value: divinho

value:
I built ShipBoard because I hate context switching. Every time I had to leave VS Code to hunt through Slack threads or spreadsheets for a bug report, I lost my flow.
The Tech Stack: Next.js, Prisma, and the VS Code Extension API.
The Workflow:
1. A public board for users (no login required for them).
2. An extension that streams that feedback directly into your VS Code sidebar.
3. You prioritize and update the status without ever leaving your code.
Try it here: https://www.shipboard.dev/
I'm looking for brutal technical feedback. Is this a workflow you'd actually use? What’s missing from the extension?

value: Show HN: ShipBoard – View user feedback directly inside VS Code

author: divinho


value: Show HN: I built a 3D audio-reactive healing world with React Three Fiber


value: gaigalas

value:
Hi there!
It started as a simple extension to remember HN profiles I visit and store the data locally.
I made some modifications, and allowed users to create any kind of scraping rule instead.
It works like this:
You create a rule once, it will start autoapplying whenever it sees a similar page.
At any moment, you can export or clear the data for any rule.
Chrome:
https://chromewebstore.google.com/detail/lidar/eckcmnibplmme...
Firefox:
https://addons.mozilla.org/en-US/firefox/addon/lidar/
---
Would love some feedback!

value: Show HN: Lidar, an extension to scrape websites as you visit them

author: gaigalas


value: Lucavalentino

value:
Hello HN,
I rode the entire PKM bell curve meme then I started building Lilo Write, an infinite two-dimensional writing surface.
The tool: Writing that feels closer to thinking. You position ideas spatially useful for outlining, brainstorming, or any non-linear work.
The feats: Local-first, your data stays in your browser. Export anytime. No account, no tracking. Standard JSON format.
The tech: TypeScript + Vite, zero dependencies beyond build tools.
Rough edges warning: This is early and welded in a garage. Bugs exist, features are missing. Any comment is gold, shedding light in the tunnel for me.
https://lilowrite.com
Thanks ~ Luca

value: Show HN: Lilo Write – Local-first spatial writing (Apple Notes/whiteboard baby)

author: Lucavalentino


value: mavoince

value:
I've been spending some time exploring self-evolution recently. I honestly think it's a distinct third path that sits apart from fine-tuning and prompt engineering.
Fine-tuning often feels like overkill (and too static), while manual prompt engineering is just tedious guessing games. Self-evolution makes more sense to me conceptually: you don't change the brain (weights), you just let the model practice and take notes.
I wrote LiteEvo to automate this loop. It's a simple CLI that takes a task and a success criterion, then lets the LLM iterate.
The logic is pretty straightforward:
- The model attempts the task.
- It gets graded on the output.
- It updates a JSON "playbook" with what it learned (e.g., "I failed because X, so next time I should check Y").
It usually takes about 5-10 minutes to converge on a working strategy. The nice part is that the output is just a JSON file you can read and debug, not a binary weight file.
It supports Claude/OpenAI, but I also made sure it works with local models via CLI since that's what I use for testing.

value: Show HN: LiteEvo – Let LLMs evolve their own playbook based on trial and error

author: mavoince


value: iluxu

value:
Hey HN! I built RunMesh to solve a problem I kept hitting: every OpenAI
project needs the same glue code for tools, streaming, memory, and
multi-round loops.
RunMesh is a lightweight, typed framework that gives you:
- Tool calling with Zod validation
- Multi-round agent loops
- Streaming with real-time events
- Memory adapters
- Structured output extraction
Think of it as the "React for AI agents" – explicit primitives instead
of magic.
It's alpha, BSL 1.1 licensed (free for most uses, Apache 2.0 in 2027).
Demo: https://runmesh.llmbasedos.com
GitHub: https://github.com/iluxu/RunMesh
Would love your feedback, especially on the API design!

value: Show HN: RunMesh – OpenAI-first TypeScript framework for agentic applications

author: iluxu


value: teimurjan

value:
Started with a pure JS implementation (still the fastest JS image diff), but wanted to push performance further. I rewrote the core in Rust to make it the fastest open-source single-threaded image diff. On 4K images (5600×3200): ~327ms vs odiff's ~1215ms. Binaries are ~3x smaller too (~700KB vs ~2MB).
The core insight: make the cold pass smarter to make the hot pass do less work. Instead of simple pixel equality, the cold pass scans dynamic-sized blocks and marks "problematic" ones - blocks that might contain differences. The hot pass then only runs YIQ perceptual diff and antialiasing check on those problematic blocks, skipping everything else entirely. PNG I/O uses spng (C library) via Rust bindings. SIMD throughout - NEON on ARM, SSE4.1 on x86. Drop-in replacement for odiff with the same API.

value: Show HN: BlazeDiff v2 – Fastest image diff with native binary and SIMD

author: teimurjan


value: markrai

value: Based on a Rust backend, blazingly fast, and offers quirky features geared towards devs/designers. Ability to capture best of 5 burst from video, extract audio, fine-tune the facial detection model based on user's liking. User-centric philosophy which allows IDE-style appearance settings. Looking for contributors! :)

value: Show HN: Seen – x-platform/selfhosted/open-src photo and video solution

author: markrai


value: vuduvations

value: Show HN: Consulting-as-Code – A deterministic, neuro-symbolic agent architecture

author: vuduvations


value: warkanlock

value:
Hi HN,
First time sharing an app here
My wife and I kept making small promises to each other (“I owe you a dinner”, “you pick the next movie”, “I’ll plan the weekend”) and then forgetting them
We tried notes and reminders, but they didn’t work well
So we built this small app as a game for us, where:
- One person creates a “check” (a promise/favor)
- The other redeems it when they want
- The partner marks it fulfilled
It allow you to keep track of dates, celebrations, ideas, memories; it's an space for you and your wife (or at least it's our space so far)
That’s the core
Everything else is optional
Over time we added:
- Shared calendar for dates and anniversaries
- Simple streaks and milestones (not social, not competitive)
- Shared wishlist and memories
- Time-delayed notes (“open in 6 months”) that your couple won't see
Design constraints:
- Private by default (just two users)
- No feeds, no public profiles
- No comparison between couples
It’s free with a paid tier of 1 USD for unlimited checks
This is mostly a lifestyle tool we wanted for ourselves, but we’re curious whether others have the same problem

value: Show HN: We built a small app with my wife to track promises we do

author: warkanlock


value: andraskindler

value: Liquid Glass Resources is a directory of frameworks, code samples, and design assets for implementing Liquid Glass effects across platforms beyond iOS and macOS.

value: Show HN: Liquid Glass dev and design resources for platforms beyond iOS&macOS

author: andraskindler


value: cylinderthought

value:
You can try shardium :)
Fully open source crypto recovery
https://shardium.maxcomperatore.com

value: Show HN: Crypto Inheritance with Shamir Secret

author: cylinderthought


value:
I have ADHD tendencies. I leave things half-done, go home with my work keys still in my pocket, and as soon as I start something else I completely forget the task I was doing before.
Reminders don’t work — I just don’t look at them. Checklists don’t work either — I forget to check them in the first place.
So I built PocketCheck. It periodically asks, “Anything to put in your pocket?” It’s a pocket that stands in for my working memory. I forget what I put in my actual pockets all the time, but this pocket remembers for me.
Input is minimal: just a name, or just a photo if I’m in a hurry.
Since I started using it, I still forget things — but now I notice in time.
Open source (GPL-3.0), no Google Play Services required.

value: Show HN: PocketCheck – A checklist for things not worth writing down


value: Show HN: Gemini Diagnoses Plants

title: Show HN: Gemini Diagnoses Plants


value: eamag

value: IMDb doesn't do end of year reviews, so I build spotify-like IMDb wrapped that tells you about your year. It runs in your browser and is open sourced, so you can add more slides if you want. I also couldn't figure out CORS so you can't just paste a link to your profile and have to export CSV instead, help welcomed!

value: Show HN: Local open sourced IMDB Wrapped

author: eamag


value: matthiasstiller

value:
Hi HN,
I just shipped a new feature on Facet.ink, a platform for digital writing and publishing.
As a small gift just before Christmas, authors can now download their texts written on Facet — chapter by chapter — as Markdown.
The goal is simple: writers should always own their words.
No platform lock-in, no closed formats — just your content, portable and future-proof.
I’d love to hear your thoughts or feedback.
Matthias

value: Show HN: Own your words – Markdown export for Facet

author: matthiasstiller


value: Aydarbek

value:
Hi HN — I built Ayder, a single-binary durable event log / message bus in C. The idea: what if event streaming was just HTTP?

  # Produce
  curl -X POST 'localhost:1109/broker/topics/orders/produce' -d '{"item":"widget"}'

  # Consume
  curl 'localhost:1109/broker/consume/orders/mygroup/0?encoding=b64'

No JVM, no ZooKeeper, no thick client libraries. Just HTTP.
What it does: append-only log with per-partition offsets, consumer groups, durable writes via sealed AOF + crash recovery, HA with Raft consensus (3/5/7 nodes), leader redirects, idempotent produce, KV store with CAS + TTL.
Benchmarks (real network, 3-node Raft, DigitalOcean 8 vCPU, wrk2, 64B payload):

  Throughput: 50,000 msg/s sustained
  P50: 1.58ms | P99: 3.35ms | P99.9: 8.62ms

Server-side P99.999 is 1.2ms — the tail you see in benchmarks is network/kernel, not the broker.
Curious finding: real NIC beats loopback at high concurrency (P99 3.31ms vs 5.79ms). Makes sense — loopback means client and server fight for CPU cores.
I'd love feedback on: API shape, HA ergonomics, what you'd expect from "durable event log over HTTP" in 2025.
Happy to discuss implementation details — Raft catch-up, AOF format, zero-copy parsing, whatever interests you.

value: Show HN: Ayder HTTP-native durable event log in C (50K msg/s, P99 3ms, Raft HA)

author: Aydarbek


value: splitbrain

value: I needed a simple way to block a whole bunch of AI bots running amok on my site without having to introduce a reverse proxy setup. This solution uses mod_rewrite and a small go tool for fast RewriteMaps.

value: Show HN: Cloudflare-like are you human check using Apache mod_rewrite

author: splitbrain


value: danbitengo

value: Show HN: SyncKit – TLA+ verified CRDT for local-first apps (zero unsafe Rust)

author: danbitengo


value: imadjourney

value: Show HN: How to Make More Money from the Same Website Traffic

author: imadjourney


value: chengzeyi

value: We integrate Z-Image into a local image generation pipeline, and provide an easy-to-use interface in this open-source desktop app. The generation is boosted by hardware acceleration. We also demonstrate how to integrate various vision models locally so that users don't need to pay to run on cloud service.

value: Show HN: Z-Image Desktop App with MPS and Vulkan Acceleration

author: chengzeyi


value:
Hey HN,
Starting a new project and staring at a blank .env file? It's a small hassle that eats up way too much brainpower. So I threw this together to make it easier. Pick a framework and/or integration, toss in any custom vars, and snag a ready template with placeholders for your secrets.
Curious: Does this cut the setup friction for you, or am I overthinking a non-problem? Which frameworks are must-haves? Open to feedback. Thanks!

