value:
Hi HN, I'm the creator of TensorWall. While building LLM-powered applications, I noticed a recurring gap: developers either give their apps a raw API key (risky) or spend weeks building custom proxies to handle rate-limiting, security, and the ever-present fear of exploding API bills. TensorWall is an open-source control plane designed to sit between your applications and your models. It gives you the visibility and guardrails needed for production. Key Features:
Unified API: One endpoint for OpenAI, Anthropic, Ollama, and LM Studio
Cost & Budget Control: Set hard spending limits and granular rate-limiting per app to prevent "bill shocks"
Security: Prompt injection detection (PII redaction on the roadmap)
Observability: Full audit logs of every request and token usage (essential for compliance)
Deploy in 60 seconds:
git clone https://github.com/datallmhub/TensorWall.git
cd TensorWall && docker-compose up -d
Why Open Source? Security and financial infrastructure shouldn't be a black box. Your AI gateway should be auditable, self-hostable, and community-driven. I'm looking for brutal feedback on the architecture. What are you currently using to keep your LLM costs and security under control?
GitHub: https://github.com/datallmhub/TensorWall

value: Show HN: TensorWall – Open-source LLM gateway with budget controls and security


value: Show HN: Specil – A minimal and helpful tool for Spec-Driven Development


value: lulzx

value:
Most S3 usage is PUT, GET, DELETE, LIST with basic auth. This does exactly that.
SigV4 auth, multipart uploads, range requests. Storage is just files on disk.
No versioning, no ACLs, no encryption. Use MinIO or AWS if you need those.

value: Show HN: Zs3 – S3 server in ~1K lines of Zig, 250KB binary, zero dependencies

author: lulzx


value: ersinesen

value: This piece reframes populism not as personal aberration but as adaptive response to the mismatch between hyper-mobile capital and territorial states. Particularly this bit on why inconsistency is a feature:<p>This explains several otherwise puzzling features of populist politics. Ideological inconsistency is not a bug but a requirement. Ethical incoherence is not accidental but adaptive. Reversals, contradictions, and symbolic excess are tolerated because responsiveness, not consistency, is the optimization criterion. The leader’s role is not to resolve systemic tension, but to remain aligned with its most salient expressions.

value: Show HN: Why Populism Isn't the Disease, It's the Symptom

author: ersinesen


value: hellokaton

value: Show HN: Artifox – no-signup AI photo tools with auto-delete uploads

author: hellokaton


value: ford

value:
Get a "Year-In-Review" for arbitrary data.
Yirgachefe was borne out of 3 ideas:
1) Spotify Wrapped is delightful
2) In many countries, services are required by law to provide your exported usage data in a timely manner.
3) Many services don't (or can't) provide years-in-review. I also love the idea of summarizing data people may _not_ want, a la the recent SNL "UberEats Wrapped" sketch.
Now you can "wrap" anything if you have the data - traditional sources like video & music streaming, or more creative ones like terminal history, amazon purchases, git contributions, etc.
If you bring-your-own API key, all processing exclusively happens in your browser + the Anthropic API (minus some anonymized usage data sent to Umami).
Outputs aren't perfect (it's AI after all), but it's also a demo of what you can do with the latest LLM coding tools and a weekend.

value: Show HN: Open-source Spotify Wrapped for arbitrary data

author: ford


value: mckinnonr

value:
I built Meter to keep scraped website content in sync over time.
Meter uses an LLM once to generate a scraping plan, then runs entirely on raw HTTP requests (no Selenium, no LLMs) to periodically detect changes and re-extract content.
I built it after spending years writing custom scrapers: parsing sites, wiring the output into databases, and keeping everything working as pages evolved. Meter follows the same approach I use in practice — do heavy analysis up front, then run fast, cheap scrapes continuously.
I’m really interested to hear from people maintaining scraping jobs or RAG pipelines in this context. I’d love any feedback on the product - thanks!

value: Show HN: Meter – Scrape sites and keep content in sync automatically (no LLM)

author: mckinnonr


value: firedexplorer

value: Show HN: Garmin Year in Review (annual stats from your device data)

author: firedexplorer


value: kristintynski

value:
The functional equation ξ(s) = ξ(1-s) identifies σ with 1-σ. Topologically, this turns the critical strip into a torus. The critical line σ = ½ is the throat.
Now treat ξ(s) as a stream function. Its gradient is a velocity field. The flow is automatically:
• Incompressible (ξ is holomorphic → Cauchy-Riemann → ∇·v = 0)
• Symmetric (functional equation → v(σ) = v(1-σ))
THE CONNECTION

    Zeta Function          Fluid Dynamics
    ─────────────          ──────────────
    ξ(s)                   Stream function
    |ξ|²                   Pressure
    Zeros of ξ             Pressure minima (p = 0)
    σ = ½                  Torus throat

THE THEOREM
For symmetric incompressible flow on a torus, pressure minima must lie on the symmetry axis.
Interactive:  https://cliffordtorusflow.vercel.app/
Why? A symmetric function p(σ) = p(1-σ) can only have a unique minimum at σ = ½.
Zeros are pressure minima → zeros at σ = ½ → Riemann Hypothesis.
NOW FOR NAVIER-STOKES
Beltrami flows (where vorticity ∥ velocity, i.e., ω = λv) have a similar structure. The vortex stretching term—the thing that causes blow-ups—becomes:

    (ω·∇)v = (λv·∇)v = (λ/2)∇|v|²

That's a gradient. Gradients have zero curl: ∇ × (∇f) ≡ 0.
No curl contribution → no vorticity growth → no blow-up.
THE PUNCHLINE
Both problems are: "Given a symmetric structure on a torus, prove things concentrate at the throat."
• RH: Zeros (pressure minima) → throat (σ = ½)
• NS: Flow (enstrophy) → Beltrami manifold (no blow-up)
Same geometry. Same mechanism. Same problem.
Interactive visualization: https://cliffordtorusflow-git-main-kristins-projects-24a742b...
WHAT I VERIFIED
• 40,608+ points with certified interval arithmetic
• 46 rigorous tests pass
• Pressure minima all at σ = 0.500
• Enstrophy bounded (ratio = 1.00)
Repository: https://github.com/ktynski/clifford-torus-rh-ns-proof
Paper (18 pages): https://github.com/ktynski/clifford-torus-rh-ns-proof/blob/m...
Either I've found a deep connection, or I've made an error that connects two unrelated problems in the same wrong way. Both would be interesting.

value: Show HN: Are the Riemann Hypothesis and Navier-Stokes the Same Problem?

author: kristintynski


value: PxldLtd

value:
Hey HN,
In an effort to familiarise myself with Rive and to try out SvelteKit's new remote functions I've put together a fun little daily game.
It's a simple premise: you have 60 seconds to guess a celebrity from a distorted image that clears up over time.
The backend is running on Cloudflare and there's no ads or tracking, just a fun little side project.
I'd love to hear what you think and if you run into any issues playing.

value: Show HN: Muggle – Daily Guessing Game Using SvelteKit Remote Functions and Rive

author: PxldLtd


value: idoco

value: We built this while developing an MCP server internally and got tired of manually testing changes by chatting. Unit and integration tests passed, but issues still appeared once a real MCP client and an LLM were in the loop.<p>test-mcp is a small, headless MCP client for running end-to-end test flows against MCP servers using real LLMs. It’s early, and I’m mainly looking for feedback from MCP builders.

value: Show HN: Test-MCP, end-to-end testing for MCP servers

author: idoco


value:
I was not happy with existing MCPs for browsers, so I decided to write my own.
What's the problem?
1. Official MCPs (Playwright and Chrome dev tools) spawns new browser instance in headless mode, without existing sessions, easily detectable as bots. So if you want to automate something behind authentication, you have to do it in every session.
2. Browser MCP which is top in Chrome store is Playwright under the hood with browser extension.
3. All 3 operate on snapshots sending huge dumps, which do not fit limit of MCP answer. Even if it fits, it eats conext quickly. Without snapshot it is not possible to interact with page.
4. There is a bunch of less known mcp tools, with way less functionality.
5. Claude extension did not exist at that time. When it first appeared and I tried it - I was constantly refusing automate anything related to passwords/keys/bank etc, sometimes in absolutely unexpected places. Initial version also did not work with Claude Code.
This makes them pretty useless for automation or debugging. Honestly I don't understand how Browser MCP got so many users, it fails on simple tasks for me.
So I decided to make my own MCP + extension. Currently for Chromium-based browsers and Firefox (with some limitations).
The idea is to allow to operate on pure css selectors (with :has-text() extension). So now LLM can make a screenshot, see there is a "Submit" button, and simply use click tool on selector button:has-text("Submit").
It supports screenshot with lower quality, and partial screenshots (it can make a screenshot of some area or some css-selector). It turns out that if you want to debug some part of the page, partial screenshots work better (I understand there is some image-to-text under the hood, and on big images it may simply not describe the area you are interested in).
There are also many other tricks that helps LLM to work more efficiently. Like listing scrollable areas, detecting tech stack on current page, presence of iframes, setting pseudo states, listing css styles on element and many more.
It turned out, that it easier for me to use my mcp and the browser with session to read Jira tasks, rather than use official Jira MCP, which requires re-authentication every day and constantly hangs.
It also solved a vicious loop "there is a bug - llm says fixed - you check it does not work - llm says fixed - you check it does not work". Now it can check results and see if it works. There are tools to extract logs, network requests, so it can debug frontend-side problems efficiently.
Long story short, here it is.
Extensions links: Chrome: https://chromewebstore.google.com/detail/blueprint-mcp-for-c... Firefox: https://addons.mozilla.org/ru/firefox/addon/blueprint-mcp-fo...
It was released quite a while ago already, got some traction, some positive comments, so I think it worth sharing a bit wider.
It is completely free and open source on both ends (extension and mcp server). All works locally, no external calls or telemetry or analytics collection.
There is optional paid relay service. It allows you to have multiple simultaneous connections, including on different machines (and probably with mobile browser, firefox on android supports extensions, though I did not check it yet). But then requests/anwers go through my relay. No data is logged or analysed, but you must be aware.
Also I plan to make Safari extension, but it is much harder to debug, so I'm postponing it.
If you ever tried browser automation and it failed - give a try to my extensions.
If you have some samples of when LLM fails on browser automation for some reason - drop in comments, so I can see if I can help you with that.

