value: kingcauchy

value:
Go 1.26 adds native SIMD via GOEXPERIMENT=simd. This library provides a portability layer so the same code runs on AVX2, AVX-512, or falls back to scalar.
Inspired by Google's Highway C++ library.
Includes vectorized math (exp, log, sin, tanh, sigmoid, erf) since those come up a lot in ML/scientific code and the stdlib doesn't have SIMD versions.
algo.SigmoidTransform(input, output)
Requires go1.26rc1. Feedback welcome.

value: Show HN: Go-Highway – Portable SIMD for Go

author: kingcauchy


value: robust-cactus

value: Show HN: Jot - Offline, source available notetaking/assistant app

author: robust-cactus


value:
Over the festive break, I built a simple web app to solve a problem I run into every year: planning time off.
If you’re trying to maximize your PTO around public holidays, this tool helps you find the best dates to take off.

value: Show HN: StretchBreak, a simple web app to solve planning time off


value: gustrigos

value: Show HN: Runtm- open-source runtime and control plane for agent-built software

author: gustrigos


value: aribenjamin

value: Show HN: A Bloomberg terminal for finding fresh powder (DuckDB WASM)

author: aribenjamin


value: debarshri

value: Show HN: Wip – Watch and reload any process using pluggable hooks

author: debarshri


value: Loeffelmann

value: Show HN: Website that plays the lottery every second

author: Loeffelmann


value: sr-white

value:
Hi HN! I built a Snowflake emulator for local development and testing.
Testing Snowflake code locally is frustrating – you need a real account (expensive) or mock everything (tedious). I wanted something that just works with the standard [gosnowflake](https://github.com/snowflakedb/gosnowflake) driver or REST API.
snowflake-emulator fixes this by:
- Using DuckDB as the storage engine
- Auto-translating Snowflake SQL (IFF→IF, NVL→COALESCE, DATEADD, etc.)
- Supporting gosnowflake driver protocol – no code changes needed
- Providing REST API v2 – use from any language (Python, Node.js, etc.)
```go
dsn := "user:pass@localhost:8080/TEST_DB/PUBLIC?account=test&protocol=http"
db, _ := sql.Open("snowflake", dsn)
db.Query(`SELECT IFF(score >= 90, 'A', 'B') FROM users`)
```
```bash
docker run -p 8080:8080 ghcr.io/nnnkkk7/snowflake-emulator:latest
```
GitHub: https://github.com/nnnkkk7/snowflake-emulator
Would love feedback – especially on SQL functions you'd want supported!

value: Show HN: Snowflake Emulator – Local Snowflake Development with Go and DuckDB

author: sr-white


value: rezat

value:
Shadowscoping – Test your team communications before they backfire
Solo founder here. I kept seeing smart leaders erode trust with their teams through preventable communication mistakes. Shadowscoping simulates how your team might react to a message or decision, so you can iterate before it's real. Currently focused on performance reviews and org changes. Free to try—would appreciate honest feedback.

value: Show HN: A flight simulator for difficult leadership conversations

author: rezat


value: remywang

value:
ORBIT is a pretty unique web browser designed exclusively for the Playdate console [1]. First, the cursor is inspired by the spaceship in the classic ASTEROIDS game, and controlled by Playdate's famous crank. Second, it doesn't support arbitrary web pages. Instead it implements an architecture I call the "exo web browser": for HTML, there is custom code for rendering each specific site, making sure the site will look right on Playdate's tiny, monochrome screen; alternatively, there's also another backend directly rendering Markdown. I also made the cuniform font [2] used in the browser based on GNU unifont. It's still very barebones, but I already use it to read news everyday.
[1]: https://play.date
[2]: https://github.com/remysucre/cuniform

value: Show HN: Browser in C and Lua for the Playdate Console

author: remywang


value: wdpatti

value:
I made these two extremely simple shortcuts a while back to quickly translate and read aloud into earphones info presented in a French art museum.
It allowed my family and me to look at the art while listening to information about it just like an audio guide would. This was the fastest implementation of something like this (two shortcuts on an iPhone Home Screen) I could think of, and you can even map the Action Button to it.
I could also see this being useful from an accessibility perspective, as it is great for people who only need text read out loud to them sometimes, while still being very easily accessible with a single button press.

value: Show HN: SpeakCamera – a surprisingly useful iPhone Shortcut to read text aloud

author: wdpatti


value: nicoritschel

value:
I built this service + client because there's not a reasonable way to share Claude Code + Codex CLI transcripts.
All data is e2e encrypted before it leaves your device, the share url contains the decryption key.
Self-host on Cloudflare workers if you prefer

value: Show HN: Share Claude Code and Codex CLI Transcripts

author: nicoritschel


value:
Hi Hacker News,
This is definitely out of my comfort zone.
I just wanted to show you guys because I'm super proud of it. It's a 100% faithful recreation based off of the schematics, patents, and ROMs that were found online.
So please watch the video and tell me what you think
https://youtu.be/auOlZXI1VxA
The reason why I think this is relevant is because I've been a programmer for 25 years and AI scares the shit out of me.
I'm not a programmer anymore. I'm something else now. I don't know what it is but it's multi-disciplinary, and it doesn't involve writing code myself--for better or worse!
Thanks!

value: Show HN: I used AI to recreate a $4000 piece of audio hardware as a plugin


value: sieep

value: Show HN: TCP chat server written in C# and .NET 9, used in the terminal

author: sieep


value:
We’ve open-sourced the official SDKs for the Office2PDF API.
The Node.js SDK is available today, with Python, Go, and Java/Kotlin SDKs planned next.
Feedback and contributions are welcome.

value: Show HN: Office2PDF - Official SDKs for Node.js (Python/Go/Java Coming)


value: claygood

value: Show HN: Open-source CSPM, DSPM, CIEM, and vulnerability management

author: claygood


value: modinfo

value: Show HN: Rust clipboard transformer, 40 transforms, 100% local, own recipes

author: modinfo


value:
Scan QR → web terminal → vibe coding in bed.
Mobile-first terminal via Cloudflare Quick Tunnel. No port forwarding. Feedback welcome.

value: Show HN: uvx ptn, scan a QR, get a terminal in your phone


value: waqaar-ansari

value: Show HN: EzFileChanger – Client-side WebP converter (no server uploads)

author: waqaar-ansari


value: ivanglpz

value:
I built an open-source image optimizer that runs entirely in the browser.
All compression happens client-side using the Canvas API—images are never uploaded or sent to a server.
The project was created with the help of v0 and AI tooling, but the architecture, state management, and image-processing logic were designed and implemented by me based on my own experience.
Features:
Fully client-side image optimization
Batch processing with a global quality slider
Real-time size comparison and compression savings
Individual and bulk download
Light/dark theme with system preference support
Tech stack:
Next.js (App Router)
React
TypeScript
Tailwind CSS
Repo: https://github.com/ivanglpz/v0-image-optimizer
Live demo: https://imgs-optimizer.vercel.app/
Feedback and technical critique are welcome.

value: Show HN: Client-side image optimizer built with Next.js

author: ivanglpz


value:
I've moved around a lot. Finding information is easy. Finding people is weirdly hard.
So I made this — a map where you drop a pin and see who else is around.
Filter by digital nomads, freelancers, remote workers, retirees. See if they just arrived or have been there for years.
Spot someone interesting? Send a message.
No signup. No feed. No tracking. No algorithm. Just a map.
More like exploring than scrolling.
Built with Next.js, Supabase, and Leaflet.

value: Show HN: A small web app where people appear on a world map


value: mitanshudodia

value:
I’ve been building Sakshatkar AI, an interview prep platform, and I just launched a new module: a resume editor where you chat with the document to update it.
Most AI resume builders give you a block of text to copy paste. I wanted something more integrated, so I built a split-pane interface where the PDF updates live as you chat.
Key Features:
Conversational Editing: You can say "Add my new role at XYZ" or "Shorten my summary to 3 lines," and it handles the formatting and insertion live.
Visual Diffing: Any change the AI makes is highlighted in yellow so you can verify the edits instantly.
JD-Specific Optimization: You can paste a Job Description and the AI re-aligns your bullet points for ATS keyword relevance.
Version Control: It maintains a version history, allowing you to roll back specific changes if the AI hallucinates or misses the mark.
Evaluate Suite: It provides a real-time ATS/Impact score and catches technical errors like malformed LinkedIn/GitHub URLs.
I built this because I found the feedback loop between "generating text" and "fixing PDF margins" too slow. I'm keeping this free for now to gather feedback on the UX and the accuracy of the ATS scoring.
Website: https://sakshatkarai.com
Direct Link: https://app.sakshatkarai.com
I'd love to hear your thoughts on the "chat-to-update" workflow. Does this solve a real pain point for you?

value: Show HN: A Live Chat Resume Editor that updates PDFs in real-time

author: mitanshudodia


value: nsemikey

value:
Recruiting is all wrong.
You should "Poach Employees" just like Zuck did with OpenAI.
I built "Poach". Paste a Job Link. Get the Best 10 Candidates for that Job.
Oh...I also built it in 5 minutes with Lovable. Insane. Coders should be scared.

value: Show HN: Poach Employees Like Zuck

author: nsemikey

title: Show HN: Poach Employees Like Zuck


value: Show HN: Underserved Directory Niches (Hand-picked for 2026)


value:
Hi HN,
I’ve open-sourced a small web app called Image to PDF.
It converts one or multiple images into PDF documents entirely in the browser. There is no backend, no file uploads, and no persistence. All processing is done client-side using jsPDF and HTML canvas.
Key details:
Single-page and multi-page PDF generation
Configurable orientation, paper size (Letter, A4, Legal, Tabloid), and margins
Aspect ratio preserved, images centered with margins
Live preview of layout before export
Built with Next.js (App Router), React, Tailwind CSS
The goal was to build a simple, privacy-friendly utility and keep the architecture straightforward and inspectable.
Source code:
https://github.com/ivanglpz/v0-image-pdf
Live demo:
https://imgs-pdf.vercel.app/
Feedback, critiques, and questions are welcome.

value: Show HN: Image to PDF – client-side image to PDF converter


value: itsangaris

value:
I sail and was tired of the tooling around GRIB files. GRIB is the binary format for weather forecasts (what NOAA/GFS publishes). If you want to view one, your options are: install a desktop app like XyGrib or OpenCPN, pay for a service, or wrestle with command-line tools.
I wanted something I could just open in a browser—drop a file, see wind on a map. Couldn't find it, so I built it: griblet.app
It parses the GRIB, extracts wind vectors, and renders them as animated particles over Leaflet. Supports multi-timestep files so you can scrub through the forecast. No signup, nothing stored server-side (files are parsed in memory and discarded). I'm planning to add more layers in addition to visualizing the wind.
Also added a Saildocs request builder because getting GRIB files can be a challenge. Saildocs is a free service for fetching weather via email—useful offshore where your only connectivity is satellite or SSB radio. You email a specially formatted request, it replies with a GRIB attachment that can be uploaded to giblet. The builder just helps you compose that request correctly.
Would appreciate feedback, bug reports, or feature ideas.

