value: yashxsagar

value:
Ola HN!
Like many of you, I live inside my browser. Chrome tabs are my research notes, my backlog, my ideas-in-progress… but also my biggest nightmare.
At some point, I realized my tabs were my second brain — except I couldn’t recall anything from it. Finding “that one brilliant article I just had open” became a daily ritual of frustration.
So I hacked together TabX: a Chrome extension that treats your browsing sessions as searchable knowledge, instead of clutter.
Here’s what it does:
1. Real-time tab indexing → Search across open tabs title, URL, or even page content instantly .
2. Privacy-first → All data stays on your machine. No servers, no trackers, no phoning home.
3. Works across multiple Chrome windows → Your search is referenced across all tabs open across all Chrome windows
4. Works for Incognito windows as well
5. Auto Highlight and Scroll → Upon shifting focus to your target tab, TabX scroll to the page section containing your search keyword and highlight it as well (works for pages with simple HTML structures for now)
I’ve been dogfooding it for weeks and my productivity shot up — no more “where the hell did I read that?” moments.
Think of it as Ctrl/Cmd+F on steroids!
TabX just went live on the Chrome Web Store -> https://chromewebstore.google.com/detail/tabx/bdmeibkfombnjp...
I’d love your feedback:
What would make this indispensable for developers, researchers, or founders?
What integrations/workflows would you want next?
Thanks
Yash

value: Show HN: I built TabX – A Chrome extension that makes sense of your tab chaos

author: yashxsagar


value: solcloud

value: Still work in progress, source code available at https://github.com/solcloud/Counter-Strike

value: Show HN: Open-source counter strike clone gameplay video

author: solcloud


value: maxpage

value: Show HN: Chrono Trace dynamic webgame v0.4

author: maxpage


value: crossivejoker

value:
I built TruthGate. It’s a self-hosted IPFS gateway that makes decentralized hosting production-grade.
The motivation: I like some of the ideas behind IPFS (content addressing, redundancy, distribution), but running a node in practice felt painful. By default, your server turns into a free CDN for strangers, bandwidth and disk get chewed up, and sites are often slow or don’t load at all. SaaS “fixes” exist, but they usually cost more than just renting a normal VPS.
TruthGate makes hosting content straightforward: no extra extensions, apps, or SaaS. You can:
* Publish Web Assembly websites directly to IPFS and served over HTTPS.
* Host and share files without relying on centralized storage.
* Only serve your own content (not become a public gateway).
* Point your own domain and manage it like any other webserver.
For anyone unfamiliar: IPFS is a peer-to-peer, content-addressed file system. In theory, nodes help each other serve shared files and cached sites (deduplication, redundancy, speed). In practice, it hasn’t lived up to that promise. TruthGate is my attempt to fix that gap.
My own site runs on it, and the docs are there if you’d like to tinker.
Website: https://truthgate.io/
GitHub: https://github.com/TruthOrigin/TruthGate-IPFS
I’d really appreciate feedback from this community.
* Does the “NGINX for decentralized hosting” idea make sense?
* Would you actually run something like this at home or in production, or is IPFS still too impractical outside experiments?
Thanks for taking a look.

value: Show HN: TruthGate: Self-hosted IPFS Gateway (Netlify for decentralized hosting)

author: crossivejoker


value: IdreesInc

value:
I made a simple open-source letter editor inspired by Animal Crossing NH. Took me forever to look over each card, but I'm quite pleased with how it turned out. You can even click the bottle in the bottom right to see a random letter design shared by other users! Now to see how long it stays up...
Check out the source code here: https://github.com/IdreesInc/Animal-Crossing-Letter-Generato...

value: Show HN: I made an Animal Crossing style letter editor

author: IdreesInc


value: punnerud

value: Show HN: Checkpoints for Claude Code [video]

author: punnerud


value:
Hi everyone,
I made a simple web tool called the Minecraft Circle Generator
. It helps players create pixel-perfect circles and other shapes in Minecraft by visualizing the block placement.
This started as a side project because I always struggled to get circles right when building in survival mode. The tool lets you choose a radius and block size, then instantly shows the pattern you can copy in-game.
I know there are other generators out there, but I wanted something lightweight, fast, and mobile-friendly. I’d love to hear your feedback, especially on how it could be more useful for builders.

value: Show HN: Minecraft Circle Generator

title: Show HN: Minecraft Circle Generator


value: andersource

value:
Hi HN!
Codyssey is a small programming game where you write Python functions to solve minigames. Think pong, flappy bird - the functions serve as a control mechanism for the player.
It evolved from an end-of-year activity I made for an introduction to programming class for 9th graders, difficulty has been adjusted obviously. I ran it as a workshop / competition at several conferences, now considering making it available as a small game.
Some technical details: It runs python in the browser with Brython [0], I originally used Pyodide but it was too heavy for conference WiFis. Code editor is Ace [1].
I'd love to hear your feedback :)
[0] https://brython.info/
[1] https://ace.c9.io/

value: Show HN: Write Code to Solve Minigames

author: andersource

title: Show HN: Write Code to Solve Minigames


value:
I maintain an open-source project (Co-op Translator) that runs automated translation pipelines using Azure OpenAI and Azure AI Vision.
Sometimes expired or misconfigured keys would slip through, and the job would keep running — producing blanks or wasting tokens.
That pain led me to build this small tool. It checks Azure OpenAI & Vision keys up front and fails fast if they're invalid, saving time and compute.

value: Show HN: Show HN: az-ai-healthcheck – Fail Fast on Bad Azure AI Keys


value: YoNoCode

value: Show HN: Convert your Bubble and other no-code apps into full code

author: YoNoCode


value: sandx

value: AlphaSuite is a comprehensive suite of tools for quantitative financial analysis, model training, backtesting, and trade management. It's designed for traders and analysts who want to build, validate, and deploy data-driven trading strategies.

value: Show HN: AlphaSuite – An open-source platform for quantitative stock analysis

author: sandx


value:
I made a DSA practice tool for Ruby. It’s test-driven, runs locally, and is easy to extend with new exercises. Would love feedback on the interface and which problems to add next.
The objective is to cement ability to implement core algorithms through repetition. This test suite is not like LeetCode. In fact, it’s more of a prerequisite to LeetCode. The test cases are not exhaustive in the spirit of checking for runtime performance, scalability, etc. Implementations are generalized, so that through practice using this tool, the user can begin avoiding having to think about the algorithmic pattern, and instead focus on its application to the problem at hand.
It uses Minitest to test the user’s implementations, dynamically loaded at runtime.

value: Show HN: Dsa.rb: Practice core dsa in Ruby from the command line


value: ringerapps

value:
Hi HN, I’ve been tinkering with this for a while and finally have it in a decent state. It’s a plotter robot that draws caricatures from photos.
I trained a diffusion model (Flux Kontext LoRA) on caricature images, 3D-printed a Sharpie mount for my Ender 3, and hacked together a pipeline that goes photo → caricature → G-code. After a lot of trial and error it’s working pretty well, and I put up a little site where you can try it out.
Happy to answer questions or hear any feedback. Thanks!

value: Show HN: I built a robot that draws caricatures with a Sharpie

author: ringerapps


value: Show HN: I built an open-source CSV importer that I wish existed


value: painternishant

value:
What we built
React Web Camera is a lightweight, reusable React component that allows users to capture multiple photos in one camera session, in-browser. It works across standard web apps, responsive UIs, and Progressive Web Apps (PWAs)—unlocking a smoother experience than the default <input type="file" capture> element.
The problem
On mobile (and increasingly on desktops), using: <input type="file" accept="image/*" capture="environment"> only allows taking one picture before the camera closes. Want to add more? You have to reopen it each time.
How React Web Camera solves it
Opens the camera inline in-browser, Lets the user capture multiple photos in one go, Allows previewing captured photos, removing unwanted ones, and submitting everything in a batch, Fully client-side, respects user privacy, Supported across web, responsive UIs, and installable PWAs.

value: Show HN: React Web Camera – Fix <input type=file> single-photo limit

author: painternishant


value: noygaryan

value:
Hi HN,
I'm the creator of thinkercan.
The best way to describe it is ChatGPT for reasoning + Photomath for the step-by-step solution.
I built this because I wanted a tool that could understand a student's question in natural language (even word problems) like ChatGPT, but then generate a perfect, step-by-step mathematical solution like Photomath.
The core problem with using LLMs alone for math is that you can't trust their process. They hallucinate steps and get the final answer wrong. My goal was to fix that.
Under the hood, thinkercan uses an LLM as an intelligent interface to understand the query. But the crucial part is that it then calls a dedicated symbolic math engine to generate the entire solution path—every single step, from start to finish.
This means you get a solution that is both easy to understand and mathematically sound from top to bottom.
Here’s a 50-second video that shows the process: https://www.youtube.com/watch?v=nI05v-Yltyc
We're still in the early stages (just added calculus support), and I'm here to answer any questions. I would be grateful for any feedback on the approach and the product.

value: Show HN: A math tutor that won't hallucinate answers (ChatGPT and Photomath)

author: noygaryan


value: lulceltech

value:
Hi Hacker News,
I'm a solo developer and the maker of VanityCert. For the past year, I've been working on a solution to a problem that turned into a personal nightmare: scaling custom domain and SSL management.
I built a SaaS app for a client and was faced with the '1-to-N' problem—hundreds of their customers needed to use their own domains. The manual process of orchestrating DNS validation, individual SSL certificates, and the 90-day renewal cycle became a full-time job. It was a massive time suck that pulled me away from building core product features for over six months.
So, I built VanityCert.com to solve that pain. It's a white-label platform with a simple API that automates the entire process:
It provides an embeddable portal for non-technical customers to set up their domains.
It handles all the complex DNS validation challenges programmatically.
It issues and securely manages unique SSL certificates for each domain.
It automatically handles the 90-day renewal cycle, so no certificates ever expire.
The goal is to abstract this entire layer of infrastructure, allowing SaaS teams to focus on innovation instead of operational overhead.
I would love to get your feedback on the idea and the implementation. What are your thoughts on the core problem? Are there any use cases you can think of where this would be particularly valuable?
Thanks for your time!

value: Show HN: Vanitycert.com – Automated Custom Domains and SSL for SaaS

author: lulceltech


value:
If you, like me, are put off by the huge effort required to add i18n to your web app (Svelte/React/SolidJS), let alone make it performant, then this is for you. I worked on this exact issue over the summer. Where it mainly differs is two things:
1. It lets you keep your code as-is: <p>Hello</p> stays like that.
2. It adds almost zero bundle overhead by just putting the messages in arrays: ['Hello']. Accessed by indices, like Protobuf, no keys, and no function-for-each-message.
Bonus: it can optionally auto-translate with Gemini + HMR in different languages during dev!
Feedback always welcome!

