription), which itself is a combination Simul-Whisper and WhisperStreaming.
I'm currently building an application, and I thought I would open up the backend model code for everyone to use.
I get ~15x speed increase on my M2 Macbook Pro compared to the original pytorch implementation, and I'm gonna be using the medium model, which has a nice balance between memory usage and accuracy.
The CoreML part is from whisper.cpp, and it only contains the encoder, and the mlx part is from mlx-whisper.

value: Show HN: Lightning-SimulWhisper: Real-Time ASR for Apple Silicon

author: predict-woo


value: cozis

value: Show HN: WL, a new templating engine for C

author: cozis


value: preezer

value:
I noticed that many users of my SaaS are only creating accounts, but they are not actually using them.
I wanted to see what they are doing to find out if they don't actually understand the function or if these are just bots.
There are many tools for session recording, something like Posthog or others. These tools are all really great, but I don't really need 90% of the given functions. Recently I got access to Claude, so I vibecoded this small tool.
I run this tool now for a few weeks, and I'm really happy with the function.
Maybe you want to give it a try; it's free, lightweight, and easy to install via docker compose.
URL to the project:
https://github.com/berti92/opensessionreplay

value: Show HN: OpenSessionReplay

author: preezer

title: Show HN: OpenSessionReplay


value: RizkiAnurka

value:
I built boGO, an open-source tool that generates a complete Go backend service directly from your SQL schema or queries.
You just write your tables or SQL definitions, run the generator, and boGO produces:
Models and repository code
API handlers and routes
A ready-to-run backend project structure
The main goal is to help developers bootstrap backend services quickly, explore Go project architecture, or prototype APIs without boilerplate.
The generated code follows clean architecture principles and is easy to extend or customize.
It’s also a nice starting point for learning how SQL maps into production-grade Go code.
Repo: github.com/withbogo/bogo
There’s still a lot of potential for improvement. I’d love feedback, suggestions, or contributions.
Check it out and let me know what you think!

value: Show HN: BoGO – Generate Go back end services directly from SQL files

author: RizkiAnurka


value: california-og

value:
ASCII AUTOMATA is a tool to analyze the visual connectivity of characters in textmode fonts. It works by scoring edge connectivity of each piece and finding the best matching neighbour piece. Every time it places a piece, it "grows" towards the edges it touches by placing a matching piece. The red heatmap shows how frequently each character is used, useful for analyzing the fonts.
I initially made it as a tool for myself. When I design textmode art fonts it is sometimes difficult to figure out if a specific character would actually be useful for drawing or not. I wanted a tool which would show how useful and versatile some character is, and how well it connects to all other pieces.
But, as it turned out, this tool produces unexpectedly beautiful emergent patterns, so I made it into a proper little toy-tool for anyone to play around with.
Sidenote: it was also a good opportunity to test a new method for constructing a responsive semi-complex UI.
I made a web component which renders text as SVG paths using hershey vector fonts. The SVG fills the parent element, and applies stroke after the stretching happens: so strings "a" and "aaa" take the same amount of space, while remaining legible because the stroke is independent of the text's transformations. Thus, I never have problems with overflowing text in the UI!
The layout is made with a CSS grid. For example the sidebar is simply  <div style="--cols:8;--rows:41;" class="sidebar grid">  and then each UI element gets a position and size  <vec-text style="--x:1;--y:19;--w:2;--h:1;">Cell Width</vec-text> . As a result, the layout is easy to make, the sidebar itself can be any size or shape,all the UI elements stay exactly where I put them, and all text remains legible due to the stretchy, monolined vector font web component. It's great!
The WHOLE UI layout is just 120 lines of HTML, and 40 lines of CSS (for around 90 UI elements)!
(it did take a while to fiddle with the coordinate numbers, but I'm working on a wysiwyg tool to make that easier too...)
[crossposted this comment from mastodon: https://typo.social/@gdc/115405978249292146]