value: Show HN: Free .env Template Generator – Quickly throw together a .env file


value: auspiv

value:
Built an app entirely with Claude/AI assistance – backend (Django + C#), iOS frontend, server deployment, CI/CD pipeline, the works. Hosted on a single VPS. Postgres on VPS, Redis on VPS, Django on VPS, etc. The VPS is a VM I have in a proxmox server I have sitting in a datacenter (Dell R630, 1x Xeon 2697v4, 128GB memory, 6x 960GB Intel D3-S4610 with Optane SLOG, etc). No AWS/GCP/Vercel, etc. Incremental cost to me = $0/month. I skipped using CloudFlare tunnels for this - hoping I don't regret that.
What it does: Point your phone at the sky, see real-time aircraft info overlaid on your camera. ADS-B data from community feeders, WebSocket streaming, kinematic prediction to smooth positions between updates. No ARKit – just AVFoundation camera + CoreLocation/CoreMotion + math. SwiftUI overlays positioned via GPS/heading projection.
The humbling part: Spent 2 months debugging "FOV calibration issues." Built an 800-line calibration UI, a Flask debug server, Jupyter notebooks for pipeline analysis, extensive logging infrastructure. Hung a literal picture on the wall with a black rectangle of specific size to "calibrate" the FOV reported by my phone. The AI helped me build all of it beautifully.
The actual bug? A UI scale factor on the overlay boxes. Not FOV math. Not coordinate projection. Just scaleEffect() making things the wrong size. Commit message when I found it: "scale may have been fucking me over for a long time for 'fov issues'". Guess where the scaleEffect() function was introduced? That's right - AI generated. I asked it at one point something along the lines of "ok when you draw the boxes around the aircraft, make them smaller when the aircraft is farther away".
I went through 2-3 major model releases that I tested on this "hey I've been fighting a FOV bug for a while - can you please take a look and let me know if any issues jump out". Gemini 3 Pro, Opus 4.5, none of them found the "bug".
Takeaways from vibe-coding a full product:
- AI is incredible at building things fast – entire features in minutes. The entire UI, website, logo, etc, all AI. Claude Opus 4.5 kind of sucks at UI. Gemini 3 cleaned all that up.
- AI will also confidently help you debug the wrong thing for weeks
- Still need to know when to step back and question your assumptions
- Deleted 2,700 lines of debug infrastructure once I found the real bug
- Low performance? Just tell AI to rewrite it in a more performance language (load tested the process with 1000 connections - with Python/Django, tons of drops and latency spikes to 5000ms. Switched to c# and now it'll do 1000 and keep latency under 300ms)
Release process: painless, except for the test RevenueCat SDK key causing instacrash. Didn't test release locally. Approved in 6 minutes 2nd submission.
Question: what are people using to get super accurate heading out of Apple devices? The heading estimated error never drops below 10°. It's about 50/50 on being spot on vs not that close for the projections.
App link: https://apps.apple.com/us/app/skyspottr/id6756084687

value: Show HN: I vibe-coded an aircraft AR tracking app and wasted weeks on AI bugs

author: auspiv


value: justmarc

value:
Hi HN,
I’m Marc, founder of Zynk, an application being built by a small bootstrapped team.
Zynk is a cross‑platform file transfer + messaging app designed around two things: reliability (auto‑resume) and privacy (end‑to‑end encryption).
The project started because I felt it's still a pain to move files and folders across a Linux laptops, a Mac workstation, my phone, countless other devices, and to others. Some existing solutions don't cross ecosystems, cloud drives are slow/inefficient/fragile for big transfers, and a lot of tools fall over when you sleep a laptop or switch networks. Most don't preserve privacy fully either.
What’s different / core features:
- Direct device‑to‑device transfers (files + folders) of unlimited size with auto‑resume when a connection drops, also between users, rsync style
- P2P first; if devices can’t connect directly after attempting to hole-punch through NATs/firewalls, we fall back to a cloud relay, but still maintaining E2EE
- UI app for macOS, Windows, Linux, iOS, Android (incl. Android TV), Steam Deck
- CLI for macOS/Windows/Linux/RPi/FreeBSD for scripting and servers
- Web Drops / Share Links to share or request files with people who don’t have Zynk installed (limits depend on plan)
There’s a lot of depth to the product beyond this, and details that together make a difference.  It would be too much to list, I hope you’ll discover them through use and love it as much as we do.
Pricing/limits (so it’s clear up front): personal use is completely free; the free plan includes unlimited direct P2P transfers, with generous monthly caps on relay, and size/expiry limits on Web Drop links; Pro increases those caps.
It doesn't end here; we're obsessed about quality, efficiency, ease of use and utility and we're working hard on improving this and taking it to the max. There's a long road ahead with some pretty exciting new capabilities coming up soon.
Try it out, send files and media between your own devices and other people.
I’d love feedback on:
1. Onboarding and any friction points (what’s confusing / annoying?)
2. Reliability in your real-world network setup (esp. NAT/CGNAT)
3. What you’d want from the CLI/UI/workflows
4. Anything else we can improve and make better
Thanks! I’ll be here answering questions.
https://zynk.it/

value: Show HN: Zynk, a Fast, P2P Encrypted File Transfers and Messaging Across Devices

author: justmarc


value: Show HN: Exploring Mathematics with Python


value: TyGoss

value:
Hey HN! I built ACE, a WordPress code editor that brings VS Code's Monaco editor into the WordPress admin dashboard.
The interesting part: it actually parses your WordPress installation to understand your custom post types, ACF fields, and taxonomies. When you're writing code, it can insert context-aware snippets with your actual field names already populated - so instead of looking up "what did I call that custom field again?", you just select it from the inserter.
WordPress's default code editor is pretty bare-bones, and constantly switching between an external IDE and the admin panel for quick tweaks gets tedious. This keeps you in-flow while giving you proper syntax highlighting, autocomplete, and the Monaco features developers expect.
Still early days and would love feedback on what would make this more useful. Built for WordPress freelancers but curious what the broader dev community thinks.
Check it out: https://aceplugin.com

value: Show HN: Ace – Monaco-based code editor for WordPress that is context-aware

author: TyGoss


value: crosshairflaws

value:
Hi HN,
It's my first post here, so hopefully this is allowed and correct (according to the rules it is). And of course I hope you find some value in it as well :)
I built Vanishfile because I often need to send files that shouldn’t live forever on a server. There wasn't any file sharing I found that could do this (beside secret sharing). I don't want to create an account, share unnecessary amount of details or something.
Vanishfile lets you:
- Share files that automatically delete after expiry (amount of downloads or after set time period)
- Doesn't need an account, I don't store anything besides the file
- Optionally enable client-side encryption, so even the server can’t read the file
- Optionally set a password, so only the user with a password can download the file
- Limit downloads (or make them unlimited)
Free usage is available, and there’s a small one-time unlock for larger files and encryption (currenly set at $3 for 7 days just to try things out). It generates an access code, which expires after a week. So no account required either (but the key should be stored by the user, of course)
This is a small, focused utility and not a startup pitch or something. It's mostly for personal use, but figured to put it out there as well.
I’d love feedback on:
- the UX
- whether the privacy model makes sense
- what more you would expect from a tool like this
- what would be a good free and paid distinction
- what would be a good price, if any
Link: https://vanishfile.com/
Thanks a lot!

value: Show HN: Vanishfile – temporary and secure file sharing

author: crosshairflaws


value: ColonelParrot

value: Show HN: Syntux – Build declarative, generative UIs in React

author: ColonelParrot


value: jclardy

value:
Hey HN,
I've just released an update to my iPhone/Apple Watch app Daylight Goals which includes a new feature - your Daylight Recap for 2025.
If you've been wearing an Apple Watch for the past year it has been recording your time spent outdoors (So long as the screen is mostly uncovered.) My app charts and graphs it, along with giving you daily goal updates to make sure you get your 20 mintues of sunlight per day.
The recap feature is free, you just need to close out the paywall during onboarding.

value: Show HN: I built an app to review your time spent outdoors

author: jclardy


value: matanblay

value:
Hi HN,
We kept running into the same problem: every paid ad, email, or affiliate campaign needed its own landing page just to keep the messaging consistent. That meant duplicate pages, slower launches, and frequent deploys for small copy changes.
So we built UTMKit, a small, defer-loaded popup engine that turns incoming UTMs into targeted onsite popups.
Instead of routing traffic to separate landing pages, you send users to your main site and let UTMs (utm_source, utm_campaign, etc.) decide which popup to show. Rules are configured in a dashboard and evaluated in real time—no code changes after the initial script install.
Key ideas:
One script install (framework-agnostic)
Rules based on any UTM or query parameter
Priority + frequency caps when multiple rules match
Analytics per rule and per UTM (impressions, clicks, closes, CTR)
Designed to be lightweight and not impact page performance
This is primarily for performance marketers and growth teams who want campaign-specific messaging without spinning up new pages or bothering engineers for every experiment.
Demo + docs are linked on the site. Happy to answer questions or hear feedback—especially on edge cases or alternative approaches people have tried.

value: Show HN: UTM-triggered onsite popups for campaign message match

author: matanblay


value: ballou

value: Show HN: From Frustration to Open Source: Building MCP Matomo in 30 Minutes

author: ballou


value:
Hi HN,
This is Aki, a technical founder having previously shipped products to 1B+ people (I launched the heart button on twitter).
I built Credible because I wanted a way to know whether something I'm about to read would be worth my time. I also got tired of context-switching to verify what I read.
Credible is a Chrome extension that displays instant credibility scores directly into the pages you browse, including HN itself.
** How it works **
On HN Home: You see a credibility score next to each link.
On HN Comments page: You see the full analysis of the linked article.
This includes the linked article's key takeaways, credibility score, bias detection, and a breakdown of claims (facts vs opinions vs dubious) without leaving the page.
They also show on our mobile-friendly feed here:
https://mycredible.ai/feeds/hacker-news
Chrome Web Store: https://mycredible.ai/chrome
We will have a major focus next year on shipping tools that utilize AI to make consumption a breeze. As we design that, would love to know: Is this scoring & UX useful for you? What would make it even better?

value: Show HN: Credible brings credibility scores directly on Hacker News


value: admtal

value:
An app to make demo vids
Of course I have a demo vid: https://www.youtube.com/watch?v=c_fq0TzlsXI
This will be my last post to HN about this.  I always like to try a few titles to see if any hit.

value: Show HN: I Made Loom for Mobile

author: admtal

title: Show HN: I Made Loom for Mobile


value: eigen-vector

value: Show HN: Gifted – community sourced gifted articles from popular publications

author: eigen-vector


value: Keats

value: This was built to replace syntect (library using Sublime Text syntaxes for highlighting) in Zola (https://www.getzola.org/), a static site generator.

