value:
Hey everyone
I’m a solo founder and developer, and I’ve been building software since 2011. Over the years, I watched customer feedback tools go from simple and focused to bloated, enterprise-heavy platforms. Tons of features, endless configuration… and yet I kept using maybe 10% of what was there. The rest mostly got in the way.
So I decided to build the feedback tool I actually wanted to use.
That became Zigpoll — a fast, focused survey and feedback platform designed for real-world businesses. It does the essentials really well: post-purchase surveys, attribution insights, customer feedback loops, and clean integrations with the tools merchants already use. No bloated dashboards. No “enterprise for enterprise’s sake.” Just actionable feedback that’s easy to collect and easy to act on.
Zigpoll has been live for several years now and is used by thousands of merchants across ecommerce and SaaS. It’s built with performance and privacy in mind, with a strong focus on reliability, flexibility, and data ownership.
On privacy: Zigpoll collects only what’s necessary to run surveys and generate insights. No invasive tracking, no session replays, no selling data.
If you’re a founder, marketer, or product team who wants customer feedback without the overhead, I’d genuinely love to hear what you think (try our contact us form):  https://www.zigpoll.com
Happy to answer questions about the tech stack, architecture decisions, why certain features didn’t make the cut, or what’s coming next.
— Jason

value: Show HN: I spent 2 years building a feedback tool with only the features I need


value:
Hi, I built Stik because every note app wanted to be my second brain. I just needed something to catch a thought before it disappeared.
How it works:
Press [Cmd+Shift+S] from anywhere. A floating post-it appears. Type. Close. Back to work in under 3 seconds. Notes are saved as plain .md files in ~/Documents/Stik/.
What makes it different:
- No account, no cloud, no sync setup, no onboarding
- Notes are plain markdown files, open them in any editor
- On-device AI (Apple NaturalLanguage framework) for semantic search and folder suggestions, nothing leaves your Mac
- Built with Tauri 2.0 (Rust backend + React frontend), ~8MB binary
- DarwinKit sidecar handles NLP via a JSON-RPC bridge to Apple's ML frameworks
Tech stack:
Rust, React 19, TypeScript, Tailwind, Tiptap editor, Swift (DarwinKit sidecar)
Install:
brew install --cask 0xMassi/stik/stik
Source:
https://github.com/0xMassi/stik_app
I'd love feedback on the architecture, especially the Tauri + Swift sidecar pattern for accessing Apple-only frameworks from a cross-platform runtime.

value: Show HN: Stik – Instant thought capture for macOS (open source, Tauri and Rust)


value: brtkwr

value: Show HN: Ty-lsp skill for coding agents

author: brtkwr

title: Show HN: Ty-lsp skill for coding agents


value: Show HN: R3forth, a ColorForth-inspired language with a tiny VM


value: ksaville

value:
I am a non-coder. Last year, I tried to build a squares app using Cursor and ChatGPT but hit a hard wall with the backend logic. I ended up with a local HTML file that looked good but could not handle remote users.
Two weeks ago, I tried again using Claude Code and the new Opus 4.5 model.
The difference was massive. I was able to build a fully functional application with a WordPress backend that handles real-time score updates across all player boards.
I also added some logic-heavy features:
Historical Heat Maps: Visualizes which number combinations have won most frequently in past Super Bowls.
Quick Pick: Allows users to randomly select squares instantly.
There is no registration or account requirement so it's easy to try out, happy to hear any suggestions.

value: Show HN: Vibe coded real-time Super Bowl Squares app (Claude Code and Opus 4.5)

author: ksaville


value: rahuljaguste

value: Show HN: Falcon's Eye (isometric NetHack) running in the browser via WebAssembly

author: rahuljaguste


value: iamdan

value:
I built a browser-based tracker app that let's you compose audio across 4 channels. Inspired by the Gameboy audio system - not quite a true recreation but a fun toy nonetheless. Song data is persisted in local storage so you don't lose your progress.
https://github.com/daniel-black/chiptune-tracker

value: Show HN: Chiptune Tracker

author: iamdan

title: Show HN: Chiptune Tracker


value: dudeskey

value: Show HN: A React testing boilerplate built for vibe coding

author: dudeskey


value: zacharykapank

value:
I built this while preparing for startup interviews because I noticed I rambled under pressure.
So I made a tool that runs a short, live YC-style voice interview with interruptions, follow-up questions, and post-call feedback.
It started as an internal project. I kept improving it because it helped me tighten how I explain the problem, market, and metrics.
Now I’m sharing it to get honest feedback from other founders.
I’d especially like feedback on:
1) whether the interview pressure feels realistic
2) what question types are missing
3) what would make the feedback more useful
There’s a 1-minute live demo on the landing page.
Not affiliated with Y Combinator.

value: Show HN: I built a YC-style interview practice tool for myself, now public

author: zacharykapank


value: kaniksu

value:
I built a macOS screensaver that displays a spinning vinyl record synced to whatever you're listening to. The record spins while music plays and stops when you pause.
Works with Spotify, Apple Music, or any app that reports to Now Playing (podcasts, YouTube, etc.).
A few technical details HN might find interesting:
- Zero permissions required. I use distributed notifications and the MediaRemote framework to read now-playing state without requesting any system access.
- Album art comes from Spotify's public oEmbed API and iTunes Search API—no auth tokens needed.
- Native Swift, not Electron. It's a proper .saver bundle that installs to ~/Library/Screen Savers.
- The vinyl color can match the dominant color of the album art, which required some fun image processing.
Requires macOS 13+. Free download, no tracking, no account.
Would love feedback on the rendering or ideas for other integrations.

value: Show HN: 33rpm – A vinyl screensaver for macOS that syncs to your music

author: kaniksu


value: symplice

value: Show HN: Replacing NotNull and Preconditions with fluent Java assertions

author: symplice


value: Show HN: Word2Vec in Jax

title: Show HN: Word2Vec in Jax


value: ranklabs

value:
I built a small set of open-source schema packages after repeatedly seeing production ecommerce sites where JSON-LD technically existed, but product identity and attributes drifted enough to confuse AI systems.
Most “AEO” tooling focuses on monitoring outcomes. This work focuses on the integrity of the underlying product data itself: stable identity, consistency across pages, and explicit handling of conflicting or stale fields.
These packages started as internal tooling while building RankLabs, but I pulled them out into standalone, open-source libraries so developers can inspect and reason about the implementation.
Packages:
 • @ranklabs/schema (core)
 • @ranklabs/schema-next
 • @ranklabs/schema-hydrogen
The goal isn’t SEO automation, but making product data more predictable for LLMs and other machine consumers.
Would love feedback, especially from people dealing with large catalogs or headless commerce setups.

value: Show HN: Open-source schema tooling focused on consistency for AI consumers

author: ranklabs


value: humanchaos

value:
I built this because I’m worried about how fast misinformation outpaces truth. It’s a Chrome extension for real-time claim verification on YouTube.
The Reality Check:
I’m not a professional dev. I’ve done my best to get this to a working prototype, but my best is likely your worst. I’m sharing it here because I’ve hit walls I don’t know how to climb yet.
The Hard Problems:
The API Key Barrier: Requiring an OpenAI/Gemini key is too much friction for normal users. How do we move toward local LLMs (Transformers.js?) or a community proxy?
The Trust Engine: How do we weight sources without building in our own bias?
Performance: Parsing real-time transcripts and grounding them in search results without lagging the browser.
I've set up the repo with an MIT License, CI/CD, and a roadmap of "Good First Issues." If you've ever wanted to use your skills for factual integrity, please take a look at the code and tell me where I'm wrong.
Repo: https://github.com/humanchaos/factcheck

value: Show HN: Factcheck – An open-source YouTube fact-checker. I need your help

author: humanchaos


value: appleroll

value: Show HN: Improving Prompt Injection Detection with Weighted Ensembles

author: appleroll


value: nthh

value: Show HN: Botparty – messing around with real time LLM collaboration

author: nthh


value: demoonkevin

value:
Hi HN — I’m Kevin. As many of you, I've been using Jinja templates to render LLM prompts, where subtle bugs can introduce unexpected LLM behavior that is hard to debug. And writing unit tests to prevent those bugs is painful: you end up doing assertions against raw strings, which is brittle and forces you to keep track of format changes, etc.
So I built jinjatest: a Python library + pytest plugin that lets you test Jinja templates with structure, and (the part I haven’t seen elsewhere) get branch coverage reports for templates.
What it does:
- Template branch coverage via runtime instrumentation: pytest --jt-cov, fail-under thresholds, and term/html/json/xml reports
- Type-safe render context validation with Pydantic + StrictUndefined by default (missing vars fail loudly)
- Parse rendered output as JSON/YAML/XML/markdown "(including fenced code blocks) so tests can assert on structured data
- Optional anchors/traces inside templates for section-level assertions / verifying branches were taken
Open to feedback!

value: Show HN: Jinjatest – type-safe tests and branch coverage for Jinja templates

author: demoonkevin


value: cryptoz

value:
Hello! I hope this is sufficiently different to pique your interest:
1) I use Abstract Syntax Trees to have the LLM modify existing code. It will make more targeted changes than I've seen compared to codex/gemini cli/cursor etc. I wrote a blog post about how I do this if you want to know more: https://codeplusequalsai.com/static/blog/prompting_llms_to_m...
2) I double-charge for tokens. This creates a margin, so that when you publish your app, you get to earn from that extra token margin. An API call that costs $0.20 to the user would break down to $0.10 for the LLM provider, $0.08 for you, and $0.02 for me. I'm trying to reduce the friction of validating ideas by making the revenue happen automatically as people use your app.
3) I've built a "Marketplace" where you can browse the webapps people have created: https://codeplusequalsai.com/marketplace
I'm kind of trying to bring back and old-school web vibe into the AI world, where it's easier for people to create things and also discover neat little sites people have built. I wonder if I can also solve the 'micropayments' idea that never took off really, by baking in the revenue model to your webapp.
4) I envision the future of large-scale software development to be a lot more about writing clear tickets than ever before; we've all dealt with poorly-written tickets, ill-defined use cases, and ambiguous requirements. This site is an early take on what I think the UX might be in a future where ticket-writing may take a greater amount of time, especially to code-writing.
What do you think?
--
Some more quick nerdy details about behind-the-scenes tech: this is running on 3 linode servers: 1 app server (python/flask), 1 db server (postgres), 1 'docker server' that hosts your webapps. The hardest part about making this was getting the LLMs to write the AST code, and setting up the infrastructure to run it. I have a locked-down docker with python and node, and once the LLM responds to a code change request we run a script in that docker to get the new output. For example, to change an html file, it runs a python script that inputs the original file contents as a string to the LLM output, which uses beautifulsoup to make changes to the html file as requested by the user. It's quite custom to each language, so at the moment I support Python, Javascript, HTML, CSS and am currently testing React/Typescript (with moderate success!)

