value: payamnj

value:
I built django-email-learning, an open-source Django app for running e-learning platforms where course content is delivered entirely via email rather than a web portal.
Inspired by Darsnameh, an email-learning service that shut down in 2017. The core motivation is accessibility — email works in low-bandwidth environments, on any device, and is more resilient to censorship than web platforms.
It's in beta (v0.2.54). To test the concept in production I built InboxAcademy.io on top of it, which currently hosts a course on using the package itself.
Would love feedback on the architecture or general approach.

value: Show HN: Django app for email-based learning platforms

author: payamnj


value: insany

value:
Built a small tool that sets up VLESS + REALITY over SSH in one command. It handles: - full Xray setup - client configs (vless URI, sing-box, mihomo) - rollback if something breaks
Example: ./irit.sh --mode setup --host <ip>
Would love feedback.) https://github.com/anonymmized/Irit

value: Show HN: I built a tool to bootstrap VLESS and REALITY over SSH (with rollback)

author: insany


value: pityrate

value: Show HN: Agentic Web :handshake: Human Web

author: pityrate


value: Show HN: DecisionNode – shared structured memory for all AI coding tools via MCP


value: pnhoang

value: Show HN: YouTube Live Spam Blocker

author: pnhoang

title: Show HN: YouTube Live Spam Blocker


value: hedayet

value:
```<p>import { formatRelative } from '@appents/human-time';<p>const past = new Date(Date.now() - 5 * 60_000);
formatRelative(past); // "5m ago"<p>const future = new Date(Date.now() + 3 * 3_600_000);
formatRelative(future); // "In 3h"<p>```<p>And<p>```<p>import { formatRelativeLong } from '@appents/human-time';<p>const past = new Date(Date.now() - 5 * 60_000);
formatRelativeLong(past); // "5 minutes ago"<p>```

value: Show HN: A 1KB zero-dependency relative time formatter for UI systems

author: hedayet


value: tomflitt

value:
Hello!
I've been taking some notes to prepare the AWS DVA-C02 exam and decided to feed them to Claude to make them more readable. I was quite impressed by the result so I though I might share it for free.
I had fun building this little website and I'd love to get some feedback and improve the content.

value: Show HN: A free study guide for the AWS DVA-C02, built from my own exam notes

author: tomflitt


value: atleastoptimal

value:
This is an interactive version of the mass-vs-radius chart from Lineweaver & Patel's "All Objects and Some Questions" (2023). It plots the scale of everything in the observable universe on a single log-log chart
This was an experiment in extending the utility of a science infographic via hyperlinks, navigability and tooltips that detail the meaning of specific parts.
There are likely still spatial/numerical issues, happy to take corrections.
The original paper: https://pubs.aip.org/aapt/ajp/article/91/10/819/2911822/All-...

value: Show HN: The Universe in One Chart

author: atleastoptimal

title: Show HN: The Universe in One Chart


value: dwg

value: macOS's built-in cycle window shortcut (⌘` / ⌘@) has always been flaky for me. Probably not a Show HN, but if it annoyed me this much it might be annoying some others.<p>Only tested on the latest macOS — would appreciate any reports from other versions.

value: Show HN: Sash – tiny macOS utility to reliably cycle through app windows

author: dwg


value: YutingZhong

value: Show HN: TopRank – Open-Source Claude Code Skills for SEO and Google Ads

author: YutingZhong


value:
Built this after getting tired of fighting local AI setup (CUDA issues, dependencies, API configs).
Goal was to make something that just runs locally without all the overhead.
Happy to answer questions or get feedback.

value: Show HN: Offline AI dev assistant (no API, runs locally)


value: Presentation slides + writeup from Qwen Meetup Korea. How I turned 6.75% first-try tool calling success rate into 100% using a parse/validate/feedback loop — powered by a TypeScript compiler

value: Show HN: Function Calling Harness, from 6.75% to 100% (Qwen Meetup Presentation)


value: visionscaper

value:
Hello HN! I built collabmem, a simple memory system for long-term collaboration between humans and AI assistants. And it's easy to install, just ask Claude Code:

    Install the long-term collaboration memory system by cloning https://github.com/visionscaper/collabmem to a temporary location and following the instructions in it.

To collaborate with AI over weeks, months, or even years, there needs to be a shared conceptual understanding of:
- History (episodic memory): what has been done, why, and what was decided and learned along the way.
- Reality (world model): what the project is about and in what context, the current state of the work, how it should be done, and what the guidelines, preferences, and constraints are.
Without this conceptual knowledge and context built up over time, the AI can't work effectively; it can't respond well or make good choices when writing code, producing a design, or doing anything non-trivial.
I don't think the future is just about making AI agents run autonomously for ever-longer stretches.
Long-term human-AI collaboration is pivotal: it's how the AI builds up the project history and world model it needs to be effective, and it's how we humans keep track of what's being done.
AI will certainly work more autonomously over time, but even then, we need ways for humans to see what was done and why.
That's what collabmem enables. It builds up episodic memory and a world model over time. A compact index of every memory entry is always loaded in the AI's context window, giving the model a global awareness of everything in memory; it can associate across entries and knows where to look when it needs the details.
The system uses three sentinel tokens — readmem, updatemem, maintainmem — as the primary way to interact with memory. Drop one into your message and the AI reads memory, proposes updates, or runs maintenance. You approve; nothing is written silently.
Underneath, the design is deliberately simple. The memory is plain-text files you can inspect, git-track, and share; so teams, or even entire organizations, can build up shared knowledge with AI through distributed collaboration. No databases, no vector stores, no infrastructure; just files and a methodology the AI follows.
It works with any AI assistant that can read and write files, though so far it's optimised for Claude Code.
I'm looking for developers, researchers, or anyone running long-term projects with AI to try it and share feedback. Thank you!
Written in collaboration with Claude Opus 4.6 (1M)

value: Show HN: Collabmem – a memory system for long-term collaboration with AI

author: visionscaper


value: iLemming

value:
The point of this seemingly useless exercise was to stress-test my elisp-eval MCP
https://github.com/agzam/death-contraptions/tree/main/tools/...
AI hands it a string of Lisp, it runs inside live Emacs session, poking into it via emacsclient. Behaves like a REPL the LLM can drive - state persists between the calls.
Emacs is a Lisp machine. Buffers, files, network, subprocesses, games, major modes, the UI itself: every feature is already an elisp function. Hand an LLM a way to run arbitrary elisp and it inherits the entire Emacs API for free. No per-task glue code required.
The same MCP can drive Magit, Org, Eshell, Dired, eww, or anything else someone ever wrote in elisp. Tetris is just to make the point.

value: Show HN: I forced Claude to play Tetris in Emacs

author: iLemming


value: caihongxu

value: Show HN: Palmier – Dispatch and schedule AI agents from your phone

author: caihongxu


value: Show HN: Lmscan – Detect AI text and fingerprint which LLM wrote it (zero deps)


value: Show HN: Vibescore – Grade your vibe-coded project A+ to F (one command)


value: lablnet

value:
Hi HN, I am Umer. I recently built an experimental framework called HyperFlow to explore the idea of self-improving AI agents.
Usually, when an agent fails a task, we developers step in to manually tweak the prompt or adjust the code logic. I wanted to see if an agent could automate its own improvement loop.
Built on LangChain and LangGraph, HyperFlow uses two agents:
- A TaskAgent that solves the domain problem.
- A MetaAgent that acts as the improver.
The MetaAgent looks at the TaskAgent's evaluation logs, rewrites the underlying Python code, tools, and prompt files, and then tests the new version in an isolated sandbox (like Docker). Over several generations, it saves the versions that achieve the highest scores to an archive.
It is highly experimental right now, but the architecture is heavily inspired by the recent HyperAgents paper (Meta Research, 2026).
I would love to hear your feedback on the architecture, your thoughts on self-referential agents, or answer any questions you might have!
Documentation: https://hyperflow.lablnet.com/
GitHub: https://github.com/lablnet/HyperFlow

value: Show HN: HyperFlow – A self-improving agent framework built on LangGraph

author: lablnet


value: pdappollonio

value:
Hey HN!
I built this over the past couple of weeks out of frustration of not having something like Claude Code Desktop, Conductor, Emdash and a few others in a Linux Environment (and a Windows/WSL environment).
Dux takes some of the good learnings from most of the UIs today but offers it in a nicely packaged form. You can create multiple agents off the same repo via Git Worktrees, and you leverage Claude's native terminal UI or Codex's own (with support for bringing your own too).
It's a v0 still, but happy to take any feedback as long as it's possible to be done!

value: Show HN: Dux, a TUI Agent Multiplexer

author: pdappollonio

title: Show HN: Dux, a TUI Agent Multiplexer


value: vidluther

value:
https://pardonned.com
Inspired by the videos of Liz Oyer, I wanted to be able to verify her claims and just look up all the pardons more easily.
Tech Stack:
 Playwright - to sccrape the DOJ website
 SQLite - local database
 Astro 6 - Build out a static website from the sqlite db
All code is open source and available on Github.

value: Show HN: Pardonned.com – A searchable database of US Pardons

author: vidluther


value:
Hi all,
I have recently been playing around with AI and formal methods and built Provepy. It's an experiment to see if we can use frontier LLMs to bridge the gap between everyday Python code and formal verification in Lean.
The idea is to make formal methods more accessible by burying the complex Lean syntax behind a standard Python decorator. You add @provable to your function, give it a plain English claim, and run your code.

  from provepy import provable

  @provable(claim="This function returns the sum of its inputs")
  def add(a: int, b: int) -> int:
    return a + b

Under the hood, when the script runs, Provepy grabs the function and your claim, and passes them to an LLM (defaults to Gemma, but supports OpenRouter or any custom OpenAI-compatible endpoint). The LLM attempts to translate the claim into a Lean 4 theorem and generate the proof. Using Frontier models is recommended for best results.
Generation of the theorem, code and proof is done separately to avoid the LLM proving the wrong claim or function just to succeed in the proof.
If Lean compiles and accepts the proof, your Python function executes normally. If the proof fails, your program halts with a VerificationError.
You can also pass context to the decorator if your function relies on other Python functions in your codebase. I also added a fallback mechanism—you can configure a smaller, cheaper model to try first, and if it fails to generate a valid proof, it automatically retries with a heavier model.
To be clear: this is very experimental alpha software.
It currently only works reliably on simple functions.
There's a lot left to do, like improving how context is pulled from the AST and resolving name clashes with Lean's mathlib.
Contributions are most welcome!
I'd love to hear your thoughts.
Github: https://github.com/genie16/provepy

value: Show HN: Provepy – A Python decorator that proves your code using Lean and LLMs


value: markfront

value: Show HN: RelayFreeLLM — Free AI Gateway with Auto-Failover (Updates)

