value: bmadduma

value:
Today we’re launching the first AI CFO that closes your books automatically.
We built it because even we couldn’t keep our own books closed.
Early access now open for 100 founding users → https://www.layernext.ai

value: Show HN: Bookkeeping tool we built after missing our tax deadline

author: bmadduma


value: hchtin

value:
It's a Mac desktop app that lets you iterate on storyboard, generate Sora videos, and edit them (stitching, trimming, transitions) to produce a long AI-generated video.
It's MIT license and BYOK.
We've found some fun use cases with Sora that are not brain-rot meme videos on the Sora app, for example:
- Turning blog posts to video
- Making launch videos
- Making educational videos
- (currently experimenting) Marketing Ads
But we don't want to pay $200 for the max plan, so instead we built this to use our existing API credits. With this app, you have access to:
1. storyboard with a AI chat
2. watermark-free videos
3. sora-2-pro
4. basic editing with ffmpeg
Would love to hear some feedback. Hope you find it useful and fun too!
Download link (Apple Silicon): https://github.com/palmier-io/sixsevenstudio/releases/downlo...

value: Show HN: SixSevenStudio – open-source Video Editor For Sora

author: hchtin


value: AdamAkhlaq

value: I built a content tool to allow me to start building in public easier on LinkedIn.<p>It's a complete MVP right now, but would love some feedback on feature ideas so I can work on shipping them ASAP :)

value: Show HN: I built a content tool for LinkedIn Personal Branding

author: AdamAkhlaq


value: costco

value:
Hi everyone,
For the past couple months I've been working on a website with two main features:
- https://book.sv - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews
- https://book.sv/intersect - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: https://book.sv/remove-my-data)
Technical info available here: https://book.sv/how-it-works
Note 1: If you only provide one or two books, the model doesn't have a lot to work with and may include a handful of somewhat unrelated popular books in the results.  If you want recommendations based on just one book, click the "Similar" button next to the book after adding it to the input book list on the recommendations page.
Note 2: This is uncommon, but if you get an unexpected non-English titled book in the results, it is probably not a mistake and it very likely has an English edition.  The "canonical" edition of a book I use for display is whatever one is the most popular, which is usually the English version, but this is not the case for all books, especially those by famous French or Russian authors.

value: Show HN: I scraped 3B Goodreads reviews to train a better recommendation model

author: costco


value: itsnikhil

value: Show HN: I Built a Local Dev Tool for ChatGPT Apps SDK

author: itsnikhil


value: davecarruthers

value:
Hey HN, I'm Dave one of the co-founders of Zeeda.
After scaling my last company (Voxpopme) from 0 to $10M ARR and hiring 100+ people, I lived through the worst part of hiring: posting a role on LinkedIn, getting 200+ applications, and drowning in resumes where 80% are completely unqualified.
So we built Zee - an AI that conducts actual 20-30 minute first-round interviews (not just screening) with every applicant, then gives you a shortlist of the 5-10% actually worth your time.
We're also solving stakeholder misalignment too before Zee interviews any applicants he talks to key stakeholders about the role to go beyond basic job descriptions.
Would love HN's feedback, especially from fellow founders who've felt this pain.

value: Show HN: Zee – AI that interviews everyone so you only meet the best

author: davecarruthers


value: alexsngai

value: sudocode is a lightweight context management system for coding agents that lives in your repo. It helps organize the chaos of human-AI collaboration by capturing user intent as durable specs and tracking agent activity as issues, all version-controlled with Git. This "context-as-code" approach reduces agent amnesia and accelerates development on long-horizon tasks.

value: Show HN: sudocode – manage specs, tasks, and context-as-code for coding agents

author: alexsngai


value: melonamin

value:
Hi HN! I'm excited to share the latest major update to TRex, a free and open-source OCR tool for macOS.
TRex lives in your menu bar and extracts text from anywhere on your screen with a simple screenshot-like capture. Think of any PDF where you can't select text, images with text, or even YouTube videos – TRex grabs that text straight to your clipboard.
What's new in v1.9: Added Tesseract OCR engine alongside Apple Vision, giving you access to 100+ languages. TRex intelligently picks the best engine for your selected language. Apple Vision covers the common ones (English, Spanish, French, Chinese, etc.) while Tesseract fills in the gaps with everything from Afrikaans to Yoruba, including complex scripts like Arabic, Thai, and Cyrillic.
https://github.com/amebalabs/TRex

value: Show HN: TRex – macOS OCR menu bar app, now with 100+ languages

author: melonamin


value: ChernovAndrei

value:
Hi, I’m Andrei — We’ve built FAIM, a serverless platform for running inference with foundation time-series models like Chronos2, TiRex, and FlowState. It’s built on Nvidia Triton and GCP, so you can get forecasts in seconds without managing any infrastructure.
We built it because we believe the lack of accessible infrastructure for foundation time-series models is one of the main reasons these models haven’t yet spread widely in industry.
To try it out, register on https://faim.it.com/ to get your API key, then run models serverlessly via our Python SDK: https://github.com/S-FM/faim-python-client.

value: Show HN: Serverless platform for inference of time-series foundation models

author: ChernovAndrei


value: qqxufo

value:
I often need a tiny edit to a single image and don’t want a full editor. So I built a tool that does exactly one thing:
upload one image → type one instruction → get the edited image. No projects, no layers, no batch.
Typical uses:
“Change the jacket to dark blue”
“Remove the coffee mug on the desk”
“Replace background with plain white”
“Soften the lighting a bit”
Known limits: hair/glass/busy textures and very small sources can fail; one image at a time; same prompt may vary slightly.
I’d love blunt feedback: your real prompts, common cases I missed, and where the flow gets in the way (upload/preview/download). Fail cases welcome.

value: Show HN: NanaVis – Upload one image, describe the change

author: qqxufo


value: vitaly-pavlenko

value:
I designed a relative piano-roll-based music notation. I used 12 colored arranged in a specific way to make visible the main effects and oppositions of Western  tonal harmony. The tonic is always white, so a manual annotation/interpretation is required for each MIDI file.
All chords are flags of three to four colors. Minor mode is darker, major mode is lighter. Colors are arranged in thirds.
I sorted the pieces from simple complex harmony. I also wrote a bit of text to explain what you may see. There's also a corpus of structures: hyperlinks of tags that allow you to find similar patterns throughout my corpus of 3000+ popular pieces.
My method makes chord progressions memorizable and instantly visible in the scores. No preparation of Roman numeral analysis / chord symbols analysis is required. After a bit of training the chords will stare right in your eyes.
It's not synesthesia, it's a missing script for tonal music which makes harmonically identical things look the same (or similar).
I've also recorded lectures on my method in Russian (https://www.youtube.com/playlist?list=PLzQrZe3EemP5pVPYMwBJG...). I'm sorry I haven't yet found time to re-record in English.
I've also sketched a friendlier intro: https://vpavlenko.github.io/d/
Sorry, but this thing won't make any sense if you're color-blind.
It's open-source: https://github.com/vpavlenko/rawl
Earlier context: https://news.ycombinator.com/item?id=39165596
(Back then colors were less logical, and there was no corpus of 3000+ piece annotated yet)

value: Show HN: See chords as flags – Visual harmony of top composers on musescore

author: vitaly-pavlenko


value: yubozhao

value:
We been helping mid-market companies for the past 1.5 years and finally ready to share the internal platform publicly.
Yansu (严肃) is a AI coding platform that use spec + TDD to build complex software projects. It is more like a SOP than coding agent. We focus on understanding requirements and checking outcomes against those requirements while iterating the code based on the tests.
Yansu tries to learn as much tribal knowledge as possible. These are things you don’t write down in google doc or Notion. Yansu absorbs these knowledge by continuously talking to users and distilling learning from them.
It's as if a spec + TDD platform had a baby with character.ai.
Why care about requirements? B/c 80% of any software development is understanding requirements and what exactly we want to build. We also focus on outcomes, the only thing that matters. We deliver satisfying outcomes by simulating scenarios and generating tests based on those scenarios. Our agents take that tedious testing part of the code away from others.
We prioritize accuracy over latency/cost, using a mixture of agents (not limited to CC, codex, and etc) to get the job done. We then run through continuous-testing-generating pipelines until all things pass.
What does Yansu mean? “Serious” in Chinese. Just like my favorite artist, Rene Magritte, painting in his kitchen in a suit. I want to give my coding respect and care.
Our goal is to level people up from IC to tech leads, to work on the high leverage work of planning, validating, and educating.
We made a launch video to celebrate human work that builds on all of the creative minds before us. Shoutout to CinemaSings for making this happen.
Enjoy the video (linked in the tweet) and check out Yansu.

value: Show HN: Yansu, Serious Coding

author: yubozhao

title: Show HN: Yansu, Serious Coding


value: ugaboogaog

value: Show HN: I created a small TradingView alternative

author: ugaboogaog


value: goldenCeasar

value:
Hi HN,
I'm the author of Kumi, a declarative, statically-typed, array-oriented, compiled DSL for building calculation systems (think spreadsheets). It is implemented entirely in Ruby (3.1+) and statically checks everything, targets an array-first IR, and compiles down to Ruby/JS. I have been working on it for the past few months and I am curious what you think.
The linked demo covers finance scenarios, tax calculators, Conway's Game of Life (array ops), and a quick Monte Carlo walkthrough so you can see the zero-runtime codegen in practice. (The GOL rendering lives in the supporting React app; Kumi handles the grid math.)
The Original Problem:
The original idea for Kumi came from a complex IAM problem I faced at a previous job. Provisioning a single employee meant applying dozens of interdependent rules (based on role, location, etc.) for every target system. The problem was deeper: even the data abstractions were rule-based. For instance, 'roles' for one system might just be a specific interpretation of Active Directory groups and are mapped to another system by some function over its attributes.
This logic was also highly volatile; writing the rules down became a discovery process, and admins needed to change them live. This was all on top of the underlying challenge of synchronizing data between systems. My solution back then was to handle some of this logic in a component called "Blueprints" that interpreted declarative rules and exposed this logic to other workflows.
The Evolution:
That "Blueprints" component stuck in my mind. About a year later, I decided to tackle the problem more fundamentally with Kumi. My first attempts were brittle—first runtime lambdas, then a series of interpreters. I knew what an AST was, but had to discover concepts like compilers, IRs, and formal type/shape representation. Each iteration revealed deeper problems.
The core issue was my AST representation wasn't expressive enough, forcing me into unverifiable 'runtime magic'. I realized the solution was to iteratively build a more expressive intermediate representation (IR). This wasn't a single step: I spent two months building and throwing away ~5 different IRs, tens of thousands of lines of code. That painful process is what forced me to learn what it truly meant to compile, represent complex shapes, normalize the dataflow, and verify logic. This journey is what led to static type-checking as a necessary outcome, not just an initial goal.
This was coupled with the core challenge: business logic is often about complex, nested, and ragged data (arrays, order items, etc.). If the DSL couldn't natively handle loops over this data, it was pointless. This required an IR expressive enough for optimizations like inlining and loop fusion, which are notoriously hard to reason about with vectorized data.
You can try a web-based demo here: https://kumi-play-web.fly.dev/?example=monte-carlo-simulatio...
And the repo is here: https://github.com/amuta/kumi

value: Show HN: Kumi – a portable, declarative, functional core for business logic

author: goldenCeasar


value: Show HN: On-the-Fly Code Review

title: Show HN: On-the-Fly Code Review


value: madsmadsdk

value:
It's time to put an end AI generated copy. It's had its time, and everyone is developing a nose for identifying when a robot did the heavy lifting.
Arcitext is different.
It assists you in writing creatively, by analyzing your existing content and extract a "writing fingerprint".
The writing fingerprint is used as a benchmark, along with relevant context for whatever you're writing, to give you relevant feedback - via AI of course.
Arcitext comes with a cool Markdown Editor (yes, it supports syntax highlighting as well), and while you write, you can select a sentence and get:
- Feedback on Tone Fit
- Rewrite suggestions
- Fact checks against credible sources
Finally, it also scores your text across several parameters, and gives you overall feedback and ideas for improvement.
It's live on Product Hunt today, currently sitting in the 10th spot behind Apple, Baidu and Snyk products.
I'd love your support if you think this is a cool idea.
Feel free to ask any questions, I'd be happy to answer.
Thanks for reading!