value: Show HN: ASCII Automata

author: california-og

title: Show HN: ASCII Automata


value:
Hi HN! I built Scribe Notes to solve a problem I kept having: to get organized with my daily work and solve the "explain what you did today and why you should still be employed" problem.
It's a 5-minute daily work journal that runs entirely in your browser. Zero backend, zero tracking, zero data leaving your machine. Everything is stored locally using IndexedDB and local storage.
Key features:
- Daily notes with @organization and #project tagging
- 30-minute time block tracking (click to toggle)
- Auto-generated weekly summaries for standups
- Stats and visualizations for performance reviews
- Export/import for backups
The idea is you bookmark it and spend 2-5 minutes at end of day jotting down what you worked on. Then when performance review season comes around, you're not desperately trying to remember what you did 6 months ago.
Tech stack is intentionally simple - it's a pure client-side app with no server dependencies. You can even run it offline once loaded.
Would love feedback from the community! What features would make this more useful for your daily workflow?
Live at: https://scribe-notes.com/

value: Show HN: Privacy-first work journal with no backend


value: stevenhubertron

value: A minimalist, self-hosted bookmarking service designed to be your default new tab page.<p>Assumes you have a homelab and Tailscale to keep everything secure.

value: Show HN: Quickmark Lightweight Bookmark Manager

author: stevenhubertron


value: rmtbb

value: I turned my resume into a catchy pop song.  Thought you'd all appreciate it.  Worked directly on the Song Style prompt, which you can duplicate for your own fun catchy resume song.  Just replace the lyrics!

value: Show HN: I turned my resume into a catchy song. It's a game changer

author: rmtbb


value: hunterbown

value: Open-source, reproducible modeling and analysis framework for analog Hawking radiation in laser–plasma flows.<p>Explores a hybrid fluid + plasma ‘flying mirror’ coupling under controlled assumptions; results are comparative and speculative in nature.

value: Show HN: Analog Hawking radiation modeling in laser–plasma flows

author: hunterbown


value: darkstarsys

value: I'm passionate about climate change, so as a sci viz practitioner, I want to communicate it accurately and clearly to a wide audience. I started by doing my own analysis of the OISST sea surface temp dataset (which is brilliant, updated daily) and soon realized I <i>had</i> to visualize it on a real 3d globe, without maps' areal distortions. So here it is. All open source, MIT licensed. Yes, a significant amount of it is AI-assisted, but all designed and code-reviewed by me. Please share if you like it! Comments welcome.

value: Show HN: Globe Viz: Global sea surface temps over time

author: darkstarsys


value: AzamatKh

value:
Hi HN,
I built OneClickPRD because as a solo builder I often wasted hours vibe coding without clear goals. I’d start with an idea, but it was vague, so the code got messy and I had to redo things.
OneClickPRD asks you a few questions about your product and then generates a short, structured PRD. The format works well with AI tools like Replit, Lovable, or v0, so you can go from idea to working MVP much faster.
Demo: https://oneclickprd.com/
Would love your feedback: does this feel useful for your projects, and what would make it better?

value: Show HN: OneClickPRD – Save hours vibe coding with concise PRDs

author: AzamatKh


value:
A social media built for developers, founders & tech creators — a place to write, share, and connect with others in tech.
We hit 30+ users after Day 1, and people have already started posting their first blogs!
Here’s one of the featured blogs written just 2 days ago: https://www.algosyncverse.com/blog/69ecc455-a812-4c62-b18e-0...
Check it out here → https:/www.algosyncverse.com

value: Show HN: AlgoSync – a social media built for developers, founders, & tech people


value: legitcoders

value:
Hi HN,
Built a WordPress plugin to track which AI bots (GPTBot, Claude, Gemini) are crawling your site. Includes llms.txt support (like robots.txt for AI).
Just launched on WordPress.org: https://wordpress.org/plugins/llms-central-ai-bot-tracker/
Free, open source, privacy-focused. Would love feedback on the approach and the llms.txt standard.

value: Show HN: WordPress AI Bot Tracker – Just Approved and Launched on Wordpress.org