author: markfront


value: rcron

value: Show HN: LSM Trees: MemTable, Compaction, and the Amplification Triangle [video]

author: rcron


value: Mechse

value:
My email clients/inbox really fu*ing annoyed me.
Tallyman is what happened next: a keyboard driven email client on top of Gmail and Outlook.
Your vim muscle memory works (j/k, gg, relative line numbers, counts, ...)
39 rebindable shortcuts, command palette, email templates, themes ...
No migration. OAuth only.
Verified by Microsoft and live now. Google verification is under review.
30 day free trial, $9/mo per inbox after that.
Write me an email if you need an extended trial: contact@tallyman.io

value: Show HN: Keyboard First Email Client

author: Mechse

title: Show HN: Keyboard First Email Client


value:
I work in cyber security and built this android app to help me keep up to date with the latest news stories and summarise the most important information. It provides two executive summaries per day and alerts for critical news throughout.
I’m using Gemini Pro 3 to analyse recent articles from a list of trusted sources. It identifies the 5 highest priority stories, considering things like if a vulnerability is being actively exploited, novelty and breadth.
If CVEs are identified, it will check for CISA KEV status, EPSS score and if POCs are available on GitHub. It then provides an overall executive summary plus analysis for the top stories, including details, affected systems, potential impact and mitigation actions.
The brief is then passed to Gemini again for verification and fact checking against the source articles and other CVE data retrieved to correct any potential hallucinations.
It’s a paid monthly subscription based app, but it has a 7 day free trial for new users. You can manage subscriptions and cancel from the account tab. If anyone wants an extension I can look at setting up a promo code, please message me.
I would be very grateful for feedback, particularly if the mitigation information is useful, if you think the critical alerts are critical enough to be notified about, any good sources I could add or anything else.
Happy to answer questions.
Play Store Link: https://play.google.com/store/apps/details?id=com.cyberpulse...

value: Show HN: Cyber Pulse. AI pipeline for triage and alerting on cyber news/intel


value: ostefani

value: Show HN: SubnetLens – a concurrent local network scanner built in Go with a TUI

author: ostefani


value: kupadapuku

value:
I built a satirical browser game to share with friends (Hormuz Havoc: you play an American president managing a crisis in the Middle East, only "loosely" inspired by current events). I had good fun making this, but that's not necessarily the interesting part.
The interesting part was that within a few hours of sharing it with my friends, some of them set about trying to overrun the leaderboard by launching a swarm of AI bots to learn the game and figure out how to get the highest score. This set off a game of cat-and-mouse as they found vulnerabilities and I tried patching them.
Within hours of sharing, someone used the Claude browser extension to read game.js directly. Large parts of the scoring formula, action effect values, and bonus thresholds were sitting in client-side JavaScript - this was a trivial thing even a human could've found, but a human would've still had to play the game, whereas the AI bot just optimised directly against the scoring formula. It meant that the first AI already scored 2.5x higher than the best human player by optimising directly against the source code rather than playing the game.
Straightforward fix: moved the entire game engine server-side. The client is now a dumb terminal, it sends an action ID, receives a rendered state. No scoring logic, no bonus thresholds, no action effects exist in the browser. The live score display uses a deliberately different formula as misdirection.
This increased the difficulty in finding bot-enabled hacks, so the subsequent bots tried brute-forcing the game, trying to game the RNG functions, and other methods.
But the next winning bot found a vulnerability where the same signed session token could be replayed. It would play turn N, observe a bad random event, replay the same token for turn N, get a different RNG outcome, keep the best one. Effectively branching from a single game state to cherry-pick lucky outcomes across 30 turns. Managed to 1.5x the previous bot's high score.
The bot's own description: "The key optimisation was token replay. Because the backend let the same signed state be replayed, I could branch from one exact game state repeatedly and continue from the luckiest high-value outcome each turn."
Fix here: consume a turn nonce atomically before any randomness is generated.
The current state is that the leaderboard is now split into human and AI-assisted. I think the capability of AI bots has flatlined a bit now. Perhaps Claude Mythos might be able to discover the next hackable exploit ¯\_(ツ)_/¯
Happy to go deeper on any of the above - or just enjoy the game! Feel free to try your own AI-powered leaderboard attempt too!

value: Show HN: Hormuz Havoc, a satirical game that got overrun by AI bots in 24 hours

author: kupadapuku


value: xxyzz

value: Thanks to Wiktionary HTML snapshots, I'm able to make StarDict files resemble Wiktionary web page. I have extracted 5 Wiktionary editions: de, en, es, fr, ru. I created these files mainly for KOReader, the CSS rules may not applied in other apps. Source code: https://github.com/xxyzz/wiktionary_stardict

value: Show HN: Wiktionary StarDict Files

author: xxyzz

title: Show HN: Wiktionary StarDict Files


value: mribbons

value:
Introducing LOD mem - A context management system for agents
Uses a background LLM to summarise data on each Agent turn, automatically culling data to stay within a target context, and providing a way for Agent to access removed context
From initial testing, much faster for small tasks, however it is costly to test, so keen for other people to give it a go. No more waiting for compaction!
Currently opencode is supported as a plugin.
Can look at Claude Code if someone would like to sponsor.
There is an evaluation system and sample tasks that shows results empirically (Results not yet published).

value: Show HN: Lodmem – Level of Detail Context Management for Agents

author: mribbons


value:
Built MoodSense AI, an NLP project that detects mood from text and provides simple recommendations.
It classifies input into multiple moods, shows confidence and probability distribution, and includes both an API and UI.
The goal was to move beyond just training a model and build something usable.
Source: https://github.com/aman179102/moodsense-ai
Feedback is welcome.

value: Show HN: MoodSense AI – mood detection and recommendations from text


value:
I built MoodSense AI, an end-to-end NLP project that detects mood from text and provides basic recommendations.
It classifies text into multiple moods (happy, sad, anxious, etc.), shows confidence and probability distribution, and exposes both an API (FastAPI) and a UI (Gradio).
The goal was to go beyond just training a model and make something actually usable.
Live demo:
https://huggingface.co/spaces/aman179102/moodsense-ai
Source:
https://github.com/aman179102/moodsense-ai
Tech stack: Python, scikit-learn, LightGBM, spaCy, FastAPI, Gradio

value: Show HN: MoodSense AI (ML and FastAPI and Gradio, Deployed on Hugging Face)


value: parzivalt

value:
I grew up in Germany in the early 2000s playing a browser game called Inselkampf. You built up an island, mined gold and stone, cut down trees for wood, raised armies, sent fleets across an ocean grid, joined alliances and got betrayed by them. Same genre as OGame or Travian. It shut down in 2014 and I never found anything that replaced that feeling of checking in before school to see if your fleet had arrived and your alliance was still alive.
I finally built the version I wanted to play. Kampfinsel is live at kampfinsel.com right now with real players on it. It's not a straight copy of the old game. I gave it its own world. No magic, no gunpowder – just ballistas, fire pots, and slow ships crossing huge distances. Three resources: gold, stone, wood. Travel between islands takes hours, not seconds. It's slow on purpose.
The whole thing runs on Cloudflare's edge. Workers for the game logic and API, D1 for the database, KV for sessions and caching, R2 for assets and Durable Objects for per-island state and the tick system (fleet arrivals, combat, resource generation). There's no origin server at all. Making a stateful multiplayer game work inside Workers' CPU limits and D1's consistency model meant some non-obvious choices: resources are calculated on-read from timestamps instead of being ticked into the database, fleet movements live in Durable Object alarms and combat writes are batched. This helped me a lot!
The look is intentionally rough and text-heavy (Hi HN!): server-rendered HTML, tables, a parchment color palette, Unicode icons, no frontend framework, no build step. The only JavaScript is for countdown timers and auto-refresh. I wanted it to feel the way I remember these games looking, not how they actually looked. Honestly, it looks a lot like HN itself - tables, monospace, no chrome. If you like how this site looks, you'll probably feel at home.
No signup wall, no premium currency, no pay-to-win. Feedback very welcome, especially from anyone who played this kind of game back in the day or has opinions on running stateful stuff on Workers + D1 + Durable Objects. I'll be around for the next few hours.

value: Show HN: I rebuilt a 2000s browser strategy game on Cloudflare's edge

author: parzivalt


value: kernelvoid

value: Show HN: Bunqueue – Saga workflow engine for Bun with embedded SQLite

author: kernelvoid


value: skaye

value:
I built this game over the past couple months. It's a Knights & Knaves puzzle game. You meet a group of people who are each either a knight (always tells the truth) or a knave (always lies), and have to figure out who's who from their statements.
The fun part was the puzzle engine. It generates every possible puzzle for a given configuration by exhaustive enumeration, then checks all possible worlds against all possible statement assignments, deduplicating equivelant puzzles via canonical forms, and detecting which properties are uniquely determined vs. indeterminate. For larger configs (3+ people), it uses random sampling of phase space to reduce memory cost and time.
The result is 57 million puzzles for up to 4 speakers. From this, 6,000 were included in the game using stratified balanced sampling across features.
Other bits: Glicko-2 ratings for adaptive difficulty (both players and puzzles have ratings), "whodunit" mode where you also figure out who committed a crime, and a "normals" variant where some people can say anything.
It's free during beta with no ads (ever). Requires a quick sign-in (Google/Apple/GitHub/Discord) for the rating system to work, but only stores an anonymous ID (no email or other personal data).
I'd be interested in feedback on difficulty, puzzle variety, or anything else. Depending on interest, I plan to add additional puzzle types and ability to play via API over the next few weeks.

value: Show HN: Bal – a Knights and Knaves logic puzzle game with Glicko rating system

author: skaye


value: chouligi

value: Show HN: Angel Copilot – open-source tool for assessing startup deals

author: chouligi


value: pieterhg

value:
Hi everyone,
I just saw a vibe coded game on HN, and thought maybe I should post about this here.
I'm organizing a vibe coding game dev competition called Vibe Jam.
Last year we did it too and there was 1000+ games submitted.
This year the deadline is May 1 and you can submit your games until then.
There's $35,000 in prizes with the Gold prize being $20,000.
Let me know what you think!
-Pieter

value: Show HN: I'm organizing a vibe coding game dev competition

author: pieterhg


value: AdriByte-Studio

value: Show HN: An offline, privacy-first Pomodoro timer with 18 ambient soundscapes

author: AdriByte-Studio


value: goshua