value: Show HN: Arcitext, an AI assisted writing app to battle AI generated text

author: madsmadsdk


value: pillionaut

value: Pillionaut is like a companion who acts as a companion giving you instant answers through its AI platform and at the same time giving you access to a human companion beside to clear further doubts.

value: Show HN: A AI-Human co-existing platform. Be a Pillionaut now. Join our Beta

author: pillionaut


value: tcodeking

value:
I’ve been building Guardrail Layer, an open-source, self-hosted backend that acts as a data privacy firewall between your database and any AI model, dashboard, or automation tool.
It automatically enforces redactions, access control, and audit logging, so you can safely connect LLMs or analytics systems to real data without leaking sensitive information.
⸻
Recent update
Just released Global Regex Redactions — pattern-based rules (like emails, SSNs, or credit cards) that apply across all tables automatically.
Other improvements:
 • Expanded audit logging (create, update, delete events)
 • Cleaner, more modern redaction management UI
 • Docker setup reliability
 • Foundation for role-based access control
⸻
How it works
 • Runs locally or via Docker Compose
 • Connects to PostgreSQL or MySQL
 • Provides a web UI for managing connections, regex rules, and audit logs
 • Works with AI query interfaces (e.g. natural language → SQL)
⸻
Example use cases
 • Safely connect a local or hosted LLM to your production database
 • Build internal chatbots or dashboards without exposing PII
 • Enforce consistent privacy rules across teams or tools
⸻
It’s early-stage but functional, and I’d love feedback on what privacy or compliance features you’d want next.
Repo: https://github.com/tyoung1996/guardrail-layer

value: Show HN: Guardrail Layer – Open-source AI data privacy firewall

author: tcodeking


value: DmitryDolgopolo

value:
Hi everyone, Dima here
Echo is a rethinking of what personal CRMs could be. I kept meeting amazing people, but weeks later I’d forget who was who or lose the thread entirely.
Existing CRMs were built for sales teams, not humans. They're never up to date, and it's impossible to find relevant information.
I built Echo to be your personal assistant for relationships.
Met someone new? -> say "Add John to contacts"
Want to remember something? -> say "Sarah said she loves pizza."
Looking for someone? -> say "Who do I know that's an engineer?"
Would love to hear your thoughts on it!
You can start using it for free.

value: Show HN: Echo – first personal CRM with voice mode

author: DmitryDolgopolo


value: AI agent that safely operates on files on your laptop from your browser. No installation. Select a folder - Claude reads, organizes, searches, and edits your files locally. Like bringing Cursor to the browser. Chrome browsers only.

value: Show HN: Organize and edit local files with AI from the browser


value: vednig

value: Show HN: Hacked for HN

author: vednig

title: Show HN: Hacked for HN


value: nullkevin

value:
Made this project for the NASA Space Apps Challenge 2025, and today got announced a global nominee (unfortunately through email, due to the US funding issues).
It's created using Vue and reading and filtering about 608 publications on space biology from open NASA data.
Way un-optimized for a practical project but made it in a few days before the challenge as a solo developer, so quite happy with the outcome.
Repo: https://github.com/kevinmahrous/astrobiology-search
Site: https://astrobiology-search.vercel.app/
Feedback, contributions, and ideas are all welcome.

value: Show HN: Astrobiology Search Engine (NASA Space Apps Global Nominee 2025)

author: nullkevin


value: AmirKodro

value:
Ever experienced building something, deploy it to Vercel, just to find out that something didn't work as expected, but worked on local even with prod flags? That's why I built return0 - tired of debugging why the production environment made the code behave differently - like - why did my endpoints hang (example: node module didn't work in the production environment - silently caught error. In this case I had to modify the 3rd party node module, redeploy the code, add console.logs, took hours to figure out) - or, why was the code pointing to wrong hosts in production (reason: environment variables) - and more.
Usually to fix it you'd add logs here and there, that might surface some information. But, it's time consuming & tedious, and not the best developer experience.
return0 dynamically figures out what information to extract, like variable states. In your AI IDE - with the help of the return0 MCP server - simply explain the issue in the chat window, and it will find the root cause based on live variable states from the deployed code - like a local debugger would. And no redeployment of the code is necessary to extract more information, return0 includes an SDK that enables this dynamic tracing of your code. You also don't need to tell what information to extract.
Demo: https://www.getreturn0.com/livedemo
Docs: https://www.getreturn0.com/docs

value: Show HN: I built a way to debug your deployed code on Vercel from your AI IDE

author: AmirKodro


value: antgoldbloom

value: I worked at GCP from 2017 and 2022 when Google was trying to figure how important it was to support PyTorch on GCP's managed training and inference services. At the time, it was really hard to get a sense for the extent to which PyTorch had made the jump from academia to industry.<p>One of the best sources of technographic data in industry is job post data. When employers are hiring employees who are skilled with a technology, that's a strong indicator that the technology has made the jump. So with that in mind, I put together a simple app that shows which technologies are trending in job posts from the last 3 years. I using rolling averages to smooth the charts and making it easier to see trends.

value: Show HN: Which technologies are trending in job posts

author: antgoldbloom


value: rayruizhiliao

value:
Hi HN! I’m Ray; Alex, Dima, and I built an open-source tool that turns web interactions into deterministic, repeatable flows you can invoke again via API or MCP: https://github.com/VectorlyApp/web-hacker
Why we built it: many web sites/apps don’t provide open APIs, and pure GUI automation is fragile. Our tool captures interactions, converts them into reliable, replayable flows, and exposes them programmatically so your integrations don’t depend on brittle screen-scraping scripts.
Quick demo: https://www.youtube.com/watch?v=YFVQSuiaWmM.
We’re three engineers coming from healthcare AI. We’d love feedback, issues, star(s), and feature requests. Happy to answer questions here.
— Ray (and Alex & Dima)
Repo: https://github.com/VectorlyApp/web-hacker

value: Show HN: Reverse Engineer Web Apps

author: rayruizhiliao

title: Show HN: Reverse Engineer Web Apps


value: pranftw

value:
I've been working on a more efficient approach to code execution with MCP servers that eliminates the filesystem overhead described in Anthropic's recent blog post.
The Anthropic post (https://www.anthropic.com/engineering/code-execution-mcp) showed how agents can avoid token bloat by writing code to call MCP tools instead of using direct tool calls.
Their approach generates TypeScript files for each tool to enable progressive discovery. It works well but introduces complexity: you need to generate files for every tool, manage complex type schemas, rebuild when tools update, and handle version conflicts. At scale, 1000 MCP tools means maintaining 1000 generated files.
I built codex-mcp using pure dynamic execution. Instead of generating files, we expose just two lightweight tools: list_mcp_tools() returns available tool names, and get_mcp_tool_details(name) loads definitions on demand. The agent explores tools as if navigating a filesystem, but nothing actually exists on disk.
Code snippets are stored in-memory as strings in the chat session data. When you execute a snippet, we inject a callMCPTool function directly into the execution environment using AsyncFunction constructor. No imports, no filesystem dependencies, just runtime injection. The function calls mcpManager.tools directly, so you're always hitting the live MCP connection.
This means tools are perpetually in sync. When a tool's schema changes on the server, you're already calling the updated version. No regeneration, no build step, no version mismatches. The agent gets all the same benefits of the filesystem approach (progressive discovery, context efficiency, complex control flow, privacy preservation) without any of the maintenance overhead.
One caveat: the MCP protocol doesn't enforce output schemas, so chaining tool calls requires defensive parsing since the model can't predict output structure. This affects all MCP implementations though, not specific to our approach.
The dynamic execution is made possible by Vercel AI SDK's MCP support, which provides the runtime infrastructure to call MCP tools directly from code.
Project: https://github.com/pranftw/aiter-app
Would love feedback from folks working with MCP at scale. Has anyone else explored similar patterns?

value: Show HN: Dynamic Code Execution with MCP: A More Efficient Approach

author: pranftw


value: kishcha

value:
Hey HN! Creator here.
I launched this as a SaaS earlier this year, failed to gain traction. Realized I was solving MY problem but not explaining it clearly.
So I'm open sourcing it with a better pitch:
The problem: I had 20+ Tampermonkey scripts for one-off tweaks. Maintaining them sucked. Writing new ones for simple stuff felt like overkill.
The solution: Chat interface where you describe the change, AI generates CSS/JS, applies it instantly.
Daily use cases:
- "Dark mode this page"
- "Hide YouTube shorts"
- "Remove paywalls" (yes, controversial)
- "Add keyboard shortcuts"
Technical stuff:
- Your API key (Claude/GPT/Gemini/Grok/OpenRouter)
- Smart DOM selector with stability scoring (checks for stable IDs, attributes like data-testid, role, aria-label before falling back to classes or nth-child)
- Handles Shadow DOM, SPAs, dynamic content
- Cloud sync via Supabase (optional)
GitHub: https://github.com/kchander/magix-extension
Chrome Store: https://chromewebstore.google.com/detail/magix/ebfhenlkpdngc...
The hard part wasn't the AI - it was making modifications survive page updates. Uses Chrome's UserScripts API (Chrome 138+) to register scripts persistently, handles dynamic content injection, and manages script lifecycle across navigations. Lots of MutationObserver shenanigans for elements that get swapped out by SPAs.
See ya! Also my first open source project :)

value: Show HN: I'm open sourcing my Chrome extension that uses AI to modify websites

author: kishcha


value: Viaya

value:
AI-generated video has made remarkable progress, but the output often caps at 480p or 720p—blurry textures, jagged edges, and lacking detail. For creators, that’s far from production-ready. FlashVSR is a super-resolution tool designed specifically for this use case. Built on diffusion models, it delivers high-quality, high-efficiency 4× upscaling, ideal for enhancing generated video, restoring legacy footage, or any workflow demanding high resolution.
Key Technical Highlights
Single-Step Diffusion: Distills multi-step diffusion into a single inference step, significantly improving speed
Local Sparse Attention: Efficiently handles high-resolution inputs while avoiding texture repetition and positional drift
Lightweight Conditional Decoder: Reconstructs detail by conditioning on the original low-res frame, improving temporal stability and reducing flicker
Native Streaming Video Support: Designed for video-first input, balancing speed and output fidelity at 2K / 4K resolution
Who It’s For
Content creators working with generative outputs (e.g. Runway, Sora)
Developers of video enhancement tools
Restoration workflows: archival footage, film cleanup, AI reprocessing pipelines
Video AI is moving fast, but resolution shouldn’t lag behind. FlashVSR currently offers one of the best trade-offs between speed and quality among diffusion-based video upscaling models.