value: Show HN: BlueprintMCP for Chrome

title: Show HN: BlueprintMCP for Chrome


value: deepmon

value:
Hi HN,
I just launched FeelStr, a lightweight completely vibe-coded app that recommends movies and shows based on your emotional state—not genres, ratings, or clickbait thumbnails.
You simply type in how you’re feeling, like:
“Post-breakup haze”
“Numb and need something gentle”
“Hopeful but not cheesy”
FeelStr processes the input using a custom emotional graph and NLP pipeline to match your vibe to streaming content. The goal is to make discovery emotional, not algorithmic.
How it works:
Uses LLMs to classify freeform mood input into nuanced emotional vectors
Matches those vectors to content tagged with human-curated emotional tone (e.g., comforting, chaotic, bittersweet)
Data comes from TMDb + custom overlay for tone, tempo, and theme
Currently suggests movies and shows available on major streaming platforms
It’s free, fast, and completely anonymous (no login needed).
Why I built it:
Scrolling through streaming platforms when you’re tired, overwhelmed, or just not in the mood to decide is a terrible UX. I wanted a tool that lets you lead with emotion and gets out of the way. This was also an excuse to explore vibe coding and build something intuition-first.
Would love your thoughts on:
UX clarity: is it obvious what to do?
Mood detection accuracy
What you'd want next (TV filters, mood shifting, social features?)
Try it out at https://feelstr.com
Appreciate your feedback!

value: Show HN: FeelStr – Stream what you feel instead of browsing genres

author: deepmon


value: jxnata

value:
I wanted to find a different way to show my wife that I love her, so I made LoveMemo.
Quick texts and typical presents were not my thing. I wanted something more an experience that was loving, personalized, and never forgotten. LoveMemo is the result. The app guides you through making a unique web page that includes:
- Names of the couple
- A counter displaying how long they have been together
- An amazing photo slideshow
- Some background music for the atmosphere
- A personal love message
When the work is complete, LoveMemo makes a nice card with a QR code that connects to the page. You can give the card to your partner and let the surprise work its magic. In fact, every memo results in a quickly loading public page that is created for easy sharing, while the mobile app is all about helping the user to create in a simple and emotionally engaging way.
This idea was just my personal project, but I thought maybe other people would like it too.
Your feedback would be great!
Thanks for your time.

value: Show HN: I built LoveMemo to surprise my wife

author: jxnata


value: Show HN: Building a No-Tracking Newsletter from Markdown to Distribution


value: Show HN: Implementing OAuth Authorization Code Flow in OpenAPI Based MCP Servers


value: foxz

value:
Hi HN,
I’ve always wanted a way to build native desktop applications that feel 'modern' without having to deal with the verbosity of C++ or the memory bloat of Electron.
So, for the past few months, I’ve been building Sapphire. It’s a general-purpose scripting language with a familiar C-style syntax, running on its own stack-based VM. My goal is to make it a go-to tool for creating fast, hardware-accelerated desktop apps.
Link: github.com/foxzyt/Sapphire
Why Sapphire?
Native Desktop Apps: It’s designed from the ground up for desktop environments, using SFML for hardware-accelerated rendering.
Custom VM: I implemented a dispatch table with computed gotos to keep instruction execution fast.
Immediate Mode UI: The language includes SapphireUI, a procedural UI system that renders everything in real-time.
Batteries Included: It already handles HTTP (GET/POST/Download), JSON parsing, and filesystem I/O natively.
Current Status: The core VM, the garbage collector, and the networking/JSON modules are quite solid. I’m currently refining the layout engine to make it even easier to build complex, responsive interfaces.

value: Show HN: I built Sapphire, a C-style scripting language and VM for desktop apps

author: foxz


value: ishaksebsib

value: Show HN: AI-SDK for Rust

author: ishaksebsib

title: Show HN: AI-SDK for Rust


value: tacoooooooo

value:
Lobste.rs makes its entire invitation tree public. I scraped it and built this interactive visualization.
Some interesting patterns:
- The top 10 inviters are responsible for ~40% of all users.
- The tree is surprisingly shallow: Most users are only 4-6 generations from the founder. Max depth is around 15.
Built with Sigma.js for WebGL rendering. Works on mobile too.
(I am seeking a lobste.rs invite if anyone's willing! – my email is in my profile)

value: Show HN: The Lobste.rs invitation tree, visualized

author: tacoooooooo


value: vishnukool

value:
Hey HN,
I noticed there are 1,000+ MCP skills on GitHub with zero way for creators to charge for them. So I built Agent37.
Selling Claude skills today means asking customers to download your skill, set up Claude Code on their laptops, configure MCP servers, and pray it works. No trial, no updates, often you're just handing over the source.
Agent37 lets creators upload a skill, share a link, and anyone can try it instantly (no Claude account needed). Built-in Stripe, creators keep 80%.
Looking for feedback on the approach. Would you use something like this?

value: Show HN: Agent37 – Monetize your Claude skills with shareable links

author: vishnukool


value: Hi HN, I built a native macOS app for helping interviewers for writing feedbacks. It listens to realtime conversations, takes the code, you can add your custom rules and BOOM, it will write the feedbacks in a way you want.

value: Show HN: MiddleViewer – A native macOS app for technical interview feedbacks


value: madsterO

value:
As a game designer, I had been working on variety indie game ideas for some time now. But little did I know that what had started as musings with friendly co-op, platformers, & even endless runners almost 2 years back, would leave me with two deeper questions:
1. Why “video” games?
And when I ask this, the implication is not a critical take on videogames. Rather, it’s an amazement at the stagnation in the gaming industry, as users are fed similar dizzying experiences over and over. Should a playful online recess require eye-numbing photorealistic graphics as a quintessential?
2. What are the players taking away from this?
Clearly, my despair with the existing visuals-heavy gaming experience was leading me somewhere - the question of what else, feels broken? Mechanics… yeah, the videogame mechanics of frantic clicks & keys seem to be almost the same since the time of arcade! In fact, they seem motivated by the shared ideals of driving addiction as symptom of heightened dopamine & adrenaline.
In the blazing world of SM & apps already hijacking one’s brain’s receptors, this didn’t feel right. Worse, most videogames now feel like a recipe to fry those dopamine receptors up. No wonder, cozy games are becoming THE thing now. Though even among cozy titles, it was hard to find one that had dared to go beyond the normative ideas of a videogame.
BLUEGRASS: MINDFULNESS SIMULATOR
And so, I dared. I worked full-time on a spatial-audio ASMR gaming experience that simulates the human mindfulness process, and put up the demo on Steam!
As someone whose wellness - both mental & physical - has been wonderfully enriched by the mindfulness practices I learnt at the foothills of the Himalayas in India, I felt that sharing the gift of this demo might be the worthwhile thing to do this New Year.

value: Show HN: How about some meditation in gaming?

author: madsterO


value: kajogo

value:
Paks is:
+ A CLI to create, validate, install, and publish skills
+ A public registry to browse and discover skills
+ Semantic versioning to manage skill updates
+ Open source
Skills are pakaged, versioned, and published so they can be reused across different AI coding agents like Claude Code, Cursor, Stakpak, GitHub Copilot, Goose, and more...
Agent skills are often copied between tools or stored in your file system, which makes them hard to share. Paks extends agent skills by adding packaging and a registry.
This project is an early open source project, so your feedback and contributions are highly appreciated!
Repo: https://github.com/stakpak/paks

value: Show HN: Package Manager for Agent Skills: Publish, Discover, Install Everywhere

author: kajogo


value:
Hi HN, I built a single-file, open-source affinity diagramming tool for qualitative analysis in the browser, called Splat. You can use it to cluster and organize notes on a board.
It runs entirely in the browser as a single HTML file:
– no install, no build step
– works offline
– optional semantic search with HF Transformers.js and AI assistant with Ollama (local) or OpenAI (for more power)
– JSON export/import for analysis elsewhere
I built Splat while manually clustering 1400+ notes of interview data for a research project. I wanted something I could email to collaborators and use and extend without worrying about accounts or bloat from proprietary services. We're using this internally in our research lab for affinity diagramming qualitative data.
Would love feedback, especially from people doing qual research, UX, or synthesis work. Feel free to build on or extend!

value: Show HN: Splat, an Affinity Diagramming Tool in a Single HTML File


value: tobihrbr

value: Show HN: Object – A universal file storage system

author: tobihrbr


value:
Hi HN,
I’m a solo dev building a clinical search engine to help my wife (a resident physician) and her colleagues.
The Problem: Current tools (UpToDate/OpenEvidence) are expensive, slow, or increasingly heavy with pharma ads.
The Solution: I built Evidex to be a clean, privacy-first alternative. Search Demo (GIF): https://imgur.com/a/zoUvINt
Technical Architecture (Search-Based RAG): Instead of using a traditional pre-indexed vector database (like Pinecone) which can serve stale data, I implemented a Real-time RAG pattern:
Orchestrator: A Node.js backend performs "Smart Routing" (regex/keyword analysis) on the query to decide which external APIs to hit (PubMed, Europe PMC, OpenAlex, or ClinicalTrials.gov).
Retrieval: It executes parallel fetches to these APIs at runtime to grab the top ~15 abstracts.
Local Data: Clinical guidelines are stored locally in SQLite and retrieved via full-text search (FTS) ensuring exact matches on medical terminology.
Inference: I’m using Gemini 2.5 Flash to process the concatenated abstracts. The massive context window allows me to feed it distinct search results and force strict citation mapping without latency bottlenecks.
Workflow Tools (The "Integration"): I also built a "reasoning layer" to handle complex patient histories (Case Mode) and draft documentation (SOAP Notes). Case Mode Demo (GIF): https://imgur.com/a/h01Zgkx Note Gen Demo (GIF): https://imgur.com/a/DI1S2Y0
Why no Vector DB? In medicine, "freshness" is critical. If a new trial drops today, a pre-indexed vector store might miss it. My real-time approach ensures the answer includes papers published today.
Business Model: The clinical search is free. I plan to monetize by selling billing automation tools to hospital admins later.
Feedback Request: I’d love feedback on the retrieval latency (fetching live APIs is slower than vector lookups) and the accuracy of the synthesized answers.

value: Show HN: Evidex – AI Clinical Search (RAG over PubMed/OpenAlex and SOAP Notes)


value: anticlickwise

value:
I built this for myself.
I was tired of checking Product Hunt, Hacker News, Reddit, Indie Hackers, and YouTube separately while building.
Here Be Shovels is a calm, opinionated place that aggregates tools, launches, AI signals, and YouTube channels indie makers actually watch.
It’s intentionally incomplete and updated every few hours and I had a lot of time building out the Wild West metaphor.
Happy to hear feedback

value: Show HN: Here Be Shovels – a calm supply depot for indie makers and AI builders

author: anticlickwise


value: watermelonson

value:
I didn't realize Universe Sandbox ran on MacOS, and I was in the mood to play around a bit.
Some functions it's got:
- Random system generation
- Sonification is super fun too
- Habitability Simulation (Just for fun, don't cite this please)
- Replacing, spawning, deleting objects
I've had tons of fun building this, so I hope someone else can share the joy.
It's free and runs in the browser.
I'd love to hear any feedback. I think this is at a state where I might leave it as it is, but if people are interested in other features, maybe I'll keep working on it. I've kept saying I'll stop working on this for a while now though.

value: Show HN: A solar system simulation in the browser

author: watermelonson


value: ejncman

value:
Is the service down?
Check in seconds if it's just you or everyone else.