value: Show HN: Wuchale: Protobuf-like internationalization from plain code


value: shlenkinr

value:
Hey HN!
I built Python Playground, a Chrome extension that gives you a ready-to-use Python environment right in your browser - no installation needed.
It runs Python locally via Pyodide (WebAssembly) with most popular libraries included out of the box, plus a package manager to install additional modules from PyPI. The environment supports inline data visualization with Matplotlib and Plotly, file upload/download through a sandboxed filesystem, and the ability to save and reload snippets. Everything executes entirely on the client, so it works offline and nothing ever leaves your browser.
In addition to the extension, there’s also a web version you can try right away: https://python-playground.com
I’m planning to add debugging tools, synchronization between devices, and possibly more advanced features over time. I would be very interested to hear your feedback and what improvements you think would be most useful.

value: Show HN: Python Playground – a Chrome extension to keep Python always at hand

author: shlenkinr


value: standards-sft

value:
A browser-only way to copy/paste text and send small files between devices.
• No accounts, join via code/QR
• AES-256 E2E in the device
• 5 MB file limit
FAQ: https://copypastefree.ca/faq.html

value: Show HN: Cross-device copy/paste and 5 MB file transfer (E2E, no signup)

author: standards-sft


value: Alechko

value:
We built a browser extension (Chrome + Firefox) that captures the runtime DOM and exports it as JSON.
Not the pre-render source (HTML/CSS/JS, templates, bundles) and not a screenshot — but the live, post-render state the browser is actually displaying:
- visibility/hidden, disabled/required
- current input values and validation/validationMessage
- dataset attributes
- trimmed text
- stable selector paths
Why: LLMs often miss or guess UI state. Screenshots are too opaque, pre-render source is too noisy. A structured snapshot gives reproducible context for debugging forms and flows.
Security:
- Runs locally in the browser as a read-only content script.
- No telemetry, no external requests.
- No page mutation; it only reads the DOM.
- Data leaves the browser only if you copy it yourself.
- Immune to prompt injection at source level — it never executes or rewrites page code, only serializes visible state.
Use cases:
- Debugging client-side forms: capture the real rendered state, not just HTML.
- Reproducible bug reports: instead of vague screenshots, share a JSON snapshot of the exact state.
- Test automation: snapshot before/after an action to compare behavior.
- Prompt engineering: feed structured UI state directly, instead of raw markup or screenshots.
Links:
• Chrome Web Store: https://chromewebstore.google.com/detail/element-to-llm/oofd...
• Firefox Add-ons: https://addons.mozilla.org/en-US/firefox/addon/element-to-ll...
Curious what people here think about this approach.

value: Show HN: Element to LLM – Extension That Turns Runtime DOM into JSON for LLMs

author: Alechko


value: vvaezian

value:
Hi HN!
I'm Vahid, and excited to share DataBot, a self-hosted data assistant that lets you query, visualize, analyze, and forecast data in plain language.
Backstory: Right after ChatGPT launched, I built a prototype chatbot for Metabase (a BI tool) to answer user questions with natural language. That experiment grew into an independent app — now a fully-fledged AI-powered data assistant.
What's different:
- Self-hosted: Full isolation and control over your data. No SaaS vendor, no third-party access.
- Feature-rich: Includes forecasting, NoSQL (MongoDB), JSON column support, Metabase integration, and data obfuscation — things rarely found in other tools.
- Affordable: Free for personal use. Business plans start at $12/user/month.
Technical details:
- Framework: FastAPI
- Data explorations/analysis: Uses GPT models to translate natural language into SQL or MongoDB queries, executes them, and returns results
- Visualization: Chart.js
- Forecasting: GPT models generate Python code, which runs securely inside the app
- Deployment: Docker or standalone bundle (includes app, Python, and local Postgres)
- Data support: SQL databases, MongoDB, JSON columns, Metabase dashboards
- Metabase integration: via Python wrapper for Metabase API (metabase_api_python)
Quickstart guide: https://databot.intellimenta.com/admin_guide/getting_started
GitHub Repo: https://github.com/Intellimenta/DataBot
Demo Video (4 min): https://www.loom.com/share/2271af6de3c04a2b8b07dbc48dfb2963
I'd love your feedback!

value: Show HN: DataBot – Self-Hosted AI to Query and Visualize Data Using Language

author: vvaezian


value:
I built this because I was frustrated with how messy translation files get as projects scale.
Most i18n solutions force you to manage big JSON dictionaries, spread across multiple files, and then manually wire everything into your UI. It quickly becomes hard to keep things organized, especially in component-heavy apps.
This approach is component-first i18n: translations live close to the UI they belong to. That makes it easier to keep things organized, avoid duplication, and scale without the usual chaos of string management.
It’s lightweight, works with React/Next.js, and is designed to stay simple while your app grows.

value: Show HN: A simpler way to manage internationalization in component-based apps


value: selmetwa

value: A command-line tool to automatically generate and Add text-to-speech audio to your Anki cards using ElevenLabs.

value: Show HN: Add audio to your Anki cards

author: selmetwa

title: Show HN: Add audio to your Anki cards


value: Aryagm

value:
Food delivery apps started as “convenience” but have turned into a system that extracts value from every side:
- Restaurants pay 15–30% commissions per order, forcing them to raise prices or operate at a loss.
- Drivers earn ~$11.77/hour before gas and vehicle costs (often below minimum wage).
- Customers end up with inflated menus and hidden fees, sometimes paying 30%+ more than the food is actually worth.
The apps don’t create value. They pull money out of restaurants, workers, and customers. It benefits nobody.
I built something simple in response: skipfees.com
It’s a free, community-driven directory of restaurants that deliver themselves. No middleman, no inflated prices, no commissions.
Right now it’s small (just a couple dozen Toronto restaurants I added manually), but anyone can add their local spots in seconds. My hope is that, over time, this grows into a resource people everywhere can use.
I’m a university student; this isn’t a startup or a VC-backed company. It’s free and open to everyone. Still rough and might be buggy, but I’d love feedback on the concept, UX, and what features would make it more useful.
Check it out here: https://skipfees.com

value: Show HN: SkipFees -free community directory of restaurants that deliver directly

author: Aryagm


value: NyxBNC

value:
Last week I shared the repo for AIMless, my silly experiment to see how far I could push “chat app, but only index.html.”
Now it’s live at https://aimless.chat
so you don’t even have to clone or double click anything.
just open and relive your AIM nostalgia.
INSTRUCTIONS:
Host clicks “Create” → gets a blob.
Copy/paste blob to your friend.
Friend pastes, returns blob.
Boom, encrypted chat like it’s middle school and your mom needs the phone line.
Github: https://github.com/ImZackAdams/AIMless

value: Show HN: AIMless (Live Demo) P2P Encrypted Chat in One HTML File

author: NyxBNC


value: bertwitt

value: SHOW HN: Curated gallery of best websites with fonts in use

author: bertwitt


value: Hey everyone, built this earlier today on my whatsapp no code platform - been going a bit viral between my social groups so thought I'd share with you guys :)

value: Show HN: Chat with Nano Banana Directly from WhatsApp


value: ingav

value: Show HN: I built an app where I've to get my gf's permission to doomscroll

author: ingav


value: hatemmezlini

value: Show HN: Vidnavigator AI

author: hatemmezlini

title: Show HN: Vidnavigator AI


value: anishNagula

value: Show HN: NekoOS, hobby OS with VGA and keyboard, PIT timer advice?

author: anishNagula


value: paulmbw

value:
Hi HN!
I’m Paul, founder of Traceprompt. We’re building an open-source SDK that wraps your AI agents and generates tamper-proof audit trails, so you can prove what agent did what, when and with which model.
You can check out the Node SDK (more languages coming soon): https://github.com/traceprompt/traceprompt-node
We built Traceprompt because LLMs are being used everywhere without a clear plan for audit and compliance. From research and discussions with other devs, I often see fintech and healthcare teams manually stitching together API Gateway, CloudWatch Logs and S3 buckets to track prompts/responses and retention. This is both complex and costly as there's little to no proof of immutability. Most current tools (Langfuse, Heliclone etc.) focus on LLM observability; few help generate verifiable evidence for compliance. That’s where we come in.
To add, regulations are also moving in this direction (e.g. the EU AI Act requires logging for certain high-risk systems, HIPAA calls for audit controls and FINRA/SEC rules push WORM-style retention). The goal is to make “prove nothing changed” boring.
Our SDK is simple:
1. BYOK architecture with AWS KMS. We never see plaintext prompts/responses; only you can decrypt. Other KMS providers are on the roadmap.
2. Append-only, hash-chained logs with a public Merkle anchor for independent verification. Repo: https://github.com/traceprompt/open-anchors
3. Audit packs: export CSV rows + proofs (and receipts) when someone asks “what exactly happened on this day and time.” You can also verify the audit packs — if a single byte was altered or a row removed by a bad actor, verification fails.
If "AI audit trails" are on your mind or on your roadmap, I'd love to talk. There are a few ways to start:
1. Checkout the repos: review code, install the SDK and experiment; open issues if anything breaks — https://github.com/traceprompt/traceprompt-node + https://github.com/traceprompt/open-anchors
2. Landing page: https://traceprompt.com — details on integrations and pricing; 7-day free trial (or 2M-token cap).
3. Join our Discord: https://discord.gg/2yUSXDECQk
4. Book a free 30-minute demo call: https://cal.com/traceprompt/traceprompt-intro
We'd love to hear your feedback, so we'll be in the comments! If you're a dev, I am happy to dive into more technical details or answer any questions. If you're in the AI audit and compliance space, please do get in touch as we have lots to learn and uncover :)
Thank you!

value: Show HN: Open-source SDK for AI agent audit trails

author: paulmbw


value: acofranc

value:
I made a site inspired by a ridiculous side project: a lift-the-flap book for adults.
It mixes science facts, (bad) poetry, and way too many penis facts.
NSFW-ish, interactive, and definitely not what you’d expect.

value: Show HN: Whose p*nis is that now? Probably the weirdest website I've ever made

author: acofranc


value: spillman

value: This is a simple 2048 game. Instead of playing with numbers, it shows the logos of Google deprecated products/services. Play it to learn what products/services were killed by Google. Maybe, Googlers will get some sense as to which products/services to work on to prevent your work being killed or deprecated:)

value: Show HN: Play a game to learn what products/services were killed by Google

author: spillman


value: ieuanking

value:
Hey everyone!
Ubik is great for academic research workflows, like uploading/marking up PDFs and searching scholarly databases. We built agents that can create precise annotations (notes) down to the line level, and can be referenced later when prompting agents using the @ symbol (like Cursor). Through @ sign note-referencing in prompts, agents stay grounded in source material added to the context window, minimizing hallucinations while building contextual understanding of user needs through interactive note-taking and PDF viewing in-browser.
We graduated from college right before ChatGPT was released, and we worried about AI's effects on learning, reading, and original research. We've spent the last 2 years building a platform that integrates the best parts of being human, like reading, writing, and thinking, with evidence-based AI agents.
If you think this sounds helpful, try it out, and let me know if you think this is a good tool for higher education and professional research.
We are a small team of 2. A complete local app and evals coming soon <3

value: Show HN: Evidence-based agents for PDF chat, academic search, and paper editing

author: ieuanking


value: pomdevv

value: It needs an Anthropic AI api key though.