value: Show HN: Griblet – Free browser-based GRIB file viewer

author: itsangaris


value: cameronehrlich

value: Privacy-first, works offline, includes a Claude Code skill.

value: Show HN: genex – explore your genetic data and family tree from the terminal

author: cameronehrlich


value: ivanvolt

value:
Hi HN,
I built Square Face Generator, a small free tool that helps people figure out whether they have a square face shape.
I noticed that most results for “square face” are long articles or videos, but very few tools actually help you decide. So I built a simple generator where you can upload a photo or follow guided steps to get an instant answer.
It’s completely free, no signup, and runs in the browser.
I’d love feedback on:
 • accuracy and UX
 • whether the results feel clear or confusing
 • what face-shape features people actually care about
Thanks for checking it out.

value: Show HN: Square Face Generator – A Free Tool to Tell If You Have a Square Face

author: ivanvolt


value: murataslan

value: Show HN: AI Coding Tools Benchmark – What Developers Experience

author: murataslan


value: Show HN: Instant AI assistant that works over any app


value: noveltysystems

value: Show HN: I built my marketing site in a weekend with Claude Code

author: noveltysystems


value: evoluteur

value: Open-source AI-driven web application to create personalized workout routines.

value: Show HN: AI-driven web application to create personalized workout routines

author: evoluteur


value: Chmouel

value:
Inspired by lazygit, I have been working on a TUI to manage git worktrees easily, making some workflows for creating and managing worktrees a bit more straightforward. For example, creating a worktree from a PR/MR or from local changes. Executing commands on a worktree, or running a custom tmux session directly on a selected worktree.
A lot of other tools are more CLI-oriented. I have tried to compile an honest comparison here: https://github.com/chmouel/lazyworktree/blob/main/COMPARAISO...
Some of the code and documentation was written with an LLM if that bothers anyone :)

value: Show HN: Lazyworktree, a TUI manager for Git worktrees

author: Chmouel


value:
Built a genetics engine for lovebird breeders (Agapornis roseicollis).
- Vanilla PHP, zero dependencies
- 14 loci, 310+ ALBS-compliant phenotypes dynamically resolved
- Cartesian product across all loci computed in milliseconds
- Sex-linked inheritance (ZZ/ZW avian system) with proper hemizygosity
- Wright's inbreeding coefficient with 6-generation pedigree traversal
- Near-Bayesian inference engine: reverse-estimates parental genotypes from offspring phenotypes
No frameworks, no libraries. Just math and arrays.
Live demo: http://kanarazu-project.com/gene-forge/Rosy-faced-Lovebird/?...

value: Show HN: Vanilla PHP genetics engine – 14 loci, zero dependencies


value: akshayaggarwal

value:
Hey HN,
I've been obsessed with the rise of "computer-use agents" lately—tools like Claude Code that don't just autocomplete but actually interact with the OS.
The problem is infra: giving an agent access to your host is a nightmare, and spinning up full cloud VMs for every task is slow and expensive. I spent last night building Boxed to solve this. It's an open-source (MIT) execution engine that gives agents a sovereign, ephemeral OS layer—fully under your control.
Why build it? I wanted something that handles the compute heavy-lifting (sub-second boot, native artifact streaming, persistent REPL sessions) so I could focus on building the agents themselves. No more wrestling with sandboxes that feel bolted-on.
Usage: It's simple to integrate. You run the Boxed control plane locally or in a container, and use the SDKs to spawn isolated sessions.
Example (Python):
python
from boxed_sdk import Boxed
client = Boxed(base_url="http://localhost:8080", api_key="your-secret")
session = client.create_session(template="python:3.10-slim")
# Run code with ephemeral OS access & artifact capture
result = session.run("import pandas; print('Processing data...')")
print(result.stdout)
session.close()
typescript
import { Boxed } from '@boxed/sdk';
const client = new Boxed({ baseUrl: 'http://localhost:8080', apiKey: 'secret' });
const session = await client.createSession({ template: 'python:3.10-slim' });
// Runs in a secure, ephemeral environment
const { stdout } = await session.run("print('hello from HN')");
console.log(stdout);
await session.close();
Tech:
Control Plane: Built in Go for a high-performance, concurrent REST API.
Agent: A lightweight Rust binary injected into every sandbox to manage lifecycles and stream results via WebSockets.
Isolation: Docker-based for now (with Firecracker MicroVMs on the roadmap for Phase 2).
It's early days, but it’s running smooth in my own agent workflows. I’d love your feedback on the architecture
Repo: https://github.com/akshayaggarwal99/boxed
Demo and quickstart are in the README.

value: Show HN: Boxed – Sovereign exec engine for AI agents (Vercel Sandbox inspired)

author: akshayaggarwal


value: nexgea

value:
ScrollBump inserts short, unskippable pauses while you scroll. The goal isn’t to lock you out, but to make mindless scrolling inconvenient enough that you notice what you’re doing.
How it works
You define rules like:
 • After X minutes in selected apps
 • At location Y
 • Insert a Z-second pause before you can continue
When triggered, the app is temporarily covered by a shield. The shield is customizable — you can set a short message, color theme, or even a photo of your dog or cat — but the core mechanic is the pause itself.
Why this instead of hard blocking
Hard blocks tend to fail. When an app is completely locked, people disable the blocker or route around it (iOS makes that easy).
ScrollBump is intentionally annoying, not absolute.
If you genuinely need the app, you wait a few seconds and continue.
If you’re doomscrolling, the pauses add up and break the habit loop.

value: Show HN: ScrollBump – adding friction to scrolling instead of blocking it

author: nexgea


value: zeapo

value:
Hi HN,
I built Weft over the last two weeks of Christmas break. I was teaching my kids Python and noticed the lack of interactivity in some of their learning experiences. The feedback loop was too long.
I wanted to provide them with an experience similar to Bret Victor's concepts on Learnable Programming; i.e. an easier way to learn is to see immediately the reaction to your actions. Hence the reactivity ;)
It is currently in early alpha and I’m planning to open-source the repo under AGPLv3 soon once I clean up the prototype code.
I wrote up a short blog post with more details on the "Why" and the implementation here: https://zenadi.com/posts/weft-announcement
I’d love to hear your feedback or ideas on how to make this better.

value: Show HN: Weft – A reactive and interactive Python experience

author: zeapo


value: DELTA-X

value:
This work explores a simple but often ignored question:
What if an AI system is allowed to not answer?
ΔX is a constraint-layer approach where output is conditional, not mandatory.
Silence is treated as a valid system state, not a failure.
The goal is not better text generation, but behavioral justification:
— when an answer is produced
— when it is withheld
— and why
All materials, protocols, and references are openly published on Zenodo.
This is not a product launch, but a research disclosure.
Feedback, critique, and edge cases are welcome.

value: Show HN: ΔX – When an AI System Is Allowed to Stay Silent

author: DELTA-X


value: jaspervdmeent

value:
Hey HN! We built a compliance health scanner that gives you a score in seconds.

  The problem: Compliance is fragmented. GDPR in Europe, AI Act coming, PIPA in Korea (24-hour breach notification!), LGPD in Brazil, PDPA in Singapore... each with different requirements.

  tibet-audit scans your codebase and tells you:
  - What's missing (breach procedures, consent mechanisms, DPO designation)
  - How to fix it (auto-fix with --auto)
  - Your compliance health score (0-100)

  45 checks across 10 frameworks, covering all inhabited continents.

  We call the auto-fix "The Diaper Protocol" – because compliance shouldn't require your full attention. One command, hands free, done.

      $ pip install tibet-audit
      $ tibet-audit scan

  Built by a human + AI team at HumoticaOS. Happy to answer questions!

value: Show HN: Tibet-Audit – Like Lynis, but for Regulations (GDPR, AI Act, PIPA, LGPD

author: jaspervdmeent


value: tinul

value:
Hello HN,
I'm a noob Python programmer that somehow loves CLI and movies.
I built a CLI called MVW (MoVie revieW) that catalogue your personal reviews and showcase them in a fastfetch way.
It is not something that are groundbreaking but something that I have passion about.
Feel free to land on my Github and gives some feedback. While you at there, don't forget to leave some stars!

value: Show HN: I built a fastfetch inspired movie review – MVW (MoVie revieW)

author: tinul


value: wincat

value: Show HN: Startup investing game

author: wincat

title: Show HN: Startup investing game


value: BSTRhino

value: Show HN: Easel - learn to code by making games you can play together

author: BSTRhino


value: stared

value:
Hi HN!
"Never perfect. Perfection goal that changes. Never stops moving. Can chase, cannot catch." - Abathur (https://www.youtube.com/watch?v=pw_GN3v-0Ls)
StarCraft 2 is one of the most balanced games ever - thanks to Blizzard’s pursuit of perfection. It has been over 15 years since the release of Wings of Liberty and over 10 years since the last installment, Legacy of the Void. Yet, balance updates continue to appear, changing how the game plays. Thanks to that, StarCraft is still alive and well!
I decided to create an interactive visualization of all balance changes, both by patch and by unit, with smooth transitions.
I had this idea quite a few years ago, yet LLMs made it possible - otherwise, I wouldn't have had the time to code or to collect all changes from hundreds of patches (not all have balance updates).
It took way more time than expected - both dealing with parsing data and dealing with D3.js transitions.
Pretty much pure vibe coding with Claude Code and Opus 4.5 - while constantly using Playwright skills and consulting Gemini 3 Pro (https://github.com/stared/gemini-claude-skills). While Opus 4.5 was much better at executing, it was often essential to use Gemini to get insights, to get cleaner code, or to inspect screenshots. The difference in quality was huge.
Still, it was tricky, as LLMs do not know D3.js nearly as well as React. The D3.js transition part is a thing that sometimes I think would be better to do manually, and only use LLMs for details. But it was also a lesson.
Enjoy!
Source code is here: https://github.com/stared/sc2-balance-timeline

