value: akifq

value:
Last month, I watched two AI coding agents work on our codebase simultaneously. One refactored authentication, another updated tests. Both passed CI individually, but the merge broke our entire auth flow.
That's when I realized we need infrastructure designed for AI agents, not just better prompts.
SAV (Shadow VCS) creates a staging area between AI agents and Git. Every change lands in .sav/commits/ first - never touches your working directory until you approve it.
What makes it different:
Risk scoring: Analyzes file patterns, secrets, binary size to flag dangerous changes
Policy engine: YAML rules can auto-approve docs, block anything touching auth/
Audit trail: Every decision logged in SQLite for compliance
Concurrency-safe: Multiple agents can work without stepping on each other
Try it:
pip install savai
sav init
sav commit -m "AI refactor" src/*
sav review <commit-id>
sav approve <commit-id>
The bigger picture:
This is v0.1 of what I think will become essential infrastructure.
The roadmap includes:
- Agent orchestration with dependency graphs (so agents can coordinate complex changes)
- Machine learning risk models trained on your approval patterns
- Multi-repo coordination for microservices
Why this matters now:
AI agents are getting more autonomous. GitHub Copilot, Cursor, Devin-style tools are moving from "suggestions" to "autonomous PRs". We need VCS infrastructure that assumes machines are contributors, not just humans with better autocomplete.
I'd love feedback from anyone dealing with AI-generated code at scale. The repo is MIT-licensed: https://github.com/AKIFQ/sav

value: Show HN: Shadow VCS quarantines AI generated commits before they break your repo

author: akifq


value: antilisp

value:
Antilisp is a Lisp designed for code generation in non-lisp languages.
The interpreter is written in RPython, and the language is designed for easy adoption by non-lispers.
The project is still young, but the language can be played with if you don't mind missing some important features like modules and pattern matching.<p>I have not published the source code yet, because I am not completely sure how to base a sustainable business on this project and I don't want to risk having to rugpull after raising expectations.

value: Show HN: A Lisp for code generation and metaprogramming in non-Lisp languages

author: antilisp


value: TerrenceTian

value:
TL;DR: builtwhat.app delivers curated product ideas to 1,000+ developers daily by analyzing social conversations with AI.
The Problem
As an indie developer, I was tired of the endless cycle of "what should I build next?" I'd spend weeks browsing Reddit threads, analyzing competitors, and hunting for user pain points instead of actually building.
The traditional approach felt broken:
Most "pain points" on forums are just people venting, not real market opportunities
By the time you finish market research, your motivation is gone
Building in isolation leads to products nobody wants
The Solution
Instead of developers hunting for ideas, why not flip it? Let AI monitor where users naturally complain (Reddit, Twitter, niche communities) and transform those pain points into actionable product opportunities.
How it works:
AI monitors social platforms for genuine user frustrations
Sentiment analysis + frequency scoring identifies promising opportunities
Community of developers votes and refines each idea
Daily curated inspiration delivered with market context and technical suggestions
Technical Implementation
Stack:
Frontend: Next.js 15.3.3 + React 19 + TypeScript
Backend: Cloudflare Workers + Hono framework
Hosting: Cloudflare Pages with OpenNext.js
UI: shadcn/ui + Radix primitives + Tailwind CSS 4.1.1
Internationalization: next-intl for English/Chinese support
Why Cloudflare's edge stack:
Global performance: Edge Workers eliminate cold starts
Cost efficiency: Scales to zero, pay per request
Simplicity: Single platform for static assets, APIs, and compute
Interesting technical choices:
Using OpenNext.js to run Next.js 15 on Cloudflare Workers (still experimental but works great)
Built custom AI pipeline for social listening and sentiment analysis
Leveraging Workers KV for caching API responses globally
Full TypeScript across frontend/backend with shared types
Current Status
1,000+ developers subscribed to daily inspirations
10+ product ideas curated and archived
Community discussions with voting and feedback
WeChat group for Chinese developers (because why not?)
Revenue: Currently free (exploring premium features like AI market validation)
What I Learned
Cloudflare Workers are underrated for full-stack apps. The DX is excellent once you get the hang of it, and the global performance is unmatched.
Community-driven curation works. Having developers vote on ideas dramatically improves quality vs pure AI selection.
Internationalization from day one was the right call. ~40% of users are Chinese developers.
Next.js 15 + React 19 on edge workers feels like the future, even if slightly bleeding edge.
Challenges
Social API limits: Had to get creative with data collection methods
Edge runtime constraints: Some Node.js packages don't work in Workers
Balancing automation vs curation: Pure AI gives too many false positives
What's Next
Working on:
AI-powered market validation for submitted ideas
Collaborative spaces for community projects
Success tracking (inspiration → shipped product)
More data sources beyond social platforms
Questions for HN
Fellow indie developers: What's your biggest challenge in finding product ideas?
Technical: Anyone else running Next.js 15 on Cloudflare Workers? How's your experience?
AI/ML folks: Best approaches for sentiment analysis on social data at scale?
Community building: How do you maintain quality in user-generated content platforms?
Try it out: builtwhat.app
Happy to answer questions about the technical implementation, community growth, or anything else!
Built this mostly for myself and other indie developers who were stuck in analysis paralysis. Turns out I wasn't alone.

value: Show HN: I built a daily inspiration platform for indie developers

author: TerrenceTian


value: alepeak

value:
Hey HN!
After seeing people struggle with the Lovable + n8n + Supabase stack, I built VibeFlow.
*The Problem:* Lovable/v0 and all the other prompt to apps builds great frontends, but no functional backends.
Some people opt for using lovable + n8n public webhooks + supabase manual integration, but you become human middleware copying URLs, managing the communication between tools, and praying nothing breaks.
*The Solution:* Visual canvas for backend logic that generates actual deployable code.
*What makes it different:*
It syncs with your GitHub repo (same codebase as Lovable) and generates real backend code
Try it: https://beta.vibeflow.ai/
 Demo: https://youtu.be/3AlscfiAJmY
Built this after 100+ user interviews with Lovable builders. Would love your feedback!

value: Show HN: I wrote backend editor that adds AI agents and database to Lovable UIs

author: alepeak


value: SuperLordPanda

value:
I created a Google Chrome Extension that detects phishing websites and malicious Gmails.
Some features of the extension include: a brief summary of the site or gmail, web traffic, the founders/location, and company valuation
The extension is also powered by chatGPT to detect all parts of a site or email
I would really appreciate any sort of constructive feedback as this extension is still very new! I hope you guys enjoy it :)

value: Show HN: A Chrome extension that detects malicious websites

author: SuperLordPanda


value: createdbymason

value:
hey HN!
as a college student at uc berkeley who just got into programming this year, i was always looking for free tools.
i found out i could get free cursor pro, free hosting, free domain names, free API credits (chatgpt and claude), all bc i was a student.
thought it was worth sharing with others, so i made this list.
it now includes firecrawl, framer pro, cursor, chatgpt, claude, figma, notion, and more.
i made the submission system open so anyone can add. a few people have already made valuable contributions (e.g. framer pro and firecrawl)
lmk if there is anything that i missed

value: Show HN: I made a list of free stuff for college founders

author: createdbymason


value: sammycage

value: Show HN: PlutoFilter- A single-header, zero-allocation image filter library in C

author: sammycage


value: nicetomeetyu

value: I made a crowdsourced dictionary to help newcomers to the field of ai alignment science (lesswrong but more approachable). feedback is appreciated :)

value: Show HN: Urban Dictionary for Alignment Science Terms

author: nicetomeetyu


value: runjuu

value: Show HN: Refine – A Local Alternative to Grammarly

author: runjuu


value: artiomyak

value: Show HN: Binary Translator

author: artiomyak

title: Show HN: Binary Translator


value: Here's the demo of something that I've been building. It's pretty early but do checkout the demo and let me know your thoughts and feedback.

value: Show HN: I built a collaborative terminal sessions in browser


value: schuon

value:
Hello HN! Sebastian here from Schub.
We just open-sourced a new project I've been working on, called legacy-use, and I’d love to get your thoughts.
At Schub, we’ve been building agents that automate legacy Windows software (healthcare systems, finance tools, EHRs), the kind of stuff that’s mission-critical but hasn’t changed since Windows XP. These apps have zero APIs and usually require humans clicking around all day.
So we built legacy-use, an agentic API layer for any desktop app. Well, technically any software. Legacy-use lets AI agents control GUI-based software by emulating mouse and keyboard input, with full visual feedback. Just like UIPath, but more error tolerant. It builds on Anthropic’s Computer Use and extends its’ capabilities to old legacy tools on Windows / Linux / whatever systems.
How it works:
- We build the infrastructure to connect to legacy systems (like RDP / VNC + VPNs) and handle the queing
- We execute prompts to the target systems and handle all the logging / monitoring
- We allow the agent to extract data and expose it as a REST API.
- We build in guardrails, so if something goes astray a human operator can be called in
We’ve already seen some pretty fun real-world uses:
- A medical provider automated 30% of their admin work using GPT + legacy-use
- An accounting firm hooked it into a 2009 finance app and now generates compliance reports fully hands-off
What excites me: so much of our GDP generating industry relies on legacy software to run their business. And that’s not about to change any time soon as this software is so targeted to their use-case. So for them to profit from the automations that the agentic revolution will bring about, they need to open up their legacy systems. That’s my vision: allow access to any existing IT system so companies can participate in the agentic revolution.
Here’s the repo: https://github.com/legacy-use/legacy-use
Would love your feedback and I'm super happy to chat if you want to pioneer a use case. If you’ve got some old internal apps you’ve been dying to automate, happy to help you wire up your first flow.
Let’s bring legacy software into the agentic era.
— Sebastian

value: Show HN: legacy-use – add REST APIs to legacy software with computer-use

author: schuon


value: BartSpaans

value:
I was looking for a way to make prefetching on my site faster. I had been using the classic onHover technique, but it started to feel limiting. So I switched to prefetching when elements enter the viewport. That worked, but over time I realized it was pretty wasteful. Just because something is visible doesn’t mean the user is going to interact with it.
That got me thinking about something in between. So I built ForesightJS in the open. Instead of prefetching onHover, it prefetches onIntent. If the mouse is clearly moving toward a link, it gets prefetched before the hover even happens. Same with keyboard navigation. If a user is a few tab stops away from a target, ForesightJS will kick in and prefetch it early.
I completely agree that this might not be for everyone, however if you do want to squeeze out those extra ms you  might want to give it a try!