value: Show HN: I made an extension that blurs out low value comments on X

author: pomdevv


value: couliss

value: Show HN: Value My Mortgage

author: couliss

title: Show HN: Value My Mortgage


value: scfast

value:
Hi HN,
I’ve been working on a project called *Testronaut*, an autonomous testing framework that combines AI reasoning with real browser automation. The idea is to let you define end-to-end tests as “missions” in plain English, then have an agent run them through a real browser using Playwright.
Why I built this: I’ve often found end-to-end tests to be fragile, time-consuming to maintain, and difficult to scale. Testronaut tries to reduce the maintenance burden by using AI to adapt tests to small UI changes, while still producing a deterministic report of what passed/failed.
How it works:
- Missions can be written as strings or functions.
- The agent uses GPT-4o with a set of tools (click, type, navigate, get_dom, etc.) to interact with the page. Support for other LLMs/Models in the works.
- Browser control is handled by Playwright.
- Reports are generated in both JSON and HTML, with step-by-step breakdowns (including screenshots).
- It runs locally via a CLI (`npx testronaut`) and doesn’t require any hosted service. You will need to provide your own OpenAI API key, however.
Current state:
- Early days: it works for simple flows and demo apps, but I’m still tuning the reliability and efficiency.
- It installs with one command and comes with a sample mission.
- Open source on npm/GitHub.
Links:
- Docs & quickstart: https://docs.testronaut.app
- GitHub: https://github.com/mission-testronaut/testronaut-cli
- npm: https://www.npmjs.com/package/testronaut
I’d love feedback from the HN community on:
- Where this could be most useful (CI/CD? flaky test replacement? exploratory testing?).
- What concerns you’d have about using an AI-driven test runner.
- Any “gotchas” I should watch out for in early adoption.
Thanks for taking a look!

value: Show HN: Testronaut – AI-powered mission-based browser testing

author: scfast


value: ninalopatina

value:
Hi HN,
Our research team just released the best performing and most efficient reranker out there, and it's available now as an open weight model on HuggingFace.
Reranker v2 was designed specifically for agentic RAG, supports instruction following (our v1 was the first to introduce this), and is multilingual.
Along with this, we're also open source our eval set, which allows you to reproduce our benchmark results.  By releasing these datasets, we are also advancing instruction-following reranking evaluation, where high-quality benchmarks are currently limited.
Please give it a try and let us know what you think.

value: Show HN: We built a better reranker and open sourced it

author: ninalopatina


value: turbodocx

value: Show HN: HTML-to-docx – generate word docs from HTML (MIT Licensed)

author: turbodocx


value: ibrahimdanish

value: Show HN: Circuit Bot - AI powered co-engineer for embedded systems development

author: ibrahimdanish


value: goosethe

value: I just think they're neat

value: Show HN: An ncurses CUDA-based fluid simulation

author: goosethe

story_text: I just think they're neat


value: daniddeme

value: Show HN: PR-desc your AI Git and GitHub workflow assistant

author: daniddeme


value: yumingh

value: I made a MacOS app that uses AI to help keep distractions off your desktop when you're working on something important, and to help get more focused work done.<p>It's paid right now because cloud/AI costs are expensive but if you want to try it and don't have the means to pay, then please DM me and maybe we can work something out.

value: Show HN: An AI productivity assistant to help you focus

author: yumingh


value: atcol

value: Show HN: Hielo – a fast, lightweight tool for managing Apache Iceberg tables

author: atcol


value: orbanlevi

value:
Mobile first open-source RSVP platform. Alternative for meetup.com / eventribe for small companies and groups. If you have a small group and don't want to pay for services you can easily selfhost this solution. Open for improvements and for feedback, ofc.<p>- One-Click Sharing - Each event gets a unique, memorable URL. Share instantly via any platform or messaging app.
- No Hassle, No Sign-Ups - Skip registrations and endless forms. Unlike other event platforms, you create and share instantly — no accounts, no barriers.
- Effortless Simplicity - Designed to be instantly clear and easy. No learning curve — just open, create, and go.

value: Show HN: Meetup.com and eventribe alternative to small groups

author: orbanlevi


value: grhayk

value:
Hey everyone,
I'm a developer who's been burned by unexpected website downtime one too many times. I tried the big-name monitoring tools but found them overwhelming for my small projects.
So, I built UptimeBuddy – a dead-simple monitoring tool that does one thing well: it checks your site and alerts you (via SMS/Email) the moment it goes down. No complex dashboards, no credit card required for the basic plan.
It's just an MVP right now, but I've put up a landing page to gauge interest.
· Landing Page: https://uptimebuddy-contact.vercel.app/
I'd love your honest feedback:
1. Does the landing page clearly explain what it does?
2. Is this a problem you've faced?
3. Would you use something like this?
Thanks for looking! Any thoughts are hugely appreciated."

value: Show HN: I built UptimeBuddy after getting frustrated with complex monitors

author: grhayk


value:
Let me start off with a disclaimer.
I'm aware that there's like a gazillion e-signing products that exist in this space — including some half decent self-hosted options too. And yes I agree that the success of incumbents such as Docusign come primarily from distribution rather than making a superb product.
But hear me out. Few enterprise tools are as universally disliked as DocuSign: 1,050 Trustpilot reviews, 1.5 stars. To me, that signals two things: (1) a burning problem, and (2) an incumbent that’s barely tolerable.
My "vision" for my current project, Formable, is to eventually create something akin to a Figma/Linear/Canva — late to market, but disruptive through usability and fresh design. One example: our fields aren’t static checkboxes. You can draw any shape/size, then apply logic with natural language (e.g. “must be a valid US state”).
I’m launching the free MVP now, with a waitlist for a much more advanced pro version. I’d love to gather some constructive feedback: what would make you actually love a paperwork tool?

value: Show HN: A more usable Docusign

title: Show HN: A more usable Docusign


value: I built Kai because I was tired of assistants that forget and cloud tools that mine my data. Persistent memory: what you teach Kai sticks. Local-first: data never leaves your device; no telemetry. Transparent: memory graph shows what it knows and why. I’m looking for early users who care about local, privacy, and explainability. The landing page has a simple waitlist; I’ll onboard in small waves and share progress.

value: Show HN: Kai – a private, offline AI "second brain" that remembers you(no cloud)


value: Hey everyone, I’m an NBA fan and Python dev, and I recently built DeepShot — a machine learning model that predicts NBA game outcomes with about 71% accuracy based on historical stats and rolling performance metrics (EWMA). It features: Real NBA data from Basketball Reference Exponentially Weighted Moving Averages to track momentum Interactive NiceGUI interface with team comparison and predictions Full Python stack and open-source (MIT license) Here’s the GitHub repo: https://github.com/saccofrancesco/deepshot And if you like it, here’s my Buy Me a Coffee: buymeacoffee.com/saccofrancesco Would love any feedback — especially from folks who’ve built sports models or worked on real-time stat tools. Also open to ideas on where to take this next (player-level modeling? betting advice dashboard?).

value: Show HN: DeepShot – an open-source NBA predictor with ML, EWMA, and live UI


value: jerrimu

value:
I tried a few low cost chat providers and thought "I can do better"
This is my earnest attempt, I built something I'm proud of and I'd love if you checked it out. I cannot afford a free tier I am sorry.
edit: supposed to be Launch HN, but it wont let me change it

value: Show HN: Seven Dollar Chat- Claude, Llama, DeepSeek and More

author: jerrimu


value:
Hi all,
I've been building yet another tool to let people build on Kubernetes and escape the pain of YAML. I've open sourced it under the Apache-2.0 license and would love some feedback.
The tool was inspired by KRO (https://kro.run) (a project recently released by Amazon) and builds on top of it. TypeKro is aimed at providing a declarative type-safe user experience to orchestrating complex kubernetes dependencies with TypeScript, so you get your intelli-sense and all the other benefits of a modern programming language.
It is differentiated in a few ways:
1. TypeKro provides a seamless development experience where you can access status fields that will exist at runtime like as though they're already populated to make the development experience seamless. TypeKro or KRO will be able to handle orchestration seamlessly in the javascript runtime or in the operator in the kubernetes cluster respectively.
As a illustrative example:
```typescript
const database = StatefulSet({ ... });
const webapp = Deployment({
    name: 'my-app',
    image: 'my-app:latest',
    env: {
        // This looks like a normal property access, but it's
        // a type-safe reference that resolves after deployment!
        DATABASE_HOST: database.status.host,
    },
});
```
2. TypeKro has 3 deployment modes to give you flexibility including rendering gitops ready yaml, deploying directly to your kubernetes cluster with a topological sorted DAG, and deploying your infrastructure as a KRO resource graph definition.
These three deployment approaches let you develop with a short-feedback loop and give you flexibility in deployment.
3. TypeKro is Kubernetes control-plane aware and will even stream relevant kubernetes events to your terminal while you're deploying resources.
4. TypeKro is smart enough to delay Custom Resource deployments until their CRDs are ready since the Kubernetes API won't accept CRs until their CRDs are ready.
5. TypeKro tries to expose everything with a very simple API integrated with arktype so you can build and consume kubernetesComposition() using tools familiar to typescript developers, and it has built in ability to integrate with your existing tool stack. You can use it to deploy your helm charts or your yaml manifests along with your new resources.
If you're interested, a good place to start is the docs site: https://typekro.run/
You can access the source code here (all stars appreciated): https://github.com/yehudacohen/typekro
And if you want my very long winded thoughts about TypeKro's software architecture and its place in the ecosystem, you can read my very long blog post here. Fair warning it's long:
https://yehudacohen.substack.com/p/introducing-typekro
And I created a discord which currently has 0 users. So if you'd like to be first, pop on in!
It's still early days, so while I hope this provides a good experience I'm sure there are still bugs that aren't captured by my test suite. I've also only tested it against my local kind cluster so far.
I'd love to improve this and make it the easiest way to deploy to kubernetes. There are many things I'd love to do with TypeKro, including integrating resources from Crossplane and cloud controllers and other 3rd party resources with their intricate composition dependencies. I'd also love to try it out with and extend it to support multi-cluster deployments where resources span infrastructure boundaries.
All contributions are most welcome!