author: legitcoders


value: JawadR

value:
Google Docs for Python basically.
For the past 4 months, I’ve been working on a full-stack project I’m really proud of called PyTogether; a real-time collaborative Python IDE designed with beginners in mind (think Google Docs, but for Python). It’s meant for pair programming, tutoring, or just learning Python together. It’s completely free. No subscriptions, no ads, nothing. Just create an account, make a group, and start a project. Has proper code-linting, extremely intuitive UI, autosaving, and live cursors. There are no limitations at the moment (except for code size to prevent malicious payloads).
Why build this when Replit or VS Code Live Share already exist?
Because my goal was simplicity (and education). I wanted something lightweight for beginners who just want to write and share simple Python scripts (alone or with others), without downloads, paywalls, or extra noise. There’s also no AI/copilot built in, something many teachers and learners actually prefer. Also its free lol
Tech stack (frontend): React + TailwindCSS CodeMirror for linting Y.js for real-time syncing and live cursors Skulpt to execute Python in the browser (for safety - I initially wanted Docker containers, but that would eat too much memory at scale. Skulpt has a limited library, so unfortunately imports like pygame wont work). I don’t enjoy frontend or UI design much, so I leaned on AI for some design help, but all the logic/code is mine. Deployed via Vercel.
Tech stack (backend): Django (channels, auth, celery/redis support made it a great fit) PostgreSQL via Supabase JWT + OAuth authentication Redis for channel layers + caching Fully Dockerized + deployed on a VPS (8GB RAM, $7/mo deal)
Data models: Users <-> Groups -> Projects -> Code Users can join many groups Groups can have multiple projects Each project belongs to one group and has one code file (kept simple for beginners, though I may add a file system later).
There were a lot of issues I came across when building this project, especially related to the backend. My biggest issue was figuring out how to create a reliable and smart autosave system. I couldn't just make it save on every user keystroke because for obvious reasons, that would overwhelm the database especially at scale. So I came up with a solution that I am really proud of; I used Redis to cache active projects, then used Celery to loop through these active projects every minute and then persist the code to the db. I did this by tracking a user count for each project everytime someone joins or leaves, and if the user count drops to 0 for a project, remove it from Redis (save the code too). Redis is extremely fast, so saving the code on every keystroke is not a problem at all. I am essentially hitting 4 birds with one stone with this because I am reusing Redis, which I've already integrated into my channel layers, to track active projects, and to also cache the code so when a new user enters the project, instead of hitting the db for the code, it'll get it from Redis. I even get to use Redis as my message broker for Celery (didn't use RabbitMQ because I wanted to conserve storage instead of dockerizing an entirely new service). This would also work really well at scale since Celery would offload the task of autosaving a lot of code away from the backend. The code also saves when someone leaves the project. Another issue I came across later is if people try sending a huge load of text, so I just capped the limit to 1 MB (will tinker with this).
Deployment on a VPS was another beast. I spent ~8 hours wrangling Nginx, Certbot, Docker, and GitHub Actions to get everything up and running. It was frustrating, but I learned a lot.
If you’re curious or if you wanna see the work yourself, the source code is here. Feel free to contribute: https://github.com/SJRiz/pytogether.
I’m still learning, so any feedback would be amazing (and contributions)!

value: Show HN: Open-source, lightweight collaborative Python IDE for educators

author: JawadR


value:
Hey HN,
Pluely v0.1.6 is out. I'm building this in public—no VC backing, no team, just me solving problems one commit at a time.
What Pluely is:
An invisible AI assistant that stays out of your way until you need it. Think Cluely, but open source, 10MB instead of 800MB, and built by someone who uses it every day.
What's different about v0.1.6:
Screenshot Selection - Click and drag to capture specific areas of your screen for AI analysis. The hard part wasn't building the selection tool-it was keeping Pluely completely invisible while enabling precise interaction. I integrated tauri-nspanel on macOS for non-activating panels and rebuilt Windows window management to stay non-focusable. Three rewrites to get this right.
Audio Device Control – Pick exactly which microphone and system audio source Pluely captures from. The challenge was hot-swapping audio devices without restarting the entire capture pipeline. Had to rebuild the audio layer from scratch to support dynamic device switching.
Autostart - Pluely now launches silently when your system boots. Always ready, never in the way.
Window Focus Issues Solved - The most reported issue from v0.1.5. Pluely no longer steals focus on macOS or Windows. You stay in your workflow, Pluely stays invisible until you call it.
Custom audio capture with Voice Activity Detection
Multi-LLM support: OpenAI, Claude, Gemini, Grok, Perplexity, Mistral, Cohere, plus custom/local models
Try it: https://pluely.com
Source: https://github.com/iamsrikanthnani/pluely
Full changelog: https://github.com/iamsrikanthnani/pluely/releases/tag/app-v...
I ship updates based on what users actually need, not what looks good on a roadmap. This release exists because the community told me what was broken and what is needed.
Built in public. No hype. Just shipping.