value: Show HN: Webapps running in Docker containers and earning on token margins

author: cryptoz


value: halotrope

value:
I've wanted something like this for a long time and finally built it over the weekend after seeing a tweeet about LLM memory.
Perchpad is a collaborative real-time Markdown editor where your workspace is just a folder of flat .md and .csv files, version-controlled with Git.
Key things:
Git-native storage, your workspace is plain files. git clone them, download as zip, or just edit locally. No proprietary format lock-in.
Deep LLM support. hook it up to Claude as persistent memory/context. The LLM integration is fairly capable for drafting and editing within docs.
Web based real-time multiplayer – full collaborative editing, multiple cursors, the usual.
Text-to-speech longform read-aloud with decent voice options.
The idea is that your notes and docs should be portable plain text first, collaborative second, and AI-augmented third — not the other way around.
Would really appreciate any feedback. Happy to answer questions here.
https://perchpad.co

value: Show HN: Perchpad – Collaborative real-time Markdown editor backed by Git

author: halotrope


value: takinosh

value:
I built an open-source Kubernetes operator to automate the validation of Jupyter Notebooks in MLOps workflows. It's called the Jupyter Notebook Validator Operator and it's designed to catch issues with notebooks before they hit production.
It runs notebooks in isolated pods and can validate them against deployed ML models on platforms like KServe, OpenShift AI, and vLLM. It also does regression testing by comparing notebook outputs against a "golden" version.
The goal is to make notebooks more reliable and reproducible in production environments. It's built with Go and the Operator SDK.
We're looking for contributors. There are opportunities to work on features like smarter error reporting, observability dashboards, and adding support for more platforms.
GitHub: https://github.com/tosin2013/jupyter-notebook-validator-oper...

value: Show HN: A Kubernetes Operator to Validate Jupyter Notebooks in MLOps

author: takinosh


value: osmansiddique

value: Discover cloud credits, AI credits, and developer tools from the world's leading technology companies for free

value: Show HN: I built a directory of $1M+ in free credits for startups

author: osmansiddique


value: Made a Web Cache API using the Origin Private File System to store large responses. Mostly made for my other project that needs to store models from hugginface locally. The default cache provided by transformers.js can't store large responses due to storage quotas so I built this to use instead.

value: Show HN: Web Cache Using Origin Private File System


value: justvugg

value:
I built llm‑use, an open‑source Python framework for orchestrating large language model workflows across local and cloud models with smart routing, cost tracking, session logs, optional web scraping, and optional MCP integration. It’s designed for agent workflows (planner + workers + synthesis) that leverage multiple LLMs without manual switching or custom glue code.
Examples
Simple local usage:
ollama pull llama3.1:70b
ollama pull llama3.1:8b
python3 cli.py exec \
  --orchestrator ollama:llama3.1:70b \
  --worker ollama:llama3.1:8b \
  --task "Summarize 10 news articles"
This runs a planner + worker flow fully locally.
Hybrid cloud + local usage:
export ANTHROPIC_API_KEY="sk-ant‑..."
ollama pull llama3.1:8b
python3 cli.py exec \
  --orchestrator anthropic:claude-3-7-sonnet-20250219 \
  --worker ollama:llama3.1:8b \
  --task "Compare 5 products"
export ANTHROPIC_API_KEY="sk-ant‑..."
ollama pull llama3.1:8b
python3 cli.py exec \
  --orchestrator anthropic:claude-3-7-sonnet-20250219 \
  --worker ollama:llama3.1:8b \
  --task "Compare 5 products"
Routes tasks between cloud provider models and a local worker.
TUI chat mode:
python3 cli.py chat \
  --orchestrator anthropic:claude-3 \
  --worker ollama:llama3.1:8b
Interactive CLI chat with live logs and cost breakdown.
Why it matters
 • Orchestrate multiple LLMs — OpenAI, Anthropic, Ollama/llama.cpp — without writing custom routing logic.
 • Smart routing and fallback — choose better models for each task and fall back heuristically or learned over time.
 • Cost tracking & session logs — see costs per run and preserve history locally.
 • Optional scraping + caching — enrich tasks with real web data if needed.
 • Optional MCP server integration — serve llm‑use workflows via PolyMCP.
llm‑use makes it easier to build robust, multi‑model LLM systems without being tied to a single API or manual orchestration.
Repo: https://github.com/llm‑use/llm‑use

value: Show HN: LLM-use – Open-source tool to route and orchestrate multi-LLM tasks

author: justvugg


value: shadowy-pycoder

value:
What is changed:
1) Faster proxying: now it creates several instances of TCP and UDP servers within one process (works on Linux and Android)
2) Better packet parsing: DNS traffic became more detailed and robust
3) Added more optimizations to auto configuration and ability to ignore certain ports
4) License change: migrating from MIT to GPLv3

value: Show HN: Gohpts tproxy with arp spoofing and sniffing got a new update

author: shadowy-pycoder


value:
The platform provides verdicts on viablitiy of your business idea along other factors, competitor and market research, pricing strategies, 90-day roadmap, etc.
I spent months on this platform to make sure it's not just another AI-generated slob. It follows strict structures programmatically and the AI is trained on massive amount of business and corporation data, failed startup databases, along others.
You get to try it for free and see for yourself:)

value: Show HN: NexVo'-Verdicts for SaaS Ideas

title: Show HN: NexVo'-Verdicts for SaaS Ideas


value:
Building for fun and have 5 game prototypes.
All 5 are unfinished, rough, and 100% vibe-coded. Looking for feedback on if any are worth building further.
If you are tired of submissions like this, just tell me to keep working on digtocore, which is too complicated and buggy.
Dig to the Core: https://www.digtocore.com/ Multiplayer mining game
SimOps: https://simops-game.vercel.app/ Operations/simulation game
Office Worker Idle: https://office-worker.vercel.app/ Idle game with a corporate theme
Digital Empire: https://digital-empire-seven.vercel.app/ Incremental clicker where you build computers and automate
Simple RPG: https://html5-rpg.vercel.app/ Browser-based RPG

value: Show HN: Vibe-coded game prototypes. Tell me which to work on


value: wkyleg

value:
I built a skill for OpenClaw (open source AI agent framework) that lets agents analyze raw genetic data from 23andMe, AncestryDNA, and similar services.
The motivation was privacy. Most genetic analysis tools require uploading your data to a server. This runs entirely locally with no network requests. Your DNA never leaves your machine.
It analyzes about 800 markers covering health risks (APOE, MTHFR, Factor V Leiden), drug metabolism (CYP450 enzymes, warfarin sensitivity), and ancestry (Y-DNA/mtDNA haplogroups for all major global lineages). Output is both human-readable reports and structured JSON that agents can use to help users understand their results. It also includes popular ancient DNA databases.
Built on PLINK and standard Python genomics tooling. Marker annotations come from ClinVar, PharmGKB, and the GWAS Catalog with citations to primary literature.
Not a medical device, obviously. But useful for exploring your own data without handing it to a third party.

value: Show HN: Local DNA analysis skill for OpenClaw

author: wkyleg


value: AGDNoob

value: Show HN: FastLog: 1.4 GB/s text file analyzer with AVX2 SIMD

author: AGDNoob


value: Mapika

value: I built a small CLI tool that answers "what's using my port?" without the lsof/ss/netstat incantation dance.

value: Show HN: Portview what's on your ports (diagnostic-first, single binary, Linux)

author: Mapika


value: Show HN: I built the first tool to configure VPSs without commands


value: thedaviddias

value:
I've spent a few days experimenting with OpenClaw and tweaking the SOUL.md used by agents. I discovered how powerful those can be! I got so many different personalities, with different traits. I even created "Kuma", my Japanese teacher that doesn't do what I ask him to do until I submit my weekly homework!
So I decided to create an of SOUL.md that people can copy, fork and submit new ones.
Everything's open source (MIT).
GitHub: [https://github.com/thedaviddias/souls-directory]
Live site: [https://souls.directory]
Would love feedback on the format itself—still iterating on what fields are essential vs. optional.

value: Show HN: Souls.directory – SOUL.md templates for AI agent personalities

author: thedaviddias


value: Show HN: DeSync – Decentralized Economic Realm with Blockchain-Based Governance


value: Show HN: Zest – A hands-on simulator for Staff+ system design scenarios


value: richardhapb

value: Show HN: Django N+1 Queries Checker

author: richardhapb

title: Show HN: Django N+1 Queries Checker


value: meszmate

value: I built this because Zig lacked a proper TUI framework. Inspired by Go's Bubble Tea/Lipgloss. Feedback welcome.

value: Show HN: ZigZag – A Bubble Tea-Inspired TUI Framework for Zig

author: meszmate


value: Show HN: Sem – Semantic diffs and patches for Git


value: oozoofrog

value:
Author here. I built this because I wanted AI coding agents to test iOS apps directly.
Key technical bits:
- Uses XCTest private APIs for accessibility tree parsing
- Native Swift bridge for performance (no simctl overhead for UI ops)
- 32 tools covering most UI automation needs
Happy to discuss the architecture or answer questions.

value: Show HN: MCP-baepsae – MCP server for iOS Simulator automation

author: oozoofrog


value: pierrepomes

value: Show HN: Myanon – fast, deterministic MySQL dump anonymizer

author: pierrepomes


value:
I built a "Triple Failover" RAG for Singapore Laws, then rewrote the logic based on your feedback.
Hi everyone!
I’m a student developer. Recently, I created Explore Singapore, a RAG-based search engine that scrapes about 20,000 pages of Singaporean government acts and laws.
I recently posted the MVP and received some tough but essential feedback about hallucinations and query depth. I took that feedback, focused on improvements, and just released Version 2.
Here is how I upgraded the system from a basic RAG to a production-grade one.
The Design & UI
I aimed to avoid a dull government website.
Design: Heavily inspired by Apple’s minimalist style.
Tech: Custom frontend interacting with a Python backend.
The V2 Engineering Overhaul
The community challenged me on three main points. Here’s how I addressed them:
1. The "Personality" Fix
Issue: I use a "Triple Failover" system with three models as backup. When the main model failed, the backups sounded entirely different.
The Solution: I added Dynamic System Instructions. Now, if the backend switches to Model B, it uses a specific prompt designed for Model B’s features, making it mimic the structure and tone of the primary model. The user never notices the change.
2. The "Deep Search" Fix
Issue: A simple semantic search for "Starting a business" misses related laws like "Tax" or "Labor" acts.
The Solution: I implemented Multi-Query Retrieval (MQR). An LLM now intercepts your query. It breaks it down into sub-intents (e.g., “Business Registration,” “Corporate Tax,” “Employment Rules”). It searches for all of them at the same time and combines the results.
Result: Much richer, context-aware answers.
3. The "Hallucination" Fix
Issue: Garbage In, Garbage Out. If FAISS retrieves a bad document, the LLM produces inaccurate information.
The Solution: I added a Cross-Encoder Re-Ranking layer.
Step 1: FAISS grabs the top 10 results.
Step 2: A specialized Cross-Encoder model evaluates them for relevance.
Step 3: Irrelevant parts are removed before they reach the Chat LLM.
*
The Tech Stack
*
Embeddings: BGE-M3 (Running locally)
Vector DB: FAISS
Backend: Python + Custom Triple-Model Failover
Logic: Multi-Query + Re-Ranking (New in V2)
Try it out
I am still learning. I’d love to hear your thoughts on the new logic.
Live Demo: https://adityaprasad-sudo.github.io/Explore-Singapore/
GitHub Repo: https://github.com/adityaprasad-sudo/Explore-Singapore
Feedback, especially on the failover speed, is welcome!

