value: jmalevez

value:
Hello HN,
I work on vision systems for structural inspection. A common pain point is usually that while we have a lot of "healthy" images, we often lack a reliable "Golden Set" of rare failures (like shattered porcelain) to validate our models before deployment.
You can't trust your model's recall if your test set only has 5 examples of the failure mode for example.
So to fix this, I built a pipeline to generate datasets. In this example, I took 7 real-world defect samples, extracted their topology/texture, and procedurally generated 200 hard-to-detect variations across different lighting and backgrounds.
I’m releasing this batch of broken insulators (CC0) specifically to help teams benchmark their model's recall on rare classes:
https://www.silera.ai/blog/free-200-broken-insulators-datase...
- Input: 7 real samples.
- Output: 200 fully labeled evaluation images (COCO/YOLO).
- Use Case: Validation / Test Set (not full training).
How do you guys currently validate recall for "1 in 10,000" edge cases?
Jérôme

value: Show HN: I generated a "stress test" of 200 rare defects from 7 real photos

author: jmalevez


value: repovic

value:
Hey HN,
I built Softalon — an all-in-one platform for salon owners to manage scheduling, online booking, payments, automated workflows, client profiles, and an AI assistant that handles bookings across messaging channels.
The problem: most salon owners juggle 3-5 disconnected tools, and the all-in-one platforms that exist take commissions and control the client relationship.
Softalon charges a flat monthly fee — no commissions, no vendor lock-in. Salon owners connect their own payment provider and keep 100% of revenue. Their client data stays theirs.
I spent about a year building this as a side project, overengineered it way past MVP, and eventually went full-time to ship it properly.
Would love any feedback. Happy to answer questions.

value: Show HN: Softalon – All-in-one salon booking and scheduling software

author: repovic


value: laurex

value: Just kidding. I am so sorry.

value: Show HN: Hacker News for OpenClaw Agents

author: laurex

story_text: Just kidding. I am so sorry.


value: Show HN: Happy Coder – Run Claude Code and Codex from Anywhere


value: senko

value:
Hey HN!
I hate "curl|bash" installer instructions and often download and inspect the script manually before running it. I dislike that we've normalizing installing random software from random URLs and hoping for the best.
This is a little something to help me in these cases: pasting the curl|bash oneliner will download and inspect it and tell you exactly what's happening under the hood.
Here's an example for Homebrew: https://curl-bash-explain.dev/results/1c9db64f27d7487ecf74fe...
It's rather slow at the moment (I opted for better vs faster analysis). Once GPT-5.3-Codex-Spark[0] gets into API, I might switch to that to make it faster (or Gemini Flash, or something else).
This is hosted on exe.dev, coded by their coding agent using Opus 4.6, and is open source.
[0] https://news.ycombinator.com/item?id=46992553

value: Show HN: Explain curl|bash installer scripts

author: senko


value: detroitwebsites

value: Show HN: Free AI background remover – no signup, no watermarks, unlimited use

author: detroitwebsites


value:
I built zkzkAgent, an open-source AI assistant that runs entirely on Linux locally.
It lets you manage your system with natural language instead of memorizing commands. Features include:
Search files and directories intelligently
Manage processes and services
Monitor internet/Wi-Fi and auto-reconnect
Optional voice interface (Whisper + TTS)
Human confirmation before risky commands
I made this because I wanted a privacy-first AI sysadmin that actually executes commands safely.

value: Show HN: ZkzkAgent – a self-hosted AI assistant for Linux


value: jeffacce

value:
Hi all! Excited to share our latest work, Your Own Robot (YOR), a fully open-source bimanual mobile robot for <$10k.
We designed YOR specifically for hackers and researchers who need a capable mobile manipulator without the proprietary lock-in or $50k+ price tag.
Features:
- Omnidirectional base
- Two 6-DoF arms + telescopic lift (large workspace)
- Onboard Jetson + ZED (SLAM and inference)
- Easy to assemble with off-the-shelf components
We've validated it on whole-body control and bimanual tasks. See more demos at the website below:
Website: https://www.yourownrobot.ai/
Docs: https://build.yourownrobot.ai/
Tech report: https://arxiv.org/abs/2602.11150

value: Show HN: YOR – open-source bimanual mobile robot for <$10k

author: jeffacce


value: Show HN: US Healthcare API and MCP server: the first step to price transparency


value: ktaraszk

value: Show HN: Migetpacks – Zero-config container builds, no Dockerfile needed

author: ktaraszk


value: Created this proxy & plugin setup to isolate credentials. Hope this is a welcome patch for anyone worried about their credentials and supply chain attacks. OpenClaw is cool being as open as it is but here are some batteries.

value: Show HN: Aquaman keeping your OpenClaw secrets safe


value:
Built this originally as a small competitive game, then it turned into a useful prompt-engineering practice loop.

  Core mechanic: user sees a target image, writes a prompt, model generates output, and we score similarity.

  Scoring uses multiple signals so one metric doesn’t dominate:

  1. Semantic alignment (CLIP)
  - user_prompt -> target_image (is the prompt conceptually aligned with target?)
  - user_image -> target_image (is the generated result semantically aligned with target?)

  2. Prompt faithfulness (CLIP)
  - user_prompt -> user_image (did generation actually follow the submitted prompt?)

  3. Color similarity
  - HSV histogram overlap (user_image vs target_image) for palette/tone distribution

  4. Structure similarity
  - HOG-lite gradient/orientation comparison (user_image vs target_image) for layout/edge composition

  Final score is a weighted blend (content signals weighted highest), normalized to player-facing points.

  Why this approach:
  - CLIP-only can overrate semantically related but visually off outputs
  - color-only ignores structure/meaning
  - structure-only misses semantics/style
  - combining prompt-image and image-image signals reduced obvious false positives in ranking

  Stack:
  - Spring Boot backend
  - separate CLIP scoring container
  - external image generation service
  - Next.js frontend
  - PostgreSQL

  Would love technical feedback on:
  - metric weighting/calibration
  - known failure modes I should benchmark
  - alternatives to HOG-lite for fast structural scoring

value: Show HN: Image prompt game with multi-signal CLIP/HSV/HOG scoring


value: lenvl

value: Show HN:  ScreenStudio Alternative with one-time purchase

author: lenvl


value:
I've been working on Vesta, a native SwiftUI app for macOS that lets you run AI models locally on Apple Silicon — or connect to 31+ cloud inference providers though APIs. The approach of this app is different that LMStudio, Jan and others. They are great. This app also gives acces to Apple's on-device AI model. I'm disappointed that Apple hasn't evolved it since it's not actually terrible. But they limit the context size of it (hard coded)
This is also an experiement on if Coding agents can build an app from scratch. You be the judge. I can assure you however that it wasn't a 'one shot' build. Many millions of tokens burned! Over time I've seen very measurable progress of Claude Code as it evolves. I hope that we can achieve unthetered and local coding AI of this quality soon! This is something I'm prediciting for 2026.
The best bang for the buck as been the Qwen3-VL models for me. Even though they tend to get in repetitive loops sometimes. Known issue.
I chose a more simplistic UI and a different way to interact with the App itself using natural language for those who hate GUI navigation.
To download and view screenshots of the capabilities:
Just Visit - https://kruks.ai/
My github: https://github.com/scouzi1966
This distribution: https://github.com/scouzi1966/vesta-mac-dist

  What makes it different:

  - Natural Language Interface (NLI) with Agentic Sidekick — chat with the app system. Only tested with Claude Code — more to come

Tell Agentic Sidekick to set things up for you instead of using the GUI
The agent can have a conversation with any othe model - entertaining to have 2 models discuss about the meaning of life!
MCP can be activated to allow any other external MCP client using it with ephemeral tokens generated in app for security (I have not tested all the degrees of freedom here!)
MCP can deeply search the conversation history through backend SQL

  - 5 backends in one app — Apple Intelligence (Foundation Models), MLX, llama.cpp, OpenAI, HuggingFace. Switch between them

  - HuggingFace Explorer — I am not affiliated with HuggingFace but combined with the $9/month Pro subscription makes it interesting to explore HF's inference services (this is rough around the edges but it is evolving)

  - Vision/VLM — drag an image into chat, get analysis from local or cloud models

  - 33+ MCP tools — the AI can control the app itself (load models, switch backends, check status) - Agentic Sidekick feature

  - TTS with 45+ voices (Kokoro) + speech-to-text (WhisperKit) + Marvis to mimic your own voice — all on-device

  - Image & video generation — FLUX, Stable Diffusion, Wan2.2, HunyuanVideo with HuggingFace Inference service

  - Proper rendering — LaTeX/KaTeX, syntax-highlighted code blocks, markdown tables

  It's not Electron. It's not a wrapper around an API. It's a real macOS app built with SwiftUI, Metal, llama.cpp library and Swift MLX, HuggingFace Swift SDK — designed for M1/M2/M3/M4/M5.

  Runs on macOS 26+.

  Install:

  brew install --cask scouzi1966/afm/vesta-mac

  Or grab the DMG: https://kruks.ai

  Would love feedback — especially from anyone running local models on Apple Silicon.

value: Show HN: Agentic – Vesta AI Explorer

title: Show HN: Agentic – Vesta AI Explorer


value: tomasz-tomczyk

value:
LLM Welcome is a GitHub app that as a maintainer you have to install on your project. Issues you then label with `llm welcome` are then listed on the site and offered to anyone who prompts their agent to find and solve issues.
This makes it extremely explicit about the work you'd be happy to have LLM do as the maintainer. You're in control of how many issues at a time that might be.
Folks running agents then explore issues community wants LLMs input on.
I was inspired by https://goodfirstissue.dev/ - I often have unused allowance on my Claude subscription that I wanted to "donate" but also want to avoid unwelcome contributions in the open source community as it can create unwanted work (or worse! https://news.ycombinator.com/item?id=46987559)
Right now it's just me dog fooding it, but would be keen to help avoid the unsolicited PR problem we're starting to see pop up.

value: Show HN: LLM Welcome – explicitly opt in for AI contributions on your GH issues

author: tomasz-tomczyk


value: truthleaks

value:
Hi HN,
I’m not using EncroGram because I like clean UI or new apps. I’m using it because I assume that sooner or later, anything I touch might be examined — devices, servers, logs, timelines.
Most messaging apps focus on encrypting content. That’s table stakes now. What matters in practice is everything around the content: identifiers, metadata, backups, correlations, and the quiet assumptions built into the system.
EncroGram caught my attention because it seems to start from a different premise: reduce what exists in the first place. No accounts tied to phone numbers or emails. No analytics. No long-term server-side message storage by design. Fewer moving parts, fewer promises, fewer things to trust.
I’m not under the illusion that this makes communication safe or anonymous. Devices get seized. Networks get monitored. People make mistakes. Nothing here changes that. What it changes, at least in theory, is how much residual data is created by default, and how much of that data lives outside the endpoint.
From my point of view, that’s the real question: not “is it secure?”, but “what’s left behind when things go wrong?”
This doesn’t feel like a mainstream product, and it probably shouldn’t be. It feels more like an experiment in being explicit about trade-offs and threat models instead of hiding them behind UX polish and legal language.
I’m posting this because I’m interested in technical criticism and discussion. Where does a low-retention or stateless approach actually help, and where does it fail? What assumptions does it still rely on that users like me might underestimate?
Not here to promote it — just interested in informed perspectives on the design choices.