value: Show HN: UpDown – Simple website uptime monitoring

author: ejncman


value: ilyasseisov

value:
Hi HN!
I'm Ilyas - a web dev.
In this short post I would like to share my mini tool on getting prepared for any dev interview:
- HR
- Tech
- Behavioral
- Live coding
- Algo
- etc.
In the last five years through all kinds of interviews and summarized preparation in a bite size checklists to quickly go thru before the interview.
This definitely adds at least extra 5% to confidence.
Checklist is free to use.
In general, "checklist" is proven tool that is used by pilots and doctors. This method helps you to make sure that all necessary steps are checked by you. Unloads your memory.
If interested, check it out here:
https://www.99cards.dev/checklists
p.s. again, it's free.
Enjoy!
Ilyas

value: Show HN: Mini tool helped me to get a dream dev job (can help you too)

author: ilyasseisov


value: Show HN: Gemini has a "Concrete Bias" against minimalist software (Basecamp vs


value:
Hi HN,
Here is a late Christmas present: I rebuilt Neko [1], the classic desktop cat that chases your mouse, as a tiny, dependency-free JavaScript library that runs directly on web pages.
Live demo: https://louisabraham.github.io/nekojs/
GitHub: https://github.com/louisabraham/nekojs
Drop-in usage is a single script tag:

    <script src="https://louisabraham.github.io/nekojs/neko.js" data-autostart></script>

This is a fairly faithful recreation of Neko98: same state machine, same behaviors, same original 32×32 pixel sprites. It follows your cursor, falls asleep when idle, claws walls, and you can click it to cycle behavior modes.
What made this project interesting to me is how I built it. I started by feeding the original C++ source (from the Wayback Machine) to Claude and let it "vibe code" a first JS implementation. That worked surprisingly well as a starting point, but getting it truly accurate required a lot of manual fixes: rewriting movement logic, fixing animation timing, handling edge cases the AI missed, etc.
My takeaway: coding agents are very useful at resurrecting old codebases, and this is probably the best non-soulless use of AI for coding. It gets you 60–70% of the way there very fast, especially for legacy code that would otherwise rot unread. The last 30% still needs a human who cares about details.
The final result is ~38KB uncompressed (~14KB brotli), zero dependencies, and can be dropped into a page with a single <script> tag.
Happy to hear thoughts from desktop pets nostalgics!
[1]: https://en.wikipedia.org/wiki/Neko_(software)

value: Show HN: Neko.js, a recreation of the first virtual pet


value: namanajmera

value: Show HN: MCP-Reddit – Reddit scraper for Claude, no API keys needed

author: namanajmera


value: johnboygiz

value:
Find FREE proxy lists with the only proxy finder software you will ever need!
Grab your own free proxy lists with Proxy Snatcher & stop paying for proxy lists or using dead proxies !
Need a reliable proxy finder & tester for your privacy or online scraping projects?
Need proxies in simple IP: Port format and saved to a simple txt file?

value: Show HN: PWYW for These Proxy Tools

author: johnboygiz

title: Show HN: PWYW for These Proxy Tools


value: tomasvilte

value: Show HN: MateCommit – A CLI to fix your "fix" and "oops" commit history

author: tomasvilte


value: jmkni

value:
So this is a little app I built over Christmas, the idea is to help you very quickly put together key words and phrases when on holiday, just by tapping your way through a menu (no typing), and then be able to see a phonetic translation and also listen to an audio of the phrase
I've tried to make it "smart", for example "Can I use" -> "Charger" will give you a translation for "Can I use the charger?" which makes sense in that language, it doesn't just dumbly glue words together
This app is
- Completely free
- Has no ads
- Works entirely offline
- Contains no analytics or tracking
- Collects no data
- Has no authentication
It's a passion project, and I plan to keep adding to it over the coming year, let me know if you have any feedback, cheers :)

value: Show HN: Lunar Languages – an offline travel phrasebook

author: jmkni


value: vinserello

value:
We often find ourselves waiting for Excel just to do simple tasks like merging two CSVs, checking for duplicates, or converting a PDF table to CSV.
Thus, I built ilovecsv.net.
It's a suite of 30+ free tools that run in your browser.

value: Show HN: I built an "ilovepdf" for CSV files (and I called it ILoveCSV)

author: vinserello


value: acaronlex

value:
Hey HN! Pivor v1.1 just dropped and I'm pumped to share it with you.
Pivor is a self-hosted CRM for small businesses who want to own their data. No per-seat pricing, no cloud lock-in, no BS.
What's new in v1.1:
  • Gmail & Outlook integration, two-way email sync, send directly from the CRM
  • Full REST API,  token auth, full CRUD, filtering, pagination
  • Task reminders, email notifications before deadlines
  • Role-based access, admin, manager, user with granular permissions
  • CSV import/export, migrate your data in minutes
Stack: Laravel 12 + Livewire 3 + Tailwind. Runs on SQLite, scales to MySQL/PostgreSQL.
Your customers, your servers, your data. AGPL-3.0.
GitHub: https://github.com/Lexaro-Software/pivor
What would make Pivor useful for your team? I'm listening!

value: Show HN: Pivor v1.1 is out, open source CRM with email sync and API

author: acaronlex


value: sunnynagra

value:
I built Sous because I was tired of:
1. Scrolling through 2000-word life stories to find a recipe
2. Trying to copy recipes from Instagram captions
3. Squinting at my phone with messy hands while cooking
The app uses Google Gemini to parse recipes from any URL (handles messy blog formats),
photos of cookbooks/handwritten notes, and even TikTok/Instagram/YouTube videos.
How it works:
- AI extracts structured ingredients and steps, pre-converts all measurements to both metric and imperial
- Live Activities show timers on your Lock Screen
- Siri control for hands-free cooking ("Hey Siri, start my pasta timer")
- AI chat assistant that knows your recipe context for substitutions and troubleshooting
I'd love feedback, especially on the parsing accuracy and cooking experience.
Learn more: https://getsousapp.com/
Try it out here: https://apps.apple.com/us/app/sous-smart-recipe-companion/id...

value: Show HN: Sous – App that imports recipes from URLs, and cookbook photos using AI

author: sunnynagra


value:
Rev-dep is a dependency analysis and optimization CLI for JavaScript and TypeScript projects.
It helps answer questions like: where is this file used, what are the entry points, are there circular dependencies, and which files or node_modules are unused.
I recently rewrote it from scratch in Go to focus on performance and low memory usage. On large codebases (500k+ LOC), circular dependency detection runs in a few hundred milliseconds and is up to 175x faster than Madge in my benchmarks.
Feature-wise it overlaps partially with tools like madge, dpdm, skott, and dependency-cruiser, but aims to provide fast, list-based, actionable output rather than graphs.
Feedback and performance comparisons on other large codebases are very welcome.

value: Show HN: Rev-dep – JavaScript/TS circular deps detection, 175x faster than Madge


value:
Hi HN!
tldr: openground lets you give controlled access to documentation to AI agents. Everything happens on-device.
I'm sharing my initial release of openground, an opensource and completely on-device RAG tool that let's you give controlled documentation access to your coding agents. Solutions like Context7 MCP provide a likely source of truth for docs, but their closed-source ingestion and querying pose security/privacy risks. openground aims to give users full control over what content is available to agents and how it is ingested. Find a documentation source (git repo or sitemap), add it to openground via the CLI, and openground will use a local embedding model and vector db (lancedb) to store your docs. You can then use the CLI to install the MCP server to your agent to allow the agent to query the docs via hybrid BM25 full-text and vector search.
Again, this is an initial release, so it is pretty barebones. Upcoming features I am working on:
- specific library version handling (it currently only supports latest versions)
- docs "registry" to allow pushing and pulling of documentation embeddings within organizations to S3
- lighter-weight package
Suggestions and PRs welcome! I'll also be around for discussion.

value: Show HN: Openground, on-device RAG pipeline with hybrid search for coding agents


value: artpar

value: Show HN: Long Term Planner MCP

author: artpar

title: Show HN: Long Term Planner MCP


value: I built a Lumon Terminal into my personal finance app. It's an incredibly inefficient way of categorizing expenses, but the more I use it, the better I recognize my own spending. I'm now looking at 12.49s and instantly know it's Netflix → Subscriptions<p>The URL is a demo that won't make lots of sense since it's not your own numbers/transactions, but you can import your own for free and try it out!

value: Show HN: I made a Macrodata Refinement simulator for bank transactions


value: ckardaris

value: Show HN: Template Files with Nix Home Manager

author: ckardaris


value: cliftonk

value: Show HN: Cmt is an AI powered commit generator

author: cliftonk


value:
I love vibe coding. I hate writing docs.
Every time I shipped something fast with Cursor / Replit / Antigravity, docs became the bottleneck. GitBook felt heavy, manual writing felt dishonest (the code kept changing), and most AI doc tools wanted me to “explain” my own code.
So I built a small tool that does the opposite:
Connects to a repo
Reads the code + structure
Infers architecture, flows, APIs, and components
Outputs GitBook-style documentation automatically
This started as a personal hack while shipping a side project. The first version was rough (hallucinated edge cases, weak on frontend state), but it already saved me hours per project.
What surprised me:
Repo structure matters more than comments
Vibe-coded apps are harder to document than “clean” ones
Docs don’t need to be perfect to be useful—they need to be current
What’s still broken:
Complex monorepos
Heavy frontend logic
Long-running background jobs
I’m sharing this mostly to learn:
How are others handling documentation for fast, AI-assisted builds?
Do you write docs manually, skip them, or automate somehow?
Link is in the comments for anyone curious.

value: Show HN: I built an AI that generates clean docs for vibe-coded apps


value: tgirotto

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

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

author: tgirotto


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

value: Show HN: Treasury – The personal finance app built for you (now on iOS)


value: ramoz

value:
Plannotator works via hooks and therefore it's fully integrated with the Claude Code planning mode capability.
I found myself constantly wanting to annotate verbose plans. I've also wanted to copy and share  plans on occasion - gathering others' feedback. So I built this. Sharing is private and was inspired by the recently featured textarea.my
If you're on desktop, play here: https://share.plannotator.ai/
Or watch a video demo: https://www.youtube.com/watch?v=a_AT7cEN_9I
https://github.com/backnotprop/plannotator

value: Show HN: Interactive plan annotation and sharing for Claude Code

author: ramoz


value: Show HN: Word Game's New Home

title: Show HN: Word Game's New Home


value: zawakin

value:
Hey HN,
I built agtrace because I kept losing track of what was happening in my Claude Code sessions – context pressure, tool calls, costs.
It's basically `top` for AI coding agents:
- Live dashboard showing context window usage and activity
- Session history you can query and diff
- Works with Claude Code, Codex, and Gemini CLI
- 100% local, reads existing logs, no cloud
Install: `npm i -g @lanegrid/agtrace`
The core idea is pointer-based indexing (no log duplication) and
schema-on-read (resilient to provider schema changes).
Would love feedback, especially from heavy Claude Code / Codex users.