value: Show HN: Pluely v0.1.6 -Open Source Invisible AI Assistant,Alternative to Cluely


value: miuchan

value: 生命、宇宙及一切的终极答案 — My Theory on the Question of Everything

value: Show HN: Answer to Life, the Universe and Everything – interactive exploration

author: miuchan


value: HardikVala

value:
A minimal implementation of ModernBERT in pure C, inspired by karpathy's llama2.c. The core (tokenizer + inference code) is around 1000 lines of code with no dependencies (except OpenBLAS for fast matrix multiplication, and PCRE for regex).
If you don't know, ModernBERT is a new encoder-only model from answer.ai. Unlike decoder-only models like Llama, encoder models process all input tokens in a single pass (no autoregression), which makes them great for tasks like token classification.
The implementation supports loading any ModernBERT checkpoint from Hugging Face. I've tested it with the base model and a token classification model for anonymizing PII. You can get >1200 tokens/s throughput on a single thread (slightly better than pytorch implementation), though that's not directly comparable to decoder models since there's no token-by-token generation.
I hard-coded the architecture to keep things simple and readable. The tokenizer is a from-scratch BPE implementation that handles most cases (though it's missing some edge cases). The main goal was to support a lightweight deployment of this model, without the heavy baggage of the pytorch ecosystem.
Enjoy.

value: Show HN: ModernBERT in Pure C

author: HardikVala

title: Show HN: ModernBERT in Pure C


value: aretecodes

value:
Hey everyone,
I’ve been working on Astrae, a library of beautifully animated components and templates built for Next.js, Tailwind CSS, and Framer Motion.
As a designer and creative developer, I noticed that while there are tons of UI kits out there, very few make it easy to add motion and personality without starting from scratch. Astrae aims to fix that — you can literally plug in animations that look polished out of the box.
Some highlights:
Ready-to-use templates for landing pages and portfolios
Animated UI components powered by Framer Motion
100% built for Next.js + Tailwindcss
Focused on design quality and performance
Right now I’m slowly rolling out new components and showcasing them on socials.

value: Show HN: Astrae, Build Animated Next.js Sites 10× Faster

author: aretecodes


value: Areibman

value:
Bottleneck is a desktop app for code reviewing AI-written pull requests.
The goal is to be able to track PRs on GitHub authored by agents (i.e. Codex, Devin, Cursor, Claude Code) and compare branches. So if you throw multiple coding agents at a ticket, this would be an easier way to let agents "bake off" against each other and pick the best one. (No need to open the Github website and switch between slow loading tabs).
I'd love feedback from any power users who are deep with AI coding agents.

value: Show HN: Code review for AI native teams

author: Areibman


value: mgriley

value:
I had some time between jobs, so I made this tool called JungleWriter where you can create weird, free-form websites.
You drag around widgets on a big canvas to create your site. No coding required. When you're done, it exports your static site as a zip, which you can upload to a hosting provider.
It's more for hobby/web-enthusiast use right now than for serious projects. I'll probably use it myself to make sites for small events and to brainstorm designs.
Hopefully someone out there also finds it useful, lmk :)

value: Show HN: Make funky websites without coding

author: mgriley


value: tekodu

value:
I spent 3 months building and validating an AI engine that generates production-ready Python code. Just finished comprehensive testing: 14/15 diverse specs worked first try (93% success).
What it does:
Generates working Python tools from plain English descriptions. Not toy code - actual production-grade with security, error handling, and logging.
Validation results:
• 15 diverse tests (data processing, APIs, business logic, file handling)
• 14 passed completely (93%)
• 1 failed: real-time streaming processor with 12+ requirements (distributed systems complexity)
• Average: 1.5 iterations to production-ready
Example generations that worked:
• Invoice matching system (18K chars, fuzzy matching, reconciliation)
• JWT authentication API (Flask + SQLite + bcrypt)
• Web scraper with ethical safeguards (robots.txt, rate limiting)
• Multi-API integration (OpenWeather + Twilio)
What struggled:
• Extremely complex specs (10+ independent requirements)
Try it: https://bauform-engine.fly.dev
Feedback form: https://docs.google.com/forms/d/e/1FAIpQLSfH_4s_pheVTePcaIt0...
Looking for honest feedback from developers - what works, what breaks, what would make this useful for your workflow.
Beta stage. Python only (for now). Best for focused tools with clear requirements.