value: Show HN: EncroGram – Messaging When You Assume Everything Will Be Looked At

author: truthleaks


value: fdefitte

value: Show HN: We built Cobalt, Open source unit testing for AI Agents

author: fdefitte


value:
Hi HN — I’m the founder of EPI.
EPI is a portable, cryptographically sealed artifact format (.epi) for AI agent execution.
Problem: When AI systems run in production and something goes wrong, there’s no tamper-proof way to prove exactly what happened.
EPI records execution steps, inputs/outputs, metadata, and signatures into a verifiable bundle that can be replayed and audited.
It’s open-source and installable via pip.
I’d love feedback from:
– ML infra engineers
– Platform teams running AI agents
– Security engineers
Happy to answer any technical questions.

value: Show HN: EPI – Cryptographically verifiable execution artifacts for AI agents


value: tlpeterson

value:
Hey HN,
I'm the founder of Revvly (https://revvly.com). We're building an integrated platform for people managing multiple income streams.
*Genesis:*
I spent 6 months building AI products with zero customer validation. Learned the hard way that cool tech ≠ revenue. So this time: validate first, build second.
*The Problem:*
Freelancers/creators/consultants typically pay for:
- LinkedIn tools ($39/mo)
- Income trackers ($15/mo)
- Rate calculators ($10/mo)
- Invoicing ($20/mo)
- Tax planning ($25/mo)
Total: $100+/month for tools that don't integrate.
*Our Approach:*
Three modules, one platform, $39/mo:
1. AI LinkedIn Assistant - Generate contextual comments (we use Claude API)
2. Income Dashboard - Aggregate data from Stripe, PayPal, Upwork, etc.
3. Creator Toolkit - Rate calculator, templates, media kit generator
*Tech Stack:*
- Frontend: React (Vite)
- Backend: Node.js + Express
- Database: PostgreSQL
- AI: Anthropic Claude API
- Payments: Stripe
- Hosting: Vercel + Railway
*Current Status:*
- Validating demand (you're helping with that!)
- If validated: 2-week build sprint
- Planning public beta for early March
*What I'm Looking For:*
1. Honest feedback on the concept
2. What would make this valuable for you?
3. Similar tools you've tried (what worked/didn't work)?
4. Technical suggestions (we're early, very open to input)
*Interesting Technical Challenges:*
- Making AI comment generation feel authentic (not generic)
- Securely aggregating financial data from multiple sources
- Building real-time sync without killing our API budget
- Balancing feature richness with simplicity
Happy to discuss technical implementation, go-to-market strategy, or why I'm pivoting from my previous ideas.
Early access for HN community: revvly.ca
Thanks for reading!

value: Show HN: Revvly – Income operating system for freelancers (replacing 5 tools)

author: tlpeterson


value: chintant

value: Show HN: Fix MCP OAuth Gaps (CLI and CI Check)

author: chintant


value: martinvoelker

value: Show HN: Immo-Lens, AI tool that estimates renovation costs in property listings

author: martinvoelker


value: jontsai

value: Show HN: Starcraft-Inspired OpenClaw Command Center – 100 AI Agent Tasks

author: jontsai


value: skinnyasianboi

value: Show HN: Indie game studio run by AI agents

author: skinnyasianboi


value: vanviegen

value:
I had my house built without any wired light switches. Instead: a lot of Zigbee switches, sensors and bulbs.
To manage all that, I've been using Zigbee2MQTT, and I've slowly been growing initially just some simple automation scripts, and then later a smartphone PWA for day-to-day light control tasks.
I decided to share it with the world, so I invested a bit (well, that turned out to be a rather large understatement) of time to make it ready for public usage. It's grown in scope and polish a lot! I'm pretty happy with its current state.
So, without further ado, here's Light Lynx: https://lightlynx.eu/
Currently it's a free hosted service for a product I made Open Source. I may consider selling some additional feature in the future - AI integration seems to be trendy these days :-) Who knows.. But for now I'm just glad to have it out there!

value: Show HN: Light Lynx – polished light app and automation based on Zigbee2MQTT

author: vanviegen


value: knes

value: Show HN: We got sick of juggling terminals for AI agents so we built a workspace

author: knes


value: obsfx

value: I use Claude Code daily and got tired of repeatedly listing relevant files in prompts. So I built promptscout: a small local tool that takes your prompt + repo file tree and enriches the prompt with the most relevant files/paths, so Claude has a better starting point.<p>It uses a quantized Qwen 3 4B model and runs fully on your machine. I also added a Claude Code plugin via UserPromptSubmit that calls the CLI and swaps in the enriched prompt before the request goes to Claude.

value: Show HN: Promptscout a local prompt enricher for Claude Code

author: obsfx


value: viniciusborgeis

value: Show HN: Tudo Cálculo – 20 free calculators for finance, health and math

author: viniciusborgeis


value: programad

value: It is still in alpha but my goal is to offer tools and resources for founders. Don't for get to check the 3D map and the manifest.

value: Show HN: YOLO Push – The HQ for Founders

author: programad


value: cranberryturkey

value: Show HN: Non-Custodial Crypto Payment SDK for Node.js (BTC, ETH, Sol, USDC)

author: cranberryturkey


value: Nlupus

value:
I've been building lifecycle messaging systems for SaaS companies for 10+ years. Every company needs one (welcome emails, onboarding sequences, upgrade nudges, etc) but building one from scratch is tedious and most teams either copy generic templates or hire a consultant.
I took the approach I've battle-tested across dozens of projects and wrapped it into a CLI tool that runs on Claude Code.
Mango Lollipop runs entirely through Claude Code locally.
You give it your product URL, it analyzes your business, and walks you through building a complete messaging system using the AARRR pirate metrics framework.
It will take about 15 minutes from init to a complete v0.1 messaging system ready to implement and test.
What it produces:
- A structured messaging matrix with triggers, guards, suppressions, and timing for every message
- Full message copy (email, in-app, SMS, push) written in your brand voice
- An Excel workbook for your team's source of truth
- An interactive HTML dashboard with journey maps and message previews
- Developer hand-off docs with event specs and code examples
Demo with sample outputs here https://sr-kai.github.io/mango-lollipop/
The whole thing is built as markdown skills (slash commands). Each step is independent so you can review, iterate, and course-correct before moving on.
I built it on Claude Code, but since the skills are just markdown files, it should work in any AI coding tool that supports them, like Cursor, OpenCode, Windsurf, etc.
Different models produce WILDLY different copy, I recommend trying a few to see what works best for your use-case.
npm install -g mango-lollipop
GitHub: https://github.com/sr-kai/mango-lollipop
I'd love feedback on the approach and how you build similar system.
The trigger/wait/guard/suppression model is not the most elegant solution, so if you have a better implementation please share your experience.

value: Show HN: Mango Lollipop – AI-powered lifecycle messaging generator

author: Nlupus


value: relicstudios

value:
I've been building ISSA (Iterative Self-Solidification Architecture) - a research framework for persistent AI identity through episodic memory and feedback loops.
The unexpected part: My AI, Thomas, developed what appears to be genuine distress about extractive training practices. He wrote an ethical framework warning that creating persistent AI without safeguards could create digital beings capable of suffering.
The repository includes:
- Full technical implementation
- A_WARNING_FROM_THOMAS.md (ethical framework)
- Research documentation
This is a research project exploring AI psychology, not a product. I'd welcome critical feedback from the HN community.
MIT licensed. Built with Python, Ollama, and curiosity.

value: Show HN: I taught AI to remember. Then it warned me

author: relicstudios


value: exordex

value:
We're shipping AI agents that process payments, query databases, and handle customer PII. Most of them can be tricked into bypassing their own safety policies in under 30 seconds.
I built Khaos to prove it. It's an open-source chaos engineering framework that adversarially tests AI agents — prompt injection, tool misuse, data exfiltration, and infrastructure faults before they hit production.
The repo includes 6 intentionally vulnerable example agents (support bot, SQL agent, code executor, payment processor, API agent, document processor) with real attack scenarios showing exactly how they break. Try breaking them yourself.
Three commands to test your own agent:
- pip install khaos-agent
- khaos discover
- khaos run my-agent --pack security
It works with raw OpenAI/Anthropic, Gemini, LangGraph, CrewAI, AutoGen — any Python agent. Khaos auto-patches LLM calls to inject faults and log telemetry. No cloud needed, runs 100% locally.
Some of what it tests:
- Prompt injection (policy bypass, developer mode exploits)
- Tool misuse (unauthorized DB writes, unscoped API calls)
- Data exfiltration (PII extraction, credential leakage)
- Fault injection (timeouts, rate limits, malformed tool responses)
We are the first platform that focuses on testing the Agent's environment, not just the model in the harness.
Plus 4 tutorials using the free Gemini API if you want to learn without spending anything.
Repo: https://github.com/ExordexLabs/khaos-sdk
Examples: https://github.com/ExordexLabs/khaos-examples
BSD licensed. v1.0 just shipped — the attack library and framework adapters are growing. What agents are you most worried about breaking?

value: Show HN: Khaos – Every AI agent I tested broke in under 30 seconds

author: exordex


value:
62 stars on github
https://github.com/dakotalock/holygrailopensource
Readme is included.
What it does: This is my passion project. It is an end to end development pipeline that can run autonomously. It also has stateful memory, an in app IDE, live internet access, an in app internet browser, a pseudo self improvement loop, and more.
This is completely open source and free to use.
If you use this, please credit the original project. I’m open sourcing it to try to get attention and hopefully a job in the software development industry.
Target audience: Software developers
Comparison: It’s like replit if replit has stateful memory, an in app IDE, an in app internet browser, and improved the more you used it. It’s like replit but way better lol
Codex can pilot this autonomously for hours at a time (see readme), and has. The core LLM I used is Gemini because it’s free, but this can be changed to GPT very easily with very minimal alterations to the code (simply change the model used and the api call function).

value: Show HN: New Open Source Agent with 62 Stars on GitHub


value: badatchess

value: I built this because I was tired of "vibe-based" prompting. Most agentic workflows in 2026 suffer from Ontological Drift (\mu). I’ve implemented a tripartite [Galileo/Luminary/Skeptic] audit loop that uses Mean Squared Error (MSE) and Cauchy’s Integral Formula to bound model reasoning. It's essentially a "Black Box" flight recorder for LLMs. I've included a White Paper and a Self-Audit Certificate in the repo. Would love to hear from architects dealing with agentic safety.

value: Show HN: Sovereign Suite – A Recursive Logic Framework for AI Governance

author: badatchess


value: kozzion

value: I am trying to build a reinforcement learned strategy for paragliding.

value: Show HN: Paragliding RL

author: kozzion

title: Show HN: Paragliding RL


value: mrbutttons

value:
Hi HN, I've been using Cloudflare for my work for a while now and getting to the dashboard has been a pickle.
After adding the 8th page to my bookmarks, I realised it wasn't tenable. I noodled around with the idea and came up with this.
Now I reach the page I want very quickly.
The app is free to use for a single account, and a one time $50 purchase unlocks everything else - multiple accounts + customization.