value: Show HN: Agtrace – top and tail -f for AI coding agent sessions

author: zawakin


value: LumiHelia

value: Show HN: I built an AI VC to roast my ideas using Gemini, Claude, and Streamlit

author: LumiHelia


value: crap

value: Show HN: swab – A configurable project cleaning tool

author: crap


value: bluepeter

value:
It's called Cumbersome because you have to wrangle API keys (versus just using ChatGPT directly). So, I think of it like driving with a manual transmission, and all the benefits you get there, versus everything-on-auto. Not always easy, but often better (well, for some). This means you can bypass the monthly subscriptions for AI providers and just pay directly. This can work out to be a lot cheaper (or not!) depending on your use.
Actually I made this because I much prefer the API sandboxes/playgrounds of the AI providers, versus their consumer apps. You can edit context, delete bad context, rewrite AI messages. So you can do all that in Cumbersome, and a bunch of other things too.
I also threw in some features like Face/Off Mode where you can get 3 responses at once, and the AI picks the best. (Cursor recently added an ensemble feature like this.) This can be really spendy, but it's worth it for high-value AI sessions. (And it's probably never something that the all-you-can-eat consumer plans will offer as they are incentivized to cheap out.)

value: Show HN: Cumbersome – iOS/macOS API client for OpenAI, Anthropic, Z.ai

author: bluepeter


value: Solo indie dev from Denmark. Built this to organize my own renovation chaos – budgets, photos, tasks, time tracking in one place. Just shipped v1.4. Feedback welcome!

value: Show HN: Home Stories – Track renovations with budgets, photos, timers


value:
AI, agents, and other robots frequently get domain details wrong. The AI Domain Data Standard fixes this by letting domains publish a canonical JSON profile at /.well-known/domain-profile.json that AI tools read directly.
We've just completed the full tooling suite:
PLATFORM INTEGRATIONS:
- WordPress: https://wordpress.org/plugins/ai-domain-data/
- Jekyll: https://rubygems.org/gems/jekyll-ai-domain-data
- Next.js: npm install @ai-domain-data/nextjs
- Cloudflare Worker: https://github.com/ai-domain-data/cloudflare-worker-ai-domai...
DEVELOPER TOOLS:
- CLI: npm install -g @ai-domain-data/cli
- GitHub Action: https://github.com/ai-domain-data/ai-domain-data-validate-ac...
- Resolver SDK: TypeScript/Node.js for programmatic access
WEB TOOLS:
- Generator: https://ai-domain-data.org/generator
- Checker: https://ai-domain-data.org/checker
The standard is intentionally minimal (name, description, website, contact, plus optional fields) and works with any domain type. All tooling is open source (MIT) and self-hosted by design.
Spec: https://ai-domain-data.org/spec/v0.1
GitHub: https://github.com/ai-domain-data/spec
What platforms should we support next?

value: Show HN: AI Domain Data Standard – Complete Tooling Suite


value: ckugblenu

value: Hi HN,<p>I kept getting annoyed having to track down which process was sitting on a port so I could kill it, so I built a tiny CLI tool that shows you exactly what’s using a given port and having the option to kill it. Sharing it here in case it helps someone else too.

value: Show HN: Portdetective – A tiny, fast rust CLI for port inspection

author: ckugblenu


value: mhosayny

value: AST-aware chunking of code for contextual retrieval

value: Show HN: Code-Chunk for RAG

author: mhosayny

title: Show HN: Code-Chunk for RAG


value: MintyPyro

value: Built a quick demo for something that could help players understand the nuances of a position without needing to translate a bunch of complicated lines. Could be helpful for new players or maybe even moderately experienced players. Let me know what you guys think and if this is something worth working on. (Please excuse the mobile version, couldn't get around to making it look good)

value: Show HN: An automated tutor to help you understand chess

author: MintyPyro


value: Show HN: NeuronDB – Embeddings and vector search for PostgreSQL


value:
Hi HN,
I’ve built HN-Brief, a self-updating static digest of Hacker News.
It is a static daily digest that fetches the top 20 stories summarizing both the article content and the community discussion (digest) into a cohesive 5-minute read.
How it works:
A Cloudflare Worker runs on a cron schedule, fetches stories via the Algolia API, generates markdown summaries, and commits them directly to GitHub. Cloudflare Pages then serves the static site.
While excellent tools like hndigest.com exist for email subscriptions, HN-Brief is a web-first alternative that is fully open source.
Source: https://github.com/jnd0/hn-brief
I’d appreciate feedback on the summary quality, reading experience and the UI!

value: Show HN: HN-Brief – Catch up on the top stories in 5 minutes


value: SaveYourChanges

value:
Hi HN — I built Revertly (https://revertly.io/
) after getting burned one too many times by Shopify product edits.
A title tweak, price change, or variant update would go live, something would break (ads, conversions, reporting), and there was no clear way to answer: what actually changed? or how do I undo this safely?
Revertly does a simple thing:
it keeps a history of product changes, shows you clear diffs, and lets you restore an earlier version with one click.
It works automatically in the background - no manual snapshots, no remembering required.
Right now it supports:
Field-level tracking for product changes
Before/after diffs that are easy to scan
One-click rollback
An audit trail that’s useful for teams and agencies
What I’m building next: metafield tracking, deeper variant diffs, and better tooling for agencies managing multiple stores.
This is early, but already useful. I’d love feedback - especially from anyone who’s ever thought “I wish Shopify had undo.”
Thanks for reading! Any feedback is appreciated!

value: Show HN: Revertly – version control for Shopify products

author: SaveYourChanges


value: austinbaggio

value:
I got tired of Claude Code forgetting all my context every time I open a new session: set-up decisions, how I like my margins, decision history. etc.
We built a shared memory layer you can drop in as a Claude Code Skill. It’s basically a tiny memory DB with recall that remembers your sessions. Not magic. Not AGI. Just state.
Install in Claude Code:

  /plugin marketplace add https://github.com/mutable-state-inc/ensue-skill
  /plugin install ensue-memory
  # restart Claude Code

What it does: (1) persists context between sessions (2) semantic & temportal search (not just string grep). Basically git for your Claude brain
What it doesn’t do: - it won’t read your mind - it’s alpha; it might break if you throw a couch at it
Repo: https://github.com/mutable-state-inc/ensue-skill
If you try it and it sucks, tell me why so I can fix it. Don't be kind, tia

value: Show HN: Stop Claude Code from forgetting everything

author: austinbaggio


value: cylinderthought

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

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

author: cylinderthought


value: soyzamudio

value:
I've been an on-call engineer at startups where "incident management" meant someone panic-creating a Slack channel called #fire-fire-fire, or all incidents handled in the #incidents channel at the same time... and the postmortem was whatever someone remembered to write in Notion three days later.
So I built a Slack bot to fix my own workflow. Figured I'd share it.
What it does:
"/incident start sev2 API latency spike" creates a dedicated channel, invites whoever's on-call, pins the details, and starts recording a timeline.
When you run "/incident resolve", it uses GPT-4 to analyze the entire channel conversation and generate a postmortem draft: summary, root cause, event timeline, action items.
The key insight: the actual diagnosis usually happens in casual messages ("wait, I think the connection pool is exhausted") not in formal status updates. So the AI reads everything, not just what was labeled important.
Stack:
- TypeScript + Slack Bolt
- Prisma + Postgres
- OpenAI API for postmortem generation
- PagerDuty integration for escalations
Other stuff it handles:
- Update severity of an incident with "/incident severity <sev1|sev2|sev3|sev4>"
- On-call scheduling with automatic weekly/daily rotation
- Paging with escalation chains (Slack DMs → PagerDuty if configured)
- Jira ticket creation for incidents within slack with "/incident ticket <title>"
- Basic analytics (incidents per on-call, MTTR)
What I learned building this:
1. Slack's API is actually pretty good now. The Bolt framework handles most of the OAuth/event subscription pain.
2. Getting AI to write useful postmortems required being very explicit about event types. Without context about what's a "status update" vs a "debug message," it would hallucinate causes.
3. On-call scheduling is surprisingly complex. Timezone handling, rotation boundaries, handoff notifications, each is a rabbit hole.
Honest limitations:
- Only works for teams already living in Slack
- AI postmortems need human review, it can miss context from calls/video chats
- Only a couple of integrations (the ones I use, but can add more, like Linear, github issues, etc...)
Code isn't open source (yet?), but happy to answer architecture questions. Been running this with my own team for approx. 2 months.
Landing page: https://incidentops.io
Would appreciate feedback, especially from SREs who've built similar internal tools. What am I missing?

value: Show HN: Incident management for Slack with AI-generated postmortems

author: soyzamudio


value: Show HN: A privacy-first bulk image compressor and HEIC converter in the browser


value: tomdesantis

value:
We know that security is often the weak point in vibe coding.
To help the community catch common issues before deployment, I built SafeVibe.
It is a free, non-commercial, and collaborative "observatory" for tracking security vulnerabilities specifically found in AI-generated applications.
What it does:
-Vulnerability list: a database of common security issues observed in vibe-coded apps (seeded with 36 issues gathered from recent discussions).
-Fixes: clear guidance on how to patch or mitigate each specific vulnerability.
-LLM Audit Prompts: you can select specific vulnerabilities and the site generates a prompt you can paste into your LLM/agent to audit your code for those exact problems.
This is a community project and completely open to contributions. I’d love your feedback on the current data and the audit prompts.

value: Show HN: SafeVibe, a collaborative database to fix security gaps in vibe coding

author: tomdesantis


value: Follows NIP-44 encryption for your private note keeping

value: Show HN: Notion-like private Markdown pages on Nostr


value: Show HN: J_PyDB – tiny encrypted file-based Python DB


value: DHDEV

value: Show HN: DynamicHorizon – Dynamic Island for macOS

author: DHDEV


value: codenote

value:
GitHub: https://github.com/giselles-ai/giselle
Giselle is an open source tool for designing and running AI workflows using a visual node-based editor. Apache 2.0 licensed.
Key features:
- Drag-and-drop workflow builder for chaining AI apps
- Multi-model support (OpenAI, Anthropic, Google Gemini) in one canvas
- GitHub integration for automating issues, PRs, and code reviews
- Built-in knowledge store with vector search
- Self-hostable with Docker or run locally
We built this because we needed a way to compose multiple AI models for complex tasks without writing glue code. The visual editor makes it easy to see the flow and debug when things go wrong.
Tech stack:
- Frontend: Next.js, TypeScript, Tailwind CSS
- Visual Node Editor: React Flow, Zustand, Tiptap
- AI: Vercel AI SDK, AI Gateway
- Database: PostgreSQL with Drizzle ORM
- Auth & Storage: Supabase
- Background Jobs: Trigger.dev (for long-running agent tasks)
- Analytics: PostHog
Happy to answer questions about the architecture or implementation.