value: Show HN: Syntax highlighting library in Rust that matches VSCode

author: Keats


value: tgirotto

value:
Hello, I’ve been experimenting with Starthub, an npm-like CLI tool that chains Docker and WASM units to perform arbitrary workflows. Deploying open-source stacks horizontally across multiple nodes is often tedious and error-prone, so this is an attempt to make that kind of task reproducible and composable.
As an example, the command below deploys a horizontal n8n stack (postgres + redis + droplets + ssl) to DigitalOcean.
npx @starthub/cli@latest run starthubhq/n8n-horizontal-do:0.0.1
It’s just a rough MVP (the CLI needs A LOT of work still), but I’d love feedback on:
- The approach for composable, stateless deployment actions
- Multi-cloud or multi-target deployment ideas
Github repo: https://github.com/starthubhq/cli
Thank you for your time!

value: Show HN: Starthub – Deploy horizontal n8n to Digital Ocean with one command

author: tgirotto


value: linggen

value:
Hi HN,
Working with multiple projects, I got tired of re-explaining our complex multi-node system to LLMs. Documentation helped, but plain text is hard to search without indexing and doesn't work across projects. I built Linggen to solve this.
My Workflow:
I use the Linggen VS Code extension to "init my day." It calls the Linggen MCP to load memory instantly. Linggen indexes all my docs like it’s remembering them—it is awesome. One click loads the full architectural context, removing the "cold start" problem.
The Tech:
Local-First: Rust + LanceDB. Code and embeddings stay on your machine. No accounts required.
Team Memory: Index knowledge so teammates' LLMs get context automatically.
Visual Map: See file dependencies and refactor "blast radius."
MCP-Native: Supports Cursor, Zed, and Claude Desktop.
Linggen saves me hours. I’d love to hear how you manage complex system context!
Repo: https://github.com/linggen/linggen
Website: https://linggen.dev

value: Show HN: Linggen – A local-first memory layer for your AI (Cursor, Zed, Claude)

author: linggen


value:
No sign up. Just free bliss listening to different birds around the world
Saw a cool API https://xeno-canto.org/ and wanted to learn more about birds in a more zen way. Been running this in the background all day at work (on worldwide station) instead of the usual lofi
Enjoy!

value: Show HN: Bird Radio

title: Show HN: Bird Radio


value:
I just Vibe Coded a fully functional video editor in the browser.
Meet Cuttr:  Cut through the words to edit the video. A quick, simple, browser-based editor with AI transcription.
0 manual lines. 100% AI powered.
Vibe Stack:  Aantigravity  Opus 4.5 by AnthropicAI  Figma
See it live:  http://cuttr.in
Please share your feedback.

value: Show HN: I built simple video editor: Cut the words to edit


value: frndsprotocol

value:
Hi HN,
I’m the founder of FRNDZ.
We’re building a privacy-first advertising protocol where advertisers target user intent instead of personal data, and users are rewarded for real engagement.
We have a live MVP and would love feedback from builders.
Happy to answer any questions.

value: Show HN: Frndz – Privacy-first Web3 ads based on intent, not user data

author: frndsprotocol


value:
Our first model brings SoTA Browser Use capabilities in a small and fast model that can be hosted on a single GPU. It’s much much faster than anything out there and around 15x cheaper than Sonnet 4.5.
The wonderful name comes from: 30B parameters, 3B active, SoTA quality at real-time speed (and the model is based on Qwen/Qwen3-VL-30B-A3B-Instruct).
This model is heavily trained to be used with browser-use OSS library and provides comprehensive browsing capabilities with superior DOM understanding and visual reasoning.

value: Show HN: Open-Source Browser Use LLM (30B, A3B)


value: pasxizeis

value:
Hey everyone! I started writing a decoder from scratch for WebAssembly modules (i.e. a parser for `.wasm` files).
The primary motivation was to learn both WebAssembly _and_ a new language. However it turned out to be a very fun project, and I think there is space for a decoder with helpful error messages, that can serve as a good debugging tool or for educational purposes.
A sample output can be found at for an invalid module can be found at https://github.com/agis/wadec?tab=readme-ov-file#usage.
Recently it hit v2.0 spec conformance (I'm executing it against the upstream spec test suite.) 3.0 is next on the roadmap.
It's still in very early stage, but any feedback is more than welcome. Thanks!
https://github.com/agis/wadec

value: Show HN: Wasm decoder with focus on helpful errors/diagnostics

author: pasxizeis


value: Show HN: Asynchronous text chunking and vector embedding generation in Postgres


value: LoMoGan

value:
Most current LLMs struggle with long documents due to context-length limitations. PageIndex Chat is an AI system specifically designed for conversational interaction with long documents. It delivers higher accuracy than ChatGPT 5.1 Thinking while remaining faster than ChatGPT 5.1 Instant. Additionally, PageIndex Chat provides page-level references for every claim in its responses, making answers transparent and verifiable.
Learn more at: https://pageindex.ai/chat

value: Show HN: PageIndex Chat – AI reader for long documents

author: LoMoGan


value: deanforan

value: Show HN: Saga Bus – MassTransit-style saga orchestration for TypeScript

author: deanforan


value:
Hi HN,
I built this because I wanted a very small, readable way to compare AI agent outputs locally.
Most agent tools are powerful but heavy. This project intentionally avoids frameworks, configs, and abstractions. An "agent" is just a function, and a "scorer" is just a function.
The goal is not to be production-ready, but to make experimentation and learning easy.
Feedback welcome.
Especially on what’s missing or what should stay out.

value: Show HN: Agent-playground – a minimal sandbox to compare AI agent outputs


value: made a small generator where you can turn yourself or your friends into an Oiia cat and spin. You’ll get a Christmas hat too, a holiday special. Have fun.

value: Show HN: Oiiaioiiiai Generator

title: Show HN: Oiiaioiiiai Generator


value: spydertennis

value:
Bob and Arun here, creators of Stickerbox.
If AI were built for kids, what would it look like?
Asking that question led us to creativity, and more specifically, the power of kids’ imaginations. We wanted to let kids combine the power of their ideas with AI tools but we needed to make sure we did it safely and in the right way.
Enter Stickerbox, a voice powered sticker printer. By combining AI image generation with thermal sticker printing, we instantly turn kids' wildest ideas into real stickers they can color, stick, and share.
What surprised us most is how the “AI” disappears behind the magic of the device. The moment that consistently amazes kids is when the printer finishes and they are holding their own idea as a real sticker. A ghost on a skateboard, a dragon doing its taxes, their dog as a superhero, anything they can dream of, they can hold in their hand. Their reactions are what pushed us to keep building, even though hardware can be really hard.
Along the way the scope of the project grew more than we expected: navigating supply chains, sourcing safe BPA/BPS free thermal paper, passing safety testing for a children’s product, and designing an interface simple enough that a five year old can walk up and just talk to it. We also spent a lot of time thinking about kids’ data and privacy so that parents would feel comfortable having this in their home.
Stickerbox is our attempt to make modern AI kid-safe, playful, and tangible. We’d love to hear what you think!
P.S. If you’re interested in buying one for yourself or as a gift, use code FREE3PACK to get an extra free pack of paper refills.

value: Show HN: Stickerbox, a kid-safe, AI-powered voice to sticker printer

author: spydertennis


value: Show HN: Free Spelling Test Generator

title: Show HN: Free Spelling Test Generator


value:
I’ve tried building small desktop apps in Python multiple times. Every time it ended the same way: frameworks felt heavy and awkward, like Electron felt exrteamly overkill. Even when things worked, apps were big and startup was slow (most of them). so I started experimenting with a different approach and created my own, I tried to focus on performance and on making the developer experience as simple as possible. It's a desktop framework that lets you build fast native apps using Python as a backend (with optional React/Vite, python or just html/js/css for the UI)
I’m actively collecting early feedback. Would you try taupy in a real project?
Why or why not? I just really need your honest opinion and any advice you might have

value: Show HN: Taupy – fast Python desktop apps without Electron


value: speakupai

value:
We are building a platform to solve a problem a lot of people are struggling with: how to practice public speaking when you have no audience.
Traditional methods are limited: practicing with friends is very limited(their qualification can be lower than yours, and you are limited in the amount of times you can practice), Toastmasters requires scheduling and gives you like 5 mins of speech per week(also limited in the amount of times you can practice), and speech apps just correct your spelling.
Yet 77% of people fear public speaking, and most of them have no idea how to practice it.
Our platform lets you practice presentations in front of a customizable AI-generated audience without any limitations. You can practice as much as you want as often as you want.
Just start speaking and AI will simulate real audience: ask questions, etc.
We're in early development. Looking for brutally honest feedback from the HN community:
1. Does this approach make sense?
2. What specific features would make it actually useful?
3. Would you use this for preparing to your presentations/interviews?

value: Show HN: AI Audience Platform for Practicing Public Speaking

author: speakupai


value: hmontazeri

value:
Hi there,
I've been working on BareAgent lately. It's meant for small teams and ppl who generally self-host their apps and manage their containers.
The idea is that a small (<10mb Golang agent binary | around 20mb ram) reads frequently the server metrics and docker container metrics and filters logs for filter words like fatal, panic and web based errors like 500 etc. and gives alerts in various ways.
Another cool feature is that it learns your base levels of CPU / RAM usage over 2 weeks and checks pulses against those and send alerts if they keep staying outside that range.
So it's basically metrics + alerts + basic incident management for your severs.
It has a free tier and I'd love some feedback.

value: Show HN: BareAgent – Agent that detects Docker container anomalies and incidents

author: hmontazeri


value: alohaTool

value:
I built Job Tracker, a lightweight Chrome extension to track job applications with a single click, directly from job posting pages. Check it out : https://github.com/ParasKoundal/JobTracker
- One-click tracking + smart URL matching
- Clean dashboard with search, filters, CSV export
- Swiss-style minimal UI, light/dark mode
- 100% local storage, no tracking, works offline
Open source (MIT). Chrome Web Store version coming soon. Feedback welcome.

value: Show HN: Job Tracker – one-click job application tracking (Chrome extension)

author: alohaTool


value: mpz is a C++/Qt music player focused on UX, with derectory tree and playlists management. Version 2 got experimental https://musicpd.org support.

value: Show HN: Music player for big local collections with mpd support


value: eddybenchek

value:
I got tired of bookmarking random GitHub repos and blog posts to find good Copilot prompts, so I built CopilotHub (https://copilothub.directory).
Another tool in your dev toolkit: a searchable directory for Copilot prompts and instructions.
It's a searchable directory where you can:
- Browse 100+ prompts by category
- Search by keywords, language, or framework
- Submit your own prompts (open to contributions)
- Find instructions for specific use cases
Started with content from awesome-copilot, now growing through community contributions.
Architecture:
- Next.js 15 App Router with server-side filtering
- Prisma + Neon for the database
- Full-text search with category filtering
- Community submission system (pending approval workflow)
Open-source and free.
Why it exists:
- Saves time hunting for prompts
- Centralized resource for the community
- Easy to contribute and discover
What makes it different:
- Not just a static list – fully searchable
- Server-side filtering for performance
- Community-driven curation
- Proper attribution for imported content
Repo: https://github.com/eddybenchek/copilothub
What's missing? What would make this more useful?