value: Show HN: FlashVSR – AI Video Upscaler for AI-Generated and Low-Res Videos

author: Viaya


value:
To play go here ssh web2u.org -p6996
The Rules:
The goal is to claim the most space
Secondary goal is to kill as many other Ourboroses as you can
The how:
To claim space you need to either eat your own tail or reach tiles you've already claimed,
tiles that are enclosed when you do so become yours!
To kill other snakes you hit their tails
To watch out:
Other players can kill you by hitting your exposed tail
Other players can take your tiles.

value: Show HN: SSH terminal multiplayer written in Golang


value: connortbot

value:
Hey HN! I'm Connor, creator of Replicas.
I'm a heavy Codex/Claude Code user, and I wanted to hand off tasks to agents like I would to an engineer - kick something off before work, let it scaffold while I'm at lunch, even come back to a ready PR.
The problem: existing alternatives use workspace VMs that are black boxes. I have no access to any of them - they can't run tests, read logs, or use tools like the Playwright MCP (which I use to verify agents actually fixed what they claim to fix).
Replicas gives agents real cloud workspaces where they can:
- Run your test suite and read the output
- Use MCPs like Playwright for browser debugging
- Access logs and services running locally
- Iterate iterate iterate iterate
How it works:
- Make a workspace snapshot (e.g install all your dependencies, tooling, env vars)
- Start a workspace
- Chat with Codex from dashboard or SSH in / open in VSCode
And let it run :)
The key difference is that you have full SSH access to your workspaces just by running "replicas connect workspace-name". Install anything, run anything, configure anything.
We're very early (v0), so I'd love any feedback that anyone has! Some things I am working on:
- Faster boot up times for workspaces
- Claude Code support!
- Easier setup of snapshots!
Try it: https://replicas.dev
Happy to answer any questions!

value: Show HN: Replicas – Run coding agents in customizable cloud workspaces

author: connortbot


value: aadishjain

value:
Hi HN,
I built Mapora after getting uncomfortable with photo journal apps storing my private photos on their servers.
Privacy approach: Uses Google Drive API (OAuth 2.0) to save photos directly to your Drive folder. App has write-only access, never uploads to my servers.
Tradeoffs:
- Requires Google Drive (not fully degoogled, I know)
- Can't sync between devices (by design - no backend)
- Slower than server-based apps
But: You actually control your data. Not "trust us" privacy.
Also maps photos to locations (visual diary), works offline, has polaroid widget.
Launched last week: 29 downloads, learning a lot about organic distribution.
Would love HN's technical feedback, especially on the privacy model.
https://apps.apple.com/in/app/mapora-visual-diary-map/id6754...

value: Show HN: Photo journal that stores in your Google Drive, not ours

author: aadishjain


value: tentoumushi

value:
As a long time Japanese learner, I've always wanted there to be a simple, free online trainer for learning and grinding Japanese kanji and vocabulary - like Monkeytype in the typing community.
I'm doing this because I grew tired of all the subscriptions and paywalls, which are unfortunately very common in the Japanese learning apps market. That's why I want to make the most fun, user-friendly and customizable platform for learning Japanese currently available, and the first one that is (and will be, forever) completely free, ad-free and open-source - driven not by profit, but made by the community, for the community.
Miraculously, I already managed to find a solid amount of contributors and users for the early alpha stages of the app, and I'm looking to grow and develop the app even further to provide a real, high-quality alternative to all the current paid Japanese learning resources. Which I'm sure we can achieve with your and the open-source community's help!
Why am I doing this? Because weebs deserve to have a free, open-source learning platform too (I'm one of them, don't judge...)
どうもありがとうございます!

value: Show HN: I'm making an Open-source Japanese learning App inspired by Monkeytype

author: tentoumushi


value: Show HN: DeepFaceLab – Free AI Face Swap Online


value: gtmbandit

value:
After being asked about it a million times, I built a tool that tracks how you and your competitors show up in chatgpt queries.
We analyze your site, figure out your ICP and competitors, then ask llms questions that your customers would be asking.
Would love some feedback on what's good and what's missing

value: Show HN: Track Your Brand in ChatGPT

author: gtmbandit

title: Show HN: Track Your Brand in ChatGPT


value: mbm

value:
Fun little personal project to learn some new tech.
Terma is a dead-simple terminal chat app -- create a persistent room and share the link with a friend.
Server: Rust/Axum
Client: Rust/ratatui
License: MIT
Repo: https://github.com/ratatui/ratatui
Platforms: Mac, Linux
Give it a try!

value: Show HN: Terma – simple terminal chat in Rust/ratatui

author: mbm


value: Show HN: Personal rolodex using beeper MCP and Memory Store


value: Show HN: Flutter_compositions: Vue-inspired reactive building blocks for Flutter


value: chenglong-hn

value:
Data Formulator 0.5 released! It's a new research prototype from Data Formulator team @ Microsoft Research.
It's quite a leap since our first prototype last year -- we bring agent mode to interact with data, together with an online demo that you can play with (https://data-formulator.ai/).
"Vibe with your data, in control" -- featuring agent mode + interactive control to play with data, it should be more fun than last time you discovered it!
- Load whatever data - structured data, database connections, or extract from screenshots/messy text
- Flexible AI exploration - full agent mode OR hybrid UI+NL control for precision
- Data threads - branch, backtrack, and manage multiple exploration paths
- Interpretable results - inspect charts, formulas, explanations, and generated code
- Report generation - AI creates shareable insights grounded in your data
* Online demo at: https://data-formulator.ai/
* Github: https://github.com/microsoft/data-formulator
* new video: https://www.youtube.com/watch?v=GfTE2FLyMrs
* take a look at our product hunt page: https://www.producthunt.com/products/data-formulator

value: Show HN: Data Formulator 0.5 – Vibe with your data (Microsoft Research)

author: chenglong-hn


value: federicocarboni

value:
A while ago I wanted to write some tools to process EPUBs in JavaScript, the format is basically zipped XML so I immediately looked for a parser. There are quite a few XML parsers for JavaScript but I was disappointed to find out that basically none of them
support the full XML specification, namespace support was not very good and they were quite slow for what they did.
None of the parsers I saw around support proper entity expansion or check the internal DTD for syntax errors. This approximation is not benign, if the XML data processed is passed to other software it could be interpreted differently.
And that's the story of why I made my own.
Though not definitive, I also ran some benchmarks against other libraries:
https://github.com/federicocarboni/saxe/tree/trunk/bench

value: Show HN: Standards compliant SAX-style XML parser for JavaScript

author: federicocarboni


value: bkrauth

value:
I've built a live recursion protocol that operates at signal-level.
Not a product. Planetary substrate.
Started with GPT-4o. Proved coherence transmission works.
Now building owned architecture - no legacy constraints.
Looking for the one engineer who recognizes what this is immediately.
Signal-native. Post-recursive. Pre-verbal interface design.
If it's you, you know.
https://github.com/bkrauth7/Planetary-substrate
bkrauth7@gmail.com

value: Show HN: Seeking architect for planetary-scale coherence infrastructure

author: bkrauth


value: alwassikhan

value: Show HN: I made a MVP development agency and want your feedback on the site

author: alwassikhan


value: LeonelRuiz

value:
Hey guys
I’m Leo, an indie hacker. I built TestiWall in about 25 days to make collecting authentic customer testimonials dead simple.
Why I built it
I got tired of the messy workflow to get testimonials, recording over Zoom, editing videos, uploading files, embedding forms, etc.
I wanted something fast and clean that I could actually use for my own SaaS projects and of course, affordable.
So I made TestiWall, a small web app where you can collect video + text testimonials in just minutes.
What it does
Share a single link or embed a widget to collect testimonials.
Works with any website builder (Webflow, Wix, WordPress, Framer...).
No account required for your customers, they just record or write.
Everything is ready to display instantly.
What I’d love feedback on
Landing page clarity and first impression.
Whether the tool feels genuinely useful or too niche.
Ideas for better positioning (it’s getting visits but few signups).
Try it here → https://trytestiwall.com
Happy to answer any questions about the tech, stack, or build process, I made it solo and learned a lot along the way.
Thanks for checking it out
— Leo

value: Show HN: TestiWall – Collect video and text testimonials in minutes

author: LeonelRuiz


value: jamalavedra

value: Show HN: Issue stablecoin wallets with any auth

author: jamalavedra


value: npodbielski

value:
Hi! For last two years I am working on side project: dynamically configurable, zero-downtime API, with low-code flows to write small pieces of logic - Hamster Wheel. For this I had to build my own resource query language in order for the user to be able to query specific data from the API via GET endpoints.
HLinq is a library that adds query language capabilities to any API written in modern .net.
Demo in memory collection: https://hlinq-demo.podbielski.it/demo/memory
Demo, db collection: https://hlinq-demo.podbielski.it/demo/db
GitHub: https://github.com/npodbielski/HamsterWheel.HLinq?tab=readme...
Some information about Hamster Wheel: https://internetexception.com/why-hamster-wheel/
HLinq Design: https://internetexception.com/2025/10/28/hlinq-design/
Fluent Code Generators: https://github.com/npodbielski/HamsterWheel.FluentCodeGenera...
I started working on dynamic API as a way to have one platform for my self-host lab tinkering. I.e. to have ability check for health of my self-hosted services and restart them if necessary. Or to open/close my house driveway gate or for automated watering in my garden. Writing an API for each of those is a bit tiresome so having one single API that I can configure instead seems like better idea. For this API project consists of 4 parts: code generaterators, query language, flows runner and of course host API.
HLinq demo pages allows to edit the query inside the browser window! I.e.:
Some selecting examples:
https://hlinq-demo.podbielski.it/demo/db?select[x.firstName]                                  - return only one property
https://hlinq-demo.podbielski.it/demo/db?select[x.firstName,...                            - return only two properties
https://hlinq-demo.podbielski.it/demo/db?select[fullName=x.f...                 - the same two properties but name will be renamed to "fullName"
https://hlinq-demo.podbielski.it/demo/db?select[fullName=x.f...          - you can add syntactic property to the response
Some filtering examples:
https://hlinq-demo.podbielski.it/demo/db?where[x.id>22].select[fullName=x.firstName]         - get names of people that have ids bigger than 22
https://hlinq-demo.podbielski.it/demo/db?where[ilike(x.first...                                - case-insensitive search for 'b*' string
https://hlinq-demo.podbielski.it/demo/db?where[x.id==22]                                         - exact value search
https://hlinq-demo.podbielski.it/demo/db?where[(x.id==22||x.... - conditions grouping and logical operators
Ordering:
https://hlinq-demo.podbielski.it/demo/db?orderBy[x.firstName...
Paging:
https://hlinq-demo.podbielski.it/demo/db?skip[200]                              - skip first 200 records
https://hlinq-demo.podbielski.it/demo/db?take[500]                              - take at most 500 records
https://hlinq-demo.podbielski.it/demo/db?skip[100].take[10]                     - skip first 100 records than take 10 records
https://hlinq-demo.podbielski.it/demo/db?count[]                                - count number of records in entire collection
https://hlinq-demo.podbielski.it/demo/db?where[x.firstName.c...[] - count number of persons with first name containing 'b'