value: Show HN: Giselle – open-source visual editor for building AI workflows

author: codenote


value: jnamaya

value:
I’ve spent the last year building SAFi, an open-source cognitive architecture that wraps around AI models (GPT, Claude, etc.) to enforce alignment with human values.
Safi is a "System 2" architecture inspired by classical philosophy. It separates the generation from the decision:
The Intellect: proposes a draft.
The Will: decides to block or approve the drafts.
The Conscience: audits the drafts based on set core values
The Spirit: An EMA (Exponential Moving Average) vector that tracks "Ethical Drift" over time and injects course-correction into the context window.
Feedback is welcome!

value: Show HN: SAFi, a Governance Engine for LLMs

author: jnamaya


value: netIsNewWrldOrd

value: I appreciate any feedback on my protocol

value: Show HN: McNeal – Encrypted messaging over acoustic channels

author: netIsNewWrldOrd


value:
Hi HN,
I built Beauty Arena to solve a data problem I've always found annoying: absolute rating scales (1-10) are terrible for subjective data. They suffer from massive inflation and inconsistent user baselines (one person's 7 is another person's 5).
I wanted to test if pairwise comparison (1v1) could produce a cleaner, strictly relative dataset.
Instead of asking "How beautiful is this person?", the system asks a simple question: "Who do you choose?". It uses a ranking system inspired by competitive games (Elo/Glicko) under the hood. As users vote, a global ranking emerges based on win/loss ratios against others rather than accumulated points.
I'm curious about the "wisdom of the crowd" limits here. Does a pairwise sort actually converge on a clear consensus, or does it cycle indefinitely due to intransitive preferences (A > B, B > C, but C > A)?
I'd love feedback on the ranking methodology and the overall UI.

value: Show HN: Beauty Arena – An Elo-based experiment to measure aesthetics


value: isbeingto

value: Show HN: OMyTree – Turning AI chats into durable process assets

author: isbeingto


value: karthikeyjoshi

value: Show HN: BSH – A Git-aware, IntelliSense-style predictive Zsh history daemon

author: karthikeyjoshi


value:
Hi HN,
I’m sharing ARES Dashboard v1.0.0, an open-source platform for AI red-teaming, evaluation, and governance.
ARES is built to support structured red-team campaigns against LLMs, with a focus on safety, misuse resistance, and enterprise-style controls rather than one-off prompt testing.
Key features:
-Campaign-based AI red-teaming workflows
Multi-tenant architecture with role-based access control
-Audit logging and compliance-oriented design
-Demo mode for users without API keys
-Extensive documentation covering security boundaries, threat models, and operational practices
The project is early but intentionally designed with production and enterprise readiness in mind. Current gaps are mostly operational (monitoring, load testing, production deployments), which are the next priorities.
GitHub:
https://github.com/Arnoldlarry15/ARES-Dashboard
I’d really appreciate feedback from folks working in AI safety, security, infra, or governance — especially around real-world red-teaming workflows and operational concerns.
Thanks for taking a look!

value: Show HN: ARES Dashboard – Open-Source AI Red-Teaming and Governance Platform


value:
Hey HN!
As I sat to write my 2025 reflection, I realized one thing I was missing was a ‘year wrapped’ for my relationships — I got my music, got my photos, got my fitness wraps — but what about my relationships?
Specifically, I wanted to figure out what my text conversations say about my relationships and myself, and if there’s been evolution throughout the year.
Who reaches out more? What’s our tone and conflict resolution? What were our month by month successes and conflicts?
So I built an app that analyzes WhatsApp conversations (.txt files) and surfaces the patterns — using Anthropic’s API for the AI-generated analysis and Instant as my database.
It’s called Text Unwrapped.
You sign up, and upload a conversation from WhatsApp. That’s sent to Anthropic's Claude AI with a bunch of different prompts. Here are some of the things you get:
- Relationship score & synopsis on overall communication
- Personality Profiles (Myers Briggs, tone analysis, top themes & emojis)
- A month-by month timeline, outlining key texts and themes for that month
- Actionable insights for each person
- A deep dive on a topic of your choice (say you want to dive into defensiveness or avoidance)
- POVs from different schools of psychology, like CBT and Jungian
You can try this yourself. I made it so each sign up gets 1 free credit (1 credit = 1 conversation analysis).
I am not a technical person: I vibe-coded this. I used Claude Code (Opus 4.5), and Instant as the backend.
I’ve been playing around with making apps for the last few years, but it was always hard to make a leap. As of this March, I was able to start turning a lot of my passion projects into real ideas. I’ve made a few personal apps, but this is the first one I wanted to share on HN.
It took me about 3 days to build this. Once I had a strong spec in place, I needed to make very little changes to Claude (mainly upgraded the design and double checked permissions). Outside of that, Instant was a big help: Claude was able to use it and add auth in less than 2 minutes.
The hardest part was adding Stripe - but mainly because I hadn’t done this before. Claude Code guided me through the Webhook setup, and the main challenge was listening to the ‘checkout complete’ to validate payment and add credits to the user.
I know privacy is a big concern here. For what it’s worth, I don’t store the actual conversation file — it’s deleted as soon as the conversation analysis is completed. I only store the analysis in the database.
Hope you enjoy it!

value: Show HN: I created a 2025 Wrapped for WhatsApp Conversations


value: stanyy

value:
I built an open source web app that generates cover letters using local AI models (Ollama, LM Studio, vLLM, etc.) so your resume and job application data never leaves your machine.
No placeholders. No typing. Letters are ready to copy and paste.
The workflow is:
1. Upload your resume (PDF) - it gets parsed and cached in your browser.
2. Paste the job description
3. Get a personalized cover letter in ~5 seconds
It connects to any OpenAI-compatible local LLM endpoint. I use it with Ollama + llama3.2, but it works with any local model server.
Key features:
- 100% local and private depending on the LLM of your choice
- Smart resume parsing with pdf-parse
- Multi-language support (you can add more languages)
- Editable output with one-click copy
I made this because I was tired of wasting time with writing letters while applying for jobs. All other tools I tried weren't as quick as I wanted because I still needed to modify the letters to replace placeholders.
I also didn't find any tool that let's me use my local LLM for free, and I didn't want to pay for ChatGPT/Claude API calls for every job application.
The output quality is good, and it can bypass some AI detectors.
It's open source too and free to use. You can self-host it or run it locally in development mode.
GitHub: https://github.com/stanleyume/coverlettermaker
Cheers :)

value: Show HN: Cover letter generator with Ollama/local LLMs (Open source)

author: stanyy


value: RobTheFrog

value:
Hey HN,
I built a REST API for capturing screenshots and generating PDFs from URLs.
Why: I got tired of managing Puppeteer/Playwright in production. Memory leaks, zombie processes, Docker issues. So I wrapped it in an API.

  Stack:
  - Node.js + Fastify
  - Playwright (more stable than Puppeteer in my experience)
  - Self-hosted on Hetzner

  Features:
  - Screenshots: PNG, JPEG, WebP
  - PDFs: A4, Letter, custom sizes
  - Full-page capture, custom viewports
  - Cookie consent auto-accept
  - Lazy-load handling

API example:

      curl -X POST "https://www.screencraftapi.com/api/v1/screenshots" \
        -H "Authorization: Bearer YOUR_KEY" \
        -d '{"url": "https://example.com"}'

  Free tier: 250 requests/month
  Docs: https://www.screencraftapi.com/docs

Would love technical feedback. What's missing? What would make this useful for your projects?

value: Show HN: ScreenCraft – Screenshot and PDF API Without the Puppeteer Headaches

author: RobTheFrog


value: kako-jun

value: Built this for my mother-in-law's guesthouse. Hope it helps other small businesses too.

value: Show HN: Min. Calendar – Monthly business hours image maker for small shops

author: kako-jun


value: Show HN: Signing Room – Stateless Bitcoin Multisig Coordinator


value: scanset

value:
Endpoint State Policy (ESP) is a policy-as-data system that keeps policy intent separate from execution.
Policies define desired state and evidence as structured data, not scripts. They’re compiled into constrained contracts that execution engines must follow, producing attestations instead of free-form output.
The contract model limits what execution can do, preventing policy logic from turning into ad-hoc tooling, while allowing the same policy to run across different environments and backends.
ESP focuses on portable intent, constrained execution, and verifiable outcomes — not embedding policy into tools.

value: Show HN: Endpoint State Policy – Policy as Data

author: scanset


value: teamtrayd

value: Built an MCP server that connects Claude Code to Robinhood. Check portfolio, get quotes, place orders - all through conversation.

value: Show HN: I built an MCP server to trade Robinhood through Claude Code

author: teamtrayd


value: ericvtheg

value: I've been wanting a way to fire prompts from anywhere (especially on mobile) and then have Claude Code chug away and open a PR. So I built a Docker image that uses your authenticated Claude Code session to execute prompts via HTTP, with a built-in dashboard to submit and monitor tasks.

value: Show HN: Self Hosted Claude Code Runner

author: ericvtheg

title: Show HN: Self Hosted Claude Code Runner


value:
We’re building Cascade, an AI-driven platform for in-house growth teams running multi-channel ads. It connects GA4 + ad platforms, explains what changed (and why), and proposes concrete actions like budget reallocation, pausing waste, and prioritizing next experiments—designed to close the loop from analysis to execution with approvals.<p>It’s not self-serve yet — if you want to try it, request a demo.
Would love feedback: where’s your biggest bottleneck today — analysis, decision-making, or execution?

value: Show HN: Cascade – AI agent that optimizes your ads across channels


value: artigent

value:
Graph RAG for C/C++ Development
1. Overview
This project enables deep code analysis with Large Language Models. By constructing a Neo4j-based Graph RAG, it enables developers and AI agents to perform complex, multi-layered queries on C/C++ codebases that traditional search tools simply can't handle. With only 4 MCP APIs and a vanilla agent, it is already able to accomplish lots of tasks related to the codebases.
2. How it works
Using clangd and clang, the system parses and indices your source files to create a high-fidelity code graph. It captures everything from high-level folder structures to granular relationships, including entities like Folders, Files, Namespaces, Classes/Structs, Variables, Methods, etc.; relationships like: CALLS, INCLUDES, INHERITS, OVERRIDES, and more.
The system generates summaries and embeddings for every level of the codebase (from functions up to entire folders) using a bottom-up approach. This structured context helps AI agents understand the "big picture" without getting lost in the syntax.
To get you started easily, the project includes: an example MCP (Model Context Protocol) server, and a demonstration AI agent to showcase the graph’s power. You can easily build your own custom agents and servers on top of the graph RAG.
3. Efficiency & Performance
Incremental Updates: The system detects changes between commits and updates only what’s necessary.
Parallel Processing: Parsing and summary generation are distributed across worker processes with optimized data sharing.
Smart Caching: Results are cached to minimize redundant computations, saving you both time and LLM costs.
4. A benchmark: The Linux Kernel
When building a code graph for the Linux kernel (WSL2 release) on a workstation (12 cores, 64GB RAM), it takes about ~4 hours using 10 parallel worker processes, with peak memory usage at ~36GB. Note this process does not include the summary generation, and the total time may vary based on your LLM provider.