value: Show HN: 15 Years of StarCraft II Balance Changes Visualized

author: stared


value: yigremT

value:
Hi HN,
I’m the founder of AXION One. We are building a neuro-symbolic microkernel in Rust (Stable 1.78) that attempts to replace heuristic resource allocation with a Vector Symbolic Architecture (VSA) and a Joint Embedding Predictive Architecture (JEPA).
The Problem:
Running modern AI workloads on Linux leaves policy decisions (scheduling, memory security, energy profiles) to opaque legacy subsystems. You effectively have a black box (the model) running on top of another black box (the kernel), with user-space scaffolding trying to bridge the gap. We argue that sovereign AI requires the kernel itself to be an agent capable of continual learning and verifiable reasoning.
The Architecture (The "How"):
• VSA (Vector Symbolic Architecture): We encode syscalls as high-dimensional holographic vectors (D=10,000). This allows the kernel to perform symbolic reasoning (e.g., capability verification) using constant-time algebraic operations (binding/bundling) rather than complex branching logic. This provides a "verifiable glue" for IPC.
• JEPA Scheduler: Instead of CFS, we use a tiny, self-supervised JEPA model trained on syscall traces to predict latency distributions and select Pareto-optimal thread orders.
• Offline Consolidation: During idle cycles, the kernel "replays" event graphs via SGD to refine its embeddings, effectively "dreaming" to optimize future performance.
Current Status (The "Why" I'm Posting):
We have a GCP prototype (linked above) that validates the VSA/JEPA architecture. It passes 93% of LKDTM tests in emulation and demonstrates the self-learning loop.
However, the bare-metal Rust kernel on Raspberry Pi 4 is hitting a wall. We are seeing 8–12ms core stalls due to cache-line contention when the JEPA scheduler writes updated embeddings back to memory. We also have race conditions during warm resume causing SDHCI timeouts.
The Ask:
I am looking for a System Architect Co-Founder who sees that 8ms stall not as a failure, but as a challenge. I need someone who understands:
• Rust nightly and unsafe boundaries in a kernel context.
• Lock-free data structures to fix the embedding write contention.
• How to implement causal masking in the replay buffer (currently missing).
I’m here to answer questions about why we think VSA is the future of kernel IPC
AXION One stands at the precipice of a major breakthrough in systems programming. The combination of Rust, VSA, and JEPA offers a theoretical solution to the most pressing problems in AI infrastructure: sovereignty, auditability, and efficiency. However, the transition from a verified cloud prototype to a robust  bare machine kernel requires a specific caliber of engineering talent that a System Architect who thrives on complexity and is unafraid of low-level optimization.
Citations:
 Show HN: A DOS-like hobby OS in Rust.
VEKOS: Verified Experimental Kernel OS.
Show HN: Vect AI OS.
 Hacker News Posting Guidelines & Analysis.
 Vector Symbolic Architectures (VSA/HDC) Research.
 Joint Embedding Predictive Architecture (JEPA) Research.
Redox OS & Rust Kernel Discussions.

value: Show HN: Axion One NeuroSymbolic Microkernel Prototype InRust(Help on Scheduler)

author: yigremT


value: Parameswar

value:
Hi HN — I built Zolly after getting frustrated with AI app builders that generate code but make even small UI changes painful.
Most tools stop at code generation. The moment you want to tweak layout, spacing, text, or colors, you’re back to manually editing code. That breaks the fast-iteration promise for solo builders.
Zolly generates websites and simple web apps from a prompt, but the core idea is a real visual editor on top of the AI output. You can click, drag, and edit the UI instead of digging through generated code.
You can:
– Generate websites, landing pages, and small web apps from prompts
– Visually edit layout, text, and styles
– Upload existing HTML and modify it visually or with AI
– Generate from an image or screenshot
– Choose between multiple AI models
– Publish with one click or export clean HTML
This is still early, and I’d really appreciate honest feedback — especially on whether a visual-first AI workflow actually solves a real problem, or if I’m missing something obvious.

value: Show HN: I built an AI app builder where you can edit the UI visually

author: Parameswar


value:
I built a small tool to compare the sizes of things visually
(animals, vehicles, planets, etc).
The idea came from watching one of those space videos that makes
everything else feel tiny. Numbers never really worked for me,
so I wanted something more visual.
It’s live here:
https://objectsizecomparison.com
This is very much a work in progress.
I’m a backend dev and UI is not my strong side, so parts of this
are probably awkward or just wrong.
Would love feedback on what feels confusing or pointless,
and what comparisons you’d actually want to see.

value: Show HN: Compare object sizes visually – from animals to planets


value: onurkanbkrc

value: Show HN: An NPM-style registry for AI agent skills

author: onurkanbkrc


value:
Hey everyone!
I built HackLens, a modern Android/IOS app for browsing Hacker News with a cleaner and smoother reading experience.
Why I built it
I love Hacker News, but wanted:
a cleaner reading experience
better topic discovery
modern UI
lightweight performance
So I built HackLens.
Key features
1.  Clean, modern UI inspired by minimal reading apps
2.  Fast browsing of Hacker News stories
3.  AI-powered summaries to quickly grasp key points
4.  Read full articles directly from the source
5.  Trending stories (last 1h, 6h, or 24h)
6.  Built-in search to find stories and topics
7.  Topic discovery based on your interests and Get notified
8.  Light & dark mode with adjustable font size
9.  Bookmark stories to read later
10. Cross-device sync for bookmarks, topics, and preferences
Download
Google Play: https://play.google.com/store/apps/details?id=com.berranova....
App Store: https://apps.apple.com/fr/app/hacklens-hacker-news-reader/id...
I’d love your feedback
If you have suggestions, find bugs, or want features, I’d genuinely appreciate it — I built HackLens to learn and improve.
Thanks for checking it out!

value: Show HN: HackLens – A fast and clean Android/iOS Hacker News reader


value: johntalkstech

value:
Hey HN, I’m the founder of TikTask. I launched it about 3 months ago.
TikTask started as a message scheduler, but I kept running into the same reality: people don’t just want “send later”, they want reliable follow-up routines for business and for daily life.
What TikTask is: a message scheduler + on-device automation app for Android. You schedule messages and routines, and the execution happens locally on your phone, not on my servers.
What “on-device automation” means (and why it matters):
On-device (TikTask): your phone executes the scheduled actions at the right time. No cloud worker sending messages, and no need to wire up official APIs for common personal workflows.
Official APIs: great for businesses at scale, but usually require a backend, verification/compliance, templates, and per-message costs, and they don’t fit many “personal phone” workflows.
Cloud automations: powerful, but your automation and often your content lives off-device, which can add cost and privacy considerations.
With on-device automation, the model is simple: private by design (your data stays on your phone) and cheap to operate (no per-message infrastructure), which is especially useful when you’re growing a small business and want to automate follow-ups without building a backend.
Use cases I built for:
Business: lead follow-ups, client reminders, appointment nudges, promo announcements
Daily life: birthdays, family reminders, recurring check-ins, routines you don’t want to forget
The hard part (and the part I’m proud of): reliability.
Android background limits and OEM battery managers can silently kill automations. A lot of the work went into a “System Monitor” style checklist that helps users enable the right settings so schedules actually run.
I’d love feedback from HN:
Does this positioning make sense, or does it confuse people (“message scheduler” vs “automation”)?
If you’ve built anything that depends on Android background execution, what reliability traps did you hit?
What would you want to see to trust a scheduler like this for business follow-ups?
If anyone wants to test premium/locked features and give blunt feedback, reply or DM and I’ll share a promo code.

value: Show HN: TikTask – schedule messages and automate follow-ups on Android

author: johntalkstech


value: StabilityFirst

value:
Hi HN, I'm researching the link between time and memory in neural dynamics.
This repo contains a proof-of-concept experiment ("The Lazarus Effect"):
1. I train a network until convergence.
2. I destabilize it (catastrophic forgetting).
3. I restore accuracy NOT by retraining on data, but by applying a stability operator to the recursive dynamics.
It suggests that memory can be treated as a stability parameter rather than just stored information. I'd love your feedback on the code and the approach.

value: Show HN: Stability First AI – Recovering memory without training data

author: StabilityFirst


value: Show HN: Neurosymbolic music generation with Claude Opus 4.5


value: Tanishmittal

value:
Hi HN / YC
I just shipped Speechable — a free, open-source AI text-to-speech solution for WordPress that runs entirely in the browser with no API keys, no paid services, and no server infrastructure.
I wrote a full write-up here:
https://tanishmittal.com/speechable-ai-text-to-speech-for-wo...
What it does
Adds a native audio player to WordPress posts
Uses browser-based neural TTS
Supports 12+ languages
Offers multiple voice presets & effects
Lets visitors download audio
Highlights words in sync with narration
Why I built it
Most TTS plugins depend on expensive APIs, external servers, and data sharing. I wanted:
Privacy-first experiences
No recurring costs
High-quality voice output
Easy install for any WordPress site
Try it
Repo & docs: https://github.com/tansihmittal/speechable
Would love feedback — especially on:
UX pain points
Performance in real blogs
Edge cases you think it should support
If you try it or build something cool with it, shoutout on X: @glowdopera
Thanks!

value: Show HN: Speechable – AI Text-to-Speech for WordPress Websites

author: Tanishmittal


value:
Hi, I built a website that lets you preview, customize, and export Flutter UI components with real-time theme changes along with the UI components themselves (lib) kinda like tweakcn+shadcn
Link: https://docs-flutterstudio.vercel.app
Repo: https://github.com/TejasS1233/flutter-studio
The components themselves have 0 external dependencies and I think they should be integratable with all apps with Flutter 3.0.0.+ (data layer and ui layer is seperated so integration friendly as well)
The main reason I actually built this is cause I hate having to be confined with the structure the packages define so I made this to actually fetch the whole source code itself so customisation is possible and also for quick building in hackathons.(component addition extremely similar to shadcn)
Currently the theme presets look ugly , will gradually add better ones and start including cooler UI features like rive/shaders.  So yeah , do check it out and lemme know what you think.
I would really really appreciate any contribution/suggestion whatsoever, wanna include tons of things and too little timeee.

value: Show HN: Flutter Studio – A Shadcn-like UI library for Flutter (0 dependencies)


value: dailydev

value:
Hi HN
I built DailyDev.in, a simple collection of developer utilities in one place.
Key principles:
No login
No credit card
No paywalls
Just tools
Includes:
JSON / SQL / HTML / XML formatters
JWT decoder, UUID & password generator
JSON ↔ CSV / YAML converters
Regex tester, diff checker, image utilities
The motivation was to reduce context switching — instead of opening multiple sites for basic tasks.
I’d really appreciate feedback:
Missing tools?
Performance issues?
UX improvements?
Link: https://dailydev.in
Thanks for checking it out!

value: Show HN: Dailydev.in – 30 Free Developer Tools, No Login Required

author: dailydev


value: atchett

value:
This is a small personal project around code-inspired clothing.
I’ve been working as a developer for a long time, but I’ve never owned a developer t-shirt. I’ve always found most of them too loud or too jokey for everyday wear, so I eventually stopped looking.
With freelance work being quieter recently, I finally spent some time on an idea that had been in my head for years — seeing if I could make the kind of thing I’d actually want to wear.
From a technical point of view it’s pretty simple: Shopify for the storefront with Printful handling print-on-demand and fulfilment. It’s just me working on it alongside other commitments.
I’d be interested in feedback on a few fronts: whether this resonates at all, what people think of the choice of languages so far, whether there are others you’d expect or like to see, and — if the concept makes sense — whether anyone here has experience with a Shopify + Printful setup (good or bad). I have a longer list of languages in mind if there’s interest.
Link: https://slashstar.store
Thanks for taking a look.