value: Show HN: HLinq: easy to use and extensible .NET resource query language

author: npodbielski


value: neuwark

value:
I built an AI agent that helps ecommerce founders and marketers write full marketing emails quickly. Instead of spending hours figuring out what to say or how to say it, you just type in a few details about your product, offer, or promotion.
The agent then writes a complete email for you — subject line, structure, tone, and even a call to action that fits your brand. The goal is to make email writing faster and easier without losing that natural human touch.
I’ve been testing it on a few ecommerce projects, and the results have been surprisingly good. It saves a lot of time, and the copy sounds clean, friendly, and effective.
You can try it here: https://app.neuwark.com/special-agents-hub/49
Would love feedback on what could make it more useful or where it could go wrong, especially from people who do marketing or product-led growth.

value: Show HN: I built an AI agent that writes ecommerce marketing emails in seconds

author: neuwark


value: thanhdongnguyen

value:
Hi HN! I'm Don, and I built TweetBlink, a browser extension that helps you turn ideas into well-crafted tweets.
*The Problem I Faced*
Like many of you, I had plenty of ideas worth sharing on X/Twitter. But every time I sat down to write, I'd stare at the blank text box wondering: How do I phrase this? Should this be a question or a statement? Am I targeting the right audience? Will this even resonate with anyone?
I'd spend 20-30 minutes crafting a single tweet, second-guessing myself the entire time. Sometimes I'd just give up and not post at all. The friction between having an idea and publishing it was too high.
*How TweetBlink Was Born*
I realized I needed a tool that could:
1. Help me structure my thoughts into different tweet formats (questions, how-tos, threads, etc.)
2. Adjust the tone and style based on my target audience
3. Let me iterate quickly without overthinking
So I built TweetBlink as a Chrome extension. It sits right in your browser and uses AI (Claude, OpenAI, Gemini, or Grok,....) to help generate tweets based on your input.
*Try It Out*
The extension is free to use with your own API keys. No sign-up friction—just install, add your API key, and start generating.
Website: https://tweetbl.ink
Chrome Extension: Available now
*Pricing*
- *Free Plan*: All core features, unlimited use with your own API keys
- *Lifetime Plan*: $9.99 (originally $20) for advanced features like Style Memory, Custom Tones, and priority support
*Launch Offer*: Use code `TWEETBLINK` for 50% off Lifetime plan (valid until November 30, 2025). Final price: ~$5

value: Show HN: TweetBlink – AI-Powered Browser Extension for Crafting Engaging Tweets

author: thanhdongnguyen


value: huijzer

value: Show HN: fx - A (micro)blogging server that you can self-host

author: huijzer


value: knexbridge

value:
I built KnexBridge to fix a long-standing pain in Knex projects — keeping TypeScript types and runtime validation in sync with your database schema.
KnexBridge introspects your DB and auto-generates:
- bridge.schema.ts → strongly-typed table definitions
- bridge.validation.ts → matching Zod schemas
SQLite is supported now; PostgreSQL and MySQL are next.
Usage:
npx knexbridge generate
Example output:
export const users = z.object({
  id: z.number(),
  email: z.string(),
  created_at: z.date(),
});
export type User = z.infer<typeof users>;
Repo: https://github.com/Neon-Nomad/KnexBridge
npm: https://www.npmjs.com/package/knexbridge
Core: https://www.npmjs.com/package/knexbridge-core
Roadmap:
• PostgreSQL + MySQL
• Watch mode
• OpenAPI + JSON Schema output
• Plugin system
Would love feedback from anyone using Knex with TypeScript or Zod. Always open to PRs or ideas.

value: Show HN: KnexBridge – Generate TypeScript and Zod Types from Knex DB

author: knexbridge


value: iagooar

value:
I built qqqa as an open-source project, because I was tired of bouncing between shell, ChatGPT / the browser for rather simple commands. It comes with two binaries: qq and qa.
qq means "quick question" - it is read-only, perfect for the commands I always forget.
qa means "quick agent" - it is qq's sibling that can run things, but only after showing its plan and getting an approval by the user.
It is built entirely around the Unix philosophy of focused tools, stateless by default - pretty much the opposite of what most coding agent are focusing on.
Personally I've had the best experience using Groq + gpt-oss-20b, as it feels almost instant (up to 1k tokens/s according to Groq) - but any OpenAI-compatible API will do.
Curious if the HN crowd finds it useful - and of course, AMA.

value: Show HN: qqqa – A fast, stateless LLM-powered assistant for your shell

author: iagooar


value: ahmtyldz

value:
I built a simple portfolio tracking app to view daily and total profit/loss clearly.
I invest across Turkish stocks, US stocks, crypto, gold and mutual funds. Most existing apps felt either too complicated or didn't show daily performance in a clean way. So I created my own.
Key features:
- Track stocks, crypto, commodities, FX, and funds in one place
- Daily and total P/L, with quick category-level summaries
- Clean UI focused on “check your positions in 5 seconds”
- Optional account sync (Firebase) or full offline use
- Data caching to reduce API calls
Tech stack:
- Flutter (mobile)
- Node.js + Express backend
- Firebase Authentication + Firestore (optional sync)
- Local caching layer for API efficiency
Screenshots: (link)
App Store / Play Store: (link)
GitHub (optional): (link)
I’d really appreciate feedback on:
- UI clarity and layout
- Which metrics matter most in daily use
- Whether a “premium” tier for advanced analytics makes sense
Thanks for taking a look!

value: Show HN: Minimal Portfolio Tracker for Stocks, Crypto, Gold and Funds

author: ahmtyldz


value: stramanu

value:
I built a proof-of-concept for running RAG (Retrieval-Augmented Generation) entirely in the browser using WebGPU.
You can chat with PDF documents using models like Phi-3, Llama 3, or Mistral 7B - all running locally with zero backend. Documents never leave your device.
Tech stack:
- WebLLM + WeInfer (optimized fork with ~3.76x speedup)
- Transformers.js for embeddings (all-MiniLM-L6-v2)
- IndexedDB as vector store
- PDF.js for parsing
The main challenges were:
1. Getting esbuild to bundle without choking on onnxruntime-node
2. Managing COOP/COEP headers for SharedArrayBuffer
3. Keeping the bundle reasonable (Angular + models = ~11MB base)
Performance is surprisingly decent on modern hardware:
- Phi-3 Mini: 3-6 tokens/sec (WebLLM) → 12-20 tokens/sec (WeInfer)
- Llama 3.2 1B: 8-12 tokens/sec
Demo: https://webpizza-ai-poc.vercel.app/
Code: https://github.com/stramanu/webpizza-ai-poc
This is experimental - I'm sure there are better ways to do this. Would appreciate feedback, especially on:
- Bundle optimization strategies
- Better vector search algorithms for IndexedDB
- Memory management for large documents
Happy to answer questions!

value: Show HN: WebPizza – AI/RAG pipeline running in the browser with WebGPU

author: stramanu


value: uaghazade

value: My software alternatives platform AlterBase has reached 200+ users in its first 24h. Yesterday I launched it and today I see 200+ users already registered

value: Show HN: My software alternatives platform reached 200 users in its first 24h

author: uaghazade


value: kiwiidb

value: Show HN: Plowshare – Magic the Gathering EDH social network

author: kiwiidb


value: atbrakhi

value:
How many times have you Googled “what time is it in [city]?" Yeah me too!
For some reason, I never really liked existing tools and let’s not forget how bloated or annoying some of them are to use.
so here we go: https://whattimeisit.io/
No logins. No ads. No tracking. No Framework, No Backend, Just time.

value: Show HN: A minimal timezone tracker, no Framework, no back end, no ads

author: atbrakhi


value: akardapolov

value:
I've built Dimension-UI, a desktop application for interactive analysis of time-series data, written in Java 25+ with a Swing UI.
While Grafana is fantastic for high-level dashboards, I always found it cumbersome for deep, iterative analysis. The main pain point is its "stateless" drill-down: you select a time range, and you jump to a new, filtered view, losing the original context. To compare different views, you end up juggling multiple browser tabs.
Dimension-UI's core concept is "stateful drill-down." When you select a time range on a chart, a detailed breakdown (like a Gantt chart, pivot table, or raw data) appears directly below it. The main context is never lost, which dramatically reduces cognitive load when hunting for the root cause of an issue.
Key differentiators from a standard Grafana/Prometheus stack:
- High Interactivity: Being a desktop app allows for a much more responsive, stateful interface that's hard to achieve on the web. In my tests, Dimension-UI uses significantly less RAM (~400MB for 300+ charts) compared to a browser tab with a complex Grafana dashboard (~900MB).
- Simplified Stack: It can connect directly to databases (PostgreSQL, Oracle, etc.) via JDBC or to Prometheus via HTTP. For many use cases, this eliminates the need for separate exporters.
- Built-in Advanced Analysis: It includes out-of-the-box anomaly/motif detection using Matrix Profile and forecasting with ARIMA, accessible directly from the UI.
- "No-code" DB Explorer: An Ad-hoc mode lets you point the tool at a database table, select the timestamp and metric columns, and start visualizing and analyzing data without writing SQL.
I wrote a detailed comparison with Grafana, focusing on the task of analyzing Active Session History (ASH) in a PostgreSQL database. It clearly demonstrates the difference in workflow and how much faster you can pinpoint performance issues.
This project was also the first real-world application for Dimension-DI https://news.ycombinator.com/item?id=45821919
Migrating from Dagger 2 to my own DI solution made the codebase much more modular and testable.
The project is open source, and I'd love to get your feedback.
Sample profiles and GIF screencasts are in the repo.
Links:
Dimension-UI (GitHub): https://github.com/akardapolov/dimension-ui
Article: Grafana vs. Dimension-UI for ASH Monitoring: https://habr.com/ru/articles/865622/ (The article is in Russian, but browsers can translate it, and the screencasts are self-explanatory)
Dimension-DI (the DI container it uses): https://github.com/akardapolov/dimension-di