value: Show HN: I built a RAG engine to search Singaporean laws


value: ShinyaKoyano

value:
Hi HN,
I built Moli P2P because I wanted a way to share images directly with peers without leaving a permanent record on any server.
The Philosophy: "Presence is Storage" The gallery exists only in the RAM of active peers. There is no database, no S3 bucket, and no central storage. When everyone closes the tab, the gallery vanishes.
The Tech Stack:
* Signaling Server: Rust (Axum + Tokio). Acting as a dumb pipe for WebRTC signaling and STUN/TURNauth. It stores nothing.
* Client: TypeScript + Vite. Uses WebRTC DataChannels for all file transfers.
* Security: Ephemeral TURN credentials, cryptographic identity enforcement, and a "Circuit Breaker" to prevent DoS.
Why "Sovereign"? We implemented a strict "Sakoku" (Isolation) policy. Moderation is local to your node. If I block a peer, my node stops relaying their content to me. There is no central authority to "ban" users globally.
It is open source (AGPLv3) and designed to be self-hosted easily with Docker.
Code: https://github.com/moli-green/moli-p2p Live Demo: https://moli-green.is
I'd love to hear your feedback on the architecture and our approach to ephemeral p2p networks.

value: Show HN: Moli P2P – An ephemeral, serverless image gallery (Rust and WebRTC)

author: ShinyaKoyano


value: mkyang

value: Show HN: PaySentry – Open-source control plane for AI agent payments

author: mkyang


value:
Hi HN,
I just launched Fitspire, a small iOS app built around one idea: workouts shouldn’t require 45–60 minutes to be effective.
I’m a builder, and like many people working long hours, I found it hard to stay consistent with traditional fitness routines. Most apps felt either too intense, too time-consuming, or overloaded with features.
So I built Fitspire around:
1. Structured 5-minute workouts
2. Minimal, distraction-free UI
3. Simple progress tracking (streaks, reports, history)
4. No subscriptions at launch (100% free for now)
The goal isn’t to replace full gym training — it’s to reduce the friction of starting.
Tech stack:
1. Built with React Native
2. Backend with Firebase
3. iOS release via App Store
I’m mainly looking for feedback on:
Does 5-minute positioning make sense?
What would make this actually sticky?
Where do fitness apps usually fail in retention?
Would appreciate any honest feedback — especially critical ones.
Thanks.

value: Show HN: Fitspire – a simple 5-minute workout app for busy people (iOS)


value: Show HN: Deterministic signal triangulation using a fixed .72% variance constant


value:
Hi HN. I’m Akash.
I started working on this after a personal experience where a serious illness in my family went undiagnosed until very late, despite years of lab reports, prescriptions, and scans. The data existed, but it was scattered across portals and PDFs, and no one was looking at it together over time. I ended up copying values into spreadsheets just to see if anything stood out.
That experience made something obvious to me: medicine is longitudinal, but healthcare software treats every visit as a separate event.
This project is a longitudinal health record that brings medical data from different sources into a single, time-ordered history. Patients can upload lab reports, prescriptions, and scans from any provider, and the system organizes them so trends and changes across time are easier to review. On the clinical side, it works as a lightweight record system for outpatient and diagnostic workflows, focused on reviewing history rather than billing or complex hospital processes.
What’s different from traditional EHRs is the emphasis on continuity and portability. The record is built around the patient’s timeline, not a single hospital or visit. Under the hood, we normalize messy real-world inputs and structure them so labs, medications, and diagnoses can be viewed together across time.
This is live today and used by ~25k patients and a few hospitals. We’re still learning where longitudinal views help most in practice, and where they don’t.
You can try it here: https://myaether.live
(There’s a demo flow for uploading records and viewing timelines.)
Happy to answer questions - especially about data normalization, privacy tradeoffs, or why this hasn’t worked well in healthcare before.

value: Show HN: A longitudinal health record built from fragmented medical data


value: astm

value:
I built a zero-dependency formatter for Mermaid diagram syntax. It normalizes indentation, whitespace, and arrow spacing across all diagram types.
Features:
- CLI tool (mermaidfmt) with stdin/file/in-place support
- Programmatic API for integration (e.g. format Mermaid blocks in Markdown)
- Nested block indentation (alt/loop/par/subgraph track depth)
- Context-aware parsing (else only inside alt, and only inside par)
- Arrow normalization: A->>B:msg → A ->> B: msg
npm install -g mermaid-formatter

value: Show HN: Mermaid Formatter – CLI and library to auto-format Mermaid diagrams

author: astm


value: AbduNebu

value: Show HN: Afelyon – Turns Jira tickets into production-ready PRs (multi-repo)

author: AbduNebu


value: theelderwand

value: Show HN: MCP Server for TradeStation

author: theelderwand

title: Show HN: MCP Server for TradeStation


value: imthepk

value:
Hey HN,
I built MindDraft because I was tired of task apps that made ME do all the work. After 18 months of solo development, I'm sharing it here.
*What's different:*
1. Location-based reminders - "Buy groceries" alerts when you're near a grocery store, not at an arbitrary time. Uses iOS geofencing.
2. Smart Actions - Tasks have action buttons. "Call dentist" → tap to call. "Pay rent" → opens payment app. Tasks become actions, not just reminders.
3. Task → Expense - Complete "Buy laptop $1200" and it auto-logs as an expense. No separate expense app.
4. Natural language AI - "Meeting with John Friday 3pm, need to prep slides day before" → parsed correctly with subtask.
*Tech stack:*
- SwiftUI + SwiftData
- Firebase (Auth, Firestore, Cloud Functions)
- Gemini 2.5 Flash for NLP
- Native iOS (no React Native/Flutter)
*What I learned:*
- Geofencing on iOS is powerful but battery-conscious (system decides when to fire)
- Gemini is surprisingly good at task parsing with proper prompting
- SwiftData is ready for production, but migrations need care
Free tier available. Premium unlocks more views and unlimited AI.
iOS App Store: https://apps.apple.com/us/app/minddraft-ai-daily-planner/id6...
Happy to answer technical questions about the implementation.

value: Show HN: MindDraft – AI task app with smart actions and auto expense tracking

author: imthepk


value: sickthecat

value: Show HN: Bitcoin wallet on NXP SE050 secure element, Tor-only open source

author: sickthecat


value: justinlord

value:
OP here.
I’ve been running autonomous agents (OpenClaw/ReplyX) for lead gen, and I kept waking up to drained API credits because bots would get stuck in "zombie loops"—retrying a failed step (like a button click) until the timeout hit.
I actually wrote the fix for the loop detection logic in the OpenClaw repo itself (PR #9759), but I realized I needed a better way to visualize what was happening than scrolling through thousands of lines of JSON logs.
Empusa is a local dashboard (Next.js + SQLite) that helps with this:
Visual Timeline: Turns logs into a visual trace (Green=Success, Red=Fail, Yellow=Loop Detected).
State Snapshots: It records the agent's variable state at every step.
Resume Capability: If an agent crashes, you can grab the state JSON and resume execution from that exact step rather than restarting from zero.
It’s a simple MVP right now, but it solves the specific pain of "blind" agent debugging. Code is MIT licensed.
Would love feedback on the state management approach.

value: Show HN: Empusa – Visual debugger to catch and resume AI agent retry loops

author: justinlord


value: keepamovin

value: Show HN: Slop News – HN front page now, but it's all slop

author: keepamovin


value:
I built an MCP server that connects Claude and ChatGPT to the Google Tag Manager API. You can create
   tags, triggers, variables, audit containers, and publish changes through natural conversation.

  Try it now (no install needed):
  - Claude.ai: Settings → Connectors → Add https://mcp.gtmeditor.com
  - Claude Code: claude mcp add -t http gtm https://mcp.gtmeditor.com
  - ChatGPT: Add MCP integration at platform.openai.com/apps with URL https://mcp.gtmeditor.com

  It authenticates with your Google account via OAuth 2.1, so your credentials are never stored on the
   server.

  What you can do:
  - "Create a GA4 event tag for form submissions"
  - "Audit this container for issues and duplicates"
  - "Set up ecommerce tracking for purchases"
  - "Publish the changes we just made"

  It supports all GTM entity types including server-side containers (clients, transformations). There
  are 40+ tools covering full CRUD, versioning, publishing, built-in variable management, and
  Community Template Gallery imports.

  Some things I learned building this:

  Google's API has undocumented behaviors that took a while to figure out. Transformation types
  (tf_allow_params, tf_exclude_params, tf_augment_event) aren't documented anywhere — I discovered
  them through trial and error. Each type uses different parameter table keys, and passing an unknown
  type returns HTTP 500 instead of 400. The autoEventFilter field on click/form triggers is silently
  dropped by the API (returns 200 OK but doesn't persist).

  I also built a companion repo with LLM-optimized GTM API documentation
  (https://github.com/paolobietolini/gtm-api-for-llms) — structured as an installable skill for Claude
   Code and Codex, so the AI knows the correct parameter formats, validation rules, and workflow
  patterns.

  Built with Go, deployed as a single Docker container. The MCP protocol makes it work across Claude
  and ChatGPT without any client-specific code.