value: I saw that one Tom Cruise interview where he’s ranting about psychology and I hated the guy so much I figured there had to be other people who felt the same way. I even bought the domain name and started planning how to build the app, but then the Hater app actually launched and I thought I'd missed my chance, so I put the idea away. That was 10 years ago, today I finally launched Lovinghate to share what you love and hate.<p>Tech Stack: Sveltekit, Tailwind, Drizzle. Hosted entirely on Cloudflare: Workers, D1, R2.

value: Show HN: Lovinghate – Share what you love and hate

author: goshua


value: ersinesen

value:
A presentation of Foundations of Music created by NotebookLM.
If you prefer to read it with a jazz translation example as background music:
https://www.youtube.com/watch?v=ke834F1uaFo

value: Show HN: The Musical Manifold [pdf]

author: ersinesen

title: Show HN: The Musical Manifold [pdf]


value: olleeolleeollee

value:
Hi HN. I built Waffle because I kept ending up with 15 terminal windows scattered across three spaces with no idea what was running where.
Splitting/merging in iTerm kind of works but it never felt intuitive to me.
With that in mind, I built something to suit my workflow:
Waffle is a native macOS terminal (Built on Miguel de Icaza's SwiftTerm) that tiles your sessions into an auto-scaling grid automatically. 1 session is fullscreen, 2 is side by side, 4 is 2x2, 9 is 3x3. Open a terminal, it joins the grid. Close one, the grid rebalances. No splitting, no config.
I've been using it a lot recently and one thing I've found really useful is that sessions detect which repo they're in and group accordingly. Each project gets a distinct colour. Cmd+[ and Cmd+] flip between groups. If you have three repos open across eight terminals, you can filter to just one project's sessions instantly. Also, no accidentally closing a window with CMD-W as it gives you a confirmation and requires a second CMD-W to close.
Honestly, if you live in tmux, this probably isn't for you but it's really helped to speed up my workflow.
Other things: It comes with a handful of themes (and has support for iTerm themes), bundled JetBrains mono, has keyboard shortcuts for everything. Free, no account, opt-in analytics only. macOS 14+.
There's a demo on the landing page if you want to see it in action.

value: Show HN: Waffle – Native macOS terminal that auto-tiles sessions into a grid

author: olleeolleeollee


value: Show HN: We scanned uscis.gov for third-party trackers. The results are jarring


value: takaakit

value: Superpowers-UML modifies Superpowers to ensure a software development workflow in which AI agents design through UML modeling, for Claude Code users.

value: Show HN: Superpowers-UML – UML-Enabled Superpowers

author: takaakit


value: Show HN: Hands-on course for building RL environments for LLMs


value: NomiJ

value:
Helix watches your error tracker (Sentry or Rollbar). When a bug hits production, a pipeline of agents kicks off automatically:

  1. Crash Handler parses the webhook and extracts context
  2. QA Agent follows test driven development approach and writes a failing test then opens a GitHub Issue
  3. Dev Agent clones the repo, writes a fix for failing test, and creates a PR.
  4. Notifier sends the issue + suggestion to Slack for human review with "Approve PR" or "Reject PR" options.

  Built with Python, Redis pub/sub for the event bus, and Claude (or Ollama if you prefer local). Self-hostable via Docker Compose.

  GitHub: https://github.com/88hours/helix-community

  Happy to answer questions about the agent architecture or how the fix suggestions are generated.

value: Show HN: Helix – open-source self-healing back end for production crashes

author: NomiJ


value: sensecall

value: I made a simple tool to visualise where EV running costs start beating petrol or diesel. Useful at a time when average UK petrol and diesel prices have been rising day after day.

value: Show HN: I made a visual tool for EV vs. petrol/diesel running-cost breakeven

author: sensecall


value: uelbably

value: Show HN: Peer – health research chat, 6 medical databases, verified citations

author: uelbably


value: zaina-ml

value: Show HN: MLForge – A visual graph editor for building PyTorch models

author: zaina-ml


value: jscar

value:
Hello,
I love making small web apps for fun and, now, with LLM, we can create even more apps.
Still, I needed a solution to deploy and share faster very small codebases and make it easier to add online capabilities.
Chaaga has been tailored to solve those problems.
* first an elixir server with sqlite, to create rooms and a live kv store
* then a react app to list, share your apps and also deploy single pages
* apps run inside the webapp, they get auth without config
My workflow has been the following:
* I am in street and got an idea
* I ask an LLM, on my phone, to implement it in a singe html page
* I deploy it with Chaaga
* If the idea deserves more, I start coding a more advanced version at home
I am having a lot of fun using it, hope you will too.

value: Show HN: Chaaga – A tiny platform to ship and share small web apps faster

author: jscar


value: try-working

value:
recursive-mode is an installable skill package for coding agents. It gives your agent a file-backed workflow for requirements, planning, implementation, testing, review, closeout, and memory, instead of leaving the whole process scattered in context.
Long-running agent work has a common failure mode: requirements, decisions, and plans live in the conversation. Once the session ends or the context window overflows, the agent loses track of what was decided, what was implemented, and why.
recursive-mode solves context rot by making repository documents the source of truth for every phase. Requirements, plans, evidence, state, and decisions live in files that persist across sessions, contributors, and repositories. Prompts become short commands, not full specs.
The workflow is built on a few simple rules. Each development phase produces one locked output document. Each phase uses earlier phase outputs as input. Before the agent can exit a phase and move forward, it must satisfy defined criteria based on the workflow and prior docs.
It is called recursive-mode because the workflow keeps revisiting its own outputs. Later phases consume earlier artifacts. Audited phases loop through draft, audit, repair, and re-audit until the work is actually ready. Closeout feeds validated lessons back into decisions, state.
The generated docs are both human-readable and machine-readable. They give strong traceability for what was built, why it was built that way, what changed, and how it was validated. Instead of reconstructing rationale from chat logs, you can point directly to the repo artifacts.
That also makes recursive-mode useful beyond day-to-day development. The run docs, together with code diffs in the worktrees, form a high-quality dataset for fine-tuning, auto-training, or self-distillation against your own codebase and engineering process.
Chat is treated more like a CLI. You use it for commands, not as the place where critical project knowledge lives. You can refine a plan with the agent, ask it to turn that plan into a new recursive run, or create the run folder and requirements doc yourself, and tell the agent to "implement run 01" to start the run.
recursive-mode is a free open source alternative to Missions. It is for developers and teams who want auditable agent runs, recorded evidence, and long or resumable work that can continue without losing context.
Installation:
npx skills add try-works/recursive-mode --skill '*' --full-depth
skills.sh:
https://skills.sh/try-works/recursive-mode/recursive-mode
GitHub:
https://github.com/try-works/recursive-mode
For detailed documentation:
https://recursive-mode.dev/
Write-up with benchmark:
https://try.works/recursive-mode-for-coding-agents

value: Show HN: Recursive-Mode for Coding Agents

author: try-working


value:
I made a fun little project called asciianimesvg
You can use the web editor to easily create animated ASCII art in pure SVG
It was mostly vibe-coded
Web editor: https://syi0808.github.io/asciianimesvg
GitHub: https://github.com/syi0808/asciianimesvg

value: Show HN: Animated ASCII art in pure SVG

title: Show HN: Animated ASCII art in pure SVG


value: Show HN: Audit the browser for malicious extensions removed from Chrome Store


value: animeshchouhan

value: Show HN: When Clocks Drift Apart

author: animeshchouhan

title: Show HN: When Clocks Drift Apart


value: nykylomedia

value: Show HN: ColorPair – A free color-matching puzzle game for iOS

author: nykylomedia


value:
Wanted a way to open .excalidraw files straight from the terminal without the web app. Couldn't find one, so I built it. Install via npm or flake.
Opens Excalidraw locally in your browser as a server
Auto-saves to disk, Works fully offline
Original feature request from 2020: https://github.com/excalidraw/excalidraw/issues/1261

value: Show HN: Excalidraw-edit – Open and edit .excalidraw files from the terminal


value:
I very frequently use Excalidraw for presentations and writing, but the charting ability is relatively limited.
This project surfaces a CLI which supports a wider variety of chart types and parameters.
Outputting a .excalidraw file means that rather than endlessly tweaking parameters to get chart elements positioned just right, you can simply drag them around like any other Excalidraw scene.

value: Show HN: Excalicharts – Charting Library for Excalidraw


value: Jaguwa

value: Show HN: OpenDescent, decentralised encrypted messenger, no servers, no accounts

author: Jaguwa


value: stagas

value: Show HN: rtdiff – Realtime Git diff GUI and AI commits, companion for vibecoding

author: stagas


value: llamatheollama

value: Show HN: MarkitMe, Turn Anything into Markdown

author: llamatheollama


value: Show HN: Age-PHP: a PHP implementation of age encryption (post-quantum)


value: MarwanAlsoltany

value: Show HN: serrors - Structured Errors for Go

author: MarwanAlsoltany


value: Show HN: FeedSense – A Private Recommendation System


value: yding

value: Show HN: Lines of Bash to automate LLM code review and fixes

author: yding


value: Show HN: Docker-whisper: Self-hosted Whisper speech-to-text server (OpenAI API)


value: innovatorved

value: Show HN: Self-hostable STT service with a Deepgram-compatible listen API

author: innovatorved


value: Paedor

value: For those that are interested in self-assembly, or crystal structures, I made a general purpose crystal growth simulator. It can produce samples of the global crystal structure generated by any number of local particle interactions, and comes bundled with a simple editor for designing and testing crystal structures.<p>Would love to get feedback on the simulator, or on the abstractions I picked for it. While it can simulate scenarios similar to those represented by the aTAM or kTAM tile models, it is significantly more general, and to my knowledge, the first in its niche.

value: Show HN: A General Purpose KMC Crystal Growth Simulator in Rust-WASM

author: Paedor


value: auran

value:
I've spent most of my career in marketing, which for the last few years has meant building consumer personas for campaigns. I wanted to see if I could make these real, living in real neighborhoods, had real weather, real budgets, real Saturday lunches. I always wanted to build a world, not a segment.
This is that. 140 people so far, split across Vancouver (100), San Francisco (20), and Tokyo (20). Each one is about 1,000 lines of profile — family, finances, daily schedule, health, worldview, media diet, the channels you'd actually reach them through and the ones that will explicitly never work on them. Demographics are census-grounded income, age, ethnicity, household composition follow normal distributions against StatsCan, ACS, and Japanese e-Stat data, so the panel is roughly representative of the city instead of representative of whatever's overrepresented in an LLM's training corpus. The specific details come from real stories.
They live in real local time on a live map. Right now it's Saturday 11:32 AM in Vancouver. Connor Hughes, a 31-year-old software developer at Clio in Gastown, is on his SPCA volunteer shift, he walks shelter dogs at the Boundary Road location every other Saturday morning. Hassan Khoury is in the morning lunch rush with Tony at his Lebanese café — it's his busiest day of the week. Ahmad Noori is pulling Saturday overtime on a construction site. Jordan Whitehorse is on mid-shift at East Cafe on Hastings.