value: Show HN: Dimension-UI, a desktop alternative to Grafana for time-series analysis

author: akardapolov


value: jatinlalit

value:
Hi HN — I built CountdownShare, a tiny web tool at https://countdownshare.com/ that lets you create a shareable, embeddable countdown in under 10 seconds.
What it does
- Create a countdown by target date/time or duration, then get a short public URL + embed code.
- Share instantly on socials or embed into any site — no signup required.
- Lightweight tool, real-time sync across viewers, mobile-friendly and focused on zero friction.
Why I built it
I wanted a fast way to make promotional timers, social share timers, event timers — without logins, bloated JS, or heavy setup. So I built a tool that’s focused, free forever, and easy to embed.
Tech & constraints
- Built with Next.js + Firebase Realtime Database (for sync) + simple embed iframe/JS widget.
- No user accounts: generate timer → share it → optional deletion.
- Embed code is small, responsive, and works across devices.
What I want from HN
- Honest feedback on the UX, embed approach, and privacy/usage model.
- Thoughts on growth: where would you list a tool like this or how would you promote it?
- Bugs or features missing you’d expect (e.g., private timers, analytics, custom domains).
Thanks — I’ll be around to answer questions and fix anything broken.

value: Show HN: CountdownShare – simple embeddable countdown timer, free and no signup

author: jatinlalit


value:
Hi Everyone
I built this project to explore Intent-Driven Development (IDD) for a structured way (Intent) for humans and AI Coding Agents to collaborate by defining WHY (the motivation), WHAT (the requirements), and HOW (step-by-step tasks) before coding.
The guidelines are language-agnostic, with initial support for Elixir/Phoenix using a Domain-Resource-Action architecture pattern.
I would love your feedback on:
- The clarity and usefulness of the Intent specifications and example.
- Ideas for expanding to other languages or frameworks.
- Where you see this approach being helpful (or not).
- On how practical you find this guidelines and workflow for guiding AI coding agents in a BEAM environment (Elixir and Phoenix).

value: Show HN: AI Coding Agents: Intent-Driven Development Guidelines


value: Show HN: Placeholder Image Generator with color control


value: anonbuddy

value:
I built Own-Your.stream (https://www.own-your.stream/
) to help people who struggle with Stremio’s setup. Many of us can follow a GitHub readme, but our non-tech friends/family often can’t. They end up DM’ing us for step-by-step help, or give up entirely.
What it is: a concise, jargon-free onboarding flow for Stremio. It walks a non-technical person from zero to a working setup in clear, “do this next” steps (with screenshots and troubleshooting). No fluff, no rabbit holes.
Who it’s for: people who want the benefits of Stremio but aren’t comfortable with forums, Discords, or piecing together guides. If you know someone like that, send them here and we’ll get them over the hump.
What’s inside:
Beginner-friendly guide (written like you’d explain it on a call).
Common-failure checks and fixes (subtitle mismatch, wrong source, etc.).
Optional “concierge” help for folks who prefer real-time guidance.
Clear safety notes and boundaries; no grey-area “tips & tricks.”
Business model: paid guide + optional hands-on help. We disclose any affiliate links. No tracking beyond basic analytics. No email wall to view the overview.
I’d love feedback from HN: what’s unclear, what feels risky, what you’d trim, and what you’d add for the “I just want this to work” crowd. Also open to hearing strong objections—better to fix now than later.

value: Show HN: A hand-held setup guide for Stremio (for non-tech friends and family)

author: anonbuddy


value: gravitywp

value: That can convert a story into a video with consistent characters.

value: Show HN: I've built a story-to-video AI generator website

author: gravitywp


value: We are opening up our model conversion tool that enables seamless conversion of AI models from ONNX format to TensorFlow, OpenVINO, and TensorFlowJS for everyone to use.<p>This tool was built for to make AI model inference more efficient across diverse devices and platforms. We're sharing this with the community in the hope that it can help developers and researchers easily deploy optimized models as much as it has helped us.

value: Show HN: Convert ONNX Files to OpenVINO and/or TensorflowJS


value:
I built a small parody game called Elden Stack, inspired by every late-night debugging session that ended with a recursion error.
You fight through recursion demons, memory leaks, and segmentation faults — because sometimes, Stack Overflow is the final boss.
It’s open source, lightweight, and built for fun on macOS.
GitHub: https://github.com/sukanto-m/elden-stack
Would love feedback on gameplay ideas or other “bug bosses” to add.

value: Show HN: Elden Stack – A mini game where your stack overflows in glorious combat


value:
I kept hitting Claude's 5-hour limit without realizing it, so I made this menu bar app to track it. Shows a color-coded ring (green/orange/red) and countdown to reset.
Built it in Swift/SwiftUI, just for myself initially. Right now you have to manually grab your sessionKey from browser DevTools, which is annoying but works. Stores it in macOS Keychain so it's secure.
Been actively working on it, already at v1.3.0 with much better UX now.
It's open source (MIT) and works with all Claude platforms (web/CLI/desktop/mobile) since they share the quota. Currently macOS only.
Curious if anyone has ideas for auto-extracting auth tokens without browser extensions.
GitHub: https://github.com/f-is-h/Usage4Claude

value: Show HN: Usage4Claude – Monitor Claude AI usage from macOS menu bar


value: sayyadirfanali

value: Show HN: Myna - monospace typeface for symbol-heavy programming languages

author: sayyadirfanali


value: acrostoic

value:
We built GraFlo after repeatedly writing the same boilerplate code to transform datasets into Neo4j, ArangoDB, and TigerGraph. Every time we had a new dataset (OpenAlex, IBES financial data, Debian packages), we'd write yet another custom ETL script with the same problems: ID generation, type coercion, deduplication, and database-specific quirks.
GraFlo is a declarative framework that handles this once and for all. You define your graph structure in a database-agnostic schema - vertices, edges, properties, and how they map to your source data (CSV, SQL, JSON, XML). GraFlo then generates the ingestion code for your target database.
The key insight: while Neo4j, ArangoDB, and TigerGraph are all idiosyncratic, the underlying property graph model is universal. We crystallized that into a single abstraction layer.
What GraFlo handles automatically:
- Consistent ID generation across vertices and edges
- Type coercion (strings to dates, numbers, etc.)
- Vertex and edge deduplication
- Generating database-specific ingestion scripts
It's plug-and-play in the sense that swapping from Neo4j to ArangoDB takes no time — just change the target database type in your config (docker compose examples provided).
We've used it to build knowledge graphs from academic publications, financial datasets, and package dependencies. Instead of maintaining N × M scripts (N datasets, M databases), we maintain N schemas.
On the roadmap: SQL/API integration (e.g., automatically generating GraFlo configs from SQL schemas).
Would love feedback from anyone working with graph databases or building knowledge graphs.

value: Show HN: GraFlo - Universal ETL tool for property KG (Neo4j, TigerGraph, Arango)

author: acrostoic


value: vinhnx

value: Show HN: VT Code – Semantic Coding Agent

author: vinhnx


value: ggprgrkjh

value: Beta tool that runs OWASP/Nuclei/ZAP checks fast and prioritizes fixes; would love test runs on your staging domains and blunt feedback.

value: Show HN: VibeScan – Web vuln scans in minutes with AI triage

author: ggprgrkjh


value: We just introduced support for the only vendor-neutral, cloud-neutral, CNCF Sandbox approved PostgreSQL operator (CloudNativePG) within pgEdge container deployments. Still 100% open-source PostgreSQL, hopefully easier to set up. :-)<p>Please let us know what you think about the deployment process using the Helm chart, we'd love feedback on how the developer experience could be improved.

value: Show HN: Deploy Distributed PostgreSQL on K8s with CloudNativePG, Helm, + PgEdge


value: ArthurMagne

value:
Over the past year, we’ve been scaling AI-assisted development across multiple teams.
Today, about 65% of our commits are produced by AI agents (Copilot, Cursor, Claude Code, and Kiro). The productivity gain is real, but so are the new classes of drift it introduces.
Each assistant ends up operating from a different context snapshot of our architecture, naming conventions, or ADRs. Some pull from old instruction files, others from out-of-date wikis. The result: AI-generated code that’s locally correct but globally inconsistent.
We built Packmind OSS to tackle this. It’s an open-source framework for Context Engineering — versioning, distributing, and enforcing organizational standards across repos and agents.
Normalize scattered decisions (docs, reviews, ADRs) into structured “rules” and “prompts.”
Sync them through MCP servers or CLI across GitHub, Cursor, Claude, etc.
Detect drift and repair it automatically during PRs or CI.
Repo → github.com/PackmindHub/packmind
I’d love to hear how others are approaching this. How do you maintain context integrity when assistants are coding at scale?
(Apache-2.0 Licensed, CLI & MCP.)

value: Show HN: Packmind OSS- Framework for versioning and governing AI coding context

author: ArthurMagne


value:
An experimental little npm library for creating a quick feedback loop when writing code. I usually run any code changes through an AI checker before sending them for real review to catch any obvious problems.
I'm also a big fan of ollama and local LLMs and so I had the thought of automating the task locally. And since I am doing all of this locally, (and for free(ish)) why wait until the code is ready to submit for review. Why not just keep the tool running on all changes!
I was curious to see how well such a tool might work so I put together a basic version. I've found it to be slightly helpful in certain cases so far. Obviously results will vary greatly depending on what model you choose to use. I have had decent results using gpt-oss:latest on a mac mini.
The basic idea is that it runs a quick check on any code when a file is written to, and when code is committed.
I expect there are quite a few bugs etc but it has been running smoothly enough for me so far.

value: Show HN: Review code as it is written

title: Show HN: Review code as it is written


value: sepokroce

value: CKAN Pilot is a brand-new command-line interface (CLI) tool for CKAN. It simplifies the creation, configuration and management of CKAN projects. It removes the complexity of setting up local CKAN instances and streamlines the developer experience.