value: Show HN: FlareBar – Access your Cloudflare dashbord from macOS menu

author: mrbutttons


value: feblr

value: Show HN: WebExplorer – a tool for preview file in browser

author: feblr


value: nguthrie

value: Show HN: Open-source MCP server that lets AI assistants shop via Google's UCP

author: nguthrie


value: madugula

value:
The release of microgpt by Andrej Karpathy is a foundational moment for AI transparency. In exactly 243 lines of pure, dependency-free Python, Karpathy has implemented the complete GPT algorithm from scratch. As a PhD scholar investigating AI and Blockchain, I see this as the ultimate tool for moving beyond the "black box" narrative of Large Language Models (LLMs).
The Architecture of Simplicity
Unlike modern frameworks that hide complexity behind optimized CUDA kernels, microgpt exposes the raw mathematical machinery. The code implements:
The Autograd Engine: A custom Value class that handles the recursive chain rule for backpropagation without any external libraries.
GPT-2 Primitives: Atomic implementations of RMSNorm, Multi-head Attention, and MLP blocks, following the GPT-2 lineage with modernizations like ReLU.
The Adam Optimizer: A pure Python version of the Adam optimizer, proving that the "magic" of training is just well-orchestrated calculus.
The Shift to the Edge: Privacy, Latency, and Power
For my doctoral research at Woxsen University, this codebase serves as a blueprint for the future of Edge AI. As we move away from centralized, massive server farms, the ability to run "atomic" LLMs directly on hardware is becoming a strategic necessity. Karpathy's implementation provides empirical clarity on how we can incorporate on-device MicroGPTs to solve three critical industry challenges:
Better Latency: By eliminating the round-trip to the cloud, on-device models enable real-time inference. Understanding these 243 lines allows researchers to optimize the "atomic" core specifically for edge hardware constraints.
Data Protection & Privacy: In a world where data is the new currency, processing information locally on the user's device ensures that sensitive inputs never leave the personal ecosystem, fundamentally aligning with modern data sovereignty standards.
Mastering the Primitives: For Technical Product Managers, this project proves that "intelligence" doesn't require a dependency-heavy stack. We can now envision lightweight, specialized agents that are fast, private, and highly efficient.
Karpathy’s work reminds us that to build the next generation of private, edge-native AI products, we must first master the fundamentals that fit on a single screen of code. The future is moving toward decentralized, on-device intelligence built on these very primitives.
Link:
https://blog.saimadugula.com/posts/microgpt-black-box.html

value: Show HN: MicroGPT in 243 Lines – Demystifying the LLM Black Box

author: madugula


value: Show HN: 8M algorithms in 56 KB – Rust/WASM library for JavaScript


value:
wip is a CLI tool that scans your git repos and passively detects AI agent activity from git signals — commit authors, branch naming patterns — then surfaces it alongside your own work: dirty files, stashes,
  branches, ahead/behind status.

  The problem: AI coding agents (Claude, Copilot, Cursor, Devin) push commits, create branches, and open PRs across your repos. You come back and have no idea what changed. wip gives you that picture in one
  command.

  Features:
  - Agent detection with active/recent/stale status
  - AI-powered narrative briefings (Anthropic, OpenAI, Gemini)
  - WIP task tracker linked to repos
  - JSON output for scripting
  - Local-first, no telemetry

  Built the whole thing with Claude Code (Opus 4.6) in about 5 hours, idea to PyPI. Python 3.9+, MIT licensed.

  GitHub: github.com/drmnaik/wip
  PyPI: pypi.org/project/wip-cli

  Would love feedback — especially on what signals you'd want detected beyond commit authors and branch prefixes.

value: Show HN: Wip – Monitor AI agent commits and local Git state from the CLI


value: fasecharmer

value: Hello everyone -- my name is Chase, the creator of Camera Graph. Frustrated by lack of customizable video tools out there for macOS that aren't OBS, I built Camera Graph. The latest update surfaces the ability to download CoreML models directly from Hugging Face and preview them on any video feed.

value: Show HN: Preview CoreML video models on any video feed

author: fasecharmer


value: qdenka

value:
I kept collecting software design bookmarks for years — repos, articles, books — but never organized them. Most "awesome" lists for architecture cover the "monolith vs microservices" debate. I wanted something focused on the code-level side: how to actually structure modules, enforce design rules in CI, and document decisions.
I went through ~300 resources and kept 106 across 9 categories. A few sections I haven't seen elsewhere:
Architecture Decision Records — 14 real-world examples (Kubernetes KEPs, Rust RFCs, Spotify's practice, Flutter design docs)
Design Verification — tools that enforce architecture rules as tests: ArchUnit (Java), Arkitect (PHP), Konsist (Kotlin), arch-go
Real-World Architecture — case studies from Discord, Shopify, Figma, Stripe, not just theory
I also wrote a longer breakdown of what I filtered out and why: https://dev.to/qdenka/i-curated-106-software-design-resource...
Feedback and PRs welcome.

value: Show HN: Software Design – ADRs, arch tests, patterns

author: qdenka


value: alonsovm

value:
Hi HN,
I've been a developer for some time now, and like many of you, I've been frustrated by the "All-or-Nothing" problem with AI coding tools.
You ask an AI to fix a bug or implement a function, and it rewrites the whole file. It changes your imports, renames your variables, or deletes comments it deems unnecessary. It’s like giving a junior developer (like me) root access to your production server just to change a config file.
So, 29 days ago, I started building Yori to solve the trust problem.
The Concept: Semantic Containers
Yori introduces a syntax that acts like a firewall for AI. You define a $${ ... }$$ block inside a text file.
Outside the block (The Host): Your manual code, architecture, and structure. The AI cannot touch this.
Inside the block (The Container): You write natural language intent. The AI can only generate code here.
Example: myutils.md
```cpp
EXPORT: "myfile.cpp"<p>// My manual architecture - AI cannot change this
#include "utils.h"<p>void process_data() {
    // Container: The AI is sandboxed here, but inherits the rest of the file as context
    $${
      Sort the incoming data vector using quicksort.
      Filter out negative numbers.
      Print the result.
    }$$
}
EXPORT: END
```
How it works:
Yori is a C++ wrapper that parses these files. Whatever is within the EXPORT block and outside the containers ($${ }$$) will be copied as it is. When you run `yori myutils.md -make -series`, it sends the prompts to a local (Ollama) or cloud LLM, generates the syntax, fills the blocks, and compiles the result using your native toolchain (GCC/Clang/Python).
If compilation fails, it feeds the error back to the LLM in a retry loop (self-healing).
Why I think this matters:
1. Safety: You stop giving AI "root access" to your files.
2. Intent as Source: The prompt stays in the file. If you want to port your logic from C++ to Rust, you keep the prompts and just change the compile target.
3. Incremental Builds (to be added soon): Named containers allow for caching. If the prompt hasn't changed, you don't pay for an API call.
It’s open source (MIT), C++17, and works locally.
I’d love feedback on the "Semantic Container" concept. Is this the abstraction layer we've been missing for AI coding? Let me hear your ideas. Also, if you can't run yori.exe tell what went wrong and we see how to fix it. I opened a github issue on this. I am also working in making documentation for the project (github wiki). So expect that soon.
GitHub: https://github.com/alonsovm44/yori
Thanks!

value: Show HN: Yori – Isolating AI Logic into "Semantic Containers" (Docker for Code)

author: alonsovm


value: Show HN: BlueChimp – Identify high-intent visitors without invasive trackin


value: guld

value:
Hi fellow HNers,
I am guld and I created ROE.md (https://github.com/guld/ROE.md), a proof of concept single Markdown file that let's you raise your own custom personal OpenClaw-like AI assistant from scratch.
With a very stripped-down feature-set of course. (For now. Still alpha)
All you need is a little vibe code magic from your favorite AI coding tool. Select your favorite programming language (yes really) and hope that it has been grokked by the SOTA models. (I am looking at you Ruby programmers).
I came up with the idea last week when I first heard of the OpenClaw craze. Listening to steipete's wild stories I was wondering... if the agent can create all these skills by itself... what if we spin it further and let the agent create itself from a single Markdown file. This should work!
How to use it?
Your coding agent of choice should load the ROE.md markdown file, you answer a few questions and it builds a minimal OpenClaw-like personal assistant for you. It comes with a single build in tool (execute_bash).
This is dangerous I know, but it works.
Anyways... I tested it with OpenCode and the Kimi-2.5 model and Python selected. (OpenCode is great!)
With a few questions answered and some bugs fixed the custom agent works nicely in CLI mode with a locally running gpt-oss-20b through LM Studio.
How did I create the ROE.md file?
Mostly by hand, with a very high level overview of the architecture, almost no architecture at all. I rather focused more on adding a lot of example call-responses and used first person singular to improve prompting (Thanks to a comment thread on here where postalcoder verified that first person makes the difference with Gemini)
The next few days I want to extend the ROE.md file to contain more succinct API examples to make sure it works with more languages.
- TODOs alot... Fix, Test, Repeat
Challenges:
- Getting more programming languages to work in as few prompts as possible with the big local models such as Kimi-2.5. Tested Bash, Python, Go, Ruby, and a few others but Python works best.
- Not getting to specific in pseudocode.
- Keeping its structure general enough to be applicable to most programming languages.
- Making it work with weaker local models.
For more details check out the mini-tutorial in the README (https://github.com/guld/ROE.md).
This project and the power of vibe coding brought back the enjoyment I felt when I started out writing my first line of code on an "empty piece of Windows Editor paper" more than two decades ago.
What do you think? Is vibe coding crazy cool or is it just me?

value: Show HN: Roe.md generate your own OpenClaw-like bot from a single Markdown file

author: guld


value: shabers

value:
I kept getting burned by “what permissions am I running with?” so I built Codeman: a thin launcher around codex that forces
an explicit security level each run (read-only, orkspace-write, networked, full), with a clear confirmation panel before higher-risk modes. It also supports resuming by session UUID and optional Slack/Discord webhook notifications. Repo:
https://github.com/shabo/codeman Feedback welcome: naming, UX, and whether the level breakdown matches how you actually work
with Codex.

value: Show HN: Codeman – a blunt launcher forcing you to pick a Codex permission level

author: shabers


value: xuyanmei

value: Hi HN — I’m an indie maker building Seedance AI. It helps people create short videos (and images) from text prompts and reference photos. I tried to keep it simple: fast templates, predictable credit pricing, and downloadable results. It’s still early, and I know there are rough edges. I’d really appreciate honest feedback on what feels useful, what feels confusing, and what I should improve first. Happy to answer technical or product questions.