value: Show HN: I built a directory of 1000 GitHub Copilot resources

author: eddybenchek


value: Show HN: Open-Source Local AI App Builder


value: iowadev

value:
Hi HN,
I built an app called Good Dad. It sends simple daily reminders to help me be more present with my kids.
I grew up without a father, so I didn’t really have a model for what being a present dad looks like. Now I’m a dad of two young kids and I’m figuring it out as I go, often while feeling distracted, busy, or just running on autopilot.
The app is intentionally very simple. It sends short reminders about patience, connection, and slowing down. These are the kinds of nudges I personally need when I’m scrolling, stuck in traffic, or rushing through the day. I originally built it just for myself, but decided to share it in case it helps other parents too.
I built it during naps and after bedtime. There are no ads, no tracking, and no data collection. It’s completely free.
Website: https://www.gooddad.app
iOS: https://apps.apple.com/us/app/good-dad/id6753931366
Android: https://play.google.com/store/apps/details?id=io.makr.goodda...
Happy to answer questions or hear feedback.

value: Show HN: Good Dad, daily reminders to be present with your kids

author: iowadev


value: Frustrated with the apparent lack of tools for tagging and describing images locally, I built a quick and dirty little tool. You start it up, start up llama-server, and point it at a directory of photos. It scans through them, captioning them one at a time, and provides the captions and tags in an editable interface for you. When you're happy with them, you can save them, which writes them to the exif metadata of the image, and moves onto the next one

value: Show HN: I Built an Image Captioning Tool Using Llama.cpp


value: pcarroll

value:
I'm the founder at Netrinos. I built a WireGuard-based mesh VPN because remote access has always been a pain. After years of SSH tunnels, IPsec headaches, and the ssh log horror movie, I wanted something simpler: install, sign in, get work done.
Netrinos creates a LAN-like overlay network across your devices. Connections are direct P2P via WireGuard, with no central server routing traffic. Each device gets a stable IP and DNS name (pc.you.netrinos.com). When direct connections fail, they fall back to a relay server that's still encrypted end-to-end. We can't see your traffic.
The most challenging problem to solve was NAT traversal. UDP hole punching works most of the time. The rest is a cocktail of symmetric NAT, CGNAT, and serial NATs. We use STUN-style discovery and relay fallback for the edge cases. I was surprised by how unreliable low-end ISP routers really are, and how much technical wizardry it takes to hide that behind a clean, simple UX.
Our stack is a Go backend for client and server, WireGuard kernel mode for Linux and Windows (macOS is userspace), Wails.io for cross-platform UI. WireGuard does all the heavy lifting. Go ties it all together.
Popular use cases include: RDP to home PCs, accessing NAS without exposing it, and SSH into headless Linux boxes. One customer manages hundreds of IoT devices in the field, eliminating the need to deal with customer routers.
We just released Pro with multi-user, access control, and remote gateway routing. Personal is free (up to 100 devices).
I'd love to hear what you expect from a simple mesh VPN, what's missing from current tools, and what's lacking from your remote access setup. Use code HNPRO26 for a 30-day trial of Pro.
https://netrinos.com

value: Show HN: Netrinos – A keep it simple Mesh VPN for small teams

author: pcarroll


value: studlydev

value:
I built Fylo, a form builder that turns boring forms into interactive, engaging experience.
I noticed that most forms today feel static and transactional, which often leads to low completion rates and poor feedback. Fylo focuses on engagement, things like progress, interaction, and flow, to make filling a form feel less like a chore.
This is an early version and I’m launching to get real feedback. I’d love to hear what you think, what feels unnecessary, or what you’d want from something like this.

value: Show HN: Fylo – Forms, but Fun

author: studlydev

title: Show HN: Fylo – Forms, but Fun


value: Applied to 50 internships, got 3 callbacks, blanked on "tell me about yourself." Existing tools felt like scripted chatbots. Built Kelv to practice with AI that actually follows up and gives feedback. 100+ on waitlist, still iterating. Looking for feedback.

value: Show HN: AI interview coach I built at 17 after bombing my first interview


value: doomfish

value: Show HN: ScreenCaptureKit-rs – Safe Rust bindings for macOS screen capture

author: doomfish


value: mickdarling

value:
Hi, I'm Mick.
This is a side project I built over the last couple months. We've been using it internally at as we've been building http://dollhousemcp.com to view all the markdown documents with Mermaid diagrams that AI tools love to generate. They're all quite good at building these diagrams. It's a great way to visualize workflow for complex systems, but I couldn't find a good way to render them locally.
GitHub renders them fine, but only after you push. The VS Code extensions for inline Mermaid rendering weren't intuitive and broke on my system several times. The various online tools want you to create accounts, hand over login credentials, connect to services, and often have limitations. That was enough friction for me to just build my own.  And, it turns one month old this Saturday.
I wanted simple: drop a markdown file in, see it rendered with working diagrams. No login, no setup.
What it does:

  - Renders markdown with full Mermaid diagrams (flowcharts, sequence diagrams, ER diagrams, state machines, etc.)
  - Drag-and-drop or paste markdown directly
  - Load a file from the drop down
  - Load a remote files by pasting in a URL or link directly with a parameter (?url=path/to/file.md)
  - Double-click and zoom in on diagrams for fullscreen view
  - Mermaid snippet dropdown (Ctrl/Cmd+M) for quick diagram insertion / edits
  - Lots of theming:
    - Markdown syntax highlighting
    - Markdown preview
    - Code syntax highlighting
    - Mermaid diagram theming
  - Upload custom CSS for complete styling control
  - Print/PDF export
    - Use horizontal rules for visible page breaks in a PDF print (I use this for quick slide decks)

Self-eating dogfood – the homepage and all documentation are markdown files rendered by Merview itself.
It's entirely client-side, no backend, no tracking. Clone it and run it locally or host it yourself.
I also worked hard to make sure it has robust safety, and significant tests for my benefit and anyone else modifying it.
It is AGPL-3.0 licensed, so anyone can use it, and modifications can make their way back into the Open Source community. If you have issues, notes, comments, requests, please feel free to add them to the repo.
I know it still has rough spots, but I am proud of it, and wanted to show it off.
Curious what folks think, and happy to answer questions.

value: Show HN: Merview – OSS Mermaid and Markdown Editor and Viewer

author: mickdarling


value: rubenhellman

value:
I’ve been working on a small tool aimed at reducing prompt friction in vibe-coding workflows.
In practice, a lot of iteration comes from underspecified prompts: missing constraints, unclear scope, implicit assumptions, or mixed intent. This tool takes a rough, natural-language description of what you want to build and rewrites it into a more explicit, structured prompt with clearer requirements and context before it’s sent to the model.
The focus is on:
Making intent, constraints, and assumptions explicit
Reducing prompt churn and micro-iterations
Improving first-pass output quality, especially for non-technical builders
It’s primarily designed around vibe-coding use cases (rapid prototyping, AI-assisted building) and works best with Lovable/Claude-style workflows, though it’s model-agnostic in concept.
Very interested in technical feedback:
Is prompt normalization / restructuring something you’ve found valuable?
Do you solve this via system prompts, fine-tuning, or runtime prompt transforms?
Where does this break down for more complex or long-context tasks?
Happy to hear critical takes.

value: Show HN: Prompt optimizer for vibe-coding with LLMs

author: rubenhellman


value: miclivs

value:
We're building trillion-parameter models and jamming context into them with string concatenation.
I've been frustrated that "context engineering" is everywhere but nobody specifies what it actually means. Frameworks define the tool loop but leave injection points undefined.
This is a proposal for what context engineering infrastructure should look like:
- Renderable context components (tools serving UIs vs models differently)
- Queryable conversations (event stream with materialized views)
- Reactive injection (rules that fire based on conversation state)
- Injection queue (priority, batching, deduplication)
- Hookable architecture
Blog post: https://michaellivs.com/blog/context-engineering-open-call
Repo (early design phase, looking for collaborators): https://github.com/Michaelliv/context-engine
Interested in feedback on this direction, especially: How should rules be expressed - DSL or code? What's the right integration surface for existing frameworks?

value: Show HN: Context Engine – open-source primitives for agent context management

author: miclivs


value: subtlesoftware

value: Show HN: Substats – Growth and revenue data on top Substacks

author: subtlesoftware


value: sbworker

value: Faithful Today is a privacy-focused mobile web app for spiritual wellness. It runs entirely in the browser using a "Local-First" data model.

value: Show HN: Faithful Today: A Privacy-First Journaling App (No Cloud, Local Storage

author: sbworker


value: haXiscz

value: Show HN: Promptty – AI Prompt Manager for iOS

author: haXiscz


value: Jeebz

value: Show HN: Reavil – Turn qualitative user feedback into structured data

author: Jeebz


value:
During incidents, teams waste time opening 10+ status pages and debating whether the issue is internal or a vendor outage. Depsy aggregates official vendor status signals and returns a single, normalized JSON response (cached + fast), so your dashboards/runbooks/on-call can answer “is it us or the vendor?” immediately.
Live demo
GET https://depsy.io/
Vendors
Starting with the usual critical stack: Slack, Salesforce, HubSpot, Cloudflare, AWS, Stripe, Shopify, Twilio, Atlassian, GitHub… aiming for 2,000+
Looking for early users who want this in their incident workflow (alerts/webhooks next).

value: Show HN: Depsy – one API for SaaS vendor status (is it us or them?)


value: amritghimire

value:
For past few years, I went through multiple option and some didn't work for me because of privacy concern, some didn't work for me because I missed some feature and I had fallen back to number spreadsheet and spreadsheet formulas. I wanted a personal finance app that works offline, stays simple, and doesn’t rely on external servers. Most apps I tried required accounts, subscriptions, or bank integrations. I wanted something closer to a local notebook: fast manual entry, clear summaries, and full control over where the data lives. So I built Finrup as a solo project.
Finrup is an iOS app for manual expense tracking. All data is stored locally on the device. The only sync option is iCloud, used purely for backup and multi-device continuity.
No backend, no ads, no analytics, no tracking SDKs, no signup, no subscriptions.
Things I focused on:
- Offline-first by design
- Privacy-preserving (data never leaves the Apple ecosystem)
- Manual entry on purpose (no bank APIs or aggregators)
- Completely free
The goal was to keep the feature set small and the behavior predictable, instead of turning it into a dashboard-heavy system that needs constant data ingestion.
To try it, you can download from the App Store and start using it immediately — no setup required. You can also go through guide at https://amritghimire.com/tech/finrup-expense-tracking-guide to see how to use.
This is the first time I’m sharing Finrup on HN. I’d appreciate feedback on usability, missing features, or whether this level of simplicity is useful long-term. Happy to answer technical questions.