value: Show HN: A small personal project around code-inspired clothing

author: atchett


value:
Hi HN,
I built a macOS desktop app that lets you chat with your documents completely locally.
No cloud, no API keys, no data leaving your machine. Everything runs offline on Apple Silicon using GGUF models and llama.cpp.
What it does:
- Upload PDFs, text files, and images
- OCR for images and scanned PDFs
- Local embeddings + retrieval (RAG)
- Chat with documents using a local LLM
- Models are downloaded on first run and stored locally
Tech stack:
- Electron (frontend)
- Python backend bundled as a native binary
- llama.cpp + GGUF (currently Gemma / Mistral class models)
- SentenceTransformers for embeddings
- FAISS for vector search
- Runs entirely on-device (CPU / Metal)
Why I built this:
I wanted a privacy-first alternative to cloud document chat tools. Packaging a full local LLM + OCR + RAG pipeline into a single macOS app turned out to be much harder than expected (Gatekeeper, PyInstaller, dylibs, model size, etc.).
Download:
GitHub release (macOS Apple Silicon):
 https://github.com/navid72m/chatbot/releases/tag/v.0.1.2
Note on macOS security:
Because the app is not signed yet, macOS may block it on first launch.
You can run:
xattr -rd com.apple.quarantine "/Applications/Document Chat.app"
I’d really appreciate feedback on:
- UX for document chat
- Model choices / performance
- How others approach local RAG on desktop
Happy to answer technical questions.

value: Show HN: A local, offline document chat app for macOS


value: aeyonblack

value: Show HN: Developer first market research for product validation

author: aeyonblack


value: con

value: I've built this small website today and wanted to share with HN. I hope it's a cute reminder that people achieved great things even when they were older. Let me know if I should add other people and their achievements - I'd be happy to!

value: Show HN: Whentheywere.com

author: con

title: Show HN: Whentheywere.com


value: robcholz

value: Lumen is a small open-source hardware node designed to live next to your computer long-term. Instead of being a single-purpose tool, it’s meant to act as a persistent bridge between software and physical feedback — lights, UI, and sensors — that can evolve over time. The focus is on extensibility and long-running use, not one-off debugging or fixed workflows. Everything is open-source, from firmware to hardware. Repo: https://github.com/robcholz/Lumen Happy to answer questions or hear feedback.

value: Show HN: Lumen: an open-source desktop hardware node

author: robcholz


value: shawnreilly

value:
Today I am releasing AGent Based Access Control (AGBAC) under Apache 2.0. This is a new Access Control Security Specification that introduces the concept of dual-subject authentication. Included is AGBAC-Min, which allows you to implement agent and human aware authentication today (right now!) with your existing IAM Solution!
https://github.com/kahalewai/agbac
AI agents broke one of the oldest assumptions in identity. For as long as I can remember, IAM has assumed a simple model: One action → one subject → one authorization decision. That worked when only humans were accessing things. But AI agents changed everything. So now we an evolution of Access Control Models (RBAC, PBAC, ABAC, now AGBAC).
When an AI agent acts on behalf of a human, there are two identities involved: The agent executing the request and the human whose authority triggered it. From a security standpoint, the correct rule becomes obvious: An action should only be allowed if both the agent and the human are authorized to perform it. Not agent or human. Agent and human.
The good news? This is now solved. And it works with your existing IAM solution protecting your enterprise today. Do you want to start enforcing (and logging) both agent and human authorization today? Or move one step closer to Zero Trust alignment within AI agent architectures? Check out AGBAC and drop a star if you think this is awesome!

value: Show HN: AGent Based Access Control Agbac for AI Agents and IAM

author: shawnreilly


value: eloh

value:
I built a tool that lets you run any package from nixpkgs without having Nix installed on your system.
uvx nixwrap ripgrep --version
uvx nixwrap jq --help
uvx nixwrap bat README.md
How it works:
1. Queries nix-index-database to find packages
2. Fetches NARs from cache.nixos.org
3. Uses patchelf to fix binaries with hardcoded /nix/store paths
4. Runs via bundled ld-linux with the correct library path
There's also a PEP 503 package index, so you can install tools via pip/uv:
uvx --index-url https://elohmeier.github.io/nixwrap fd --version
Requires Python 3.14+ (uses stdlib compression.zstd for zero external dependencies). Linux only for now.
Works great for simple CLI tools. Complex packages with hardcoded runtime paths (like neovim) have limitations.

value: Show HN: Nixwrap – Run Nix packages from Python without Nix installed

author: eloh


value: AISovereignDev

value:
I am a builder, not a researcher. I got tired of watching monolithic Transformers choke on non-text data and crash under the weight of real-time sensory streams, so I bypassed the whitepaper cycle to build OMNI_GENESIS (v0.7.0)—a modular framework designed for high-velocity AGI execution and frontal lobe emulation.
Repository: https://github.com/AI-Sovereign/Multimodal-AGI-Architecture-...
The architecture handles 132 asynchronous modalities—from biological signals to network entropy via Scapy—flattened into a 1344-dimensional causal manifold. By utilizing a tiered "Tri-Brain" pipeline across decoupled packages (see /packages and /TCS), the system maintains cognitive stability where standard models fail.
Technical Specifications:
* Modular Architecture: Distributed logic across specialized directories, separating the Sensory Cortex from the Causal Inference engine.
* Tiered Processing: Integration of snnTorch (Spiking Neural Networks) for reflexive temporal triggers and torch_geometric (GNNs) for episodic memory retention.
* TCS-25 Plasticity: Implementation of Hebbian-modified logic prioritizing "Surprisal" (MSE delta) over static weights, enabling online learning without backprop latency.
* Performance: Optimized with Polars for sub-millisecond entropy checks across the full 132-modality buffer.
Current Status:
The Sensory Cortex and HTSP (Hierarchical Temporal) units are fully operational. The system handles the 512-to-4096 latent space expansion with high precision. This is an implementation-first project; the codebase is the proof.
I am releasing this v0.7.0 codebase for architectural peer-review and technical validation. I am specifically interested in discussing the HLS projection math and the orchestration of modular inference engines with anyone working on non-transformer AGI paradigms.

value: Show HN: Omni_genesis – A 132-modality AGI core built for execution

author: AISovereignDev


value: kread

value: Show HN: GemBack – Smart fallback library for Gemini API rate limits

author: kread


value: The idea of this bible app (Thy Word) is to be completely clutter free and self contained, using absolutely no permissions from the phone other than the basic ones. Includes Strong's dictionary for hebrew and greek original, John Wesley's notes on the Bible and the ability to write, save and search for notes you take during sermons. Thy Word is and will always be free, offline and clutter free.

value: Show HN: An offline, free and ad-free Bible app with study tools


value: kidandcat

value:
I built ccc to control Claude Code sessions from my phone via Telegram. It lets you start sessions remotely, get notifications when Claude finishes tasks, and seamlessly switch between phone and PC.
Features:
- 100% self-hosted, runs on your machine
- Multi-session support with Telegram topics
- Voice messages (transcribed with Whisper)
- Image attachments for Claude to analyze
- tmux integration for session persistence
Built with Go. Would love feedback!

value: Show HN: CCC – Control Claude Code Sessions Remotely via Telegram

author: kidandcat


value: daimh

value:
Hi HN,
I built todotree to solve a problem I kept running into: TODO lists scale
poorly once tasks start depending on each other.
Instead of a flat list or grep output, todotree treats TODOs as nodes in
a dependency tree (inspired by Makefiles structure and Markdown format). This
makes it easier to see what’s actionable, what’s blocked, and how tasks
relate.
Key features:
- Tree-based visualization of TODOs and their dependencies
- Automatically highlights actionable tasks (red)
- Supports completed (~ / ~~) tasks, shown in blue
- Multiple output formats: terminal, HTML, Markdown, JSON
- Simple Markdown-based input format
- Fast, standalone CLI tool (written in Rust)
I primarily use it to track technical debt and project planning in
Markdown files, but it can also be used to visualize TODOs in any
structured task list.
Demo + examples are in the repo:
https://github.com/daimh/todotree
I’d really appreciate feedback:
- Is the dependency-tree model useful for TODOs?
- Are there similar tools people prefer?
- What would make this more useful in real projects?
Thanks!

value: Show HN: Todotree – visualize TODOs as a dependency tree

author: daimh


value: zuluwill

value:
Hi HN,
I’ve been quietly building an experiment called Society Speaks to explore a question that’s bothered me for a long time:
Can we measure public opinion without flattening it into polls, comments, or outrage?
Instead of comments or demographics, the system uses:
 • clear statements
 • simple voting (agree / disagree / unsure)
 • optional short explanations
People aren’t grouped by who they say they are, but by how they actually respond. Using ML techniques inspired by Pol.is, this reveals natural opinion clusters, areas of genuine consensus, ideas that bridge different groups, and honest fault lines where agreement doesn’t yet exist.
There’s a small daily entry point (inspired by Wordle):
one question per day, ~2 minutes to answer, insight revealed only after you respond. Over time, this creates a longitudinal, more nuanced view of public opinion than polls or comment sections usually provide.
We also take curated news articles and podcasts and turn them into structured prompts for deliberation, rather than reactive debate.
This isn’t a finished product and I’m not claiming it “solves” democracy. I’m mostly interested in whether this approach is:
 • methodologically sound
 • useful compared to traditional polling or forums
 • flawed in ways I haven’t spotted
If you’re curious, today’s question is here:
https://societyspeaks.io/daily
I’d genuinely value critique, especially from people who’ve worked on polling, deliberative democracy, or large-scale opinion systems.

value: Show HN: A daily 2-minute civic question revealing consensus and divides

author: zuluwill


value: A lightweight macOS utility for keyboard-based mouse control. Use WASD for movement with progressive acceleration, Space for click/drag, and Ctrl/Shift for right/middle click. Toggle with Caps Lock.

value: Show HN: MouseKeys – Control your mouse with your keyboard (macOS)


value: maciejonos

value: Show HN: Qbitwebui – Modern web UI for managing multiple qBittorrent instances

author: maciejonos


value: Layogtima

value:
Hallo HN, wrote a short sci-fi story called Moonfall.
It got stuck in my teeth and I couldn't shake it off, and here's where I've reached right now.
If you read it, would LOVE your take! I've not written for YEARS, and I wanted to get back to it.
Disclaimer on AI usage: Used Claude Sonnet for iterating through this

value: Show HN: Moonfall

author: Layogtima

title: Show HN: Moonfall


value: docuagent

value:
Before you say another RAG clone, please hear me out for a second.
The Problem:
As a creator: You have to screen record, edit, annotate, and then present. If anything changes, you redo the process.
As an end user: You have to watch a 5-minute video when all you need to know is 5 seconds of that video to perform a specific task.
The Solution:
For creators: Record and upload your raw screen captures. No further effort.
For end users: You ask a question, and you get exactly the document for your specific question with annotated screenshots.
How is this different from Scribe or RAG?
* vs. Scribe: Scribe is for active capture (clicking while you work). DocuFine is for passive extraction—it turns your existing raw videos or demos into guides after the fact.
* vs. RAG: Most video RAG just searches transcripts. DocuFine "sees" the UI using an LLM and then uses OCR to "snap" the annotations to the actual buttons, so the guides are spatially accurate even if the video is silent.
The site isn't live yet—I'm currently gathering feedback on the concept and demo before opening it up, as I'm still optimizing the LLM costs and extraction logic.
Demo Links:
- Initial Recording: https://streamable.com/c5gom5
- Query Asked: How do I find orders placed by a customer?
- Generated Output Guide: https://streamable.com/9c4ncj
End-to-End-Demo: https://streamable.com/hqb6te
Honest feedback appreciated!