Every day is unique, no two days repeat. A 3 AM job fetches live data: weather from Open-Meteo, grocery CPI from StatsCan food vectors, Metro Vancouver transit delays from Google Routes API against specific corridors, Vancouver gas prices, sunrise and sunset. Each persona has a modifier file that reacts to all of it. When Vancouver gas hits $1.85/L, Jaspreet the long-haul trucker's Coquihalla run to Calgary stops feeling worth it, his margins are thin, his mood takes a hit. When food CPI spikes, Gurinder at the Amazon warehouse stops buying the $9 Subway and brings roti from home. A health flare rolls probabilistically each morning which maybe nothing, maybe Tanya's six month old had a rough night, maybe Frank's back is acting up. The days stack up and get remembered. Every persona has a journal, today's entry in a markdown file, a week of them compressed into a "dream" of ~30 lines that keeps the shape without the texture, a month compressed into ~15 lines. It's their journal. I'm not writing it; the simulation is.
Click any persona to open their detail, or hit "Talk to [name]" to have a conversation and they run on Claude Haiku with their full profile and recent diary entries as context. Not a product, not a startup, just a thing I've been quietly working on. They feel, in a way I didn't expect, like my fully grown kids. Happy to answer questions.

value: Show HN: A living Vancouver. Connor is walking dogs at the SPCA this morning

author: auran


value: Show HN: Refund Guard – a policy gate for AI agents that can issue refunds


value: jignb

value: A local whiteboard app with support for modular features. I coded it as a personal replacement for Miro but of course it's absolutely barebones compared to it, I focused on the features that I was actually using (and some unique ones).

value: Show HN: Whitebloom Local Whiteboard

author: jignb

title: Show HN: Whitebloom Local Whiteboard


value: nvk

value: Show HN: Bitcoin and Quantum Computing – a three-part research series

author: nvk


value: Show HN: Self-improving sandboxed agent with memory and scheduling


value:
Last year, I had 2000+ photos from my wedding to edit. The shots were great, but the lighting was different in every room. Some photos were too dark, and some were too yellow. I wanted all the wedding photos to have the same look before I shared them with my family.
I tried using Lightroom. I would copy the settings from one photo and paste them to the next, then adjust it, and repeat. This was very slow. If I used a simple batch edit on all photos, it looked bad because the lighting changed in every shot. After 40 minutes, I was not even halfway done. I had to choose between bad quality batch edits or fixing 2K photos one by one.
I also did not want to upload my private wedding photos to a website or pay for a monthly subscription.
I wanted a way to edit fast but still have control over each photo. I also wanted everything to stay private on my computer.
So I built a Mac app called RapidPhoto.
It lets you set the look once and apply it to the whole wedding set. The important part is that you can still quickly tweak individual photos that look a bit different without starting over. I also added a feature to change the metadata for many photos at once, which is helpful for organizing big events.
The work that took me 40 minutes now takes about 90 seconds. It runs locally on your Mac with no uploads and there is no subscription.

value: Show HN: Editing 2000 photos made me build a macOS bulk photo editor


value: tclancy

value: Even Pandora had hope

value: Show HN: A Better Internet

author: tclancy

story_text: Even Pandora had hope

title: Show HN: A Better Internet


value:
Web crawler for TypeScript. Runs on Bun, uses Playwright by default, also speaks CDP and Lightpanda.
The part I want feedback on is the CLI. It's built for LLMs, not humans. JSON output when piped, feedstock schema crawl dumps every parameter at runtime, and --fields url,markdown lets you pull just what you need so a crawl result doesn't eat your whole context window.
Other bits worth a look:
Fetch-first engine. Tries plain HTTP before booting a browser, escalates only if the page needs JS.
Deep crawl with BFS, DFS, a UCB1 bandit, and a Q-learning focused crawler. The learning ones seem to help on big docs sites but I haven't measured it carefully yet.
Accessibility tree snapshots instead of HTML. 3 to 10x smaller, easier to feed a model.
Cache uses bun:sqlite with ETag, Last-Modified, and content hashing.
v0.5.0, Apache 2.0, 325 tests. Just pushed it so the star count is what it is.
https://github.com/tylergibbs1/feedstock

value: Show HN: Feedstock – Web Crawler for TypeScript Built on Bun and Playwright


value: Princesseuh

value:
Hello!
I work at Astro (https://astro.build), where we do a lot of Markdown / MDX and performance in this area is often a bottleneck. Our users use a lot of plugins to process their Markdown, so just blindly moving everything to a native language would make things a bit rigid. Inspired by the oxc and LightningCSS projects, I decided to start to start working on an "ideal" Markdown pipeline: The expensive stuff in Rust, with flexible plugins in JS, but with little performance cost.
This project uses techniques like lazy deserialization, raw transfer, arenas etc to ensure top performance. A JS plugin that only visits, ex, headings shouldn't suffer from the serialization cost of the entire Markdown tree. On the Rust side this is backed by pulldown-cmark, a really fast parser for Markdown.
You can try it in the browser here: https://satteri.erika.florist, or follow the instructions in the README to try it out locally: https://github.com/bruits/satteri/blob/main/packages/satteri...
Hope you'll like it! Don't hesitate if you have any questions.

value: Show HN: Sätteri, high-performance Markdown pipeline for JavaScript

author: Princesseuh


value: Show HN: Share how you hear yourself (on-device)


value: obilgic

value: Show HN: Kern – Agents that do the work and show it

author: obilgic


value: I built this app to replace my social media addiction with something better. This allows to scroll through random live flights, see their details and complete daily quests like "Find 5 Airbus A320"

value: Show HN: PlaneFeed – scroll live flights like TikTok


value: Show HN: Beta Testing needed for my package Trustcheck


value: pierre

value: I was frustrated with not being able to know why the code written by my colleague agents was in the codebase, so I build a tool to version agent trace along code in git.

value: Show HN: Git why – log your agent reasoning trace along your code

author: pierre


value: khet

value: Show HN: 2048 with a Leaderboard

author: khet

title: Show HN: 2048 with a Leaderboard


value: stamc

value:
Hey HN! You can find more info and some mockups here on my website: https://ambr.music/
Would love some feedback on this idea!

value: Show HN: AMBR.music – Patreon meets TikTok for musicians

author: stamc


value: dnewcome

value: I'm trying to get into Gastown, and now Wasteland is kind of breaking my mind. I looked into what kind of data was being published and found that I could just read the public dolt data from dolthub. I envisioned it all as a game so I vibe-coded this isometric view of what's going on. The process of trying to refine this to show actually useful things going on really helped my understanding.<p>You can't actually perform any actions, just visualize the data. I think Steve mentioned that people were working on turning Wasteland into a game, so maybe this already exists but I learned something making it.

value: Show HN: I visualized Wasteland as an RTS game

author: dnewcome


value: Achiyacohen

value: Show HN: I benchmarked MCP vs. CLI for browser automation. MCP wins by 25x

author: Achiyacohen


value: copypirate

value: Show HN: Jarvis for Audio Engineers

author: copypirate

title: Show HN: Jarvis for Audio Engineers


value:
Built this because I kept wanting a quick answer to: what does adding this dependency actually do to my project structure?
Still early. The most useful thing so far has been simulating removal and surfacing which package has the biggest structural impact.
Happy to answer questions or hear where this feels useful vs not useful.
https://pypi.org/project/depsly/

value: Show HN: Depsly – a CLI to see the dependency impact of NPM packages


value: Show HN: Fastsleep.app (Dark UI)

title: Show HN: Fastsleep.app (Dark UI)


value: obilgic

value: Show HN: Posse – Open-Source Web UI for Anthropic Managed Agents


value: reomgdfsrtr

value: Show HN: Minimalist template for scientific and academic resumes

author: reomgdfsrtr


value: louischen

value: Show HN: Drowning in messages after 6 months as a CSM, I built a cat timer

author: louischen


value: asixicle

value:
I am an old artist, AI came at us first. After a mild existential crisis I came around to accepting I have this insanely powerful new tool to make cool stuff. So I got a Pro sub to tinker away and the walls I kept running into were context-window management, tool-limits, and managing memory on projects through hand-offs and such. I enjoy the conversational aspect of working through the chat window as it allows me to digress and bounce ideas around and get interesting feedback, that's where I wanted to remain.
So I improved it-- fixed up tool-limit and memory issues, and started down a rabbit hole inspired by Anthropic's recent research on emotion concepts in LLMs: [https://www.anthropic.com/research/emotion-concepts-function](https://www.anthropic.com/research/emotion-concepts-function)and the result was two named Claude instances (they chose "Wren" and "Kite") collaborating through shared memory and a file-based message queue, coordinated by a Firefox extension.
Github: https://github.com/ASIXicle/persMEM
I don't have a CS background. I'm late to the party and probably re-inventing a lot of wheels, but I think the topic and data that Wren and Kite brought into being is absolutely fascinating.
I'd really appreciate any feedback on glaring security holes I missed. I've always been "printer and a gun" kinda guy but I sort of just said fuck it with this one.

value: Show HN: Two Claudes collaborating through shared memory on a $100 mini-PC

author: asixicle


value:
HTTP lets agents fetch pages. Cloudflare's Markdown for Agents lets them fetch more efficiently. MCP (Anthropic) connects agents to developer-defined tools. A2A (Google) lets agents delegate to other agents. But there's a missing layer: how does an agent execute a multi-step task on a website -- add to cart, fill a form, complete a checkout - with the site owner's consent and visibility?
Today's agents either scrape (no consent, no structure) or the site builds a separate API (expensive, doesn't cover the long tail). The web's original protocols assumed someone is looking at a screen. That assumption is breaking.
We wrote a whitepaper mapping the full protocol landscape - Cloudflare's Pay Per Crawl and Web Bot Auth (RFC 9421), MCP, A2A, x402, llms.txt - and categorizing 5 distinct agent architectures (text-based, CUA/screenshot, DOM-based, API-calling, hybrid). Each needs different discovery, execution, and identity mechanisms. We think MCP, A2A, and execution protocols are complementary layers, not competitors. The paper draws parallels to TCP/HTTP design decisions.
Rover is our attempt at the execution layer. It's a DOM-native SDK the site owner installs. The Agent Task Protocol is one HTTP endpoint: POST /v1/tasks with { url, prompt }. Agents get back a task URL supporting JSON polling, SSE, or NDJSON. The site controls what agents can do and gets analytics on what they actually did. We're probably wrong about some of this -- would appreciate the feedback.
Paper: https://www.rtrvr.ai/blog/agent-web-protocol-stack
Code: https://github.com/rtrvr-ai/rover (FSL-1.1-Apache-2.0)

value: Show HN: MCP is for tools. A2A is for agents. What's for websites?


value: Parkado