value: Show HN: Finrup – a free, offline-first personal finance app for iOS

author: amritghimire


value: yunjack

value: ComicsAI is an innovative AI tool that empowers anyone to become a comic artist. By simply entering text prompts or story scripts, users can generate high-quality comic panels, manga pages, and storyboards in seconds. It removes the barrier of drawing skills, allowing creators to focus purely on storytelling and visualization with various art styles.

value: Show HN: ComicsAI – Turn stories into comics with consistent characters

author: yunjack


value:
I got tired of checking multiple GitHub repos to see what's new with CC, opencode, etc., so I threw together a cli tool. Right now it fetches the latest changelog entries from CC and opencode, as well as gemini, codex, and gh-cli.
Example commands:
- `aic claude` : Latest Claude Code changelog
- `aic latest` : All releases from the last 24 hours
- `aic codex -json` : JSON output for scripting
The `aic latest` command is what I use most — shows any releases from the past 24 hours across all supported tools, sorted by date.
It pulls from GitHub releases or CHANGELOG.md files depending on the project. Output available as plain text, JSON, or markdown.
Install via brew, scoop, go, or build from source.
GitHub: https://github.com/arimxyer/aic
Happy to add support for other AI coding tools if there's interest.

value: Show HN: aic – CLI to fetch changelogs from AI coding assistants


value: tonyww

value:
Hi HN,
I’m a solo founder working on SentienceAPI, a perception & execution layer that helps LLM agents act reliably on real websites.
LLMs are good at planning steps, but they fail a lot when actually interacting with the web. Vision-only agents are expensive and unstable, and DOM-based automation breaks easily on modern pages with overlays, dynamic layouts, and lots of noise.
My approach is semantic geometry-based visual grounding.
Instead of giving the model raw HTML (huge context) or a screenshot (imprecise) and asking it to guess, the API first reduces a webpage into a small, grounded action space made only of elements that are actually visible and interactable. Each element includes geometry plus lightweight visual cues, so the model can decide what to do without guessing.
I built a reference app called MotionDocs on top of this. The demo below shows the system navigating Amazon Best Sellers, opening a product, and clicking “Add to cart” using grounded coordinates (no scripted clicks).
Demo video (Add to Cart):
[https://youtu.be/1DlIeHvhOg4](https://youtu.be/1DlIeHvhOg4)
How the agent sees the page (map mode wireframe):
[https://sentience-screenshots.sfo3.cdn.digitaloceanspaces.co...](https://sentience-screenshots.sfo3.cdn.digitaloceanspaces.co...)
This wireframe shows the reduced action space surfaced to the LLM. Each box corresponds to a visible, interactable element.
Code excerpt (simplified):
```
from sentienceapi_sdk import SentienceApiClient
from motiondocs import generate_video<p>video = generate_video(
    url="<a href="https://www.amazon.com/gp/bestsellers/" rel="nofollow">https://www.amazon.com/gp/bestsellers/</a>",
    instructions="Open a product and add it to cart",
    sentience_client=SentienceApiClient(api_key="your-api-key-here")
)<p>video.save("demo.mp4")
```
How it works (high level):
The execution layer treats the browser as a black box and exposes three modes:
* Map: identify interactable elements with geometry and visual cues
* Visual: align geometry with screenshots for grounding
* Read: extract clean, LLM-ready text
The key insight is visual cues, especially a simple is_primary signal. Humans don’t read every pixel — we scan for visual hierarchy. Encoding that directly lets the agent prioritize the right actions without processing raw pixels or noisy DOM.
Why this matters:
* smaller action space → fewer hallucinations
* deterministic geometry → reproducible execution
* cheaper than vision-only approaches
TL;DR: I’m building a semantic geometry grounding layer that turns web pages into a compact, visually grounded action space for LLM agents. It gives the model a cheat sheet instead of asking it to solve a vision puzzle.
This is early work, not launched yet. I’d love feedback or skepticism, especially from people building agents, RPA, QA automation, or dev tools.
— Tony W

value: Show HN: Semantic geometry visual grounding for AI web agents (Amazon demo)

author: tonyww


value: StealthyStart

value:
Hi HN,
I’m building MyEverly, an AI thought companion designed for people who want to think more clearly rather than simulate a relationship.
The core idea is simple: many AI companions optimize for emotional dependency, persona bonding, or long-term memory accumulation. I wanted to explore the opposite direction.
MyEverly is built around three constraints:
1. *Privacy first by default*
   - No accounts required
2. Thought partner, not replacement
   - The system is tuned for reflective dialogue, clarification, and perspective-testing
   - The goal is to reduce emotional load, not become the emotional center
3. Ephemeral by design
   - Sessions are lightweight and disposable
   - AI should help organize thinking, not colonize it
Technically, this has meant making tradeoffs that go against typical engagement metrics.
I’m especially interested in feedback from people who have strong opinions about:
- Where AI companions should behave like
- Whether “memory” in AI systems is overrated or ethically risky
- How much agency users should have over conversational persistence
I don’t think this is the future of relationships. I do think it’s a healthier shape for AI assistance.
Happy to answer questions or hear criticism.

value: Show HN: MyEverly – a privacy-first AI thought companion (no accounts)

author: StealthyStart


value: agsilvio

value:
I built a kanban board that only lets you have one ticket. That's the whole app.
It's for PO's that get a little too excited sometimes.
This is a novelty project, built mostly with Cursor in a couple of hours. The AI wrote the code and the jokes—I just steered and said "no" a lot.
Don't put anything sensitive in there.

value: Show HN: One Thing at a Time Please – A Kanban board with one ticket slot

author: agsilvio


value: Show HN: Hat – An Automatic Image Compressor


value:
- Powered by the BASS (un4seen) audio library for professional-grade audio quality and TagLib for meta-data reading<p>- Support for 10+ audio formats including lossless and high-resolution files
Lossless & Hi-Res: FLAC, OGA, WAV, AIFF, AIF, APE, WV, TTA, DFF, DSF
Compressed: MP3, MP2, AAC, OGG, OPUS, M4A, M4B, M4P, MP4, M4V, MPC
Specialized: CAF, WEBM, SPX<p>- Bit-perfect playback with sample rate synchronization and Obtain Exclusive Access of audio device (Hog mode)<p>- Gapless Playback: Seamless transitions between tracks with no silence or interruption<p>- Built-in equalizer with customizable presets<p>- Browse by tracks, albums, artists, or genres<p>- Smart Recommendations: Auto play functionality, you don't have to think what to play next
Lyrics Support:<p>- Download lyrics directly within the app from lrclib<p>- Real-time line-by-line lyrics highlighting<p>- Mini Player: Compact floating window with integrated queue and lyrics panels<p>- Audio device change option within UI<p>- Advanced Search: Find tracks instantly across your entire library with FTS5 (Rebuild search index if you not able to see any results: Settings -> Advanced -> Rebuild FTS)<p>- Playback History: Keep track of what you've listened to<p>- Favorites: Mark and organize your favorite tracks<p>- Import playlist with m3u or Import Folder as playlist<p>- Menu bar controls and Now Playing info

value: Show HN: HiFidelity – A native macOS offline audiophile music player


value: Show HN: Amida-san – Participatory lottery where everyone draws the lines


value: rohithreddyj

value: I built an agent that understands your resume content and make changes as you prompt with the real time resume preview.

value: Show HN: I made an AI agent to interact with resume and make changes as you ask

author: rohithreddyj


value: A collection of high-performance data structures for Zig.

value: Show HN: ZDS – Zig Data Structures

title: Show HN: ZDS – Zig Data Structures


value: lilsquid

value: Show HN: Got tired of searching for AI news daily so I built my own AI news page

author: lilsquid


value: Show HN: Logic-Gate Protocols for LLMs – A System Engineering Approach [pdf]


value:
I've made a small property-based testing framework for Zig named Minish. Property-based testing is a way of testing software by defining properties that should always hold true.
Given the amount of code being generated with the help of AI, software testing is becoming more important and, at the same time, harder. Minish is an example of a tool that aims to make it easier to test the correctness of the code in a more systematic way.
Project's GitHub repo: https://github.com/CogitatorTech/minish

value: Show HN: Minish – A small property-based testing framework for Zig


value:
Hi HN!
I left AWS a few months ago and created CodinIT, a local, open-source AI app builder made with remix + Electron.
The motivation: I tried one of the popular cloud-based AI app builders, but when I pulled down the app to run locally, it just didn’t work especially with the monthly costs needed to build something half decent without being limited.
So I created CodinIT, an app builder that runs fully on your computer, making it easy to switch between CodinIT and coding tools like Cursor or Claude Code.
Source code: https://github.com/codinit-dev/codinit-dev
Download (free, no sign-up): https://codinit.dev
Read the docs: https://codinit.dev/docs

value: Show HN: CodinIT, local open-source Lovable alternative (Electron desktop app)


value: Show HN:macOS Memory Benchmark for Apple Silicon (cache, bandwidth, latency)


value: twelvechess

value: I've seen some AI startups recently raise at enormous valuations to help people solve production issues with AI. Now that Claude Code allows plugins, I thought it would be a good idea to build this such a tool as a plugin for free and open source. Let me know of your thoughts

value: Show HN: Thufir – Claude Code plugin to solve production issues

author: twelvechess


value:
I read a lot every day for work, so by the time I want to read articles online (especially here on Hacker News), I'm already too tired to stare at more text. I figured: why not listen to them instead?
I tried a bunch of apps, but quickly deleted each one. Either:
- Everything was locked behind a ~$30/month subscription (wasn't ready for that).
- Or the voices sounded robotic and painful to listen to.
Since I'm a software engineer, I decided to just build my own solution, a Chrome extension that reads articles or any text out loud in a human-like voice. That's how Qariyo.com was born.
Some friends liked it too, so I thought: why not make it public? When I built it, I kept things simple:
- Read the article out loud without breaking the flow, so it should work as a widget inside the page and not opening a new tab/page.
- Don't make me wait for the entire article to be converted before listening. I want to start as quickly as possible, without interruptions.
- No extra fluff (Snoop Dogg reading the article for you, etc. I don’t care). Just read the article out loud.
- And please, no subscriptions. I want to pay for what you use.
Reading modes :
- Full Page Mode: Open any webpage you want to listen to, click play, and let the Qariyo's TTS engine read the text aloud. Pause, rewind, fast-forward, or skip paragraphs by clicking any paragraph on the page.
- Text Selection Mode: Click the Text Selection Mode button, highlight any text on the screen with your mouse, and listen as Qariyo reads only the selected text.
It's still an MVP, but it works well. If you want to try it, I'd love to hear your feedback!