value: Show HN: C/C++ source code graph RAG based on Clang/clangd

author: artigent


value: cloudwaddie

value: Show HN: Huntr – An all in one leak checker tool

author: cloudwaddie


value: xiupos

value: Show HN: Cloud Chamber in Browser

author: xiupos

title: Show HN: Cloud Chamber in Browser


value: This helps me focus by letting me stay in the terminal

value: Show HN: CLI app to control your Mac written in Rust


value: Pockets

value: Show HN: SmartZip Pro – A ZIP, RAR & 7Z utility for iOS

author: Pockets


value: Zavora

value:
Hi HN,
I’ve been working on ADK-Rust, an open-source framework for building and deploying AI agents in Rust.
The motivation came from building agent systems where performance, safety, and predictable behavior mattered more than rapid prototyping. Most agent frameworks and workflow tools today are Python- or JS-first and tend to be runtime-heavy when taken to production.
Recently, I added ADK-Studio — a visual, low-code environment for building AI agent workflows on top of ADK-Rust.
You can think of ADK-Studio as a Rust-native alternative to tools like n8n, but focused specifically on AI agents:
- Visual, drag-and-drop workflow design (sequential, parallel, loop, router agents)
- Tool integration (functions, MCP servers, browser automation, search)
- Real-time execution with SSE streaming and event traces
- Code generation: visual workflows compile down to production Rust code
- Build and run agents as native executables directly from the studio
The goal is to let people prototype agent workflows visually, then ship them as fast, memory-safe Rust binaries instead of long-running JS/Python services.
Making AI Agents with ADK Studio is super simple:
1. ADK-Studio install: `cargo install adk-studio`
2. Start ADK Studio server: `adk-studio --port 6000`
3. Open in browser: open http://localhost:6000
I would really appreciate feedback from folks building agent systems, workflow engines, or AI inference infrastructure — especially around design tradeoffs vs existing tools like n8n.
Project site: https://adk-rust.com
GitHub: https://github.com/zavora-ai/adk-rust
Best
James

value: Show HN: ADK-Studio – a visual builder for creating AI agent workflows with Rust

author: Zavora


value: danieljhkim

value: Show HN: DevBox – An execution contract to end AI agent instruction fatigue

author: danieljhkim


value: jinqueeny

value: Show HN: CATArena – Evaluating LLM agents via dynamic enviroment interactions

author: jinqueeny


value:
Hi HN — I made keifu, a small Rust TUI for browsing Git commit graphs.
I like VS Code’s Git Graph, but I’ve been moving my workflow into the terminal. Recently I’ve been using AI coding tools more, which tends to create more parallel branches as I iterate, and I wanted a very quick way to jump between branches without leaving the terminal.
I also wanted something that works well on Windows Terminal: Serie was the closest match visually, but I couldn’t use it there.
keifu focuses on the graph + fast branch navigation:
- Unicode commit graph with per-branch colors
- commit details and changed-file stats (+/-)
- checkout / create / delete local branches, plus fetch
To try it:
- cargo install keifu
- run `keifu` inside a git repo
Feedback on graph readability and keybindings would be really helpful.

value: Show HN: keifu — a Git commit graph TUI with per-branch colors (Rust)


value: atharvtathe

value: Show HN: I made AI virtual staging tool for real estate listing

author: atharvtathe


value: EfimovSD

value: Show HN: Revieko – architecture drift hotspots in every pull request

author: EfimovSD


value: anticlickwise

value:
Hi HN,
AI coding tools change fast and I kept running into comparisons that were outdated within weeks. So I built DevCompare to automate the whole thing.
DevCompare regenerates comparisons daily across 18 criteria, including features, pricing, repo understanding, latency, and real developer sentiment from Reddit and HN.
The site is fully automated via cron, cached at the edge, and loads as a simple table with expandable details. No signups, no paywall.
https://devcompare.io
Happy to answer questions

value: Show HN: DevCompare – a live, auto-updating comparison of AI coding tools

author: anticlickwise


value: bchaipats

value:
I built this because I kept rewatching YouTube tutorials to find the one part I needed.
Existing summarizers give you walls of text. Videx keeps everything under 300 words — a one-liner, few takeaways, and a clickable timeline.
No signup needed to try it. There's sample videos on the homepage. I'd be happy to answer questions or hear what's missing.

value: Show HN: Videx – Summarize YouTube without the fluff

author: bchaipats


value: Bayram

value: Show HN: Agent Skill + Activity Watcher = productivity hack for 2026

author: Bayram


value:
IDDQD Internet builds zero-DB, zero-signup tools powered by pure HTML/JS for instant browser execution. Even with AI features, we keep it stateless and record-free.
Hi HN,
I built Reveal, a simple web-based tool to preview suspicious or potentially NSFW/jump-scare links safely. Instead of clicking blindly, you load the target URL behind a heavy blur curtain and use a slider to reveal the content at your own pace.
Key Technical Highlights:
Pure Client-Side: No database, no backend logging.
Stateless Architecture: The target URL is stored within the browser's address bar (URL parameters), making it easy to share without saving a single byte on our servers.
Vanilla Stack: Built with Vanilla JS, Bootstrap 5 (Glassmorphism), and Web Audio API for real-time feedback.
If you’re tired of "link-roulette" from friends or colleagues, this might be a useful addition to your toolkit. I'm an indie developer focused on "Vibe Coding" and keeping the web record-free.
App: https://app.iddqd.kr/reveal/ Source: https://github.com/iddqd-park
Park Sil-jang
Dev Team Lead at IDDQD Internet. E-solution & E-game Lead. Bushwhacking Code Shooter. Currently executing mandates as Choi’s Schemer.
HQ (EN): https://en.iddqd.kr/
GitHub: https://github.com/iddqd-park

value: Show HN: Reveal – A stateless, zero-DB blur curtain for suspicious links


value: surrTurr

value:
Built a tiny mac app that let's you create a depth map from any image.
Under the hood it uses apples coreml-depth-anything-small model to create the maps.
The app itself is a lightweight wrapper around it.

value: Show HN: Depth Map Extractor (macOS)

author: surrTurr

title: Show HN: Depth Map Extractor (macOS)


value: jasimvk

value: Show HN: StudyBuddy - AI study tool

author: jasimvk

title: Show HN: StudyBuddy - AI study tool


value: dimitarbak

value: For years, outsourcing contracts have been governed by a simple assumption: engineering effort roughly scales with people and time.<p>AI breaks that assumption.

value: Show HN: Who Captures the AI Efficiency Gains in Software Outsourcing?

author: dimitarbak


value: yunjywork

value: Show HN: Wirey – GUI socket client for TCP/UDP testing

author: yunjywork


value:
Hi HN,
I built Smart Meme Search to solve a small but very real personal problem.
Over the years I saved thousands of memes from X and Telegram. I often remembered what a meme meant or the situation it fit, but not any visible text or filename — so finding the right meme at the right moment was almost impossible.
This app indexes your local meme collection using:
CLIP for semantic image understanding
OCR for embedded text
a local ONNX-based inference pipeline (CPU-only)
You can search memes by intent or meaning (e.g. “awkward silence”, “overconfident but wrong”) and get relevant results instantly.
Everything runs locally; no images are uploaded anywhere.
I learned a lot building this, especially around native dependencies, model packaging, and deployment on Windows. Happy to answer technical questions or hear feedback.
Free version (With adds): https://apps.microsoft.com/detail/9NLLW6HFJ3VN
Thanks for reading.

value: Show HN: Smart Meme Search – search for your meme collection using CLIP and OCR


value: riccardoruspoli

value:
Hi HN,
My YouTube “Watch Later” playlist keeps growing, and I rarely have time to watch long informational videos end-to-end. Most of the time, I just want the key ideas in a format I can skim, search, and save.
reko is a local-first tool that converts YouTube videos into clean Markdown summaries using transcripts + LLMs (Ollama by default, cloud optional). The Python app is the engine; the CLI is the main interface for automation, and I added a tiny localhost web UI to make “summarization sessions” fast (paste a link, tweak settings, get rendered Markdown; settings are cached in the browser).
Feedback is very welcome.
Repo: https://github.com/riccardoruspoli/reko

value: Show HN: Reko – Local-First YouTube-to-Markdown LLM Summarizer

author: riccardoruspoli


value: Yusufshunan

value:
Built this as an experiment in pre-decision analysis.
DT Gatekeeper doesn’t recommend actions or optimize outcomes.
It evaluates proposed decisions and surfaces what futures get closed once you proceed.
Curious whether this kind of decision “gate” is practically useful, or just interesting in theory.

value: Show HN: DT Gatekeeper – a GPT that maps decision closures before they lock in

author: Yusufshunan


value: einshutoin

value: Show HN: Lazy-image – Node.js image library with static binaries (Rust/NAPI)

author: einshutoin


value:
Hi HN,
I built FlowStateOS Companion as an experiment in treating human life as a system with measurable forces.
Instead of focusing on decisions or optimization, it works with a small set of core life forces (energy, meaning, freedom, connection, etc.) and helps users:
observe imbalances
evaluate drift or overload
reflect on how daily behavior affects system health over time
The goal isn’t advice or prescriptions, but making internal state legible the way an operating system exposes resource usage.
It’s intentionally non-performative and slow. No motivation layer, no scoring, no productivity framing.
I’m curious whether this kind of systems-level self-diagnostics is useful, or whether it collapses under real life complexity.
Link: https://yusufshunan.com/gpt
Feedback welcome, especially from people interested in systems modeling, human factors, or reflective tooling.

value: Show HN: FlowStateOS Companion – experiment, modeling human life forces with GPT


value: vnglst

value:
It's almost New Year's and here's an original way to show the countdown using Tetris. I'm sure something similar has been done before, but I had a lot of fun building this (TDD style) using Claude Code.
After New Year's it can also function as a regular clock:
https://tetris-time.koenvangilst.nl/
For the impatient, it's possible to increase the speed using url parameters:
https://tetris-time.koenvangilst.nl/?to=2025-12-31T23:00:00....