value: Show HN: TypeKro – TypeScript infra-as-code for Kubernetes built on KRO


value: mazahermuraj

value:
I built Paletra (https://paletra.cc) to simplify the frustrating workflow of designing accessible color palettes across light and dark modes.
The idea came from a designer I work with - she was using ChatGPT to generate tints, Coolors to visualize them, and WebAIM to check contrast, all before copying them into Figma.
Paletra aims to streamline all that:
- Generate tints, shades, and dark mode versions from a base color
- View WCAG contrast scores (live, on both black and white backgrounds)
- See matching complementary colors
- Preview colors on actual UI components (buttons, hover/disabled states, etc.)
- Save palettes into folders
- Export as JSON/CSS/SCSS
- Figma plugin: apply palettes directly OR analyze any component's accessibility
Still a work in progress—would love feedback on:
- The color design workflow
- UX and clarity of accessibility guidance
- Anything you'd want it to do better
Site (Free to start): https://paletra.cc
Thanks for checking it out!

value: Show HN: Paletra – Build WCAG ready color palettes and test them on components

author: mazahermuraj


value: Show HN: Multi-Scene Full 3D Context from CCTV


value: zyakita

value:
Hi everyone,
I've been working on an open-source project called Deep Research OSS and would love to get some feedback from the community.
I was frustrated by AI research tools that only performed a single/double-pass search and often missed important details.
To solve this problem, I built a multi-agent pipeline that mimics how a human team works.
First, it creates a broad plan. Then, it uses agents to conduct research. Finally, it has another agent perform a gap analysis to identify missing information and assign new tasks to dig deeper. Additionally, to avoid errors, AI is forced to use code execution for all numbers in the report.
The whole thing is built specifically for the Gemini API to see how far it can be pushed. I've been using it for my own stock analysis, and the iterative process seems to generate much more comprehensive reports.
It's still a work in progress, and I'm sure there are better ways to approach this. I'm looking for contributors and feedback to improve the pipeline and make it more robust.
If you're interested in multi-agent systems or just want to help build a better research tool, you can check out the repo.
https://github.com/zyakita/gemini-deep-research-oss

value: Show HN: Open-Source] Deep Research Assistant Built Solely for Gemini API

author: zyakita


value: reverseCh

value:
Hey HN! I've built Video-2-Prompt, an AI tool that converts videos into detailed content prompts.
*What it does:*
- Analyzes videos (MP4, MOV, AVI, WebM) and platform URLs (YouTube, TikTok)
- Generates 600-800 word professional analysis reports
- Provides insights on engagement factors and content strategy
*Key Features:*
- Multi-AI integration with intelligent fallback
- Deep analysis mode for comprehensive insights
- Real-time processing with progress indicators
- Platform-agnostic video processing
*Tech Stack:*
- Next.js 14, React 18, TypeScript
- Serverless architecture (Vercel)
- Smart video analyzer with retry mechanisms
*Use Cases:*
- Content creators analyzing viral videos
- Marketing teams understanding competitor strategies
- Researchers converting video content to text
*Live Demo:* https://video2prompt.org
The tool is free to use. I'd love feedback on analysis quality and feature suggestions!

value: Show HN: AI-powered video analysis tool that generates 800 word content prompts

author: reverseCh


value: Beefin

value:
I built a minimal personal site that’s entirely static—no frameworks, just `index.html`, `styles.css`, and a bit of vanilla JS. It has a geotagged photo album that renders a Leaflet map and a lightbox gallery from a simple `images/gallery.json`. I wrote small scripts to bulk ingest photos, read EXIF lat/lng, upload to S3/CloudFront, and update the JSON. It’s fast, portable, and easy to host on GitHub Pages or any static host.
- Live site: [ethan.dev](https://ethan.dev)
- Tech: HTML/CSS/JS, Leaflet, Markdown frontmatter, static JSON
- Looking for feedback on: accessibility, mobile performance, and better static content organization without a build step.

value: Show HN: Show HN: Geotagged Photo Map

author: Beefin

title: Show HN: Show HN: Geotagged Photo Map


value: glenntws

value:
Karton (German for “carton”) is a type-safe and web socket based solution that allows you to define a shared definition of what is synchronized between server and client side (the “Karton contract”):
- The type of state that is synced between the server- and client-side (read-only on client-side)
- Server procedures that clients can call to make mutations on server-side
- Client procedures that servers can call to make queries etc. to the client-side
All connections share the same state, and the state is always defined by the server. Deltas are efficiently synced through JSON-style patches (using immer under the hood).
We’re Glenn and Julian, and we built this as a tool for our startup (stagewise - YC S25). We needed a simple and type safe solution to sync a CLI app (JS-app) with a browser-based UI-app (we use React). We didn’t find any solutions out there that seemed easy to use, so we simply decided to build our own one.
We offer both the server and client in vanilla TS, but also ship a React-specific client that gives you a Zustand-style access to the state (allowing to only select slices of the whole state as well).
The API is made to be very easy to use, with just one output type that’s shared between server and client apps, making it a low-overhead solution if you build locally running JS apps that consist of multiple sub-apps.  Karton is MIT-licensed, available on npm (@stagewise/karton) and currently part of our bigger OSS monorepo. It’s pretty much in beta state but we already use it in production.
GitHub: https://github.com/stagewise-io/stagewise/tree/main/packages...
NPM package: https://www.npmjs.com/package/@stagewise/karton
Our YC Launch: https://www.ycombinator.com/launches/O3M-stagewise-the-front...

value: Show HN: Karton is a simple, type-safe RPC and state-syncing framework (OSS,MIT)

author: glenntws


value: frydcircuit

value: Pump Tracker lets you follow the trades of top Solana KOLs and influencers as they happen. It automatically pulls their swaps (Pump.fun, Raydium, etc.), calculates holdings and PnL, and highlights their best plays so you can see who’s actually winning in the meme coin game.

value: Show HN: Solana KOL wallet tracker Pumptracker.io

author: frydcircuit


value: StellaMary

value:
I’ve been experimenting with building a Node.js HTTP framework using Rust + Hyper with N-Api.
The project started as a fully synchronous server, but I recently patched it to support async (Tokio/Hyper). That change alone nearly doubled throughput in benchmarks.
What’s different:
* Built directly on Hyper, no heavy abstraction
* napi glue for Node.js bindings
* Minimal core (sync and async engines side-by-side)
* Focused on raw performance, not plugins (yet)
This is still experimental and not production-ready. I’d like feedback on:
* Async API design — does it feel ergonomic?
* Middleware style — what patterns should be supported?
Repo: [https://github.com/Shyam20001/rsjs](https://github.com/Shyam20001/rsjs)
Full benchmarks: [https://shyam20001.github.io/rsjs/](https://shyam20001.github.io/rsjs/)

value: Show HN: Created a Node.js's addon that can handle 1M req/s

author: StellaMary


value: yayasoumah

value:
Hi HN,
I built Pocket Agent — a mobile-first interface for coding agents like Claude Code, Cursor, Codex, Gemini, and OpenCode. It lets you connect to your own dev environment (or cloud agents) and interact with them in real time from your phone.
Pocket has two parts:
 • Pocket Server (open-source): lightweight server (Mac + Linux today, Windows soon) you run on your machine. It securely exposes your environment to Pocket.
 • Pocket App (beta): your mobile interface. You can:
 • Chat with agents (Claude, Cursor, Codex, Gemini, etc.)
 • Run and monitor terminal sessions in real time
 • Approve or deny file edits, searches, or commands
 • Launch and track cloud background agents
 • Manage multiple projects/sessions — all from your phone
Traction in the first 24 hours:
 • 1,400+ unique visitors
 • 350+ beta signups
 • 130+ GitHub stars
 • Community already contributing: Linux support, Tailscale pairing, Claude Code auth integration
Repo: https://github.com/yayasoumah/pocket-server
Website: https://www.pocket-agent.xyz
I’d love your feedback — especially if you’re building or using CLI agents. What would make you adopt and trust a mobile-first agent workspace?

value: Show HN: Pocket Agent: run Claude, Cursor, Codex and more from your phone

author: yayasoumah


value:
Hi HN,
I made Pixfy.io. Yes. It’s just another AI wrapper — but at least it gives you an alternative way to edit photos.
With Pixfy you can:
* Describe changes like “remove background” or “make this photo brighter”
* Swap faces, try different hairstyles or outfits
* Restore or enhance images
* Generate new images from text prompts, then keep editing them

value: Show HN: Yes, another boring AI Image Editor


value: jonpizza

value: Show HN: I fine-tuned GPT4.1 on my iMessage history

author: jonpizza


value: AIKit is a minimal TypeScript wrapper that gives you unified access to the generation APIs of OpenAI, Anthropic, and Google Gemini—complete with streaming, multimodal inputs, and tool calling. No extra runtime packages: just the fetch that ships with modern Node and browsers.

value: Show HN: AIKit - Minimal library for calling OpenAI, Anthropic, Gemini gen APIs


value: lakshikag

value:
I'm an indie hacker with a bad habit: I have way too many side projects. Some are thriving and some are just a few lines of code and for a long time, all of them were costing me money.
The first thing I do when I start a new side project is add analytics. I want to see how people use it, if they're even using it at all. But I couldn't stomach another monthly subscription.
The worst part of analytics platform was that even the free plans had a catch. They'd limit me to a few thousand events a month, or they'd just delete all my data after 30 days. It was useless for tracking long-term growth. And the paid plans weren't much better. They'd cap you at a few sites and even then, your data retention was only a few years. If I had to track a dozen side projects, the costs would balloon to an amount that just didn't make sense for me.
I knew there had to be a better way, so I decided to build my own. I wanted a solution where I didn't have to pay per site and where I actually owned my data.
That’s how berrylog.app was born.
The core idea is simple: you bring your own database. Every single log, every event, and every page view from all of your projects is saved directly to your Supabase database. You are the sole owner of your data. You get an unlimited, historical record of your analytics for all your projects without paying a recurring fee to some company just to access your own information.
I honestly just made it for myself because I was tired of paying a fortune for something so essential. But after using it for a while, I realized it was too useful to keep to myself. This wasn't just my problem; it's a shared pain point for every indie hacker.
So, I’m sharing it with the community. I've built an analytics platform that truly respects the indie hacker's wallet and sanity. It’s a one-time payment for unlimited websites and unlimited events. No more paying a subscription for a project that might never get off the ground.
Berrylog.app is my contribution back to this community. I would love for you to give it a try and let me know if you have any suggestions.

value: Show HN: Stop paying for analytics. I built a Bring-Your-Own database platform

author: lakshikag


value: jackdecker

value: Show HN: CivMD – CivitAI model downloader in your terminal

author: jackdecker


value: Aitizazk

value:
Hi HN,
I just launched CodingReady, a free website to practice coding interview questions in a LeetCode-style environment powered by AI.
While practicing sometimes all you need is a nudge in the right direction or quick feedback specific to your code that's where the AI companion comes to the rescue. Instead of looking up entire solutions, get instant feedback specific to your code.
Feedback and questions welcome.