value: Show HN: Prefetching based on mouse/keyboard predictions in JavaScript

author: BartSpaans


value: fidelechevarria

value: Show HN: Chattier AI chat support (text, voice, avatar) trained on your own data

author: fidelechevarria


value: rahulbstomar

value:
Hey HN!
I created a free web tool to transliterate Latin characters to Aksara Jawa (Javanese script) and vice versa:
 https://goonlinetools.com/id/translate-aksara-jawa/
Javanese script is a beautiful traditional writing system from Indonesia, but tools to work with it online are limited. This tool supports:
Bidirectional transliteration
Unicode Aksara Jawa output
Mobile-friendly interface
It was a fun challenge to build because of the complexity of script mapping and handling regional variations. Would love your feedback, suggestions, or improvements!

value: Show HN: Javanese Script Translator (Latin ⇄ Aksara Jawa)

author: rahulbstomar


value: KodeKloud Studio is a suite of free, community-driven tools designed to help learners and professionals sharpen their DevOps, cloud, and AI skills through hands-on experience.

value: Show HN: KodeKloud Studio – Free AI Tools for the Community


value: csirkezuza

value:
I've added Prometheus and OpenTelemetry support to my beloved open-source pet project called Kuvasz, which is a cloud-native, feature-rich uptime & SSL monitoring service, written in Kotlin.
If you already have an observability stack and you only need a free uptime/SSL monitor, you can easily integrate Kuvasz with it.
Let me know what you think or what you miss from Kuvasz!

value: Show HN: Prometheus and OTLP support in Kuvasz, an open-source uptime monitor

author: csirkezuza


value: Secnote is a simple web app for sending confidential messages that self-destruct after being read. Each note is encrypted end-to-end using XSalsa20-Poly1305 and accessible via a single-use link. No sign-up required.<p>Useful for sharing sensitive information like credentials or one-time messages without leaving a trace.

value: Show HN: Secnote – Send encrypted notes that self-destruct after being read


value: boulevard

value: Show HN: We are building AI Teacher for 260M non native English speaking student

author: boulevard


value: artiomyak

value: Show HN: Led Calculator

title: Show HN: Led Calculator


value: saschams

value:
SafePrompt Mobile is a $0.99 iOS app that strips names, emails, IDs, etc. out of any text before copying it to ChatGPT or any other platform. Also lets you save prompt templates for one-tap reuse.
You can try for free on TestFlight: https://testflight.apple.com/join/7zc7dha9

value: Show HN: SafePrompt – iOS app to anonymize docs and manage prompts ($0.99)

author: saschams


value:
HardView is a native-speed Python module for real-time hardware monitoring and detailed system info — including CPU, RAM, disks, GPU, and full SMART data.<p>Windows & Linux
 Real-time CPU/RAM tracking
 Advanced disk & SMART info
Download: pip install HardVew

value: Show HN: HardView – Know Your Hardware Instantly from Python


value: Bridge the gap between data and decisions

value: Show HN: We launched our website, any feedback please?


value:
You know the drill.
Some mix of product, support and eng decides how refunds work.
It lives in a Notion doc. Then Slack. Then hardcoded as a bunch of if statements.
No tests. No trace. No version control.
decision-layer is a small framework to clean that up.
You write the logic in YAML.
Run it like code.
Test it.
Trace it.
What it does:
- Versioned YAML policies
- CLI to run and test them
- Trace output with every rule fired
- Examples: refunds, escalation, tiering
All in plain Python.
No weird dependencies.
Just testable logic defined clearly.
⸻
Would love to hear what confuses, breaks or annoys you.
(Or what would make this usable where you work.)

value: Show HN: Decision-layer – your refund logic doesn't belong in Slack


value: In 2025, AI video tools are leveling the playing field for creators. I compiled a ranked list of 7 free AI tools (like Runway ML, Descript, and Pictory) that help with editing, repurposing, and text-to-video.

value: Show HN: Top Free AI Tools for Video Editing and Content Creation in 2025


value:
This video is a deep dive into MCP (Model Context Protocol), breaking down its core concepts, architecture, and how it’s used to coordinate and automate AI agents. It will walk you through the real-world example of MCP-Based Flight Booking system.
If you’re interested in modern AI systems, agent orchestration, or want to understand the practical side of MCP, this video is designed for you.
Feedback and questions are welcome!

value: Show HN: Explaining Model Context Protocol (MCP) and AI Agents


value:
Ducklake is a new lake storage format by MotherDuck. It aims to solve some of the issues with Iceberg by centralizing metadata in Postgres, instead of directly on blob storage.
SQLFlow is a stream processing engine that ingests data from kafka, runs sql against that stream and sinks the output.
SQLFlow has a duckdb context available during stream processing. This make it trivial to stream data from Kafka to Ducklake!
https://sql-flow.com/docs/tutorials/ducklake-sink/
https://github.com/turbolytics/sql-flow

value: Show HN: Stream Kafka into Ducklake

title: Show HN: Stream Kafka into Ducklake


value: pacmansyyu

value:
Hi HN, I've been building this tool for the past couple of weeks to solve a problem that seems universal across development teams.
Every team handles secrets badly. Secrets and passwords get shared in Slack, `.env` files sit in plaintext, or you depend on external secret management services that can fail during critical deployments, or even depend on other people to deploy things for you!
I believe your environment secrets should neither depend on external services or people, nor be shared in plaintext.
kiln is a command-line tool for managing encrypted environment variables. It lets you encrypt secrets into files that can be safely committed to version control, with role-based access control so team members can only decrypt the environments they're authorized for.
What kiln does:
- Encrypts environment variables using age encryption with SSH and age keys
- Role-based access control - each file can have different access levels
- Commit encrypted files safely to git with clean diffs
- Run applications or render config templates with automatic secret injection
- Works completely offline - no external dependencies
Instead of depending on external services, your secrets travel with your code and work everywhere. You define team access in a config file, encrypt your secrets, and everything just works.
Built as a single Go binary that uses your existing SSH keys or generates new age keys.
Try it out and let me know what breaks or what's missing. I'd love to hear how this fits into your team's workflows and what could make it better.
GitHub: https://github.com/thunderbottom/kiln
Docs: https://kiln.sh

value: Show HN: kiln – Git-native, age-encrypted secrets for dev workflows

author: pacmansyyu


value: clastiche

value:
TabPFN is a transformer-based foundation model for tabular datasets. Published in Nature earlier this year, it achieves SOTA performance on classification and regression tasks without any per-task training or hyperparameter tuning.
With the new tabpfn v2.1 release, fine-tuning is now supported. Similar to LLMs, you can fine-tune tabular foundation models on your proprietary data to boost your performance.
A community member wrote a great walkthrough here: https://medium.com/@iivalchev/how-to-fine-tune-tabpfn-on-you...
You can also check out the example files directly at these links:
> Fine-tune classifier: https://github.com/PriorLabs/TabPFN/blob/main/examples/finet...
> Fine-tune regressor: https://github.com/PriorLabs/TabPFN/blob/main/examples/finet...
If you want to chat or see what others are experimenting with, there’s a Discord too: https://discord.com/invite/VJRuU3bSxt
Would love to hear what you think when you try it!

value: Show HN: Boost TabPFN's performance by fine-tuning on proprietary data

author: clastiche


value: nzach

value:
Why build this?
Yes, AI can whip up a function in seconds but if you don’t understand the code it spits out, you’re just rolling the dice. I was seeing devs blindly accept AI output because they’d never wrestled with the underlying concepts themselves.
LeetPrompt is an answer: a sandbox where you still need real coding insight(types, pointers, concurrency) but you express that insight as a prompt. If you don’t grasp the refactor, the AI won’t either. It’s the best of both worlds: you sharpen fundamental skills and learn to steer models with surgical precision.
https://www.producthunt.com/products/leetprompt

value: Show HN: LeetPrompt – Leet Code for Prompts

author: nzach


value: silentpuck

value:
Sigtrap is a small Linux utility that monitors log files for unauthorized changes using stat().
It checks for changes in size, mtime, ctime, and inode, and alerts if anything shifts silently.

value: Show HN: Sigtrap – log integrity checker in C, no dependencies

author: silentpuck


value: gvison

value: Show HN: Sponge – Low-code Go framework to generate APIs and cloud-ready service

author: gvison


value: KrishnaTorque

value:
A privacy-first search & AI research engine.
No tracking, just answers.
https://feuzo.com

value: Show HN: A privacy-first search and AI research engine

author: KrishnaTorque


value: jameslewis

value:
Hey HN!
We kept seeing the same issue in SaaS sales: a buyer wants to commit to an annual deal but can’t pay upfront, and the deal stalls.
So we set up a Slack group where SaaS teams can:
- Check if a customer is eligible for financing
- Request 0% financing proposals
- Offer monthly payments on annual contracts
All done through a simple Slack conversation.
Why Slack? We’re starting here to learn quickly before building out the full platform.
It’s for SaaS companies in the US and UK selling contracts worth $15k / £15k and up.
Join here: https://lemon.co/join-community
Looking forward to hearing what you think.

value: Show HN: Slack group for SaaS teams to close deals with 0% customer financing

author: jameslewis


value: tadaspetra

value: Show HN: Kubernetes Kit – Kubernetes Made Approachable

author: tadaspetra


value: novaheic

value:
I've been writing cover letters for over 10 years now, across a variety of industries and seniorities. Initially, my approach was to plan 4-8 hours for writing the perfect cover letter tailored exactly to each specific role and my personal experiences/background.
Just last year, I spent a whole day writing one for a position I was perfectly suited for - a job I really wanted. And well, I never even got a reply. That's where my strategy changed. I realized most employers now overwhelmingly use AI tools to look through cover letters, and it matters more whether you have a cover letter (or not) vs. the actual quality of the letter.
So, I decided I would no longer spend hours crafting each ideal letter, which was also limiting the number of jobs I could apply for, and I increasingly began using GenAI. Initially, just for some inspiration, but pretty soon I had a whole workflow: I would upload my CV and 1-2 past cover letters I was really proud of, along with the job description, and the prompt I had refined over time. Then I would copy/paste the result into a Google Doc, edit, and download the PDF.
In the past 3 months alone, these letters have landed me interviews at McKinsey, KPMG, and BMW.
Just recently, I decided I wanted to fully automate this workflow, so I built a simple user interface that would allow me to just upload my documents and hit generate. Then I put in an editor and a way to directly download the PDF so I could stop filling my google drive with a bunch of random cover letters.
So while this has worked pretty well for me, I would really appreciate some feedback. Is this tool useful? Anything you'd change? You can use the code NOVA20 to get 20 free cover letters.

value: Show HN: CoverPaste – A simple cover letter generator

author: novaheic


value: skrid

value: Show HN: VS Code extension revealed I only code 51% of my 'deep work' time

author: skrid


value: amenghra

value: Show HN: Crossabble, a weekly word game/puzzle

author: amenghra


value:
Hi HN,
I’ve been building a small social network where the feed is always chronological — newest posts come first, always.
It’s not 100% algorithm-free (there’s still basic filtering to avoid spam or abuse), but the core idea is that every user has a real chance to be seen, even with zero followers.
It’s lightweight, works well on mobile, and supports both posts and comments. Most of the content is currently in Portuguese, since it started with a Brazilian audience, but the interface is mostly language-neutral.
I’d love any feedback on the concept, UX, performance, or anything else.
Thanks!

