value: bahaAbunojaim

value:
Hey HN! I'm Baha, creator of Mysti.
The problem: I pay for Claude Pro, ChatGPT Plus, and Gemini but only one could help at a time. On tricky architecture decisions, I wanted a second opinion.
The solution: Mysti lets you pick any two AI agents (Claude Code, Codex, Gemini) to collaborate. They each analyze your request, debate approaches, then synthesize the best solution.
Your prompt → Agent 1 analyzes → Agent 2 analyzes → Discussion → Synthesized solution
Why this matters: each model has different training and blind spots. Two perspectives catch edge cases one would miss. It's like pair programming with two senior devs who actually discuss before answering.
What you get:
* Use your existing subscriptions (no new accounts, just your CLI tools)
* 16 personas (Architect, Debugger, Security Expert, etc)
* Full permission control from read-only to autonomous
* Unified context when switching agents
Tech: TypeScript, VS Code Extension API, shells out to claude-code/codex-cli/gemini-cli
License: BSL 1.1, free for personal and educational use, converts to MIT in 2030 (would love input on this, does it make sense to just go MIT?)
GitHub: https://github.com/DeepMyst/Mysti
Would love feedback on the brainstorm mode. Is multi-agent collaboration actually useful or am I just solving my own niche problem?

value: Show HN: Mysti – Claude, Codex, and Gemini debate your code, then synthesize

author: bahaAbunojaim


value: thekotik

value:
Hi HN
We're Vitalik (Ex-Bolt) and Stas (Ex-Grammarly, Wix)
We build a MacOS vibecoding tool for native Swift iOS apps, that replaces Xcode for non-developers.
Features:
 - Project Creation agent makes a real Xcode project in the background.
 - Design Agent creates design system using SwiftUI (supports glassmorphism )
 - Coding Agent is quite efficient with caching and parallel tool calls.
 - The app gets build on your mac using iOS simulator and runtime, any bugs are passed back to coding agent.
Requirements:
- You still need an Xcode on your machine, but don't need to open it.
Website: https://www.superappp.com
Would love feedback for our Beta

value: Show HN: Superapp – Native Swift iOS App Builder

author: thekotik


value: AiStyl

value: Show HN: Praqtor – AI intelligence platform for ML engineers

author: AiStyl


value: selfhost

value: Show HN: Postastiq – A single-binary, SQLite-backed blogging platform

author: selfhost


value: mcbetz

value: Inspired by physical countdown timers like TimeTex, I created a minimal open source app with countdowns up to 60min.<p>I used Tauri, vanilla CSS and JS, the app is ~9mb.

value: Show HN: Visual Countdown Timer (Open Source)

author: mcbetz


value: danamajid

value:
i built Botkit because i couldn’t find a good way to see what other people are actually working on in robotics. a lot of projects seem to be scattered across Discords, X, private chats, or never shared at all.
Botkit is a simple experiment: you add a WhatsApp number and post project updates (text, photos, videos) while you’re building, like you’re messaging a friend. those updates become a project others can follow.
you can also forward purchase receipts and it’ll automatically extract the parts used, mostly so people can learn what actually goes into real builds. here's an example of what your AI assistant on Whatsapp will keep up to date for your followers to see: https://botkit.com/danamajid/olly
i’m trying to learn a few things:
- are there good robotics communities or Discords I’m missing? i'd love for the directory to include all sorts of projects
- if you’re working on a robotics project, would you be willing to share it publicly?
this is early and pretty raw. curious to hear what people think.

value: Show HN: Botkit – Share robotics projects by posting updates via WhatsApp

author: danamajid


value: logicallee

value:
This is an experiment. It does include blocking and rate limiting, so it should meet the minimum requirements.
As you can see in the write-up, it is a tree+mesh architecture. It might eventually be able to act as a replacement for YouTube Live or TikTok.
For now, there isn't much there.  You can start a livestream or join the livestream of anyone else.  You can see view counts. Chat isn't saved.
Have fun exploring it and let me know any features you'd like added.

value: Show HN: Experimental) livestream anything, tree+mesh network

author: logicallee


value: sidarcy

value:
Blobbing is a small web toy I made as an experiment in generative doodling.
It starts with randomly placed dots and gradually turns them into an animated “blob”. The idea comes from a simple pen-and-paper practice: placing dots on a page and connecting them intuitively until a shape emerges.
This is a digital version of that idea — playful, a bit meditative, and intentionally imprecise. There’s a Zen mode that continuously generates blobs, and you can interact with finished shapes to nudge them into new forms.
It’s built with plain HTML canvas and JavaScript. No frameworks.

value: Show HN: Blobbing – a doodling experiment where dots become blobs

author: sidarcy


value: zpusmani

value: Show HN: DeckBuilder Single-file HTML slide editor (750KB, no build step)

author: zpusmani


value: blueskyline

value: Show HN: Opinara – Global polling with real-time map visualization

author: blueskyline


value: pierridotite

value: Show HN: A cyberpunk-style terminal dashboard for monitoring markets

author: pierridotite


value: chapiware

value:
Hi HN,
I built a small mobile app that shows you all the photos and videos you took on this exact calendar day across past years.
I started working on it after realizing that I almost never revisit my photo library. I take thousands of photos, but most of them just sit there. Existing “On This Day” features worked, but they always felt tied to cloud uploads, accounts, and algorithmic resurfacing — which didn’t sit well with me for something as personal as memories.
So I wanted to explore a different approach.
Ayer is fully local-first:
 • 100% on-device (no cloud, no sync)
 • No account, no login
 • No analytics, no tracking
 • Zero network calls — airplane mode works
The idea is very simple: you pick a date (or open the app today) and you see what you captured on that same day in previous years. Sometimes it’s joyful, sometimes it’s heavy, sometimes it’s completely ordinary — but revisiting memories by date gives them context rather than ranking them by “importance”.
A few details that might interest this crowd:
 • It handles large photo libraries (30k+ photos) smoothly
 • Navigation is date-first, not feed-based
 • Optional “then vs now” collages are generated locally
 • Built with React Native, using system photo APIs only
This isn’t meant to replace Apple Photos, Google Photos, or Lightroom. It’s more like a quiet, focused space that sits alongside them, designed for reflection rather than optimization.
If anyone is curious, it’s available on iOS and Android:
https://www.chapiware.com/ayer/
I’d genuinely love feedback — especially from people who care about local-first software, privacy trade-offs, or performance with large datasets.
Thanks for reading.

value: Show HN: Ayer – Relive your photo memories by date, 100% offline

author: chapiware


value: justmarc

value:
Merry Christmas! Your file transfer gift from Santa is here.
I built Zynk because I was tired of file transfer being a "solved problem" that still sucked.
AirDrop? kinda Apple-only. Cloud drives? Size limits? Waiting for uploads? WeTransfer? caps caps caps. Browser uploads? No folders supported? One network hiccup and you start over.
What Zynk does:
    Transfers files between any devices/OS on your local network, also without Internet connectivity, P2P + E2EE
    Transfers files between you and anyone else over the Internet, P2P + E2EE
    Resumable: close your laptop, lose WiFi, switch networks, pick up where you left off
    End-to-end encrypted (we can't see your files)
    No size limits, folders, send anything rsync-style, terabytes welcome
    All platforms supported: Mac, Windows, Linux, iOS, Android, Android TV, Steam Deck
    UI for everyday use + CLI for power users and automation
    Web share and drop links for sharing with anyone via browser
    Media viewer/player built into the app
I wanted something that "just worked" the way AirDrop does, but across everything I own and the people I care about. Built-in chat and file previews because context can matter when you're bouncing stuff between people. Even my mom uses it and helps with UX (mom test passed).
It's being developed to be a one stop shop tool that is capable of covering any file transfer need and scenario very well.  We have a long roadmap ahead. Expect new and unique feature announcements soon.
Free, ad free, with Pro tier available for those who need a lot more. Would love feedback from the HN crowd!
Happy to answer any questions.
https://zynk.it

value: Show HN: Zynk, Cross-platform, private file transfer that works

author: justmarc


value: shikaan

value: Show HN: A terminal-based KeePass password manager

author: shikaan


value: reynaldi

value: Show HN: Same-Same, But Different – AI Image Matching Game

author: reynaldi


value: Aydarbek

value:
Hi HN — I built Ayder because Kafka in 2025 feels like starting a car with a hand crank.
# That's it. You're streaming.
curl -X POST localhost:1109/broker/topics/orders/produce -d '{"item":"widget"}'
Single binary. Zero dependencies. HTTP-native (not a proxy). Built in C with libuv.
Performance (wrk2, real network, 3-node Raft, sync-majority writes):

  Throughput:     50K msg/s sustained
  P99:            3.46ms
  P99.9:          12.94ms
  Server P99.999: 1.22ms (HTTP isn't the bottleneck)

Recovery time — the number nobody talks about:

  Kafka after unclean shutdown: 2+ hours (real production reports)
  Ayder after SIGKILL: 40-50 seconds with 8 million offsets

Tested repeatedly. Zero data loss. Follower replays AOF, asks leader for missing offsets, catches up automatically. No manual intervention.
What you get:
- Append-only log with consumer groups + committed offsets
- Raft consensus (3/5/7 nodes) with mTLS
- KV store with CAS + TTL
- Stream processing: filter, group_by, windowed joins (including cross-format Avro+Proto)
- Idempotent produce, retention policies, Prometheus metrics
I'm a solo founder from Kazakhstan, self-taught systems programmer. Spent 8 months building this. Looking for feedback on the API, what's missing, and early design partners.
GitHub: https://github.com/A1darbek/ayder

value: Show HN: Ayder – Nginx for event streaming (50K msg/s, P99 3ms, 40s recovery)

author: Aydarbek


value: Show HN: Deep Learning (Global Linear Solvers), Polyharmonic Splines (No SGD)