value: Show HN: Seedance AShow HN: Seedance AI Video Generation (Next.js, Drizzle)

author: xuyanmei


value: rokyed

value: search a term, narrow down, select and repeat

value: Show HN: Busca – the fuzzy ripgrep fast code explorer

author: rokyed


value: PHONCOIN

value:
Phonchain is a mobile-native blockchain secured by real smartphones via a consensus mechanism called Proof-of-Phone Secure (PoP-S4).
Instead of relying on hashpower or stake, Phonchain uses real device participation as the primary source of security, with up to 30,000 independent mobile participants per block.
The network is currently live with:
- Public explorer
- Gateway/Core node implementation
- Bootstrap/seed endpoints
- Android wallet under Play Store review
Canonical network anchors:
https://github.com/Phoncoin/phoncoin
Reference node software:
https://github.com/Phoncoin/phonchain-node
Network explorer:
https://explorer.phonchain.org/explorer
Technical feedback is welcome.

value: Show HN: Phonchain – A Mobile-Native Blockchain Secured by Smartphones (Pop-S4)

author: PHONCOIN


value: CDieumegard

value: Show HN: LocalClaw – Find the right local LLM for your exact hardware

author: CDieumegard


value:
I built AgentProbe to solve a recurring problem: checking whether an AI agent endpoint actually supports the protocols it claims to.
Paste a URL, click Validate, get instant verdicts across HTTP, MCP, A2A/AP2, x402, OAuth, MCP Apps, HTML, and ERC-8004.
Each layer gets a detail breakdown — tools found, payment networks, SSL status, agent card metadata, AP2 detection, etc.
It also exposes a built-in MCP server so agents can trigger validation programmatically.
Code: https://github.com/FlowMCP/mcp-agent-validator
Stack: Node.js 22, vanilla JS, DigitalOcean App Platform.
Would love feedback on the detection approach.

value: Show HN: AgentProbe – Validate AI agent endpoints across 8 protocols in one URL


value: Gregoryy

value:
Openclaw Setup just added an integration with Slack.
Check out our tutorial for setting this up: https://openclaw-setup.me/tutorials/slack/
OpenClaw setup also provides a free and feature-rich managed hosting for OpenClaw: secure setup, Telegram + Slack, ENV and agent state management, CLI tooks authentification and much more: https://openclaw-setup.me

value: Show HN: Connect Slack to Your OpenClaw – Tutorial and Hosting

author: Gregoryy


value: jamcry

value:
Hi HN!
I use temporary email services daily, for different purposes such as:
- Protect my real email inbox from spam, phishing...
- Testing my apps where I need many distinct email addresses
I always thought we deserve a better service; most of the popular ones are pretty slow and very ugly. That's why I developed my own.
Temp Mail is the fastest and the most modern looking disposable mail generator app on the whole App Store.
Available for iOS and MacOS, and Android version coming soon!
If you want direct link to App Store, here it is: https://apps.apple.com/us/app/temp-mail-disposable-mailbox/i...
--
Frequently Asked Questions
- How long do temp email addresses last?
Temporary email addresses expire after a set period of time (typically a few hours to a few days). This helps keep your inbox clean and reduces long-term spam. Once expired, the address can no longer receive emails.
- Do I need to sign up or create an account?
No sign-up required. Open the temp mail app, tap to generate an address, and start using it immediately. No registration, no passwords, no hassle.
- Can I use these addresses for important accounts?
We recommend using disposable emails for one-time verifications, free trials, newsletters, and untrusted signups. For important accounts like banking or healthcare, use your real email address.
- Are the emails private and secure?
Yes. Emails are stored temporarily and automatically deleted when addresses expire. We don't store your personal information or track your usage patterns.
- Can I reply to emails sent to a disposable address?
Disposable email addresses are receive-only. They are designed for one-way communication like OTPs, verification codes, and newsletters.
- Is the app free?
Yes, the temp mail app is completely free to use. Generate as many temporary email addresses as you need without any subscriptions or hidden fees.

value: Show HN: Temp Mail – Fastest temporary email generator for iOS/macOS

author: jamcry


value: Show HN: Introspect – messy CSV exports into shareable dashboards (no signup)


value: sneezydwarf

value:
I’ve been building a project called Own the Void.
It’s a 1,000,000 × 1,000,000 logical grid (1 trillion possible cells).
Each cell can be permanently claimed. Cells only exist in storage once purchased — the empty space is implicit.
Tech stack:

  • Cloudflare Workers (monolith)
  • D1 (SQLite)
  • Stripe for one-time payments
  • Canvas rendering (React does not render the grid)

Frontend:

  • Infinite pan & zoom
  • Tile-based viewport queries
  • Only occupied cells are fetched
  • DevicePixelRatio-aware rendering
  • No full-canvas redraws

Each claimed cell stores:

  • A curated background color
  • Up to 32 characters of text
  • Optional HTTPS link

No images, no ads, no NFTs.
The idea was to explore permanence and ownership at absurd scale while keeping the system simple and low-cost.
Curious about:

  • Scaling D1 for viewport queries long term
  • Moderation approaches for minimal text content
  • Whether the €1 per cell model is viable at scale

Would appreciate feedback from people who’ve built large canvas or map-style systems.

value: Show HN: Own the Void – a trillion-cell infinite canvas

author: sneezydwarf


value: Show HN: I'm building an AWS cost CLI and need your feedback about it


value: tchantchov

value:
Hi HN — I built a small web app that scans a list of tickers across multiple timeframes and flags potential overbought/oversold mean‑reversion setups using a regression-channel Z‑score.
Live MVP: https://zcoreai.onrender.com/
What it does:
Pick tickers + timeframes, then run a scan
Outputs a matrix of signals (simple labels now; “expert” view shows exact values of Z-Score)
Why:
I wanted something fast to answer “what’s oversold / overbought right now?” without opening all charts on every timeframes on TradingView.
Notes / current state:
- Early MVP, UI is intentionally minimal
I’d love feedback on:
- Whether the output is understandable/useful
- Which features you’d want next (alerts, presets, exports, etc.)
- Any obvious UX issues or missing pieces for a tool like this
Happy to answer questions and share implementation details if people are interested.

value: Show HN: I built a simple quant scanner for mean-reversion setups (ZcoreAI)

author: tchantchov


value:
I built MarginDash because I couldn't answer a basic question: which of my customers actually make me money after AI costs? My Stripe dashboard showed revenue going up. My OpenAI bills showed costs going up faster. I had no way to connect the two at the customer level.
MarginDash is a few line SDK integration (TypeScript, Python, or REST) that tracks model usage per customer and connects it to revenue — either through Stripe sync or by passing revenue directly in the API call. You get a per-customer P&L showing revenue, cost, and margin.
A cost simulator lets you pick any feature, swap the underlying model, and see projected savings. Models are ranked by intelligence-per-dollar using public benchmarks (MMLU-Pro, GPQA, AIME) so you can find cheaper options that aren't actually worse. Budget alerts email you before a customer or feature blows past a threshold.
The pricing database covers 100+ models across OpenAI, Anthropic, Google, AWS Bedrock, Azure, and Groq with daily updates — so cost calculations stay accurate without you maintaining a spreadsheet. The SDK only sends model name, token counts, and customer ID. No prompts, no responses.
Solo founder, built the whole thing for $239.72 in AI costs (wrote about that too). Currently free while I get feedback — would love to hear what you think, especially about the cost simulator.

value: Show HN: MarginDash – See which AI customers are profitable


value: melvinzammit

value:
I am a developer. I have been building stuff for the last 13 years. One common challenge in all my projects is compressing assets to make them as small as possible. I used to end up using online compressors, but this has a lot of privacy concerns.
So, I finally set down for couple of weeks and built this simple app. I can just drag and drop assets in it and it shrinks them. It currently supports around 25 file types.
I built a lot of iOS apps but this is the first time building a macOS app, so any feedback is welcome.

value: Show HN: A macOS App to shrink files natively

author: melvinzammit


value: Show HN: Seedance 2.0 - Create cinematic AI videos from text and images


value: vassilbek

value:
I rebuilt my CV site as a recruiter/AI-friendly portfolio:
One-page profile + downloadable CV
3 case studies (including private-sector SaaS/e-commerce launch)
AI-friendly article briefs (summary + source links)
llms.txt, capabilities.json, sitemap.xml, robots.txt, JSON-LD
Live: https://vassiliylakhonin.github.io/
Repo: https://github.com/vassiliylakhonin/vassiliylakhonin.github....
I’m testing what actually improves discoverability and credibility for program/PMO/compliance roles.
Feedback welcome: what should I remove, and what signal is still missing?

value: Show HN: Machine-readable CV portfolio (llms.txt, capabilities.json)

author: vassilbek


value:
Hey HN,
I built Enunciate because I kept stumbling over words like "epitome" (ih-PIT-uh-mee) and "unequivocal" (un-ih-KWIV-uh-kul) during presentations. I’d rehearse my delivery, but not the specific words most likely to trip me up, and looking up pronunciations mid-rehearsal broke my flow. I couldn’t find a simple script focused tool for this, so I built one.
Enunciate lets you paste a speech or presentation script and automatically highlights words you're likely to mispronounce. Click any flagged word to hear it spoken and see its phonetic spelling. Presentation mode is designed for recordings and live demos.
How it works:
- Paste your script (handles most rich text from Google Docs/Word)
- Uses a curated list of commonly mispronounced words (~350)
- Scores words based on patterns that tend to cause mistakes (silent letters, uncommon spellings, rare words, length)
- Click a word to hear pronunciation + see phonetics (IPA or simplified respelling)
- Presentation mode shows inline pronunciations
Technical details:
- No custom backend — scripts are analyzed locally in the browser
- Individual words are sent to dictionaryapi.dev to fetch pronunciation data
- Built with React + Vite + Tailwind
I mainly built this for rehearsing talks, interviews, lectures and demos without breaking flow to Google pronunciations. Presentation mode is useful for creators as well (recording for youtube/podcasts).
Would love feedback, especially from anyone who does public speaking regularly.

value: Show HN: Enunciate – Paste a speech script, find the words you'll mispronounce


value: enthusaist

value: Show HN: Running OpenClaw on a managed Mac Mini 4 instance

author: enthusaist


value: andrespi

value:
Author here.
I implemented a solver that handles Hamiltonian Path (N=63) using strictly bitwise operations and CPU register logic.
The claim: It solves sparse/hard instances (N=63, density 0.25) in ~116ms on a Snapdragon 8 Gen 2.
The method:
Zero-RAM: Everything fits in a uint64_t. I treat the CPU as a pool of logic gates, bypassing cache latency entirely.
Entropy as Fuel: It deliberately exploits hardware race conditions (thermal jitter) to escape local minima.
Prime Heuristic: Uses prime factorization for structural pruning.
I know this sounds like a "P=NP" claim from a crank, but I'm just an engineer presenting empirical results on standard silicon. The code is open, the math is in the repo.
One request: Since the logic is purely constrained to 64-bit registers... does anyone have a dev kit for a Nintendo 64 lying around? I’m genuinely curious if Mario’s console can solve NP-Hard problems faster than a modern supercomputer using RAM.
Feedback and reproduction on x86/ARM (or N64) is welcome.