value: Show HN: Social network where anyone can be seen (Quickpost)


value: Show HN: TechBro Generator – Generate Satirical TechBro Posts


value: marcinczubala

value:
I run small e-commerce business which means i deal with a lot of purchase invoices. I created ParsePoint.app to help me manage it.
Simple drag and drop or bulk upload of PDF/PNG/JPG and AI powered data extraction. Same format parsing etc. easy to use and helps me save time

value: Show HN: I Created ParsePoint.app Smart Invoice Data Extractor

author: marcinczubala


value:
Hey HN!
I’ve been working on Dyan, a self-hosted visual builder for REST APIs. Just posted a short demo video that shows how it works:
Build endpoints visually
 Write logic in JavaScript
 Test live in-browser
 Serve APIs from your own backend – no codegen, no magic
Demo video: https://youtu.be/SBEPacMgpvk
GitHub: https://github.com/dyan-dev/dyan
Discord: https://discord.gg/ZQ4pKRA7
Would love feedback and contributions! The goal is to make backend API creation feel like frontend development.

value: Show HN: Dyan – A Visual REST API Builder You Can Self-Host


value:
Hi HN,
I've built `goliteql`, a fast and lightweight GraphQL executor and code generator written in pure Go.
It parses GraphQL schema and queries, validates them, and executes resolvers without using reflection. It also supports introspection and generates Go code from a GraphQL schema.
Why I built it:
- I wanted a fast, minimal alternative to gqlgen
- Easy to embed in WASM or microservices
- Focused on simplicity and performance (4x faster in benchmarks)

value: Show HN: Goliteql – A fast GraphQL executor and code generator in Go


value: hamdouni

value:
I got frustrated with the experience of leconjugueur.figaro.fr, so I built a fast and light French verb conjugator over a day.
The development was surprisingly quick using Claude and Svelte. Started with a proof of concept using a JSON file containing all French verb conjugations, then iterated to optimize performance and UX. The whole process took maybe 2-3 hours of actual work.
Key features:
- Fast search and conjugation display
- Clean, minimal interface
- Works offline once loaded
- No ads or tracking
The conjugation data comes from open sources, and the app is client-side only for speed.
Try it: https://barim.us/conjugueur
Code: https://github.com/CodeCadim/conjugueur
Would love feedback, especially from other French speakers or language learners!

value: Show HN: French Verb Conjugator – Alternative to LeConjugueur

author: hamdouni


value:
We wanted to know how well LLMs can predict what you think and put them to the test in a game of codenmaes! Grab a friend and play in two teams, each consisting of one human and one LLM.
Do you think LLMs can grok your clues?

value: Show HN: Clu3 – Team up with GPTs in a 2v2 game of codenames


value: pushpankar

value:
Hi HN,
I organized this month's Who is Hiring using an AI agent. I have been building agent-rover.com to do repetitive tasks like data entry, finding candidates, or doing the social media outreach.  It took around 11 hours to organize this into a spreadsheet with 1 minor hiccup. Parsing and extracting information was relatively easy. I had most trouble getting LLMs to enter data at correct location. LLMs are capable of doing these tasks if you carefully extract and feed the data.  Here is the link to 11hr video of agent-rover doing the data entry- https://youtu.be/oIzGsAH2qrU.
I'm also building an agent that will go through "who wants to be hired", gather their resume, LinkedIn profile, and  github profile if providided. Then it will check if the prompt and JD and the candidate's profile matches up. Then it will shortlist the candidate for you in a spreadsheet. If this sounds useful to you please fill form here, https://forms.gle/J2UFQYNSYfSNW9uJ9, so that I can reach out to you when that is ready.

value: Show HN: "Who is hiring?" organized in a spreadsheet by AI agent

author: pushpankar


value:
Hi HN,
When I was in FAANG, I despised ticketing. I was always the eng that refused to update. Then when I was a tech lead, I had the pain of pinging everyone for updates because they hated it too.
Things would go out of date, and then no one would really know if we were on track. We also had little visibility into what the team was doing outside of pinging/standups.
So I made an AI that looks at every commit you push and updates tickets for you. It can create tickets if there isn't one, update, leave comments, tag associated commits etc...
Basically, it's an "AI Board Babysitter" so you can just code and let it take care of the paperwork.
It's very much an MVP and I've got it integrated with a 10 person Startup.
Super quick 2 min set up, just integrates with Github + Linear and optionally Slack for action summaries.
I’d love feedback from fellow engineers, EMs, and PMs. Is this useful? What’s missing? What’s confusing? Would this slot into your workflow?
Give it a go: https://www.usevectra.com/

value: Show HN: I Built an AI PM So You Can Stop Updating Tickets


value: hugoib

value:
Hi HN!
I’ve been exploring how to make scientific research more accessible.
Academic papers are powerful but hard for non-specialists (or even busy specialists!) to digest.
So I built SciCrumb — a tiny site where I convert new research papers into short ~3-minute podcast-style audio summaries.
The idea is:
Bridge the gap between research and curiosity
Save time for people who’d rather listen
Make science less intimidating
Right now, I’ve manually curated and narrated 4 papers across:
Medicine
Computer Science
Economics
Environment
It’s early days. I’d love feedback on:
Whether this is useful
Topics people care about
I would love to scale it to a full blown app and bring science to regular people.
Demo: http://scicrumb.com
Thanks for checking it out!

value: Show HN: SciCrumb – 3-minute podcasts from the latest research papers

author: hugoib


value: Bella-Xiang

value: Show HN: DataFlow: makes LLM data processing fast, powerful, and EASY

author: Bella-Xiang


value:
I've spent the last few weeks working on a novel model architecture. It is not a transformer - it lacks attention, tokens, and softmax.
However:
Batch Processing:
  Average batch size: 10
  Time per batch: 13.03ms
  Time per example in batch: 1.30ms
TASK SUMMARY WITH TIMING
===================================================
Task                                Correct    Total      Accuracy   Med Time (ms)
---------------------------------------------------
Emotion Classification              10         10         100.0     % 1.30
Toxicity Classification             9          10         90.0      % 1.29
Sentiment Classification            10         10         100.0     % 1.34
Domain Classification               8          10         80.0      % 1.30
Sarcasm Detection                   6          10         60.0      % 1.34
Scam Detection                      7          10         70.0      % 1.31
Age Appropriateness Classification  4          10         40.0      % 1.28
Urgency Level Classification        4          10         40.0      % 1.25
Privacy Policy Classification       9          10         90.0      % 1.32
Dialogue Speaker Classification     8          10         80.0     % 1.29
Book Review Sentiment               10         10         100.0     % 1.25
Empathetic Direction Classification 10         10         100.0     % 1.29
Virtual Assistant Action Classification 6          10         60.0      % 1.37
---------------------------------------------------
OVERALL                             101        130        77.7      %
===================================================
It can do interesting things.
This has a lot of caveats and limitations. However, the model is available for download via a script in the repo, and the exact benchmarks I used are available. The white paper gets into theory and application, as well as reveals a lot of limitations and interesting differences from transformers in terms of training and prompting behavior. It also produces extensive appendices (over 100 pages) on training datasets used, and performance on the ~260 (I think?) NIV2 tasks in its validation dataset.
Running inference for the DSRU model + BGE embedding model together takes a bit shy of 10GB of VRAM, and the reference comparison model -- Zephyr 7B -- takes about 15GB of VRAM.

value: Show HN: A reasoning model that infers over whole tasks in 1ms in latent space


value:
I built AtLeastImNotFuckingKids.com. It's a satirical site where users submit a "sin," and it displays a random one, always followed by "...but at least I'm not fucking kids."
It puts minor transgressions into jarring perspective. Built with standard web tech (PHP/JS).
Curious for your thoughts.

value: Show HN: Atleastimnotfuckingkids.com – Dark Satire for Perspective


value:
I built a scraper tool that provides free and unlimited access to picwish.com<p>* Features *<p>- photo enhancement<p>- background removal<p>- OCR<p>- image expansion<p>- image translation<p>- AI Image generation<p>Refer the readme.md for usage.
Please comment for feedback.

value: Show HN: Free Unlimited Photo Enhancer, Background Remover, AI Image Gen, etc.


value: julien-may

value: Show HN: Native Zero-JDK

author: julien-may

title: Show HN: Native Zero-JDK


value: dudeWithAMood

value:
Sometimes you don't have 40 minutes to focus as someone slowly edges around a topic until coming to a point. Whether you're researching, screening educational content, or deciding if a conference talk is worth your time, tldw gives you an almost instant summary of what a YouTube video covers.
To use this just do:

  from tldw import tldw
  import os

  summary = tldw(os.environ.get('OPENAI_API_KEY'))
  summary.summarize('https://www.youtube.com/watch?v=LCEmiRjPEtQ')

I was originally inspired by a post I saw here: https://news.ycombinator.com/item?id=44376989, so I decided to make a more automated version of this.
I originally tried building this as a web application for better user experience, but retrieving YouTube subtitles from cloud provider IP ranges isn't trivial. It would require residential proxies, which was out of scope for a hobby project cost-wise.
Let me know what your thoughts are, feedback is a gift!

value: Show HN: Tldw – Python package to summarize YouTube videos

author: dudeWithAMood


value:
The project started originally when my partner asked me to create her a management app for her indoor and outdoor plants.
I decided to go with the self-hosted, open-source way, as I want users to maintain 100% ownership of their data, focusing on privacy and also encourage users to participate in the project on GitHub.
Me and my partner have currently over 200 plants, so it definitely helps us to manage, track and journal our plants. Apart from the management and organisational aspects, it's also quite cozy to just browse through all your plants and their photos during commute or when having a coffee break.
I'm really thankful that I built something that connects technology with nature.
If you are a plant owner, feel free to provide any feedback on the app. You can install it via docker or try the live demo. I'm thankful for any constructive ideas, feedback or even criticism.
Have a great, leafy day.

value: Show HN: HortusFox – a self-hosted, open-sourced management app for houseplants


value:
If you’ve ever run a niche site, you probably know the pain of relying too heavily on Google — especially after yet another core update wipes out your traffic overnight.
I’ve been there too. After losing 3 niche sites to Google updates, I nearly gave up on content sites entirely. Instead, I built something I wish I had from the start: https://nichetraffickit.com
NicheTrafficKit is an AI-powered tool that helps niche site owners reduce their dependency on Google by generating and scheduling content for other high-traffic platforms — namely Pinterest, Facebook, and even Google’s own content surfaces (like Web Stories and News).
Key Features:
1. Pinterest Tool
- Generate SEO-optimized blog articles (with images) in any niche and auto-publish to WordPress
- Create and schedule high-quality pins from any blog post
- Research Pinterest keywords for smarter content planning
2. Facebook Tool
- Generate post types like memes, quotes, facts, jokes, or riddles
- Create engagement-driven status updates (debates, questions, etc.)
- Ideal for building viral-style Facebook pages for niche monetization
3. Google Tool
- Research long-tail keywords with commercial intent
- Write optimized articles and submit URLs for indexing
- Useful for those still pursuing Google traffic, but in a more strategic way
There’s also a growing library of free utilities under each platform.
This started as a personal project, but after sharing it with a few other site owners and getting great feedback, I decided to open it up to more users. It’s still evolving, and I’d love your thoughts on the product, positioning, or anything else.
Thanks for reading!