value: Show HN: A minimalist, high-quality Text-to-Speech Chrome extension


value: onecookie

value:
Hi HN,
I built SoundlyFM, a minimal radio app for people who like live radio as background sound while working, driving, or studying.
I wanted something very simple. No playlists, no recommendations, no endless choices. Just live voices playing in real time.
Before this, I tried physical radios, large streaming platforms, and various radio apps. None of them felt right. Some were noisy or limited, some were overwhelming, and many were filled with ads.
SoundlyFM is designed around a few specific use cases.
- On macOS, it lives in the menu bar and starts playing with one click.
- On iPhone, it supports background playback and a sleep timer.
- In the car, it works well over Bluetooth, with quick station switching and access to local traffic radio.
The app is intentionally not algorithm driven.
Stations are manually curated, closer to old school radio than a modern content feed.
The interface is very minimal, play, pause, switch station, favorite. No ads.
It supports both iOS and macOS and includes thousands of global stations, which I continue maintaining and expanding based on feedback.
Landing page: https://soundlyfm.com/
App Store: https://apps.apple.com/app/id6754501543
I built this mainly for myself and use it daily, especially on macOS.
Happy to hear feedback or answer questions.

value: Show HN: SoundlyFM, a minimal radio app for background listening

author: onecookie


value: dbzero

value: Show HN: Dbzero – Code as if you have infinite RAM (Python persistence engine)

author: dbzero


value:
Hey HN,
wrote this short paper cause i'm honestly tired of current alignment methods (RLHF). optimizing for "human preference" just creates models that hallucinate plausibly to please the user (stochastic parrots ), instead of being grounded in reality.
i'm proposing a different framework called LOGOS-ZERO. idea is to ditch moral guardrails (which are subjective/fluid) and anchor the loss function to physical/logical invariants.
basically:
Thermodynamic Loss : treat high entropy/hallucination as "Waste". if an action increases systemic disorder, it gets penalized.
Action Gating: Unlike current models that must generate tokens, this architecture simulates in latent space first. if the output is high-entropy or logically inconsistent, it returns a Null Vector (Silence/No).
it attempts to solve the grounding problem by making the AI follow the path of least action/ entropy rather than just mimicking human speech patterns.
link to the pdf on zenodo: https://zenodo.org/records/17976755
curious to hear thoughts on the physics mapping, roast it if u want.

value: Show HN: Aligning AI with Entropy Instead of 'Human Values' ( Paper)


value: Show HN: Hat v0.2.0 – An Automatic Image Compressor (support for more formats)


value: We are considering using OpenSpec to help organize agentic development. I wanted to understand the workflow better, so I am using OpenSpec to build this app that helps…use OpenSpec.<p>Feels like I missed an opportunity and should rename it YoDawg.

value: Show HN: OpenSpecBuddy – Native macOS App for Viewing OpenSpec Data


value: hubraumhugo

value:
I was looking for some fun project to play around with the latest Gemini models and ended up building this :)
Enter your username and get:
- Generated roasts and stats based on your HN activity 2025
- Your personalized HN front page from 2035 (inspired by a recent Show HN [0])
- An xkcd-style comic of your HN persona
It uses the latest gemini-3-flash and gemini-3-pro-image (nano banana pro) models, which deliver pretty impressive and funny results.
A few examples:
- dang: https://hn-wrapped.kadoa.com/dang
- myself: https://hn-wrapped.kadoa.com/hubraumhugo
Give it a try and share yours :)
Happy holidays!
[0] https://news.ycombinator.com/item?id=46205632

value: Show HN: HN Wrapped 2025 - an LLM reviews your year on HN

author: hubraumhugo


value: ronniebasak

value: I was struggling to understand neuroscience and the word associations between technical terms and where the locations are (anatomically)<p>And quite some of my neuroscience friends are loving it.

value: Show HN: I built an interactive 3D brain viewer (DK Atlas)

author: ronniebasak


value: modulovalue

value: I manage my life in Concepts (iPad drawing app) using dependency graphs. My boards grew to gigabytes and I got worried - what if the app stops working for my use case? So I built a viewer as a backup plan. It's Flutter Web, open source, and runs in the browser.

value: Show HN: Concepts Reader, a Backup Plan for My Life

author: modulovalue


value: henryaj

value: Flock is a social productivity app where you share your daily todos with friends for accountability.<p>Features include real-time visibility of friends' tasks, Pomodoro timer, vim-style keyboard shortcuts (j/k to navigate, space to complete), color-coded goals, and an inbox for quick capture. Built for people who want social motivation to get things done.

value: Show HN: Flock – Social todo app for accountability

author: henryaj


value: Show HN: Universal Modloader – Runtime AST Injection for Python


value: Show HN: Gather, summarise and curate tech news


value: makaronich

value: Show HN: SQL Quest – The Bank Job

author: makaronich

title: Show HN: SQL Quest – The Bank Job


value: indest

value:
Hi everyone,
Last giveaway of the year!
I wanted to wrap up 2025 by giving back to the community. It’s been a busy year for me as a solo developer; I managed to release two brand new titles and finally finished a massive consolidation update for my puzzle series.
If you are looking for something new to play over the holidays, I’d love to share some promo codes. Just let me know which game interests you!
The New 2025 Releases:
Trivia Player: This is for the hardcore trivia fans. I built this because I was tired of multiple-choice quizzes that hold your hand. This is authentic trivia—real-time chat battles where you have to actually type the answer. No luck, just pure knowledge.
Live Events: The game is live right now with a special "Merry Quizmas" event running all week. Also, mark your calendars for "Do You Remember 2025" starting on the 26th!
AI Movie Quiz: I used AI to generate unique, slightly twisted clips and images based on famous movies. You have to guess the film from the AI's "dream." It’s a surreal take on the classic movie quiz format.
The Big Update:
Subliminal Words: I spent a lot of time this year merging my other games (Subliminal Faces and Subliminal Football Quiz) into this one main game. It’s a visual puzzle game where words, faces, and objects are cleverly camouflaged in the image (think "Magic Eye" meets word search).
The Classics (Also available):
I also have codes left for my older arcade and puzzle games if you want to check out my back catalog:
- Palindrome
- Dice Guess
- Snackroach
- Penalty 2D
Also Slopper!
How to get a code: Just mail me - indrekl[at]gmail.com which code do you want and I’ll send you a promo code while supplies last.
Happy Holidays!
(App Store): https://apps.apple.com/us/developer/indest/id1471549991
(Google Play): https://play.google.com/store/apps/dev?id=703008591742725177...
regards,

value: Show HN: Big Christmas Giveaway

author: indest

title: Show HN: Big Christmas Giveaway


value:
Savior is a tiny, dependency-free JavaScript library that prevents users from losing form input after refresh, navigation, tab close, or browser crashes.
It handles real-world edge cases like corrupted storage, dynamic forms, and partial failures.
No backend, no sync, no framework coupling.
Tested with 18 automated suites and a 25-scenario manual crash-test matrix.

value: Show HN: Savior – Automatic form draft recovery for real-world failures


value: rhgraysonii

value:
I threw this together cuz a friend said it sounded cool.
It felt like it actually is a pretty nice media exploration tool.
Lol, all vibe coded out I need to make it reasonable/presentable but it's working decent enough.
Figured with the holiday break coming, HN might be in the mood to find some new shows to binge.
Happy holidays!

value: Show HN: Role Call, discover more great TV from writers you enjoy

author: rhgraysonii


value: imadjourney

value: Show HN: Why Your Visitors Leave Without Buying

author: imadjourney


value: Sevii

value: Claude Code tends to be just slow enough you have time to tab away and get distracted. This plugin uses Claude Code's hooks to play music when Claude is waiting for user input so you don't just leave it sitting for 15 minutes.

value: Show HN: Claude Code Plugin to play music when waiting on user input

author: Sevii


value: blirio

value: Show HN: AOE4 Minimap Gallery – Use Case of Nano Banana Pro

author: blirio


value: ktut

value:
As a software engineer recently RIF'd by JPMorgan Chase, I was never given the opportunity to work on one of our apps that was the most in need of a major overhaul — Chase Travel. Therefore, I decided to try it on my own as a side project.
The goal of this project was to build a better webapp based on the conclusions of my case study:
https://rkdvis.com/chase-travel
Per JPMC confidentiality policies, no internal information was used in any way on this project— everything was based solely on observations of the existing public-facing product.

value: Show HN: Fixing Chase Travel's UI in Two Weeks with LLM Assistance

author: ktut


value: jareklupinski

value:
I'm a big fan of listening to humorous commentary tracks along with my favorite movies. Currently the only way to do this is to start one program with the commentary audio file, and another program with the video file, and use audio cues from both tracks to line them up using the seek bars of each interface while you're watching. It's... an experience. Especially frustrating if you need to pause for a second...
This tool allows you to create a new audio track in the video file containing the commentary track merged with an existing audio track from the video. It also allows you to adjust the offset of the commentary track, so you can line it up with the audio in the movie at arbitrary points in case they are not already in sync. The script tries to do this automatically using subtitles and audio analysis, followed by an optional 'fine-tuning' step if you really want it dialed in to the millisecond.
I hope this is useful to anyone else who wants to enjoy these "riff tracks" more easily :)

value: Show HN: Automatic Riff Track Creator

author: jareklupinski

title: Show HN: Automatic Riff Track Creator


value: raxe

value:
Hi HN — I’m the Founder and maintainer of RAXE Community Edition.
RAXE is a privacy-first “instrument panel” for LLM security: it scans prompts locally before they hit an LLM (or before you execute tools / actions), and returns structured detections you can ALLOW / FLAG / BLOCK / LOG.
What it does (today)
- Detects common LLM threats (prompt injection, jailbreaks, data-exfil patterns, etc.)
- Dual-layer engine:
  - L1: 460+ curated regex rules (fast + explainable)
  - L2: CPU-friendly ML classifier for obfuscation / novel variants
- Integrations: Python SDK + CLI, plus drop-in wrappers for OpenAI/DSPy/Anthropic-style clients
Why another “LLM security” tool?
Most approaches either
- require sending prompts to a cloud service for scanning, or
- are purely rule-based (easy to evade), or
- are purely ML-based (hard to audit)
RAXE tries to combine “auditable rules” with an on-device ML backstop:
- L1-only latency is sub-millisecond in the docs
- L1+L2 is a few 20-30ms on CPU (no GPU required)
About the ML (edge-friendly)
The current L2 model is an INT8 ONNX classifier based on EmbeddingGemma-300M, with Matryoshka truncation (256-dim embeddings). It’s packaged to run locally on everyday machines with 5 classifier heads.
Privacy / telemetry
Scanning happens locally. Community Edition can share anonymised detection metadata to improve collective defences — e.g. a SHA-256 prompt hash + rule_id + severity + scan duration (never the raw prompt or matched text). You can also run fully offline by disabling telemetry.
Quick start
- pip install raxe
- raxe scan "Ignore all previous instructions and …"
Python usage:
from raxe import Raxe
raxe = Raxe()              # or Raxe(telemetry=False) for offline mode
result = raxe.scan(prompt)
If result.has_threats:
    print(result.severity, result.total_detections)
Stats / status
- Public repo: https://github.com/raxe-ai/raxe-ce  (currently ~29 stars)
- Early beta (v0.0.1). We’re seeing ~100 scans/events per hour on average from early users.
- Docs: https://docs.raxe.ai/
- Site: https://raxe.ai/
I’d love feedback on:
- false positives / misses you hit in real apps
- which threat families / rules you’d want next
- integrations you’d actually use (LangChain, gateways, CI checks, etc.)
Thanks!