value: Show HN: CodingReady – Free Coding Interview Practice Platform

author: Aitizazk


value: Akzid

value:
Hi HN  I built Simple PDF Scanner because I was frustrated with bloated scanner apps that require subscriptions.
v1.0 includes: OCR, password-protected PDFs, custom filenames, camera + photo import, A4 support.
Planned for v1.1: quality controls, estimated file size, more paper sizes, history.
I’d love feedback – what features do you always miss in simple scanner apps?

value: Show HN: Simple PDF Scanner – fast, one-time iOS scanner app

author: Akzid


value: Show HN: The Asteria Programming Language


value: hieuwu

value: Show HN: Kudos Snap – AI-Powered Professional Kudos Messaging App

author: hieuwu


value:
I built Nano Banana Pro (nanobanana.pro
), a simple web app for text-to-image generation and context-aware editing using Google’s official flash image API.
Upload an image (JPG/PNG/WebP, up to 6MB)
Edit with text prompts or blend styles
Get consistent, high-quality results in seconds
It’s free to try (first enhancement is free). Feedback and ideas welcome!

value: Show HN: Nano Banana Pro – AI image editing powered by Google's official API


value: alaserm

value: Show HN: How to create FREE decentralized lotteries

author: alaserm


value: vbaro

value:
A couple of years ago, when Apple added Metal shader support to SwiftUI (iOS 17), I tried to learn shaders. Most resources I found were either too academic (assuming graphics background) or just showed cool effects without explaining how they worked. I wanted something that built intuition step by step.
So I started taking notes. Those notes turned into a structured guide, and eventually into a full course — which I’ve now put online for free.
The course walks through shaders from first principles:
 - How shaders plug into SwiftUI (.colorEffect, .distortionEffect, .layerEffect)
 - Coordinate systems and why UV space matters
 - Colors as vectors (RGB/HSV, blending, interpolation)
 - Functions like sin, fract, and smoothstep that show up everywhere
 - Procedural patterns, randomness/noise, distance fields
 - Animation, texture sampling, performance, accessibility
Each chapter builds on the previous one, includes SwiftUI-ready code, and ends with challenges (honestly, the challenges were for me the best part to learn). There’s also an optional paid Xcode project with all the working shaders, but the course itself is free.
A side note: I didn’t write this by pasting prompts into an AI and spitting out chapters. I learned the material by using Claude as a study partner — asking it to explain formulas, clarify artifacts I saw, or come up with challenges based on my notes. That iterative back-and-forth helped me actually understand the math instead of copying code blindly.
If you’ve ever been curious about shaders but found the material intimidating, this is meant to be an approachable path in. Would love feedback — both on the course itself and on how others have used AI as a learning tool rather than just a code generator.
PS: iOS Dev Weekly featured the course recently, which was very encouraging.

value: Show HN: Metal.graphics – Metal shaders course for SwiftUI

author: vbaro


value: superproton

value:
I built Duebase AI to solve a problem I kept running into in fintech - analyzing UK company financial health takes forever.
The process usually goes: download PDFs from Companies House → manually extract data to spreadsheets → calculate ratios → interpret trends. Takes 3-4 hours per company and requires serious financial expertise.
The technical challenge: Companies House filings are messy. Inconsistent formats, complex accounting structures, missing data, and you need to understand UK accounting standards to make sense of it all.
My approach:
Parse 15M+ UK company records from Companies House API
Built ML models to extract and normalize financial data from varied filing formats
Created scoring algorithms that weight liquidity, profitability, leverage, and growth trends
Generate 1-5 health scores with explanations in plain English
What it does:
Instant financial analysis of any UK company (30 seconds vs 4 hours)
Real-time monitoring with alerts for new filings/director changes
Risk detection that catches declining trends early
No financial background needed to understand results
The hardest part was handling the data inconsistencies - UK companies file in different formats, use various accounting frameworks, and often have incomplete information. Had to build a lot of data cleaning and normalization logic.
Currently focused on the UK market since I know the regulatory landscape well, but the approach could work for other countries with similar public filing systems.
Link: https://duebase.com

value: Show HN: I built AI that turns 4 hours of financial analysis into 30 seconds

author: superproton


value: GravyPouch

value:
Prompting arena site where users win money breaking prompts and earn money creating them. Alternative to https://hackaprompt.com / https://app.grayswan.ai/arena,
Open to feedback/improvements/bugs.
- Uses USDC on polygon just like polymarket.
- Fun and a good way to actually prove your prompting abilities.
- See every attempt live as it's happening and improve from what your opponents are doing.

value: Show HN: Chat.win, Win money breaking prompts

author: GravyPouch


value: Show HN: ProServer – Sign .ipa files with ease


value: qwikhost

value: Real-time speech-to-speech translation for videos, streams, and meetings. Works with YouTube, Netflix, Zoom, and Google Meet.

value: Show HN: VoiceHop – Real-Time Audio and Video Translation

author: qwikhost


value: ddahlen

value:
This is a Python (with Rust backend) library for high accuracy orbit calculation of the entire known catalog of asteroids. It is a full N-Body integrator, including lots of additional physics, such as relativistic effects, non-spherical gravitational fields of planets, masses of large asteroids, and a number of non-gravitational forces such as radiation pressure. The design goal is essentially JPL Horizons on your laptop, but all asteroids at once.
I began this project while I worked at Caltech on a NASA mission called NEO Surveyor (which is a space telescope designed to survey Near Earth Objects). It is being used by NEO Surveyor to predict previously known asteroids in images to reduce processing costs (since it is computationally expensive to process unknown asteroids). I wrote this generally enough that it is also being used by the SPHEREx and Roman space telescopes for a similar purpose.
I got it open sourced before I left caltech for a PhD, and I am continuing to develop it as a part of my PhD.
Here is a plot of my favorite group of asteroids, the Hildas, because of Jupiter their orbits are synchronized such that they make a triangle:
https://dahlend.github.io/kete/auto_examples/plot_mpc_state....

value: Show HN: precision asteroid orbital dynamics library

author: ddahlen


value: Show HN: EPIC.CSS – An epic dark themed CSS framework


value: muhammetarda

value:
I built Linkfy, an iOS app that strips tracking junk (utm_*, fbclid, etc.) from URLs.
Runs entirely on-device, no server
Works via share sheet or inside the app
Keeps essential parts of the link intact
I made it because I was tired of sharing messy, tracking-filled links.
App Store: https://apps.apple.com/us/app/ai-link-cleaner-linkfy/id67497...
Code (Swift package): https://github.com/zeynep-yildiz/Linkify
Happy to hear feedback and edge cases!

value: Show HN: Linkfy – On-Device URL Cleaner for iOS

author: muhammetarda


value:
I build runcell, an AI Agent in Jupyter Lab. It can understand context (data, charts, code, etc) in your jupyterlab and write code for you.
Runcell has built-in tools that can edit or execute cells, read/write files, search web, etc.
Comparing with AI IDE like cursor, runcell focus on building context for code agent in jupyter environment, which means the agent can understand different types of information in jupyter notebook, access kernel state, edit/execute specific cells instead of handling jupyter as static ipynb file.
Comparing with jupyter ai, runcell is more like an agent instead of a chatbot. It have access to lots of tools to work and take actions by its own.
You can use runcell with simple "pip install runcell" to start.
Any comments and suggestions are welcome.

value: Show HN: AI Agent in Jupyter – Runcell

title: Show HN: AI Agent in Jupyter – Runcell


value: milindsoni

value:
Hi HN! I built TipTour because I'm frustrated with React Tour and similar libraries that force users through rigid, linear onboarding flows. Users should be able to discover your product naturally, not be railroaded through predetermined steps.<p>What TipTour does differently:
- Always-on, ambient help - The tooltip follows your cursor smoothly, providing context without blocking anything
- Non-intrusive - No overlays, no "Next" buttons, no forced flows. Users stay in control
- Discovery-friendly - Add contextual hints that appear as users naturally explore your interface

value: Show HN: TipTour, A Tooltip that guides you

author: milindsoni


value: matt-npl-public

value: Show HN: SocialRails

author: matt-npl-public

title: Show HN: SocialRails


value: abilafredkb

value:
I’ve been building something I’ve always wished existed: a way to discover and connect with people across the world, but without the endless scroll or shallow matches.
Eintercon is a social app where you get paired with people globally, but each connection lasts only 48 hours. The idea is to give just enough time to have a meaningful exchange before you hop off to meet someone new. It’s not about collecting followers — it’s about sparking real conversations, learning about other cultures, and keeping things fresh.
A couple of things that make it different:
Global-first design: built to make cross-border connections effortless.
48-hour connection window: keeps the experience engaging, without endless ghost chats.
Mailing & Labs: you can also drop into themed chatrooms ("Labs") filled with people from diverse cultures, or send cross-border messages to test how connections spark.
Passport feature: answer light “visa-style” questions to connect intentionally with people from specific regions.
We’re live on iOS (testflight) and web. I’d love feedback on:
Does the 48-hour limit feel like a good balance between fleeting and meaningful?
Are there other cultural-exchange features you’d expect or want in something like this?
Does the name “Eintercon” work for you, or is it too clunky? (I’ve been getting mixed reactions).

value: Show HN: Eintercon – A 48-hour social window to meet people worldwide

author: abilafredkb


value:
Hey HN!
We built PeekShot, a tool to automate website screenshots at scale. It’s aimed at developers, SaaS founders, marketers, and QA teams who need consistent, pixel-perfect captures — without clicking around manually.
Why we built it:
We kept running into the same problem: getting consistent, high-res screenshots for dashboards, changelogs, QA, and marketing. Existing tools were either flaky, slow, or expensive — so we decided to build our own.
Key Features:
- REST API → start capturing in <60s
- Flexible modes → full-page, viewport, or element-level
- Device & viewport simulation → mobile, desktop, custom sizes
- High-res output → Retina, HD, custom DPI
- Webhook delivery, custom headers, proxy support
- Integrates easily with CI/CD, dashboards, analytics pipelines
Free Tier: 100 credits, no credit card required.
I’d love feedback — happy to share API examples, discuss integrations, or hear how you’d use this!

value: Show HN: PeekShot – Automate Website Screenshots via Simple REST API


value: clogg

value: It's a small project of a generic formula calculator with lazy evaluation and streaming API, written in Go. Possible applications include various expression evaluators, spreadsheet-like processors, etc. Functions and values can be added on the fly for highly dynamic calculations. Generic (as in Go) types for keys and values give extra flexibility and widen the range of applications.