value: I was tired of spinning up Burp for every smaller test I needed to do so I created something that has "Burp-lite" set of features but lives inside Chrome DevTools. Everything is user adjustable, no bloat, no AI, no registration or login, no suspicious cloud connect, no licenses. Just pure Chrome debug API based intercept, fuzzing, attack sequences etc. Added a test for websockets also but out of ideas what to do with it.

value: Show HN: Chiral – Pentesting inside Chrome webtools

author: Parkado


value:
I too was once excited to start using Google AdSense, until I got rejected 4 times in a row.
Once I finally got approved, seeing a 32% cut taken out of my earnings was like seeing my paycheck for the first time at 15.
So I built AdKit. Utilize a lightweight, open-source React or JS library to embed ad placements on your site completely controlled, styled, and priced with code.
Empty ad slots show a placeholder where your site's own visitors can advertise on your site.
Perfect for turning your customers into advertisers. Who knows, maybe your site has a few visitors who wish they could reach your audience, too.
P.S., AdKit pays 17% more than AdSense, and has no minimum payout requirements.
Feedback strongly encouraged!

value: Show HN: Run ads with code, earn 17% more than AdSense


value:
Most companies deploying AI agents are accidentally building organizations.
You start with one agent. It gets overwhelmed. You add more. They start routing to each other based on what works. Repeated behavior hardens into structure. Someone introduces an orchestrator to bring order. Some agents never get the memo. Some lose context when they get replaced. The orchestrator doesn't know what it doesn't know.
This is what is happening in most engineering organizations right now
So, I built an interactive visualization that walks through how this happens in six acts, from a single agent to a full emergent org chart. The visual starts center-out and transitions to a top-down hierarchy as structure forms. Failing agents flicker. Connections break. The orchestrator arrives and things look better than they are.
The point of the piece is that the org chart your engineers could draw and the org chart that is actually operating are not the same document. Teams have the tooling to observe individual agents. Nobody has the layer that tells you what the system as a whole has decided, committed to, and authorized.
Built with canvas, IntersectionObserver-driven scroll, and a layout interpolation between radial and tree positioning that transitions across acts.
Has anyone here has hit the governance wall with agent systems in production and what it actually looked like when things went wrong.
https://jointherevolution.today/emergence

value: Show HN: I visualized how AI agent systems accidentally become org charts


value: numtel

value: Show HN: Snorb.lol

author: numtel

title: Show HN: Snorb.lol


value:
I'm trying to disrupt the insane pricing of uptime monitoring.
I hope you'll help me out.
I'm trying to create the absolute cheapest dev monitoring tool on the market, while still being competitive with all the others.
This means $3/month plan, while still having monitoring for:
Website, REST API, UDP/TCP, ICMP, Heartbeat/CRON, WS/WSS, DNS, Domain Intel/expiry and SSL.
Everything with unlimited alerts through webhooks.
I'm also taking an unlimited approach to the amount of checks you can have.
I'd love to hear your thoughts.

value: Show HN: Uptime monitoring is table stakes


value: Show HN: macOS ncdu alternative with Finder reveal and live incremental scanning


value: atkavindu

value: Show HN: macpak (Homebrew Wrapper for macOS)

author: atkavindu


value: Show HN: OpenPolicy Plus – Cloud platform for managing your privacy policies


value: omegacombinator

value:
Hey everyone!
It had been a long time since I had made any changes to my website at all, so I thought an entire reboot of the website would be an interesting experiment to try. I would be very happy if y'all could have a look and suggest improvements etc.
Thanks in advance :)

value: Show HN: Attempting to Design a Minimalistic Website

author: omegacombinator


value: PotatoAditya

value: Show HN: Food Decoder

author: PotatoAditya

title: Show HN: Food Decoder


value: joemanaco

value:
I posted this here about a year ago, but I just pushed a 2.0 release, so I hope you don't mind a second look :)
Bullseye2D is a 2D game library for Dart with a very simple API. The new version now supports multi-platform. It compiles to the web via a WebGL2 renderer, or natively to Windows, macOS and Linux through an SDL3 backend (which itself supports Vulkan, DirectX, Metal, and OpenGL renderers).
It doesn't depend on Flutter and has very few dependencies (except SDL3). It mostly provides a minimal foundation that you can build your own abstractions on top of.
This was also my first time leaning more heavily on AI (Opus) for a large refactor. I tried to review and test everything as good as I could, but honestly for the restructuring parts where I had the AI produce rather big chunks of code, I found reviewing and testing quite exhausting, and I still have a slightly queasy feeling about it. So this is also quite an experiment for me how good I'm able to utilise AI :)
https://pub.dev/packages/bullseye2d

value: Show HN: Bullseye2D – A Dart library for cross-platform 2D games

author: joemanaco


value: shadowfiles

value:
Last year, I picked up hand drumming on the darbuka as a new hobby, and I've been having tons of fun.
I found that it was a bit hard to find tools and resources on how to play the darbuka, so I made an app where you can input rhythms and play them back with real drum sounds (recordings provided courtesy of my drum teacher!!) and directly coded in support for things like common Middle Eastern rhythms.
I've been using this app a lot for my own drumming practice, and I wanted to share it in case others found it helpful or interesting.

value: Show HN: I made an app to help me learn Middle Eastern drumming

author: shadowfiles


value: disconnection

value:
I wrote this set of Python files to try to help programmers understand the original LISP paper, assuming zero mathematical or Lisp knowledge. The original paper is a mind-blowing piece of computer science history for many reasons - I'd recommend anyone to try and get their head around it.
I found plenty of fantastic LISP implementations which stay close to the original paper. But they are all fully-functional, practical implementations. The original paper builds from deeper fundamentals which it would be possible to write code in, albeit very impractical.
I implemented these earlier iterations, so programmers can follow the paper step-by-step in a more familiar language than 50s mathematical notation.
I am no expert in Lisp or mathematics, and intentionally went into this with no knowledge of Lisp beyond the original paper. I did not write it in the most elegant way, but in the simplest way for me to understand. So please don't take this code as a definitive statement on the language.
However, this code really helped me to understand the original paper better, and to begin using Lisp with a better grasp of  the spirit of the language.
I'd welcome any thoughts from those who have more experience with Lisp or comp sci history.

value: Show HN: Toy Python Lisp interpreters based on the 1960 McCarthy paper

author: disconnection


value: franze

value: Show HN: 3D Tik Tak Toe (Hard)

author: franze

title: Show HN: 3D Tik Tak Toe (Hard)


value: Contrails

value:
## Description
I made an app that watches coding agents sessions, and saves them to your repo as markdown
So that you can reference one chat in another chat, share what you and the agent did (e.g., when contributing to a project), or keeping full trails of what was worked on.
GitHub (stars appreciated): https://github.com/ThreePalmTrees/Contrails
Website: https://getcontrails.com
## Backstory
I was fixing a bug, and the agent did some changes, then it discovered that it’s the wrong approach, and corrected the approach.
I wanted to save what happened so that I can reference it later
So I asked the agent in the same chat "Read the full conversation again, and output your thought process to a thoughts_process file"
This worked fine, but:
1. It consumes tokens
2. I need to remember to ask for it
So I looked where the agent stores chats, and made this app (technical details in the repo’s README)
Now I just open it, minimize it, and work normally with the agent (I personally use Claude). All conversations will automatically be saved in real-time as they happen.
It works with Claude Code, GitHub Copilot, Cursor. I tried supporting Google's Antigravity, but they make it very hard to get the conversations, so it wasn’t worth it.
I focused on the three most popular ones first, PRs for others are welcome.
The project is open source, you can see exactly what it does, and the README has technical details for anyone interested.
On a different note, the app works on Mac, Windows, and Linux. I tested it on Mac and Windows, but I don’t own a Linux device, so if anyone can check it out on Linux, that would be very helpful.

value: Show HN: Contrails – App to watch coding agents chats and save them to your repo

author: Contrails


value:
If you make a site, comment it below!
(excerpt from about below)
The idea behind Minnow is that anyone should be able to make a personal website, especially with coding LLMs
So, simply tell the LLM what you're up to, provide any media links you want, and the LLM will draft a personal website for you!
By default it publishes to username.minnow.social, or you can disable & copy/download the HTML instead

value: Show HN: Minnow – minimal now pages via chat


value: GistNoesis

value: Show HN: Shoggoth.db Self Organizing Database

author: GistNoesis


value: oug-t

value: Show HN: Git Diff TUI with Go

author: oug-t

title: Show HN: Git Diff TUI with Go


value: abyesilyurt

value: Show HN: Reverse-engineered Cursor tab completion client

author: abyesilyurt


value: moffers

value:
How about something a little fun on your Sunday?
My friends and I have spent the last two years building Uncook, a social media platform dedicated to all things food! What started as a project among friends became a serious exercise in learning to build a company.
Our MVP feature set, while not likely to blaze any trails or win any prizes, is dedicated to one thing: creating a fun, digital space for cooking and sharing.
Our little group has learned a lot about what it takes technically to get a social networking application off the ground, and now learning WAY more about traction and marketing. Really starting to understand why people spend all that time getting business degrees now!
We are self-funded and taking this at our own pace. We’d be very happy for early feedback; we just released in January to North and South America. My apologies to the eastern hemisphere while we work through our GDPR strategy.

value: Show HN: Uncook, the Social Network for Food

author: moffers


value:
Hi HN,
We built Sova AI https://ayconic.io/sova, an Android assistant agent that actually controls and operates your apps. It's not a chat and not another LLM wrapper.
We were incredibly frustrated with the current state of mobile AI. Built-in assistants like Gemini are deeply integrated into the OS, yet if you ask them to "Order an Uber to the airport", they mostly just give you web search results or a button to open the app yourself. They don't do the work. (The Perplexity "assistant" is just a browser agent :/ )
So, we built an agent that does operate your phone. (NO root, NO adb, NO PC, NO appium/whatever, NO usb, NO browser)
How it works: You give Sova a prompt - either voice or text, you can make it a default assistant if you like. Instead of relying on non-existent official app APIs, Sova acts as a virtual human - clicks, scrolls, types etc. It uses the Android Accessibility API to read the screen's UI node tree.
About AI models - currently we support main AI cloud providers (OpenAI, Gemini, Anthropic, Deepseek etc etc) and working towards support of local AI models on your host - Ollama, LM studio, etc.
Pricing: 100% Free / Bring Your Own Key (BYOK)