value: Convia

value:
MistSeeker started from a very simple observation.
When something goes wrong in a large codebase,
the hardest part is usually not fixing it,
but knowing where you should touch — and where you shouldn't.
In real development work, we usually know one of two things:
- Something has already broken, or
- Nothing has broken yet, but something feels fragile.
What we usually don't know is:
- Which parts of the system are structurally safe
- Which changes are likely to amplify risk
Reading all the code is unrealistic.
So we started by making this judgment visible.
## What MistSeeker actually does
MistSeeker is not a bug-finding tool.
The question we're trying to answer is simpler:
Is this code structurally suitable for change,
or is it likely to fail during modification?
To answer that, we evaluate code from three independent perspectives.
1) COI — Structural fitness
COI looks at how code is organized.
- How responsibilities are divided
- How deeply logic is nested
- How much structural duplication or entanglement exists
A high COI does not mean "perfect code."
It means the structure is less likely to cause unexpected ripple effects when changed.
Low-COI code, on the other hand, often turns small edits into wide-reaching consequences.
2) ORI — Execution stability
ORI focuses on behavior, not structure.
- Hidden I/O dependencies
- Global state mutations
- Logic dependent on time, randomness, or environment
Code can look clean and well-organized, yet still be fragile at runtime.
ORI surfaces these invisible execution risks.
3) GSS — Semantic stability
GSS addresses a pattern that appears frequently in AI-assisted coding environments:
Code works correctly and passes tests,
but its intent collapses easily with small changes.
MistSeeker does not claim to "understand" code semantics.
Instead, it measures how much structural and behavioral change is triggered by small edits.
If minor modifications cause disproportionate shifts, GSS drops.
This pattern appears often in generated code or after repeated refactoring.
## What the scores tell you
Each file or module ends up with a profile:
- Is it structurally fit for change?
- Is it risky from an execution standpoint?
- How easily does its meaning break when modified?
From this, we derive a single stability score (GSI) and a risk level.
The goal is not to rank code.
There is only one question we want to answer:
When upgrades or refactoring are needed,
where is the safest place to start?
And which areas require extra caution?
## Try it (no signup)
HN readers: 5-day Pro evaluation key (no credit card required)
If the command fails, pull the image directly. Windows (CMD/PowerShell): see the install guide on our site.
Just set the license key to the value below:
License key: 716f3617b11685ba1af36bea74f929a3
Docker image: tongro2025/mistseeker
## Where this has been useful
In practice, it helped in situations like:
- Setting refactoring priorities instead of changing things blindly
- Reviewing AI-generated code changes
- Identifying areas that should not be touched during upgrades
- Finding structurally fragile areas even when tests pass
## What this is not
- It does not replace linters
- It is not a bug detector
- It is not an auto-fix tool
MistSeeker is not a mechanic.
It's a map.
## Why I’m sharing this
I'm curious about others' experiences.
- How do you decide what is "safe to change" in large codebases?
- Have you had systems that passed tests but became increasingly hard to modify?
- Does the idea of structural fitness and change risk resonate with you?
Opinions, counterarguments, and real-world examples are all welcome.
If useful, I'm also happy to discuss boundaries and limitations.
guide / project(manual) page:
https://convia.vip

value: Show HN: MistSeeker – a map of what is safe to change in large codebases

author: Convia


value:
I built this for myself to manage the chaos of juggling multiple high-priority freelance projects with ADHD. The problem is I have tasks in my head, emails I need to respond to, calendar events, git repos I'm working on, and recurring habits I'm trying to maintain. I need to ensure I make meaningful progress on each one every week to some extent. This tool helps me figure out what tasks are lagging as the week progresses, and helps me quickly jot down new tasks as they arise without breaking my workflow substantially.
utility-explorer (ue) pulls everything into a unified CLI dashboard. The main ideas:
- Block tracking: Recurring activities (workouts, focused work, etc.) as weekly targets rather than repeating tasks. The system warns you when you're at risk of missing your target for the week.
- Structured routines: ue am and ue pm commands for morning standup and evening review, so there's a ritual rather than a blank canvas.
- AI focus: Optional integration with Claude to recommend what to work on next based on all your context.
It syncs Gmail, Google Calendar, and git commits via GitHub. All data stays local in SQLite.
Built with Python, Click, and Rich. Check it out and give me feedback, I'm planning on adding more integrations and features.

value: Show HN: A CLI for ADHD Productivity, Aggregates Gmail, Calendar, GitHub


value: junhoyeo

value:
I've been through all of them. Claude Code, Codex CLI, Gemini CLI, Cursor. Finally settled on OpenCode.
But I had billions of token data scattered across 5 different tools with no unified view. So I built Tokscale.
The problem:
- Token usage scattered across multiple AI coding tools
- Each stores data in different formats and locations
- Claude Code deletes your history after 30 days by default (!)
- No way to see total spend or compare with other developers
What Tokscale does:
- One command (`bunx tokscale`) shows all your AI usage in one place
- GitHub-style contribution graph (2D/3D) for your token consumption
- Global leaderboard to see where you rank among other developers
- Spotify Wrapped-style year review image to share
Built with:
- Rust native core for fast parsing
- OpenTUI, the same terminal UI framework that powers OpenCode
Check it out: https://tokscale.ai
CLI: `bunx tokscale`
GitHub: https://github.com/junhoyeo/tokscale

value: Show HN: Tokscale – See who's burning the most tokens across all platforms

author: junhoyeo


value: Norris-Eng

value:
Hey HN, I'm a SysAdmin turned Dev.
Leaving your mining rigs unmonitored for the holidays is a risk, so I built a tool to automate it.
I got tired of the delayed/fragmented data from ISOs like PJM and ERCOT, so I built a normalized API to track grid stress in real-time.
The Stack:
Scrapers: Python 3.11 (Pandas/Requests) handling the messy ISO formats.
Compute: Azure Functions (Consumption Plan) to keep costs near zero.
Storage: Azure Data Lake Gen2 (Parquet) for historical retention.
I also wrote a simple Python client 'kill-switch' for Bitcoin miners to automate curtailment during price spikes.
Repo is here: https://github.com/Norris-Eng/gridwatch-kill-switch
API is here: https://rapidapi.com/cnorris1316/api/gridwatch-us-telemetry
Happy to answer questions about the scraping logic or Azure costs!

value: Show HN: I Built a US Grid Telemetry API Using Azure Functions and Python

author: Norris-Eng


value: superhuang

value: Show HN: Textbooks are hard. I made AxisY to visualize complex topics

author: superhuang


value:
I open-sourced a minimal starter kit for building ChatGPT Apps backed by an MCP server
(Streamable HTTP + SSE) deployed to Vercel.
I kept it intentionally small and auditable after repeatedly running into issues with
SSE headers, CORS preflights, runtime differences, and deployment quirks.
The core repo is MIT licensed and meant as a working reference, not a framework.
There’s also an optional Pro package with extra examples, but the free repo stands on its own.
Feedback welcome.

value: Show HN: Minimal ChatGPT App Starter Kit (MCP and SSE and Vercel)


value: hauxir

value: Show HN: BBC2Podcast – Geo-unblocking proxy for BBC radio as podcasts

author: hauxir


value: tchantchov

value:
Hi HN,
Like many here, I started trading the "Wheel" strategy (selling puts/covered calls) to generate income. I quickly realized that tracking my true cost basis was a nightmare.
My brokerage shows the stock's purchase price, but it doesn't "know" that I've collected $400 in premiums over the last three months. I ended up with a massive spreadsheet that I had to update manually every Friday just to see if I was actually profitable.
I built PremiumFlow to automate this. It treats the option premium and the underlying stock as a single financial unit.
What’s inside:
Automatic Basis Adjustment: If you sell a call against a LEAP or stock, your "break-even" price updates instantly.
Assignment Risk: It monitors how close your short puts are to being ITM.
No Spreadsheet Mess: All the math (annualized returns, DTE, etc.) is handled in the dashboard.
I’d love for you to kick the tires and let me know what you think. I'm especially interested in feedback on the UI and any edge cases in your own options strategies that I might have missed!

value: Show HN: PremiumFlow – Track true cost basis for the "Wheel" strategy and LEAPS

author: tchantchov


value: fheysen

value:
Hi HN!
I built Openinary because Cloudinary and Uploadcare lock your images and charge per request.
Openinary lets you self-host a full image pipeline: transform, optimize, and cache images on your infra; S3, Cloudflare R2, or any S3-compatible storage.
It’s the only self-hosted Cloudinary-like tool handling both transformations and delivery with a simple URL API (/t/w_800,h_800,f_avif/sample.jpg).
Built with Node.js, Docker-ready.
GitHub: https://github.com/openinary/openinary
Feedback welcome; especially from Cloudinary users wanting the same UX but on their own infra!

value: Show HN: Openinary – Self-hosted image processing like Cloudinary

author: fheysen


value:
Hi HN,
I built VeriMed while working on an AI-powered telemedicine platform. I quickly realised that verifying whether a healthcare provider is legitimately licensed is surprisingly hard.
Every country has a different registry, a different API format (REST, SOAP, FHIR), and different access requirements. I couldn't find a unified solution, so I built one.
VeriMed:
- Connects to 5 national medical registries:
  - USA (NPI)
  - France (RPPS)
  - UAE (DHA)
  - Netherlands (BIG)
  - Israel (MOH)
- Falls back to AI document verification when registries aren't available
- Uses fuzzy name matching to handle variations (e.g., "Greg" vs "Gregory")
- Production-ready with Docker, Kubernetes manifests, and health checks
- MIT licensed, free to self-host
- Enterprise extension available:
  - SSO (SAML 2.0 & OIDC)
  - RBAC (Admin, Reviewer, Viewer)
  - Audit Dashboard & CSV Export
  - Bulk CSV Import Wrapper
Tech stack: NestJS, TypeORM, OpenAI (optional), Fuse.js
GitHub: https://github.com/daretechie/verimed
I'd love feedback on the architecture or suggestions for additional country adapters to prioritise.

value: Show HN: VeriMed – open-source medical license verification


value: tiagoantunespt

value: Turn messy clipboard history into smart, context‑aware suggestions that appear right where you work—without leaving your current app.

value: Show HN: Clip Ring – Your clipboard, context-aware

author: tiagoantunespt


value: Successful entrepreneurs always say that the most profitable tools are the ones that help you solve the issues you’re facing. The problem is, I apparently have no issues. So instead, I built a PoC of a minimalistic ideas directory focused on issues others are facing. Feedback is welcome

value: Show HN: "What Should I Build?" A directory of what people want


value: technoabsurdist

value:
Hi HN, I’m Emilio. We’re launching the Wafer extension for the popular IDEs (VS Code, Cursor and Antigravity).
Wafer exists to make performance engineers more efficient. Most of the work perf engs do is extracting signal and turning it into the next experiment. You spend hours per kernel doing interpretation and bookkeeping: which counters matter, what changed, what hypothesis you’re testing, what to try next.
Wafer is building an environment where profiling, compiler analysis, and docs are first-class context in your workflow, so iteration is cheap. long-term, that same structured context becomes the interface for an automation layer that can read the evidence, propose a change, and rerun the loop.
NVIDIA has poured an insane amount of truth into their tooling. NCU, compiler output, SASS, the counters, the sections, the warnings, the “this is why you’re slow” breadcrumbs. Serious perf engineers already live in this stuff. The real problem is that it’s still not packaged as a tight loop. You run a profile, you get a giant report, then you spend a bunch of time translating it into a plan, mapping it back to the right lines of code, deciding what to ignore, deciding what to try next, and keeping track of what you’ve already tested. That translation step is where a ton of time goes, and it’s also the part that doesn’t scale.
We're just starting out and today, Wafer makes that translation step cheaper by keeping the evidence and the code in one place. You can run Nsight Compute profiling from your editor and view results where you’re editing, so you’re not flipping between terminals, report viewers, and screenshots. You can compile CUDA and inspect PTX and SASS mapped back to your source, so “what did the compiler actually do” is something you can answer in seconds and iterate on quickly. And you can query GPU documentation from inside the editor with the exact context you’re working in.
What we’re adding and moving towards is making that loop not just faster, but more automatic and more reproducible. We’re rolling out GPU Workspaces, where you keep a persistent CPU environment for your repo and dependencies, and only spin up GPU execution when you actually run something. A lot of GPU dev time is editing, debugging, and iterating on hypotheses, not burning GPU cycles - but today the workflow forces you to keep a GPU box alive just to preserve state. We want the “run the experiment” part to be on-demand and reliable, without killing your environment.
The bigger direction is the same theme: take the evidence perf engineers already use and make it machine-legible, so an automation layer can actually act on it. We're working on tool-driven loops: read the profile, identify the highest leverage bottleneck, propose a concrete code change, run the diff, re-profile, and keep a history of what worked and what didn’t.
If you’ve ever wished you could hand an agent your kernel plus the profiler and compiler evidence and have it do real work instead of vibes, that’s what we’re building towards.
You can see more about us here: https://wafer.ai
Or download directly from here:
VS Code: https://marketplace.visualstudio.com/items?itemName=Wafer.wa...
Cursor: https://open-vsx.org/extension/wafer/wafer
Would love feedback from anyone doing CUDA, CUTLASS/CuTe, Triton, training or inference perf. If you try it and something feels slow, confusing, or missing, email me at emilio@wafer.ai

value: Show HN: Wafer – Profile, inspect assembly, and iterate on CUDA within your IDE

author: technoabsurdist


value:
I built a simple Random Word Generator that instantly generates nouns, verbs, adjectives, or mixed words.
The original motivation was pretty practical:
- I often need placeholder words for UI prototypes
- quick prompts for writing or brainstorming
- random inputs to test small tools or scripts
No login, no tracking, just click and get words.
It’s free and web-based:
https://randomwordgeneratorapp.top/
Would love to hear:
- how you’d use something like this
- what kinds of generators you find useful (phrases, constraints, themes, etc.)