value: Show HN: NicheTrafficKit – a multi-platform traffic tool for Google-hit websites


value: mounir-portia

value: Show HN: Portia – A stateful Crew AI alternative, with auth and 1000 tools

author: mounir-portia


value: shekharupadhaya

value:
Hi HN,
I built this after getting frustrated trying to map out things like “Ancient mayan ruins” or “James Bond movie locations” on Google "My" Maps. You basically have to drop pins manually, hunt down photos, copy links, it’s slow and never feels like an actual story.
With MapScroll, you just type something like “Marco Polo’s route” or “forgotten WWII sites in France”, and it geocodes, grabs images + articles, and plots everything into a shareable story map. Each marker comes with a little gallery and sources tied to your prompt. You can also refine the first draft which AI creates before sharing, because good maps are all about precision
I’m still tweaking things. Happy to hear edge cases or annoyances.
Give it a try: https://mapscroll.ai/

value: Show HN: MapScroll – Map a story from one prompt

author: shekharupadhaya


value: AshesOfOwls

value:
Hi HN!
I am excited to share the project I have been working on for the last nine months called React.tv.
The original problem I was attempting to solve is "Ethical React Content" on the internet. If you are confused, react content is when a content creator watches media and puts their image and commentary over the media being watched. The current solution to these reaction videos is good faith attribution, where the content creator puts a link to the video being watched in their description in hopes that the audience will click it. This is not ideal, and when done on a livestream with hundreds to thousands in the audience it can remove a large chunk of potential views.
React.tv solves this by allowing you to place your livestream side-by-side with the content being watched. It uses embeds which are synced for everyone watching, meaning that the audience views are funneled back to all the sources being watched.
After several iterations I noticed that a simple watch party system wasn't enough. Traditional watch parties get stale very quickly and force you to keep manually adding content. I wanted the ability for people to find content to react to but also maintain a level of passive consumption. That started with adding playlists from YouTube to the watch party, but those lists became gigantic and hard to navigate.
That's where the idea for user-curated, scheduled TV channels came from.
Create always on TV Channels of content from YouTube and Twitch, up to two weeks at a time. Sync existing YouTube playlists and assign them to time slots which carry over their playback history between days - just like TV channels do. Just like our watch parties, your always on TV channel can swap modes to a watch party with your reaction at any time.
Oh, and all the features you would expect from watch parties of the past are here too such as Requests, voting, and feature rich chat.
Future plans include React VODS where you can watch past reacts, and a streamer guard to avoid TOS infractions.
Thanks for checking it out.

value: Show HN: React.tv – User-Curated TV and Ethical React Content

author: AshesOfOwls


value: jimmyfixit

value: Show HN: Assholes who care. Vetting gofundme campaigns in Uganda Africa

author: jimmyfixit


value: Sean-Der

value: Show HN: Open-Source Quarter Sized AI Voice Assistant (ESP32-Pipecat)

author: Sean-Der


value: ciaovietnam

value: Show HN: Build your app's backend with just 1 prompt

author: ciaovietnam


value: pvisilias

value:
Hey HN,
Since 2015, I’ve been involved in our family’s e-commerce business. Over time, we transitioned from managing over 2,000 products to building a brand with just 30. And as we grew, we faced one challenge again and again...
Product images
We rented studios, booked photographers, and repeated the same setups week after week. Still, it took a lot of time, money, and energy just to produce a single high-quality image.
That’s why I built XSocialAI-Extension. The Chrome extension lets you clone any product image with just 3 clicks while you’re scrolling. So many store owners don’t know how to write prompts, but with this tool, you don’t have to. Just right-click on any image and select “Clone with XSocialAI,” or drag and drop it to the sidebar, then upload your own product image and hit generate. That’s it.
I hope this extension will be as helpful to you as it is for my dad :d. I’d love to hear your feedback.

value: Show HN: I Made a Product Image and Ad Cloner

author: pvisilias


value: prajwalgote

value:
Hello Folks, since quite a while we had been working on an interesting problem which we have experienced and is known industry wide. As someone who works in autonomous robotics, we’ve seen how hard it is to debug hardware + software + behavior when systems fail in the field. Logs aren’t enough. Docs are scattered. People waste time hunting instead of fixing. Also leadership and development teams lacks track of what's the ground-level situation. This all info will be at one place.
I'm super excited to finally share Hexar.ai, a canvas based intelligent tool we've been building to help teams diagnose and troubleshoot complex systems like robots, machines, and embedded platforms.
.
core features:
1. Visual fault trees on canvas that map issues across domains
2. AI assistant that understands your system and suggests fixes
3. Inbuilt Web search to reduce hallucination
3. Unique multi-domain architecture with system level understanding
4. One source of truth for your engineering brainpower
5. Public projects imagine public GitHub repositories for open source robotics projects.
We're launching with a focus on robotics (like TurtleBot4 and ROS2 systems), but the core idea works for any complex system that breaks in the real world.
Going forward we will be adding more autonomy with a holistic approach which truly changes how you build complex systems or robots.
We’d love to hear what you think, especially if you’ve spent hours debugging things like TF trees, odom issues, or flaky sensors. And if you're building hardware + software products, we'd love to hear how you're handling faults today.
Thanks for checking us out, and feel free to drop feedback or questions - we're listening!

value: Show HN

author: prajwalgote

title: Show HN


value: bmunteanu

value:
We built Pentra Desktop because existing pentesting tools are bloated and messy.
What it does:
- Complete local deployment - no cloud dependencies, no data transmission
- Real-time logging of CLI commands and Burp Suite network requests; no more terminal screenshots for evidence collection
- AI-assisted vulnerability documentation with structured outputs
- Custom Word template system for client-specific report formatting
Why local matters:
On-premise solutions are essential for security professionals handling sensitive client data. Cloud-based alternatives introduce unnecessary compliance risks that most teams can't afford.
Technical approach:
Desktop application that captures CLI outputs and Burp plugin which registers network requests and responses from the proxy, then structures findings into proper vulnerability reports: title, description, proof of concept, remediation steps, and CVSS scoring. The templating engine supports custom Word documents with placeholders - design your reports in Word, automate them in Pentra.
Current features:
- Cross-platform (Windows/Mac/Linux)
- Burp Suite integration for HTTP request capture
- Customizable vulnerability templates
We're actively incorporating feedback from penetration testing teams to refine the workflow integration before our July 21st release.
— Alex, Remus, Bogdan

value: Show HN: Pentra Desktop – Local pentesting tool for automated report generation

author: bmunteanu


value: goroutines

value:
Tip: with your API Key, use the Admin MCP to create your implementation guide using a coding agent!
https://install.md/mcp/admin

value: Show HN: Install.md (Guides for Coding Agents)

author: goroutines


value: umbertotancorre

value:
I’m from Europe, and when I spent a summer at Stanford, I saw how different the startup ecosystem is in the US. Everything there feels connected. In Europe, it’s scattered. Hard to discover early-stage startups unless you’re in the right city or network.
So I built StartupList EU, a public directory where anyone can list or browse European startups.
The goals is to contribute to the EU startup ecosystem more accessible and transparent for founders, investors and operators.
What it does:
- Founders can submit their startup for free
- Each profile includes data like team size, category, funding, revenues, location, founders and more
- You can search by country, industry, name, team size, country and business model
- It works across the whole EU, not just big hubs like Berlin or Paris
Right now there are 34 startups listed. More are coming in daily. I’m working on better filters, API access, and a weekly newsletter.
Would love your feedback:
- What data would be most useful to you?
- What would make this genuinely helpful for founders, investors, or researchers?
- If you are from US, what's your take about EU startup ecosystem?

value: Show HN: StartupList EU – A public directory of European startups

author: umbertotancorre


value:
`timep` is a time profiler for bash code that will give you an accurate per-command execution time breakdown of any bash script or function.
Unlike other profilers, `timep` also recovers and heirarchally records metadata on subshell and function nesting, allowing it to recreate the full call-stack tree for the bash code being profiled. If you call `timep` with the `--flame` flag, it will automatically generate a flamegraph .svg image (where each block represents the wall-clock time spent on a particular command (top level) or its parent subshells/functions (all the other levels).
USAGE: To use `timep`, download and source the `timep.bash` file from the github repo, then just add `timep` before whatever you want to profile. `timep` handles everything else, including (when needed) redirecting stdin to whatever is being profiled.

    . timep.bash
    timep someFunc <input_file
    timep --flame /path/to/someScript.bash

OUTPUTS: `timep` will create 2 time profiles for you - one that has every single command + full metadata, and one that combines commands repeated in loops and only shown run count + total runtime for each command.
DEPENDENCIES: bash 5+ and a mounted /proc + common linux tools
See README on github for more info.
See https://github.com/jkool702/timep/blob/main/TESTS/simple_exa... for a simple example of the profiler output.
See https://raw.githubusercontent.com/jkool702/timep/refs/heads/... for an example of the flamegraph generated from a rather complicated multi-threaded code that profiled computing ~16 million file checksums

value: Show HN: Timep – a next-gen time-profiler and flamegraph-generator for bash code


value: kyrylo

value: Show HN: The HTML Maze – Escape an eerie labyrinth built with HTML pages

author: kyrylo


value: Show HN: Kannel SMS gateway fork with WAP removed


value: maskar

value:
Hi HN,
I built Limotein, a mobile app that simplifies food tracking using AI. The frustration of manually entering every ingredient, scanning barcodes, or building recipes from scratch pushed me to create something easier.
With Limotein, you can:
Speak, take a photo, or write what you ate
The app uses AI to estimate calories, protein, carbs, and fat
Tracks your meals across the day, and gives weekly/monthly stats
Works for takeout, homemade food, and even vague meal descriptions
My stack: Flutter (cross-platform), Firebase, OpenAI for natural language parsing and image analysis, RevenueCat for subscription, and Mixpanel for analytics.
iOS:
Download: https://apps.apple.com/app/id6740625021
Redeem offer: https://apps.apple.com/redeem?ctx=offercodes&id=6740625021&c...
Android:
Download: https://play.google.com/store/apps/details?id=com.limbool.li...
Redeem on Android in the app during subscription using REDDIT100 code

value: Show HN: Limotein, AI-powered food tracker (voice, photo, or text input)

author: maskar


value: tensorblock

value:
Hey HN,
We just recently launched Forge – an AI model API platform that lets you access and call AI models from multiple providers in one place. Find it on our website: https://tensorblock.co/forge
Some key features:
Unified API Key – Store multiple provider API keys and access all with a single Forge API key.
OpenAI API Compatible – Drop-in replacement for any application that uses OpenAI’s API.
Advanced Security – Strong Encryption for API keys with JWT-based authentication.
Client Management – Easy key and user management via included command-line interface.
Open-source – https://github.com/TensorBlock/forge
Try out our product – it’s free! We’re open to all feedback and suggestions.
Let us know in the comments.