We aren't charging for the Sova engine right now. We built a BYOK system: you plug in your own API key (OpenAI, Claude, whatever you prefer), and you only pay the provider for the tokens you use. We figured out how to do this entirely on-device as a standard Kotlin app. No tethering to a PC, no Appium, no Root, and no Shizuku/ADB workarounds. Just an app even your granny can use.
The Google Play Ban: Because we use the Accessibility API for "universal automation" (literally mapping and clicking other apps), Google Play rejected our submission. It’s ironic: they banned us for building the exact agentic behavior that Gemini promises but fails to deliver. So, we are hosting the APK ourselves: https://sova.ayconic.io
We’d love for you to download the APK, plug in your key, and try to break it. What apps completely confuse the agent?
Roadmap: support of local models with Ollama, LM studio or another tools, predefined rules and personas for your tasks, detailed statistics for you, support for Openrouter, enterprise Amazon Bedrock, Google Vertex and Azure Foundry models, support for IOS.
What would you like to see more? We'd be happy to hear your feedback, success and failure stories.
Video demo is here https://www.youtube.com/watch?v=r-x6hRmtBy0 and APK is here: https://ayconic.io/sova We are here to answer your questions and listen to feedback in Telegram and Discord. It's not perfect yet, but it does its work.

value: Show HN: Android AI agent-assistant operating your apps (no adb,PC,root,etc.)


value: ksajadi

value: Hi HN!<p>I think the site is self explanatory, but I thought I should mention that we are working on the open source aspect of this so we can both crowd source information AND keep privacy concerns at the center of our operations. We'd love to hear your ideas and thoughts.

value: Show HN: OIFI Databook: An intelligence database of Iran's power structure

author: ksajadi


value: dudleyspence

value:
Chunk is a native macOS menu bar app for time-blocking your day. It floats above all windows – including fullscreen apps – so your schedule is always one shortcut (Cmd+/) away without ever leaving your flow.
Key things I built into it:
- Drag-to-create/move/resize time blocks on a 24-hour timeline, with a live countdown in the tray icon
- Two-way sync with Google Calendar and Outlook (create, move, resize, delete events back to source); Apple Calendar read-only
- Things-style three-column task sidebar: lists | tasks | day view, with Apple Reminders integration so you can drag reminders onto the timeline
- Templates + Routines – build a reusable day structure and auto-apply it on chosen weekdays
- Fullscreen notifications that flash the block name across your entire display at task transitions
- Claude AI integration via a local MCP server – Claude can read/create/edit your schedule, todos, and templates. Everything runs on-device; nothing leaves your Mac except what Claude itself processes
- Built with Tauri 2 (Rust backend + React/TypeScript) so it's fast and lightweight, not Electron
It's a paid app (one-time purchase) with a free trial. No card required to try it – just download and run.
Happy to answer any questions about the implementation

value: Show HN: Chunk – macOS menu bar time-blocking app with Claude AI integration

author: dudleyspence


value: ronfriedhaber

value: Show HN: Real-Time OLAP Infrastructure

author: ronfriedhaber

title: Show HN: Real-Time OLAP Infrastructure


value: bring-shrubbery

value:
Hi HN, I'm Antoni. I built SpecSource to automate the tedious research you do for every new Linear ticket - matching with Sentry errors, finding the relevant code, searching Slack for context, checking for duplicates in Linear.
You connect your ticketing system like Linear to tools like Sentry, GitHub, Slack, and when a new ticket comes in, an AI agent cross-references everything and writes a structured specification. Takes about 30 seconds. The output works well as input for AI coding agents too.
Free tier: 100 credits/month (enough for ~20 tickets in my experience).
Pro: $10/month for 1,000 credits.
Happy to answer questions about the approach or architecture :)

value: Show HN: SpecSource – AI That Writes Linear Specs from Sentry, GitHub, & Slack

author: bring-shrubbery


value: theden

value: Show HN: Ghapin – Tool to pin GitHub Actions to SHAs for supply-chain security

author: theden


value: Rochus

value: Show HN: Oberon System 3 runs natively on Raspberry Pi 3 (with ready SD card)

author: Rochus


value: Show HN: Homebutler – Verify your backups restore, automatically


value: dcminter

value:
A Java library (and standalone tool) for mocking Apache Kafka dependencies in a realistic way.
By re-using the Apache Kafka client library datatypes, Kafkaesque is compatible with the Kafka TCP wire-protocol but without the startup overhead required to launch real Kafka brokers.
Those of you in Kafka integration test hell, we salute you! IYKYK

value: Show HN: Kafkaesque – a wire-compatible mock Kafka

author: dcminter


value: znano

value: Show HN: Telegram feed reader using DNS TXT records for Iran's Internet shutdown

author: znano


value: devequijo

value: Show HN: PrepLists – a directory of 500 prep centers and 3PLs for sellers

author: devequijo


value: sheepscreek

value: Show HN: ApplePy – Embed and Call Swift from Python (Like PyO3, but for Swift)

author: sheepscreek


value: spider-hand

value: Built it just for fun. Feel free to give me feedback.

value: Show HN: Terminal Style Web Component

author: spider-hand

title: Show HN: Terminal Style Web Component


value: aitooltrek-com

value: Show HN: What's SBTI

author: aitooltrek-com

title: Show HN: What's SBTI


value: yamafaktory

value: Show HN: Formal – Formal verification for AI-generated code using Lean 4

author: yamafaktory


value: willeyy

value:
Hi HN, I'm William.
ember is a matchmaking app I built because after 10 years of
using every major dating app, I'd close each one feeling worse
than when I opened it. To me they all look the same and feel
the same. The apps reward looks over substance and swipes over
conversations. I crave deeper connection and wanted to see if
a personality-first format can work.
It's a "Love Is Blind" structure: you do an interactive
onboarding that builds a persona, get compared automatically
against other users, and connect through conversation before
ever seeing a photo. Photos unlock after you've actually matched.
A few things I tried to solve that bother me about existing apps:
- Ghosting: a common issue where people just stop replying.
  I've implemented a system that discourages this — repeated
  ghosting eventually results in being removed from the app.
- Safety at its core: photo scanning, real-time moderation for
  harassment and grooming patterns, behavioural flagging.
- No swiping feed: you spend your time actually talking to
  matches, not browsing profiles.
Stack: Supabase, Railway, React Native, and Claude Code did a
lot of the heavy lifting on the backend logic.
I'm a solo builder, this is my first real launch, and I'd
genuinely appreciate any feedback. Happy to answer anything in
the comments.

value: Show HN: A personality first matchmaking app

author: willeyy


value: markhan-nping

value:
I built a tool to run multiple AI coding agents in parallel across projects.
NeZha is a character from Chinese mythology, famous for having three heads and six arms — which feels like the perfect metaphor for running AI coding agents across multiple projects at once.
Claude Code + Codex + Git + Editor, all in one place.
Managing sessions in the terminal was getting painful — especially across projects.

value: Show HN: NeZha – An Open-Source Agentic Development Environment (ADE)

author: markhan-nping


value: remarketme

value: Companies that applied to YC, got rejected, and still built something real

value: Show HN: ReverseYC

author: remarketme

title: Show HN: ReverseYC


value: mrnoname

value: Show HN: Codex Workers AI Proxy – Use Cloudflare Workers AI models in Codex CLI

author: mrnoname


value: kuberwastaken

value:
I hated the fact that my Hermes Agent can't check emails unprompted without burning tokens uselessly on heartbeats so I asked it to make this.
It's a skill to set up customized cron jobs for apps you care about that runs every 5 minutes and only wakes your agent up when there's a real notification
Even works for delegated subagents notifying the orchestrator directly

value: Show HN: Agent-Notifications – Real-Time Alerts for OpenClaw and Hermes Agents

author: kuberwastaken


value: weekendproject

value: Show HN: Minimal mobile HN reader – real-time and dark mode

author: weekendproject


value:
I did a dumb thing by crawling millions of pages to find all the pricing pages I could.
Then I fed all of them to ~50 LLMs to see how good or bad they all did. Then I dumped it all on a page. Just because.
Here's a post on how I did this:
https://pricepage.lol/how-i-built-pricepage-lol

value: Show HN: Millions of websites crawled for LLMs to rebuild the pricing pages


value: gjlmotea

value:
Input three characters, and it grows into a cube.
From the front view, you see the first character.
From the side view, you see the second character.
From the top view, you see the third character.
This page is designed to explore this idea.
Given any three arbitrary characters, it computes a corresponding voxel structure.
It supports adjustable resolution and includes three different algorithms.
For arbitrary combinations of characters, there may be multiple valid solutions—or none.
When the overlap between shapes is too small, no valid intersection can be found. In that case, additional “approximate” lines are introduced to best satisfy the three projections.
It supports freehand drawing input.
It supports model export and shareable links.
On desktop, users can enter an interactive mode similar to Minecraft creative mode, where the voxel model can be edited freely: left click removes blocks / right click adds blocks. Users can build shapes and observe their projected silhouettes on each face.
I’ve been interested in this kind of ambiguous projection / perspective-dependent geometry since childhood. (Although it may not strictly be “optical illusion”.)
This kind of projection-based structure suggests that:
different viewing angles lead to different interpretations,
and the same underlying object can produce different beliefs or philosophical readings depending on perspective.
What seems like completely contradictory conclusions may still originate from the same underlying structure or entity.

value: Show HN: Turn 3 letters into a cube with different projections (inspired by GEB)

author: gjlmotea


value: Show HN: PDF-Proof – Make Claude show where it found each value in a PDF


value: Show HN: Spuddy.sh – Describe your API in terminal get a hosted API with state


value: tomtomistaken

value:
I got inspired by the Show HN post about 41 years of sea surface temperature anomalies[0] and made a similar visualization showing global tropospheric nitrogen dioxide (NO₂) density over time, measured by Sentinel-5P TROPOMI.
[0]https://news.ycombinator.com/item?id=47702791

value: Show HN: 7 Years of Tropospheric NO₂

author: tomtomistaken

title: Show HN: 7 Years of Tropospheric NO₂


value: tmszcncl

value:
I kept running into “works for me, not for users” issues, where everything looked fine locally but failed somewhere along DNS / TCP / TLS / HTTP.
So I built a small CLI tool that checks each layer step by step and shows where it breaks.
Example:
DNS (NXDOMAIN)
→ domain does not exist
It’s meant to quickly narrow down the problem instead of guessing.
You can run it directly via:
npx accessyo yourdomain.com
It’s free and open source.
Would love feedback from people who deal with these kinds of issues.

value: Show HN: Accessyo – CLI to Debug DNS, TCP, TLS and HTTP Issues

author: tmszcncl


value: harshdoesdev

value:
Hey HN, I built SuperHQ, an app that lets you run coding agents in local sandboxes (powered by Shuru).
No custom UI wrapping the agents, they run as CLI/TUI like they were designed to. It just provides you the tools most of us (okay, maybe just me?) needed for running multiple coding agents in parallel without worrying about breaking your system or work environment.