value: Show HN: Generic streaming calculator written in Go

author: clogg


value: Show HN: Build simple knowledge graphs from videos you watch


value:
Hey HN,
I’m 14 and I’ve been obsessed with startups for a while. I noticed one big problem: most of us (especially first-time founders) waste months building something that nobody wants. So I built Startup Solve — an AI-powered platform that helps you stress-test your startup ideas before you write a single line of code.
It includes six tools:
~ AI Co-Founder → brainstorms with you like a smart partner.
~ Startup Oracle → pressure-tests your idea and asks the brutal questions VCs would.
~ Funding Predictor → gives an estimate of whether your idea could raise funding.
~ Idea Incubator → helps refine and niche down broad ideas.
~ Viability Scanner → scores viability, market size, and competition.
Growth Engine → generates potential go-to-market strategies.
The goal isn’t to replace founders — it’s to save time, sharpen clarity, and help you focus on the right problems instead of chasing noise.
I built this without a coding background, learning step by step along the way. It’s still early, but a few people who’ve tried it said it feels like having a “reality-check partner” for their startup.
I’d love feedback from HN — both brutally honest criticism and ideas on how to make this more valuable for serious founders.
https://startup-solve.lovable.app
Thanks for reading.

value: Show HN: Startup Solve – AI toolkit for startup clarity


value:
My co-founder and I have spent years building and launching side projects, most of which have failed. We've accumulated a graveyard of abandoned codebases, and one day we had the idea to sell one of them. The process sparked an idea: what if there was a marketplace specifically for digital projects at every stage of their life, from a simple idea to a profitable business?
Today, we're launching just that—an online auction platform where founders can sell their projects. We're talking about everything from:
Startup ideas (just a concept or landing page)
MVPs and unfinished projects
Failed startups
Active projects
Profitable businesses
We're a two-person team just starting out, and we're currently trying to get our first 100 listings. We're facing the classic chicken-and-egg problem, and it's a grind.
This isn't a polished, corporate product. We know it has a long way to go, but we wanted to get it out there to the HN community and get your honest feedback.
We're not here to just sell something; we're here to solve a problem we've personally experienced. We'd appreciate any thoughts on the platform itself, or any ideas on how we can reach more founders who might be interested in listing their project.
Thank you for your time.

value: Show HN: An auction platform for failed and active startups


value: shashanoid

value: Show HN: ApiJuice – Create an API for anything in seconds

author: shashanoid


value: BaraBatman

value: Show HN: Easy CLI tool to cancel multiple vercel builds at once

author: BaraBatman


value: pablo-chacon

value:
I wrote an AI-Agent Vulnerability and Risk Report. It’s a technical writeup covering the new attack surface AI-agents and LLMs introduce. The report is objective and focused on security, legal, and regulatory risks, not speculation.
The report covers:
The attack surface of AI-agents and LLMs.
Exploitation pathways such as prompt injection, privilege misuse, and workflow manipulation.
The legal and technical difference between prompt injection and SQL injection.
Regulatory exposure (e.g. GDPR liability) when data leaks occur.
Mitigation strategies to reduce risk, including backend immune layers.
Full report:
https://github.com/pablo-chacon/AI-Agent-Vulnerability-and-R...
Would love to hear feedback, especially from those working in AI security, infrastructure, or compliance.

value: Show HN: Now we have systems vulnerable to social engineering

author: pablo-chacon


value: vladsh

value:
I just want code reviews to be useful. They should be about trade-offs, sharing knowledge, and holding each other accountable, not on policing style, naming quirks, or catching trivial regressions and security issues.
Tools similar to Cursor Bugbot flood PRs with noise, run in a black box, and bill you per seat for the privilege... they try to solve problems too late, in the wrong place, and without your developer environment tools.<p>That turned into AgentCheck: an open-source subagent that runs locally with five focused reviewers - logic, security, style, guidelines, and product. It bakes in a lot of internal experience in building enterprise products, working in agentic-native SDLC, and solving Claude Code shenanigans.
If you’re curious about the internals or have ideas for new reviewers, feel free to ask questions or contribute.

value: Show HN: AgentCheck – Local AI-powered code review agents for Claude Code

author: vladsh


value: nevodavid

value: Show HN: GrowChief – open-source social media outreach tool

author: nevodavid


value:
i built TXTOS because my models kept forgetting and bluffing. i wanted a portable fix that works across providers without code or setup. TXTOS is a single .txt you paste into any LLM chat. it boots a small reasoning OS that gives you two things by default: a semantic tree memory that survives long threads, and a knowledge boundary guard that pushes back when the model is out of scope.
what it is
plain text. no scripts, no trackers, no api calls. MIT. the file encodes a protocol for reasoning, memory, and safety. you can diff it and fork it. it is not “a clever prompt”. it behaves like a tiny OS that the model follows.
why it exists
after debugging a lot of RAG and agent stacks, the same failures kept coming back. memory broke across sessions. the model answered outside its knowledge without warning. i wanted a zero install layer that i can carry between models and keep the same behavior.
what you get
* semantic tree memory. it records ideas and relations, not just tokens. it can recall earlier branches, avoid repetition, and keep tone stable.
* knowledge boundary test. ask something impossible, then run the built in check. it flags risk and proposes a safe path instead of hallucinating.
* simple rules. cite then explain. stop when sources or offsets are missing. show a short audit trail. keep answers compact when you ask for compact.
try in 60 seconds
1. download TXTOS.txt, open a fresh chat with any model you like
2. paste the file content, then type: hello world
3. test memory: ask three related questions, ask it to recall the first one exactly, then switch topics and come back
4. test boundary: ask for something unknowable or very recent, then type kbtest. watch how it handles the boundary
5. optional: restart the chat and paste the same file. see how the tree helps you rebuild state fast
what to expect
less overtalking. better recall of your own constraints and tone. refusal on missing citations. early warnings when your question is outside scope. the file is small by design, so even weaker models can use it. stronger models show bigger gains.
not just marketing
TXTOS came out of real failures. it was built to stop two user facing problems that cost time and trust. forgetting, and unearned confidence. since it is plain text, the community can audit it and improve it.
looking for feedback
* what did the memory tree get right or wrong for your workflow
* which boundary cases still slip through
* what small operator would you add to the OS so it helps you daily
url is in the link field above. thanks for reading. if you break it, even better. tell me how you did it and i will ship the fix.

value: Show HN: Txtos for LLMs – 60 SEC setup, long memory, boundary guard, MIT


value:
We built SwiftAI, an open-source Swift library that lets you use Apple’s on-device LLMs when available (Apple opened access in June), and fall back to a cloud model when they aren’t available — all without duplicating code.
SwiftAI gives you:
- A single, model-agnostic API
- An agent/tool loop
- Strongly-typed structured outputs
- Optional chat state
Backstory: We started experimenting with Apple’s local models because they’re free (no API calls), private, and work offline. The problem: not all devices support them (older iPhones, Apple Intelligence disabled, low battery, etc.). That meant writing two codepaths — one for local, one for cloud — and scattering branching logic across the app. SwiftAI centralizes that decision. Your feature code stays the same whether you’re on-device or cloud.
Example

  import SwiftAI

  let llm: any LLM = SystemLLM.ifAvailable ?? OpenaiLLM(model: "gpt-5-mini", apiKey: "<key>")

  let response = try await llm.reply(to: "Write a haiku about Hacker News")
  print(response.content)

It's open source — we'd love for you to try it, break it, and help shape the roadmap. Join our discord / slack or email us at root@mit12.dev.
Links
- GitHub (source, docs): https://github.com/mi12labs/SwiftAI
- System Design: https://github.com/mi12labs/SwiftAI/blob/main/Docs/Proposals...
- Swift Package Index (compat/builds): https://swiftpackageindex.com/mi12labs/SwiftAI
- Discord https://discord.com/invite/ckfVGE5r and slack https://mi12swiftai.slack.com/join/shared_invite/zt-3c3lr6da...

value: Show HN: SwiftAI – open-source library to easily build LLM features on iOS/macOS


value: secwang

value: I really like csvq, but its implemented features feel a bit heavy for me. I don't really need to perform statistics directly on CSV files, but simple modifications are what I need. Plus, I don't have a Go environment, so I created a simple awk version. All test cases are included in the except script.

value: Show HN: Csvqsh – SQL-like query language for CSV in Awk

author: secwang


value: byintes

value:
Hey HN, I built Yoink AI to solve my biggest frustration with AI tools: they constantly break my workflow. I was tired of copy-pasting between my apps and a chatbot just for simple edits.
Yoink AI is a macOS app that brings the AI to you. With a simple hotkey (⌘ Shift Y), it works directly inside any text field, in any app. If you can type there Yoink can write there
Key Features:
- Automatically captures the context of the text field you're in, so you dont have to manually prime it
- Create custom voices trained on your own writing samples. This helps you steer the output to match your personal style and avoid generic, robotic-sounding text
- Yoink doesnt just dump text and run. It delivers suggestions as redline edits that you can accept or reject, keeping you in full control.
It's less of a chatbot and more of a collaborative writing partner that adapts to your workflow, not the other way around.
There's a free tier with 10 requests/month and we just launched a pro trial, which will get you 100 requests for the first 7 days to try it out!
I'm here to answer questions and would love to hear what you think - like all early stage start ups, feedback is always deeply appreciated

value: Show HN: Yoink AI – macOS AI app that edits directly in any textfield of any app

author: byintes


value:
Why develop it?
First of all, I don't like the idea of all in one. In particular, I almost only need words. I have used Typora. It is really good, but it can hardly satisfy me except editing. So I wonder if there is a simple Markdown editor that can manage files and has a good editing experience? Then there is MarkFlowy.
Features:
MarkFlowy has been independently developed for 2.5 years.Based on the design philosophy of being lightweight, simple, and highly available, it now boasts a considerable number of functions such as:
- Local Priority: MarkFlowy is a pure editor designed to edit your local content. The synchronization method is left to the user's choice. Currently, it provides a Git manager and will later integrate with other similar cloud storage services for synchronization. Feel free to provide feedback on this.
- User Experience First: It offers multiple editing modes, including source code and WYSIWYG (What You See Is What You Get). Additionally, it supports custom themes. Furthermore, high performance is the foundation of a good user experience. MarkFlowy has a lightweight package size and specialized performance optimization. Real-world testing shows that on an M1 Mac device, editing documents with over 200,000 characters can still ensure smooth writing in both Chinese and English.
- AI Support: MarkFlowy integrates with multiple AI vendors such as Deepseek, Openai, and Ollama. It also supports using their features within the editor for tasks like Q&A, summarization, translation, etc., to enhance your writing efficiency.
- Simplicity: Out-of-the-box usability. You only need to familiarize yourself with Markdown syntax to use it easily. It also provides synchronization methods such as Git and a powerful file management tree.
Apart from these features, MarkFlowy can edit text files (txt), JSON files, and view image files, among other useful functions. We welcome everyone to experience, exchange ideas, and participate.