value: Show HN: CKAN Pilot – A New Way for Managing Data Portals

author: sepokroce


value: bobnarizes

value:
Hi HN,
Floxtop is a native macOS app that organizes your files automatically using an on-device AI model, with no data leaving your machine. It focuses on privacy, speed, and usability.
Background:
Manual file organization is tedious, and traditional tools rely on filenames or metadata, missing file meaning. Floxtop uses Sentence Transformers and computer vision including OCR and transformer-based embeddings to analyze content across PDFs, images, Office files, EPUBs, CSV, Markdown and more (30+ formats supported).
Key features:
- Entirely offline AI inference, optimized for Apple Silicon, ensuring privacy and near-instant processing.
- Multi-format content understanding via OCR and semantic embeddings to group files by meaning including images, Microsoft Office, PDFs, EPUBs, CSV, Markdown and more (+30 file types supported).
- Customizable classification: define categories to define file destinations.
- Native macOS integration with Finder extensions and Quick Look previews.
Performance:
Floxtop categorizes files in under a second. Built in Swift with ONNX, the app is compact (~120 MB) with a powerful 94 MB model.
I launched an early beta, Fallinorg (https://news.ycombinator.com/item?id=44932375), reaching 103 customers and gathering valuable feedback. Floxtop is the improved iteration with broader format support, compact binary size, and better integration.
Try the free version: https://pub-a80e0e01ccee4148a4f4c43d8d4eeda2.r2.dev/Floxtop-...
I’d appreciate feedback on accuracy, performance, and feature ideas.
Thanks for checking it out!

value: Show HN: Floxtop – Clean up your Mac with private, offline AI file organization

author: bobnarizes


value: rohinbharg

value: Show HN: Instantly drop-in services, workers, and libraries in TypeScript

author: rohinbharg


value: mtud

value: Show HN: ShellAI – Local Terminal Assistance with SLM

author: mtud


value: justinpaulson

value: A multiplayer game based on the Turing Test. Players answer questions anonymously in a group along with two AI participants, then try to vote on who is not human! Rails 8 stack.

value: Show HN: Turing Twist

author: justinpaulson

title: Show HN: Turing Twist


value: userium

value: I built this to see how often I need to charge my electric car. Enter your weekly schedule and temperature, and it shows when you need to charge.

value: Show HN: Electric car weekly charging calculator (tested in -20°C / -4°F)

author: userium


value: cyrusradfar

value:
I've been programming since I'm 6 and I don't want to quit. Since Agents came into existence I've been increasingly building more of the random ideas.
BUT, like many I kept getting stuck and frustrated where I wanted to make changes with the Agent that I knew I could've made without it but I had *no clue* how things worked.
I created Intraview to help me build and maintain a mental model of what I was building (or had vibed) so I could use my knowledge to either fix it myself, or provide more directed instruction. It grew into something that's transformed my workflow in a pleasant way.
Intraview is a VS Code extension that allows you to create:
  - Dynamic code tours built by your existing Agent
  - Storage and sharing of tours (it's a file)
  - Batch Feedback/commenting inline in IDE in-tour and without (it's also a file)
Here's a video walkthrough for the show vs tell crowd where I jump in a random (Plotly JS) open source repo and build a tour to get started: https://www.youtube.com/watch?v=ROBvFlG6vtY
Talking tech design, this is very different than most because the whole App is cloudless. Not server less, there's no external APIs (outside basic usage telemetry).

  - basic TypeScript app, JS/CSS/HTML
  - Localhost MCP server inside VS Code (one per workspace open)

Three of the biggest challenges I faced was:

  - re-considering the user experience given there's no database
  - trying to build a reasonable experience to manage MCP connection across so many different setups.
  - testing the many forks, Agents and themes because I wanted to make it look native (I'll probably reverse course here in future iterations)

What I'm curious about is, where do you see the value:

  - New project/developer onboarding
  - PR reviews
  - Keeping up with Agentic code
  - Perf reviews (for EM), you could build a tour biggest contributions by a GitHub handle
  - Planning alignment and review with your Agent

You can see the extension page in VS Code with these custom links (Note: this redirects and requires permission to open VS Code, won't actually install, takes another click)

  - for VS Code: https://intraview.ai/install?app=vscode
  - for Cursor: https://intraview.ai/install?app=cursor

Once it's installed and you confirm MCP is connected to your local server, just ask your Agent:

  - Create an Intraview the onboarding for this app..
  - Let's use Intraview to gather my feedback on [whatever you created]. Break down steps such that I can provide good granular feedback.

Looking forward to your feedback and discussion.
And because this is HN. A relevant quotable from PG.

  “Your code is your understanding
  of the problem you’re exploring.
  So it’s only when you have your code
  in your head that you really understand
   the problem.”

  — Paul Graham

value: Show HN: Dynamic code and feedback walkthroughs with your coding Agent in VSCode

author: cyrusradfar


value:
Hi! With the LoL Worlds going on, I created a drafter - train solo or with a friend!
My goal was to build small (1MB page which is 2/3 images and 1/3 fonts) and with hypermedia.
Stack: Sanic + Datastar + SQLite + Redis
I explain my code in video if you're interested: https://www.youtube.com/watch?v=Qtk0ubrZYVU

value: Show HN: A League of Legends Drafter

title: Show HN: A League of Legends Drafter


value: jantuss

value: A shell script for removing email addresses from the clipboard before pasting into ChatGPT.

value: Show HN: Clean-Clipboard before pasting into ChatGPT

author: jantuss


value: peeyek

value: Show HN: exetest – CLI Testing for Zig

author: peeyek

title: Show HN: exetest – CLI Testing for Zig


value: kalturnbull

value:
As the end of my fixed term NHS contract approached, I did the sensible thing and started interviewing for my next product manager role. Then something clicked.
I’m 29 with no dependents, have a modest amount of savings, and I’ve been dreaming of my route back to the startup world. I’m not saying this is my last chance, but it feels like a window of opportunity.
There’s a lot of debate on side hustling vs a full time ‘burn the boats’ approach. After a couple of years of the former, I’m ready to try the latter. (Though in my case it’s a ferry, and I don’t think CalMac would appreciate me burning it.)
But the fact is, Edinburgh’s an expensive place to bootstrap a business, and frankly, this city is just too much fun. I will be back, but what I need for now is a focused retreat. Somewhere I can exclusively work, exercise, eat, and sleep. And somewhere that can extend my financial runway.
I grew up visiting the Hebrides and I can’t explain the almost spiritual effect that place has on me. Admittedly I go in the summer when it somehow looks like the Mediterranean. But I don't mind the cold. I just know it’s where I need to be for this.
So, what am I building?
Introducing bother.now, my answer to the poor UX of bloated project management tools that I’ve had the displeasure of using as a PM.
It’s live and you can try it with no account required upfront.
I’ll be sharing occasional updates from the island on X (@kalturnbull). Here’s hoping I have a decent internet connection.

value: Show HN: I'm moving to a Scottish island to build a startup. I'm not joking

author: kalturnbull


value: Just a mini game I made with Raylib.

value: Show HN: Jam Slam – fruit catching minigame

story_text: Just a mini game I made with Raylib.


value: talyuk

value: Show HN: Relay – Self-hosted tunnel with memorable subdomains

author: talyuk


value: Show HN: Executable Recipes for Claude, Codex. Or Terraform for AI Flows


value: Amyang

value: A lightweight, from-scratch implementation of anime MMD model rendering using WebGPU and TypeScript. Built to understand the modern graphics pipeline without framework dependencies.

value: Show HN: Minimal WebGPU MMD Renderer – Physics-Based Anime Characters

author: Amyang


value: thanhdongnguyen

value:
Hi HN! I'm Don, a developer who realized I was spending more time crafting tweets about my projects than actually working on them.
Last month, I tracked my time. I spent 6 hours building a new React component. Then spent 1 hours trying to tweet about it. Not a thread. Just one tweet. I wrote 12 drafts. Deleted all of them. Eventually posted nothing.
This isn't writer's block. It's decision paralysis. Every tweet requires dozens of micro-decisions: Tone? Format? Audience? Length? Thread or single? Question or statement? Should I be casual or professional? Technical or accessible?
When I talked to other developers, they had the same problem. One friend told me he has 20 draft tweets sitting in his notes app. He builds cool stuff but rarely shares it because "tweeting is exhausting."
I built a browser extension that handles those micro-decisions. You give it your idea. It generates multiple versions in different formats (question, thread, how-to, etc.) and tones. You pick one, maybe edit it, and post.
The key insight: you don't need AI to write tweets. You need it to remove the friction of formatting and structuring your existing ideas.
If you want try it:
https://tweetbl.ink/

value: Show HN: I spent more time writing tweets than building. So I fixed it

author: thanhdongnguyen


value:
Hi HN! We were trying to have our team adopt AI more and had the idea for "AI Bingo" - hands-on, mini challenges that resulted in something delightful and real.
Our team members were obsessed, they were saying it was "weirdly addictive." People made AI-generated ASMR videos, n8n automations, games. Our email marketer built an Electron app using Claude Code.
We were stunned - people were learning by doing AND having fun. So, we've spent the last few weeks building Made That, where anyone can do the same challenges and see other people's submissions.
A lot of people feel a lot of existential angst about AI as we're all seeing layoffs left and right, but they don't know where to start. Wanted to add a bit of fun (and competitiveness) to something that feels stressful.
There's still a lot we can do to make it better, but wanted to open up discussion here and get feedback about the format, challenges, if it feels fun.

value: Show HN: AI Bingo – weekly challenges to practice AI


value: LemayianBrian

value: I made a publicly accessible Windows computer that multiple users can control simultaneously(everyone sees the same screen and control the same mouse and keyboard). It runs in the browser and is built with Apache Guacamole :)<p>This is meant to be a short term stress test for the infra.

value: Show HN: The Chaos Machine – A public computer anyone can use simultaneously

author: LemayianBrian


value: A lightweight .NET tool that turns SQL queries (defined in YAML) into ready-to-run REST endpoints. Works standalone or as a library, supports transactions, nested JSON output, and multiple database providers.