value: Show HN: Turn raw screen recording into precise guide with annotated screenshots

author: docuagent


value:
Hi HN,
I built fp-pack, a small TypeScript functional utility library focused on pipe-first composition.
The goal is to keep pipelines simple and readable, while still supporting early exits and side effects — without introducing monads like Option or Either.
Most code uses plain pipe/pipeAsync.
For the few cases that need early termination, fp-pack provides a SideEffect-based pipeline that short-circuits safely.
I also wrote an “AI agent skills” document to help LLMs generate consistent fp-pack-style code.
Feedback, criticism, or questions are very welcome.

value: Show HN: FP-pack – Functional pipelines in TypeScript without monads


value: BenjaminHas

value:
Hi HN, I’m the creator of this tool.
I built it because I noticed that most photos contain metadata that people often don’t realize — things like GPS location, device info, timestamps, and other hidden EXIF fields. This can be a privacy concern or a nuisance if you just want to repurpose your images.
The tool lets you:
Strip GPS, device info, and other EXIF metadata
Edit or replace metadata for reuse
Process everything locally on your machine, so nothing is uploaded to any server
I focused on keeping it simple, fast, and secure, and I’d love feedback from HN on:
UX improvements
Edge cases in metadata handling
Any additional features that could make it more useful for developers or privacy-conscious users
I’m happy to dive into technical details about how it parses and edits EXIF data client-side if anyone is interested.

value: Show HN: I made a tool to strip GPS and EXIF metadata from photos client-side

author: BenjaminHas


value:
Hi HN, I built Jetbase — a Python-based database migration tool.
Jetbase has:
- strict validation to detect altered or removed migration files after they’ve been applied (prevents drift, fails fast)
- database locking so multiple migration processes can’t run at the same time
- full rollback support
- ascending version numbers enforced directly in filenames so migration history is obvious
- uses raw sql instead of ORMs
The main Python tool is Alembic, but it’s mainly used with an ORM and doesn’t include things like validation checks.
So I built Jetbase to add the features I was looking for.
Some other things I ran into:
- tools with more validation checks than Alembic were usually Java-based, not Python
- some tools gate rollback support behind paid tiers
- wanted a way to easily see migrations history
Moved off ORMs to raw SQL, which made Alembic’s ORM integration not necessary. Why I moved off:
- explored queries directly in DB UI tools (DBeaver, Snowflake) and didn’t want to rewrite them in ORM syntax
- ORMs didn’t make sense for Python data pipelines (S3 → Snowflake → Postgres)
- raw SQL was more efficient for things beyond basic CRUD
- shared a database with a sister team and didn’t want to create extra ORM models in API to query their stuff
Repo (with a quick start guide): https://github.com/jetbase-hq/jetbase
It currently supports Postgres and SQLite (planning to add more databases)
Would love to hear any feedback!

value: Show HN: Jetbase – A Python database migration tool (Alembic alternative)


value: bashmaster

value:
I used to take on way too many projects at once. One day, I opened my laptop and just couldn't work on the same thing again. Staring at my growing to-do list only made it worse. I was basically paralyzed by my own plans.
Then I tried something ridiculously simple I remembered from school: Pomodoro technique. 25 minutes. One task. Stop if you want. Somehow that tiny bit of focus actually worked.
That's why I built Planndu. It combines tasks, notes, and a Pomodoro timer in one place.
Sometimes, the simplest trick is all it takes to get moving again.

value: Show HN: I built Planndu when task lists stopped working for me

author: bashmaster


value: ssunboyy

value: Show HN : A game to document my electronics learning journey.

author: ssunboyy


value: Show HN: I built a "Do not disturb" Device for my home office


value: ckkampy

value:
Hi everybody, Happy New Year 2026!
I started the indie road by creating small util OverlayAI(dot)app can run on MacOS by the inspirations of:
- Pomodoro Focus session (block apps and distract websites)
- Audio Live-Transcript (also instantly answer questions)
- and Ask AI Anything window anywhere on screen (with OCR or selected text content)
Happy to share with you, hope it can help if you need to find alternatives thats fit.

value: Show HN: macOS Util for Deep Focus, Live Transcript and AskAI Anywhere

author: ckkampy


value:
Hi HN,
I’m building Corviont, a self-hosted offline maps appliance (tiles + routing + search) for edge/on-prem devices.
Hosted demo (no install): https://demo.corviont.com/
Self-host (Docker Compose repo): https://github.com/corviont/monaco-demo
Docs: https://www.corviont.com/docs
What’s inside:

  - Vector tiles served locally (PMTiles)
  - Routing served locally (Valhalla)
  - Offline geocoding/search + reverse (SQLite Nominatim-based index)
  - MapLibre UI wired to the local endpoints

After the initial image + data pulls, it runs fully offline (no external map/routing/geocoding API calls).
Next (if people need it): a signed on-device updater for regional datasets (verify → atomic swap → reload).
I’d love feedback: where offline maps/routing/search matters for you, and what constraints bite (hardware, fleet size, update windows, regions, deployment style).

value: Show HN: Offline tiles and routing and geocoding in one Docker Compose stack


value: gabrycina

value:
I built FocusMode because I was paying $10/mo for Raycast Pro just for workspace switching.
It's a menu bar app that lets you define a "focus workspace" - a set of apps that should be visible. Press ⌘⇧P and everything else hides instantly.
Features:
- Multi-monitor: assign apps to specific screens
- Per-app maximize toggle
- Native Swift, ~2MB
- MIT licensed
Website: https://gabrycina.github.io/FocusMode/
GitHub: https://github.com/gabrycina/FocusMode
Happy to answer questions about the implementation.

value: Show HN: FocusMode – Free macOS Raycast Pro alternative workspace switcher

author: gabrycina


value: humility

value: Basically considering goals, constraints and decisions as explicit state

value: Show HN: I built an LLM based planner to learn GenAI/RAG fundamentals

author: humility


value:
I built DeepShot to predict NBA game outcomes using machine learning and advanced rolling statistics.
The model scrapes historical data from Basketball Reference and uses Exponentially Weighted Moving Averages (EWMA) to capture recent team momentum and form. It's powered by XGBoost and achieves roughly 70% prediction accuracy.
The web interface (built with NiceGUI) lets you visualize upcoming matchups, see key statistical differences between teams, and get real-time predictions.
All data is free and public, and it runs locally across all platforms. The model training notebook is included if you want to retrain or experiment with the features.
Would love feedback on the approach or suggestions for improving accuracy!
Live demo: https://deepshot.onrender.com

value: Show HN: DeepShot – NBA Game Predictor with 70% Accuracy Using EWMA and XGBoost


value: KhazP

value: Templates and workflow for generating PRDs, Tech Designs, and MVP and more using LLMs for AI IDEs

value: Show HN: I built a systematic workflow for 'vibe coding'

author: KhazP


value: DenisDolya

value:
I was annoyed by reverse engineering binaries and .so traditional tools like objdump, readelf and even strings fail at one critical task: finding numeric constants.
Sections .rodata та .dynstr contain only names. That's why I created DYNUM scanner numeric constants, enough digging in Hex!
How it works:
1. Scans files as raw bytes, interpreting them as every possible numeric type (int8, int32, float, double, both endianness)
2. Filters out noise: aligned addresses, reasonable values, no NaN/Inf
3. Optionally integrates with nm/readelf to show symbol names
4. Architecture-aware (x86, x64, ARMv7, ARM64)
5. Follows Unix philosophy: text output, pipes friendly, single purpose
GitHub: https://github.com/Ferki-git-creator/dynum

value: Show HN: Missing puzzle in binary analysis: finding num constants in .so/.bin

author: DenisDolya


value: chrka

value: Show HN: Law of Large Numbers or Why It's a Bad Idea to Go to a Casino

author: chrka


value: shahinsalehiin

value:
AI systems learn from public explanations across the web.
LLM Ready is a diagnostic tool that estimates how consistently AI systems can understand and describe a product based on public information.
It does not track live AI responses, rankings, or prompts. It focuses on structural clarity, reference sources, documentation, community discussions, and educational content.
I built this after seeing many early products misrepresented by AI due to missing or fragmented explanations.
Feedback is very welcome.

value: Show HN: A diagnostic tool to see how AI systems understand your product

author: shahinsalehiin


value: fkratzer

value:
  Hey HN,

  I built Underpriced AI to solve a problem I had as a part-time reseller: standing in a thrift store trying to figure out if something is worth buying.

  How it works:
  - Snap a photo of any item
  - AI identifies the brand, model, maker, era, etc.
  - Pulls recent sold prices from eBay and other marketplaces
  - Gives you an instant valuation with confidence score

  You can also generate SEO-optimized eBay listings and publish directly from the app.

  Tech stack: Next.js, Claude API for vision/analysis, eBay API for market research and listing.

  The "Quick Scan" feature is designed for mobile – get a price check in seconds while you're out sourcing.

  Free tier available. Would love feedback from anyone in the reselling space or who's worked on similar pricing/valuation problems.

  https://underpricedai.com

value: Show HN: Underpriced AI – Snap a photo, get instant resale value with AI

author: fkratzer


value:
I built a Rust drop-in replacement for svelte-check that's 10-30x faster for Svelte 5 projects.
What it does:
- Parses Svelte files with a custom Rust parser
- Transforms them to TSX in parallel using Rayon
- Runs type-checking via Microsoft's tsgo (the native Go port of TypeScript)
- Maps errors back to original .svelte locations via source maps
Why it's fast:
The official svelte-check uses TypeScript's Language Service API optimized for IDEs with persistent connections. Great for autocomplete but slow for batch CLI checks.
svelte-check-rs writes real TSX files to disk and runs tsgo as a standalone compiler. This enables incremental builds with persistent .tsbuildinfo, so subsequent runs only re-check changed files.
Benchmarks on a 650-file SvelteKit monorepo (M4 Max):

  Cold: 17.5s vs 39.6s (2.3x faster)
  Warm: 1.3s vs 39.4s (30x faster)
  Iterative: 2.5s vs 39.8s (16x faster)

The AI part:
I built this in ~2 days using Claude Code (Opus 4.5) and Codex CLI (GPT-5.2 xhigh). The entire Svelte parser, TSX transformer, diagnostics engine, and CLI were written entirely by AI. I focused on architecture decisions and testing against real codebases while the models handled the implementation.
My motivation was actually to make AI coding agents more effective. When agents write code, they need to verify it works, and waiting 40 seconds for type-checking kills the feedback loop. With 1-2 second checks, agents can iterate much faster and catch their own mistakes immediately on our large and growing production SvelteKit codebase.
Website: https://svelte-check-rs.vercel.app/

value: Show HN: I built a 30x faster svelte-check in 2 days with AI


value:
I would like to share an open database focused on link-level metadata extraction and aggregation, which may be of interest to researchers.
The project maintains a structured dataset of links enriched with metadata such as:
- page title
- description / summary
- publication date (when available)
- thumbnail / preview image
- etc.
The goal is to provide a reusable, inspectable set of link metadata that can be used for experiments in areas such as:
- RSS and feed analysis
- news analysis
- link rot analysis?
The database is publicly available here:
https://github.com/rumca-js/RSS-Link-Database-2025
There are also databases for previous years

value: Show HN: Open database of link metadata for large-scale analysis


value: gravitycode

value:
I made a wealth inequality visualization that converts net worth to lifespan.
The math: $1 = 1 second of life
At the median US net worth (~$192k), you'd live about 2.2 days.
Elon Musk gets 23,000+ years.
https://immortals.georgekal.com
Tech: React, TypeScript, Cloudflare Pages, Framer Motion.
Curious to hear thoughts on the concept and execution.