value: Show HN: Random Word Generator–a simple tool for instant writing&naming prompts


value: ezekg

value: Show HN: analog.watch - read 3 analog clocks as fast as you can!

author: ezekg


value:
Hi HN,
We built Chargenda after realizing how many business subscriptions we were paying for - and how often renewal dates were missed.
Most teams use dozens of tools today. Some are forgotten, some renew automatically, and some keep charging long after they’re useful. We wanted a simple way to see everything in one place.
Chargenda lets you:
track all subscriptions
get alerts before renewals
see where subscription money goes
avoid surprise charges
It’s intentionally simple and privacy-friendly (manual tracking, no bank connections).
We’re still early and would really appreciate feedback from people managing subscriptions for teams or businesses.
Happy to answer any questions.

value: Show HN: A Simple Way to Track Business Subscriptions


value: properbrew

value: Show HN: FormAIt – Free, private offline note formatting using LLMs

author: properbrew


value: mywork-dev

value:
Quick update: MCPShark now supports Codex’s config.toml.
If you use Codex CLI / the Codex VS Code extension, MCPShark will auto-detect ~/.codex/config.toml (or $CODEX_HOME/config.toml), parse the [mcp_servers] entries, and convert them into MCPShark’s internal config automatically (stdio + HTTP supported).
Repo: https://github.com/mcp-shark/mcp-shark
VS Code extension: https://marketplace.visualstudio.com/items?itemName=MCPShark...

value: Show HN: MCPShark adds config.toml support for Codex

author: mywork-dev


value: My friends and I worked on a language to allow agents to build some crazy structures in Minecraft. Peep some of the results in the README.md!

value: Show HN: Open-source 3D language for agents in Minecraft


value: samkim

value:
A colleague and I made a kids' picture book that introduces authorization concepts.
We work at AuthZed and explain these concepts regularly. We thought it'd be fun to put them together in a format accessible and appealing to kids and grownups alike. It would also be helpful when explaining what we do for work and make a unique gift for our families.
The goal was a fun story first and foremost. We aimed to present concepts accessibly but made conscious decisions to simplify, knowing we couldn't be comprehensive in a picture book format.
We also wanted visually appealing illustrations, so we built a custom tool to streamline exploring ideas with AI. It does reference-weighted image generation (upload references, weight which ones matter most), git-like branching for asset organization, and feedback loops that improve subsequent generations. It was built with Claude Code. Here's a screenshot: https://content.authzed.com/hubfs/screenshot-design-asset-ma...
We'd love feedback on where we chose to simplify. Did we get the tradeoffs right or did we oversimplify? And lastly, did you enjoy the story?
You can read the book online: https://authzed.com/resources/dibs-and-the-magic-library

value: Show HN: A kids book that introduces authorization and permissions concepts

author: samkim


value: joshcartme

value: This is a toy project I've been working on.  It was mostly something I realized I could make so I decided to go for it.

value: Show HN: ChatSMTP – Email an AI

author: joshcartme

title: Show HN: ChatSMTP – Email an AI


value: vedant-pandey

value: Hi Everyone, I created a 3d rasterizing engine in zig, with culling, clipping, and projection (check out demo on github)

value: Show HN: 3D Rasterizing Engine Written in Zig

author: vedant-pandey


value: vseplet

value:
What is Morph?
Morph is a zero-build fullstack library for creating web interfaces with server-side rendering. Built on HTMX and Hono, it works with Deno, Bun, and Node.js
Perfect for: Admin panels, dashboards, Telegram Web Apps, internal tools, and small projects where you don't want to maintain a separate frontend
Why Morph?
No build step — just write TypeScript and run
Server-side rendering — components render on the server with full access to your backend
HTMX-powered — update parts of the page without writing JavaScript
Embed anywhere — add a web UI to any existing Deno/Bun/Node project
Minimal footprint — no project structure requirements, no config files

value: Show HN: Embeddable fullstack web library without a build step

author: vseplet


value:
Hi HN,
I recently started running, and like most people who get into it, I began caring a lot about the data.
I use an Apple Watch and Apple Health to track everything, but there’s no wrapped-style summary for that data which I can be proud of. So I built one.
Apple Health Wrapped connects to your Apple Health data and generates a beautiful, shareable yearly summary (similar to Spotify Wrapped, but slightly more fun)
This is a small side project and still early. I’m mainly curious:
Did you liked it? What part exactly?
What would you want to see in a fitness wrap that’s missing today?
Happy to hear feedback here.
(Disclosure: I built this, with 2 of my friends - Shivay and Aman)
— Nihal
If needed, you can also reach me at: nihalgoyal10@gmail.com

value: Show HN: Apple Health Wrapped 2025

title: Show HN: Apple Health Wrapped 2025


value: claygood

value: Show HN: Block AI Ads

author: claygood

title: Show HN: Block AI Ads


value: nikhonit

value:
I built this because running yt-dlp in production (especially on serverless/Vercel) is a nightmare of IP blocks, cold starts, and binary dependencies.
TranscriptAPI is a lightweight wrapper that handles the extraction, formatting, and proxy rotation. It prioritizes manual captions over auto-generated ones and returns clean JSON with timestamps, ready for RAG pipelines.
The MCP (Model Context Protocol) Integration: I recently added native MCP support. If you use Claude Desktop or other MCP-compliant agents, you can add this API as a tool to 'watch' videos directly in your chat context without manually copying transcripts.
Technical Stack:
Backend: Python (FastAPI) on AWS Lambda (for burst scaling)
Caching: Redis (to prevent hitting YouTube for the same video twice)
Challenge: Handling 'drifting' timestamps in long livestreams where the auto-generated captions lose sync with the video frame.
It has a free tier for hobbyists. I’m curious to hear how you’re handling the context-window limits when feeding full 3-hour transcripts to LLMs

value: Show HN: A JSON API for YouTube Transcript with MCP Support

author: nikhonit


value: rogimatt

value:
Hi HN,
I’m building Draped, a virtual try-on product focused on helping people choose outfits using clothes they already own.
The idea came from a simple problem:
People have clothes, but still don’t know what to wear each day.
With Draped, users can:
Digitize their wardrobe
Get outfit suggestions based on context (weather, day, occasion)
Preview outfits via a lightweight virtual try-on (still early)
I’m currently iterating fast and validating use cases before scaling.
I’d really appreciate:
Feedback on the problem itself
Thoughts on the virtual try-on approach
Any similar products I should study
Website: https://draped.me
Happy to answer questions and share what I learn building this.
Thanks

value: Show HN: Draped – Virtual try-on to help people choose outfits they own

author: rogimatt


value: JohannesGlaser

value:
I built a small ingestion layer that turns visual inputs (screenshots, photos, exports) into structured, machine-readable memory for LLM assistants.
Instead of treating each image as a one-off prompt, scanOS normalizes recurring visual inputs into the same schema over time — even when the source formats differ. The goal is to accumulate state from visual data, not just extract text.
It’s not an OCR tool and doesn’t rely on embeddings, RAG, or fine-tuning. The output is either human-readable text or explicit machine-readable JSON that can be stored, inspected, and reused as part of a file-based memory system.
scanOS is part of a larger file-based architecture I’m using daily, but this module stands on its own.
Code + docs: https://github.com/johannes42x/scanOS

value: Show HN: ScanOS – normalizing visual inputs into persistent LLM memory

author: JohannesGlaser


value: Show HN: AudioGhost AI – Run Meta's Sam-Audio on Consumer GPUs (4GB-6GB VRAM)


value: aamatte

value:
Hey HN, I'm Andres. I've been building Kapso as a solo founder, and just crossed 4,000 developers, all organic.
WhatsApp has 3B+ users and 98% open rates. You'd expect developers to be building tons of stuff on it, especially when the US is the fastest-growing market in WhatsApp usage.
But it’s not happening… And I'd bet it's because the DX is painful.
Every team needs to build the same features again and again. Meta fires webhooks for everything. There's valuable data in there for debugging, but no way to make sense of it without building your own tooling.
That’s why I built Kapso. What you get:
- Working WhatsApp API + inbox in 2 minutes, not days
- Full observability: every webhook parsed, every message tracked, actual debugging tools
- Multi-tenant platform: generate a setup link, customer connects their Meta account, done
- Workflow builder for deterministic automations and AI Agents
- WhatsApp Flows: build mini apps inside WhatsApp using AI + serverless functions
- Docs that work for humans and LLMs
We're up to 95% cheaper than Twilio, with a generous free tier (2,000 messages per month).
We also open source several tools: a TypeScript client for the WhatsApp Cloud API, a reference WhatsApp Inbox implementation, and a voice AI agent for WhatsApp.
GitHub: https://github.com/gokapso/
Happy to answer questions!
https://kapso.ai/

value: Show HN: Kapso – WhatsApp for developers

author: aamatte


value: andrew-ld

value: A simple, TV-friendly, gamepad-navigable frontend for the Lutris game launcher on Linux. This application provides a "10-foot UI" designed for couch gaming, allowing you to browse and launch your Lutris library entirely with a gamepad.<p>Developed as a hobby project, this software allows for full system management for gaming via gamepad. It was created to streamline the couch gaming experience by enabling users to launch Lutris games, change window focus, control volume, and force-quit applications without a keyboard.

value: Show HN: Lutris Gamepad UI

author: andrew-ld

title: Show HN: Lutris Gamepad UI


value: gokh

value: Show HN: UTM Manager – Lightweight UTM persistence for marketing attribution

author: gokh


value: A real-time multiplayer trivia game designed for in-room group play. One person hosts on a shared screen while everyone else joins from their phones. Currently just stress-testing the core experience.