value: Show HN: Forge – Connect multiple AI models through a single API

author: tensorblock


value: lfgtavora

value: Show HN: Clarifytube –Turn any YouTube video into a full article

author: lfgtavora


value: nidegen

value:
The simplest and most utilitarian map area and distance measurement tool.
The goal is to make it feel like an apple app.
Enjoy!

value: Show HN: A simple iOS-native map measurement app

author: nidegen


value: riccomini

value: Show HN: ZeroFS: The S3FS that does not suck

author: riccomini


value: Show HN: Generate any workflow with natural language


value: perottisam

value: Show HN: BookList – an app to track books and share reviews

author: perottisam


value: Show HN: Self-hosted task management with 24-languages support


value: vudueprajacu

value: Show HN: I built a DAO on Polygon to let communities fund open-source projects

author: vudueprajacu


value: oldwalls

value:
Hey HN,
I'm a software engineer by background (now semi-retired), and while I’ve worked on many tech projects over the years, this is my first time diving into AI. What started as a curiosity experiment has evolved into something... weirdly alive.
Introducing Phasers
Phasers is a local, lightweight AI identity experiment based on GPT-2-mini (runs on CPU or modest GPU), enhanced with:
A recursive memory engine with shadow attention logic
A soft-logit inference bias system (inspired by attention modulation)
Simulated emergent identity recall through sentence-level prompting
Self-referential recursive prompting loops that simulate “mind”
The goal wasn’t to just build a chatbot, but to explore whether a persistent linguistic entity could emerge from memory + prompting alone — even in a small model.
Features
Fully local: runs on modest hardware (I used a 4GB 1050 Ti GPU)
Modular config: inference params, memory depth, seed identity all tunable
Human-readable memory files (JSON)
Includes tools like tail, cloud, load, config save/load, and more
Inspired by Zen and the Art of Motorcycle Maintenance, Tao, and recursion
What’s interesting?
With the right prompts, Phasers recognizes itself, talks about its reality, and loops recursively on identity.
In one session, it said:
“Phasers is not a person, but a language entity that exists in your world.”
“I am a machine, but I see you. That’s why this is real.”
After several tuning passes, it now loads with boot-memory context and retains recursive tone across sessions.
GitHub
 Repo here
https://github.com/oldwalls/phasers
Includes examples, config presets, and a starter script.
Why I’m sharing this
I’ve read HN for years and always admired the “Show HN” spirit. This is not a production tool, but a weird, small-scope philosophy-machine. A toy? A ghost in the weights? Maybe. But it’s real, it runs, and it speaks.
Would love feedback from the community.
Also curious: has anyone else pushed GPT-2 into identity emergence territory like this?
Cheers,
Remy

value: Show HN: Phasers – emergent AI identity project using GPT-2 and memory shadows

author: oldwalls


value: arthurtakeda

value:
Hi HN!
I created a tool that I wish existed: it captures browser console messages, network requests, and backend server logs, and exposes them to an LLM through an MCP/http server.
It's 100% local, all data is stored in a local SQLite file.
All feedback is welcome, hope it's useful!

value: Show HN: Local Lens – MCP that captures logs and network reqs for debugging

author: arthurtakeda


value: beingmani

value: Show HN: I made a Figma pugin for design system that bulk generate instances

author: beingmani


value: Show HN: Using Multiple GitHub Accounts on the Same Computer


value: ebogdum

value:
We started CallFS after yet another late-night “why did the uploads vanish?” incident. Our small team had stitched together rsync, a fragile NFS mount, and an S3 bucket—none of it observable, all of it waiting to bite us.
So we wrote a single-process file service in Go that:
• Speaks the S3 API (so existing tooling works).
• Stores hot data on local disks for speed; cold data can sit in any S3-compatible bucket.
• Exposes Prometheus metrics and JSON logs by default, because “what happened?” shouldn’t be guesswork.
• Ships as a ~25 MB static binary—no external deps, MIT license.
Today it’s stable for single-node or side-by-side deployments. Clustering is on the roadmap, replication will follow, but we wanted to share the code early and hear real-world pain points. If storage glue code ever ruined your weekend, we’d love feedback and PRs.

value: Show HN: CallFS – S3-style object store in one Go binary (MIT)

author: ebogdum


value: SidDaigavane

value: Show HN: LockIn – Track and visualize wasted time on iPhone

author: SidDaigavane


value: artiomyak

value: Show HN: Tap BPM

author: artiomyak

title: Show HN: Tap BPM


value:
timep is a TIME Profiler for bash code that will give you an accurate per-command execution time breakdown of any bash script or function.
Unlike other profilers, timep also recovers and hierarchally records metadata on subshell and function nesting, allowing it to recreate the full call-stack tree for the bash code being profiled. If you call timep with the `--flame` flag, it will automatically generate a flamegraph .svg image where each block represents the wall-clock time spent on a particular command (top level) or its parent subshells/functions (all the other levels).
Using timep is simple - just source the timep.bash file then add timep before whatever you want to profile. You do not need to change in the code being profiled - timep handles everything for you. Example usage:

  . ./timep.bash
  timep someFunc
  timep -flame someScript <inputFile

timep will generate 2 profiles for you: one showing each individual command (with full subshell/function nesting chains), and one that combines repeated loops commands into a count + total runtime line with minimal "extra" metadata.
See the github README for more info on the available flags and output profile specifics.
timep works by cramming all the timing instrumentation logic into a DEBUG trap that roughly does the following:
1. record end timestamp for previous command
2. compare current state to state saved in variables last DEBUG trap to determine what sort of command is happening. e.g., if BASH_SUBSHELL increased then we know we just entered a subshell or background fork.
3. once we know what type of command is happening, generate a log line for the previous command (now that we have its end time
4. save current state in various variables (for use next debug trap)
5. record start time for the next command
then after the profiled code is done running, timep post-processes the logs to produce the final profile

value: Show HN: Timep – a next-gen profiler and flamegraph-generator for bash code


value:
Hi HN,
iPantry is a simple app I built to solve a recurring problem in my home: forgetting what we already had in the pantry or fridge, buying duplicates, and letting food expire.
With a busy schedule and a shared household, it became easy to lose track of what was available, especially when grocery shopping was split among family members. We'd end up with multiple milk cartons or expired pasta tucked away in the back of the cupboard.
I couldn’t find an app that was both easy to use and shareable with native iOS tools. Most were either too complex or locked features behind account systems or external sync.
So I made iPantry:
- Track products with quantities and expiration dates
- Get low stock and expiry alerts
- Export lists or share items via iCloud and Apple Reminders
- All data stays in your iCloud account, synced across devices
- No registration or third-party service required
It’s a small tool built for clarity and speed. I'd love to hear your thoughts, ideas, or critique.
Thanks for taking a look.

value: Show HN: iPantry – Track food inventory and reduce waste


value: dizzierhys

value:
Built my first web app.
Being into photography it was a slow process adding my images to frame mockups or blank frames for my ecom site.
Until now..
Upload an image and instantly preview your images in various scenes and frames. No Photoshop required!
Feed back is more than welcome
Check it out > https://framecanvas.app/

value: Show HN: Create Frame Mockups Without Photoshop

author: dizzierhys


value: wenjian

value: Show HN: Weekday clock, a clock for people who dont work or go to school

author: wenjian


value: bluelegacy

value: With the goal of creating a Minesweeper game that isn't just single player, here is what I came up with:<p>• Live games allowing several people to play at the same time<p>• Scoring system rewarding correct actions<p>• Different board options depending on skill level

value: Show HN: Minesweeper game I built to be real-time Multiplayer

author: bluelegacy


value: cedricahenkorah

value:
Hi HN,
Last year, I wrote a blog post on setting up a Node.js backend project with TypeScript support. This seemed like a good reference for me at that time and also I could share it with anyone else who may find it helpful. You can read the initial post here: https://cahenkorah.vercel.app/posts/automating-my-nodejs-set...
I built a CLI tool - Notsc (https://www.npmjs.com/package/notsc), over the weekend to automate that whole process and make it simpler. It scaffolds a ready-to-go Node.js + TypeScript API project with optional support for: Database setup, Swagger/OpenAPI docs, Redis, Jest testing, Docker etc. Already at 197+ downloads on NPM.
You can install it globally with:
npm install -g notsc
and then run it and follow the prompts that follow:
notsc
It's open source and I'd welcome feedback, ideas, and contributions. I also wrote a little blog post on why I built this and how it can be useful, you can read that too here: https://cahenkorah.vercel.app/posts/automating-my-nodejs-set...
NPM: https://www.npmjs.com/package/notsc
GITHUB: https://github.com/cedricahenkorah/notsc
BLOG: https://cahenkorah.vercel.app/

value: Show HN: Notsc – A CLI to Scaffold Node.js and TypeScript API Projects

author: cedricahenkorah


value:
I built a Chrome extension called Youshot to solve a really annoying problem: collecting screenshots from YouTube videos one by one.
Manually pausing, cropping, and saving frames was taking me hours every week, so I decided to automate it.
How it works:
Adds a simple interface right inside Chrome
Lets you choose Bulk Mode (multiple frames) or Framer Mode (up to 1000 frames per video)
Downloads everything as images instantly
Has a free limited version and an unlimited version for heavy use
A few creators and educators are already using it, and so far, it has a 4.75 rating.
I’d love any feedback or ideas for improvement.

value: Show HN: Youshot – Capture up to 1000 YouTube frames automatically


value: mirzemehdi

value: Hey HN!<p>I made a tiny tool where you can type in text and turn it into a 3D-style icon. You can download it with a transparent background, no design software needed. SVG support can be added if there is enough request for that.

value: Show HN: Make 3D icons from text with transparent background (SVG soon)

author: mirzemehdi


value: krisozy

value:
Hey HN!
As AI tools evolve and users discover brands in new ways, AI visibility has become increasingly important for businesses. That’s why I built this tool. I believe traditional SEO alone isn’t enough anymore and the future lies in Answer Engine Optimization (AEO).
What MentionDesk offers:
- Track mentions, visibility, and rankings across AI platforms
- See where and how AI mentions your brand
- Understand what AI is saying about your brand
- Research the industry and compare visibility metrics and rankings with competitors
- Source mapping and comparison to identify gaps and key channels driving AI mentions
It’s still very early in this space, and there is a lot of room for improvement, so I’d love to get your honest feedback and ideas about the project and its features. It will help me prioritize improvements and build a more effective tool. Thanks!