value: Show HN: Tetris Time

author: vnglst

title: Show HN: Tetris Time


value: Show HN: LLMRouter – Stop using GPT-4/o1 for everything (16 routing strategies)


value:
Hello HN, I made a simple web app for drawing up plays for amateur sports coaches: https://playmaker.click/playbook
It's inspired by Excalidraw; I love how you can hit buttons and go into different modes so I tried to replicate that for desktop users. It should work nicely on tablets, phones not so much.
The main motivation was to learn some Rust and WebAssembly, the recent WASM 64-bit address space update is interesting to me, so I put together a lttle playbook engine that handles player movement, trail recording, and ball possession tracking. It runs the animation/interpolation stuff in the browser.
The rest of the stack is Laravel/Vue on the frontend, Tailwind for styling. Nothing too fancy. I wanted something where I could drag players around, record movements, and play them back like a little animation (double tap outside the field). Supports football (soccer), basketball, rugby, and hockey fields, reasonably badly drawn
There's rough edges for sure; the Rust code could probably be cleaner, I got a bit of help from Claude but where applicable I dug down into the reasoning as to "why", so it was a good learning experience figuring out how to expose structs to JS via wasm-bindgen and learning about serde.
The playbook all runs locally in the browser, and saves the play config into the URL using LZ-String so it can be easily shared without blowing browser URL address bar limits, but I haven't tested silly-big plays on it yet.
Happy to answer any questions about the setup or my approach in general. Cheers! PG

value: Show HN: I built a standalone WASM playbook builder for sports coaches


value: kaant

value: I built Omnvert as a fast, privacy-first toolbox for the checks I repeat during incidents and deployments: ping/MTR, DNS propagation (recursive vs authoritative), redirect chains + headers, TLS handshake details, RDAP/WHOIS, ASN/prefix, subnet math — plus a small PCAP toolkit to turn captures into scriptable output (JSON/NDJSON), top conversations, and flow exports.

value: Show HN: Omnvert Network Toolkits Ping/MTR,DNS,Headers,TLS,RDAP,PCAFlowsP→JSON

author: kaant


value:
I built this for myself after failing to stick with most habit trackers.
Instead of streaks, I use RPG mechanics: daily quests, XP, gold, rewards,
and penalties for missed habits. Everything runs inside Google Sheets
using formulas and a small script.
I’ve been using it daily for over 8 months.
Curious if others here have tried gamifying personal systems,
or have ideas on how to improve this approach.

value: Show HN: Habit Tracking as an RPG in Google Sheets


value: bradpeters

value:
Hi HN! I built HyperCell to solve a problem we kept hitting at our company (Scoop Analytics):
Business teams model everything in Excel – pricing logic, risk calculations, financial forecasts. Engineers then rewrite that logic in Java. Every time. This translation process causes bugs, delays, and drift between "what the business thinks the logic is" vs "what's actually running."
HyperCell takes a different approach: load the Excel file, compile the formulas into a DAG, and execute them in-memory. No translation. The spreadsheet IS the code.
Technical details:
- Parses Excel formulas using ANTLR4
- Builds a dependency graph for intelligent recalculation
- 200+ functions (SUM, VLOOKUP, INDEX/MATCH, IF, NPV, IRR, etc.)
- Cross-validated against Excel with 82,881 formulas at 100% accuracy
- ~50ms to load a typical workbook, sub-millisecond recalculation
Use cases we've seen:
- Insurance companies running rating models
- Fintech doing real-time pricing
- E-commerce with vendor-specific shipping rules
It's Apache 2.0 licensed. We extracted it from our production system where it's been running for 2+ years.
Happy to answer any questions about the architecture or implementation!

value: Show HN: HyperCell – Open-source Excel calculation engine for Java

author: bradpeters


value: stanyy

value:
I made an open source web app that generates cover letters using local AI models (Ollama, LM Studio, vLLM, Openrouter, etc) so your CV and job application data never leaves your browser.
No placeholders. No typing. Letters are ready to copy and paste. 100% local and private depending on the LLM of your choice. Multi-language support (so you can add more languages).
It connects to any OpenAI-compatible local LLM endpoint. I use it with Ollama + llama3.2, but it works with any server.
The generated letters unique since they are based on your unique experience and skills from your resume. They will also be written as if directly responding to that job posting, so all letters are unique.
I honestly don't feel bad about using or making this becuase while actively applying for jobs, I see that a high percentage of recruiters now use AI to generate job descriptions and also during the interview process.
I was tired of wasting time with writing and personalising letters while applying for jobs. All other tools I tried weren't as quick as I wanted because I still needed to modify the letters to replace placeholders.
I also didn't find any tool that let's me use my local LLM for free, and I didn't want to pay for ChatGPT/Claude API calls for every job application.
The output quality is good, and it can bypass some AI detectors.
It's open source too and free to use. You can self-host it or run it locally in development mode.
GitHub: https://github.com/stanleyume/coverlettermaker
Cheers :)

value: Show HN: Cover letter maker with Ollama/local LLMs (Open source)

author: stanyy


value: sidarcy

value:
I built a minimal browser game where you have to make 67 from four numbers.
https://simondarcyonline.com/67/

value: Show HN: Make 67 – a tiny maths game

author: sidarcy

title: Show HN: Make 67 – a tiny maths game


value: VivienMahe

value:
I wanted something simple to capture my day to day life without having to create montages manually.
So I built Snappit. Record 5 seconds every day. The app automatically compiles them into weekly, monthly, and yearly montages.
No sign up. No account. No server. Everything stays on your phone.
The constraint is the feature. 5 seconds is low enough friction that you actually do it, but enough to capture the texture of a day.
Built with Kotlin Multiplatform (Android + iOS from a shared codebase).
Happy to answer questions about the tech or the product. Feedback welcome.

value: Show HN: I built an app to capture 5 seconds of my life every day

author: VivienMahe


value: wenjian

value:
Inspired by a post in reddit (I lost the link), I build this simple web application, where you an mark your daily mood of the full year in a single page.
all data stored in the local
you can print the page to get a clean display
it can show 3 years data (data in last year and the year before is read only)
that's it.
hope you enjoy your every day

value: Show HN: Rate my day – rate your daily mood for the full year in the single page

author: wenjian


value: yahoai

value: Show HN: Ornix – Zero-setup folder organizer for macOS

author: yahoai


value: kasznar

value: I wanted to understand how CAD programs work, so I built a simple geometric constraint solver from scratch.

value: Show HN: A Simple Geometric Constraint Solver

author: kasznar


value: togido

value:
Hello everyone,
Yet another Hacker News reader :)
Free iOS app, built for fun and personal use.
Posting here in case anyone wants to try it. Feel free to give feedback if you want, thanks.

value: Show HN: A simple, free Hacker News reader for iOS

author: togido


value:
I built this because I was tired of the focus music dance—opening YouTube, fighting autoplay recommendations, getting distracted by comments.
As a developer, I spend hours in deep work mode. I wanted something that just works: one click, instant focus music with actual binaural beats (not just labeled that way).
I'd love your feedback:  Which genre do you reach for when you need to focus? Any features you'd want to see added?

value: Show HN: Simple Chrome extension to play focus music


value: willswire

value:
Hey HN,
On a recent flight home for Christmas, I tried to get some side-project work done on my MacBook… and immediately lost the battle with Seat 28C. I could barely open the laptop, let alone type comfortably.
The real constraint on planes these days is elbow room. That got me wondering: could a small, handheld keyboard and trackpad setup make in-flight work tolerable?
After failing to find anything compelling on Amazon, I realized something obvious: my iPhone already has a great keyboard and touch experience. So why not use it directly?
I looked for existing apps, but the top options felt dated and required both devices to be on the same Wi-Fi network—which isn’t always possible (or desirable when paying ridiculous prices for airplane Wi-Fi).
So over the last few days I’ve been tinkering with a project I call Magic Input. It turns your iPhone into a wireless keyboard and trackpad for your Mac.
How it works (high level):
• The iOS app discovers nearby Macs using MultipeerConnectivity
• Keyboard input and touch gestures are streamed directly to macOS
• The macOS app injects events at the system level (requires Accessibility permissions)
• No shared Wi-Fi network required; devices connect peer-to-peer
It’s very early, but already supports basic typing and cursor control—especially useful in cramped spaces like planes.
Here’s the TestFlight link for the brave. You’ll need to install the same app on both macOS and your iPhone:
https://testflight.apple.com/join/T1PgucDs
Happy to answer questions or dig into implementation details if anyone’s curious.

value: Show HN: Magic Input – Use your iPhone as a keyboard and trackpad for your Mac

author: willswire


value: I wanted a way to download exactly what the browser loads for a webpage  (i.e. all JS, CSS, images, fonts, etc.) with the original directory structure preserved (like what you see in DevTools → Network/Sources). Primarily for context/ prompting an LLM for creating similarly designed websites.<p>Couldn't find a tool, so here it is:)

value: Show HN: I built a CLI to dump all JavaScript/CSS/assets from any webpage


value: pllu

value:
Hi HN! I built a simple, self-hostable AI chat app that focuses on voice input and privacy.
Why I made this: I want to talk to AI (voice or text) without my conversations being logged, used for training etc.
Requires an OpenRouter account. No subscription: pay-as-you-go with most conversations costing a fraction of a cent (a couple of cents for requests with search enabled)
Privacy:
- Uses OpenRouter to route to Gemini 3 Flash on Google Vertex with Zero Data Retention (ZDR).
- Prompts and responses aren't stored or logged at any layer.
- OpenRouter keeps only metadata like timestamps and token counts.
How it works:
- Single HTML file – download and self-host, or use the hosted version https://martinpllu.github.io/spraff
- Log in to OpenRouter via OAuth (API key kept in browser local storage until logout)
- Audio recorded in browser, sent directly to Gemini for processing (no separate transcription step)
- Text-to-speech via your device's local voices
Technical notes:
- Gemini 3 Flash is currently the only model that supports both audio input and ZDR
- Provider is pinned to Google Vertex with zdr: true in the API request
- No build step, no dependencies – just one HTML file
- Current status: beta, tested on MacOS, Android and iOS. Should work but issues/feedback welcome.
Code: https://github.com/martinpllu/spraff

value: Show HN: Spraff – Voice and text AI chat, self-hostable, no data retention

author: pllu


value: alessiopel

value: Show HN: Imesde – A tiny, ephemeral vector engine for streaming data (Rust)

author: alessiopel


value:
Hi HN,
I built PII Firewall because I got tired of watching "privacy" APIs secretly pipe user data to cloud AI models. If you're using GPT/Claude to redact PII, you're literally giving the AI your PII.
What makes this different:
- Zero AI – deterministic regex + 30 checksum validators (Luhn, Verhoeff, Mod 11/97)
- Zero storage – processes on Cloudflare edge, no logs, no persistence
- 152 PII types – SSN, Aadhaar, 50+ country IDs, 20 API key formats, crypto wallets
- Two modes: `/fast` (2-5ms) for structured PII, `/deep` (5-15ms) adds names/addresses via 2000+ name gazetteer
The technical approach:
Instead of ML inference, I use combined V8-optimized regex with heuristic pre-scanning. Clean text (90% of requests) skips pattern matching entirely. For IDs that require it, I implemented full checksum validation:
- Credit cards: Luhn
- Indian Aadhaar: Verhoeff
- Chinese ID: ISO 7064 Mod 11
- Brazilian CPF/CNPJ: Dual Mod 11
- IBAN: Mod 97
Runs on Cloudflare Workers (pure JS, no WASM), so no cold starts.
Why I'm sharing:
Enterprise PII solutions cost $50K+/year. I wanted to make this accessible to indie devs, startups, and anyone building AI features who doesn't want to become a data liability. The $5/mo tier covers most use cases.
Would love feedback on the detection coverage or edge cases I might be missing.