value: Show HN: QuackKing – real-time multiplayer trivia for living-room play


value: avipeltz

value:
Hey HN, we’re Avi, Kiet, and Satya. We’re building Superset, an open-source terminal made for managing a bunch of coding agents (Claude Code, Codex, etc) in parallel.
- Superset makes it easy to spin up git worktrees and automatically setup your environment
- Agents and terminal tabs are isolated to worktrees, preventing conflicts
- Built-in hooks [0] to notify when your coding agents are done/needs attention,
- A diff viewer to review the changes and make PRs quickly
We’re three engineers who’ve built and maintained large codebases, and kept wanting to work on as many features in parallel as possible. Git worktrees [1] have been a useful solution for this task but they’re annoying to spin up and manage. We started superset as a tool that uses the best practices we’ve discovered running parallel agents.
Here is a demo video:
https://www.youtube.com/watch?v=pHJhKFX2S-4
We all use Superset to build Superset, and it more than doubles our productivity (you’ll be able to tell from the autoupdates). We have many friends using it over their IDE of choice or replacing their terminals with Superset, and it seems to stick because they can keep using whatever CLI agent or tool they want while Superset just augments their existing set of tools.
Superset is written predominantly in Typescript and based on Electron, xterm.js, and node-pty. We chose xterm+node-pty because it's a proven way to run real PTYs in a desktop app (used by VSCode and Hyper), and Electron lets us ship fast.
Next, we’re exploring features like running worktrees in cloud VMs to offload local resources, context sharing between agents, and a top-level orchestration agent for managing many worktrees or projects at once.
We’ve learned a lot building this: making a good terminal is more complex than you’d think, and terminal and git defaults aren’t universal (svn vs git, weird shell setups, complex monorepos, etc.).
Building a product for yourself is way faster and quite fun. It's early days, but we’d love you to try Superset across all your CLI tools and environments, we welcome your feedback! :)
[0] https://code.claude.com/docs/en/hooks
[1] https://git-scm.com/docs/git-worktree

value: Show HN: Superset – Terminal to run 10 parallel coding agents

author: avipeltz


value: axeluser

value: Show HN: CarryFit – Open-source carry-on compliance checker for 170 airlines

author: axeluser


value:
I was paying $200/month for analytics while making $0. Built Prysm as the tool I wished existed.
Waitlist: prysmhq.com
Features indie hackers actually need:
  • Real-time 3D globe (surprisingly motivating to watch)
  • $10/mo, not $50+ (I'm an indie hacker too)
  • 3.8kb script vs GA's 45kb
  • No cookie banners
  • AI chat instead of complex dashboards
Tech: Next.js, Supabase, Mapbox. Tracking script is 200 lines of vanilla JS.

value: Show HN: Analytics for indie hackers – $10/mo with a live 3D globe


value: ediblepython

value: Show HN: Word Snake

author: ediblepython

title: Show HN: Word Snake


value: ProbDashAI

value:
Hi HN,
Like many people, I was frustrated that the released Epstein/Maxwell court documents were mostly scanned images (PDFs) with no text layer. This made them impossible to Ctrl+F or analyze programmatically.
I built a pipeline to fix this using Python, Tesseract, and OpenSearch.
The Site: https://epsteinfilez.com
The Stack:
Ingestion: Python workers using ocrmypdf (Tesseract) to perform parallel OCR on raw files.
Search: OpenSearch for indexing the extracted text.
Frontend: Next.js (SSR) for the UI.
Infrastructure: Self-hosted Docker swarm.
Features:
Sub-second full-text search across all files.
Highlights search terms directly on the PDF page.
Deep linking to specific pages/documents.
This is a transparency tool, not a political one. I wanted to make the raw primary sources accessible to researchers and journalists.
Feedback on the search relevance or indexing pipeline is welcome!

value: Show HN: Full-text search engine for Epstein docs (OCR and OpenSearch)

author: ProbDashAI


value: iowadev

value:
End of year seemed like a good time to thank people who helped me this year. Research shows gratitude letters boost well-being, but most people never send them.
Built this with Lovable in a weekend. Pick a template, write your message, download as a beautiful letter design. No sign-up required.
Try it: https://grateful.ly