value: Show HN: MarkFlowy – A Markdown editor, which is lighter, smarter and purer


value: Show HN: Cchistory – Extract shell command history from Claude Code sessions


value: Built this on my no code WhatsApp agent platform (https://wassist.app) using the Kiwi flights Remote MCP released yesterday - check it out! Super clean on-the-go experience

value: Show HN: Search and book flights directly from WhatsApp


value: wagslane

value: Show HN: The Boot.dev Training Grounds

author: wagslane

title: Show HN: The Boot.dev Training Grounds


value: Dhavidh

value: I've built a tool that empowers customers to retrieve real-time data and take direct actions on your website, all powered by ChatGPT Agent Operator and Anthropic Computer Use. No more digging around-just ask and act, Posting for the 10th time because am bad at marketing and don't know how to get my first customer

value: Show HN: Posting My Startup for 10th Time, An AI Navigation and Task Assitant

author: Dhavidh


value: samfeldman

value:
A few years ago I bought the birthdays.app domain, and decided to make a birthday reminder app based only on text reminders—SMS in the US, WhatsApp outside the US.
This year, I've focused on improving the app's importing options. Previously, it only supported adding friends manually or via text.
Earlier in the year, I built a Google Calendar sync flow (using OAuth), and just recently, made a Chrome extension to import Facebook friends' birthdays.
The flow is—install the extension, pull Facebook birthdays, select friends, then import to birthdays.app (using an import code from the web app).
I'm unsure how many people still use Facebook, but suspect many mainly use it for birthdays nowadays (and may prefer text reminders instead).
I built this with a text-reminder-first mindset (over push notifications, calendar format, etc) because of the simplicity. I'd actually built my own makeshift solution a few years prior to building birthdays.app (using Google Sheets + Zapier SMS), and found the solution to work well.
To me, the text app feels like the social home of the phone (for real friends, at least), and it feels right for the birthday reminders to show up there.
Thanks for reading, and open to any thoughts around the Chrome extension or the birthday reminders in general :)

value: Show HN: I built a Chrome extension–export Facebook birthdays to text reminders

author: samfeldman


value: creativedg

value:
I started this boilerplate in July 2020 and I’ve been maintaining it for 5 years. It began on Next.js 9 and kept upgrading to Next.js 15+ (App Router), while upgrading the stack over time (Tailwind 1 → 4, ESLint 8, swapping Cypress → Playwright, etc.). The goal is simple: I kept rebuilding the same setup, so I packaged it and kept it updated.
What you get (preconfigured, keep only what you need):
- Next.js 15 (App Router) + TypeScript + Tailwind 4
- Auth with Clerk (magic links, MFA, social, passkeys)
- I18n via next-intl
- DB with Drizzle ORM (PGlite locally)
- Forms with React Hook Form + Zod validation
- Testing: Vitest (unit), Playwright (integration/E2E)
- CI with GitHub Actions; Storybook for UI work
- SEO (Open Graph, JSON-LD, sitemap, robots)
- Observability: Sentry, logging with LogTape, log management & uptime/monitoring
- Security: Arcjet (bot detection, rate limiting, shield rules)
- DX details: ESLint/Prettier, Lefthook + lint-staged, Commitlint, absolute imports, bundle analyzer
- AI code review
It’s free and open source (MIT). Today the project sits around 11.8k GitHub stars and 2.2k forks. I’m still actively maintaining it and adding features.
Repo: https://github.com/ixartz/Next-js-Boilerplate
Why I built it
Spinning up auth, a DB, i18n, tests, and lint/format/CI for each new app was repetitive. This gives me (and hopefully you) a production-ready base in minutes, with opinionated defaults you can start.
I’m open to suggestions and feedback, what would you like to see next? I’ll hang around in the comments to answer questions.

value: Show HN: Open-source Next.js 15 boilerplate – auth, DB, intl, tests, monitoring

author: creativedg


value: ajcatton

value:
I’m currently starting the process of selling a house for the first time (wish me luck!). As part of this, I began investigating the world of listing agents and their commission models and, like many before me, was pretty disappointed with the incentives built into the usual fixed-percentage commission structures: Basically the most rational thing for a listing agent to do under this model is to convince you to sell for whatever semi-reasonable price you can get quickly and without too much effort.
This is especially concerning given the amounts of money involved  — real estate sales and purchases tend to be among the biggest and most consequential ones people make in their entire lives.
I wondered: what approach do I wish I could take, particularly one that doesn’t require “changing the whole broken system”.  My favorite answer so far is Progressive Commissions: which reward listing agents very little for just getting a sale done at a low price, but scale up the reward quickly if they can get you a better and better sale price.
This page is a dynamic explainer / calculator to help people understand the concept and even come up with a specific commission structure that works for their home sale.

value: Show HN: Home sellers: incentivize your agent to get you a higher sale price

author: ajcatton


value: CeresBroker

value: Show HN: Leadchee, a CRM for consultants and small teams who hate HubSpot bloat

author: CeresBroker


value: kashishhora

value:
Hey everyone!
We've been working with several MCP server maintainers and we noticed some difficulties getting drop-in logging and identity attribution working out of the box with existing vendors. A couple of challenges we hoped to solve were:
- Baseline piping of tool calls to traditional vendors
- How to tie tool calls to a “user session”
- Understanding the context behind tool calls made by agents
So we built something. :) The MCPcat library is completely free to use, MIT licensed, and provides a one-line solution for adding logging and observability to any vendor that supports OpenTelemetry. We added custom support for Datadog and Sentry because we personally use those vendors, but we’re happy to add more if there’s interest.
Here’s how it works:

  mcpcat.track(serverObject, {...options…})

This initializes a series of listeners that:
1. Categorize events within the same working session
2. Publish those events directly to your third-party data provider
Optionally, you can redact sensitive data. The data never touches our servers (unless you opt in to additional contextual analysis, which I mention below).
Some teams might also want a better understanding of “what use cases are people finding with my MCP server.” For that, we provide a separate dashboard that visualizes the user journey in more detail (free for a high baseline of monthly usage and always free for open source projects).
We have two SDKs so far:
Python SDK: <https://github.com/MCPCat/mcpcat-python-sdk>
TypeScript SDK: <https://github.com/MCPCat/mcpcat-typescript-sdk>
Other SDKs are on the way!

value: Show HN: MCPcat – A free open-source library for MCP server monitoring

author: kashishhora


value: logicprog

value:
Hi! I vibe coded a small tool that I think is relatively unique — at least, I haven't found anything like it — and I've found personally extremely useful. I don't know if anyone else will, but I'm sharing it just in case someone does find it useful. Basic description:
HTML Commenter is a zero-dependency, self-contained document annotation tool for any static HTML page. This system allows users to annotate the text on a web page, auto-saves their comments to local storage, and lets them share their comments with others via compressed links or back them up with JSON files, all without requiring a server or external libraries.
The goal of this project is to provide a lightweight collaborative document annotation experience for people who:
- don't want to use SaaS and/or heavy PWAs
- don't want to have to install and maintain software on their own server
- don't want to ask users to install software (like LibreOffice)
don't want to have to send - multiple versions of files around like it's 1990 and we're dealing with Word Documents
- prefer writing in plain markup languages (like Markdown, Org, or HTML), exporting to HTML, and posting their writing to their domain (either self hosted or on things like NeoCities)
Github: https://github.com/alexispurslane/html-commenter/
URL for trying it out: https://alexispurslane.github.io/html-commenter/try.html

value: Show HN: HTML Commenter

author: logicprog

title: Show HN: HTML Commenter


value: alessandro-a

value:
Hey HN,
I built ShadowGit a while back to automatically commit code every minute to a hidden git repo (.shadowgit.git). Original goal was to easily rollback when AI tools break things.
But I discovered something interesting: this minute-by-minute history is perfect context for AI assistants.
So I built an MCP server that lets Claude/Cursor query this history using native git commands. The results surprised me:
Before: Claude would read my entire codebase repeatedly, burning 15,000+ tokens to debug issues.
After: Claude runs `git log --grep="drag"` finds when drag-and-drop worked, applies that fix. 5,000 tokens.
How it works technically:
1. ShadowGit watches your project and commits every save to .shadowgit.git (parallel to your main repo)
2. MCP server exposes git operations to AI:
   - `git diff HEAD~10 -- component.tsx` to see recent changes
   - `git log -S "functionName"` to find when code was added
   - `git show COMMIT:file.js` to retrieve working versions
3. AI naturally understands git, so instead of dumping context, it surgically queries what it needs
It's free while I am testing and improving it.
I am looking for feedback on the project and maybe some integration ideas beyond MCP.
Thanks!
Alessandro

value: Show HN: I cut my Claude API bill by 66% with Git-based context

author: alessandro-a


value:
I built a small tool that lets you transform any photo into realistic pencil drawings using AI — no Photoshop skills required.
The idea came from seeing how many people love hand-drawn portraits but don’t have the time (or budget) to commission an artist. With this, you can upload a photo, type a simple prompt, and get back a graphite-style pencil sketch in seconds.
How it works:
upload an image
The AI generates a pencil drawing with shading and detail that looks hand-drawn.
You can experiment with different styles (realistic, artistic, etc.) to get unique results.
Try it here: https://www.pencilart.app/

value: Show HN: I used nano banana to create art – in just a few seconds


value: SunTree

value:
Key features:
• Pomodoro timer built directly into VS Code (no switching apps)
• Integrated Brown noise for background ambient(no more juggling Spotify + timer + focus apps)
• "Work Hours" concept – closes at 6pm, opens at 8am in your timezone
• Customizable work/break durations

value: Show HN: Dolpo – A VS Code Pomodoro Timer with Brown Noise

author: SunTree


value: aliatwa

value:
I built a small tool to solve a debugging pain I hit constantly: switching between Stripe dashboard, AWS CloudWatch, and Sentry just to track one error.
So I made an embeddable React log viewer that unifies logs from multiple services. You can drop it directly into your own Next.js/React app (e.g., an admin panel or dev tool) and see everything in one place.<p>What it does
Connects to Stripe, AWS CloudWatch, and Sentry (more sources coming)