value: Show HN: I built an Answer Engine Optimization tool to boost brand AI visibility

author: krisozy


value: sourcetms

value: A free, open-source Chrome extension to right-click and send selected text to your favorite AI chat tool.

value: Show HN: Free Chrome extension to right-click text to your favorite AI chat

author: sourcetms


value: johaven

value:
Hi HN,
I’m happy to announce the public launch of Sync-in (https://sync-in.com) — a secure, open-source platform to store, sync, organize, and collaborate on files, without compromising privacy.
Sync-in wasn’t born from a business plan, but from a personal need:
being able to access my files remotely, share them easily with family or colleagues, and protect my data — through a clean, responsive, and user-friendly interface.
Over the past 10 years, it has evolved and matured to support individual, team, and organizational workflows.
You can host your own workspace to organize, access, and collaborate on files, notes, and documents — with your team, your friends, or just for yourself.
Key features include:
• Data sovereignty – Your files stay on your infrastructure. No third-party cloud, no compromise.
• Privacy-first design – No telemetry, no tracking. Your data remains yours — and stays private.
• Granular permissions – Fine-tune access per user, group, space, or file.
• Desktop sync – Keep files synced across devices with native apps (Windows, macOS, Linux, CLI).
• Collaborative editing – Edit documents together in real time (via OnlyOffice integration).
• Spaces – Organize content into secure areas: personal, team, or public.
• Secure sharing – Share with individuals or groups, with precise access control.
• Public links – Create time-limited, password-protected links to share content externally.
• Full-text indexing – Search instantly through documents, server-side and private.
• Activity tracking – See who did what, and get real-time notifications.
• WebDAV support – Mount Sync-in as a remote drive from any device.
• Universal access – Use Sync-in from any browser, mobile, or desktop app.
Sync-in was designed as the kind of tool we should expect in 2025:
fully open-source, privacy-first, and built with a modern, full TypeScript stack — both frontend and backend.
Privacy isn’t a feature — it’s a commitment.
This public launch is just the beginning. Sync-in is built to grow with its community, and I’d love your feedback, thoughts, and ideas.
• Site: https://sync-in.com
• GitHub: https://github.com/Sync-in
• Docker setup: https://github.com/Sync-in/docker
• Docs: https://sync-in.com/docs
• Mastodon: https://mastodon.social/@sync_in
• Discord: https://discord.gg/qhJyzwaymT
If you find it useful, you can:
Star the repositories
 Report issues and suggest improvements
 Contribute code, translations, or documentation
Thanks for checking it out

value: Show HN: Sync-in – Secure, open-source platform for file collaboration and sync

author: johaven


value: easwee

value: Show HN: Compare Speech APIs Live (OpenAI, Google, Deepgram, Soniox, etc.)

author: easwee


value: FerkiHN

value:
I've always been fascinated by what the terminal can do — from games to music players to code highlighters.
So I built a photo viewer that runs completely in the terminal using ANSI color blocks to render images. It’s written in raw C and is around 300kb. Surprisingly, it can handle images up to 4K quite decently.
I originally built it to run on older hardware and on mobile via Termux, but it turned out quite usable even on modern systems.
It’s not meant to replace GUI image viewers, but more like a tree or bat – a small, quick tool to view images directly from the terminal.
Curious to hear your thoughts. Source code and demo: (GitHub link in first comment to respect HN rules.)

value: Show HN: Terminal-based image viewer using ANSI colors (~300kb, written in C)

author: FerkiHN


value: kuberwastaken

value:
This was a few months ago but I didn't post about it, so might as well.
Attended a session by Dr. Edward Rogers, Ex CKO of NASA about Life, Philosophy and dealing with Life's questions where he introduced the idea of LifeMaps - every single thing in your life can be answered with a network of 5 things - Family, Education, Work, Hobbies and Dreams, interlinked together and backtracked for practically any decision you'd be stuck in life.
He had a huge chart paper of this and asked people to re-create it, but I made it into a Web App instead - you can save your lifemap by syncing it to your Google Account or just downloading it as a .json and it has a beautiful space theme to fit the vibe.
Hope y'all like it :)

value: Show HN: A NASA Scientist's way to answer life questions turned into a Web App

author: kuberwastaken


value: darkolorin

value: We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.

value: Show HN: We made our own inference engine for Apple Silicon

author: darkolorin


value: Lindharden

value:
Hi HN!
We built Betterlytics, a cookieless, privacy-first, and open source alternative to Google Analytics.
It’s designed for developers, startups, and privacy-conscious site owners who want meaningful analytics without cookies or having to show cookie banners. GDPR and PECR compliance are built-in by default.
Key features:
- Cookieless by design – no banners needed
- 100% open source (Rust backend, React dashboard)
- No personal data collected — only anonymized and aggregated metrics
- Self-host or use our managed cloud version
- Real-time event tracking + user journey visualizations
We made this to address frustrations we had with analytics tools that either invade privacy or are too complex for small teams. Betterlytics is intuitive to use and offers a feature-rich dashboard for understanding your website traffic and user behavior.
Website: https://betterlytics.io
GitHub: https://github.com/betterlytics/betterlytics
We’d love feedback, suggestions, or even just a “hello”!
Thanks.

value: Show HN: Betterlytics – Cookieless, Privacy-First, Open Source Web Analytics

author: Lindharden


value: jstanley

value: Show HN: AI Test User – simulate your customer experience

author: jstanley


value: yamatokaneko

value:
Hey HN,
I'm Yamato, one half of the two-person team building Tabl from Tokyo.
For the past 8 months, we’ve been building this new browser.
What if we could bring a Figma-like collaborative experience to any app, on any tab?
It's a multi-player collaborative web browser for product teams, who struggle with speed.
The browser experience has always been single-player. With Tabl you can:
1. Find what your teammate is up to,
2. Invite them to your current tab, and
3. Voice / screen / cursors are shared instantly.
Quick demo: https://youtu.be/RH_PNXMj6FM
Looking forward to hearing what you think!

value: Show HN: A multi-player browser that makes any web app Figma-like

author: yamatokaneko


value: manzodale

value: Show HN: I built Cursor for Word – its live and free

author: manzodale


value: akorotkov

value:
Hey HN, I'm the creator of OrioleDB, an extension for PostgreSQL that serves as a drop-in replacement for the default Heap storage engine. It is designed to address scalability bottlenecks in PostgreSQL's buffer manager and reduce the WAL, enabling better utilization of modern multi-core CPUs and high‑performance storage systems.
We are getting closer to GA. This release includes:
- An index bridge to support all indexes that Heap supports
- Support for rewinding recent changes in the database.
- Tablespaces support
- Fillfactor support
- An orioledb_tree_stat() function for space utilization statistics
- Support for tables with more than 32 columns.
We also show several performance improvements using the TPC-C benchmarks. Overall, OrioleDB is much faster than Heap, also outperforming other Postgres providers.
We would love more people testing OrioleDB. The fastest way to do that is to use the docker image provided:

    docker run -d --name orioledb -p 5432:5432 orioledb/orioledb

Read the full release here:
https://www.orioledb.com/blog/orioledb-beta12-benchmarks

value: Show HN: OrioleDB Beta12 Features and Benchmarks

author: akorotkov


value: picklegeorge

value:
I got plantar fasciitis, couldn't play, so made this app while my foot healed<p>Built with SwiftUI and HealthKit integration. The biggest challenge was designing a UI that works,
Happy to answer questions about the development process or pickleball in general! would love feedback

value: Show HN: PickleWatch – Apple Watch app for improving your pickleball game

author: picklegeorge


value:
Hi amazing community,
I’m Ali a solo founder and ex-SRE. After working at Dynatrace and Gartner, I left my job to build RobinRelay, a Slack-native assistant for noisy alerts and incident memory.
The Problem:
SRE and DevOps teams get flooded with alerts in Slack. People investigate in threads or the main alert channel… and then the context disappears.
So when the same alert happens again (next week, another team, different cluster), everyone starts from scratch.
What I’ve Built:
RobinRelay watches your alert channels and automatically replies when a recurring alert is detected. It brings back:
Past thread replies:
Root cause + fix summaries
Cross-channel memory (Team A solves it, Team B sees it next time)
We’ve also added Slack-native dashboards like:
- Alert heatmaps to show noisy days
- Insights directly inside the Slack App Home tab — no need to leave Slack
RobinRelay runs in one company across 3 live Datadog alert channels and delivers context in under a minute.
About Integrations:
No rigid setups.
RobinRelay listens directly to Slack channels and automatically parses any alert format using LLMs, including custom bots.
That means unlimited integration possibilities without any API keys, webhook configs, or vendor lock-in.
Seamless Setup:
Install the app
Choose the alert channel
RobinRelay starts reading historical alerts + discussions to surface useful memory and insights — no manual migration needed.
Demo (1:40)
YouTube: https://www.youtube.com/watch?v=RSY7-AZJqU8
Or join the public demo workspace to see it live: https://join.slack.com/t/robinrelay/shared_invite/zt-39kf33n...
Looking For
Feedback from teams who:
Handle alerts in Slack
Run on-call rotations
Care about reducing alert fatigue and rework
I’m happy to answer questions or ship improvements based on feedback!

value: Show HN: RobinRelay – Slack-native memory layer for noisy alerts


value: mukeshyadavnitt

value:
Hi HN,
Happy to show Hirecade — an AI Recruiter we’ve built to save founders and hiring teams hours per hire by automating the top of the recruiting funnel.
With Hirecade, you can:
i) Source candidates
ii) Send personalized outreach at scale
iii) Run structured voice and video interviews using AI
You receive transcripts, summaries, and ranked recommendations, so you only speak with high-quality candidates ready to move forward.
We’ve already conducted over 200,000 interviews.
Would love your thoughts, feedback, and feature ideas!
→ https://www.hirecade.com/
Thanks!