value: Show HN: Built a gratitude letter generator in a weekend with Lovable

author: iowadev


value: rmdes

value: Show HN: OSINTukraine – Telegram Archive V2

author: rmdes


value: enovation

value:
Hello PeopleSoft Developers! Santa has a gift for you!
It was around a year ago, when the Integration Team at Azusa Pacific University, frustrated by the steps required to expose an HTTP endpoint from the Integration Broker, took the bold step to look elsewhere. Since we are a Node.js shop, we were dreaming of an API that would allow us to invoke get, create and save CI methods by passing a plain old Javascript object. The dream:
```<p>const appServer = Appserver.fromEnv();<p>const myCI = appServer.ciFactory('MY_CI_NAME');<p>const result = myCI.get({ PARAM1: 'value1', PARAM2: 'value2' });<p>console.log(result);<p>```
After some R&D sessions, and thanks to GraalVM, that dream is now a reality!
Ladies and Gentlemen, allow me to introduce you PSSDK:
https://github.com/azusapacificuniversity/pssdk
The PeopleSoft SDK for Node.js (PSSDK) is a library that allows you to connect to PeopleSoft Component Interfaces (CIs) using Node.js (or Python). The project consists of a Java library and an npm package. The project is meant to be used on GraalVM, namely GraalNode.js. GraalPython is also supported but not heavily tested.
There are many benefits to use PSSDK. It allows you to:
- Use a plethora of available libraries for data manipulation
- Use popular Node.js/Python web frameworks, like express.js, fastify.js, and Flask
- Properly version control your project and allow git collaborations with pull request reviews, etc...
- Allow automated unit and end-to-end tests and proper CI/CD
- Containerize and orchestrate highly available deployments with Docker and Kubernetes
We are releasing this project as open source under the Apache v2 license with love. We are excited to see what the PeopleSoft community can do with it.
Merry Christmas and Happy New Year!
-- The APU Integration Team

value: Show HN: PeopleSoft Meet Node.js (and Python)

author: enovation


value: sathvikchinnu

value: Show HN: Lume.js – 1.5KB React alternative using zero custom syntax

author: sathvikchinnu


value: bscript

value: Show HN: Developer-friendly HTTP request testing inside the browser

author: bscript


value: a-abit

value:
I wanted to get feedback on my project MinuteShark.
As a freelance developer, I was frustrated by needing to use a time tracker, a project manager, and a note taker - each of which had too many features and buttons and pages for me.
I needed something basic I could use to get my freelance work done.
I personally love it.
I would love to know if you guys think it hits the spot as well. Am I on the right track?
You can see it here https://app.minuteshark.com/#/register
Thanks for your feedback!

value: Show HN: MinuteShark Time Tracking for Freelancers

author: a-abit


value: nickytonline

value:
Built this because I needed a decent ChatGPT app template. TypeScript,
MCP server, React widgets, Vitest, Storybook, Pino… all the proper tooling.<p>Feedback welcome.

value: Show HN: TypeScript template for building ChatGPT Apps

author: nickytonline


value: rubenhellman

value:
Hi HN,
I’ve been spending a lot of time vibe-coding with LLMs and kept running into the same bottleneck: translating a vague idea into a prompt that produces usable output without multiple correction cycles.
Most of the time wasn’t spent coding, but refining prompts. Adding constraints, clarifying intent, restating context, fixing assumptions. It felt like a repeatable preprocessing problem rather than something that should require manual effort every time.
So I built a small tool that takes rough, unstructured input and rewrites it into a more explicit, structured prompt optimized for vibe-coding style workflows. The focus is on intent extraction, constraint clarification, and reducing prompt entropy so the first generation is closer to what you actually want.
Under the hood, it’s tuned to recognize common failure modes in AI-assisted building like underspecified requirements, implicit assumptions, and missing system-level context.
This isn’t meant to replace iteration or thinking, just to reduce prompt churn and help people stay in flow, especially non-technical builders.
Link:
https://vibecodeprompts.cloud
Curious to hear thoughts from people building with LLMs. Is prompt friction something you accept as inherent, or do you think it’s worth systematizing like any other part of the pipeline?

value: Show HN: VibecodePrompts – Reduce prompt friction for vibe-coding

author: rubenhellman


value: jakedahn

value: Show HN: Oblique Strategies for Claude Code

author: jakedahn


value:
Greed.js is a javascript library that allows executing PyTorch code right in the browser. It uses the WebGPU of the browser to execute PyTorch operations.
We have written the core PyTorch operations in WGSL, which speedup the calculations.
Also for devices/browsers which don't have GPU support. We have implemented fallback to CPU with NumPy polyfills implementing the PyTorch functions.

value: Show HN: Greed.js – Run PyTorch code in browser using WebGPU


value: gregsadetsky

value: Show HN: Drive Time Shaver Calculator

author: gregsadetsky

title: Show HN: Drive Time Shaver Calculator


value: ChadNauseam

value: Show HN: Languagecat, a free dataset for people making language-learning apps

author: ChadNauseam


value: starlitlog

value:
Built this during my PhD research after getting frustrated with traditional web scrapers breaking every time sites updated their HTML. DR Web Engine uses declarative JSON5 queries instead of imperative scraping code.

  Key features:
  - JSON5 query language (can still use XPath for field selectors)
  - AI-powered element selection (describe what you want in natural language)
  - Plugin architecture for extensibility
  - Modern browser automation (Playwright-based)
  - Handles dynamic content and JavaScript

  The core insight: define WHAT data to extract in a structured format, not HOW to extract it step-by-step. Makes scraping more maintainable and less brittle.

  Started as academic research but rebuilt it for real-world use. Published the methodology in arXiv and now actively developing the open source version.

  Looking for feedback on the query language design and real-world use cases!

value: Show HN: DR Web Engine – JSON-based web scraping that doesn't break on change

author: starlitlog


value: dboon

value:
Claude Code added a /stats command. The stats are cached in $HOME/.claude, so I made a fun program with Bun + WASM that pulls the stats (nonsensitive, nonidentifiable), posts them to a database, and lets you see where your usage lies.
The code is here[^1] if anyone's interested in the rendering code or wants to make sure I'm not exfiltrating their credit card.
[^1]: https://github.com/tspader/claude-wrapped/

value: Show HN: Claude Wrapped in the terminal, with a WASM raymarcher

author: dboon


value: SahilAdBlocker

value:
Hi HN
I built a simple, lightweight ad blocker Chrome extension and wanted to share it with the community for feedback.
What it does:
Blocks common ads, popups, and banners
Handles dynamically loaded content using DOM observation
Very lightweight and fast
No tracking, no unnecessary permissions
Why I built it:
I wanted a minimal ad blocker that doesn’t slow down browsing or overcomplicate settings.
Chrome Web Store link:
https://chromewebstore.google.com/detail/ajmonbkfjffgdhhacjm...
I’m especially interested in:
Performance feedback
Bug reports
Feature suggestions
Thanks for checking it out!

value: Show HN: A lightweight ad blocker Chrome extension I built

author: SahilAdBlocker


value: GenLabs-AI

value:
I built an extension that gives developers cheaper access to AI models in a Kilo Code fork.
Free tier: 200 requests/day to open source models (DeepSeek, Qwen, Minimax)
Paid tier ($20/month): $45 in credits for Claude/GPT-5/Gemini-3 + 1000 daily open source requests
Works in VS Code, Cursor, and Windsurf. Your data isn't used for training.
Just launched today, still rough but functional. Would appreciate feedback.
agentica.genlabs.dev
Download extension from:
https://open-vsx.org/extension/agentica/agentica

value: Show HN: Agentica – 200 reqs/day for free, data not used to train our LLMs

author: GenLabs-AI


value: botbuilder

value:
Built this after losing money on Pump.fun rug pulls.
Scans tokens for:
- Liquidity verification
- Whale concentration
- Contract safety
- Token age
Takes 10 seconds vs 10 minutes of manual checking.
Tech: Supabase + Netlify + vanilla JS. Would love feedback!

value: Show HN: SafeSnipe AI – Rug pull detector for Solana meme coins

author: botbuilder


value: Gricha

value: Show HN: A holiday-themed text-based adventure game played over MCP

author: Gricha


value:
I built this because I hate writing tests but I also hate shipping bugs.
qckfx records what you're already doing. You tap through the simulator to check your work, and it replays those sessions to catch visual regressions and crashes. No SDK, no code changes, no test files.
How it works: finish a feature, click around in the simulator, hit Cmd+Shift+S. That's your test. Cmd+Shift+T to run it before you push.
It's free, runs locally, and installs via Homebrew: brew install --cask qckfx/tap/qckfx. I'm a solo dev (10 years iOS, ex-Meta) and this is early. Feedback welcome.
https://qckfx.com

value: Show HN: qckfx – Record your iOS simulator sessions, replay them as tests


value: simullab

value:
I got tired of buying stocks because some guy on Twitter said "this is the next 10x" or watching my crypto portfolio tank because I bought at the peak of FOMO season. We've all been there, right?
So I spent the last few weeks building BuffettlyAI on Poe using their script bot builder. The goal was simple: create something that would slap me in the face with reality before I make dumb financial decisions.

value: Show HN: I automated Warren Buffett's brain on Poe. It's uncomfortably accurate

author: simullab


value: Mincirkel

value:
Hi HN,
We kept running into unstable WebRTC call flows in hybrid apps:
permission prompts, app lifecycle issues, and notification → call transitions breaking.
Instead of stacking more workarounds on top of the browser, we moved the critical call flow into a native layer and exposed it through a custom JS ↔ native bridge.
This allows an incoming notification to reliably open an accept/decline UI and transition directly into a live audio/video call.
It currently powers MinCirkel (a closed family network), but the bridge itself turned out to be the real asset.
I’m mainly looking for feedback, similar experiences, or teams that have struggled with WebRTC reliability in hybrid environments.
Happy to answer technical questions.

value: Show HN: We built a native bridge to make WebRTC calls reliable in hybrid apps

author: Mincirkel


value: KasamiWorks

value: Show HN: I hired AI to fix my memory, but made it 100% Offline for privacy

author: KasamiWorks


value: thomaswc

value:
It's a a long puzzle, somewhat akin to a 2000 piece jigsaw puzzle.  Solving it collectively with others is encouraged.
If you are the sort that likes rules, the rules are you can use Google or whatever external sources you like for help.  View page source, however, is cheating.
Hat tip to Octagon and the TND crew for help in play testing and proposing categories.

value: Show HN: A 45x45 Connections Puzzle To Commemorate 2025=45*45

author: thomaswc


value: ahsekka

value:
Hi HN — sharing ragctl, an open-source CLI for the most failure-prone part of RAG pipelines: document ingestion, OCR, parsing/cleaning, and chunking.
Vector DB setup is fairly standardized now, but getting high-quality, consistent text + metadata into it still takes a lot of brittle glue code. ragctl aims to make that “pre-vector” step repeatable: turn messy documents into retrieval-ready chunks in a few commands.
Features
 • Multi-format input: PDF, DOCX, HTML, images
 • OCR for scanned/image-based docs
 • Semantic chunking (LangChain)
 • Batch runs with retries + error handling
 • Output: direct ingestion into Qdrant (for now)
Looking for feedback
 • DX: is the CLI intuitive?
 • Performance / edge cases: weird PDFs, mixed layouts, tables
 • Roadmap: which connectors (S3, Slack, Notion) or vector stores should be next?
Repo: https://github.com/datallmhub/ragstudio
Happy to answer questions about the architecture and chunking approach.

value: Show HN: Ragctl – document ingestion CLI for RAG (OCR, chunking, Qdrant)

author: ahsekka


value: gaganyatri

value:
ASR + TTS + Chat + Vision + Docs
Try demo : https://workshop.dwani.ai
Built dwani.ai over the past 11 months.
Talk to AI in Indian languages and get response back via Voice and Text.
Combining Open weight models to build AI for Indian languages.
Github : https://github.com/dwani-ai
Setup : https://docs.dwani.ai

value: Show HN: Dwani.ai – AI for Indian Languages

author: gaganyatri


value: acoyfellow

value:
I've been building t2t, a voice-to-text app that works system-wide with a twist: it supports MCP (Model Context Protocol) servers for extensible automation.
*What it does:*
- Hold `fn` → speak → text appears anywhere (local Whisper transcription)
- Hold `fn+ctrl` → agent mode that connects to any MCP server
- All processing is local except OpenRouter API calls for agent mode
- Cross-platform: macOS, Windows, Linux
*Why I built it:*
I wanted voice-to-text that wasn't tied to a specific app, and I got excited about MCP as a way to make the agent mode extensible. Instead of hardcoding integrations, you can connect to any MCP server (databases, APIs, file systems, etc.).
*Architecture:*
- Rust + Tauri for the desktop app
- Local Whisper model (whisper-rs) for transcription
- Local MCP client (stdio/HTTP/SSE) - no remote workers
- OpenRouter API for AI (direct calls from Rust)
- Svelte 5 frontend
The MCP client runs entirely in Rust, so it works in both dev and production builds. Tool execution happens locally via JSON-RPC.
*Open source:* https://github.com/acoyfellow/t2t
I'd love feedback on the architecture, especially the local MCP client approach. Also curious if others find the MCP integration useful or if I'm overcomplicating things.
What do you think?

value: Show HN: T2T – Voice-to-text with MCP support local and cross-platform

author: acoyfellow


value: alvinunreal