value: Show HN: The Immortals – See how long you'd live if $1 = 1 second of life

author: gravitycode


value:
Phantom Guard is a CLI tool that catches "slopsquatting" attacks before they compromise your supply chain.
The attack vector: AI assistants hallucinate package names → attackers register those names with malware → developers install malware thinking it's legit.
How it works:
1. Checks if packages exist on registries
2. Matches against 10+ AI hallucination patterns
3. Detects typosquats of top 3000 packages
4. Analyzes metadata (age, downloads, maintainers)
```
pip install phantom-guard
phantom-guard validate flask-gpt-helper
# HIGH_RISK: Package not found, matches pattern
```
Performance: <10ms cached, <200ms uncached.
Try the live demo: https://matte1782.github.io/phantom_guard/
GitHub: https://github.com/matte1782/phantom_guard

value: Show HN: Phantom Guard – Detect AI-hallucinated package attacks


value: ubergeekady

value:
Hi HN
I’ve been building Crispforms, a conversational form builder inspired by Typeform — but with one big difference: it’s completely free for unlimited forms and unlimited submissions.
I started this after getting tired of paywalls and usage caps kicking in the moment a form actually starts working. Crispforms lets you create high-converting, multi-step forms with logic, analytics, exports, and integrations — without worrying about limits.
This is still early and very much shaped by user feedback. I’d genuinely love to hear how you use forms today, what feels broken, and what you’d expect from a tool like this.
Happy to answer any questions and take all criticism

value: Show HN: A full free for unlimited usage alternative to Typeform

author: ubergeekady


value:
Hey everyone, built another tiny thing. This time for a wider audience BUT i think it might appeal more to those with kids.
The idea was this: no matter how much effort you put in to reduce screen time for kids or yourselves, it inevitably happens which leads to eye strain.
So I built a Look puzzle game that is designed for people to have to look around their actual surroundings in order to finish the puzzle.
You take a picture of your surroundings, that picture becomes a puzzle and then you complete it while looking at what you just clicked.
There is no reference photo on your screen to refer to, the actual scene (what you just clicked) is your reference. Thus your eyes are forces to take a natural break if you want to complete the puzzle on time.
Super happy to hear any feedback or thoughts.

value: Show HN: LookPuzzle: A real time puzzle game that forces eye breaks


value: -i

value: click any word once an hour to get new info & learn more just like Wlkpedia, every word is a URL & Ai checks all URL's every hour and writes a new article based on the URL but the Ai hallucinates sometimes, share link 2 connect with anyone, press space to chat with the Ai, realtime infinite Wlkpedia Ai

value: Show HN: Realtime Infinite Wlkpedia AI

author: -i

title: Show HN: Realtime Infinite Wlkpedia AI


value: lcnmrn

value: Show HN: Turbo – Python Web Framework

author: lcnmrn

title: Show HN: Turbo – Python Web Framework


value: lastFitStanding

value: Show HN: I made a free AI tool for SWEs who struggle to land interviews

author: lastFitStanding


value: pici

value:
I built an offline-first, open-source invoicing app because I wanted a simple way to generate invoices without SaaS lock-in. It runs fully locally, keeps all data on disk, and exports PDF invoices.
I’m keeping it lightweight on purpose - it’s not a CRM. Feedback is very welcome.

value: Show HN: Invoice Builder – Offline first, self-hosted invoicing app

author: pici


value: shreyasdeshmukh

value:
Hey HN,
I got tired of JSON parsing failures and manually switching between LLMs on task basis. So, I built Perf, a single endpoint that picks the right model and guarantees valid JSON back.
The problem: LLMs are unreliable with structured output. You end up writing retry logic, validation, fallback chains or you end up overpaying for a flagship model for trivial tasks.
What Perf does: You call perf.chat(messages) and it analyzes the prompt, routes to the best model for cost/quality, validates the output, and repairs or retries if needed.
Benchmark results (~500 prompts):
- 100% valid JSON (vs ~85-95% from direct API calls)
- 90% cheaper than GPT-4o baseline (with identical prompts and no retries)
Tradeoff: Routing and validation add 1-3s latency. Good for batch/async workloads, not ideal for real-time chat yet.
Live demo: https://demo.withperf.pro
Landing page: https://withperf.pro
Would love your feedback and questions.

value: Show HN: 100% valid JSON across OpenAI/Anthropic with automatic model selection

author: shreyasdeshmukh


value:
Hi HN,
I’ve been working on RustAPI, an open-source Rust API framework designed with AI-first development in mind.
While frameworks like Actix and Axum are powerful, I noticed that LLMs struggle with their boilerplate-heavy and rigid patterns when generating APIs. RustAPI focuses on readability, composability, and predictable structures so both humans and AI can work with it more effectively.
It includes built-in OpenAPI/Swagger support and is optimized for SSE, MCP, and LLM-driven workflows.
I’m sharing this to get early feedback from Rust and backend developers — especially those experimenting with AI-generated code.
Repo:
https://github.com/Tuntii/RustAPI

value: Show HN: RustAPI – An AI-first Rust API framework


value: joelS

value: Show HN: Everyday.live – An MMO you control only through daily text intentions

author: joelS


value: jgautsch

value: 20% time for Claude Code. Fork or follow along!

value: Show HN: 20-Pct-Time

author: jgautsch

title: Show HN: 20-Pct-Time


value:
I built this for plumbers, electricians, and contractors who hate paperwork but need to invoice on the job.
Say something like "Invoice John for plumbing repair, 2 hours at $50, parts $30" → professional PDF.
100% free, no limits
No signup required
All data stays in your browser
Works offline (PWA)
Would love feedback on the voice parsing and overall UX.

value: Show HN: Utilioo- Speak your invoice, get a PDF (built for tradespeople)


value: morcules

value:
Hello. My name is Morcules.
I’ve had a unique idea of making a programming language that has 0 optimizations. Everything works as expected.
The goal is to:
- provide maximum control for the user.
- zero hidden optimizations.
- Assembly, but more readable, std library and structured.
- Provide many compile time features and maybe even optimizations using manual specification: @[unroll]
I started to work on it only like a month ago.
I’ve had some starting points from like a year ago, but most of them were discarded.
It’s mostly made for embedded, os dev or anything that needs to just work as written.
It compiles to assembly (Right now only to arm64) tested only on mac apple silicon.
If you have any questions feel free to ask them.
I would really appreciate some help, because I see myself using this language.
Even small things like descriptive comments would really help me.
I don’t consider myself as a programmer that writes good code. Many things were written fast without considering many things.
Github: https://github.com/Morcules/byte-lang
Example Source Code:
// This doesn’t make sense it’s just random instructions showing everything that works right now //
// Comment //
void : term(i64 exit_code : [reg(x0)]) {
    asm(format("mov x1, #{}\nmov x16, #1\nsvc #0x80\n", 10));
}
void : test(i32 test_var : [stack], i64 test_var_two : [stack]) {
}
void : main() {
    i32 var = 5;
    i16 var2 = 2;
    i8 var3 = 1;
    i64 exit_code = 30;
    i64 exit_code_clone = exit_code;
    u64 test = 10;
    u8 something = 30;
    u8 something_clone = something;
    i64 exit_code_success = 0;

    bl(test, var, exit_code_clone);

    bl(term, exit_code_success);
}

value: Show HN: Byte Lang – a unique programming language with zero optimizations

author: morcules


value: rcarmo

value: Show HN: Asterisk - A small text embedding model for low-resource hardware

author: rcarmo


value:
Ghost is my preferred blogging software. But, logging in is a bit of a pain - relying on email magic links.
I wanted lower-friction sign-in, so I built Trivet: a small middleware that does Google OAuth, checks/creates the member in Ghost, then uses the Ghost Admin API to mint a one-time member sign-in URL and redirect you into the site.
Setup is straightforward, and it relies on Google One Tap to avoid the need for theme edits.
I host it for free on my Mac Mini [1], but it's MIT + self-hostable.
Try it out and let me know what you think.
Blog post with more details: https://www.contraption.co/trivet-adds-google-sign-in-to-gho...
GitHub: https://github.com/contraptionco/trivet
[1] https://www.contraption.co/a-mini-data-center/

value: Show HN: Trivet – Google sign-in for Ghost blogs, free and OSS


value: Simuletic

value:
Hi HN,
I’m Fredrik, the founder of Simuletic. I’m an engineer working in Computer Vision, and I kept hitting a wall with object detection models in security scenarios: they are terrible at detecting "edge cases" that are rare or dangerous in the real world.
Some of the most difficult problems I have faced is "weapon detection" and "lying / falling detection".
To train an AI to detect someone falling or lying injured, you need thousands of images. But getting real data is an ethical nightmare—you can't ask elderly people to fall down stairs, and you can't use real accident footage due to privacy.
The current industry solution is using actors. But actors instinctively protect themselves when falling; they don't capture the chaotic, dead-weight reality of a true medical incident. Models trained on actors fail on real victims.
The Solution: A Rendering Pipeline Instead of scraping the web for bad data, I built a rendering pipeline to generate physically accurate and highly realistic synthetic data specifically for CCTV angles.
By using a AI generation local pipeline (no data leaves my own server) I can simulate any scenario with high realism, unlike 3d simulation tools... including bounding boxes.
The pipeline automatically generates perfectly annotated ground truth (bounding boxes and segmentation masks) for every frame, solving the massive headache of manual labeling.
I’ve released the first batch on Kaggle for different scenarios. It’s designed specifically to bridge the "sim-to-real" gap for overhead cameras.
Kaggle Link: https://www.kaggle.com/datasets/simuletic/cctv-incident-data...
Specs: CCTV resolution and noise profiles, varied lighting (indoor/outdoor), and high-angle perspectives.
Format: Annotations are YOLO-ready out of the box.
What I want from HN: I know many of you work with YOLO and other CNNs. I’d love for you to throw this dataset into your training mix and see if it improves recall on "lying down" classes in real-world tests.
I’m here to answer questions about the rendering pipeline, the domain randomization techniques used, or the challenges of sim-to-real transfer.
Thanks, Fredrik

value: Show HN: I built a render pipeline to generate 'impossible' CCTV training data

author: Simuletic


value: ultra-meta

value: I built a system that maps its own "DNA" using AST to enable self-healing capabilities. Instead of a standard release, I’ve hidden the core mapping engine inside a New Year gift file in the repo for those who like to explore code directly.<p>It’s not just a script; it’s the architectural vision behind Ultra Meta. Check the HAPPY_NEW_YEAR.md file for the source

value: Show HN: A New Year gift for Python devs–My self-healing project's DNA analyzer

author: ultra-meta


value: relatedcode

value: Show HN: No human should read logs anymore

author: relatedcode


value: Show HN: ZELF – A modular ELF64 packer with 22 vintage and modern codecs


value:
Baby-name lists felt overwhelming, so we built a small tool that narrows things down to a short ranked list based on the kind of name you’re leaning toward.
It’s intentionally simple: no sign-up, no saving, nothing final.
Feedback welcome.