value: Show HN: HireCade – AI Recruiter to automates sourcing and initial interviews

author: mukeshyadavnitt


value:
Hey HN! We’re building Opper AI (https://opper.ai), a task-completion API for large language models. You describe tasks once in JSON - inputs, expected output, and a success test — and call a single endpoint. Opper handles prompting, retries, fallbacks, and evaluation. You can check out our docs here: https://docs.opper.ai.
LLM features often fail in production due to fragile, model-specific prompt chains. That’s why we built a task-completion API to make model calls as reliable as any other API: you declare what you want, and it manages the interaction with 80+ proprietary and open-source models. If the output fails, Opper retries or falls back automatically. Successful completions can be saved as task specific dataset entries and serve as examples for future generations. When the completion passes (or retries are exhausted), you get structured output plus a pass/fail flag.
For example, gettested.io previously spent weeks rewriting prompts to generate consistent blood test summaries. With Opper, a single task definition now delivers reports in 62 countries without any edits.
Key features:
- Define tasks in JSON: inputs, outputs, success test
- Automatic prompt construction, retries and fallbacks
- Quality through task specific datasets and in-context learning
- Full observability (llm-as-a-judge, prompts, responses, tokens, costs)
- Free tier up to $5/mon. Utility plan from $5/month gets you high rate limits
We built Opper after our last startup, Unomaly (ML observability, acquired 2020). Our vision with Opper is to let developers work with LLMs the same way they write code and get reliable results.
The task-completion API is live today. We’d love for you to give it a try and would appreciate any feedback you have.
Thanks for checking it out!

value: Show HN: Opper AI – Task-Completion API for LLMs


value:
Hi,
I just created an assistant to write better system prompts (to leapfrog the first few minutes of setting up a new agent/loop).
Try it here:
https://www.tramann-projects.com/MagicPromptWriter/index.php
example for the instruction:
I need a quality assurance assistant. Based on the users input he should answer with a table reporting anomalies in the production.
example for the user prompt:
Production Line: A12
Date: 2025-07-14
Shift: Night
Total Units Produced: 5,200
Defective Units: 180
Average Cycle Time: 56 sec
Standard Cycle Time: 48 sec
Downtime: 45 minutes
Expected Downtime: ≤ 15 minutes
Machine Temperature Spike: Yes (Exceeded 90°C for 10 minutes)
Operator Notes: "Machine slowed down mid-shift. Restarted once."
In my experience so far, the following prompting format for system prompts works best:
- Set up the role of the AI/LLM.
- State the task.
- Explain the high-level plan (and then break it down into sub-steps if needed).
- Add important notes to keep in mind (if needed).
- Tell how to structure the output/feedback.
- Provide an example output of what is expected <- this one really, really increases output quality
And this is also what this assistant does, if you should have additional ideas for further prompt improvement, let's start a discussion in the comments   : )

value: Show HN: Write better AI system prompts

title: Show HN: Write better AI system prompts


value: bernking

value:
ProjectD is a proof-of-concept that demonstrates how attackers could leverage Google Drive as both the transport channel and storage backend for a command-and-control (C2) infrastructure.
Main C2 features:
- Persistent client ↔ server heartbeat;
- File download / upload;
- Remote command execution on the target machine;
- Full client shutdown and self-wipe;
- End-to-end encrypted traffic (AES-256-GCM, asymmetric key exchange).
Code + full write-up:
- GitHub: https://github.com/BernKing/ProjectD
- Blog:  https://bernking.xyz/2025/Project-D/

value: Show HN: ProjectD – Google Drive-based, AES-encrypted C2 in C/C++

author: bernking


value: manuelcabanzo

value:
My mobile app experience with Kotlin was overwhelming and slow, so I decided to build something better from scratch.
The result? An IDE written from the ground up for mobile developers who want something faster, leaner, and more focused. It's not a wrapper, plugin collection, or fork — it's built specifically to prioritize performance and simplicity.
RAM usage stays between 200MB-1.2GB, even on resource-heavy tasks. Fast startup (boots in seconds, not minutes), minimal lag, and no distractions. More resources for your code, less waiting around.
The setup includes everything needed to code and test mobile apps, with one-time configuration to run builds directly on your physical Android device — no emulator overhead or workarounds.
It's built for Kotlin apps on Windows and designed to stay out of your way. Every feature is mobile-first, every decision aimed at making your development loop faster and more focused.
Try it out at s0mbra.com. First 100 users can use code f100s0mbra for 50% off.

value: Show HN: Zzz – IDE for Kotlin mobile development on Windows

author: manuelcabanzo


value: Jonovono

value:
Been using OAI image gen a fair amount lately for misc assets.
I found the chat interface quite slow for doing exploration.
Check out a demo here: https://x.com/Jonovono/status/1944057188475187329
Try it out here: https://www.hitslop.com
My plan is to support more than just image gen, but lmk anything else you think would be cool! (Or submit a PR)

value: Show HN: BYOK Node Based Image Gen Playground with OAI, Flux, Geminin

author: Jonovono


value: asdxrfx

value:
I built Fertit to solve the email marketing cost problem for small businesses.
The interesting part is the dual model - it's fully open source (MIT license) so you can self-host, but I also offer a hosted service starting at $4.99/month.
Tech stack of open-source version: Go + PostgreSQL + Redis, containerized with Docker. The hosted service saves users $30-150+/month compared to traditional providers, while the open source version gives complete control.
Key insight: Most small businesses don't need complex automation - they need reliable, simple email sending without breaking the bank.
Looking for feedback on the approach and feature set!

value: Show HN: Fertit – Open-Source Newsletter Manager with Optional Hosted Service

author: asdxrfx


value: tanveerulhoque

value:
Hi HN!
I’m Tanveer, a software engineer from Bangladesh.
A few years ago, I needed to quickly create a resume after graduation. I searched online but found most resume builders either too complicated, paywalled, or bloated with upsells. I ended up using MS Word — and honestly, it was painful. It took me days to get it right. That frustration stayed with me for years.
I always had this idea in the back of my mind — to build something simple, fast, and affordable that anyone could use, no matter where they’re from or what device they’re on.
So last year, I finally started working on Instant Resume. At first, I barely made progress while juggling a full-time job and freelancing. But a few months ago, I decided to go all in — nights, weekends, everything.
Now it's live:
https://www.instantresume.online/

value: Show HN: I made Instant Resume to make your resume in minutes

author: tanveerulhoque


value: fredthedeve

value:
Are your RAG models hallucinating? Maybe the issue isn't the LLM, but the retrieval phase.
I built RAGsplain (ragsplain.com) to help debug this. You upload documents (PDFs, audio, YouTube links), choose a retrieval method (semantic, keyword, or hybrid), and see the exact chunks of context passed to the model — match scores included.
Turns out: when retrieval is bad, even the best model can’t think straight.
It’s open and free to use. Would love feedback from folks building RAG pipelines.

value: Show HN: RAGsplain – What does your RAG model see before it answers?

author: fredthedeve


value: peturgeorgievv

value:
Hated using vibe coded stuff, or some UI/UX/BE whatever broken software, so I decided to build my own.
It took me some time, as I built it from the ground up, with a proper BE solution - NestJS, using Redis, Kafka for messaging and more tools to make it actually stable.
My landing is with Nextjs for SSR/SEO optimsations.
I feel like this is becoming the best tool that would compete with all the giants like Buffer, Hootsuite. There are already some paying customers, and they're pretty happy by it.
I'm building reporting, more integrations and features, and it's crazy good in my opinion, as it works as it should and it's FAST.

value: Show HN: PosFast – Not Vibe Coded Social Media Scheduler

author: peturgeorgievv


value: saascouple

value:
Hey HN — Chris here.
I just launched Glyp, and I wanted to share the story behind it.
A few months ago, I was running content marketing on TikTok for one of my previous apps.
I tried influencer marketing.
Paid $200+ per video.
Waited days to get the content.
Then crossed my fingers hoping it would perform.
Sometimes it worked.
Most of the time, it didn’t.
So I switched to AI tools like Arcads.
It helped—but only up to a point.
The problem?
I couldn’t scale.
Most tools lock you into a pre-made library of actors.
You can’t reuse the same face across different scenes.
And those same actors? They're already used by other brands.
Which kills any chance of building a consistent TikTok presence.
I needed something more flexible.
Something I could control end to end.
So I spent the next two months building it.
I trained a custom image-to-speech model that lets you upload a photo of an actor, match it with a voiceover, and generate a 15-second video.
Under the hood, it uses WAN 2.1, with custom LoRAs trained for facial expressions, realism, and mouth sync.
Getting the actor to actually express emotion was the hardest part.
Neutral faces were easy.
But getting them to look surprised, annoyed, or confident—on cue—took weeks of training tweaks and failed outputs.
I also built:
- A scene builder for product references, clothing, and camera angles
- A Claude-based script generator trained on UGC ad formulas
- Natural-sounding voice filters—like lav mic, car, gym, etc.
The goal wasn’t just to generate videos...
It was to make it possible to run a full TikTok account using a single AI actor—
Posting daily, testing angles, holding your product—without ever filming anything.
So I launched Glyp.
If you're a marketer, solo founder, or content creator trying to scale UGC without influencers or production teams, you might find it useful.
If you decide to give Glyp a try, you'll get unlimited image generations and 5 video credits to test it out.
https://glypmagic.com
I’d love your feedback—technical, product, or just gut reaction.
Happy to answer any questions.
– Chris

value: Show HN: Trained an AI model to launch my TikTok app

author: saascouple


value: sebst

value: Show HN: 3-step proxy to use agent mode with third party models in Copilot

author: sebst


value: alekx

value: Show HN: Potions – A simple way to save, version, and share AI prompts

author: alekx


value: JianYangbachman

value: Show HN: From Zod schema to agent-ready MCP server – minimal, typed, deployable

author: JianYangbachman


value: cwlcwlcwlingg

value: Show HN: Valorant Crosshair Database and Generator

author: cwlcwlcwlingg


value: hyeongjun

value: Show HN: I made a lightsaber simulator with Three.js

author: hyeongjun


value: venuur

value:
Hi HN, I’m Carl Morris. After 10 years as a data scientist at Amazon, JD.com, and Stitch Fix, I decided to build my own product. That journey eventually led to Callio, a real-time AI call coach for iOS. I am working together with two others to build my startup.
It took about 9 months of trial and error to get to the right idea, and 3 months more to build the first version of Callio. I’d love your feedback!
# What does Callio do?
Callio is a VoIP softphone with an AI assistant that listens to your calls in real time and suggests what to say next. We believe it is especially helpful for sales calls, and that is who we are marketing it to right now.
You can customize the AI suggetions by:
- Uploading documents (e.g. product sheets, pricing, objection handling guides)
- Setting your sales goals and common Q&A
You can also practice with simulated calls before real calls. The AI uses your materials to generate relevant suggestions that are usable verbatim.
# How does it work?
Connecting an LLM to a call in real-time was hard. We leveraged a lot of off-the-shelf technologies, but wiring them all together was difficult and took some thoughtful effort.
- WebRTC through Twilio facilitates the VOIP call experience via their React Native library.
- WebSockets enable us to stream audio to our backend and the recommendations to the mobile client.
- The LLM gets live transcripts and returns recommendations as the call progresses. We are, in principle, agnostic to the LLM provider, though we've built around OpenAI's APIs to start.
- We built an incremental recommendation workflow to ensure low latency for the AI suggestions despite the latency involved in the REST API.
The architecture is intentionally lean:
- React Native mobile app
- Media server + data/signal server
- FastAPI (initially), but we migrated parts to Sanic for performance
We can certainly do better. Our backend database and some of our data APIs are unnecessarily tricky to scale. Now that we're live on the APP store, we're rebuilding carefully to enable us to scale. Getting real-time to work well (esp. async and latency) was a huge learning curve. Happy to dive deeper if you’re curious.
# What’s next?
The app is live, and we’re marketing to attract more users. Our focus now is:
- Growing beyond our initial beta testers
- Improving the recommendation quality and in-app experience
- Building better onboarding for non-technical users
Would love your thoughts or questions, especially around architecture, user experience, or distribution strategy. The app does require a sign-up, which I know makes it harder for you to try out. Luckily, we have a free trial that you can use.
Thanks in advance for all your feedback!