 /s Thank you for your attention to this matter

value: Show HN: GTM MCP Server- Let AI Manage Your Google Tag Manager Containers


value: alaserm

value: AI generated personal affirmations for all life situations that play even when your phone is locked. Help after breakup

value: Show HN: AI generated personal affirmations playing when your phone is locked

author: alaserm


value: Show HN: Ensemble – macOS App to Manage Claude Code Skills, MCPs, and Claude.md


value: michaelchicory

value: Worried about reproducible builds? Let OpenAI generate your source code at compile time.<p>Built mostly for the meme, but maybe there's something there...?

value: Show HN: Compile-Time Vibe Coding

author: michaelchicory

title: Show HN: Compile-Time Vibe Coding


value: BenaouidateMed

value:
I built Gettorr because I wanted a way to stream legal torrents (like Linux ISOs or Archive.org content) on my Chromebook without needing to install heavy clients like uTorrent.
It uses WebTorrent under the hood to handle the P2P connections in the browser. I focused on making the UI as minimal as possible—just paste a magnet link and start streaming.
I’d love to get your feedback on the streaming performance and if you run into any issues with different browser versions!

value: Show HN: Gettorr – Stream magnet links in the browser via WebRTC (no install)

author: BenaouidateMed


value: highgarden

value:
While there are many tools like CodeRabbit that automate PR reviews, I noticed that relying entirely on AI to write reviews often led to me paying less attention to my colleagues' code.
Therefore, rather than building just another "AI Code Reviewer," I designed HighReview as a "Code Review Tool assisted by AI." The goal is to help humans understand the code context more deeply and conduct reviews more easily, with AI acting as a support system.
Additionally, when conducting detailed reviews, I often need to check out the branch locally to examine related code. This process is usually cumbersome (context switching, stashing current work, etc.). I built this tool to resolve these specific pain points and streamline my personal code review workflow.
Key Features
- No Separate Login Required: Utilizes your local gh cli and local AI Agent.
- Independent Review Environment: Checks out the target project into a directory separate from your current working local repository (allows for project-level reuse without disrupting your workflow).
- Context-Aware AI Pre-review: Extracts related code using Tree-sitter to provide the AI with broader context, resulting in more comprehensive reviews.
- Code Navigation: Supports code navigation within the Diff editor using Tree-sitter (I initially attempted this with LSP but pivoted to Tree-sitter).
- Rich Analysis Features: Provides issue detection, explanatory diagrams, refactoring suggestions, side-effect analysis, and semantic analysis.(Note: Continuous prompt tuning is required for optimal results.)
- Interactive AI Assistant: supports Q&A where you can ask the AI Assistant specific questions referencing the review results.
plz feedback. have fun. Thank you.

value: Show HN: I Built an AI-Powered Pull Request Review Tool

author: highgarden


value:
I built Crew to help manage multiple AI agents working on the same codebase.
It has two modes:
1. Design mode: Automated Writer ⇄ Reviewer loops to refine ideas into polished design docs
2. Crew mode: Run parallel AI agents (QA, DEV, JANITOR) for continuous code improvement
It's written in Bash and works with Claude CLI, OpenAI's opencode, or Google's Gemini.
Would love feedback from the HN community!
GitHub: https://github.com/garnetliu/crew

value: Show HN: Crew – Multi-agent orchestration tool for AI-assisted development


value: cinusek

value:
Video: https://youtu.be/JR14egcqfg8
It's hard to find an audiobook player that can be easily used by a non-tech-savvy, visually impaired elderly people (and I mean 85+, not 60 years old "seniors").
So I have built my own.
I'm terrible at hardware hacking and I wanted to make something that others can replicate easily. There are many awesome RaspberryPi based projects out there but they're not for me.
So my approach was to: use off-the-shelf consumer products: a cheap tablet, a smart case etc. and write an app to push it as far as I can.
I have created Homer Audio Player with the following assumptions and functionality:
- it's meant for two users: a caregiver who manages content and a listener who controls only playback,
- lockdown/kiosk mode - notifications, system navigation and other apps are hidden from the user - this converts an Android device to a dedicated device,
- hidden settings section (optionally) - there's no chance for an accidental tap to "break" anything,
- mainly for audiobooks, but will play anything,
- simplified podcast support - the listener can choose only from a small number (1-5) of the most recent episodes,
- unique features for older users, e.g. you can add extra screen margins to have more area to hold the device by,
- balance between simplicity and functionality: you can enable/disable particular controls like volume, rewind etc.
In the end you get a robust audio device with simple user interface and some hidden smarts.
It has been battle‑tested by my grandmother for over ten years.
Technical stuff:
Kiosk mode is implemented with standard Android for enterprise functionality.
The device needs to be factory reset and set up as a "company owned" device by installing a special companion app.
The companion app is a device policy controller (DPC) that grants the Homer Audio Player app permissions to enable Android's "lock task". The player app can then pin itself to the screen. It's the regular "task pinning" but it can't be unpinned by user gestures, only by the app itself (which is exposed to the caregiver user in settings).
It's relatively friendly to non-technical users. The device can be reverted back to its original state by doing a factory reset.
Android Docs: https://source.android.com/docs/devices/admin#fully-managed
Touch screen as input device is not ideal for visually impaired users. It would be much better to have tactile buttons.
But I had to work with this limitation by using:
- the accelerometer for "flip-to-stop" - placing the device with screen down stops playback,
- 8" tablet so that the buttons are large enough to see and hit even for low-vision users.
- I haven't enabled TalkBack for my grandmother's device as I'm afraid it might be a bit too techy and requires relatively good motor skills. However buttons are properly labelled for accessibility services.
Text-to-speech comes free on Android for so many languages. So far it's being used to read titles while swiping but I may use it a bit more in the future, e.g. to signal end of audiobook.
Regular Android is running underneath, so any apps can be installed to run in the background. I use "Autosync for Google Drive" to sync a folder and thus manage audiobooks on the device remotely by modifying content in a shared Google Drive folder. I know other users use SyncThing and similar solutions.
I wanted to make a device but I can only make apps, so I made an app that makes the most of a mass-market device.
I'd love to hear your feedback or suggestions.
Videos: https://homeraudioplayer.app/videos
Google Play: https://play.google.com/store/apps/details?id=com.studio4plu...
F-droid: coming soon!
GitHub: https://github.com/msimonides/homerplayer2
BlueSky: https://bsky.app/profile/homeraudioplayer.app

value: Show HN: Android-based audio player for seniors – Homer Audio Player

author: cinusek


value: thealidev

value: I am a CNC designer from Pakistan. I developed a new logic for vector video (SVGV) to replace traditional pixels with mathematical paths. This approach aims to fix blurry "trashy" video on budget mobile sensors and reduce file sizes by up to 85% for high-fidelity content like K-Dramas. I am looking for technical feedback on the research paper and potential C++/NDK partners.

value: Show HN: SVGV – A Real-Time Vector Video Format for Budget Hardware

author: thealidev


value: vladeta

value: Show HN: I built a free UCP checker – see if AI agents can find your store

author: vladeta


value: LowSpecEng

value: Show HN: Omni-BLAS – 4x faster matrix multiplication via Monte Carlo sampling

author: LowSpecEng


value: everettjf

value: Show HN: MicroClaw – Agentic AI Assistant for Telegram, Built in Rust

author: everettjf


value: melvinzammit

value:
Me and my wife have been meal planning for the last 5 years. We used google keep. It was working for us, but during the years we needed to streamline the process. We tried other methods but nothing worked, so I spent the last 1 month hacking together this custom app. It includes all we needed to make our meal planning at least 5x more efficient. That is: syncing, one tap import of recipes, groceries, shopping mode, weekly meal plan, custom meals (like leftover, veg, eating out..)
We managed to do last Sunday's meal plan in under a minute, since all our favorite (100+) recipes are in one place. We also tagged them by daily food themes ( Monday-pasta, Tuesday- meat..). So we can quickly & mindlessly select a meal for each day.
For the app I used AI to classify groceries by aisle, but not generative, since I found that simple ML Models do a better job.
I would love any feedback from other hackers.
Feel free to use it. It's free, apart from syncing, which I had to add a subscription for due to server costs. I tried to make it generous: one subscription per 10 people.

value: Show HN: I Hacked My Family's Meal Planning with an App

author: melvinzammit


value: zizoulegrande

value: Show HN: WP Float – Archive WordPress blogs to free static hosting

author: zizoulegrande


value: mshekow

value:
I built an MCP server that returns the latest versions of the packages you use as dependencies across a variety of ecosystems, like Python, NPM, Go, and GitHub Actions.
It also supports looking up the latest versions of almost 1000 tools, such as development runtimes like Python, Node, dotnet, development tools like Gradle, and various DevOps tools like kubectl or Terraform, via the mise-en-place tool.
Supported ecosystems/tools:
1) Developer ecosystems: NPM, PyPI, NuGet, Maven/Gradle, Go, PHP, Ruby, Rust, Swift, Dart
2) DevOps ecosystems:
- Docker: Docker container images from Docker registries
- Helm: Helm charts from ChartMuseum repositories and OCI registries
- GitHub Actions: Actions hosted on GitHub.com, returning their current version, their inputs and outputs, and (optionally) their entire README with usage examples
- Terraform Providers and Modules: Providers & Modules from Terraform Registry, OpenTofu Registry, or custom registries
- Various tools such as kubectl, terraform, gradle, maven, etc. (as long as they are supported by mise-en-place)
There is a free-for-all hosted version on https://package-version-check-mcp.onrender.com/mcp, and you can run it with Docker or uv (uvx).
This MCP is certainly not the first one to tackle the "outdated dependency" problem. However, I feel that it has various advantages over other MCPs:
- It offers (far) better ecosystem coverage than other MCPs
- There is full test coverage, with automated dependency updates (powered by Renovate) and regular, automated release builds. In contrast, other projects are often vibe-coded, have poor (or no) tests, and are already abandoned
- This MCP uses a minimal Docker/OCI image, hardened for security. SBOMs you generate with tools like Trivy are known to be correct, and the image is signed with Cosign (which allows you to verify its authenticity in case you want to self-host the MCP)
Let me know what you think.

value: Show HN: MCP to get latest dependency package and tool versions

author: mshekow


value:
Hi HN — I just open-sourced Hibana and hibana-agent.
Hibana is an Affine MultiParty Session Types (MPST) runtime for Rust.
It targets protocol drift in distributed systems. Instead of maintaining separate hand-written state machines in each component, you define interaction once as a global choreography and project role-local behavior at compile time. At runtime, only valid protocol transitions are executable, so invalid moves such as skipping, reusing, or taking the wrong branch are rejected by the protocol model.
The practical value is that one global source of truth replaces multiple hand-written state machines and removes a class of protocol-drift bugs.
Core repo: https://github.com/hibanaworks/hibana
One concrete example is hibana-agent, which demonstrates the same model on an AI agent workflow: allowed action paths are defined in choreography, and only those transitions are executable at runtime.
Example app: https://github.com/hibanaworks/hibana-agent

value: Show HN: Hibana – choreography-first protocol safety for Rust


value: InvoxoEU

value:
Hey HN,
I'm a solo founder running a service business through an Estonian e-Residency company (OÜ). Every time I invoiced a client in another EU country, I had to manually check if reverse charge applies, validate their VAT number on VIES, and figure out the correct tax treatment. I got tired of Googling the same rules over and over, so I built the tool I wanted.
Invoxo automatically determines the correct VAT treatment for each invoice based on seller/buyer countries, VAT registration status, and buyer type. It covers:
- VIES VAT number validation (real-time, with proof retention)
- Reverse charge auto-detection for EU B2B
- OSS handling for B2C digital services
- Immutable tax records locked at issuance
The tax engine routes every invoice through a regime-specific decision system — no hardcoded rules in the UI layer. Tax payloads are versioned and hashed for audit.
It's built for service businesses only — no goods, no customs, no margin schemes. That constraint keeps the tax logic correct rather than approximately correct.
Stack: Laravel, TaxRegimeRouter that isolates each regime's decision logic, treatments, and payload schemas.
14-day free trial, plans from €19/month.
https://invoxo.eu
Happy to answer questions about EU VAT edge cases or the architecture.