value: Show HN: A calm baby-name decision tool (not a generator)


value: stets

value:
Hi! I took a cheap $25 walmart phone and put a static server on it? Why? Just for a fun weekend project.
I used Claude Code for most of the setup. I had a blast.
It's running termux, andronix, nginx, cloudflared and even a prometheus node exporter.
Here's the site:
https://walmartphone.stetsonblake.com/

value: Show HN: Vibe Coding a static site on a $25 Walmart Phone

author: stets


value: julienreszka

value: Explain what you wish existed. Get anonymous votes. Connect with interested users. No surveys, no meetings—just clear signals.

value: Show HN: CoinFountains – Validate demand before you build

author: julienreszka


value: Show HN: I computed a 109M-digit prime candidate for the EFF prize using Python


value: batels

value: Simple recurring tasks, enforced by email — with proof of execution.<p>No projects. No boards. No noise. No bloated tools like Asana or ClickUp.

value: Show HN: DonePing – Simple Recurring Tasks

author: batels


value: ultra-dev

value:
I built Sitewatch because I often want a fast way to sanity-check a site without installing tools, setting up monitoring, or creating an account.
Sitewatch runs instant checks for SSL configuration, DNS resolution, HTTP status, and common security headers. Everything is usable without logging in or providing an email.
The focus is on keeping the checks quick and frictionless. Monitoring and alerts are planned next, but I wanted to share the core tools early to get feedback on what’s useful and what’s missing.
I built this myself and I’ll be around to answer questions or discuss implementation details. Feedback is very welcome.
https://sitewatch.io

value: Show HN: Sitewatch – fast checks for SSL, DNS, HTTP status, and security headers

author: ultra-dev


value: muyenlee

value:
I built a production-ready full-stack template optimized for Claude Code and AI-assisted development.
The problem: You can vibe code features in hours. But shipping to production takes weeks of CI/CD, security scanning, infrastructure, testing, and deployment pipelines.
This template gives you the entire production stack from day one:
- Backend: Go + Echo
- Frontend: Next.js, Swift/iOS, Kotlin/Android
- Infra: Google Cloud Run + Pulumi IaC
- CI/CD: GitHub Actions with security scanning
- Database: Firebase (Auth, Firestore, Storage)
AI-native features include Claude Code integration with MCP servers, pre-built AI commands, and quality gate hooks.
MIT licensed. Feedback welcome!

value: Show HN: Vibe to Prod – Production-ready template for AI-assisted development

author: muyenlee


value: evoluteur

value: Show HN: Query and visualize the graph of your GitHub repositories

author: evoluteur


value:
Many teams now allow AI systems to draft customer-facing messages across support, CRM, and billing workflows.
This introduces a specific failure mode:
AI can generate text that constitutes an irreversible business commitment
(refunds, credits, billing changes, contractual promises).
Once emitted, the commitment exists. Detection after delivery is irrelevant.
This project implements a hard authority boundary.
Model
--------
AI systems propose messages.
They do not decide whether those messages are allowed to commit the company.
A gateway enforces that decision.
AI drafts message
        ↓
Authority Gateway
        ↓
Send | Block → Approval
Behavior
---------
For each outbound message:
Inspect text for commitment signals
Classify outcome as reversible or irreversible
If reversible → allow
If irreversible → block and require explicit approval
Log decision and evidence
No attempt is made to assess advice quality, intent, or correctness.
Only enforceability is considered.
API surface
------------
/v1/messages/send
Enforces authority on outbound messages
/v1/support/messages/decide
Decision-only endpoint for support systems
Returns structured reasons and a safe fallback reply
Properties
----------
Deterministic enforcement
Idempotent execution
Explicit approval for irreversible actions
No reliance on prompt discipline or training hygiene
No agent autonomy
This is a sandbox implementation to test whether a narrow authority layer is useful in practice.
Feedback welcome from teams already routing AI-generated messages into real customer workflows.

value: Show HN: An authority gate for AI-generated customer communications


value: atmiya

value:
Hey HN, I’m Atmiya.
I built Invoce.ai because invoicing always felt unnecessarily slow and bloated.
Most tools make you click through forms and settings just to send an invoice. With Invoce, you simply chat with AI, describe the work you did, and it creates a clean invoice instantly.
It’s early and intentionally simple. I’m curious how others here handle invoicing today and what feels most frustrating. Happy to answer questions and hear feedback.

value: Show HN: Chat with AI to create invoices instead of filling forms

author: atmiya


value: spieglt

value: Show HN: Walkie-Talkie Toy over WiFi/Tailscale on Raspberry Pi

author: spieglt


value: fenilsonani

value:
Built this after getting frustrated with the paying to a google for email. Now running it for 1 months in production.
What it does:
- Full SMTP server (inbound/outbound, DKIM signing, SPF/DMARC checking)
- IMAP with IDLE support
- CalDAV/CardDAV (replace Google Calendar/Contacts)
- Web admin panel with Prometheus metrics
- Greylisting for spam prevention
- Auto-discovery (mail clients configure themselves)
- Audit logging for compliance
What it doesn't do:
- Webmail (use Roundcube, etc.)
- ML-based spam filtering (greylisting + basic heuristics only)
- Clustering/HA

value: Show HN: Self-hosted email server for 2026 – single binary, CalDAV

author: fenilsonani


value: douxx

value:
PR: https://github.com/dpipstudio/botwave/pull/25
Project: https://botwave.dpip.lol

value: Show HN: Live audio streaming to FM radio via ALSA on Raspberry Pi

author: douxx


value: I Made Arzuno Humanizer to get rid of the ai robotic sounding Text

value: Show HN: Fixing Robotic AI Text with a Simple Browser Extension


value: I Made Arzuno Humanizer to get rid of the ai robotic Text

title: Show HN: Fixing Robotic AI Text with a Simple Browser Extension


value:
Eight years ago, I was job hunting and felt embarrassed by my Gmail address. I tried registering firstname.lastname — taken everywhere. So I thought: "I'm in IT, I'll set up my own domain with my own IMAP/SMTP. How hard could it be?"
A lot went wrong. Forgotten renewals. rocky DNS mistakes. I ended migrating toward Google Workspace, but in the end it felt overkill, having a subscription there plus the whole registrar subscription, and the whole GAFAM sticker on it. Moreover, the final price was way too high for me at the time.
Six months ago, someone approached me and related to that issue, then said: "What if we automated all of that? Want to build it together?" I said yes.
So Happymail was born. Anecdotal, My wife did found this name, excellent one!
You search for an available email address using simple keywords (like your name), pick one, and you're set up in minutes. That's it.
Zero config – works instantly with Outlook, Thunderbird, any mail app. Just email + password.
You own your data – no GAFAM, no tracking, ever
We handle the hard stuff – DNS, deliverability, renewals, domain reputation
How it works
We own and manage the domains — registrations, renewals, DNS, reputation — so you don't have to. Multiple people can share a domain without affecting each other (e.g., john@doe.re and jane@doe.re coexist independently). You get the benefits of a custom domain without any of the technical responsibility or even the whole registrar yearly fee upfront.
Privacy by design
GDPR-compliant – fully hosted in France under strict EU privacy laws
We can't read your emails – Isolated from our main infrastructure, only you have the password.
Backend secured on Azure France – isolated, heavily guarded, no US jurisdiction
We never store your password – we set it up, you control it. You can reset it, but even we can't retrieve it.
No backdoors – only a serious court order could compel a password reset (extremely rare)
Your inbox is yours. We're in the business of infrastructure, not surveillance.
Pricing
€2 to €6.99/month depending on the domain (TLD costs vary). No hidden fees. We pay registrars, and infrastructure — you get a real email address you actually own.
What's not ready yet
We're launching early to get real feedback. The main missing piece: a migration tool to import your old emails from Gmail, Yahoo, GMX, etc. It's coming soon, but not today.
We didn't want to build in the dark. We have enough to be useful — now we want your criticism.
FAQ
What if Happymail shuts down? We've planned for the worst. We have reserved funds in a locked account specifically for this scenario. If we ever have to close: first we stop accepting new subscriptions, then everyone gets at least a year's notice to export their data and get their affairs in order. Let's hope it never happens.
Can I bring my own domain? No — we manage the domains to keep things simple and hassle-free.
Why not Proton or Fastmail? They're great, but you're still @protonmail.com or @fastmail.com. With Happymail, you're @yourself — no branding, ever.
What's the long-term business model? Subscriptions only. No ads, no data selling, ever. We plan to add optional extras down the road (more storage, a simple personal page for resumes, etc.), but the core stays simple.
The new year is coming, and what better realistic new resolution than having a clean email that you are proud of? Happy new year! Happymail!

value: Show HN: Happymail – Make email great again


value: TedOS

value:
I built a task/goal tracker where the entire UI is one input field.
The idea: your goals live in four quadrants (daily, weekly, monthly, yearly). Everything happens through commands. The app docks to the side of your screen.
Adding a goal:
/d 50 pushups
Chain them:
/d 50 pushups /w 3 gym sessions /m finish project /y learn piano
Updating progress:
Create an alias with /alias p pushups, then just type 25 p to add 25. Three characters.
Review your week with /review 7d. Rename goals, change targets, convert between quadrants—all through commands.
Each quadrant auto-resets at the right interval (daily at midnight, weekly on Monday, etc). You don't manage anything.
Why I built it this way: I kept bouncing between productivity apps looking for something faster. Nothing stuck because they all wanted me to click through menus and organise things. I just wanted to type and move on.
So I made something deliberately constrained. One input. Four quadrants. No settings screen. No integrations. The lack of features is the point.
Curious what the HN crowd thinks—especially if the command syntax feels intuitive or too obscure. Still iterating.

value: Show HN: Lock In – A goal Mac tracker controlled by commands (7 Days Free)

author: TedOS


value: ahmgeek

value: Show HN: I built an international calling platform for the past 6 months

author: ahmgeek


value: cesncn

value:
Built Respectlytics because I always got confused about how to deal with privacy questionnaires when publishing my own mobile apps in app stores. For people who have read a little bit about those privacy regulations like GDPR, ePrivacy Directive, CCPA, etc., I guess it is a bit difficult to say that an analytics solution is fully compliant and no user consent is needed. But what I have come to is that the key thing is to build something that is extremely defendable, transparent, and very easy to explain if and when needed.
Privacy: API rejects custom fields with 400 errors. Only stores 5 fields. Session IDs rotate every 2 hours with daily salt. Open source SDKs. It is technically impossible to say which event data belongs to whom.
Automation: Auto-discovers conversion paths, calculates lift scores, finds drop-offs. No manual funnel setup needed. These analysis are done per session. Multi-session tracking is blocked by design.
Tradeoff: Can't track MAU/DAU (session rotation prevents it). Not free because this platform costs money and I am unfortunately not in a position to offer this service for free. But I did everything I could and will keep doing to maintain it as a premium service for people/organizations/companies who need high level of privacy in their analytics solutions.
Current Mobile SDKs: Swift, Kotlin, Flutter, React Native. All are open source.
Already using it for my own apps. Curious what HN thinks about the architecture.