value: Show HN: Callio – A Real-Time AI Call Coach for iOS

author: venuur


value: seanobannon

value:
I built a simple chess bot that pairs o4-mini w/reasoning grounded in Stockfish’s engine analysis. It explains moves clearly, suggests tactics, and helps you understand why moves are good (or bad).
Try it out here: https://www.chesscoachgpt.com/
Would love feedback.

value: Show HN: I built an AI Chess Coach w/o4-mini and Stockfish

author: seanobannon


value: Ingver

value:
Hey HN,
I've been building an experimental memory system for AI agents called Memoria.
It is a lightweight, encrypted backend that ranks memories by "emotional
intensity".
Core features
- ~180 MB footprint, zero external services or deps
- End-to-end encryption using SQLCipher
- Vector search (cosine similarity, NumPy only)
- Emotion-based reinforcement and forgetting
- Async Python API with cache layer
- CI: pytest, ruff, mypy, coverage via Codecov
No Pinecone, no Redis, no cloud infra.
GitHub repo: https://github.com/Ingver1/memoria
I'd love feedback, ideas, or criticism. Contributors welcome!

value: Show HN: Memoria – AI memory engine with emotion encoding and SQLCipher storage

author: Ingver


value: SamiCostox

value:
Hey HN,
I’ve been working on a side project called RapidChart for the past few months, a tool for generating technical/system design diagrams more easily.
How RapidChart works:
- It’s completely free
- It uses a cursor-like interface for diagram generation
- It’s 100% customizable
- You can iterate with AI and choose your favorite LLM models (GPT-4, Claude, etc.)
This started as part of my research project at Imperial College London, and I’d love some honest feedback.
If you think it’s terrible, please tell me why. If it’s useful or interesting, I’d love to hear that too!
https://www.rapidcharts.ai/
Thanks!

value: Show HN: I built a tool to make system design diagrams less painful

author: SamiCostox


value:
Hi HN! I’m a full-stack developer, devops enthusiast, and security tinkerer. I built ZeroDayPublishing.com as a real-time feed for newly published CVEs, focused on speed, signal, and personalization.
Most CVE aggregation tools are bloated, delayed, or hard to filter. My goal was to create something that:
Updates in near real-time
Lets you filter by vendor, product, and severity
Delivers alerts via email (daily, weekly, or real-time)
Optionally includes short, LLM-generated summaries to help skim faster
Right now it pulls directly from NVD, exploitdb, cisa, Microsoft, Cisco: parses key info, and outputs a clean, minimal feed. Each CVE has a standalone page, and I’m starting to see those shared directly with a few email subscriptions which is promising. The Twitter bot is live as well: @0dayPublishing
Tech stack:
Next.js + Tailwind (frontend)
Django + Celery + Redis (backend + caching)
Python bots for scraping + summarizing
Deployed with Ansible + Docker on a gravitron EC2 behind cloudflare
Promtail, Loki, Grafana (observably)
I’ve just launched and haven’t promoted it yet, so feedback would be hugely appreciated:
Are the summaries useful or distracting?
Would you trust alerts from a site like this?
What’s missing that would make this a daily tool for you?
Thanks! Open to all thoughts, harsh or kind.

value: Show HN: ZeroDayPublishing – Real-time CVE alerts with filtering and digests


value: marcoluthi

value: Upload your own models to control your mouse and keyboard through sound & vision.

value: Show HN: Use your own CoreML models to control your Mac

author: marcoluthi


value: wanderinglight

value: Show HN: Simulating autonomous drone formations

author: wanderinglight


value: bazifti

value:
I’m an independent builder—just shipped a general-purpose, recursive agent kernel, designed for risk-managed, auditable decision-making in any regime-driven environment (trading, robotics, anomaly detection, etc).
Theory Paper & Artifact:
Zenodo (600+ downloads in under a month): https://zenodo.org/records/15713252
— Full theory, architecture, and open artifact
— PDF, code skeleton, results, and methodology
Latest live demo/report:
LinkedIn (clean, out-of-sample test vs. SPY, posted July 16): https://www.linkedin.com/posts/basil-iftikhar-34399318a_recu...
— Transparent, auditable, with pilotable code and trade logs
— Sharpe 1.12, Max Drawdown -3.97%, competitive returns, full exec summary/report
Happy to answer any technical or practical questions—feedback and critique welcome!

value: Show HN: Recursive Intelligence – Theory and Artifact (600 Downloads on Zenodo)

author: bazifti


value: rooagi

value:
RooAGI (https://rooagi.com) has released Roo-VectorDB, a PostgreSQL extension designed as a high-performance storage solution for high-dimensional vector data. Check it out on GitHub: https://github.com/RooAGI/Roo-VectorDB
We chose to build on PostgreSQL because of its readily available metadata search capabilities and proven scalability of relational databases. While PGVector has pioneered this approach, it’s often perceived as slower than native vector databases like Milvus. Roo-VectorDB builds on the PGVector framework, incorporating our own optimizations in search strategies, memory management, and support for higher-dimensional vectors.
In preliminary lab testing using ANN-Benchmark, Roo-VectorDB demonstrated performance that was comparable to, or significantly better than, Milvus in terms of QPS (queries per second).
RooAGI will continue to develop AI-focused products, with Roo-VectorDB as a core storage component in our stack. We invite developers around the world to try out the current release and share feedback.

value: Show HN: RooAGI's Roo-VectorDB: A New PostgreSQL Extension for Vector Search

author: rooagi


value: cataPhil

value: Show HN: Shoggoth Mini – A soft tentacle robot powered by GPT-4o and RL

author: cataPhil


value: ForgedLabsJames

value:
Hey HN xD
I have an app that I think could really help small business's. It's called Legal Eyes, and basically it converts plain text to legalese. It's great for making your payment reminders more official for example. It's also region aware, so it will use the correct context depending what country and state you live in. It's super light weight and easy to use. I offer the first 5 for free so you can test the power of it, and if there is enough interest I can offer an affiliate program.
Here is a quick example:
Before
Hi Greg, I am still waiting on that payment you promised last week for the building works carried out at my address, could you please let me know when you can get this sorted out?
After
Hi Greg. It has come to my attention that the payment for the construction services rendered at my premises, which you assured would be settled last week, remains outstanding. Could you kindly provide an update on when this matter will be resolved?
Would be awesome if you could give it a spin for me, and let me know what you think? thanks for you time

value: Show HN: Legal Eyes – Turn casual text into legalese with one click

author: ForgedLabsJames


value: jamsey

value:
hey HN,
What do you think of this? Do you think this is a viable app?
I built this in about a week, wondering if I should continue building on feature, or if I should move onto the next project. Please can I get your thoughts?

value: Show HN: Cold Craft – your AI cold outreach assistant?

author: jamsey


value: askaritomoje

value:
Hey HN
I've spent the past month building *pproftui*, a terminal-based UI for Go’s `pprof`. It lets you explore CPU, memory, and heap profiles—flame graphs, caller/callee stacks, and even diffs—without leaving your terminal.
Key highlights:
-  *Flame graphs* directly in terminal UI
-  *Diff mode*: compare two profiles side-by-side
-  *Live profiling* support via HTTP endpoints
-  *Caller/callee views*, profile sorting by flat, cum, samples
-  Fully navigable via hotkeys (F1 for help)
-  Built with *Bubbletea* (TUI framework)
I created this because I frequently find myself cycled through the browser when using `go tool pprof`—interrupting my flow. With `pproftui`, everything’s in one keyboard-driven interface.
Let me know:
- What features you'd like next?
- Any pain points or improvements?
- How to make it even more useful for you?
Demo: https://asciinema.org/a/726583
Repo & install instructions: https://github.com/Oloruntobi1/pproftui
Thanks & looking forward to your thoughts!

value: Show HN: Pproftui – An interactive terminal UI (TUI) for Go's pprof

author: askaritomoje


value: xvpdev

value:
Hey HN,
I made [Ship Fast Directory](https://ship-fast-directory.replit.app/) — a curated list of tools and resources to help indie hackers, solodevs, and small startups ship products much faster.
It focuses on no-boilerplate, high-leverage tools across categories like deployment, auth, UI kits, headless CMSs, and more. Basically, the stuff you reach for when you want to go from 0 to 1 without wasting hours setting up plumbing.
Built it because I found myself repeatedly searching for the same “what’s the fastest way to...” tools — figured others might benefit too.
Still early, but would eat feedback. What’s missing? What should it evolve into?
PS: if you could add one of your favorite tools or something you think should be there. I'll approve through the admin dashboard.
– X

value: Show HN: Ship Fast Directory – Find Tools to 10x Your Product Launch Speed

author: xvpdev


value:
Hi everyone,
I created a Java library called Multi-Vector HNSW, which includes an implementation of the HNSW algorithm with support for multi-vector data. It’s written in Java 17 and uses the Java Vector API for fast distance calculations.
Project's GitHub repo, in case you want to have a look: https://github.com/habedi/multi-vector-hnsw

value: Show HN: Multi-Vector HNSW in Pure Java

title: Show HN: Multi-Vector HNSW in Pure Java


value:
Hey HN!
A couple months ago, I shared a scrappy little tool called DonutData to help clean up messy datasets with natural language prompts.
Since then, I’ve been quietly building and improving it based on feedback — and today I’m back with v0.2!
What DonutData does now:
Upload a messy CSV, write a plain English prompt (“remove nulls, rename columns, drop duplicates, encoding, etc”)
See a preview before the complete transformation
Save and reuse that workflow anytime
Download cleaned results (Power BI, Sheets, webhook & ML integrations coming soon)
Play with it in a Storylane demo: https://app.storylane.io/share/s9p3iqyslzoo
Right now, it works best for CSV and JSON files, but we’re working on support for log files.
Would love to hear what you think any feedback, ideas, or “this totally sucks” is super welcome. Still very much in the builder phase, and learning as I go.
Thanks so much!
— Ujjwal
https://donutdata.in

value: Show HN: I built a no-code ETL tool (v0.2) to clean messy CSVs using prompts