value: Show HN: AI code generator with 93% validation success (Python)

author: tekodu


value: carverauto

value:
ServiceRadar is an open-source platform for distributed, secure network management and observability, scaling to 100k+ devices. Born from frustration with complex traditional NMS tools like Zabbix, it bridges legacy (SNMP/syslog) and modern (gNMI, OTLP) protocols for cloud-native environments.
We built ServiceRadar to simplify monitoring hybrid telecom networks, evolving it into a Kubernetes-native solution with Helm and Docker support. It uses mTLS with SPIFFE/SPIRE, NATS JetStream for event streaming (90M+ EPS), and SRQL for intuitive queries. Integrated with OpenTelemetry, Prometheus, and CloudEvents, it fills the network visibility gap in CNCF’s application-focused observability stack.
We’re seeking early adopters to try our demo or deploy locally—no sign-up needed. Feedback on usability or contributions for new protocols would be awesome.
Quick Start: helm install serviceradar carverauto/serviceradar or docker compose up -d
GitHub: https://github.com/carverauto/serviceradar (please star!)
Docs: https://docs.serviceradar.cloud
Join our Discord or use GitHub Issues to share thoughts.

value: Show HN: ServiceRadar – open-source Network Observability Platform

author: carverauto


value: crschnick

value:
Hello there, I am proud to present KickstartFX, a ready-to-use JavaFX desktop application template. You can clone it and get started instantly or try out the pre-built releases on GitHub. If you have an interest in the Java desktop application landscape, this might be for you.
Relating to the times JavaFX is mentioned in HN discussions, you can also see this as a showcase about what is possible with modern Java + JavaFX if used correctly. While JavaFX might not be the most trendy desktop framework out there, it is very much alive and still a very solid solution for creating stable desktop applications in Java.
It comes with the following features that you won't find in other templates:
- A fully up-to-date build using the latest features of JDK25, Gradle 9, JavaFX 25, WiX 6, and much more
- Native executable and installer generation for all operating systems using native tools
- A fully modularized build, including fully modularized dependencies and the usage of jmods
- JDK 25 Leyden AOT cache generation logic and customizable training run implementations
- A ready-to-deploy GitHub actions pipeline to automatically build and release your application on all platforms
- Close-to-native theming capabilities with AtlantaFX themes as the basis combined with many manual improvements
- Automated tracking of the system appearance settings with light/dark mode and accent colors
- Advanced error handling and issue tracking with built-in support for Sentry
- Markdown rendering capabilities out-of-the-box with flexmark and the JavaFX WebView
- Integrated ability to automatically codesign the application on Windows and macOS
- Robust state management for caches, persistent data, and more
- Many customization options available to users in a comprehensible settings menu
- Update check capabilities and notifications for new GitHub releases
- Built-in troubleshooting tools for developers and users, including debug mode, heap dump, and more
- Hot-reload capabilities for all resources, including reapplying stylesheets
- Plenty of checks to warn users about problems with their system configuration, environment, and compatibility
- Desktop and registry access support classes
- Robust dependency Linux package management and font handling, your application will even run in WSL
- Application instance management and coordination via inter-process communication
- System tray icon support and proper handling of AWT/Swing alongside JavaFX
- Built-in support for Jackson and Lombok
- Integrated translation support with user interface language changes applying instantly
- Application logo templates that look native on every operating system, including a macOS 26 liquid glass icon
- Included third-party open source licenses of all dependencies, plus the required license display in the application
So as you can see, this is not a basic template but instead a full application, just missing your custom application content to be plugged in. You can of course also customize any of the codebase, the idea is to fork the repository, not depend on it using a library. There is also documentation available at https://kickstartfx.xpipe.io
The licensing model is designed to allow open source projects to use it under an Apache 2.0 license and other proprietary forks to contact me for licensing. Essentially, the base license for everyone is GPL3, but you can contact me to get the permission to license it under the Apache 2.0 license. If you developing a personal project, the only thing you need to show for that is that you are developing your application in an open source repository. If you are a company, developing a closed-source application, and want to license it under something else than GPL3, you can also contact me for an offer. All dependencies are compatible with a permissive license like Apache as well.