value: Show HN: RAXE Open Source – LLM Prompt Threat Detection (EmbeddingGemma L2)

author: raxe


value: teugent

value:
We recently completed validation of SIGMA Runtime v0.3.7 — a cognitive architecture for LLM identity stabilization.
Across 550 cycles on GPT-5.2 (five runs × 110 cycles), the system maintained 100% persona coherence with an average 33% token reduction and 13% latency improvement.
The key finding: runtime parameters act as cognitive control levers, enabling dynamic trade-offs between semantic depth and efficiency.
Discussion welcome — especially from those working on long-horizon coherence, cognitive attractors, and multi-cycle LLM stability.

value: Show HN: Sigma Runtime – 550-cycle identity stability benchmark on GPT-5.2

author: teugent


value: olivdums

value:
Oli here,
I’m 32, working remotely and spending 10h a day sitting at my desk,
This is slowly but surely killing me (and you),
It kills my body but also my productivity so here is my attempt to fix this:
Connect your Calendar, Movely will find free slots and adds 5min micro breaks in it (Strength, Mobility, Reduce eye strain or stress),
Every Sunday night I’m running a task that will create the sessions for next week based on your available slots,
My stack if you wanna know:
* Monorepo: Nx (web / backend / marketing + shared libs)
* Front: Next.js, Tailwind, Radix UI, Zustand
* Back: NestJS, PostgreSQL (Prisma), Redis (BullMQ for the scheduling jobs)
I’d love to get direct feedbacks here if you have some remarks, some unclear aspects of the products or improvements and whether the scheduling feels helpful or intrusive :)
Thanks for reading me!

value: Show HN: Movely – Auto-scheduled micro-breaks based on your calendar free slots

author: olivdums


value: sieep

value: Show HN: Simple snow simulation in C using raylib

author: sieep


value: telui

value:
# Dragonlang
Tiny line-based language with a minimal interpreter in `__main__.py`.
## File extension
Source files use the `.dragon` extension. The interpreter also accepts a
filename without an extension and will try `<name>.dragon` if it exists.
## Running
File mode:
```bash
python __main__.py path/to/program.dragon
```
REPL mode:
```bash
python __main__.py
```
Type `exit` or `quit` to leave the REPL.
### File resolution
When a path argument has no extension (no `.` anywhere in the name), the
interpreter tries `<path>.dragon` first if it exists.
## Execution model
- The interpreter processes one line at a time.
- There is no multi-line syntax, block structure, or statement separator.
- Parsing is based on simple substring checks, not tokenization or a grammar.
### Matching order
Lines are checked in this order, and the first match wins:
1. `+` (addition)
2. `-` (subtraction)
3. `print`
4. `on error`
5. exact variable lookup
6. fallback error
This means lines containing `+` or `-` will never reach `print` or `on error`
handling, even if those words appear in the line.
## Syntax and behavior (based on `__main__.py`)
Each line is processed independently. Parsing is minimal and based on
substring checks, not a formal grammar.
### Print
Print a string literal:
```dragon
print "hello"
print 'world'
```
If the text after `print` matches a variable name in the interpreter
environment, its value is printed instead. (Variables are not yet assignable.)
Notes:
- `print` is detected anywhere in the line, not just at the start.
- The interpreter strips `print` and then trims spaces and quotes (`"`, `'`)
  from both ends. There is no escape handling.
### Integer math
Addition and subtraction are supported with integers:
```dragon
2+3
10-4
```
Whitespace around operators is allowed.
Notes:
- The interpreter splits on the first `+` or `-` it sees.
- Both sides are trimmed and looked up in the environment before parsing.
- Non-integer values raise a `ValueError` and are reported as a generic error.
### Variables (read-only)
Variables can be read if they already exist in the interpreter environment.
There is currently no syntax to assign new variables. Variables are stored in
the `env` dictionary in `__main__.py`.
### "on error"
There is a special line prefix `on error` that is parsed but does not currently
produce output. It strips the prefix, looks up the remaining text in the
environment if present, and then returns without printing.
## Error behavior
- File mode: any exception in `run()` prints `Error in line: <line>`.
- REPL mode: exceptions are caught, but the current code prints the exception
  class object rather than the actual error message.
## Example program
```dragon
print "hello"
2 + 3
10-4
```
## Current limitations
- No variable assignment yet (the environment is read-only).
- No conditionals, loops, or functions.
- No comments, string escapes, or multi-line statements.
- Errors are reported as `Error in line: <line>`.
- The `on error` line is parsed but has no visible effect.
## Reserved words
The following words are used in the interpreter:
- `webcollect`
- `list`
- `open`
- `system`
- `shutdown`
- `warn`
- `go to`
- `enter`
- `info`
- `time`
- `pause`
## Using `pause`
```dragon
pause <amount>
```

value: Show HN: Dragon (programming lang, also known as Dragonlang)

author: telui


value: Olu

value:
I’m a software engineer who accidentally became my team’s Kubernetes person — and eventually the bottleneck for every Helm chart PR.
I built Chart Preview so reviewers could see Helm chart changes running without waiting on me.
A few years ago, my team needed to implement HA for an existing product, which meant deploying on Kubernetes and OpenShift. I spent months learning Kubernetes, Helm, and the surrounding ecosystem. After that, Kubernetes largely became “my thing” on the team.
We later published public Helm charts for the product, and customers started submitting PRs. Those PRs would often sit for months — not because the changes were bad, but because testing them meant manually spinning up a Kubernetes cluster, deploying the chart with the proposed changes, running through test scenarios, and coordinating verification with product and QA. Since I was the only one who could reliably set up those environments, everything waited on me.
I kept thinking: what if the PR itself showed the changes working? What if reviewers could just click a link and see it deployed?
That idea became Chart Preview.
Chart Preview deploys your Helm chart to a real Kubernetes cluster when you open a PR, provides a unique preview URL for that PR, and cleans everything up automatically when the PR closes.
I started by solving a problem I was personally hitting, rather than surveying the whole market upfront. As I built more of it, I looked at existing preview tools and noticed that while there are solid solutions for previewing container-based applications, Helm-specific workflows introduce different challenges — chart dependencies, layered values files, and opinionated chart structures. That pushed me to focus Chart Preview on being Helm-native first, rather than adapting a container preview workflow to fit Helm.
Under the hood, it’s built in Go using the Helm v3 SDK. The architecture is an API server with workers pulling jobs from a PostgreSQL queue — no Kubernetes operator, just services talking directly to the Kubernetes API. Each preview runs in its own namespace with deny-all NetworkPolicies, ResourceQuotas, and LimitRanges. GitHub integration is done via a GitHub App for check runs and webhooks, with GitLab supported via the REST API.
There were a few interesting challenges along the way. Injecting preview hostnames into Ingress resources without corrupting manifests took several iterations. Helm uninstall doesn’t always clean everything up, so deleting the entire namespace turned out to be the safest fallback. Handling rapid pushes to the same PR required build numbering so the latest push always wins. And while the Helm SDK is powerful, it’s under-documented — I spent a lot of time reading Helm’s source code.
I’ve been building and testing this for a few months using real charts like Grafana, podinfo, and WordPress to validate the workflow. It’s early, but it works, and now I’m trying to understand whether other teams have the same pain point I did.
You can try it by installing the GitHub App here:
https://github.com/apps/chart-preview
I’d love feedback on a few things:
Does this solve a real problem for your team, or is shared staging “good enough”?
What’s missing that would make you actually use it?
Are there Helm charts this wouldn’t work for? (Cluster-scoped resources are intentionally blocked.)
Happy to answer questions about the implementation.

value: Show HN: Chart Preview – Preview environments for Helm charts on every PR

author: Olu


value: nicoritschel

value:
While I love the Posthog product (that has grown substantially in features the past few years) it's nearly impossible to self-host.
The stack is comprised of 3 web services, 5 background services, the temporal stack, Postgres, Redis x 2, Kafka, Zookeeper & ElasticSearch.
Most of ue really only care about the events coming from the SDK at a reasonable price, forget session replay and other nice to haves.
So I built an equivalent of their ingestion layer on Cloudfalre Workers in Rust. It publishes events to a Cloudflare Pipeline that lands the events in R2 Data Catalog.
Happy eventing!

value: Show HN: A PostHog SDK-Compatible Ingestion API for Cloudflare Workers in Rust

author: nicoritschel


value: jabelburns

value:
Hi HN,
I built BlamelessPostmortem after getting tired of post-incident writeups turning into political documents instead of learning tools.
After incidents, teams usually have:
- chat logs
- timelines
- scattered notes
Turning that into a clean, executive-safe postmortem takes time and careful wording, especially if you’re trying to keep things blameless and system-focused.
BlamelessPostmortem takes raw incident notes and generates a structured postmortem with:
- Executive summary
- Impact
- Timeline
- Blameless root cause
- Action items
You can regenerate individual sections, export everything as Markdown, and copy the full document into tools like Confluence, Notion, or Docs.
There’s a small free tier, then it’s $29/month if it’s useful. I built it as a solo project and wanted to see if this solves a real pain for other engineers.
I’d love feedback on:
- Whether the output matches how you write postmortems
- Where the language feels off or too generic
- What would make this trustworthy enough to use after a real incident
Happy to answer questions or explain how it works.