value: Show HN: I built a zero-log PII redaction API – no AI, just regex and checksums


value: linkeli

value:
Hi HN,
I built Terminalot, a local-first SSH terminal that runs entirely on your own infrastructure (Docker) and connects to real Linux servers.
The goal was to make an AI-assisted terminal that infra people can actually trust:
- every command is shown and requires explicit approval
- no hidden execution
- no mandatory cloud backend
- all security-critical logic is open and auditable
It’s open-core. The free version is fully functional; paid licensing only unlocks higher limits (e.g. saved connections). You can inspect or modify everything.
Quick start:
docker run -p 3001:3001 linkeliai/terminalot:latest-beta
Repo: https://github.com/linkeli-labs/terminalot
This is the first public beta. I’d really appreciate feedback, especially around security assumptions, UX, and deployment expectations.

value: Show HN: Terminalot – A local-first, open-core SSH terminal with AI copilot

author: linkeli


value:
Hi HN,
I'm a solo founder working on a project that uses AI to help with finding files in Google Drive.
The Problem: With Google Docs and Sheets, it's easy to make documents but very hard to find them again unless you organize them manually, which takes time.
This is a big problem for startups, who often use Google Docs for the convenience but struggle with information management. It leads to a lot of time wasted searching for documents and a lack of clarity.
The Solution: Paper Tray uses AI to organize Drive files automatically. It 'tags' each file so you can then use a filter interface to find them.
By default, it tags by the type of document (meeting notes, plan, pitch deck etc), the topic of the document, and the department it belongs to (product, engineering, sales etc).
The result is that it takes just a few seconds to find most of your documents, in an intuitive and satisfying way.
You can add your files easily with a Chrome Extension that adds a button to the header of Google Docs and Sheets. Click this button to add the file and manage the tags.
Business model: a 7-day free trial followed by monthly/annual subscription, currently priced at $12/mo, or $9/mo with an annual plan.

value: Show HN: Paper Tray – dramatically better file organization for Google Drive


value: micouay

value: Show HN: Collaborative code playground in a single <1MB HTML file

author: micouay


value: huali

value: I built Vue-Hook-Optimizer (VHO) to solve a pain point: refactoring "mega-components" where logic is so tangled that it's hard to see where one Hook ends and another begins.

value: Show HN: Refactor Vue/React Components with Label Propagation and Claude Code

author: huali


value: kenshozone

value:
I keep burning out and quitting projects. Built this to stop myself.
Hard rules:
- Work 100 minutes - owe 10 minutes rest (enforced via meditation/breaks)
- Complete 5 tasks - owe 1 reflection
- Hit 10:1 ratio - focus timer locks until you rest
- No overrides, no "just one more"
Terminal UI, monospace everything, ASCII progress bars. Black screen, functional colors only.
Live at: app.kensho.zone
Built it because every other productivity app let me work myself into the ground. This one physically stops me.
Feedback welcome, especially from people who've quit projects from burnout.

value: Show HN: Productivity app that blocks you at 10:1 work-to-rest ratio

author: kenshozone


value:
Hey,
When working on complex projects involving multi-document or multiple tasks process, I've stumbled upon a problem that LLMs can't guide themselves efficiently through context. I've seen some tools for LLM graph interface (https://branchcanvas.com/, http://grafychat.com/) but:

  - They are closed source and cost $
  - They are ugly (subjective) or clunky
  - They do not support merging the branches back

So I built Bushchat: an opensource, browser-based tool that turns LLM conversations into a tree structure, allowing you to expand and merge back when needed. This is basically LLM 'thinking' but with manual thread control, which increases the reliability of steering the azimuth of the thought process, and efficiently work on context.
In the future I envision that making this a collaborative tool where users could share and work together on live data trees or part of their context with each other could increase the productivity in heavy-AI reliant teams.
It's free, hosted on gh-pages and you can access it https://bushchat.xyz/ or run it yourself https://github.com/srakai/bushchat
Everything is DOM based, there is no server, but you need your API key or local LLAMA with OpenAI compatible API. Also disclaimer: There is posthog analytics, which is used to collect solely usage metrics (gh-pages does not facilitate that). Messages, API keys, responses, chat names are out of scope (https://bushchat.xyz/privacy ).
Happy to hear your thoughts!

value: Show HN: Bushchat – open-source graph LLM interface


value: techabroad

value: Show HN: StackOverflow Jobs inspired website to find jobs with visa sponsorship

author: techabroad


value: xukeek

value:
Built a small vibecode site that tracks and summarizes the $500+/month side projects shared in the recent Ask HN thread, using AI + scheduled updates.
https://hackernews-side-projects.vercel.app/
Curious if this kind of vibecode aggregation is useful or just redundant.

value: Show HN: I built a site that tracks and summarizes $500M projects from Ask HN

author: xukeek


value:
I built a free invoice generator for my own use and ended up adding multiple invoice formats for different use cases.
It’s intentionally simple and runs entirely in the browser. Feedback welcome.

value: Show HN: I built a free invoice generator for freelancers and small businesses


value:
I love Hacker News but I rarely have time to read full articles / comments, so I built a simple site to summarize the top posts & comments to make it easier to get a big picture.
A few implementation details:
 - It pulls the top stories from the HN Firebase API (`/v0/topstories`)
 - Filters stories with >200 points
 - Uses Claude Sonnet for post + comment summaries
 - Tries to capture the sentiment of the comments (still working on improving this)
 - Refreshes both posts + summaries hourly
It has already helped me I hope it helps you! Please let me know if you have any feedback -- still actively iterating on it.

value: Show HN: Tl;dr HN – Summaries of the top HN posts and comments (powered by AI)


value: potetotown

value:
A production-ready mobile client for Google's Jules AI coding assistant built with React Native + Expo.
Key features:
- Expo SDK 54 + React Native 0.81 with TypeScript
- Expo Router for file-based navigation
- Custom hooks for Jules API integration (sessions, activities, sources, plan approval)
- expo-secure-store for encrypted API key storage
- Real-time session chat with 5-second polling
- Markdown rendering with syntax highlighting
- Dark/light theme + i18n (English/Japanese)
- Performance optimizations: memoized components, FlatList virtualization, efficient pagination
Architecture highlights:
- Clean layer separation (presentation/components/hooks/data)
- Context API for global state management
- Comprehensive TypeScript types for all API responses
- Detailed documentation with architecture diagrams
Docs: https://github.com/linkalls/jules-mobile-client/blob/master/...

value: Show HN: Jules Mobile Client – React Native App for Google's Jules AI Assistant

author: potetotown


value: anybodyy

value:
I built surfgram because I was tired of heavy Telegram libraries and inconsistent types
Key technical decisions:
 — Zero Dependencies: No node-fetch, no axios, just native calls
 — Source of Truth: Instead of manual typing, I wrote a generator that scrapes the official Telegram Bot API docs
—  Type Safety: Focus on strict null checks to avoid undefined at runtime.
The repo is here: https://github.com/surfgram/surfgram
 I’d love to hear your feedback on the code generation logic and the API design

value: Show HN: Surfgram – A 0-dep Telegram SDK with types generated from official docs

author: anybodyy


value: Show HN: My voice-to-text tool gets 10x more traffic from Bing than Google


value: velocitatem

value: Show HN: I modeled Rio's traffic noise as a sub-optimal Nash Equilibrium

author: velocitatem


value: NexAIOS

value:
Hey HN,
I'm 18, built this solo over 7 months in India. Launched on Product Hunt this morning but wanted to share here for technical feedback.
What it is:
Nex Sovereign is a cognitive OS with 9 apps that make AI transparent:
Mind App – See its beliefs, values, goals + real-time dopamine/cortisol levels
Memory Graph – Auditable knowledge graph (not hidden RAG)
PDAR Thinking – Watch reasoning process (Perceive → Decide → Act → Reflect)
Boardroom – Internal CEO/CTO sub-agents make recommendations, you mandate approval (governance layer)
Chat, Memories, Goals, Settings
Tech stack:
Python/FastAPI (async event loop for cognitive simulation) + Next.js 14 + SQLite (local-first)
Why I built it:
Every AI is a black box. You never see what it believes, how it thinks, or why it decided. I wanted to solve interpretability at the architecture level (not as a research problem, but as a product design problem).
Current status:
Invite-only beta (8 concurrent users). Already got integration interest from Giselle (workflow orchestration platform).
Looking for feedback on:
Is the cognitive transparency useful or just "neat"?
Which app is most compelling? (Mind, Memory Graph, PDAR, Boardroom)
What would you build on a Nex SDK?
Product Hunt: https://www.producthunt.com/products/nex-sovereign
Happy to answer technical questions about the architecture.

value: Show HN: Nex Sovereign – AI OS with visible reasoning and governance

author: NexAIOS


value: mcramblett

value: Show HN: Novel Novel Generator. Recursive Gemini pl wrote 97p coherent nerdy pdf

author: mcramblett


value: nLight

value:
Summit is a local-first macOS app for meeting recording and transcription.
All audio processing, transcription, and summarization happens on your Mac - nothing is sent to the cloud (unless you chose to).
Built for privacy-sensitive work such as NDAs, legal, healthcare, consulting.
Features:

  - Works with any call app: zoom, meet, teams, etc.
  - Automatic call detection
  - On-device speaker identification
  - Custom summary templates (decisions, action items, notes)
  - Supports online and in-person meetings

Would love feedback from people who care about local-first software and privacy.

value: Show HN: Summit – local AI meeting insights

author: nLight


value: KirisameMarisa

value:
I built VideoReview because tools like Frame.io work well for external reviews,
but were hard to use in our actual game development workflow.
In our studio, reviews often involve a large number of short cutscene clips
on internal or restricted networks.
so we needed a lightweight, self-hosted solution.
After switching to VideoReview:
- feedback became more focused
- fewer “which frame are you talking about?” moments
- review cycles got noticeably shorter
It’s a small, on-prem tool built for internal teams, not client delivery.
I’d love feedback from people who regularly review animation or gameplay footage.

value: Show HN: VideoReview – an on-prem, Frame.io-like video review tool for game dev

author: KirisameMarisa


value: ryjo

value: Show HN: CLIPSmqueue: A POSIX message queue (mqueue) library for CLIPS

author: ryjo


value: poperk

value: Show HN: SatoriDB – Query 1B vectors on a laptop (SQLite for embeddings, Rust)

author: poperk


value: Show HN: Octopii – Custom distributed state machines with Raft (Rust)