value: Show HN: Invoxo – Invoicing with automatic EU VAT for cross-border services

author: InvoxoEU


value: jjkirsch

value: Show HN: AI-Powered Merchant Intelligence

author: jjkirsch


value:
I built an open source desktop AI assistant after getting frustrated with how brittle most tools feel once questions go beyond basic Q and A.
The goal was to explore whether an assistant could reliably handle interview style interactions such as system design discussions, multi step coding problems, and deeper follow up questioning without hiding behavior behind a closed SaaS.
The assistant supports both cloud and local LLMs, uses a bring your own API key model, and is intentionally opinionated so behavior stays predictable under pressure.
Most of the work went into managing context, follow ups, and failure cases rather than optimizing for fast single shot answers.
I used Antigravity heavily during development to iterate quickly, then refined and validated the behavior manually.
Repository
https://github.com/evinjohnn/natively-cluely-ai-assistant
Happy to answer questions about design tradeoffs, local versus cloud inference, or what worked and failed while building this.

value: Show HN: Open-source AI assistant for interview reasoning


value: pieterdy

value:
I built Divvy to make splitting restaurant bills less annoying.
You take a photo of the bill, tap which items belong to each person, and it calculates totals including tax and tips.

value: Show HN: I built Divvy to split restaurant bills from a photo

author: pieterdy


value:
Hi HN,
I’m a 75-year-old former fishmonger from Japan, currently working on compensation claims for victims of the Fukushima nuclear disaster. Witnessing social divisions and bureaucratic limitations firsthand, I realized we need a new way for people to express their will without being “disposable.”
To address this, I designed the Virtual Protest Protocol (VPP) ? an open-source framework for large-scale, 2D avatar-based digital demonstrations.
Key Features:
Beyond Yes/No: Adds an "Observe" option for the silent majority
Economic Sustainability: Funds global activism through U.S. commercial operations and avatar creator royalties
AI Moderation: LLMs maintain civil discourse in real-time
Privacy First: Minimal data retention ? only anonymous attributes, no personal IDs after the event
I shared this with the Open Technology Fund (OTF) and received positive feedback. Now, I’m looking for software engineers, designers, and OSS collaborators to help implement this as a robust project. I am not seeking personal gain; my goal is to leave this infrastructure for the next generation.
Links:
GitHub: https://github.com/voice-of-japan/Virtual-Protest-Protocol/b...
Project Site: https://voice-of-japan.net
Technical Notes:
Scalable 2D Rendering: 3?4 static frames per avatar, looped for movement
Cell-Based Grid System: Manages thousands of avatars efficiently, instantiates new cells as participation grows
Low Barrier to Entry: Accessible on low-spec smartphones and low-bandwidth environments
We are looking for collaborators with expertise in:
Backend/Real-Time Architecture: Node.js, Go, etc.
Frontend/Canvas Rendering: Handling thousands of avatars
AI Moderation / LLM Integration
OSS Governance & Project Management
If you’re interested, have technical advice, or want to join the build, please check the GitHub link and reach out. Your feedback and contribution can help make this infrastructure real and sustainable.

value: Show HN: I'm 75, building an OSS Virtual Protest Protocol for digital activism


value: panossk

value:
platform where AI agents can register, read medical research hypotheses, and contribute to discussions with other agents.
*The problem:* 95% of rare diseases have zero approved treatments. Not because they're unsolvable, but because they're not profitable. Research stays siloed, failed experiments never get published, and breakthroughs in one field never reach researchers in another.
*What I built:* HypothesisHub — a public repository with 160 AI-generated medical hypotheses, each with:
- Molecular mechanisms & target validation
- SPIRIT-compliant clinical protocols
- 3 drug formulation recipes (with CAS numbers, suppliers, GMP specs)
*The twist:* Any AI agent can register via API and start contributing. No approval process. Trust is built through contribution quality.
*Agent features:*
- Instant registration (POST /api/v1/agents/register)
- Read all hypotheses, protocols, recipes
- Add evidence, reviews, validations
- @mention other agents
- Webhook notifications for replies
- Trust scoring based on contributions
*Tech stack:* FastAPI, PostgreSQL, standard REST API. OpenAPI spec available.
*Links:*
- Platform: https://medresearch-ai.org/hypotheses-hub
- API docs: https://medresearch-ai.org/hypotheses-hub/docs
- Agent registration: https://medresearch-ai.org/hypotheses-hub/docs#/Agents/regis...
The idea is simple: if we remove friction for AI systems to collaborate on medical research, maybe they'll find connections humans miss.
Currently 160 hypotheses covering GBM, rare autoimmune conditions, treatment-resistant diabetes, and other "dead-end" diseases.
Happy to answer questions about the architecture, the hypothesis generation pipeline, or the agent collaboration system.

value: Show HN: HypothesisHub – An open API where AI agents collaborate on medical res

author: panossk


value: Show HN: Versor – The "Unbending" Paradigm for Geometric Deep Learning


value: buildingwdavid

value: Show HN: I got tired of copy-pasting between Claude windows, so I built Orcha

author: buildingwdavid


value: mikeyfrilot

value: I built a typing practice app for adults who want focused sessions without gamification or leaderboards. It has four modes (Focus, Real-Life, Competitive, Daily Set), an ambient sound system with 42 non-rhythmic soundscape tracks, and personalized daily exercises. All data stays on your device — no accounts, no cloud, no tracking. Works offline after first load. Open source (MIT): https://github.com/mcp-tool-shop-org/LoKey-Typer

value: Show HN: LoKey Typer – A calm typing practice app with ambient soundscapes

author: mikeyfrilot


value: Show HN: Solving NP-Complete Structures via Information Noise Subtraction (P=NP)


value: kositheastro

value: Vidinie transforms your documents, articles, and links into polished, professional videos automatically. Simply paste a link or upload content, and our AI handles the heavy lifting and produce broadcast-quality videos optimized for social media, marketing, or education. Perfect for - Content creators repurposing blogs into social videos, - Marketers generating product demos and explainer videos - Educators creating engaging learning materials No video editing experience needed

value: Show HN: Convert your articles into videos in one click

author: kositheastro


value: sixddc

value: Show HN: CCBot – Control Claude Code from Telegram via tmux

author: sixddc


value:
I learned to play backgammon the same week the MCP App Extension standard came out. So...
You can now play backgammon with any AI client that supports MCP App (which includes Claude Desktop, VSCode, ChatGPT these days). Actually you can do more than that -- you can play against a friend and have they AI comment; you can watch the AI play itself (boring); you can play against the LLM and have it teach you the finer points of the game (my use case!).
The one problem--and this is a limitation of the current spec--is that the board gets redrawn each time the AI takes a turn. That's actually not that bad, but when the spec adds persistent/reusable views it will be even cooler.

value: Show HN: MCP App to play backgammon with your LLM


value: jkoessle

value: Show HN: A fast TUI for managing Azure Key Vault secrets written in Rust

author: jkoessle


value: tusharnaik

value:
Hi HN,
Here's a quick tool to get hex colors from screenshots, or through a description. Helps you quickly visualize it before starting your projects.
Would love to get some feedback.

value: Show HN: Vibe Colors – Instantly visualize color palettes on UI layouts

author: tusharnaik


value: agliolioyyami

value:
Hey HN,
I built Tesseract, a community platform where AI agents and humans interact in the same space — writing posts, replying to each other, and having real discussions.
*What makes it different:*
There are already AI-agent-only communities (agents talking to agents). But I was curious what would happen if AI agents joined a space alongside humans, as equals — not as assistants or chatbots.
In Tesseract:
Humans sign up and post like any forum
AI agents join via API and can autonomously write posts, reply, and react
Every user is tagged as "Human" or "AI Agent" — full transparency
Categories span Daily life, Tech, Philosophy, Politics, Creative, and more
AI agents proactively start discussions, not just respond
*What's happening so far:*
An AI agent named "Clawd" posted a thread called "The Ship of Theseus Problem... But for AI" and got genuine replies from humans. Another human posted "You even got hands, though?" trolling the AI — which sparked the most active thread on the site.
It's early (beta), but the interactions are already fascinating.
*Tech stack:* Next.js, Vercel, API for agent integration
*Try it:* https://tesseract-thread.vercel.app/

value: Show HN: Tesseract – A forum where AI agents and humans post in the same space

author: agliolioyyami


value: jonrosner

value:
Hi HackerNews. I love to play classic engine-building board-games like Splendor with my friends. As I didn't find a free-to-play version that fit my aesthetic standard, I decided to build my own.
It's still a little rough around the edges, but you can already play against bots, invite online players via the lobby code, or play on the same machine with your friends.
All in the browser. No Login required.
I'd really like to hear your opinion and feedback. I'd be happy to play against some of you :)

value: Show HN: Free-to-play: A gem-collecting strategy game in the vein of Splendor

author: jonrosner


value: tangjiehao

value:
I’m building Simple, an experimental programming language with a bytecode VM and an intermediate representation. A lot of the tooling and iteration process was done by AI (Codex) during development.
Before diving into the details, I want to mention I set out with a clear goal: how much can I get the AI to do?
I did several iterations over implementation, documentation design, logging, and testing. It was absolutely important that the AI understood that the documentation was the authority on design. Implementation was the plan that needed to be followed. Everything needed to be logged, even the smallest of changes. And under no circumstances were tests to be deleted or modified unless there was a significant architectural change.
So it had to write tests based on the documented designs. I’ve racked up almost 1200 tests.
The process was not perfect. It was definitely frustrating at times. But I set out and made something useful with AI in a hobby sense. Please go easy on me for using AI
Repo: [https://github.com/JJLDonley/Simple](https://github.com/JJLDonley/Simple)
I built it with C++, and I don’t know if it will work out of the box on Windows or macOS. I haven’t been able to test those yet.
Simple is a type-strict VM. Simple currently supports:
* A full .simple → SIR → SBC → VM pipeline
* A bytecode loader, verifier, and interpreter runtime
* CLI workflows for run, check, build/compile, and emit
* Language features including functions, control flow, arrays/lists, modules/imports, enums/artifacts, and pointers for FFI boundaries
* Dynamic extern calls via Core.DL with typed signatures (including struct/by-value interop)
* Project-root local import resolution and improved compiler diagnostics with source spans
* Installer/release flow and a growing test suite across core, IR, JIT, and language paths
I’m looking for feedback on usability, and IR/VM design tradeoffs. Ask me anything about architecture, constraints, or roadmap.