value: Show HN: Turn raw HTML into production-ready images for free

author: alvinunreal


value:
Hi HN,
I built a small web app that lets users write letters and schedule them to be delivered to their future selves or to other people, for free.
The idea came from wanting a simple way to reflect on goals and thoughts over time without ads or paywalls.
How it works:
- Write a letter
- Choose a future delivery date
- The letter is delivered automatically when the date arrives
Tech stack:
- Next.js
- TypeScript
- Resend
- Inngest
I’d really appreciate feedback on:
- Use cases you find interesting
- Privacy concerns or improvements
- Features you’d expect from something like this
Happy to answer questions.

value: Show HN: Send free letters to your future self or others


value:
Hi HN! I built Depsy (depsy.io), an API that returns normalized status for your SaaS dependencies in a single call, during incidents I kept losing time to "status-page roulette" (Slack,Okta,GitHub, Cloudflare, etc)... Depsy is meant to answer one question fast: "is it us, or the vendor?"
What it does:
- Query 1–2000+ vendors in one request
- Cached + normalized output (so you don’t parse different status formats)
- Designed to plug into on-call flows (dashboards, alerts, runbooks)
Why I'm posting:
I'm looking for feedback from SRE/DevOps folks:
- What fields would you need in the response to trust it during an incident?
- What vendors are "must-have" for you?
- Would you rather send a vendor list, or have Depsy manage "your stack" profiles?
Happy to share details on caching/normalization approach if useful!

value: Show HN: Depsy – normalized SaaS dependency health in one API call (cached,fast)


value:
I built an open-source Planning Poker web app focused on being simple,
free, and unrestricted.
It uses WebSockets and Cloudflare Workers with Durable Objects
to manage real-time room state.
GitHub: https://github.com/rie03p/planning-poker

value: Show HN: Open-source Planning Poker web app with no voting limits


value: sotoplatero

value: Lets you publish Markdown via API and get a temporary public link perfect for automation flows or scripts.

value: Show HN: Dead-simple publishing for Markdown from your automation flows

author: sotoplatero


value: agentictime

value: Show HN: MCPTotal – Run MCP servers in isolated containers instead of locally

author: agentictime


value: metaist

value: Bundle up a pure python project into a single Cosmopolitan Python file that runs on Linux/Mac/Windows with no changes.

value: Show HN: Cosmofy – bundle your Python code for Linux/Windows/MacOS

author: metaist


value: Ralmidu

value: I spent the last 10 months building this multi‑app AI platform as a non‑programmer by directing AI tools instead of writing code myself. I was interested in computer science years ago, but stepped away from it right as OOP was becoming standard, so I never learned to program in any practical sense. This project is my first real attempt at building software, done entirely through prompt‑driven development.<p>The platform includes document chat, OCR, translation, tutoring, agents, voice chat, a to‑do list, and a working Stripe subscription system. My goal is to show what one person can build now that AI can act as the developer. This is the live demo.

value: Show HN: A Multi‑App Platform Built by One Person Using AI as the Developer

author: Ralmidu


value: turbosanchezz

value: Show HN: Shh – Speech-to-text CLI with AI formatting and translation

author: turbosanchezz


value:
Hi HN,
We’re building PoliteHub (https://politehub.com), an AI-first team messaging tool for small and growing teams.
Like many teams here, we used Slack daily. Over time, two things became increasingly painful for us:
1. Pricing: Per-seat pricing punishes growing teams.
2. AI tools are fragmented — chat in Slack, thinking in ChatGPT, docs elsewhere
PoliteHub is our attempt to fix this by making AI a "member" of the channel that understands the full workspace context, rather than just a sidebar chatbot.
What we’re experimenting with:
- Slack-like channels and messaging
- Shared AI that understands workspace context
– Workspace-based pricing instead of per-user
– Built mainly for SMBs and early-stage teams
The product is still early (beta), and we know replacing existing habits is hard. We’d really appreciate any feedback from the HN community, especially from teams that have tried integrating AI into daily collaboration.
What we’re most curious about:
- Does workspace-based pricing make sense?
- Should AI be shared across a team, or remain personal?
- What would make you seriously consider switching from Slack?
Thanks for taking a look, and I'm happy to answer questions.

value: Show HN: PoliteHub – an Slack alternative with workspace-based pricing


value: galapago

value: Show HN: MCP support to guide smart contract fuzzing campaigns in Echidna

author: galapago


value: arreow

value: Show HN: Knowimg – An AI clothes changer and image editor for the web

author: arreow


value:
Yo HN! I’m Souhail. I made this tool called Kolors. How is it different from Coolors? Kolors let you map primitive and semantic colors, which means they are customizable to match any design system.
This tool is live on PH, so it would be great if you could upvote it: https://www.producthunt.com/products/kolors-2?launch=kolors-...
PS: NOT vibe-coded

value: Show HN: Create color palettes for design systems (primitive, semantic tokens)


value: Show HN: Free True or False Quiz Maker

title: Show HN: Free True or False Quiz Maker


value: kenshozone

value:
I got tired of productivity apps that let you override breaks and burn out.
Kensho Zone enforces a 10:1 focus-to-rest ratio. After 100 minutes of focus,
the timer won't start again until you log 10 minutes of meditation/rest.
It also locks your task list if you complete 5 tasks without writing a reflection.
Terminal aesthetic, no gamification, no social features. Just discipline.
Try it: kensho.zone

value: Show HN: I built a system that locks you out until you rest

author: kenshozone


value: Show HN: Free QR Code Generator – Dynamic Tracking and Custom Logo – QrBarKit


value: joeeverjk

value: Show HN: SatoriDB – embedded vector database written in Rust

author: joeeverjk


value: mayankkgrover

value:
The one-liner: You give it a script, it generates a portrait video in 10 minutes. No stock footage, no templates—it writes code that renders as video.
## Why I built this
We were building game dev courses at Outscal and needed to produce a lot of video content fast. Traditional video production was slow. AI video generators looked weird. So we tried a different approach: what if video was just code?
Turns out it works. We can replicate specific art styles, and the output is consistent and editable.
## What it can do
- Generate 30-60 second shorts from a script
- Match specific visual styles
- Let you fix individual scenes via chat if something's off
## What it can't do (yet)
- Custom art styles (we have a few presets)
- Run in a web UI (API costs are too high right now, so it runs through Claude Code in terminal)
Occasionally a shape might render slightly off, but you can tweak individual scenes through chat.
## How it actually works
The tool uses Claude Code to generate React components for each scene in your script. These components animate and render exactly like a video would. When you're done, you get a real video file.
It's not "AI video generation" in the usual sense. There's no diffusion model hallucinating frames. It's closer to programmatic motion graphics—but you don't write the code, Claude does.
## What I'm looking for
10 people who want to try it. Not paying customers—just people who make (or want to make) Short form  and are comfortable enough with a terminal to run Claude Code.
You'd get access to our setup, and I'd want your honest feedback on what works and what doesn't.
If this sounds interesting, try the project on git, it is open-source.

value: Show HN: I built a tool that creates videos out of React code

author: mayankkgrover


value: aashirpersonal

value: I built this because I was tired of guessing why my RAG system was failing. It projects user queries vs. documents into 2D space to find 'Red Zones' (high user intent, low documentation). Open source, built with FastAPI + React. Would love feedback on the clustering logic.

value: Show HN: Semantic Coverage – A tool to visualize RAG blind spots using UMAP

author: aashirpersonal


value: alizainf

value:
For the last 4 months, I've been programming exclusively with AI. One of the most painful parts has been getting correct and up-to-date documentation for third-party libraries. So I made an agent to fix that.
You can add it as an MCP tool to anything.
It works because it's just focused on doing research for code. You can check out the prompts https://github.com/803/sensei/blob/main/sensei/prompts.py. I combined this with a couple specialized tools for this use-case:

  - Kura - Query cache. Works even better because the prompt instructs the agent to decompose queries into sub-queries, and usually the sub-queries are more generic. Each gets cached as a reusable building block.
  - Scout - Source code explorer. Let's the agent glob, grep, and tree any public repo at any tag/branch/SHA. When docs are unclear or unspecific, there's nothing better than reading what the code actually does.
  - Tome - llms.txt ingestion. Super simple. Does it on-demand for any domain and then stores the results in postgres for search.

I'm not charging for this right now and it's completely open-source and you can run it yourselves with your own API keys. If things go nuts I'll have to change course.
I made it to scratch my itch, maybe it'll scratch yours as well.

value: Show HN: Sensei, documentation agent for coding agents

author: alizainf


value: Show HN: I created a free pdf to quiz maker tool


value: sawirricardo

value: Show HN: BunPush, Modern Realtime pusher compatible built on Bun

author: sawirricardo


value: SHow HN: Prompt-RAG – Fix low-quality AI images using a 500 prompt vector DB


value: Kaadz

value:
Hey HN! I just shipped WatchLLM - a semantic caching layer for LLM APIs that sits between your app and providers like OpenAI/Claude/Groq.
The problem: LLM API costs add up fast, especially when users ask similar questions in different ways ("how do I reset my password" vs "I forgot my password").
The solution: Semantic caching. WatchLLM vectorizes prompts, checks for similar queries (95%+ similarity), and returns cached responses instantly (50ms). If it's a miss, we forward to the actual API and cache for next time.
Built in 3 days with Node.js, TypeScript, React, Cloudflare Workers (edge deployment), D1, and Redis. Just added prompt normalization today to boost cache hit rates even further.
It's drop-in - literally just change your baseURL and keep using your existing OpenAI/Claude SDKs. No code changes needed.
Currently in beta with a generous free tier (50K requests/month). Would love feedback from anyone building LLM apps - especially on the semantic similarity threshold and normalization strategies.
Live demo on the site shows real-time cache hits and savings.

value: Show HN: WatchLLM – Semantic caching to cut LLM API costs by 70%

author: Kaadz


value: AshBuk

value: Show HN: Clean Hyprland 0.52 RPM Builds for Fedora via COPR

author: AshBuk


value: germa

value: I watched MegaLag's video on the honey's public database of their network. I scraped it and now sharing the full thing.

value: Show HN: I scraped Honey's affiliate database and now sharing the analysis

author: germa


value: fwsgonzo

value: Show HN: LoongArch Userspace Emulator

author: fwsgonzo

title: Show HN: LoongArch Userspace Emulator


value: zibisco

value: Show HN: InfiniDB – The Unreliable Database of Everything

author: zibisco


value: araldhafeeri

value:
The first thing a new developer needs to do when getting a new device is to configure their dev environment.
Stackup allows you to do that in a single command, given you write the configuration yaml once.
It's free open-source under the MIT license.
This is my first Go project (as an experienced developer).
Feel free to give me any feedback.

value: Show HN: Stackup – Configure your dev env for cross-platform

author: araldhafeeri


value: melihmucuk

value:
AI coding agents like Claude Code can hallucinate dangerous commands. Wrong paths, wrong directories, and suddenly you're running "rm -rf ~/" instead of "rm -rf ./". I built Leash to catch these mistakes before they execute.
It's a simple hook that runs before each tool call. Blocks dangerous commands outside the working directory, protects sensitive files like .env and .git even inside the project, and stops destructive git operations like reset --hard or push --force.
Some things it handles that weren't obvious at first: agents doing "cd ~/Downloads && rm -rf folder" to escape the sandbox, compound patterns like "find -delete" or "xargs rm" targeting paths outside the project, and symlink-based escapes.
Works with Claude Code, OpenCode, Pi, and Factory Droid. Setup is just "npm install -g @melihmucuk/leash" followed by "leash --setup claude-code".
Not a full sandbox. If you need real isolation, use containers. This just catches the common hallucination patterns that cause accidental damage.
https://github.com/melihmucuk/leash

value: Show HN: Leash – Security guardrails for AI coding agents

author: melihmucuk


value: pehli

value:
Hey HN,
I’ve been working with Kubernetes for a while now, and one thing that has always been a friction point for me is dealing with Custom Resource Definitions (CRDs).
We use them for everything—monitoring, cert-manager, custom controllers—but the tooling around them always felt a bit raw. Dealing with them usually meant running `kubectl get crds`, piping output to grep, or staring at 5,000-line YAML files just to figure out what fields were available in the schema.
It got frustratingly worse when I started managing multiple clusters. I found myself constantly context-switching just to check if a CRD was installed or to diff versions between environments. It felt like I was spending more time memorizing `kubectl` flags than actually working.
So, I started building *CRD Wizard* to scratch my own itch.