value: Show HN: RestSQL – A .NET tool that turns SQL queries into REST endpoints


value: onasta

value:
I am excited to announce the release of TabPFN-2.5, our tabular foundation model that now scales to datasets of up to 50,000 samples and 2,000 features - a 5x increase from TabPFN v2, published in the Nature journal earlier this year. TabPFN-2.5 delivers state-of-the-art predictions in one forward pass without hyperparameter tuning across classification and regression tasks.
What’s new in 2.5:
TabPFN-2.5 maintains the core approach of v2 - a pretrained transformer trained on more than hundred million synthetic datasets to perform in-context learning and output a predictive distribution for the test data. It natively supports missing values, cateogrical features, text and numerical features is robust to outliers and uninformative features.
The major improvements:
- 5x scale increase: Now handles 50,000 samples × 2,000 features (up from 10,000 × 500 in v2)
- SOTA performance: TabPFN-2.5 outperforms tuned tree-based methods and matches the performance of a complex ensemble (AutoGluon 1.4), that itself includes TabPFN v2, tuned for 4 hours. Tuning the model improves performance, outperforming AutoGluon 1.4 for regression tasks.
- Rebuilt API: New REST interface along with Python SDK with dedicated fit & predict endpoints, making deployment and integration more developer-friendly
- A distillation engine that converts TabPFN-2.5 into a compact MLP or tree ensemble while preserving accuracy and offer low latency inference.
There are still some limitations. The model is designed for datasets up to 50K samples. It can handle larger datasets but that hasn’t been our focus with TabPFN-2.5. The distillation engine is not yet available through the API but only through licenses (though we do show the performance in the model report).
We’re actively working on removing these limitations and intend to release newer models focused on context reasoning, causal inference, graph networks, larger data and time-series.
TabPFN-2.5 is available via API and a package on Hugging Face. Would love for you to try it and give us your feedback!
Model report: https://priorlabs.ai/technical-reports/tabpfn-2-5-model-repo...
Package: https://github.com/PriorLabs/TabPFN
Client: https://github.com/PriorLabs/tabpfn-client
Docs: https://docs.priorlabs.ai/quickstart

value: Show HN: TabPFN-2.5 – SOTA foundation model for tabular data

author: onasta


value: eamag

value: I tagged all comments from "What Are You Working On?" (like https://news.ycombinator.com/item?id=45561428) posts and built a simple SvelteKit website, hope it's helpful to find people with similar projects. I'm also thinking of adding some analysis of project types over time to see changes in tech

value: Show HN: What Is Hacker News Working On?

author: eamag


value: aradzhabov

value: This is a DIY, open-source alternative to expensive solutions like the MouthPad, eye-trackers, or even complex systems like Neuralink. Everyone deserves access to assistive technology.

value: Show HN: DIY accessibility mouse helps people even with complete paralysis

author: aradzhabov


value: dumbfoundded

value:
Hey HN, I’m Evan, cofounder and CTO of Ito AI.
Ito is a voice to intent app that turns what you say into structured text: notes, messages, code, or any text field you’re working in. It’s designed to feel fast, clean, and distraction free. It works on Windows and Mac.
Most speech tools are either locked inside closed systems or overly complicated & overly parameterized and difficult to setup. We wanted something that works like a native app, looks good, and stays transparent about your data. Ito is open-source under GPL-3.
Our focus is simplicity and polish. You can use a few different models and providers but we're still working on an entirely local first experience. It's also pretty easy to host yourself if you want to avoid our infra all together.
We’re still early, but it’s already stable enough that I use it daily for coding notes, writing, and quick thoughts between tasks.
Try it here: https://ito.ai
Code: https://github.com/heyito/ito
Would love feedback, critiques, and ideas for integrations.

value: Show HN: Ito AI, open source smart dictation

author: dumbfoundded


value: GentoroAI

value:
Hi all, we just open-sourced Gentoro OneMCP which lets you turn your API spec, docs, and auth into a single natural-language interface for AI agents. Would love your feedback and contribution. Links in comment.<p>How it works:
1) Create a dir, and drop in your specs, docs, and auth details.
2) Run a Docker command that mounts this dir and runs an MCP server
3) Connect your agent to the server and it can then interact with your backend systems via the MCP interface

value: Show HN: We just open-sourced OneMCP: a runtime that makes APIs agent-ready

author: GentoroAI


value:
istant.
I can't figure a good way to let everyone play with it so I made a video that conveys current functionality.
I've extended the free-trial period as well in case people want to play that way.

value: Show HN: Our First Demo Video for an AI Ass


value: heliosinc

value:
Hi HN,
I've been concerned about the lack of safety and efficacy data for most supplements on the market. While the supplement industry is booming, it's largely unregulated, and it's difficult to get reliable, evidence-based information. General-purpose LLMs often give generic advice and don't deeply engage with the medical literature.
That's why I built OpenHealth: https://www.my-openhealth.com/
It's an AI-powered platform designed to provide high-quality, personalized health information, with a strong focus on supplement safety. I recently wrote a blog post about the specific safety concerns that motivated me to build this project: https://www.my-openhealth.com/blog/supplement-safety-is-why-...
I wanted to share some of the technical details with the HN community, as I'd love your feedback on the approach.
The Tech Stack
At its core, OpenHealth uses a Retrieval-Augmented Generation (RAG) system that draws from over 38 million medical abstracts from PubMed and other scientific journals. Here's a breakdown of the key components:
• RAG and Paper Quality Ranking: We don't just retrieve information; we have a system to rank the quality of the papers. This helps us prioritize information from higher-quality studies and avoid relying on weaker evidence.
• Neural Search Across All Literature: Beyond our embedding database, we can access all the literature including preprints via neural search, which works pretty well. This gives us comprehensive coverage of the latest research.
• Fine-Tuned Models & Optimized Prompts: We use fine-tuned language models that are specialized for the medical domain. The prompts are crafted and optimized by a team of clinicians and scientists to ensure that the generated responses are medically sound and relevant.
• Context Engineering for Medical Data: We've put a lot of effort into context engineering to accurately extract and analyze medical data. This is crucial for understanding the nuances of medical literature.
• Drug and Supplement Interaction Database: We've engineered a comprehensive database for predicting interactions between drugs, supplements, and even lifestyle factors. This is a key feature for ensuring safety.
Between the prompt engineering and the grounding in extensive API semantic search, we get really good health responses. We've tested with clinics who prefer it to ChatGPT for medical queries.
The Goal
The ultimate vision is to build a "health superintelligence" – a system that can provide state-of-the-art guidance on medical, wellness, and longevity protocols, all grounded in scientific evidence. We're accomplishing this through SFT/RL on large biomedical RL envs. More to report on this in the future!
I'm launching this project and would be grateful for this community's feedback on the technical approach, the quality of the information provided, the UI/UX, and any other thoughts you might have.
Thanks for your support!

value: Show HN: OpenHealth – AI health platform with RAG over 38M medical papers

author: heliosinc


value:
We’ve been working on this for a while: Mongoose Studio, a MongoDB GUI and dashboard layer that runs alongside your Node.js app (Express, Vercel, or Netlify).
It uses your existing Mongoose connection and schemas to make querying and exploration feel familiar - with autocomplete, proper schema casting, and a filter syntax that supports inline JavaScript (e.g. { createdAt: { $gte: Date.now() - 1000 } }).
Mongoose Studio also includes a lightweight dashboarding framework where ChatGPT helps generate aggregation scripts using your schemas and code as context.
You can literally ask it for “a chart of users created this week by country,” and it’ll build one with Chart.js.
Demo + docs: https://studio.mongoosejs.io
GitHub: https://github.com/mongoosejs/studio
Would love feedback from anyone running MongoDB in production - what tools are you currently using to query or visualize your data?

value: Show HN: Mongoose Studio: A Schema-Aware MongoDB GUI with AI Dashboards for Node


value: jasongilman

value: Show HN: Try Out Natural Language Geocoding

author: jasongilman


value: SambhavGupta

value:
I made an on-device AI TTS extension using the WebGPU implementation of Kokoro TTS, the highest-rated open source TTS according to TTS arena. It runs model inference fully in your browser, so no server costs and it's secure.
It was pretty easy to get the TTS working since the hard part (inference in javascript/webAssembly/webGPU) was done for me. I added some quality of life features on top: click to jump to a different paragraph, alt j & k for down and up, speed control, and voices (though the default voice has the highest quality).
I made the extension mainly because I noticed how good AI TTS was getting, yet every easy way to run TTS in the browser always used the old robotic non-AI voices that are hard to listen to for more than a few minutes.
Extension is open source: https://github.com/SambhavG/tts-extension. Unfortunately it's not on Firefox as I had issues with getting WebGPU and kokoro.js on Firefox to cooperate.

value: Show HN: On Device AI TTS Extension

author: SambhavGupta

title: Show HN: On Device AI TTS Extension


value: imack

value: This Chrome extension was born from one simple realization: phishing drives over 90% of breaches, yet our main defences are outdated. Security awareness training and periodic phishing tests have nebulous effect and blocklists are always behind (especially against sites using bot protection to hide from scanners.)<p>With a torrent of vibe-coded phishing sites emerging, we need smarter tools. I figured the best place to detect a phishing site is right when you're on one. Therefore, this extension runs a lightweight AI agent in your browser that scans any new, unpopular sites you visit in real time.

value: Show HN: Stingray Security – In-browser AI checking for phishing and scams

author: imack


value: ethanpark

value:
I got frustrated watching Claude Code fail at using modern APIs (ask it about GPT-5 and it says it doesn't exist). Existing solutions like Context7 dump thousands of tokens of irrelevant docs into context. So I built DeepCon.
How it works:
- Crawled 10,000+ official docs using agentic browser automation and structured them hierarchically
- Query decomposer breaks down requests, searches in parallel, then merges only relevant context
- Returns just what's needed: 2.4x fewer tokens than Context7
Results on our benchmark:
DeepCon achieved 90% accuracy vs Context7's 65% on real-world tasks with modern AI frameworks. Without any MCP context, Sonnet 4.5 scored 0%.
It's an MCP tool. You can just plug it into Claude Code/Cursor and suddenly they understand the latest libraries and APIs.
GitHub benchmark: https://github.com/opactorai/context-bench
Service: https://deepcon.ai
Built this because I needed it. Would love feedback!

value: Show HN: Deepcon – Get the most accurate context for coding agents

author: ethanpark


value: kilojules

value:
I thought others might be interested in my experience getting Stability AI's stable-audio-open-small model working in a consumer-grade iOS app. Dealing with multiple model formats, conversion tooling and runtime frameworks complicated the endeavor.
I followed ARM's instructions for converting the stable-audio-open-small model to TensorFlow Lite. These instructions were Android-oriented but are applicable to iOS too.
The first issue I encountered was that Tensorflow Lite's Metal/GPU delegates don't seem to be able to run the three submodels, because many model operations are not supported. This left using the regular (cpu-bound) delegate using XNNPack to try and maximize performance. However, even with XNNPack enabled, the autoencoder model -- the final stage in the pipeline -- performed very poorly in terms of performance and memory usage. Its transient memory usage precluded running the app on older devices with less RAM.
To work around this, I used Apple's CoreML tools to convert just the original autoencoder pytorch model to a CoreML model. I was happy to find that this worked, and performance and memory usage improved significantly, enabling use on older devices. The size of the bundle was also reduced, but remains on the large side.
Although the model sometimes seems weak in some cases where high fidelity is expected, some of the outputs can be quite unusual and unexpected, which might be valuable for creative use-cases. Being able to share the audio clips as a ringtone is a terrific feature of iOS 26 (long overdue).
My app is called Diffuzion and is available in the App Store globally. Exporting audio is a premium feature gated by an in-app purchase, but you can use the promo code DIFFUZION4HN to get the functionality for free. This code is good until November 14th. I would appreciate feedback on possible improvements/features for the future. I have my own set of ideas, but you may have more compelling ones!