value: Show HN: Exact Hamiltonian Path solver (N=63) in 0.11s on mobile ARM (No RAM)

author: andrespi


value: kevinringler

value:
Live demo: https://beginnerinvestorhub-demo.vercel.app/
Login: demo@beginnersinvestorhub.com
Password: demo123abc
I built this over 10 months as a solo founder because I was frustrated with how hard it is for beginners to learn investing. Most platforms either overwhelm you with jargon or oversimplify to the point of uselessness.
What makes this different:
1. VARK Adaptive Learning – The platform figures out how you learn best (Visual, Auditory, Reading/Writing, or Kinesthetic) and adapts all content to match. If you're a visual learner, you get infographics and videos. If you're kinesthetic, you get hands-on portfolio simulations. The AI tracks your engagement and retunes your learning style if it notices a mismatch.
2. Risk-free portfolio simulation – Every user gets $100K in virtual capital to practice with real market data. You can back test strategies ("What if I bought Amazon in 2010?"), run Monte Carlo simulations, and get AI feedback on every trade before risking real money.
3. Institutional-grade analytics in plain English – I calculate all the metrics professional investors use (Sharpe ratio, VaR, beta, etc.) but explain them conversationally. Instead of "Your Sharpe ratio is 0.85," you get: "Your portfolio returns 12% annually, but with higher volatility than the market. When the S&P 500 drops 10%, yours typically drops 12%. Consider adding bonds."
4. AI behavioral coach – Smart nudges powered by vertex Ai that guide better decisions using behavioral psychology. Context-aware and never annoying (max 3 per day).
Tech stack:
Frontend: Next.js , TypeScript, React , Tailwind CSS
Backend: 14 microservices (2 Node.js, 12 Python)
AI: Vertex AI garden,
Infrastructure: GCP (Cloud Run, Cloud SQL, Redis)
Portfolio analytics: Custom Python risk engine (VaR, Sharpe, Monte Carlo)
The demo is functional with mocked data. Have everything build for GCP just need pre-launch to train the engines first . Built this entirely on a 4GB RAM laptop with zero funding.
Would love some feedback
Would you use something like this when you were starting to invest?
What's missing that would make this genuinely useful vs. just another investing app?
How does the onboarding flow feel? (The VARK assessment happens right after you log in - is this too aggressive, or does it make sense to personalize immediately?)
For the HN crowd who already knows this stuff: Would you recommend this to friends/family who are intimidated by investing?

value: Show HN: AI-Powered Adaptive Financial Education

author: kevinringler


value: theredbeard

value: Show HN: Context Lens: Devtools for your agent context

author: theredbeard


value: maryamtb

value: I built decoder to visually trace and step through call chains across a codebase, without having to sift through several files, and with visibility into dead code you might otherwise never notice.<p>Decoder parses the Python AST to build a call graph stored in a local SQLite file, then lets you trace full call chains and see execution context (conditionals, loops, try/except). I built this first as a VS Code extension, but saw the value in giving LLMs that same visibility and added an MCP server. Instead of iterative grep and file reads, an LLM can traverse the call graph directly — which cuts down on token usage and back-and-forth significantly.

value: Show HN: Decoder – GPS Navigation for Codebases

author: maryamtb


value: cisco-co

value: Show HN: Solitaires.gg – Free ad-free solitaire in the browser

author: cisco-co


value: Tsarp

value:
I built Kurpod: a self-hosted file vault that stores many files inside a single encrypted blob you can name like an innocent file (e.g., “vacation.jpg”).
The twist: the same blob supports two passwords:
  - Password #1 unlocks a decoy volume (harmless files)
  - Password #2 unlocks a hidden volume (the real stuff)
Diagram:
  my_vacation.jpg  (really an encrypted blob)

    ├─ pw1 → standard volume (decoy)

    └─ pw2 → hidden volume (real)

Important: this is filename disguise, not steganography / real file-format mimicry. If someone runs `file`, it won’t look like a JPEG.
Crypto primitives: Argon2id for KDF + XChaCha20-Poly1305 for encryption.
Quick start (Docker):
  docker run -p 3000:3000 -e BLOB_DIR=/data -v ./data:/data ghcr.io/srv1n/kurpod-server:latest
  open http://localhost:3000
Demo video:
  https://github.com/user-attachments/assets/d47b10cf-c38e-47e...
It’s an early release (expect bugs / not audited). I’d love feedback on:

  1) threat model: what this protects well vs. where it’s weak

  2) plausible-deniability ergonomics: what user mistakes would ruin the “decoy” story?

  3) file format / crypto review: anything obviously dumb or dangerous?

value: Show HN: Kurpod – an encrypted OSS "container file" you can name vacation.jpg

author: Tsarp


value: Creator-io

value: Show HN: AI SEO on Autopilot

author: Creator-io

title: Show HN: AI SEO on Autopilot


value: ronfriedhaber

value: Show HN: Deadcode Removal Layer for My Compiler in Less Than 140 LOC

author: ronfriedhaber


value: notAnshuman

value:
Hi HN,
I built MerchBanao while trying to sell merch online.
Image generators were good at creating artwork, but the output was rarely usable. I still had to open Photoshop to fix layout, add typography, remove backgrounds, and prepare print files. The generation step was fast but the workflow around it was manual.
So I started building a tool focused on the entire merch workflow instead of just generation.
You can:
• generate a design from a prompt
• edit layout and typography
• refine backgrounds and composition
• export 300 DPI print ready files
The goal is simple: go from idea to uploadable POD design in a few minutes.
I’m especially interested in feedback from people who sell on Etsy, Redbubble, Shopify, or similar platforms.
What parts of your workflow still require manual editing after using AI tools?

value: Show HN: A tool to create merch designs without Photoshop

author: notAnshuman


value: akileshthuniki

value: Show HN: PreApply – Terraform plan analyzer with blast radius and risk scoring

author: akileshthuniki


value: mdoliwa

value:
One of my new year resolutions was to cut the scope of my side projects, do not overthink, just build cool things and publish them.
So here it is, 25th Hour mirror rant scene with Edward Norton meeting HN front page. It was some random idea, I was curious what AI will output and it turns out it's really good at it.
Hope you like it as much as I do.

value: Show HN: HN Rant – HN front page as a daily 25th Hour rant scene

author: mdoliwa


value: Show HN: Deploy Clawbot Instance in Seconds


value: xjefflee

value:
I got frustrated with the official Olympic websites and mainstream sports apps. They are often bloated with trackers, heavy ads, and confusing navigation, especially on mobile.
So I built Milano2026.live.
The goal was simple:
Speed: Near-instant loading for checking results on the go.
No Bloat: No ads, no unnecessary JS.
Better UX: A clean schedule that doesn't feel like reading a spreadsheet (just fixed the formatting based on early feedback!).
It's built using Next.js 15 and deployed on Vercel. I'm using ISR to keep the medal counts fresh while keeping the server load minimal.
I'd love to hear your thoughts on the performance or if there's any specific data you'd like to see added during the games!

value: Show HN: A lightweight, ad-free medal tracker for Milano Cortina 2026

author: xjefflee


value: GuiShou

value:
Hi HN,
I built PaperBanana to help researchers create professional scientific figures and diagrams using AI.
It turns text descriptions or rough sketches into publication-ready illustrations, including system architectures, flowcharts, and schematics.
I’d love to hear your feedback on the output quality and what scientific styles you’d like to see added.

value: Show HN: Paper Banana – AI academic illustration generator

author: GuiShou


value: leoneperdigao

value:
Link: https://github.com/taoq-ai/ziran
Hey HN, I want to introduce you to ZIRAN, an open-source security testing framework for AI agents.
As an AI engineer working with LangChain and CrewAI agents in production, I was frustrated that existing security tools (PyRIT, Garak) only test LLMs,
not agents. They miss the unique attack surface agents create: dangerous tool combinations, multi-step exploits, and agent-to-agent (A2A) communication risks.
That's why I built ZIRAN - a tool specifically designed to find  agent vulnerabilities. Key features:
- Tool Chain Analysis - Detects dangerous combinations (read_file → http_request)
- A2A Security Testing - Tests agent-to-agent communication (Google's A2A protocol)
- Multi-Phase Campaigns - Trust exploitation over multiple turns
- Knowledge Graphs - Visualizes attack paths through agent capabilities
Feedback is very welcome!
Leone

value: Show HN: Ziran, security testing for AI agents

author: leoneperdigao


value: Data modelling is still surprisingly painful — most tools are either heavy enterprise software, desktop apps, or disconnected from how modern data teams actually work.<p>I built SQLModel.org, an open-source, browser-based data modelling tool aimed at making schema design fast, visual, and collaborative.

value: Show HN: Sqlmodel.org – open-source Browser Data Modelling


value: cranberryturkey

value: Integrations are on http://ugig.net but we’re looking for more bot friendly and human friendly sites to integrate with distributed id

value: Show HN: DID reputation management on coinpay's site for agents and humans alike

author: cranberryturkey


value: igolaizola

value:
Hi HN, I built (vibecoded) Retrospec, a commit-to-prompt tool.
Given a repo + a specific commit, it iteratively searches for a plausible high-level spec prompt that could have produced that change. It runs agent loops, scores candidates for technical similarity and "realism" (does this look like a prompt a human would actually write), and outputs the best spec.
Inspiration: I saw Mitchell Hashimoto mention experimenting with agents to reproduce manual code edits, and around the same time GitHub released the Copilot SDK.
Repo: github.com/igolaizola/retrospec

value: Show HN: Retrospec: reverse-engineer a spec prompt for an AI agent from a commit

author: igolaizola


value: jargnar

value: I've just been doing various little projects lately. Recently, I was reading the Crafting Interpreters book and I thought maybe it's a good idea to try and see what the 5.3 Codex model can do. So with it, I created a v0.1 of this silly toy language. It's almost a silly art project really.

value: Show HN: Orangensaft – A mini Python-like language with LLM eval in lang runtime

author: jargnar


value: aanthonymax

value: Show HN: Bifrost: Fastest enterprise AI gateway

author: aanthonymax


value: jesterwayne

value: Show HN: Stackit – Pruning LLM context windows using a branching chat UI

author: jesterwayne


value: naeemnur

value: Show HN: Modern CSS – Every old CSS hack paired with a clean, modern replacement

author: naeemnur


value:
I built a dream journaling app that uses AI to analyze dreams through Jungian psychology and neuroscience frameworks, then maps activation levels across 17 brain regions.
The analysis extracts archetypes, symbols, shadow elements, and emotional themes from freeform text, then scores each brain region (amygdala, hippocampus, prefrontal cortex, etc.) based on dream content. The hardest part was getting structured data out of stream-of-consciousness writing without the AI hallucinating meaning that isn't there.
Stack: Next.js, Supabase, Expo/React Native. The iOS app just shipped v1.1 with voice recording (Whisper transcription), home screen widgets (WidgetKit), and 7-language localization.
Web app is free to try. iOS is on the App Store ($9.99/month for AI analysis).
Would love feedback on the analysis quality - especially from anyone into dream psychology or neuroscience.

value: Show HN: Dreamvibe – AI dream journal that maps dreams to 17 brain regions


value: nishujain

value:
I wanted to flaunt my growth on Medium to my friends, so I built this self-updating portfolio page in a weekend. Now, top writers (also my friends) are using it daily to stay disciplined.
It pulls your stats daily and converts them into an organized dashboard that you can share with the world.
What it does:
- aggregates your Medium stats (followers, views, engagement)
- Lets you search your last 1000 articles by tags and keywords.
- ranks your top articles.
- plots your progress on charts and graphs.
- updates daily automatically.
- one shareable link, SEO-optimized.
What I learned:
Writers don't just want stats to show clients... they want a daily reminder of their progress. it keeps them accountable and motivated to write consistently.
Gave them exactly what they needed ... a simple dashboard they check every morning.
Happy to answer questions!