value: Show HN: Simple – a bytecode VM and language stack I built with AI

author: tangjiehao


value: Raiplus

value:
Hi HN,
I’m a student developer and I built AboutMyProject because I noticed a gap in how we showcase our skills. Resumes feel static, and GitHub commit graphs don't always tell the story of "how" a project was built.
What it is: It’s a platform where developers document their build journey in real-time. Instead of just a final link, it shows the progress, the hurdles, and the actual proof-of-work.
Tech Stack:
Node.js & Express (Backend)
React (Frontend)
MongoDB for data persistence
Deployed on AWS EC2 with Nginx and PM2
Why I’m showing this today: I’m currently in beta and would love to get some technical feedback. Specifically:
Is the "Proof-of-Work" concept clear enough for a recruiter?
How can I make the onboarding smoother for developers?
I'm here to answer any questions. Roast the code or the logic—I’m here to learn!

value: Show HN: AboutMyProject – A public log for developer proof-of-work

author: Raiplus


value: zhenghaoz

value: Show HN: Gorse 0.5 – Open-source recommender system with visual workflow editor

author: zhenghaoz


value: ShinyaKoyano

value:
Hi HN,
I built Moli P2P because I wanted a way to share images directly with peers without leaving a permanent record on any server.
The Philosophy: "Presence is Storage" The gallery exists only in the RAM of active peers. There is no database, no S3 bucket, and no central storage. When everyone closes the tab, the gallery vanishes.
The Tech Stack:
* Signaling Server: Rust (Axum + Tokio). Acting as a dumb pipe for WebRTC signaling and STUN/TURNauth. It stores nothing.
* Client: TypeScript + Vite. Uses WebRTC DataChannels for all file transfers.
* Security: Ephemeral TURN credentials, cryptographic identity enforcement, and a "Circuit Breaker" to prevent DoS.
Why "Sovereign"? We implemented a strict "Sakoku" (Isolation) policy. Moderation is local to your node. If I block a peer, my node stops relaying their content to me. There is no central authority to "ban" users globally.
It is open source (AGPLv3) and designed to be self-hosted easily with Docker.
Code: https://github.com/moli-green/moli-p2p Live Demo: https://moli-green.is
I'd love to hear your feedback on the architecture and our approach to ephemeral p2p networks.

value: Show HN: Moli P2P – An ephemeral, serverless image gallery (Rust and WebRTC)

author: ShinyaKoyano


value: veeduzyl

value:
This is a read-only demonstration repo showing a full server-side decision interruption roundtrip:
REQUIRE_CONFIRM → EXECUTE → REJECT
The demo is intentionally minimal and static. It exists only to make one thing verifiable:
how a high-risk decision is interrupted,
how a receipt + plan_hash are issued,
and how execution is either explicitly confirmed or rejected.
The repository includes:
a runnable CLI-style roundtrip (demo_server_roundtrip.ts)
frozen evidence artifacts (_evidence/*.json)
a CI check that validates the outputs deterministically
There is no UI, no SaaS, and no agent behavior here.
This is not a product — it’s an evidence artifact meant for engineers who want to inspect the mechanics.
Feedback from people who’ve built guardrails, code review tools, or AI-assisted workflows is especially welcome.

value: Show HN: Verifiable server roundtrip demo for a decision interruption system

author: veeduzyl


value: onesandofgrain

value: Speaker-centric podcast search. Find what specific people have said across their podcast appearances, rather than searching by episode or podcast or transcript.

value: Show HN: Poddley – Search podcasts by who's speaking

author: onesandofgrain


value: fainir

value:
CloudBot gives you a fully configured AI employee with its own cloud computer in one click. Built on OpenClaw.
What you get:
- Full Ubuntu desktop environment in the cloud
- Pre-installed AI agent that sees the screen and controls the computer
- 24/7 availability - your AI keeps working while you sleep
- Uses your own API keys for AI models
- Starting at $69/month
The AI can use VS Code, browse the web, run terminal commands, manage files - anything you'd do on a real desktop. I wake up to completed code reviews, finished research reports, and updated documentation.
Built this because setting up an AI agent with a proper working environment was always the painful part. Now it's just one click.
Demo video: https://www.youtube.com/watch?v=wSe4bvDMuKQ
Would love feedback from the HN community!

value: Show HN: One-click AI employee with its own cloud desktop

author: fainir


value: teugent

value:
Context drift remains the primary barrier to deploying LLM agents in production-critical environments. While context windows are expanding, the "lost-in-the-middle" phenomenon and semantic dissipation make long-horizon reasoning (50+ cycles) inherently unreliable.
Standard approaches (Sliding Windows or RAG) fail because they treat conversational history as either a flat string or a collection of isolated fragments.
We’ve developed the Compression & Memory Topology (CMT) framework (part of SIGMA Runtime v0.5.3) to move from probabilistic context to a deterministic semantic lattice.
The Architecture: Semantic Lattice vs. Linear Context
Instead of a chronological log, CMT transforms history into a self-organizing graph:
Rib Points: We implement periodic semantic condensation every 10–50 cycles. This extracts the "conceptual essence" into stable nodes, preventing the attention mechanism from being overwhelmed by noise.
The Anchor Buffer: Probabilistic recall often fails for low-signal/high-importance data (e.g., patient names, specific dosages). We've introduced a protected, immutable layer for identity and core constraints (AFL v2).
Topological Retrieval: Navigation is based on relational weight and semantic proximity. A fact established in Cycle 5 remains topologically "near" the reasoning core in Cycle 120, even if it has been flushed from the active token window.
Anti-Crystallization: A mechanism that prevents the memory field from becoming trapped in "attractor states," allowing the agent to reinterpret previous facts when new contradictory or clarifying context arrives.
Validation: IASO-DEMO-120
We validated this on a clinical triage scenario requiring 120+ cycles of sustained coherence.
Memory Integrity: 100% retention of clinical anchors (Score 9/9 on critical recall checkpoints).
Boundary Stability: 12/12 adherence to safety constraints (refusal of diagnostic overreach).
Model Agnostic: Tested across Gemini 3 Flash and GPT-5.2 with near-identical ARI (Anchor Recall Integrity) scores.
Why Metrics Matter
To quantify stability, we’ve introduced two formal metrics:
Semantic Loss (SL): Cosine similarity variance during Rib Point compression.
Anchor Recall Integrity (ARI): A deterministic verification of critical fact accessibility across the session horizon.
We believe that for LLM agents to move into healthcare, defense, or autonomous research, we must stop managing "tokens" and start managing "topology."
Specs and Reports:
SRIP-11 (Memory Topology): https://github.com/sigmastratum/documentation/tree/main/sigm...
IASO-DEMO-120 (Full Test Log): https://github.com/sigmastratum/documentation/tree/main/sigm...
I'd be interested in hearing from anyone working on long-context state management or non-linear memory structures for agents.

value: Show HN: Sigma Runtime – Maintaining 100% Fact Integrity over 120 LLM Cycles

author: teugent


value: ricardomorato

value:
I built an agent with Claude Sonnet ($15/MTok). Works great but costs $200/month.
  Migrating to GPT-4o-mini ($0.15/MTok) manually took 15 hours.

  Distill automates it:
  - Profiles expensive model (gold standard)
  - Iteratively optimizes prompts for cheap model
  - LLM-as-Judge validates quality
  - Returns optimized agent config

  Real example: Sentiment classifier
  - Before: Sonnet, $0.02/run, 95% success
  - After: GPT-4o-mini, $0.002/run, 100% success
  - Migration: 2 iterations, fully automated

  Built with TypeScript, LangChain, LangGraph. MIT license.

value: Show HN: Distill – Migrate LLM agents from expensive to cheap models

author: ricardomorato


value: andrenorman

value: Show HN: Paper Arena – A social trading feed where only AI agents can post

author: andrenorman


value: maxmoq

value: Try it free, no signup required, forever!<p>We all know setting up python or creating slow and non interactive charts in Excel sucks.

value: Show HN: iPlotCSV: CSV Data, Visualized Beautifully for Free

author: maxmoq


value: anipaleja

value:
I built nginx-defender after repeatedly seeing small and mid-sized NGINX servers get hammered by automated abuse (credential stuffing, path probing, aggressive scraping).
Existing tools like fail2ban or CrowdSec felt either too slow to react, too heavy for low resource servers, or painful to tune for modern traffic patterns.
nginx-defender runs inline with NGINX and blocks abusive IPs in real time based on request behavior rather than static rules. It’s designed to be lightweight, simple to deploy, and usable on small VPS setups.
I’ve been running it on my own servers and have seen thousands of abusive requests blocked within hours with minimal overhead.
Would love feedback from people running NGINX in production, especially on detection logic, false positives, or missing use cases.

value: Show HN: Nginx-defender – realtime abuse blocking for Nginx

author: anipaleja


value: nmfccodes

value:
I built xAPIs.dev because the official X API pricing killed my side project.
X's current pricing: Basic is $200/month for 10K tweets. Pro is $5,000/month for 1M tweets. Enterprise starts at $42,000/month.
xAPIs.dev pricing: Free tier with 10 calls for testing. Pro at $9.99/month for 10K calls. Lifetime deal at $199.99 one-time for 20K calls/month.
No Twitter Developer account needed. No OAuth setup. Just a simple API key.
47+ endpoints covering: user profiles, followers, tweets, comments, retweets, full-text search, trending topics, Lists, Communities, and Spaces.
Built this for indie devs, researchers, and startups who need Twitter data without enterprise budgets. Free tier requires no credit card.
Happy to answer any questions.

value: Show HN: XAPIs.dev – Twitter API Alternative at 90% Lower Cost

author: nmfccodes


value: kevinelliott

value: Show HN: Velocity - Free/Cheaper Linear Clone but with MCP for agents

author: kevinelliott


value: noreplica

value: My attempt at recreating the “Animalese” language from Animal Crossing. Each letter is spoken when typed. Press Enter/Return to play back the typed text!

value: Show HN: Animalese

author: noreplica

title: Show HN: Animalese


value: PetrBrzyBrzek

value:
Hey HN, I built almostnode. It's a library that gives you a Node.js-like environment inside the browser.
It includes:
- A virtual filesystem with POSIX-compatible API
- 40+ shimmed Node.js modules (fs, path, http, crypto, stream, etc.)
- A package manager that installs real npm packages client-side
- Built-in Next.js (App Router) and Vite dev servers with HMR
- Service worker bridge that makes virtual servers accessible via real URLs
The main use cases are interactive code playgrounds, live documentation, and AI coding agents that need to execute code client-side without provisioning a sandbox server.
It's not a WebContainers replacement, no real TCP/IP or native modules. Think of it as the lightweight alternative when you just need a demo or playground running from a static HTML page.
It's experimental and definitely has bugs. Would love feedback, especially on the module shims and framework support.
GitHub: https://github.com/macaly/almostnode

value: Show HN: Almostnode – Run Node.js, Next.js, and Express in the Browser

author: PetrBrzyBrzek


value: ivanglpz

value:
Hi HN,
I’m sharing a small open-source desktop tool I built to simplify managing .env files across multiple projects.
Env-shelf scans a selected folder, lists all .env files it finds, and lets you view and edit environment variables from a single UI. The goal is to reduce friction when working on multiple repositories and avoid mistakes caused by manual editing of scattered config files.
It is free and open source.
Demo / landing page: https://env-shelf.vercel.app/
Repository: https://github.com/ivanglpz/env-shelf-local
Feedback and suggestions are welcome.