value: Show HN: KickstartFX – The most advanced JavaFX template for your app with JDK25

author: crschnick


value: zachpark

value:
Building interactive widgets for ChatGPT turned out to be way more complex than I expected.
Between the MCP protocol, widget registration, build setup, CSP policies, and resource serving — I kept writing the same boilerplate over and over just to get something basic working.
That’s why I built FastApps, an open-source framework that lets you build ChatGPT apps with only two files: a Python backend and a React frontend.
It removes all the glue code and focuses on developer experience:
- Auto-discovers and registers MCP widgets automatically
- Builds and bundles React UIs with zero config
- Manages CSP, metadata, and resource serving out of the box
- Type-safe data flow with Pydantic and React hooks
- Runs as an MCP server, fully compatible with ChatGPT and other MCP clients
If you’ve been experimenting with MCP or building ChatGPT tools, I’d love your feedback.
It’s fully open source — and I’m looking for contributors who want to help shape this ecosystem together.
GitHub → https://github.com/DooiLabs/FastApps
Docs → https://www.fastapps.org/

value: Show HN: FastApps – zero-boilerplate framework for building ChatGPT apps

author: zachpark


value: tomfal

value: Show HN: Cmtly – turns staged Git diff into a polished commit message

author: tomfal


value: Yaramsa-Gautham

value:
So, I built Videoyards Extension which is a screen recording tool helps founders and indie creators to create professional screen recording videos or product demos of their SaaS or apps. It's basically like screen studio alternative for like windows... (This is currently stable for windows OS, chrome, brave, edge browsers and for mac, linux users it will be coming soon..)
Here are the results after 6-weeks (1&half month ) since launch:-
- 6000+ visitors
- 200+ users signups
- 150+ videos are exported
- 13 total paid users
- USD 705 in total revenue
- 112 install on my chrome extension
- 4-Five star review on my chrome extension
- My extension got featured by chrome
It took me 8 months to build this application and now after seeing the responses, feedback I feel so happy that I've finally built something that people actually need.... This is still int he beta version, I'm constantly improving from the feedbacks and user requests...

value: Show HN: I Built Videoyards to create professional demos videos

author: Yaramsa-Gautham


value: vnglst

value:
We have another election coming up at the end of this month here in the Netherlands, and how we use our limited land is a hotly debated topic.
Last year also I shared this animated hexagonal map of Dutch land use. I've now expanded this to include several more European countries, so you can see what makes Dutch land use so special - or not.
If you want to help add more countries, I'd appreciate PRs on GitHub. The map rendering is already there, it only requires land use data which can be found in the SQLite database (at least for EU countries).
Github: https://github.com/vnglst/onsland
Previous HN: https://news.ycombinator.com/item?id=40599763

value: Show HN: Land use visualization for European countries

author: vnglst


value: dchest

value: Show HN: Find Closest Named Color

author: dchest

title: Show HN: Find Closest Named Color


value: pauxel

value:
Hey HN,
I built BlinkDisk, a modern, minimalistic, and privacy-first backup app that’s designed to make backups effortless for everyone. It’s built on top of Kopia and combines strong encryption, deduplication, and cross-platform support with a simple, friendly UI.
Key features:
- Modern & minimal UI – clean, distraction-free design that anyone can use
- End-to-end encryption – your data is encrypted before it ever leaves your device
- Cross-platform – works on Windows, macOS, and Linux
- Fair source – built transparently in the open
GitHub: https://github.com/blinkdisk/blinkdisk
Website: https://blinkdisk.com
Would love your feedback!

value: Show HN: BlinkDisk – Modern File Backups

author: pauxel


value: melvinzammit

value:
I got tired of uploading sensitive assets to random websites just to make them smaller. Especially when those files end up in apps used by millions of people. I wanted a faster, safer way to optimize images and videos without ever leaving my device.
So I built a MacOS app to quickly do this natively on my mac. I built a lot on iOS Apps but this is the first time I’m building a MacOs App.
I would really appreciate any feedback. I’m looking for new features to build.

value: Show HN: 1 SEC to reduce file size of PNGs, jpg, pdfs, mp4, mov, GIF

author: melvinzammit