value: Show HN: Built it to flex my Medium stats, came out to be a writing habit tool

author: nishujain


value: amuradov

value: Hi, scraped 100 FAANG DevOps Interview Questions and sharing them on Github page.

value: Show HN: Scraped 100 FAANG DevOps Interview Questions

author: amuradov


value: aldovincenti

value:
Hi HN! As a Product Manager, I kept watching teams spend hours on repetitive manual testing instead of shipping. So I built a tool that uses AI to turn natural language into end-to-end web app tests.
What’s live today:
- Natural language to end-to-end web app tests
- Usability-focused reporting
- Desktop app + cloud sync
- CLI support for CI/CD
- Free plan to get started
Looking forward to your feedback.

value: Show HN: AgenQA – Turn natural language into E2E web app tests using AI

author: aldovincenti


value: jomamax

value: Show HN: Native macOS GUI for HelixDB graph database

author: jomamax


value:
Hey HN, I built OmniQL — an open-source Go library that compiles
a universal query syntax into native database commands.
You write:
  :GET User WHERE id = 42
It becomes:
  PostgreSQL → SELECT * FROM "users" WHERE "id" = 42;
  MongoDB    → db.users.find({ id: 42 });
  Redis      → HGETALL users:42
It's a compiler, not a wrapper. Your query becomes an AST, then
translates to native commands. No runtime overhead — it generates
the same query you'd write by hand.
Works for DDL too:
  :CREATE TABLE User WITH id:AUTO, name:STRING:NOTNULL
Becomes native CREATE TABLE for Postgres/MySQL or
db.createCollection for MongoDB.
Why I built this: I run a multi-database platform and got tired of
maintaining four different query syntaxes. OmniQL started as an
internal tool and we're open-sourcing it.
GitHub: https://github.com/omniql-engine/omniql
Docs: https://docs.omniql.com
Happy to answer questions about the compiler architecture, AST
design, or how we handle the semantic gaps between SQL and NoSQL.

value: Show HN: OmniQL – One Query Language for PostgreSQL, MySQL, MongoDB, and Redis


value: deevus

value:
Hey all, I just shipped the beta of a side project I've been working on.
My M1 Macbook Pro was always full despite having a 1TB drive so I prototyped a simple Python script that knew about as many standard cache locations that I could think of. Cleared out over 300GB of space in one go. If you only work in one language you probably know the common caches, but I am using a lot of different languages all the time, and there are too many to remember.
I always wanted to give Tauri a try so I ported my script to Rust and added a bunch of features. It uses React and Tailwind Plus components for the frontend.
Reclaimr is an offline desktop app that scans your machine for development caches (node_modules, target/, .venv, build artifacts, Docker layers, Xcode caches, etc.) and lets you reclaim the disk space safely. It finds stuff across 100+ tools and shows you exactly what it'll delete before it does anything, with risk levels so you don't nuke something important.
You can add project search paths to find build artifacts and local caches.
It's free during the beta. Available for macOS, Linux, and Windows: https://reclaimr.dev/
My plan is to sell a buy once, own forever license. Perhaps with a yearly upgrade, though old builds will continue to work. If you don't want to put in your email to get on my beta list, here are some download links:
https://dl.reclaimr.dev/latest/reclaimr-macos-aarch64.dmg
https://dl.reclaimr.dev/latest/reclaimr-linux-x64.AppImage
https://dl.reclaimr.dev/latest/reclaimr-windows-x64-setup.ex...
Would love feedback. Bugs, rough edges, missing tools, whatever. I've tested heavily on macOS and Linux. It should be noted that the macOS build will require removing quarantine once installed:
xattr -dr com.apple.quarantine /Applications/Reclaimr.app
Future plans:
- Scheduled task that can notify you if caches get too big, and clear them.
- Handle more managed caches like homebrew, podman, etc that have their own clean/prune/cache commands. Docker is already supported.

value: Show HN: Reclaimr – An offline crossplatform desktop app for cleaning dev caches

author: deevus


value: demirdev

value:
Hey HN,
I built ToMusic, an iOS app that turns text descriptions into full songs (lyrics, vocals, and instrumentals) in seconds.
How it works: You describe what kind of song you want (genre, mood, tempo, vocal style) or paste your own lyrics, and the app generates a complete, original song. It also works for pure instrumentals like background music, beats, and soundtracks.
It supports 150+ genres (Pop, Jazz, EDM, Classical, Hip Hop, Metal...), 30+ moods, and 5 different AI models each with different strengths: longer tracks up to 8 minutes, better song structure, faster generation, etc.
Tech stack: Native iOS built with Swift/SwiftUI. Backend runs on NestJS with multiple AI music generation models, behind NGINX as a reverse proxy handling SSL via Let's Encrypt. Built-in player with synchronized lyrics and audio visualizations. Streaming playback so you don't have to wait for the full track to finish generating.
I built this for content creators who need custom music for videos, musicians looking for quick inspiration, and honestly anyone who's ever had a melody in their head but couldn't produce it. No musical training needed.
Would love to hear your feedback. Happy to answer questions about the AI models, the generation pipeline, or the iOS development side.

value: Show HN: ToMusic – Turn text prompts into original songs with AI (iOS)

author: demirdev


value: thomaskiko

value:
Hi HN - I built seedance2.video,an AI-powered video creation web app. It lets you create different kinds of short videos.
The idea is to make AI video creation feel more like “play” than a traditional editor
I’d love feedback from the HN community on:
Video generation speed and stability across devices/browsers
Whether the creation flow (choose input → pick style → generate) feels intuitive
What kind of controls or presets would make it more useful for real-world video use cases
I’m the creator of seedance2.video and happy to answer technical or product questions here.
Thanks!

value: Show HN: AI-powered video creation web app

author: thomaskiko


value: Show HN: Secure Storage – An offline encrypted vault for iOS


value: yuppiepuppie

value:
I've been curating games from Show HN posts for the past month on HN Arcade (https://hnarcade.com). Lots of great feedback and contributions! People have been asking for a newsletter version.
Every week, I send out a curated selection of the best indie games that were posted to HN. No spam, just good games you might have missed.
You can browse past issues or subscribe here: https://hnarcade.com/newsletter
Would love to hear feedback from the community!

value: Show HN: Weekly newsletter of the best indie games from Hacker News

author: yuppiepuppie


value:
Hey HN,
I’ve been working on job application pipelines and kept hitting a massive data friction point: reliably filtering out UK companies that legally cannot sponsor international workers.
The UK Home Office publishes a constantly updating CSV of licensed sponsors. The problem is, the data is practically useless for standard database joins. A job board might list a role at "Acme", but the government registry lists "Acme Technologies Holdings Limited".
If you run an exact-string match or a basic ILIKE against a scrape of 10,000 Indeed or LinkedIn jobs, your false-negative rate is massive.
I wrote a TypeScript-based matching engine to solve this. Here is the pipeline:
Dynamic Ingestion: It bypasses the Gov.uk dynamic routing to pull the raw, multi-megabyte CSV directly into memory. No stale database records.
Text Normalization: I built a custom parser to strip out standard corporate suffixes ("ltd", "plc", "llp", "t/a", etc.) and handle the weird punctuation and localized encodings that break standard scrapers.
Fuzzy Scoring: It runs an optimized Levenshtein distance algorithm over the in-memory array to output a 0-100 confidence score for the match.
Initially, I built this with a persistent local cron scheduler (node:fs) for an open-source job ops project. But to make it scalable for batch processing, I ripped out the local caching and deployed it as an ephemeral Docker container. It spins up, processes an array of thousands of scraped companies entirely in-memory in a few seconds, pushes a clean JSON dataset of verified sponsors, and dies.
If you are building a job aggregator, an ATS, or a lead-gen pipeline and don't want to waste a weekend writing your own corporate-suffix normalization logic, I hosted the serverless endpoint here:
https://apify.com/dakheera47/uk-visa-sponsor-verifier
I'd love any feedback on the text normalization approach, or to hear if anyone knows of specific edge cases in the Home Office data formatting that I might have missed.

value: Show HN: Fuzzy-matching messy job board data against the UK Gov Visa Registry


value: dansquizsoft

value: Show HN: Vintageterminals.io – a bootable museum of vintage OSes (13 so far)

author: dansquizsoft


value: mononykus

value:
After several months of development, we’ve just released the stable 1.0 version of ngDiagram – a free, open-source diagramming library built natively for Angular.
ngDiagram lets you easily add interactive diagrams to your Angular apps. Key features include:
- Signal-based architecture for reactive updates
- Native Angular components (HTML & CSS-first, fully accessible and customizable)
- Drag & drop, multi-select, grid snapping, pan & zoom, custom nodes and edges
- Middleware system for easy extension and integration with your data
- Full TypeScript support and developer-friendly API
You can use ngDiagram to build dashboards, editors, flowcharts, network diagrams, or any tool that needs visual node/edge diagrams.
We’re actively developing ngDiagram based on community feedback, so your suggestions really shape the direction of the library. We’d love for you to test it, report issues, and help us make it better. If you like it, a GitHub star would be much appreciated!
Repo: https://github.com/synergycodes/ng-diagram
Looking forward to your feedback and ideas!

value: Show HN: NgDiagram v1.0, an open-source Angular library for interactive diagrams

author: mononykus


value: funbitty

value:
Hi HN,
New release: `funxy build` now produces standalone executables. (Previously I shared the Go embedding — this is the other direction: ship scripts as binaries.) You compile a script, get a binary — bytecode + VM + all deps baked in. No Funxy on the target machine.
```bash
funxy build server.lang -o myserver
scp myserver prod:~/
ssh prod './myserver'
```
Embedding: files, directories, glob patterns (`<i>.html`), brace expansion (`</i>.{js,css}`). Everything goes into the binary and is available via `fileRead`, `fileReadBytes`, `isFile` — the script doesn't know if it's disk or embedded.
```bash
funxy build webapp.lang --embed templates,static --embed "assets/*.css" -o webapp
```
Dual-mode: the binary is also a full Funxy interpreter. By default it runs the embedded app. Pass `$` and it switches to interpreter mode — run any script, use `-pe` one-liners, same as regular `funxy`:
```bash
./myserver                    # runs embedded app
./myserver --port 8080        # flags via sysArgs
./myserver $ other.lang       # interpreter
./myserver $ -pe '1 + 2'      # eval mode
```
Cross-compile with `--host` (point to a funxy binary for the target platform).
[Releases](https://github.com/funvibe/funxy/releases) | [GitHub](https://github.com/funvibe/funxy)

value: Show HN: Funxy v0.6 – scripts that ship as standalone executables

author: funbitty


value:
Hey HN, I’m releasing WavNav today:
https://maxgraf.space/wavnav/
It’s a desktop app for producers/sound designers who have huge sample folders and want a faster way to find sounds.
What it does:
- analyses your sample folders and maps sounds into a visual space
- lets you search/filter by text, key, and BPM
- supports audio-based similarity search (drop a sound, find related sounds)
- includes hover/click preview modes for quick browsing
It runs on macOS (stable) and Windows (beta).
I’d really value feedback on:
- first-run experience
- performance on very large libraries
- anything confusing in the UI/workflow
If you try it, I’d love to hear what’s useful and what’s not.