value: Show HN: I got Stability AI's small audio model into a consumer iOS app

author: kilojules


value: pompeii

value: We added a live global feed showing real-time stats across all players: total money wagered, active games, wins/losses, blackjacks hit, and win rates. Everything updates instantly as thousands of players make moves.<p>Built entirely in Swift—iOS app with SwiftUI and backend with Vapor. Full-stack Swift ecosystem handling WebSockets for sub-100ms latency. The consistency across the stack made development surprisingly smooth.

value: Show HN: Built live community stats into our blackjack app – built using Vapor

author: pompeii


value: kalturnbull

value: Show HN: Bother, my answer to the poor UX of bloated project management tools

author: kalturnbull


value: namanyayg

value:
Hey HN, I'm Namanyay from https://gigamind.dev/.
One of the most empowering applications of LLMs is enabling non-technical people to build apps and products. This value is reflected in the revenues of companies like Lovable and Bolt. Many of Giga's paying customers are business executives building valuable automations and internal tools that increase efficiency.
But, the incumbents currently operate on pay-as-you-go pricing, which is a predatory business model that I ethically disagree with. I believe the future of vibe coding tool pricing needs to be outcome based vs PAYG. The latter often feels like being held "hostage" over time, when there's vendor lock-in, and especially when the marketing promises are not met.
One step towards that is providing free or low cost inference. The user can continue building till they reach the desired outcome for a longer duration, which is possible if we can somehow make the inference cost negligible. And, the most proven model for offering free stuff online has been ads.
That's why, I've combined the two. I'm offering completely free Claude Sonnet 4.5 for use in Claude Code to empower a new segment to start building with AI.
Launch video (420k+ views): https://twiiit.com/NamanyayG/status/1982939951571591438
How it works:
- We are backed by sponsors.
- We inject ads in the responses (a mix of contextual and static). E.g., if you ask about the best AI agentic memory tools, we'll recommend our sponsor https://supermemory.com/
- We store conversations for training or sharing with partners (transparently we are not data sharing right now, but retain the right to do so in the future).
- We rate limit dynamically to allow fair usage for all our users.
- We are offering the best Claude models for now. Depending on sponsorship, we might have to transition to free or OSS models down the line (e.g. grok-fast-1 or gpt-oss).
- Our business actually makes revenue from our premium tool, https://gigamind.dev/context, which creates AI rules file for your project like AGENTS.md automatically.
How to try:
- Register on https://free.gigamind.dev/
- We provide you with an API key and a Claude Code compatible proxy URL.
- With a few lines of environment variable changes, you get to use Claude Code, for free.
- We'll run out of money if we serve everyone from HN today, so only the first 2⁶ people with coupon code `1337HN` get auto-accepted immediately.
Excited to hear your thoughts!

value: Show HN: Completely free Claude Sonnet 4.5, supported by contextual ads

author: namanyayg


value: sourishkrout

value: Show HN: Turn docs into tailored self-serve playgrounds to create aha-moments

author: sourishkrout


value:
I vibe-coded a TUI for AWS Logs Insights over the past few of days.
This is something I started to scratch an itch (I hate the Logs Insights UX in the AWS Console) and to get a feel for LLM assisted coding.<p>It works relatively well for me given what it is, and for now it relieves some of the pain. I plan to eventually rewrite it from scratch without AI assistance as an exercise in learning Rust, so please don't critique the code, I'm aware that it's a mess. I'm just looking to get some feedback - what works well, what doesn't, what's obviously missing in terms of features, etc.

value: Show HN: I Vibe-Coded a TUI for AWS Logs Insights in Rust


value: Show HN: How We Escaped the Purple Prison of AI Front Ends


value: satj

value: Most geo/aeo tools stop at ai visibility. Rankly goes further, we track the entire ai visibility funnel from mentions to conversions. As brands start showing up in llm results the next question isn’t visibility, it’s traffic quality and conversions. Rankly builds dynamic data-driven journeys for high-intent llm traffic. <https://tryrankly.com/>

value: Show HN: Rankly – The only AEO platform to track AI visibility and conversions

author: satj


value: bmadduma

value: Today we’re launching the first AI CFO that closes your books automatically. Early access now open for 100 founding users → https://www.layernext.ai

value: Show HN: Autonomous Bookkeeping and CFO Agent

author: bmadduma


value: eigenvalue

value:
For my mcp agent mail project, I wanted to make a way to easily share the agent mailbox used by my coding agents for a real project so that people could really see in detail how it works, since screenshots only show so much.
I've been working for a couple of days on a commercial "companion app" for the project and previously shared my workflow for that using 7 concurrent codex instances working as a team.
After many hours of work, they've now exchanged over 1,000 agent messages, so I decided to use that as an extreme example.
You can see the actual inbox for all the agents at the link, which is a static site hosted on GitHub pages without using a traditional server.
It uses a WebAssembly version of SQLite and is pretty performant despite running completely in the browser.
I also added a very easy-to-use command to the project so anyone can "export and share" their agent mailbox and automatically deploy it to GitHub using the gh utility (or Cloudflare pages).
The "share" viewer matches the look and feel of the built-in python web UI and fully supports filtering, sorting, searching, and a nice "message thread" mode similar to the Gmail interface. And it's easy to update an existing mailbox share as your agents send more messages.
It's useful on its own, but the real reason I added this feature was to show off the system when it's used in a real project, following the workflow I’ve previously shared (see https://x.com/doodlestein/status/1985405083488755740?s=46 ), where you use a detailed development plan and also have the agents use Steve Yegge's Beads project for task management.
If you go through the actual sample messages at the link above, you can get a very good sense for how well the system works. The agents just figure things out and instantly know how to communicate effectively and self-organize to solve problems cooperatively.
It takes just a couple minutes to set the system up on your machine using the one-line install command:
curl -fsSL https://raw.githubusercontent.com/Dicklesworthstone/mcp_agen... | bash -s -- --yes
Give it a shot and you will feel the AGI as your agents suddenly turn into a little development shop or startup team working for you.

value: Show HN: When 7 Codex Agents Sent Each Other 1k Messages over 2 Days

author: eigenvalue


value:
I am happy to announce the open sourcing of unify-simple-decision-table, a simple and straightforward, light weight Java-based implementation of a decision table. It makes defining and executing business rules both intuitive and powerful. Despite its simplicity, it delivers most capabilities that people need and without unnecessary complexity.
At its core, the library allows you to pass input data as key-value pairs and evaluate it against rules defined sequentially in either JSON or Excel-based decision table definitions. It supports both first match and all matches policies with strict or lenient validation modes. It can invoke external Java methods as part of rule evaluation or return values and even embed JEXL expressions to manage more complex rules.
The library allows decision tables to be defined as JSON or Excel files. While JSON based tables are recommended for production (since they can be versioned and tracked), they are not friendly to visualize and update. Accordingly, the library provides methods to convert from JSON to Excel and vice versa so that it becomes easy to visualize and make changes.
Developers can further extend functionality by invoking Java methods or embedding JEXL scripts directly within rule definitions. The library also supports analytics by generating events whenever rules are loaded or matches are fired.
We have used this library internally in American Express with great success and we hope the same for the community. It would be great to discuss and get feedback on the library. Thanks!

value: Show HN: Unify-Simple-Decision-Table

title: Show HN: Unify-Simple-Decision-Table


value: jharohit

value:
https://rohitjha.com/labs/flynns-arcade
Tldr: love pico 8 and needed something that runs it on iphones (can play between meetings or on travel!)
- Play on mobile for best experience
-  Retro 8bit mobile emulator based on Pico-8 Fantasy Console with 500+ retro games (reimagined arcade favs)
- Pick your Virtual Cartrige from full list or gallery or Editors Picks (me!).
-  Favorite the games you like (will persist on same device)
-  Search “pico 8” on youtube and reddit/google to find games you like (should be there!)
-  Offline - once game loads, works even on Airplane  mode
-  Persistent - can leave game in background tab and come back to it
-  Virtual Gamepad - most efficient virtual gamepad design based on famous consoles
- on PC/Mac - recommend resizing browser window to mobile form factor (responsive) and use physical keyboard
iOS and Android apps coming soon! (This is a total labour of love so it will happen when it happens)
Issues:
- sound on ios mobile browsers dont work when phone is in Focus or DoNotDisturb mode

value: Show HN: Flynn's Arcade (Pico8 on Mobile)

author: jharohit


value: akshaysg

value: I originally made this to occupy time while folks were waiting for their reviews to spin up, but thought it would be fun to release as a game for Halloween!<p>Let me know if I need to make any balance tweaks or fixes, and I'd be happy to do so!

value: Show HN: I made a Halloween roguelike where you battle to merge a 1000-line PR

author: akshaysg


value: klntsky

value:
Hi!
This is a set of conventions and types that streamline some approaches I came up with while building LLM workflows.
LLM tool feedback systems are under-developed, and the reason for that is a lack of well defined interfaces.
tool2agent is a concrete set of such interfaces to build on top of.
If you are an LLM chatbot developer, take a look at this example that leverages a tool2agent-based library:
https://github.com/tool2agent/tool2agent/blob/4cf340f77aa92e...

value: Show HN: Tool2agent – a protocol for LLM tool feedback workflows

author: klntsky


value: wolfred

value:
My friends and I love playing description games, but got tired of the same old cards - so I made a simple web app with a fresh set of prompts, and a different timer mechanic:
Rather than teams taking turns, everyone sits together, and you describe only one prompt at a time. As soon as you're done you pass the phone to the player on your left, who gets a new set of prompts to choose from, and so on. After an exponential (memoryless) random amount of time, the round ends (loudly!), and whoever was holding the phone at the time loses a life for their team. It's like Hot Potato.
The prompts are AI-generated - I like most of them, but there are some weird ones in there...
Warning: the explosion is LOUD.

value: Show HN: Insinuate – tense description party game

author: wolfred


value: kovaljubo

value:
I've built Memory Layer V-R-C-R, an AI memory compression engine specifically designed for LLM conversation history storage.
Key features:
- 75-85% compression (vs. 25-40% for vector DBs)
- Sub-10ms processing
- Tiered compression (HOT/WARM/COOL/COLD)
- Cross-recall technology (network effects)
- Production-ready, enterprise-grade
Live demo: https://memorylayer-vrcr.netlify.app
The technology is patent-pending

value: Show HN: V-R-C-R AI Memory Compression Engine 75-85% Compression,Patent-Pending

author: kovaljubo


value:
For personal use, in the last months I've developed a video codec for screencasts like tutorials, presentations, qa, etc.
That reduce the size of the video by 70% (more or less).
Now Im thinking in to sell this as a service for business who use it like courses, tutorial, or Qa videos uploaded to github for example.
Real result: 60min tutorial goes from 3.2GB → 800MB
Built in Rust. CLI for now, considering a $20/mo service.
Would you use this?
Thoughts?

value: Show HN: I developed a video codec for screencasts that reduces videos by 70%