It started as a simple TUI to view resources, but I realized I needed more visual context. Now it’s a full desktop app (Go backend + Next.js frontend) that auto-discovers your local kubeconfigs and gives you a unified interface to explore CRDs across all your clusters.
Over the last few weekends, I’ve added a few features that I really wanted:
1.  *Multi-Cluster Support:* It loads all your contexts automatically. You can switch between clusters instantly without touching your terminal.
2.  *Documentation Generator:* I realized I often needed to share CRD specs with devs who don't have cluster access. I added a generator that turns any CRD (from your cluster or a Git URL) into a clean, searchable static HTML or Markdown page.
3.  *AI Integration:* I hooked it up to local LLMs (Ollama) and Google Gemini. You can click a button to have the AI explain a complex schema or generate a sample manifest for you.
I wrote it in Go because I wanted it to be snappy and easy to distribute as a single binary.
It’s open source, and I’d love to hear what you think or what other pain points you run into with CRDs.
Repo: https://github.com/pehlicd/crd-wizard

value: Show HN: CRD Wizard – A GUI for Kubernetes Custom Resource Definitions

author: pehli


value: danielvers

value:
Hi HN,
I built Nano Banana Images to solve a specific problem: generating consistent, commercial-grade assets (like product photos or branding visuals) is still too "random" with standard text-to-image tools.
Instead of fighting with long prompt strings, I built a library based on "structured prompts"—separating subject, lighting, and camera settings into reusable components.
The app wraps these presets into a simple UI where you can generate high-quality results without prompt engineering. It's built with Next.js and uses current SOTA models under the hood.
I'd love your feedback on the prompt curation and the quality of the outputs for professional use cases.
URL: https://www.nanobananaimages.com/

value: Show HN: Nano Banana – Structured AI prompts for commercial design

author: danielvers


value:
Full Paper (Zenodo):
https://zenodo.org/records/18027729
Help Needed:
I am looking for feedback on the experimental setup.
The main engineering challenge is maintaining GHZ coherence long enough to isolate the effect from environmental noise.
If you have
Hi HN. Standard General Relativity posits that time dilation is caused solely by mass-energy. But what happens when information entropy reaches a critical density?
I have released Version 2.0 of my paper, proposing the Information-Induced Time Dilation (ITD) hypothesis.
The Hypothesis:
I propose that local information entropy (\Delta S_{info}) acts as a "computational load" on the spacetime metric. Just as mass curves spacetime, extreme information density might "lag" the local clock.
The Experiment (Strictly Falsifiable):
To test this, I designed a differential measurement using Sr-87 optical lattice clocks (Section 6):
Compare: A system in a GHZ Entangled State (High Info) vs. a Product State (Low Info).  Control: Mass and energy are kept identical.  Prediction: If my derivation is correct (\alpha \neq 0), the entangled sector will show a frequency redshift relative to the control group.
expertise in quantum metrology, I would deeply appreciate your technical insights.
(Optional) Request for ArXiv Endorsement:
Endorsement Code: E3Y83D (physics.gen-ph)

value: Show HN: Does Information Density Cause Time Dilation?


value: KalaINC

value: Show HN: Turning code execution logic into support-friendly flow diagrams

author: KalaINC


value: Key details: 2.1s generation, no watermarks, commercial license included. Supports multi-AI models (Google Veo 3.1 etc.) & aspect ratios (1:1/16:9/4:5). Free trial, no tech skills needed.

value: Show HN: Nano Banana Video – AI Text/Image-to-Video in 2.1s


value:
A lightweight Python CLI that turns Terabox links into downloadable files.
No browser required. Works well for automation and scripting.

value: Show HN: Tera-dl – Terabox downloader for the terminal


value: qzcanoe

value:
Trellis 2 is a new image generation model, but I found it surprisingly hard to quickly try, compare prompts, or understand what it’s good at without digging through docs or code.
So I built a small, fast site that focuses on one thing: letting people experiment with Trellis 2 through real prompts and instant results.
It’s intentionally simple:
* No account required
* Prompt-in, image-out
* Optimized for people coming from search or just curious about the model
This started as a way to explore Trellis 2 myself, and then I realized others might want the same thing.
Would love feedback from people who are testing new models or building AI tooling.

value: Show HN: A fast, simple playground for Trellis 2 image generation

author: qzcanoe


value: Cyao

value:
I've made an ARM based single-board computer that runs Android and Linux, and has the same size as the Raspberry Pi 3!
Why? I was bored during my 2-week high-school vacation and wanted to improve my skills, while adding a bit to the open-source community :P
These were the specs I ended up with:
- H3 SoC
    - Quad-Core Cortex-A7 ARM CPU @ 1.3GHz
    - Mali400 MP2 GPU @ 600MHz
- 512MiB of DDR3 RAM (Can be upgraded to 1GiB)
- WiFi, Bluetooth & Ethernet PHY
- HDMI display port
    - 1080p resolution
- 5x USB Slots: 2x USB-A, 1x USB-C Host, 1x USB-C Host & OTG, 1x USB-C PD for power (Negotiating up to 25W. No power socket, yay!)
- 32 GB of eMMC 5.1 storage (Optional)
- 3.5mm audio jack
- SD Card slot
- Lots of GPIO
I've picked the H3 CPU mainly for its low cost yet powerful capabilities, and it's pretty well supported by the Linux kernel. Plus, I couldn't find any open-source designs with this chip, so I decided to contribute a bit and fill the gap.
A 4-layer PCB was used for its lower price and to make the project more challenging, but if these boards are to be mass-produced, I'd bump it up to 6 and use a solid ground plane as the bottom layer's reference plane. The DDR3 and CPU fanout was truly a challenge in a 4-layer board.
The PCB was designed in KiCAD and open-source on the Github repo with all the custom symbols and footprints (https://github.com/cheyao/icepi-sbc). You can also check it out online with kicanvas: https://kicanvas.org/?github=https%3A%2F%2Fgithub.com%2Fchey...
At large quantities, the price can probably reach less than 20$! (exc. taxes, tariffs and other costs)
It has been a wild journey, even making me learn how to use crypto as I needed to pay someone to download some "confidential" files from a baidu drive...
Read about more details on Github! Everything is open-source under the Solderpad license, aka do what you want: sell it, build it, modify it! :-)

value: Show HN: I built an open-source Linux-capable single-board computer with DDR3

author: Cyao


value:
Hi HN,
I’m sharing OpenSEO Studio v1.0, a fully static SEO article generator.
The idea is simple:
– No backend
– No accounts
– No tracking
– BYOK via OpenRouter
– Runs entirely in your browser
– Outputs clean Markdown only
I built this because most SEO/AI writing tools are SaaS-first, opaque, and over-engineered. I wanted something transparent, cloneable, and easy to self-host.
What it does:
– Generate long-form SEO articles
– Contextual generation options (not hidden in settings)
– Export Markdown or download .md files
– Ghost / WordPress exports are prepared (UI only for now)
What it deliberately does not do:
– No user accounts
– No database
– No server-side storage
– No “AI platform” lock-in
The project is MIT-licensed and fully client-side. You can verify everything by reading the source.
Live demo: https://openseo.studio
Repo: https://github.com/Vellis59/openseo-studio
Happy to answer questions or get feedback.

value: Show HN: OpenSEO Studio – a static, BYOK SEO article generator (v1.0)


value: afrederico

value:
Instead of waiting for OpenAI to scrape the whole web so your products get listed and people can buy through it, we’ve enabled our own ACP abstraction layer over Shopify to make your store agent ready right now.
Merchants get a one-click setup: connect your store and agents can query structured product data (inventory, pricing, availability) and initiate purchases through ACP-compatible flows. Everything is structured and permissioned; no scraping, no prompt-hacking storefront HTML. Full agentic payments via Stripe are on the roadmap, solving the hardest part: safely handing off agent intent while preserving authorization, fraud controls, and auditability.
For developers, ACP provides SDK-style endpoints that make "AI can shop this store" as simple as calling an API, not building custom glue code per merchant.
We're running a limited beta while we learn where this breaks in real usage—currently free for smaller stores as we focus on stability and feedback.
Early interest has been strongest from merchants selling technical or niche products, where agent-driven discovery actually makes sense. We're live at https://actory.ai and looking for blunt feedback from anyone running Shopify stores, building agents, or thinking about what happens when almost all commerce flows through agents in the next few years.

value: Show HN: ACP is the HTML of agentic commerce

author: afrederico


value: zieg

value: open source ui components for your next project. install via shadcn cli.

value: Show HN: Beste.ui – Shadcn/Tailwind blocks for developers

author: zieg


value: Show HN: Opensource"BeMyEyes"alternative(Java/Go/Python)built as a learning pjet


value: OzanYldz

value:
Hi HN,
I'm Ozan, the developer behind MonumentAI.
I built this app because I enjoy traveling but find traditional audio guides and plaques incredibly boring. They usually focus on dates and architectural styles, skipping the interesting human stories—the scandals, exiles, and secrets.
I wanted a "Shazam for Buildings" that feels like a local friend whispering the gossip in your ear, rather than a history textbook.
How it works:
1. You take a photo of a landmark.
2. The app identifies the location using Google Gemini's vision capabilities.
3. It generates a short, engaging story focused on the "gossip" or hidden history (also via Gemini).
It's currently an iOS app built with SwiftUI.
The app is free to download (with a paid tier for unlimited scans), but you can try the core functionality without paying.
App Store Link:
https://apps.apple.com/us/app/monumentai-scan-explore/id6756...
I'd love to hear your feedback on the UX and the quality of the stories.
Thanks!

value: Show HN: MonumentAI – Shazam for buildings (History without the boring parts)

author: OzanYldz


value: franze

value: Show HN: MicroQuickJS WASM – A 100% Claude Code Port

author: franze


value:
I've had a goal for the past three years to learn Assembly language. This December, I finally put some real effort into learning the language. I followed the tutorials in Oscar Toledo G's "Programming Boot Sector Games" and used Gemini as a tutor to help explain the concepts I was stuck on. It was tempting at points to vibe code some of the trickiest pieces, but I found resisting the temptation and using Gemini just to be an expert tutor to answer my ignorant questions brought back the joy of coding again. The result is a Christmas-themed Space Invaders clone written in 8086 Assembly.
The code isn't perfect, and neither are my line-by-line comments, but it's a good exercise if anyone else is trying to learn a language.
The game compiles to a .com file and runs in DOSBox. I'm not trying to fit it into a boot sector (512 bytes) like the original tutorial (https://github.com/nanochess/Invaders/blob/master/invaders.a...), so my code assembles to more verbose (around 700 bytes).

value: Show HN: I wrote a Christmas-themed Space Invaders clone in 8086 Assembly


value: bpcreech

value:
Now with V8 14.3, and native support for Windows on ARM!
https://bpcreech.com/PyMiniRacer/
Release history: https://bpcreech.com/PyMiniRacer/history/

value: Show HN: PyMiniRacer 0.13.0

author: bpcreech

title: Show HN: PyMiniRacer 0.13.0


value:
I built this extension to eliminate the friction of context-switching to a browser just to hunt for documentation. It brings djangopackages.org directly into your command bar.
Features:
 * Instant Search: Query the full package index.
 * Deep Navigation: One-keystroke jump to Documentation, PyPI, or Source Code.
 * At-a-Glance: View package metadata and grids without leaving the modal.
Link: https://www.raycast.com/saadmk11/djangopackages

value: Show HN: Search Django Packages on Raycast – Jump to Docs, PyPI, and Repos


value: languse

value: Show HN: Android Use – Automate Android with AI Agents via XML Parsing

author: languse


value: Show HN: Timescope – infinite, smooth-scrolling timeline UI with charts


value: julienreszka

value: Discover how economic forces shape prosperity. Explore 6 fundamental curves that explain everything from government spending to capitalism's wealth explosion.

value: Show HN: Master Economics Through Interactive Simulations

author: julienreszka


value: shubhmx

value:
Hey HN, I’m Shubham, building Infina AI (https://infina.so
)
I noticed I was creating ~30 Linear issues a day. Often I’d be in the middle of designing something or thinking through GTM, remember a bug or task, switch to Linear, write it up, assign it, set priority, then switch back. Same pattern with Slack—context switching dozens of times a day.
It wasn’t the time per action that bothered me as much as losing focus repeatedly. I kept thinking: when I already know what needs to be done, why do I have to stop what I’m doing just to type it somewhere?
That led me to experiment with voice-first workflows.
What we built
Infina is a desktop app that lets you execute common work actions using voice, without switching tabs.
• Voice execution – Hold a key and say something like:
“Create a Linear ticket: fix navbar bug, assign to Sarah, high priority.”
or
“Send a Slack message to the engineering channel: deployment is done.”
The action happens in the background while you stay in your current app.
• Voice dictation – This is for writing text anywhere (Slack, email, docs). You can speak naturally and it cleans up filler words and structure, instead of raw transcription.
• Voice search – Ask a quick question (product, debugging, general knowledge) and get a short answer in a small popup, without opening a browser or new tab.