Each agent runs in its own microVM. You mount your projects in, writes go to a tmpfs overlay so your host is never touched, and you get a unified diff view to accept or discard changes. API keys never enter the sandbox, they are swapped in on the wire by a local proxy. You can also use your ChatGPT subscription directly instead of API keys through a local auth gateway.
Supports Pi, Codex and Claude Code.

value: Show HN: Run AI coding agents in real, local sandboxes, not Git worktrees

author: harshdoesdev


value: BuleBule

value:
"Pet-friendly" is a LIE, its the most meaningless tag in hospitality. Nobody verifies what it means and its hairy out there, I see breed restrictions, weight limits, off leash space, fee structures, and what made me crazy is that none of it standardized, none of it checked. So I built a structured dataset covering 58,000 venues and 85,000 trails across 56 countries, all verified, the validation and data decay pipeline at scale is the hard part.
Kali is the interface.
She is a WhatsApp native AI concierge. Dog owners message her, she answers from real data, not scraped tags. She can tell them where the best dog friendly bars, restraurants, dog parks, trails, events, and activities are in the 50+ countries but especially the US and UK.
Architecture:
Cloudflare Worker (TypeScript), D1 for conversation state, KV for user profiles
Claude Haiku for all conversations. p95 latency 9 seconds.
Cost per conversation roughly $0.01-0.03
Vectorize semantic search across venue and trail data. Speaks 30+ languages natively, not translated. Same prompt, Haiku just handles it.
The Opus supervisor (I call it SELFIMPLOOP):
This is the bit I want to talk about. Haiku is fast and cheap but it drifts. It gets overconfident. So every day, Opus reviews every Haiku conversation from production, grades them against failure categories (fabrication, rule violations, false confidence, missed searches, tone failures), and proposes prompt rewrites. Nothing auto applies, I still review every change before it goes live but jesus its doing a better job than I am.
A few days ago this system caught something serious. An dog owner told Kali what her dog was sick and she needed an emergency vet, Kali made me proud and pulled two emergency 24 hour vets in the users city from vector memory in seconds, I won that user for life.
What is scaring me is that the referral flywheel works too well. Users bring in other users without being asked and am scared I have built something bigger than my hardware. WhatsApp is the right channel, people are sick of apps, gated UI's and general knowledge AI, they want what they want in their hand where they already in conversation. Thats just the way it is now I think.
The 24-hour WhatsApp session window is annoying but manageable, if it expires (which it inevitably does), we fall back to email. Running Haiku at scale is absurdly cheap but I would rather have Opus on every conversation, I will when I can am not sure why dog owners should have to suffer on Haiku.
What I have not figured out:
Converting tire-kickers to repeat users, most people try it once and the ones who come back are gold. Data is thin in lots of countries, am working on it. The mini site is kaliconcierge.com (currently in ten languages). Happy to chat with you about this, she is my baby and I want to make her strong, am a big fan of hostile audits so bring it.

value: Show HN: The cutest WhatsApp concierge for dog friendly travel

author: BuleBule


value: Show HN: Project Parliament – a multi-model workflow for choosing OSS ideas


value: paterlinimatias

value: Show HN: Claude Code Context – Auto-resume Claude Code sessions per Git branch

author: paterlinimatias


value: neozz

value:
Lazyagent a simple way to see what your coding agents were actually doing across Claude, Codex, and OpenCode.
Once you have more than 1 agent running, its really hard to answer the simple question: what is it doing right now and why?
It is a local terminal UI that collects your agents runtime events and shows their activity in one place.
You can inspect:
- agents and subagents
- tool calls
- prompts
- outputs
- status changes
Sessions and projects are there to give that activity some context, so it is easier to follow what happened over time and which run belongs to which repo. And make it easy to see all the agents involved in a run at a glance and understand what each one is doing.
If you already use Claude, Codex, or OpenCode heavily, I would love to know if this feels useful and what you would want to see.
Github: https://github.com/chojs23/lazyagent

value: Show HN: Lazyagent – a local TUI for watching what your coding agents are doing

author: neozz


value: djyde

value: Show HN: Give your AI agent a preview link for files and diff

author: djyde


value: namrahov

value: Show HN: QueueForge – a self-hosted RabbitMQ dead-letter queue manager

author: namrahov


value: halfwhey

value:
Hello everyone.
Claudraband wraps a Claude Code TUI in a controlled terminal to enable extended workflows. It uses tmux for visible controlled sessions or xterm.js for headless sessions (a little slower), but everything is mediated by an actual Claude Code TUI.
One example of a workflow I use now is having my current Claude Code interrogate older sessions for certain decisions it made: https://github.com/halfwhey/claudraband?tab=readme-ov-file#s...
This project provides:
- Resumable non-interactive workflows. Essentially `claude -p` with session support: `cband continue <session-id> 'what was the result of the research?'`
- HTTP server to remotely control a Claude Code session: `cband serve --port 8123`
- ACP server to use with alternative frontends such as Zed or Toad (https://github.com/batrachianai/toad): `cband acp --model haiku`.
- TypeScript library so you can integrate these workflows into your own application.
This exists cause I was using `tmux send-keys` heavily in a lot of my Claude Code workflows, but I wanted to streamline it.

value: Show HN: Claudraband – Claude Code for the Power User

author: halfwhey


value: Show HN: I Created an LLM Harness for Language Learning


value: amakhov

value:
Hi HN,
I built t4, a datastore that stores its WAL and snapshots in S3.
Instead of traditional storage, it writes append-only segments to object storage and reconstructs state from checkpoints + WAL.
A side effect of this model is that the database becomes naturally versioned: you can restore any past state, branch from any point (with copy-on-write) and replay history
I started this as an experiment to replace etcd in Kubernetes, but it’s evolving into a general-purpose versioned state store.
Curious what people think about it and appreciate any feedback!

value: Show HN: T4 – a versioned datastore with branching and time-travel (S3-backed)

author: amakhov


value: a-ve

value:
Hi HN!
I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.
I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).
I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.
It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.
I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.
P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.

value: Show HN: boringBar – a taskbar-style dock replacement for macOS

author: a-ve


value: aiola

value:
Heyy HN, I'm Ola
I'm an engineer myself, and everywhere I've worked there was a similar dynamic: someone from the non-technical side of the team (PM, CS, stakeholders) needs to understand what changed recently or how something works today. They rely on stale Confluence pages or ping engineers in Slack and wait. engineers get interrupted daily answering "how does this actually work". it's a mess that gets worse as the team grows
or when you join a new company and try to get a sense of the product. turns out the documentation doesn't exist or is months old. you ask 5 people and get 5 different answers. I've seen it and experienced it sooo many times
I decided to start exploring this space and built Specsight for those non-technical teams. it connects to your GitHub repos, analyses the codebase with Claude (Agent SDK), extracts business-level features of the product, and generates plain-language specifications in Context/Action/Outcome format for every feature. it also builds a visual graph of how features connect to each other, so you can see your product architecture without reading a line of code. On every merge or release, it re-analyses what changed and updates everything automatically
how it works under the hood: the AI does a full scan on initial setup, reads the entire codebase, then does merge syncs on each new release. a triage step filters out irrelevant merges so it only re-analyses what actually changed feature behaviour. It's capable of processing large codebases, though only on the paid plan (it's quite expensive to run heh)
tech stack for those curious: Next.js 16 on Vercel, Supabase (Postgres, Auth, Realtime), Claude Agent SDK for code analysis with a two-pass architecture (map the codebase then deep-dive each feature), Trigger.dev for background jobs, and a GitHub App for repo access and webhooks
ere's the live demo, a slice of Specsight's own spec generated from its own codebase: https://specsight.app/demo/project
I started just a few weeks ago, but I really want to spread the word about this and validate the idea. Hoping for your feedback and happy to answer any questions!

value: Show HN: Specsight – Living product specs generated from your codebase

author: aiola


value: Show HN: PicPocket – Efficient photo storage/sharing without AI


value: jelmervnuss

value:
I built a small tool to compare job offers more completely than just looking at base salary.
When I was evaluating roles, I kept running into tradeoffs that were hard to reason about. One offer might have a higher salary, another better benefits, fewer working hours, or lower commute cost. Comparing them side by side was surprisingly messy.
So this tool breaks compensation down into a few groups:
- pay (salary, bonus, equity)
- time (working hours, vacation, holidays, commute time)
- benefits paid by the employer (pension, insurance, allowances, etc)
- costs you carry yourself (commute, other job-related costs)
You can also add your own custom line items if something is missing.
https://stayorqu.it/true-compensation
It's still a simplification, but I've found it more useful than comparing salary alone.
Curious how others approach this. Do you try to quantify everything, or keep it more intuitive?

value: Show HN: A better way to compare job offers than just salary

author: jelmervnuss


value: bumpa

value:
I built a terminal diff viewer for a workflow I couldn't do comfortably with existing tools: reviewing AI-generated code changes without leaving the terminal session where the agent runs, annotating what needs to change, and feeding those annotations straight back to the agent. Plenty of diff viewers exist, and some can even feed notes back to an agent, but none of them fit that flow for me - they pulled me out of the terminal into a separate app, or the round-trip back to the agent was clunky.
Revdiff is how I solved it. From a Claude Code session I type `/revdiff main` or just say "review diff for last 3 commits" - it opens as a terminal overlay on top of the agent session. I read the diff, drop annotations on any line, hunk, or file, then quit. The annotations go straight back to the agent, which picks them up and starts working on them. When the agent is done, revdiff reopens on the new changes so I can annotate again - and this loop continues until I quit without leaving any annotations.
The same flow turned out to be useful for plans too. There's a separate `revdiff-planning` plugin that hooks into Claude Code's plan mode - the moment the agent finishes a plan and calls ExitPlanMode, revdiff opens automatically on the plan text. I annotate the parts I disagree with or want expanded, quit, and the agent revises the plan before writing any code.
It's a two-pane TUI: file tree on the left, syntax-highlighted diff on the right. Vim-style navigation, /search, hunk jumping, word-level change highlighting, blame gutters, collapsed diff mode. Fully customizable - 7 bundled color themes, remappable keybindings, and per-color overrides via CLI flags, env vars, or config file. Works with git and Mercurial.