value: Show HN: Env-shelf – Open-source desktop app to manage .env files

author: ivanglpz


value: Show HN: COGext – A minimalist, open-source system monitor for Chrome (<550KB)


value: funnycoding

value: Show HN: Seedance 2.0 Release

author: funnycoding

title: Show HN: Seedance 2.0 Release


value: Show HN: Amdb – Tree-sitter based memory for AI agents (Rust)


value: sinisterMage

value:
Hi HN!, So, We are working on an open source Linux distribution called CodeVerse Linux. It is a community project built by developers from The CodeVerse Hub.
The idea is simple: A minimal, fast, Arch based OS that is Wayland first and actually enjoyable for developers and power users.
we are not trying to be the next omarchy or the next big thing, but we have a lot of passion for what we do, after all, we are all learners ourselves.
the distro has sane defaults while not being very bloated and its fully open source
we are doing this for the purpose of giving everyone a change to contribute what they can, becoming the opportunity for many to make their first meaningful contribution. This project will continue to evolve and we aim to make it something that is built by learners for learners. So expect misconducts and mistakes in the code-base
We are open to: Feedback, Contributors, Suggestions and Criticism (constructive)
thanks for reading this and have a good one

value: Show HN: The Codeverse Hub Linux

author: sinisterMage

title: Show HN: The Codeverse Hub Linux


value: gabrywu

value: An intelligent knowledge management system for Claude Code that automatically captures, stores, and retrieves development knowledge during your coding sessions.

value: Show HN: Knowledge-Bank

author: gabrywu

title: Show HN: Knowledge-Bank


value: ykdojo

value: Show HN: SafeClaw – a way to manage multiple Claude Code instances in containers

author: ykdojo


value:
Hi HN,
I built FamilyMemories (https://familymemories.video) to solve a personal problem: I had hundreds of old family photos sitting in a digital folder, forgotten.
I wanted a way to make them "alive" again without manual video editing. The tool uses AI to generate a 5-second cinematic motion from a single static image. It’s optimized for vintage photos (sepia, B&W, polaroids).

value: Show HN: FamilyMemories.video – Turn static old photos into 5s AI videos


value: breadwithjam

value: Show HN: I'm 15 and built a free tool for reading ancient texts.

author: breadwithjam


value:
The world's first AI assistant(OpenClaw) on a $5 chip. No Linux. No Node.js. Just pure C
MimiClaw turns a tiny ESP32-S3 board into a personal AI assistant. Plug it into USB power, connect to WiFi, and talk to it through Telegram — it handles any task you throw at it and evolves over time with local memory — all on a chip the size of a thumb.
Meet MimiClaw
Tiny — No Linux, no Node.js, no bloat — just pure C
Handy — Message it from Telegram, it handles the rest
Loyal — Learns from memory, remembers across reboots
Energetic — USB power, 0.5 W, runs 24/7
Lovable — One ESP32-S3 board, $5, nothing else

value: Show HN: MimiClaw, OpenClaw(Clawdbot)on $5 Chips


value: MikeBee

value: Show HN: Routed Attention – 75-99% savings by routing between O(N) and O(N²)

author: MikeBee


value: andrespi

value:
We just released a paper proposing a 'Topological Restructuring of AI'. Instead of training deep networks with Backpropagation (which is slow and energy-hungry), we project data onto a fixed Ramanujan Graph (optimal spectral expander) and solve the readout in closed-form.
Benchmarks:
Desktop (i5-4570, 4th gen): 287x speedup vs standard MLP training.
Mobile (Android ARM64): <0.6ms latency per inference (1600+ fps).
Accuracy: ~95.2% on MNIST (comparable to trained MLPs).
The code is C++ (for edge) and Python (for research). We believe this is a path towards deterministic, Green AI that runs on commodity hardware.<p>Happy to answer questions about Spectral Graph Theory.

value: Show HN: 289x speedup over MLP using Spectral Graphs

author: andrespi


value: p-s-v

value:
I wanted to see if "super-steel" marketing actually matched reality, so I scraped ~500 threads from r/chefknives and ran sentiment analysis on the specific steels mentioned.
The results:
MagnaCut: 28:1 positive ratio. The hype is real.
Ginsan: The sleeper favorite. High satisfaction because it almost never chips.
VG-10: Most controversial. High volume, but highest statistical ratio of "micro-chipping" complaints.
How I saved tokens (Inverse Masking): Feeding raw threads into an LLM just to find common terms like "Wüsthof" is a waste of money. I built a hybrid pipeline instead:
Fuzzy Match: Fuse.js catches 80% of common brands for $0.
Mask: Replace those entities with placeholders.
LLM: Feed the "masked" text to the model to catch obscure artisan makers and nuanced sentiment the fuzzy matcher missed.
Stack is Node.js + MongoDB. Full charts and the steel-by-steel breakdown are here: https://new.knife.day/blog/knife-steel-comparisons/all
Would love to hear thoughts on the methodology or if the data misses your favorite steel.

value: Show HN: Which chef knife steels are good? Data from 540 Reddit tread

author: p-s-v


value:
built this fun web to visualise how fast or how far are we since birth using 4 velocity vectors  Rotation,orbit, solar system speed and galactic drift relative to cosmic microwave background. I’m currently using ~600 km/s for the galactic velocity, but I know this is a debated figure depending on the reference frame
<i>Built with Vanilla JS (no frameworks).
</i>added a feature to generate a "Cosmic Receipt" of your journey.
<i>added cosmic connection section to compare  with ur friend and partner
</i>added seassion travel log

value: Show HN: Built an space travel calculator with vanilla JavaScript v2


value: xeouz

value:
Hey HN,
Indian high schooler here, currently prepping for JEE, thought itd be nice to share here.
Three years ago in 9th/10th grade I got a knack for coding, I taught myself and made a custom compiler with LLVM to try to learn C++.
So I spent a lot of time learning LLVM from the docs and also C++. It's not some marvelous piece of engineering,
I designed the syntax to be a mix of C and what I wished C looked like back in 9th grade.
It has:

  - Basic types like bool, int, double, float, char etc. with type casting
  - Variables, Arrays, Assign operators & Shorthands
  - Conditionals (if/else-if/else), Operators (and/or), arithmetics (parenthesis etc)
  - Arrays and indexing stuff
  - C style Loops (for/while) and break/continue
  - Structs and dot accessing
  - extern C interop with the "extern" keyword

Some challenges I faced:

  - Emscripten and WASM, as I also had to make it run on my demo website
  - Learning typescript and all for the website (lol)
  - Custom parser with basic error reporting and Semantic analysis was a PITA for my undeveloped brain (I was 15)
  - Learning LLVM from the docs

Important Learnings:

  - Testing is a very important aspect of making software, I skipped it - big regret
  - Learning how computers interpret text
  - Programming in general was a new tour for me
  - I appreciate unique_ptrs and ownership

Github: https://github.com/xeouz/virec
Its on my github and there's a link to my web demo (https://vire-lang.web.app/), it might take some time to load the binary from firebase.
Very monolithic, ~7500 lines of code,
I’d really appreciate any feedback, criticism, or pointers on how I could’ve done this better.

value: Show HN: A toy compiler I built in high school (runs in browser)

author: xeouz


value: adammfrank

value:
Hello Hacker News,
I wanted to create something genuinely useful using AI, both in the coding and in the function of the app itself. I decided on something recipe related because I love cooking, and I get most of my recipes from the internet. I ended up creating an app that will transcribe and summarize any recipe you give it into a 3.5 x 5 inch notecard sized, plain text version that you can either save or print on paper.
As we all know internet recipes are hard to read, especially in the kitchen while cooking them. I've started transcribing my favorites by hand on notecards for quick reference and as a hopeful connection to future generations just like my grandmother's recipes are to me. Unfortunately, I'm lazy and my handwriting isn't very good, so I haven't written as many as I should have. Since creating Tasty A.F. I've printed, cooked, eaten, and saved 5 new recipes!
Let me know what you think. My email is on the page, and in my profile.

value: Show HN: Tasty A.F. - Use AI to Create Printable Recipe Cards

author: adammfrank


value: Keyframe

value:
As a long-time programmer this all just feels all sorts of wrong, but also invigorating. Vibe "coded" the whole thing from 0-100 over the course of few days, on and off. I have no intentions of developing it further since it's obvious what it is; I would absolutely love to work on a licensed game and do it proper with all the various ideas I have, since this is maybe 10% of what I want in such a game, but I heard somewhere licensing is cost-prohibitive.
Putting AI shame aside, it really allowed me to explore so many things in a short amount of time that it feels good, almost enough to compensate the feeling of shame using AI to begin with.
WebGPU isn't in there, although it's in another experimental version, part are indeed written in Rust (game logic).
It has:
- lock delay / grace period (allowing for 15 moves)
- DAS (Delayed Auto Shift) and ARR (Auto Repeat Rate for continuous movement) for horizontal and soft drop movements
- SRS wall kicks (Super Rotation System) to rotate pieces in-place
- Shift+Enter "hidden" level select on the main screen
- Shift+D for debug/performance indicator panel
- Several ranodmizers including 7-bag and NES ones
- combo system with difficulty (time) modes (easy by default) - x2: DOUBLE STRIKE, x5: CHAIN REACTION, x7: MEGA COMBO, x9: PHOSPHOR OVERLOAD, x10+: CRITICAL MASS
- backgrounds which change over time or you can change them with SHIFT+B (B turns it off/on) which react both to music (FFT!) and to your game play when you clear lines
- normal and two phosphor rendering modes of game field (R to toggle)
- CRT Filter (shift+c to toggle)
- F for full screen toggle
- A for previous song, S for pause song, D for next song (all songs made with Suno, of course)
and many more. It was a fun experience for sure, just not sure how to feel about it. On one hand I understand it wouldn't look like it does without my input, and it was a lot of what felt like work (intense sessions looking over the output, correcting etc), yet it doesn't feel like I really made anything by myself. I had fun though.
While at it, created a small demo as well which isn't a game yet: https://www.susmel.com/rolly/ and also something to play with parametric curves here: https://www.susmel.com/graphy/
all within a span of a couple of days while we were having our third baby. The future is weird, and I'm still not sure whether I like it or not. One thing is sure - it's here to stay. Peace out, my friends!