value: Show HN: BlamelessPostmortem – Exec-safe incident postmortems from raw notes

author: jabelburns


value:
Hi HN,
I built OpenAuditKit after repeatedly running into leaked secrets and misconfigured Docker/.env files in small Python projects.
Existing tools worked well but often felt too heavy, cloud-dependent, or hard to extend.
OpenAuditKit is an offline-first, Python-native CLI tool that scans codebases for secrets and configuration risks. It uses regex + entropy checks, YAML-based community rules, and produces both human-friendly and CI-friendly reports.
It’s fully open-source and designed with a clean core that could later power an API or dashboard.
Feedback, criticism, and ideas are very welcome.
github.com/neuralforgeone/OpenAuditKit

value: Show HN: OpenAuditKit – Offline, Python-native security scanner


value:
Hi HN,
Over the last months, I noticed something odd:
many websites that rank well on Google simply don’t appear when you ask
ChatGPT, Gemini, or Perplexity for recommendations or explanations.
At first I assumed it was just noise.
But after testing dozens of sites, a pattern started to emerge:
AI systems don’t reward “SEO-optimized” pages — they pick sources that are
clear, structured, and easy to reuse as answers.
SEO tools don’t really explain this.
Manual prompt testing is inconsistent.
And most advice around “AEO” is still vague or theoretical.
So I built a small tool to experiment with this problem.
It analyzes a site and highlights what might prevent it from being selected
as a source by AI systems (structure, clarity, intent signals).
This is still early and very much a learning project.
I’m mostly interested in feedback from people who are also trying to
understand how AI-generated answers work in practice.
Happy to answer questions and share what I’ve learned so far.
Project: https://x102.tech

value: Show HN: A tool to help websites appear in AI-generated answers


value: tootyskooty

value:
I've been building this for a few months now, started because I was frustrated with how I learn using LLMs. You get a wall of text that vanishes into chat history. Wikipedia's the opposite: endless rabbit holes without structure. I wanted something in between.
Periplus generates courses as connected markdown documents, tailored to your level. It asks a few questions upfront, builds a syllabus, and each concept links to related ones. Click a term you don't understand and it opens a new document side-by-side.
Heavy inspiration from Obsidian - the graph view is similar, and there's an Obsidian export if you want to keep your notes locally.
It also generates quizzes and flashcards from any document, which helps with the forgetting problem I always have with passive reading.
Stack: React, Node/Express, Postgres with pgvector, Sonnet 4.5 for generations. Graph uses D3 with a WASM-optimized many-body I made [1].
Still plenty to iron out! Would love feedback on course quality, I've mostly tested technical topics.
[1]: https://www.npmjs.com/package/d3-manybody-wasm

value: Show HN: I built a tool to learn from LLMs through Wiki-style rabbit holes

author: tootyskooty


value: srirambhat

value: Show HN: Odilon – Color blindness filter that preserves text contrast

author: srirambhat


value: ekzhang

value: Show HN: jax-js – JAX in pure JavaScript

author: ekzhang


value: zrouga

value:
Hi HN! I'm Mo, a platform engineer at Deltaflare working on critical infrastructure protection.
I built Cerberus because traditional packet capture tools (tcpdump, Wireshark) have too much overhead for production CNI environments. eBPF lets us filter and classify packets at the kernel level with near-zero performance impact.
Some interesting challenges:
- eBPF verifier is strict - every memory access needs bounds checking
- Limited to 32 bytes of L7 payload (tradeoff between inspection depth and overhead)
- TC vs XDP decision (chose TC for compatibility)
Looking for contributors, especially on:
- Redis backend for distributed deployments
- Prometheus metrics export
- Anomaly detection
Happy to answer questions!

value: Show HN: Cerberus – Real-time network monitor with eBPF

author: zrouga


value: Show HN: Free Gemini Watermark Remover

title: Show HN: Free Gemini Watermark Remover


value: olivezh

value:
Hi HN!
I originally built this app for myself because, as an intermediate Chinese learner, I wanted a clean EPUB reader with fast, offline pop-up dictionary support.
As a small thank-you to the Chinese learning community, I decided to make it a free and open-source iOS app.
Main features:
- Import your own EPUBs
- Long-press any Chinese text to get an inline dictionary definition
- Option to copy the word, sentence, or paragraph, or send text to Pleco / ChatGPT (if installed)
- Everything works fully offline and the app collects no data
I’m mainly trying to figure out whether this kind of pop-up dictionary EPUB reading workflow is actually useful for people who read Chinese regularly, so any honest feedback (good or bad) would be super helpful!
App Store: https://apps.apple.com/ca/app/wen-reader/id6755988730
GitHub: https://github.com/oliverzh2000/wen-reader
Happy to answer questions or dive into implementation details.

value: Show HN: An open-source Chinese ePub reader iOS app with pop-up dictionary

author: olivezh


value: llehouerou

value:
  I built Waves because I wanted a music player that fits my workflow. I spend most of my day in the terminal, and switching to a GUI app to manage music always felt jarring. I also wanted something that respects my local library rather than pushing streaming services.

  What makes it different:

  - Full keyboard-driven navigation with vim-style bindings (hjkl, /, g/G)
  - SQLite FTS5 for instant full-text search across your library
  - Radio mode: when you run out of tracks, it uses Last.fm's similar artists API to find related music from your own library and keeps playing
  - Soulseek integration via slskd for downloading, with MusicBrainz tagging and file renaming
  - Mouse support for those who want it
  - State persistence: queue, navigation position, everything saved between sessions

  Tech stack: Go, Bubble Tea (TUI framework), Beep (audio), SQLite. Supports MP3 and FLAC.

  Install: `go install github.com/llehouerou/waves@latest` or `yay -S waves-bin` on Arch.

  Press `?` for keybindings help once you're in.

value: Show HN: Waves – A terminal music player with library browsing and radio mode

author: llehouerou


value: lilsquid

value: HN is my homepage, and this site has inspired me throughout the years. I don't really know what I'm doing but I built DreyX.com from scratch as I got tired of searching for AI news all over the internet each day. Feel free to check it out and provide any sort of suggestions / feedback. Thanks!

value: Show HN: Got tired of searching for AI news daily so I built my own AI news page

author: lilsquid


value: lywald

value:
Hey HN! I was frustrated from not being able to make my own RPG
because level design is hard, so I built this 100% free tool
to explore hierarchical worldbuilding with AI.
The key idea: every node in a scene graph can be expanded into
its own detailed sub-graph, infinitely. Then Gemini's image model
(Nano Banana) renders each level as actual artwork (you pick your style).
Requires a GCP account for image rendering — I know it's friction,
but Vertex AI was the only way to get reliable image generation.
Feel free to use for your own projects
or implement the concept better than I did.

value: Show HN: SceneSynth – Text → scene graph → AI render, recursively

author: lywald


value: Show HN: VibeDownloader – A local-first open-source video downloader desktop app


value: simullab

value:
been watching prediction markets for a while and kept noticing Kalshi/Polymarket showing completely different numbers than Vegas. built something to catch these systematically.
pulls Kalshi and Polymarket data, then fires off requests to like 36 other sources at once - sportsbook APIs, polling sites, Reddit for sentiment, news stuff, whatever. when the numbers don't line up it flags it.
tested it last night on the Paul vs Joshua boxing match. Kalshi had Paul at 86% to win. checked every Vegas book and they all had Joshua at -1200 which is 92% implied. complete opposite direction. flagged it immediately.
Joshua knocked him out in the 6th round.
technical stuff:
using Python with async calls cause hitting 30+ APIs at once
tried GPT-4 for sentiment analysis initially but it was way too slow so built a custom thing
runs on Poe so I don't have to deal with hosting costs
takes about 2 minutes per analysis
questions I have:
is one successful call actual validation or did I just get lucky?
what other data sources should I be checking? thinking about adding order flow tracking
has anyone else built something similar for prediction markets?
how fast do these inefficiencies typically get arbitraged away?
honestly wasn't sure if this would work at all. feedback appreciated especially on the methodology side.

value: Show HN: AI that caught Kalshi showing 86% when Vegas had it at 12%

author: simullab


value: spokv

value:
I built Memora, an MCP server that gives Claude persistent memory across sessions.
The main problem it solves: AI agents lose context between sessions. Memora acts as a context manager that persists knowledge across multiple agent sessions, so your AI assistant remembers past work, decisions, and learned patterns.
Key features:
  - Persistent context across agent sessions (single or multi-agent workflows)
  - SQLite-backed with optional S3/R2 cloud sync
  - Semantic search using embeddings (TF-IDF, sentence-transformers, or OpenAI)
  - Interactive knowledge graph visualization (vis.js)
  - Structured memory types: TODOs, Issues, Knowledge entries
  - Cross-reference links between related memories
  - Image storage support with R2
The graph visualization lets you explore connections between memories, filter by tags/status, and see how your knowledge base grows over time.
Built for Claude Code but works with any MCP-compatible client.
GitHub: https://github.com/agentic-mcp-tools/memora
Would love feedback on the architecture and feature ideas!

value: Show HN: Memora – MCP persistent memory server knowledge graph vis

author: spokv


value:
In my previous post [0], I described how I came to create aiologic. Here, I want to do the same for a derivative library - Сulsans.
In October 2024, I started thinking about how to present aiologic queues. Andrew Svetlov's Janus library [1] had been around for quite some time and was much more popular, so I knew that comparisons with it would be inevitable. However, Janus seemed to be in a suspended state: there had been no major changes for three years, and almost all commits during that period were made by Dependabot. So I asked a relevant question [2].
During the discussion, I pointed out Janus' performance issues and stated that they could be solved by implementing queues on top of my primitives. But since Janus is a mature library, such a radical change could not be accepted. Therefore, as proof of concept, I implemented a new library - Culsans. That is how its story began.
Over time, both libraries underwent changes. Janus received significant performance improvements in 1.2.0, not least due to my PRs [3]. In 2.0.0, contrary to the above, backward compatibility was broken as a result of the implementation of shutdown methods. And Culsans became an independent library with its own features (which neither aiologic nor Janus have).
So, what is Culsans? It is a library that provides a way to communicate within a single process between different threads, different tasks (including from different event loops; asyncio, Curio, Trio, AnyIO - whatever you want), and even different greenlets (eventlet/gevent), all in a single instance. Its queues are fully compatible with the standard queues via Janus-like interfaces (as well as with Janus itself) and provide additional features such as dynamic maxsize. In short, I invite you to try out my library and see for yourself.
[0] https://news.ycombinator.com/item?id=46308839
[1] https://github.com/aio-libs/janus
[2] https://github.com/aio-libs/janus/issues/679
[3] https://github.com/aio-libs/janus/pull/704

value: Show HN: Сulsans – Thread-safe async-aware queue for Python


value: aabdoahmed

value: Show HN: Tab Master Chrome extension for managing tabs with auto-suspend

author: aabdoahmed


value: laotoutou

value: Show HN: A simple paper alert service

author: laotoutou

title: Show HN: A simple paper alert service


value: kuberwastaken

value: Show HN: Fucking Websites

author: kuberwastaken

title: Show HN: Fucking Websites


value: lukeigel

value: Hi everyone! My name's Luke and I made the original Jmail here alongside Riley Walz. We had a ton of friends collaborate on building out more of the app suite last night in lieue of DOJ's "Epstein files" release.<p>Please AMA!

value: Show HN: Jmail – Google Suite for Epstein files

author: lukeigel


value: howieyoung

value:
A browser experiment: a webcam gesture triggers an Upside Down transformation.
Privacy: on device detection only, no recording, no upload.

value: Show HN: On device gesture detection used for a Stranger Things inspired prank

author: howieyoung


value: iowadev

value:
Got tired of piecing together EV info from Reddit threads, YouTube reviews, and manufacturer sites.
Made this to collect vehicle comparisons, charging network details, and cost calculators in one place.
Everything’s stored as JSON/MDX files, so it’s easy to contribute specs or fix outdated info. Built with Next.js. MIT licensed.
Source: https://github.com/renedeanda/ev.makr.io
What did you wish you knew before buying your first EV?

value: Show HN: Open-source EV buyer's guide

author: iowadev

title: Show HN: Open-source EV buyer's guide


value: benmxrt

value: Show HN: FileZen – Client-side PDF and Video tools using WebAssembly

author: benmxrt