value: Show HN: Privacy-Paranoid Mobile Analytics with Built-In Intelligence

author: cesncn


value: akonstantin

value: Show HN: Konstantly 2.0 – AI-powered course creation for teams

author: akonstantin


value: JohnLins

value: Show HN: Chatbot Without Safety Alignment

author: JohnLins


value: zimtzimt

value:
Hey HN,
I overloaded my personal Next.js portfolio with ambient audio, sound effects, and heavy animations. It felt magical on my machine—but on mobile or slower connections, it crawled. Users bounced before hearing a note or seeing a transition.
Standard fixes (lazy loading, code splitting) helped marginally, but I wanted real progressive enhancement: deliver a fast, accessible baseline first, then upgrade to the rich stuff only when the device/network could truly afford it.
So I built Birthday-Cake Loading (BCL)—a tiny runtime that detects capability signals (memory, CPU, network, reduced-motion/data prefs), buckets them into conservative tiers (base → ultra), and lets you gate features with simple components like <CakeLayer> and <CakeUpgrade>.
Now my site loads instantly with static fallbacks, then smoothly adds motion/audio/images when safe. Lighthouse scores improved dramatically, and it respects user preferences out of the box.
Open source (Apache 2.0), tree-shakeable, Next.js App Router ready with SSR bootstrap support.
GitHub: https://github.com/shiftbloom-studio/birthday-cake-loading
npm: @shiftbloom-studio/birthday-cake-loading
Demo in the repo (examples/next-demo) (W.I.P.)
Would love your thoughts, brutal feedback, or war stories from media-heavy sites!

value: Show HN: Birthday-Cake Loading – Rich Next.js without punishing slow devices

author: zimtzimt


value: liyuanhao

value:
Your vibe coding sessions are valuable — the debugging breakthroughs, architectural decisions, code patterns worth revisiting. Stop letting them disappear into JSONL files you'll never open.
I built Yolog Desktop because I kept losing valuable vibe coding sessions. It archives, replays, and analyzes your sessions from Claude Code (more tools coming). Makes your vibe coding history searchable and useful.
Demo video: https://www.youtube.com/watch?v=77KEQciBtdc
Yolog is a native desktop app (Tauri/Rust, ~10MB):
- Full replay – timeline navigation, syntax highlighting, tool use visualization
- Error detection and highlighting
- Markdown rendering
- Search – fast full-text across all sessions
- Cost tracking
Vibe Metrics
Traditional metrics (lines of code, commits) don't capture vibe coding. When you write 50 characters and get back 5000 lines of working code, that's a different kind of productivity.
Yolog introduces metrics designed for vibe coding:
- Token Leverage – AI output vs your input
- Burn Rate – tokens/min during active coding
- Flow detection – vibing or grinding?
- 12-week streak heat map
Website: https://yolog.dev
X: @yologdev
Still early. Happy to answer questions or hear what else would be useful.
Your vibe coding sessions are gold. Treat them that way.

value: Show HN: Yolog – Archive and analyze your vibe coding sessions

author: liyuanhao


value: loondri

value:
Inspired by PG’s tweet about a chain of 4 YC startups where the founder worked at a YC startup, I vibe coded and generated these genealogy chains for all ~6k YC startups. And to make these trees easily accessible I packaged them into a hosted webapp.
Few noteworthy YC startups which have had huge impact in YC ecosystem
Airbnb - 83 YC startups spawned
Stripe - 67 YC startups spawned
Dropbox - 50 YC startups spawned
Justin.tv/Twitch - 47 YC startups spawned
More recently founded YC startups which have spawned a lot more YC startups
Rappi - 21 YC startups spawned
Brex - 20 YC startups spawned
Scale AI - 19 YC startups spawned

value: Show HN: Foundertrace – chain of YC startups founded by its employees

author: loondri


value: pazzk

value:
I ran into intermittent interrupt watchdog resets while working on ESP32-S3 firmware(an embedded microcontroller SoC) with flash encryption and PSRAM enabled.
What initially looked like a timing or scheduling issue turned out to be a structural problem in ESP-IDF: flash operations temporarily disable cache, while cache sync(`esp_cache_msync`) can still be triggered by DMA-based TLS crypto paths.
When these paths overlap, the system can stall long enough to trip the interrupt watchdog.
I wrote up the investigation, reproduction conditions, and a workaround(serializing flash operations and cache sync paths) here: https://pazzk.net/blog/esp32s3-flash-cache-issue
Interested to hear if others have seen similar issues or cleaner approaches.

value: Show HN: Debugging ESP32-S3 watchdog resets caused by flash/cache interactions

author: pazzk


value: iamlin

value: Show HN: Til.re – The URL is your timer, no signup required

author: iamlin


value: wawan

value:
As a freelancer, my monthly revenue depends on how many days I actually work.
I found it surprisingly hard to see this clearly during the month.
I built DayCount to make worked days (and what they represent in revenue) visible in real time.
Feedback welcome!

value: Show HN: Visualizing freelance revenue based on worked days

author: wawan


value: MKuykendall

value: Auxide is a Rust library for building real-time audio processing graphs. It provides a low-level, deterministic kernel for executing directed acyclic graphs (DAGs) of audio nodes, with a focus on real-time safety and correctness.

value: Show HN: Auxide- a Real-Time Audio Graph Library for Rust

author: MKuykendall


value: cylinderthought

value:
maybe i can add stenography
Hi HN, I'm Max.
I built this because I was terrified that if I die tomorrow, my family gets nothing. The existing solutions were either trusting a centralized custodian or complex hardware setups.
Shardium is a client-side tool that splits your seed phrase into 3 shards using Shamir's Secret Sharing.
Shard A: You keep.
Shard B: You give to a beneficiary (PDF).
Shard C: We hold (or you self-host).
It works as a dead man's switch: If you are inactive for 90 days (email ping), Shard C is released to your beneficiary. They combine B + C to recover the funds.
The Stack:
secrets.js-grempe for the math.
FastAPI + PostgreSQL backend.
Client-side encryption (seed never hits the network).
It is 100% Open Source and MIT Licensed. You can self-host it for free ($0), or use the managed version.
I'd love your feedback on the security model. Roast my code here: https://github.com/pyoneerC/shardium

value: Show HN: Shardium – open-source "Dead Man's Switch" for crypto inheritance

author: cylinderthought


value: hanzili

value:
Hey HN,
Claude Code is pretty agentic now. It writes scripts, calls APIs, uses CLIs. But when something requires actually clicking through a website, it stops and asks me to do it.
Problem is, I'm often unfamiliar with these platforms myself. "Go to App Store Connect and generate a P8 key" okay but where? I end up spending 10 minutes navigating menus I've never seen before.
I started delegating these tasks to Perplexity's Comet browser. It handles the clicking, returns what I need. But copy-pasting between Claude and Comet got old fast.
So I built this MCP server to connect them directly. Now when Claude needs to interact with a website that has no API, it can just ask Comet to handle it.

  Examples:
  - Grab my app ID from RevenueCat dashboard
  - Generate a P8 key in App Store Connect
  - Navigate admin panels behind login walls

I tried Playwright MCP but having Claude do the clicking itself overwhelms the context window. Comet's agentic browsing just works better in my experience.
Comet doesn't have an API, so this uses CDP to communicate with it directly.

value: Show HN: Comet MCP – Give Claude Code a browser that can click

author: hanzili


value: proletarian

value:
I missed the admission deadline for a graduate Philosophy program so I created something to guide me over the next several years.
Starting with Euthyphro next week. Feel free to join or follow.

value: Show HN: OpenGrad – Self-directed, AI-facilitated graduate programs

author: proletarian


value: debarshri

value: Show HN: Open-source alternative to Cloudflare's Sandbox SDK

author: debarshri


value: matheusdelgado

value:
Hi HN,
We’ve been building LLMSafe — a Zero-Trust Security & Governance Gateway that sits between your application and an LLM model.
The problem we’re trying to solve:
Once you connect an LLM to real data or real users, you open the door to real risks:
• prompt injection
• phishing and social-engineering via LLM
• data exfiltration
• PII leakage
• unsafe or non-compliant outputs
• lack of auditability/governance
LLMs don’t have a native “security layer”, so we built one.
Our pipeline looks like this:
Client
↓
Firewall & risk detection (prompt injection, phishing patterns, unsafe intent)
↓
Normalization & safe rewrite
↓
Policy enforcement
↓
Inbound data protection (masking/scrubbing)
↓
LLM call
↓
Outbound data protection
↓
Response governance & filtering
↓
Audit logging (trace → decision → outcome)
Everything runs as a gateway so teams can deploy it inside their own infrastructure (Docker), instead of sending data to yet another SaaS.
We also log every decision so you can trace:
input → layer → risk → block/allow → output.
Right now it can:
• detect prompt injection attempts
• detect phishing/social-engineering content
• mask PII automatically
• block risky outputs
• enforce policy rules
• provide a full audit trail
We are still actively building and refining. I’d really appreciate feedback — especially from people building real LLM products or working in security/compliance.
Demo + docs: https://llmsafe.cloud
Happy to answer technical questions and hear what we’re missing.

value: Show HN: LLMSafe – A Firewall and Governance Layer for LLM Apps

author: matheusdelgado


value:
Wanted a clean solution to build linktree style url grouping within a single url with no setup. Give it a try!
groupmylinks.com/github.com,https://github.com/apple/ml-sharp

value: Show HN: No setup linktree with url params


value: jtpagency

value: Show HN: A 3D Hitbox Debugging Approach to AI Agency

author: jtpagency


value: Xlab

value: Show HN: Upd.dev – free Git hosting with excellent reliability and modern touch

author: Xlab


value: kmewhort

value: I built a drag-and-drop optics lab where you can place mirrors, lenses and prisms to play around with real-time light ray physics. It uses standard ray-tracing with proper Snell's Law refraction and chromatic dispersion.<p>The blog post (which includes the app at the top) is the first in a series I have planned explaining this and other artful physics simulations.

value: Show HN: Spectral Lab – An optics simulator in WebGL

author: kmewhort


value: genvibe

value:
Solo dev here. Built GenVibe over the past few months to solve my own problem - repetitive React boilerplate and design-to-code work.
You describe your app in plain English (or upload Figma/screenshot), and it generates working React/React Native code.
Launched last week:
- 500 signups
- $250 MRR
- All organic
Still very early and rough. Code quality isn't perfect, but it works for quick prototyping.
Would love feedback from the HN community on:
- Code quality/best practices
- Features that would make this more useful
- Whether this actually solves a real problem
Built with remix. Happy to answer technical questions!

value: Show HN: GenVibe – AI generates React apps from text, Figma, screenshots

author: genvibe