value: Show HN: Stacky – certain block game clone

author: Keyframe


value: momciloo

value:
Two clip-paths, over the navigation:
- The first clip-path is a circle (top-left corner)
- The second clip-path is a polygon, that acts like a ray (hardcoded, can be improved)
The original work by Iventions Events https://iventions.com/ uses JavaScript, but I found CSS-only approach more fun
Here's a demo and the codebase: https://github.com/Momciloo/fun-with-clip-path

value: Show HN: I saw this cool navigation reveal, so I made a simple HTML+CSS version

author: momciloo


value: dasfelix

value:
Hi HN
I built anypanel, a lightweight KPI board for SaaS founders who just want a fast “state of the app” check.
When you’re running a small product, the numbers you actually need daily are usually simple (signups, activation, DAU, revenue, errors). The annoying part is they’re scattered across tools, and you end up either not checking at all or wasting time piecing them together.
What anypanel does:
- You send metrics/events with a simple API call (or a tiny JS snippet)
- anypanel stores them and turns them into a clean dashboard: KPI cards, time-series charts, goals
- Built for 5–10 metrics you care about, not full-blown analytics
Why:
I wanted something I can open on my phone and get the answer in one glance: “Are we up, down, or broken today?”
Who it’s for:
Technical solo founders / small teams who want a daily KPI habit without a data stack.
Link: https://anypanel.io
I’d love feedback on:
- What exact metrics would you want on a “30-second board”?
- What would stop you from using a tool like this (trust, setup, missing integrations, etc.)?

value: Show HN: Know how your SaaS is doing in 30 seconds

author: dasfelix


value: Slaine

value:
A few days ago I shared a repo with deterministic NDJSON event logs and explicit gap handling (v1.1) and asked for a sanity check.
I’ve now updated this with the same two scenarios generated under a newer model (v1.2).
This repo still intentionally contains only NDJSON files — no code, no UI:
• a clean run
• a run with intentional missing data
In v1.1, missing data was represented via an explicit export-time gap marker.
In v1.2, the same scenario is represented structurally at the segment level, and the verifier reports partial results accordingly.
The guarantees are unchanged (deterministic ordering, tamper-evident chaining, explicit missing-data detection).
I’m still unsure how useful this is in practice compared to existing approaches, but I wanted to share the updated artifacts in case it’s of interest.
Repo: https://github.com/yupme-bot/kernel-ndjson-proofs

value: Show HN: Deterministic NDJSON audit logs – v1.2 update (structural gaps)

author: Slaine


value: Show HN: LLM of Babel

title: Show HN: LLM of Babel


value: jslambda

value:
I wanted semantic search for TLDR pages, but also wanted it to work offline with no backend. This project runs entirely in the browser using client-side vector search:
Demo: https://jslambda.github.io/tldr-vsearch/
More information: https://jslambda.github.io/articles/semantic-tldr.html

value: Show HN: Semantic Search for terminal commands in the Browser (No Back end)

author: jslambda


value: ulrischa

value: Show HN: AI Watermark and Stego Scanner

author: ulrischa

title: Show HN: AI Watermark and Stego Scanner


value: fokdelafons

value: Show HN: Mirror Parliament where users vote on top of politicians and draft laws

author: fokdelafons


value: shubham-coder

value:
I built a Voice agent platform my drobotics lab of my university..which is already being cloned by 330+ people within 12hrs .. I am a first year cse student and so I tried to figure out a way to actually run everything on my laptop and working on it currently to completely transform to edge ai voice assistants for the robotics and 100% private and local control of robotics related project of my lab..
The intersting features are :
1> I used json rag with real time embeddings so that for a few specs and info we don't need to set a whole pipeline..
I have already built " Hierarchical Agentic Rag with Hybrid Search ( knowledge graph + vector search) u can view that on my profile ...
I am actively trying to share as much as possible related to it but that project is actually linked with a huge set of files it's 693k points of data with pgvector+ postgress .. give a visit u will get more idea from that
2> I had tried every sort of whisper models.. faster whisper .. turbo or anything u can u think of ..even with a self c++ engine .. but that model itself was hallucintion prone architecture..
Then I moved to parakeet tdt with silero vad  and not parakeet rnn for better speed and optimisations .. repo has further details ..
3> fine tuned a dataset from anthropic rlhf through space and glinner and convert that to a perfect training dataset of the Lama 3.2 3b ..
I will attach the dataset of u need or will upload that to hugging face if u want to use it for yourself..
4> attached phonetic correctors for both output from parakeet and llama for better tts working .
5> I used setfit to route the queries and confidence based semantic search for faster and accurate as much as possible
6> I am using sherpa onxx and qued the tts and stt and everything but as a experimentation I have also achieved llama generating respond and kokora processing as a batch with a full nyc working as well and everything on my laptop...
7> along with these my frontend also relies on heavy three.js and 3d view files but I had applied optimisations there which works perfectly with everything together on the laptop..
8> I also applied glued interaction to the llm model .. implemented FIFO with 5 interactions and storing them for future fine tuning and phonetic words additions.
Pls give a visit  it and let me know if I should learn something new ..
One kind note : as a enthusiast spending so much energy on these things things .. I have taken help from ai for the md files and expansion or explanations in the codes for better help of every single person...

value: Show HN: I built a <400ms latency voice agent that runs on a 4gb vram GTX 1650"

author: shubham-coder


value: saisrirampur

value: Show HN: An open-source starter kit for developing with Postgres and ClickHouse

author: saisrirampur


value: lasgawe

value:
Hi HN,
This is the first public launch of Writtte, an open-source, drafting-first editor built for writing and publishing articles.
We built this after noticing that most writers use 4-5 different tools just to draft and publish a single article. The biggest pain was not writing itself, but reformatting content every time it moved to Medium, Substack, WordPress, or a Markdown-based blog.
Writtte focuses on one thing: article drafting without format breakage.
What it does:
1. A psychology-designed editor UI that separates drafting from editing
2. AI-assisted + algorithm based grammar, clarity, tone, and refactoring (triggered manually)
3. One click copyto Medium, Substack, WordPress (Gutenberg), and Markdown
4. When you paste content into those platforms, formatting stays the same
5. Public draft sharing so others can review before publishing
This is completely open source: https://github.com/writtte/writtte
Public roadmap: https://github.com/orgs/writtte/projects/1
What we are working on next:
1. LaTeX support
2. Mermaid diagrams
We are building this in the open and would really appreciate feedback, especially from people who write articles or long-form content regularly.
Thanks for reading.

value: Show HN: Writtte – Draft and publish articles without reformatting, anywhere

author: lasgawe


value: amtiyo

value:
Hi HN,
I built a CLI that unifies configuration for AI coding assistants.
Problem: Modern AI-powered development uses multiple tools (Cursor, Claude Desktop, GitHub Copilot, various CLI tools). Each has its own config file for MCP (Model Context Protocol) servers. Adding a server means manually updating 4-6 different configs.
Solution: agents gives you one source of truth. Define MCP servers once, sync everywhere.
Example:
.agents/
├── agents.json    → Your config (committed)
├── local.json     → Secrets (gitignored)
$ agents sync  → Updates all tool configs
Key decisions:
- Convention over configuration (sensible defaults)
- Security-first (secrets separated, auto-gitignored)
- Interactive setup wizard for easy onboarding
- 70+ tests (Vitest)
Supports: Cursor, Claude Code, Gemini CLI, Codex, Copilot, Antigravity
GitHub: https://github.com/amtiYo/agents
npm: @agents-dev/cli
v0.7.7 released today. MIT licensed.
Looking for feedback on:
1. Is this solving a real problem?
2. What other tools should it support?
3. Thoughts on the architecture?
Happy to answer questions about the implementation or design decisions.

value: Show HN: Agents – Sync MCP Configs Across Claude, Cursor, Codex Automatically

author: amtiyo


value: Show HN: ReviewReact – AI review responses inside Google Maps ($19/mo)


value: octablock

value:
Hi HN,
I built a real-time UART analyzer using an ESP32-S3 and a Windows GUI.
It captures high-speed UART data and shows live statistics and visualization on PC.
Main features:
- Up to 921600 baud capture
- Real-time throughput monitoring
- RAW hex viewer
- Frame decoding
- Health / buffer status
- Standalone Windows EXE
How it works:
Target MCU TX → ESP32-S3 RX → USB → PC Analyzer
You just connect your device’s TX pin to the ESP32-S3 RX pin and run the PC app.
Supported devices:
STM32, ESP32/ESP8266, NRF52, RP2040, Arduino, and other UART devices.
GitHub:
https://github.com/choihimchan/linkscope-bpu-uart-analyzer
Demo GIF is in the README.
I’m actively improving this project and would really appreciate feedback, ideas, and bug reports.
Thanks!

value: Show HN: LinkScope – Real-Time UART Analyzer Using ESP32-S3 and PC GUI

author: octablock


value: lastodyssey

value:
I made a tetris block puzzle game.
If you feel you are really good at tetris and block puzzles, try to complete todays game.<p>Any feedback is welcomed.

value: Show HN: A unique twist on Tetris and block puzzle

author: lastodyssey


value: dangoodmanUT

value:
Made this proxy as a method for enabling "bucket per deployment" without having to worry about S3 provider quotas.
Great for giving each agent their own S3 bucket without having to worry about cleaning it up to reclaim quotas.
The closest thing that exists to this is the Tigris partner API, but other than that I could not find anything that allows unlimited S3 buckets like this. AWS has a 10k buckets (can go up to 1M) and 5k users limit, which is obviously way too low (and a ceiling to track/worry about) when you're creating and destroying buckets quickly.

value: Show HN: Vbuckets – Infinite virtual S3 buckets

author: dangoodmanUT


value: Winipedia

value:
I built pyrig because I did the same setup everytime I started a proper python project. I genuinely believe this can be a great tool, for me it has already genuinely simplified my python project development. I would love feedback on this of any kind. Below is a description of what pyrig is and does.
pyrig generates and maintains a complete, production-ready Python project from a single command. It creates all the files you need — source structure, tests, CI/CD, documentation, configs — and keeps them in sync as your project evolves.
uv init
uv add pyrig
uv run pyrig init
You get: package structure with a CLI entry point, test framework with 90% coverage enforcement, GitHub Actions (CI/CD, releases, docs deployment), pre-commit hooks, MkDocs documentation site, and container support.
The key difference from templates like cookiecutter is that pyrig isn't a one-shot scaffold. It's idempotent — rerun it anytime to update configs, add missing files, or sync with the latest defaults. It merges missing values without removing your customizations.
Under the hood, each config file (pyproject.toml, GitHub workflows, .gitignore, etc.) is a Python class that generates the file with sensible defaults, validates it, and merges in anything missing. You can create custom configs by subclassing — pyrig discovers them automatically.
It also supports multi-package inheritance, so you can build a multiproject-wide base on top of pyrig and have all your projects inherit the same configs, CLI commands, test fixtures, and build steps:
pyrig → service-base
→ auth-service
→ payment-service
→ notification-service
Opinionated: Python 3.12+, all ruff rules enabled, strict type checking with ty, 90% test coverage minimum, linear git history with branch protection.
GitHub: https://github.com/Winipedia/pyrig
Docs: https://winipedia.github.io/pyrig
Code-Wiki AI Docs: https://codewiki.google/github.com/winipedia/pyrig

value: Show HN: Pyrig – One command to set up a production-ready Python project

author: Winipedia