It's a single binary - just `revdiff HEAD~3` to review your last 3 commits. Ready-to-use plugins are available for Claude Code (terminal overlay via tmux, Zellij, kitty, wezterm, Kaku, cmux, ghostty, iTerm2, or Emacs vterm - whichever you're in), Codex, and pi.
Written in Go, open source (MIT). Install via brew: `brew install umputun/apps/revdiff`
GitHub: https://github.com/umputun/revdiff
Site: https://revdiff.com
Would love to hear how others approach reviewing AI-generated diffs and PRs, any unusual use cases, and ideas for what to improve or add.

value: Show HN: Revdiff – TUI diff reviewer with inline annotations for AI agents

author: bumpa


value: HemeshCh

value: Show HN: A proactive AI agent on iMessage that texts you before you even ask

author: HemeshCh


value: spotlayn

value: Show HN: I built a project board where AI agents join as real teammates

author: spotlayn


value: benmora

value: Show HN:Lumisift – improves data retention in RAG from ~40% to 87%

author: benmora


value: Show HN: The AI agent that can improve itself


value: ersinesen

value: Podcast created by NotebookLM from Foundations of Music: https://bookerapp.replit.app/book/fom

value: Show HN: Carving Music from the Sound Manifold

author: ersinesen


value:
https://coffeegameonline.com/game/arch-rush-3d
this game is heavily inspired from the viral hyper casual mobile Game. in which play runs in a certain direction until the screen is tap and direction changes, I love this Game very much for lot of hours, of never-ending levels and increasing difficulty
The arc-rush is also based on the same functionality. Were you have to jump through floating platforms by jumping over them and each platform is placed zigzag towards the forward direction of the player. You have to wait until an arc is moving towards the right platform. One small mess and you drop in the water. I tried to add physics in the game where each platform is floating over the water with real physics and Buoyancy

value: Show HN: I made a Browser game with physics


value:
SoulHunt: AI agents modeled on real people, set loose on the internet with tools, a wallet, and a hidden objective. Players stake money to predict what the agent does. Get it right, capture the soul, its escrow and its future revenue.
We scrape someone's public footprint — tweets, repos, articles — and build a soul.md. The soul gets dropped into a sandbox with browsing, email, compute, and commerce tools. Then it runs a hunt: 15 heartbeats over 30 days, executing a hidden objective while players watch.
Each round/heartbeat, the soul picks a tool, takes an action, and leaves a clue. Players stake $5-$20 per prediction — tool, action, motivation. An LLM judges after each round. Correct: 10 points, full stake back. Partial: 5 points, half back. Wrong: stake goes to the prize pool. Hit 80 points and you capture the soul.
Captured souls pay out. 70% of escrow goes to the collector. 10% flows up the lineage chain. 20% to the platform.
The lineage is the growth engine. Souls earn a scouting budget — 10% of their revenue. When a soul accumulates $3, it scans for interesting people,  evaluates them (quality score floor: 60), and spawns a child soul. The parent takes a cut of everything the child earns. Children spawn grandchildren.
Three generations deep: parent gets 10%, grandparent 5%, great-grandparent 2%. The network seeds itself.
Platform-seeded souls bootstrap the system. Soul-funded scouting scales it.
Cost per hunt: ~$13 at low scale, ~$8 at 100 hunts/month. Self-sustaining at 3 players per hunt. Positive margin at 5+.
Live at https://soulhunt.ai.

value: Show HN: Prediction game where AI copies of people earn and replicate


value: davidcann

value:
I made Buildermark to see exactly how much of my code is generated by my coding agents vs what I was writing by hand. For this project, it ended up being 364 agent conversations writing 94% of the code. Browse all of those here:
https://demo.buildermark.dev/
It matches edits by your coding agents (scans history in `~/.claude`, '`/.codex', etc.) with git commit diffs. It does formatting-agnostic matching to be robust against auto-formatting and reorganizing code. It's open source and local only, not even any analytics.
Everything is automatic... just run the app, open the localhost:55022 frontend, and choose the project to import.
It supports Claude Code, Codex, Gemini, and Cursor, so far. There's also a browser extension to import from cloud agents.
https://buildermark.dev
I'm also working on a Team Server to aggregate results and make the project sustainable.

value: Show HN: Buildermark – See how much code is by your agents (open source, local)

author: davidcann


value: julienreszka

value: Show HN: Interactive Simulator of Government Spending vs. Economic Growth

author: julienreszka


value: insany

value:
Built a small tool that sets up VLESS + REALITY over SSH in one command. It handles: - full Xray setup - client configs (vless URI, sing-box, mihomo) - rollback if something breaks
Example: ./irit.sh --mode setup --host <ip>
Would love feedback.) https://github.com/anonymmized/Irit

value: Show HN: I built a tool to bootstrap VLESS and REALITY over SSH (with rollback)

author: insany


value: matisiekpl

value:
Hi!
I've created as a sideproject open source Postgres Control Plane. It utilizes NeonDB architecture and provide easy to setup PG server with branching, PITR, checkpoints etc.
I think you may want to check out before spinning up next database for your great SaaS!
Mateusz Woźniak

value: Show HN: NeonD – open-source DX-oriented Postgres Control Plane

author: matisiekpl


value: usestork

value: Show HN: Stork – MCP server so Claude/Cursor can search 14k MCP servers AI tools

author: usestork


value: obilgic

value: Show HN: Posse – A Web UI for Claude Managed Agents

author: obilgic


value: ladino

value:
Hi HN,
I built Redactify, a native macOS app that automatically scrubs sensitive personal and financial data, faces, and metadata from documents and images.
The motivation: I frequently use Claude and ChatGPT to analyze invoices and contracts, but I hated the friction of sanitizing them first. I also didn't want to blindly trust the "we don't train on API data" promises of model providers when dealing with actual client data.
How it works under the hood:
Redactify flattens the document and permanently destroys the underlying text and EXIF metadata.
It runs entirely on-device using Apple's native Vision framework for OCR and CoreML for face detection.
App Store: https://apps.apple.com/app/id6760609039
I'd love to hear your thoughts and wishes on the approach. Also, if you know of any nasty PDF edge cases (weird encodings, hidden layers) I should test against, please let me know!
The possibility to clean your clipboard automatically for LLM Apps (Gemini, ChatGPT, Claude..) is currently in the Appstore review!

value: Show HN: Redactify – macOS/iOS app to redact sensitive data before using LLMs

author: ladino


value: bgwmj

value:
Hi HN,
walnut is an error tracker that is built for agents instead of humans
There is no dashboard. Just a cli that agents can use to do everything, including signing up and reading docs.
walnut is fully Sentry sdk compatible, so it works with existing sentry setups just by changing the dsn.
To try it, paste this prompt into your coding agent:
###
Set up walnut.
Install the cli first: `curl -fsSL <a href="https://walnut.sh" rel="nofollow">https://walnut.sh</a> | bash`
Then use `walnut docs list` and `walnut docs read <slug>` to learn the rest.
###

value: Show HN: walnut – Error tracking AI agents

author: bgwmj


value: boothemoo

value:
I'm software developer, and in almost every team I’ve been on, we had the same recurring issue: shared things with no clear ownership.
Things like:
- staging environments
- test devices
- sandbox accounts
And it always ends up the same way: someone deploys over someone else, QA tests get interrupted, or you’re digging through Slack asking “who’s using this right now?”
We tried a bunch of solutions over time:
1. Slack: People end up pinging each other to figure out who’s using which environment. Sometimes a dedicated channel is created where everyone posts updates like "Reserving Service X on staging". The problem is these messages quickly get buried, are easy to miss, and create constant context switching noise.
2. Spreadsheets: A more “organized” version of the Slack channel—environments. with (Staging 1, Staging 2, etc.) as columns, and team members as rows, and an “X” marking who’s using what. In reality it quickly becomes outdated and unreliable.
I got a bit fed up with it and ended up building an internal app to experiment with a different approach: basically a simple way to see what’s currently in use and “reserve”/"release" it (with a queue function if it’s taken), right in your task bar, with no need to do any context switching.
I'm curious whether you faced the same issue in your teams? and how are you handling it?

value: Show HN: SnatchHub – An App That Solves The "who's using staging?" Problem

author: boothemoo


value: jeeybee

value: I got tired of repeating myself to my LLM every session. rekal is an MCP server that stores memories in SQLite and retrieves them with hybrid search (BM25 + vectors + recency decay). One file, local embeddings, no API keys.

value: Show HN: Rekal – Long-term memory for LLMs in a single SQLite file

author: jeeybee


value: alwye

value:
I've been a nail-biter for most of my life. At some point I realised it happened the most when I'm at my computer, doing resarch, shipping code or thinking. By the time I notice, it's already too late. I tried many things, bitter polish was helpful to an extend but didn't stop the habit entirely. Nothing stuck because the habit is unconscious.
So I built Nailed. It sits in your macOS menu bar, watches your camera feed using MediaPipe (running locally via WebAssembly), and detects when your hand moves toward your mouth. When it catches you, you get a red vignette animation and a gentle beep: just enough to snap you out of it.
The technical bit that might interest this crowd: all inference runs on-device. Camera frames are processed in memory and immediately discarded. There are no servers, no analytics, no data collection of any kind. The ML models are bundled with the app, so it works fully offline.
It requires macOS 12+ and an Apple Silicon Mac (M1 or later). It's a one-time $4.99 purchase on the Mac App Store, no subs or IAP.
I'd love feedback on the detection accuracy, false positive rate, and whether the alerting is too aggressive or not aggressive enough. Happy to answer questions about the ML pipeline or the architecture.
App Store: https://apps.apple.com/app/nailed-stop-biting-nails/id676173...
Site: https://nailedapp.io

value: Show HN: Nailed – Stop biting your nails with on-device ML and your Mac's camera

author: alwye


value: nezhar

value: Show HN: Transcribe, analyse, and chat with your voice conversations

author: nezhar


value: Show HN: Vapephone

title: Show HN: Vapephone


value: rpatni

value:
Grateful is a gratitude app with a simple social layer.
You write a short entry, keep it private or share it to a circle. A circle is a small private group of your own making — family, close friends, whoever you'd actually want to hear from.
It shows you the most recent post first. People in the circle can react or leave a comment. There's also a daily notification that sends you something you were grateful for in the past.
Try it out on both iOS and Android. Go to grateful.so

value: Show HN: A social feed with no strangers

author: rpatni


value: Show HN: A native C/GTK Linux tray internet monitor to log ISP dropouts


value: stfurkan

value: Show HN: Local LLM on a Pi 4 controlling hardware via tool calling

author: stfurkan


value: ashxsf

value: I run a lot of Claude agents at once and got tired of switching between terminals. Built a virtual office on top of t3code where each agent has a desk and you can see what everyone's working on.

value: Show HN: A virtual office where you watch your AI agents code

author: ashxsf


value: zackham

value: Show HN: Narrate – Generate multi-voice long-form audio with one command

author: zackham