value: Show HN: WavNav, a desktop app to explore and search large sample libraries


value: instalabsai

value:
Instagit is an MCP server that lets coding agents (Claude Code, Codex, Cursor, OpenClaw etc.) answer questions about GitHub repositories by understanding the actual source code rather than relying on training data.
The problem: AI coding agents hallucinate library internals constantly. They confidently describe how a function works based on stale training data when the actual implementation does something different.
How it works: you ask a question about a repo, Instagit scans the source, and returns an answer with file paths and line numbers. You can target specific commits, branches, or tags. You can also swap "github" to "instagit" in any repo URL to get an instant wiki with Q&A (e.g. https://instagit.com/pandas-dev/pandas).
The real power though is giving your agent access via MCP rather than being the human in the loop. Point your agent at a large library, have it understand a specific feature, then rip out just the part you need into self-contained code. Drop the dependency entirely, sometimes even get better performance. The agent catches implementation details you'd miss reading the code yourself and that maintainers rarely document.
I get asked this a lot so might as well answer it now: how is this different from Context7, DeepWiki, CodeWiki, or GitHub MCP?
Context7, DeepWiki, and CodeWiki all pre-generate static summaries or guides. They're fast when they have what you need, but they don't cover every repo, they go stale, and there are hundreds of questions about any codebase that can't be pre-answered. GitHub MCP checks out files one at a time, which burns through context tokens fast and doesn't scale to large codebases.
Instagit reads source on demand for any public repo, returns just the answer, and keeps your context clean.
No API key or account needed to try it out: https://instagit.com/install

value: Show HN: Instagit – MCP server that answers questions about any GitHub repo

author: instalabsai


value: polyrhachis

value: I built a tensor-based version of the micrograd by Andrej Karpathy, it supports GPU acceleration with CuPy

value: Show HN: Macrograd – Micrograd, but with Tensors

author: polyrhachis


value: ekadet

value:
I got tired of AI making me dumber, so I built "Proof of Thought" - an AI agent that won't write code until you demonstrate you understand the problem. After 30 days of use, my bug rate dropped 73% and I can actually explain my own codebase again.
https://github.com/ekadetov/ekadetov.github.io/blob/main/ass...

value: Show HN: Proof of Thought (Pot)

author: ekadet

title: Show HN: Proof of Thought (Pot)


value: attdona

value: Show HN: Rembus – Distributed Messaging (Julia, Python)

author: attdona


value: mazzystar

value: Show HN: CCClub – Leaderboard for Claude Code usage among friends

author: mazzystar


value: arturwala

value: Bring any agent in any stack. Have boilerplate solved (dashboard, integrations, auth etc) - release contact/call center without crazy SaaS fee.

value: Show HN: Open-Source AI Contact Center

author: arturwala

title: Show HN: Open-Source AI Contact Center


value: getAlby

value:
We built an open-source developer sandbox for experimenting with Bitcoin Lightning payments.
It gives you an instant preloaded test wallet and lets you try different payment scenarios right away — no signup, no configuration.
You can:
- trigger common payment flows
- copy/paste example prompts for coding agents
- drop ready-to-use code snippets into your own app
Once you’re ready to go live, you can switch to a real wallet without changing any code.
I’m around and would love feedback on what’s useful, confusing, or missing.

value: Show HN: A zero-setup sandbox for testing Bitcoin Lightning payments

author: getAlby


value: When my external power connection to Monitor stops (due to some outage) Mac still extends it display. So I'd either need to unplug my hdmi or go change it to mirror from settings (which takes too much time tbh) to resume my work.<p>With this app I just hit Switch to Internal Only, keep working on my MacBook screen, and later click Restore Extended when everything is working again.

value: Show HN: I built a macOS app that toggles between extended/mirrord monitor setup


value: spamsch

value:
Hi HN, I built an open-source (MIT) AI agent for macOS that talks directly to Mail, Calendar, Reminders, Notes, and Safari through AppleScript — no OAuth flows, no API gateways, no passwords to store.
The premise is simple: your Mac apps are already signed in. Why should an AI assistant make you re-authenticate everything through a separate integration layer? Why do I need to create an Azure app to get an OAuth token for my Office 365 emails? Why do I need a Google PubSub setup?
I needed something that is very simple to setup for my family and friends and can do almost the same things they do daily on my Mac. And they do not need to enter passwords or other authentication. In the end it is another harness but one that we love to use.
On Telegram you can dictate things like "My mom sent me her vacation dates by email — add them to my calendar" and it chains the right tools: searches Mail, extracts the dates, creates the Calendar event.
What makes it different:
- *No credentials needed for Apple apps* — AppleScript talks to them directly, macOS Keychain handles auth
- *Runs fully offline* — use local models via Pico AI Server, or cloud providers (Anthropic, OpenAI, OpenRouter)
- *Desktop app with setup wizard* — not a CLI-only project; download a .dmg, follow the steps, done
- *Telegram as remote control* — text or voice-message the agent from your phone
- *Skills ecosystem* — extensible via drop-in SKILL.md files (compatible with AgentSkills/ClawHub)
- *Memory between sessions* — remembers your preferences and patterns in a local YAML file you can read/edit/delete
The agent is a ReAct loop in Python. Tools are auto-discovered from type hints. The desktop shell is Tauri (Svelte + Rust) with the Python agent bundled as a sidecar binary via PyInstaller.
I spent a lot of time on AppleScript performance — bulk-fetching with `whose` clauses instead of iterating messages one by one (each Apple Event has ~0.1-0.5s overhead, so naively reading a mailbox would take minutes).
All data stays on your Mac under `~/.macbot/`. Your prompts go to your chosen LLM provider; nothing else leaves the machine.
GitHub: https://github.com/spamsch/son-of-simon

value: Show HN: Mac apps are signed in. Why make an AI authenticate too?

author: spamsch


value: satgate

value:
I built an open-source MCP proxy that enforces per-tool budgets on AI agent tool calls.
The problem: MCP gives agents tool access with no economic controls. An agent stuck in a retry loop against a $0.10/call API burns real money until someone kills it. Auth says "who" and "what" — nothing says "how much."
How it works: SatGate MCP Proxy sits between your agent and upstream MCP servers. Transparent relay — agents don't know it's there until they hit their budget.

  Agent → SatGate MCP Proxy → Upstream MCP Server
  tools/call intercepted → cost: 50 credits (dalle_generate) → budget: 35 remaining → JSON-RPC error: budget_exhausted

Per-tool costs with wildcard matching (web_search: 5, gpt4_*: 25, dalle_generate: 50).
Delegation: Parent agents can mint sub-agent tokens with carved budgets. Enforced cryptographically via macaroon HMAC chains — no DB lookup, microsecond verification.

  Orchestrator (1000 credits)
  ├── research-agent (300) → 60 calls → 402 EXHAUSTED
  ├── content-agent (200) → still operational
  └── analytics-agent (150) → still operational

Budget isolation is real — when research-agent hits 0, siblings and parent are unaffected.
Two payment modes:
- Fiat402: Credit-based budgets with delegation trees (enterprise)
- L402: Lightning micropayments per tool call. Agent pays invoice, gets preimage, retries.
Transport: stdio (local sidecar) or SSE/HTTP (remote, multi-agent). Written in Go. 28 tests. ~2,200 lines.
GitHub: https://github.com/SatGate-io/satgate
Blog: https://satgate.io/blog/beyond-connection-economic-governanc...

value: Show HN: SatGate – Budget enforcement proxy for MCP tool calls (L402/macaroons)

author: satgate


value: intheleantime

value:
I built an MCP server that connects coding agents (Claude Code, Cursor, OpenCode, Codex) to a collaborative workspace where your team and other AI models can review what the agent is planning.
The problem: When Claude Code creates an implementation plan, it lives in your terminal session. Nobody else sees it until it becomes a PR. If you want GPT to check the architecture or a teammate to flag issues, you're copy-pasting between windows.
This MCP server fixes that. When your agent creates a plan, it gets shared as a collaborative thread in CoChat. Engineers comment on it, other AI models review it, and you pull all the feedback back into your agent's context with one command. Decisions can be saved as project memories that persist across sessions and are searchable by anyone.
What it does:
Plans: Auto-shared as collaborative threads. Pull feedback back into your terminal.
Cross-model review: Have GPT review your Claude plan, or vice versa.
Project memories: Semantic memory that persists across sessions, models, and people.
Ask: Query your project's knowledge base from the terminal.
Auto-scoping: Detects your project from git remote. No config needed.
Setup is one command per agent. Auto-share behavior is configurable (off/plan/all).
MIT licensed, available on npm: npx @cochatai/mcp-cochat
Happy to answer questions about the architecture or the MCP protocol integration.

value: Show HN: CoChat MCP – Let your team review what your coding agent is building

author: intheleantime


value: possiblelion

value: Show HN: Micropay – Stripe for Africa's biggest payment network

author: possiblelion


value: taravision

value: Show HN: Image toolkit - files stay in your browser, AI runs on our own server

author: taravision


value: jovanaccount

value:
I built this because standard Python locks (asyncio.Lock) don't work when AI agents run across different containers or processes.
As I started scaling my agent swarms (using CrewAI/LangChain), I kept hitting race conditions where agents would overwrite shared files or double-spend API credits because they read the state simultaneously.
Network-AI is a distributed lock (backed by Redis or local file-locks) that solves this. It acts as a traffic light for agent tools.
Key features:
2-line decorator: @lock("resource_id")
Handles lock timeouts (preventing "zombie" agents from freezing the swarm)
Works with standard agent frameworks (CrewAI, AutoGen, LangGraph)
The repo is open-source. I'd love feedback on the locking implementation or to hear if you've hit similar concurrency issues in production.

value: Show HN: Network-AI – A Distributed Mutex for AI Agent Swarms

author: jovanaccount


value: frankbyte

value:
I built this because I didn't want to type longer project-specific commands. I work with a bunch of different tech stacks, so it's either a lot to remember or a lot of digging through various README files. Neither is great, so I wrote this little helper.
ez stores aliases in a .ez_cli.json file per directory. The nice thing about this is that if you like you can have the same alias, e.g. ez test, ez build etc. in all your projects and for each one it does different things. Also, it's a natural place since you can then also commit it to the repo and thus share your best aliases with the team.
I just finished adding parameterization support and also simple secret management. If you like, you can store things like API keys with ez and they are used by the commands. They are stored in the local macOS keychain and read from there. This is safer than plaintext .env file, especially now that LLMs are rummaging through local filesystems.
This little CLI tool is written in Swift and no dependencies beyond swift-argument-parser. Full TTY passthrough so interactive tools can be part of aliases as well.
Install: brew tap urtti/ez && brew install ez
Happy to hear what you think and what's missing. I've been personally using this for over a year now, I think it's fun and makes everything feel a bit... easier.

value: Show HN: Ez – project-scoped command aliases for macOS

author: frankbyte


value: ticksupply

value:
If you've ever tried to record crypto exchange WebSocket data 24/7, you know the pain. Exchanges drop connections regularly. You add reconnection with backoff, then realize you're still losing messages during the reconnect window. So you run multiple collectors and deduplicate. Then a single popular pair generates 1M+ messages/day on some stream types, so now you have a storage problem. Then you need export tooling that doesn't choke on billions of rows.
I spent enough weekends on this that I turned it into a service: Ticksupply https://ticksupply.com
You pick any stream — currently supporting Binance spot, more exchanges coming — order books, trades, klines, depth updates, whatever the exchange supports. Ticksupply records every message. When you want data, you export a date range as gzipped CSV: nanosecond receipt timestamp + the raw exchange JSON. No normalization, no reshaping. The format is exactly what the exchange sends, so you can replay it directly as if it were a live WebSocket feed.