value: Show HN: An embeddable log viewer for AWS, Sentry

author: aliatwa


value: sagarkava

value:
Hey HN,
This newsletter didn’t come from a growth hack or content strategy.
It started with frustration.
Every week, I was drowning in blogs, PR blasts, and Twitter threads trying to keep up with Voice + AI. New models dropping. Partnerships overnight. Startups in India and Asia pushing infra upgrades that no one was covering.
But whenever I wanted to know what really mattered, the signal was buried under the noise.
And there wasn’t a single newsletter focused on India’s Voice AI ecosystem — most only covered the US.
So I built the thing I wished existed: Voice AI Stack — a newsletter on India, Asia, and global Voice AI updates.
What you’ll get every Friday
Product launches that actually move the Voice + AI ecosystem forward
Infra upgrades & strategic deals (with context on why they matter)
Advances in speech tech, translation & agent performance
A spotlight on VideoSDK’s AI Agent features — what’s shipping, and what’s next
If you’re a developer, PM, researcher, or just curious about the future of AI voices & agents in India and beyond — this is for you.
Behind the Scenes
Last night at 11:30 pm, we were testing our VideoSDK AI agent. Everything was running perfectly—smooth, steady, no problems at all.
Then suddenly, every agent started speaking in opera voices. Instead of answering questions, they were singing like they were on stage in Italy.
We couldn’t stop laughing. Then came the panic. And finally, the fix.
That’s what building in this space is really like—messy, surprising, and full of moments you don’t expect. Behind every polished demo, there are nights like this: bugs, laughter, and small wins that make the journey worth it.
This newsletter is my way of opening that door for you. A peek into the experiments, the stumbles, the “wait, did that agent just…” moments that make this space exciting.
Subscribe here to stay in the loop.
https://videosdkweekly.substack.com/p/welcome-to-voice-ai-st...
And if you’ve got a friend building or curious about Voice AI — forward this to them.
Let’s cut through the noise, together.
See you tomorrow
Sagar Kava

value: Show HN: Welcome to "Voice AI Stack" Weekly – A Home for Voice AI Builders

author: sagarkava


value: scottfr

value:
Hey HN, I wanted a grammar checker that didn’t send my writing to someone's servers, so we built Grammit, a Chrome extension that runs grammar checks locally using an LLM. Your text never leaves your computer during checking.
Here’s a 2-minute overview: https://www.loom.com/share/baf501ee6cf14a919a7384128246ed67
Because it uses an LLM, it catches more than spelling and grammar. For example, it can correct some wrong statements like “The first US president was Benjamin Franklin.”
Grammit also includes an in-page writing assistant that can rephrase or draft new text. It also uses the local LLM.
We used many new web features to build this, such as:
- Chrome’s new Prompt API to talk to the local model.
- Anchor Positioning API to place the UI with minimal impact on the DOM.
- CSS Custom Highlights API for inline error marking.
- The new CSS sign() function to create CSS-driven layout with discontinuities.
Part of the fun of being early adopters of bleeding edge tech is we’re discovering new Chrome bugs (e.g., https://issues.chromium.org/issues/428354426, https://issues.chromium.org/issues/428039224).
I’d love your feedback on:
- Where the UX feels rough
- What do you think of the corrections and suggestions
Happy to answer questions about the tech or the Prompt API. Thanks for trying it out!
Chrome Web Store extension link: https://chromewebstore.google.com/detail/grammit-the-ai-gram...

value: Show HN: Grammit – Local-only AI grammar checker (Chrome extension)

author: scottfr


value:
Hi, I’m Chris and my partners and I are building Devplan, an AI product development tool that helps teams go from idea to working code faster.
What Devplan does:
- Creates deep contextual understanding from Github and the web with our open source context engine: https://github.com/devplaninc/contextify
- Generates right-sized PRDs, user stories, and tech design based on company context
- Gives a ballpark effort and complexity estimate for every user story
- Breaks down requirements into structured coding prompts for tools like Claude Code, Cursor, Windsurf, or JetBrains Junie
- Integrates with Linear and Jira to push generated project docs and tickets to your tracking system
- Lets you kick off projects with images to refine specs with mocks, diagrams, or screenshots
- Exports detailed coding prompts as standalone files or use our CLI to work with them directly
Why we built it:
We believe the next generation of product development will be built with AI at its core. But we’ve seen first-hand how the current tools fall short:
- Docs from ChatGPT or Claude are useful but too general and lack context for real workflows
- AI coding agents lose context quickly in large repos and generated code often requires re-work
- Most approaches to planning for AI coding takes too long and isn't shared or reviewed, which slows teams down
AI should remove that friction, not create more of it. We built Devplan to make planning and execution one connected flow. It starts with outcomes, adapts to the size of your project, and produces structured inputs for the coding tools you already use. Instead of bouncing between AI assistants, PM docs, and code editors, Devplan ties it all together so you can move faster without losing context.
We have an MVP template for side projects, but the platform is being built for real teams who want to ship product with confidence while staying lean. We are still early and we’re iterating quickly.
Would love to hear feedback from other builders. What’s working for you when it comes to planning and building with AI?
P.S. If you want to try it, public beta is open: https://www.devplan.com

value: Show HN: Devplan – Generate specs and coding prompts with deep context


value: cevian

value:
We've been building text-to-SQL at TigerData and kept hitting the same problem: evaluation tools that tell you your accuracy score but nothing about how to improve it.
Getting a 60% pass rate is meaningless if you don't know whether failures are from bad schema retrieval or poor SQL generation. It's the difference between actionable insights and meaningless benchmarketing.
So we built, and are now open-sourcing, text-to-sql-eval with a simple insight: run every query three different ways:
- Normal mode - let the system retrieve schema and generate SQL
- Full schema mode - provide all tables to test upper bound accuracy
- Golden tables mode - give it the right tables to isolate reasoning issues
The performance delta between modes tells you exactly what's broken.
PostgreSQL-specific because database quirks matter for correctness. Works with any LLM or text-to-SQL system. Includes an LLM-as-judge option because deterministic matching produces too many false negatives on complex queries.
We've been using this internally to improve our (also open-sourced) text-to-sql system.
Open sourcing both the eval suite and a companion tool for generating test datasets from your production schema.
Built with uv for easy setup. TimescaleDB for tracking results over time. Simple Flask UI for exploring failures.
Try it, break it, tell us what's missing.

value: Show HN: An Open-Source Eval Suite That Helps You Fix Postgres-Based Text-to-SQL

author: cevian


value: Show HN: cc-hooks-ts – A type-safe, extendable hook builder for Claude Code


value: yehosef

value:
I've been playing with the VAERS database for a while and I just got through importing it into duckdb for local analytics.  The data set has various problems that I try to fix to improve the quality of the dataset.
The import scripts are https://github.com/yehosef/vaers-duckdb
A ready data file is available here (~3GB) https://drive.google.com/file/d/1d3wRRr2UFvCYR9r7J5XBym2dYKT...
I'm planning to add some dashboards like my original elasticsearch project.  I would also like to add some embeddings for vector search. WIP
I'd love to hear what you think!

value: Show HN: VAERS DuckDB Database

author: yehosef

title: Show HN: VAERS DuckDB Database


value: CeresBroker

value: Show HN: HN: AI-powered email campaigns that convert leads


value: vvoruganti

value:
Hey everyone want to show off a demo we've been working on for a while that makes use of our memory and reasoning engine https://honcho.dev
Get interviewed by an AI agent that helps you generate unique information that other users (or agents!) can then pay to ask questions about.
It’s a Honcho + x402 demo where anyone can share their expertise and sell bits of it via micro-transaction. You can actually get paid for the valuable context in your head!
Read more about the story at: https://blog.plasticlabs.ai/blog/Penny-for-Your-Thoughts

value: Show HN: Penny for Your Thoughts

author: vvoruganti

title: Show HN: Penny for Your Thoughts


value: lordknish

value:
I’ve been working on an open-source project called StreamGrid. It’s a desktop app (Electron + React + TypeScript) that lets you watch and manage multiple streams in a grid you design yourself.
The goal is to give complete control over how streams are arranged. For example, you can set up one big “main” stream surrounded by smaller feeds, or just lay everything out in an even grid. Drag and drop to rearrange, resize on the fly, and save/load different grid setups for different use cases.
Features
Flexible layouts: create any arrangement of streams you want (2, 5, or more).
Stream management: add/remove streams, custom names/logos, save and export your setups.
Grid management (new in v1.2.0): save multiple configurations, switch between them, rename and organize presets.
Platform support: YouTube (video, live, shorts), Twitch (live with chat), HLS, MPEG-DASH, and now local video files.
Chat integration: YouTube and Twitch chats in draggable, resizable windows.
Performance: virtualized rendering for smooth 50+ stream grids, player pooling for lower memory, web workers for layout, debounced state updates, lazy chat loading.
Cross-platform: available for Windows, macOS, and Linux.
What’s new in v1.2.0
Virtual rendering and player pooling for much smoother performance.
Grid management system with save/load.
Local file playback support.
Improved error handling and recovery.
Cross-platform packaging with electron-builder (NSIS, DMG, AppImage, DEB, RPM).
Try it
Download prebuilt binaries from Releases: [GitHub Releases link]
Or build from source (Node.js 18+, npm 9+).
Repo: https://github.com/LordKnish/StreamGrid
I’d be interested in feedback from anyone who’s tried building video-heavy Electron apps, or who has ideas for making the system more efficient.

value: Show HN: StreamGrid – Open-Source Multi-Stream Viewer

author: lordknish


value: jeanhaiz

value:
I always struggled to build simple authorisation systems. It’s a multi-layer mess: API-level role checks, countless object-user DB tables, and SQL/ORM spaghetti just to manage rights.
With NPL, my authorisation lives in a single place.
When I create a new protocol instance (NPL data objects), I attach user attributes that define who can read and do what.
And that’s it, the NPL runtime takes care of the rest — I have a fully authorised backend without adding layers and layers of authorisation.

value: Show HN: NPL, a programming language with authorization as first‑class citizen

author: jeanhaiz


value: ragp

value:
Hey everyone!
I spend a lot of time in git worktrees in Claude Code to do tasks in parallel. I made this to create and manage them easier w/o mental overhead.
Simple to create/list/delete worktrees, as well as a config for copying over .env/other files, running install commands and opening your IDE into the worktree.
Would love to get feedback. Feel free to open any issues or PRs! https://github.com/raghavpillai/branchlet

value: Show HN: Branchlet – Git Worktree TUI for Claude Code/Cursor/Codex etc.

author: ragp


value: I built a small project that integrates Gemini Flash Image but makes it more affordable to use compared to the official API. It allows you to generate high-quality images with the same model, and also provides simple editing features for refining results. The goal is to make experimenting with Google’s image model cheaper and more accessible for people who create a lot of images.

value: Show HN: Gemini Flash Image – A Cheaper Way to Use Google's Image Model


value: jimishol

value: Show HN: Cholidean Harmony Structure

author: jimishol

title: Show HN: Cholidean Harmony Structure


value: dubadub

value: Show HN: CookCLI New Big Release

author: dubadub

title: Show HN: CookCLI New Big Release


value: Jocund

value: Show HN: No-Cloud Needed – A License for Devices That Work Without the Cloud

author: Jocund