• Meeting notes – Infina runs in the background and capture meetings (Zoom, Meet, Teams, etc.) without a bot joining. You get transcripts, summaries, and searchable history. This is where the product originally started.
Over time, I found myself opening Linear and Slack much less. Tasks get captured immediately when I think of them, instead of relying on memory or context switching later.
There’s a short demo here showing ticket creation by voice:
https://x.com/shubhmx/status/2003846700532978016
Infina works on macOS and Windows: https://www.infina.so
I’d genuinely love feedback from people here:
Does voice execution fit anywhere in your workflow?
What tools would actually be worth integrating?
Where would this feel unnecessary or annoying?
Happy to answer any technical or product questions.

value: Show HN: Infina – create Linear tickets by voice command

author: shubhmx


value: codenameaustin

value:
Hi HN!
I’m a solo founder who trades options, and I got tired of paying $100–$300/month for a bunch of different tools: one for GEX, one for backtesting, one for scanning, another for trade ideas, another for volatility… it felt like death by a thousand subscriptions.
So I built a single website that replaces all of them and made it completely free.
For anyone who trades options, it’s basically all your favorite money making tools in one place, with a super generous free tier so that you never need to pay unless you’re a crazy power user.
What it currently includes:
- Options backtester
- GEX (Gamma Exposure) visualizations
- Trade ideas & flow - Unusual options activity scanner
- Probability calculators & DTE analytics
- Strategy models & edge tools
- …and a bunch more that I used to pay for across 10+ different sites
Why I built it
Over the last few years I found myself juggling way too many expensive subscriptions just to trade effectively.
Some tools were $150/mo, others $80/mo, others locked basic features behind paywalls. It created a huge financial barrier for new traders — the exact people who need the tools the most.
So I spent the last 3 months obsessing over every detail and building a single, clean, fast toolbox that gives traders everything in one place without the painful subscription stacking.
Data Transparency
The Toolbox uses ORATS institutional data, which normally costs me $600/mo.
No shady data scraping. No questionable aggregators.
Just a clean interface using high-quality options data.
Who it’s for
- Options sellers
- Directional traders
- Income traders
- People learning options
- And anyone tired of 5–10 subscriptions to do basic research
Try it here
https://optionstradingtoolbox.com
Ask me anything! I’ll be active in the comments!
Thanks for reading
Merry Christmas, Austin (solo founder)

value: Show HN: I re-built 9 options trading SaaS in 3 months and made it free

author: codenameaustin


value: ascorbic

value: host -t LOC santa.mk.gg

value: Show HN: Track Santa with DNS LOC Records

author: ascorbic

story_text: host -t LOC santa.mk.gg


value: zayoka

value: Show HN: GenresFox Version 0.4.1 Update - This time it really has WASM.

author: zayoka


value: Show HN: An open-source anonymizer tool to replace PII in PostgreSQL databases


value: bcjordan

value: Show HN: Regex Man - short 3D regex game (desktop web)

author: bcjordan


value: choraria

value:
Every product that allows usernames eventually ships the same broken solution. Someone adds a blacklist. Then a regex. Then another regex copied from StackOverflow. It works just long enough to ship, and then `admin`, `support`, city names, brand impersonation, and obvious slurs start leaking through anyway. Everyone knows it’s fragile, but it gets ignored because "it’s just usernames".
I’ve had to rebuild this logic across multiple products, and I got tired of pretending it’s a solved problem. So I built *username.dev*, an API that answers a more useful question than "is this taken?" — it tells you what a username actually represents.
Instead of returning a boolean, the API classifies usernames into real categories like brands, public figures, places, system-reserved terms, dictionary words, premium handles, and offensive content, and returns structured metadata you can actually make decisions with. That means blocking impersonation without breaking legitimate users, stopping abuse without maintaining massive regex lists, and even monetizing high-demand usernames if that’s part of your product.
Under the hood it’s intentionally boring infrastructure: Cloudflare Workers at the edge, KV for fast reads, D1 for usage and analytics, and a simple HTTP endpoint (`GET /check?input=foo`). P95 latency sits around 300ms globally. There’s no ML magic, no black box, and no attempt to be clever — just fast, deterministic classification.
Pricing is usage-based and prepaid because subscriptions for infrastructure like this are annoying. There’s a free tier with 1,000 requests and no credit card. Use it, throw it away, or rip the idea off.
If you think regex blacklists are "good enough", usernames don’t matter, or this is a trivial problem, you’re probably already shipping bugs — they’re just not loud enough yet.
Tell me why this is a bad idea, what edge cases I’m missing, or what you’ve duct-taped together instead.
— Sourabh

value: Show HN: No more writing shitty regexes to police usernames

author: choraria


value: yuedongze

value:
A lot of times I use GenAI to quickly prototype something like an app idea or a UI/UX mock for a site. I'd like this text-to-UI experience to be as fast as possible to quickly iterate.
I've tried classic LLMs like ChatGPT/Claude/Gemini and dedicated text-to-app builders like Lovable/Blink/Bolt/Replit. For the former the experience is still a bit crude - a lot of times I have to manually spin up the pages they create to see what's going on. The latter looks fancy but requires a sign up, and then by the time I enter the prompt, the spinner spins forever to bootstrap a production ready app with databases and log-in, when my intention is just to use it myself and see if it works.
So after I sign out from work yesterday for Christmas break, I decided to vibe one myself and hence created Vibe Builder. The idea is simple:
- Single page HTML. TailwindCSS. HTML components and JS blocks. No need to create fancy frameworks or templates when you can just vibe on DOM elements.
- Build the app where you enter your prompt. Zero deployment hassle.
- Stream everything, never wait for AI to fully finish their thought.
- Optimize for time-to-first-UI-change. You get to se the changes live.
- And post on HN to see if it works.
This is just a V1, as you can see it only generates dead UI. but i already had fun asking it to generate wild app ideas or clones of existing apps and see how fast AI puts things together.
Next, I'm considering using HTMX to add interactivity to the components, as well as have a vibe API router that actually handles interaction.
Let me know if it builds the app you have in mind!

value: Show HN: I built the fastest AI app builder that I can find

author: yuedongze


value: tjruesch

value:
Hi HN,
I’m one of the maintainers of Bridge Anonymization. We built this because the existing solutions for translating sensitive user content are insufficient for many of our privacy-concious clients (Governments, Banks, Healthcare, etc.).
We couldn't send PII to third-party APIs, but standard redaction destroyed the translation quality. If you scrub "John" to "[PERSON]", the translation engine loses gender context (often defaulting to masculine), which breaks grammatical agreement in languages like French or German.
So we built a reversible, local-first pipeline for Node.js/Bun. Here is how we implemented the tricky parts:
0. The Mapping
We use XML-like tags with ID’s that uniquely identify the PII, `<PII type=”PERSON” id=”1”>`. Translation models and the systems around them work with XML data structures since the dawn of Computer Aided Translation tools, so this improves compatibility with existing workflows and systems. A `PIIMap` is stored locally for rehydration after translation (AES-256-GCM-encrypted by default).
1. Hybrid Detection Engine
Obviously neither Regex nor NER was enough on its own.
- Structured PII: We use strict Regex with validation checksums for things like IBANs (Mod-97) and Credit Cards (Luhn).
- Soft PII: For names and locations, we run a quantized `xlm-roberta` model via `onnxruntime-node` directly in the process. This lets us avoid a Python sidecar while keeping the package ‘lightweight’ (still ~280MB for the quantized model, but acceptable for desktop environments).
2. The "Hallucination" Guard (Fuzzy Rehydration)
LLMs often "mangle" the XML placeholders during translation (e.g., turning `<PII id="1"/>` into `< PII id = « 1 » >`).
We implemented a Fuzzy Tag Matcher that uses flexible regex patterns to detect these artefacts. It identifies the tag even if attributes are reordered or quotes are changed, ensuring we can always map the token back to the original encrypted value.
3. Semantic Masking
We are currently working on "Semantic Masking"—adding context to the PII tag (like `<PII type="PERSON" gender="female" id="1" />`  ) to preserve (gender) context for the translation. For now, we are relying on a lightweight lookup-table approach to avoid the overhead of a second ML model or the hassle of fine tuning. So far this works nicely for most use cases.
The code is MIT licensed. I’d love to hear how others are handling the "context loss" problem in privacy-preserving NLP pipelines! I think this could quite easily be generalized to other LLM applications as well.

value: Show HN: A local-first, reversible PII scrubber for AI workflows

author: tjruesch


value: acro-v

value:
Hey HN, I'm building Aye Chat (https://github.com/acrotron/aye-chat), an open source AI-powered terminal workspace that lets you edit files, run shell commands, and ask AI to modify your codebase directly, all in one REPL session.
I built this because I got tired of the "suggest -> review -> approve" loop in existing AI coding tools. As models improve and generate proper code more often than not, manual approval started to feel unnecessary as long as there is a strong safety net to allow easy rollback of the changes.
Aye Chat applies changes automatically, but every AI edit is snapshotted locally, so you can instantly undo any change with a single command. This automatic file update with a safety net is the core idea.
In the same session, you can run shell commands, open Vim, and ask the AI to modify your code.
It supports multiple models via OpenRouter, direct OpenAI API usage with your key, and also includes an offline local model (Qwen2.5 Coder 7B).
You can watch a ~1-minute demo here: https://youtu.be/i-vGI6-kP4c
Basically, the typical workflow goes like this (instead of a chat window, you stay in your terminal):

  $ aye chat # starts the session
  > fix the bug in server.py
   Fixed undefined variable on line 42

  > vim server.py
  [opens real Vim, returns to chat after]

  > refactor: make it async
   Updated server.py with async/await

  > pytest
   Tests fail

  > restore
   Reverted last changes

I use Aye Chat both in my work projects and to build Aye Chat itself. Recently, I used it to implement a local vector search engine in just a few days.
Lower-level technical details that went into the tool:
The snapshot engine is a Python-based implementation that serves as a lightweight version control layer.
For retrieval, we intentionally avoided PyTorch to keep installs lightweight. Instead, we use ChromaDB with ONNXMiniLM-L6_V2 running on onnxruntime.
File indexing runs in the background using a fast coarse pass followed by AST-based refinement.
What I learned:
The key realization was that the bottleneck in AI coding is often the interface, not the model.
I also learned that early users do not accept a custom snapshot engine, so to make it professional-grade we are now integrating it with git refs.
What I'd love feedback on:
- Does the snapshot safety net give you enough confidence to let the AI write files directly, or does it still feel too risky?
- Shell integration: does the ability to execute native commands and prompt the AI from a unified terminal interface solve the context-switching problem for you?
There is a 1-line quick install:

  pip install ayechat

Homebrew and Windows installer are also available.
It's early days, but Aye Chat is working well and is legitimately the tool I reach for first when I want to iterate faster. I would love to get your feedback. Feel free to hop into the Discord (https://discord.gg/ZexraQYH77) and let me know how it goes. If you find it interesting, a repo star would mean a lot!

value: Show HN: AI that edits your files directly, no approvals

author: acro-v


value: brobles

value:
Google Search Console is powerful. It's also a nightmare to use daily.
You open it. 47 filters. 12 graphs. Data from 3 days ago. And zero advice on what to actually do.
So I built something simpler.
SEO Rank Tracker connects to your GSC, pulls your data, and shows you three things:
- Which pages are indexed (and which aren't)
- Your actual rankings and clicks (without the filter maze)
- What needs fixing and why
The auto-indexing part: instead of clicking "Request Indexing" one URL at a time in GSC, it submits your pages to Google's Indexing API automatically. Same API news sites use. Works for any site.
No credit card. Connect GSC, import sitemap, done.
Built with Laravel + PostgreSQL. Using Google's APIs (Search Console + Indexing).

value: Show HN: Free tool to auto-index pages and track rankings

author: brobles


value: credit to @RhysSullivan on github for creating this

value: Show HN: Epstein Files and images (4000 .png files)


value: oblivionsage

value: Show HN: Elfpeek – A tiny interactive ELF binary inspector in C

author: oblivionsage


value: ahmgeek

value:
I built Voklit because I was tired of paying $2+/min to call banks and airlines overseas. Needed a US number that works anywhere with actual affordable rates.
It's a web-based VoIP service (mobile apps coming soon). You get a real US phone number, pay-as-you-go minutes on top of a small subscription that covers the infrastructure.
Bootstrapped, solo founder. Built with [Go/Next.js/ and Websockets] if anyone's curious about the stack.
Adding more countries soon. Happy to answer questions or hear feedback.

value: Show HN: Voklit – Cheap international calls without the carrier markup

author: ahmgeek