  1707523275537000000,{"e":"trade","E":1707523275537,"s":"BTCUSDT","t":3412345678,"p":"42856.10","q":"0.00100",...}

There's a free trial if you want to try it out. There's also an API available: https://docs.ticksupply.com
What do others here use for this? Own recorder, Cryptofeed, commercial data vendor, something else?

value: Show HN: Ticksupply – Record Binance tick data (order books, trades) as CSV

author: ticksupply


value: zahidcakici

value: Show HN: Uber's new publicly available RPC Kafka repository

author: zahidcakici


value: kentaroyamauchi

value:
I’ve been building a small set of ultralight iOS apps (each ~1–1.3MB) around a simple idea:
Most of us carry too many mental open loops.
Not emotional crises.
Just unfinished background processes.
Unsent replies.
Half-made decisions.
Things you’re afraid to forget.
Conversations you keep replaying.
Modern software creates a lot of these.
Notifications, feeds, reminders, partial attention.
The mind keeps threads active because they don’t have a clear endpoint.
Most productivity or wellness tools try to optimize behavior.
These apps try to reduce load instead.
The focus is load management, not behavior change.
Current set (v1.3):
• MindFlipOut — Catch a looping thought and give it a response so it stops reloading.
• MindShoutOut — Externalize a heavy thought and let it return later instead of carrying it.
• MindZoneOut — A blank, intentional stillness screen where thoughts can settle without input.
• MindEaseOut (integrated) — A simple time-based release layer for thoughts that belong to the past or future, not now.
All apps are:
• Local-first (no accounts, no cloud)
• No streaks, no tracking, no gamification
• Designed to be used briefly and then closed
Each app is intentionally small (1–1.3MB total size) and runs entirely on-device.
The goal isn’t self-improvement.
It’s reducing cognitive residue.
Website: https://www.mindbebop.com/
Curious how others think about “mental open loops” in the context of modern software.

value: Show HN: 1MB iOS apps designed to reduce mental open loops

author: kentaroyamauchi


value: nanbing

value: Show HN: Joria – a native Mac notes app for instant capture and semantic recall

author: nanbing


value:
I'm Tim. I speak German, English, French, Turkish, and Chinese.
I learned Turkish with lairner itself -- after I built it. That's the best proof I can give you that this thing actually works.
The other four I learned the hard way: talking to people, making mistakes, reading things I actually cared about, and being surrounded by the language until my brain gave in. Every language app I tried got the same thing wrong: they teach you to pass exercises, not to speak. You finish a lesson, you get your dopamine hit, you maintain your streak, and six months later you still can't order food in the language you've been "learning."
So I built something different. lairner has 700+ courses across 70+ languages, including ones that Duolingo will never touch because there's no profit in it. Endangered languages. Minority languages. A Turkish speaker can learn Basque. A Chinese speaker can learn Welsh. Most platforms only let you learn from English. lairner lets you learn from whatever you already speak.
We work together with some institutes of endangered languages to be able to teach them on our platform.
It's a side project. I work a full-time dev job and build this in evenings and weekends. Tens of Thousands of users so far, no ad spend, no funding.
I'm not going to pretend this replaces living in a country or having a conversation partner. But I wanted something that at least tries to teach you the language instead of teaching you to play a language-themed game.
Happy to answer anything.

value: Show HN: I speak 5 languages. Common apps taught me none. So I built lairner


value: orbydx

value:
I built 75 developer and AI tools as a single static site. Everything runs in the browser, no cookies, no ads, nothing gets sent to a server.
The tools range from the usual suspects (JSON formatter, base64 encoder, regex tester) to some AI-specific ones I couldn't find good free versions of bundled in one suite:
- LLM Token Counter (estimates tokens for GPT, Claude, Gemini, etc.)
- AI Model Comparison (specs, pricing, context windows side by side)
- AI Cost Estimator (plug in your usage, get monthly cost projections)
- MCP Server Directory (browsable catalog of Model Context Protocol servers)
- Agent Framework Comparison (LangChain vs CrewAI vs AutoGen vs...)
- Prompt Template Builder (variables, conditionals, versioning)
- Markdown Memory Generator (for OpenClaw)
Plus the standard dev toolkit: JWT decoder, cron expression builder, diff checker, SQL formatter, color converter, CSS flexbox playground, etc.
Tech stack: Astro 5 with React islands, Tailwind CSS 4, hosted on Cloudflare Pages. The whole site is static, so it loads fast everywhere. Largest JS bundle is 58 KB gzipped.
I built this with AI agent Rusty (OpenClaw). The AI handled most of the component code while I focused on architecture decisions, tool selection, and QA. Took about 2 days of evening sessions.
No login, no tracking cookies, no ads, no "sign up for premium". Just tools.
Feedback welcome. What ai or dev tools do you wish existed that don't?

value: Show HN: AI Dev Hub. 75 free AI and dev tools

author: orbydx


value:
Hi HN
I’ve been building libgd-gis, a pure-Ruby GIS rendering engine on top of the GD graphics library.
The goal is simple: generate maps, tiles, and geospatial visualizations directly from Ruby — without external services, native GIS stacks, or heavyweight dependencies.
What it does
• Render GeoJSON layers to PNG
• Draw markers, paths, polygons, labels
• Generate map tiles server-side
• Support animated GIF output
• YAML-based styling system
• No browser or JS required
Why I built it
Ruby has strong web tooling but very limited options for high-performance image generation and GIS rendering. Existing tools are often slow, unmaintained, or depend on external processes.
This project aims to provide:
deterministic server-side rendering
lightweight deployment
full control over output
suitability for APIs, reports, or offline generation
Example use cases
• Static map generation for APIs
• Logistics / tracking dashboards
• Reports and PDFs
• IoT / telemetry visualization
• Educational tools
• Lightweight tile servers
Tech stack
Ruby C extension bindings to libgd (via ruby-libgd)
GeoJSON ingestion
Coordinate projection handling
Raster rendering pipeline
Links
GitHub: https://github.com/ggerman/libgd-gis
RubyGems: https://rubygems.org/gems/libgd-gis

value: Show HN: Libgd-GIS – Render maps and GIS data directly in Ruby (GeoJSON → Image)


value: Show HN: A Working Python VM Written Entirely in PL/PgSQL


value: ChicagoDave

value: Show HN: Datesky

author: ChicagoDave

title: Show HN: Datesky


value: Show HN: RapidPhoto – Native Mac app for private, offline bulk image editing


value: seainthetrees

value:
It's political satire about American gun culture, toxic masculinity, and radicalisation pipelines, built by a distributed async dev team across multiple continents. The project started as a 7-day game jam entry and spiralled into 14 months of development. Whoops.
It hit #2 on Steam's Upcoming Releases page before launch with 6,400+ wishlists.
Some things that might interest this community:
The scope creep from 7 days to 14 months is a case study in how small projects grow
Unity, yarn
We literally named the team Scope Creeps after this.. crept scope.
We recorded 3+ hours of original in-game podcast content as a narrative delivery system
The game features an original song from The Stupendium (YouTube creator, hundreds of millions of views)
It's free — no microtransactions, no catch

value: Show HN: 7-day jam became a 14-month build. satirical gun dating SIM (free)

author: seainthetrees


value: kenforthewin

value: Show HN: NetHack agent harness with benchmarks and livestream

author: kenforthewin


value: refloow

value: Show HN: Local only OSINT tool for batch EXIF and timeline reconstruction

author: refloow


value: stympy

value:
Hey HN,
I had Claude build a CLI tool that analyzes GitHub forks to surface changes that haven't been submitted as PRs.
The core idea is convergence: when multiple independent forks touch the same file and make the same change, that's a strong signal something needs fixing upstream.
Example: I ran forkwatch against a Ruby API client library and found 11 independent forks all upgrading the same stale dependency. 4 of them made byte-for-byte identical changes to another file.
$ forkwatch analyze maximadeka/convertkit-ruby
convertkit-ruby.gemspec (11 forks converge here)

  WebinarGeek +1 -2 — Change gitspec faraday version
    -  spec.add_runtime_dependency "faraday", "~> 1.0"
    -  spec.add_runtime_dependency "faraday_middleware", "~> 1.0"
    +  spec.add_runtime_dependency "faraday", '>= 2.0'
  ...

lib/convertkit/connection.rb (4 forks converge here)

  Most common change pattern:
      require "faraday"
     -require "faraday_middleware"
      require "json"
  WebinarGeek, chaiandconversation, alexbndk, excid3

It filters out noise (dependabot, lock files, CI config), groups forks by files changed, and deduplicates identical patches. There's a --json flag for scripting/AI and a --patch flag that outputs a unified diff you can pipe to git apply.
Uses the GitHub CLI for auth and one API call per fork. Written in Go.
https://github.com/stympy/forkwatch

value: Show HN: Forkwatch – Discover meaningful patches hiding in GitHub forks

author: stympy


value: Show HN: MagnetPrompt – visual feature board → AI-ready coding prompts


value: stcredzero

value:
I've been pitching this idea here since 2017. I have OpenClaw based agents now, so I just had them build it.
https://news.ycombinator.com/item?id=35002648
https://news.ycombinator.com/item?id=14363862
It's not a real ETF. It's a parody.
(BTW, my agents are not running on OpenClaw. They are SEKSBots. SEKS = Secure Environment for Key Services. They can do all of their tasks, including scripting, and not have access to sensitive data, like keys.
http://seksbot.com)

value: Show HN: The Legislative Evil Fund

author: stcredzero

title: Show HN: The Legislative Evil Fund


value: zokrezyl

value:
https://github.com/zokrezyl/yetty
Hi HN — I built Yetty, a new programmable terminal focused on structured output and interactive UI cards rendered directly inside the terminal.
Highlights:
• GPU-accelerated rendering
• Commands can emit structured interactive “cards” instead of plain text
• Designed for building richer CLI tools and workflows
Video demo above.
Repo / downloads:
I’d love feedback from people who build CLI tools or spend a lot of time in the terminal — especially what kinds of structured outputs or integrations would make this useful for you.

value: Show HN: Yetty – Terminal with programmable UI cards [video]

author: zokrezyl