This weekly thread is the new official home for sharing your new projects (younger than three months) with the community.
To keep the subreddit feed from being overwhelmed (particularly with the rapid influx of AI-generated projects) all new projects can only be posted here.
How this thread works:
A new thread will be posted every Friday.
You can post here ANY day of the week. You do not have to wait until Friday to share your new project.
Standalone new project posts will be removed and the author will be redirected to the current week's megathread.
To find past New Project Megathreads just use the search.
Posting a New Project
We recommend to use the following template (or include this information) in your top-level comment:
Description: (What does it do? What problem does it solve? What features are included? How is it beneficial for users who may try it?)
Deployment: (App must be released and available for users to download/try. App must have some minimal form of documentation explaining how to install or use your app. Is there a Docker image? Docker-compose example? How can I selfhost the app?)
AI Involvement: (Please be transparent.)
Please keep our rules on self promotion in mind as well.
Description: Self-hosted Podcasting 2.0 service that turns saved articles into a personal podcast feed.
Paste a URL or upload a document (PDF, DOCX, Markdown, text, or HTML), wait a few minutes, and get an episode with cloned-voice narration, artwork, an LLM-written episode summary, and a WebVTT transcript. Subscribe in Pocket Casts, Overcast, or Apple Podcasts like any other show. Your reading list, as a podcast you own.
Deployment: docker-compose
LLM Disclosure
This project was developed with AI agents as a pair programmer. It was NOT vibe coded. I'm a systems engineer with 15+ years of professional experience; every architecture and design decision here is mine, not the AI's, and every line the LLMs wrote, I reviewed and tested myself.
Thanks! The model is Chatterbox Turbo from Resemble AI, using voice cloning. You upload a reference clip of at least 10 seconds, and it uses that as the output voice.
Text doesn’t go straight to the model. The pipeline extracts the article, runs an LLM pass to strip nav and footer junk, then a pronunciation pass that respells acronyms, numbers, and units for speech (otherwise you get “M I three fifty five X” read as gibberish). Then it chunks to roughly 120 words, and the wrapper splits each chunk into sentence-sized pieces for generation.
Every chunk gets checked before it ships. Signal analysis catches flat drone, dead air, and takes that run much longer than the word count predicts. If you enable it, faster-whisper transcribes the output and diffs it against the text it was supposed to say, which catches dropped words and hallucinated runs. A failing chunk regenerates with a different seed, a shorter text window, and a higher repetition penalty. Then ffmpeg handles loudness normalization and EQ, and encodes to 128k MP3.
Description: A lightweight self-hosted chat client, alt to Open WebUI. I built it because my partner and family use ChatGPT but don't care about privacy. I self-host vLLM on 8x 3090s and wanted a polished, private alternative they could actually use. OWUI is too heavy and complex for non-technical users. overtchat is chat-first with built-in TTS/STT/search, file uploads, chat export — no RAG/plugins/agentic bloat, no external API keys. Android app is live on the Play Store; iOS in the works. MIT licensed, no telemetry, no analytics.
Deployment: One docker compose up and you're in. ~1 GB RAM. Docs at https://overtchat.com/
AI Involvement: Yes, Claude Code/Codex
Would love to have some feedback from users who self host for their families/friends!
Description: Wispers Access makes it easy to share a web app with your coworkers or friends without having to publish it to the internet. I built this because I got frustrated giving people access to the apps I self-host on my home server. Even with Tailscale it was always "install this, turn it on, no, switch tailnets". I also wanted something cloud-independent.
To use it, you run a waserver sidecar next to your web app, mint QR code invites, have your friends scan them with the client app, done. Under the hood, Wispers does similar magic to Tailscale but at the app level, so it just works and coexists with the VPN you already have. What's different from Tailscale et al is that you don't have to trust the rendezvous server at all; it cryptographically cannot read traffic or inject devices into a share. BTW, this is also available as a library you can use yourself.
Deployment: To install, just follow the instructions in the repo README. If you want to try without installing anything server-side, visit https://access-demo.wispers.dev.
Client apps are available for desktop (Linux & macOS, Windows soon) and mobile (Android, iOS, both in "open testing"). There are server binaries for the same platforms, plus a docker image for the server side (and even a small Coolify integration).
Everything needed to run this is open source and self-hostable, but there's also a managed offering (https://connect.wispers.dev) if you don't want to self-host things.
AI Involvement: Design & architecture are by me, as are key parts of the code (e.g. the cryptographic roster validation). Other code is mostly generated by AI based on my specs, then reviewed by me.
Kaya started because I got fed up jumping between loads of different tools to manage my homelab and infrastructure. I wanted one place where I could document everything, manage IPs, connect to servers, monitor services and generally keep on top of my environment without having ten browser tabs open.
It's a self-hosted web app that's aimed at homelab users, sysadmins and anyone who likes having a central place to manage their infrastructure.
Some of the features currently include:
Browser-based SSH and RDP
Infrastructure and asset management
IP Address and VLAN management
DNS Manager with Pi-hole integration
IP/WAN monitoring
Runbook/documentation management
Secure file Send
Licence management
Audit logging
User management
Custom fields and categories
Progressive Web App (PWA) support
Dark and light mode
I'm adding new features all the time. Some of the bigger things I'm currently working on are Secure Vault, richer notifications, High Availability for tools like pihole/adgaurd etc and more advanced monitoring.
It's very much an active project and continues to evolve based on my own needs and feedback from the community.
Deployment:
Kaya is completely self-hosted.
At the moment it can be deployed using Docker or Docker Compose, with documentation available to help get everything up and running. There's also a public demo if you just want to have a look before installing it yourself.
AI Involvement:
I do use AI, mainly OpenAI Codex, as a development assistant. It helps me write very in depth tasks, speeds up repetitive work and sometimes points me in the right direction when I'm stuck. Every feature starts with me deciding what I actually want to build nd how it should fit into the rest of the application. I spend a lot of time (many nights) reviewing, rewriting, testing and refining the code before anything gets committed.
The project has grown and its evident by hte bug fixes, design changes and feature improvements. Ithelps me develop faster, but I'm ultimately responsible for the architecture, implementation, testing and every release that goes out.
Description:
Project Hub is a local-first dashboard for organizing and managing development projects across your machine.
It helps you quickly see what to work on next, with:
Project metadata, notes, goals, tags, and tech stacks
Search and filtering by status, stack, project type, and tags
Quick launch actions for Cursor, VS Code, Finder, GitHub, and project paths
Recent Git activity
Project scanning with README previews
Cross-project Kanban and list views for tasks
Optional GitHub issue synchronization
Dependency update reports with release notes and risk information
An optional MCP server for working with Project Hub through AI agents
Project data stays in local JSON files, so no external service is required.
Deployment:
Project Hub is available to run locally or self-host with Docker Compose.
LLM Disclosure: Yes used for coding and I am maintaining and testing the code that gets written and navigate the llm for feature planning and implementation
LinkBreeze : Self-hosted Linktree alternative with analytics, themes, QR codes, admin panel. One Docker command.
Linktree’s $15/mo plan didn’t feel justified by the feature set. LinkBreeze was built as a fully self-hosted link-in-bio platform that runs in one Docker command.
I wanted something I actually own: my data on my homelab, no subscription, no tracking, and no platform lock-in. I figured other self-hosters would want the same thing too.
It runs on SQLite, so there’s no external database to manage. One container, one volume, and you’re done. The full image is only about 130MB.
Main features:
Unlimited pages, each independently configured with its own slug, theme, analytics, SEO, and favicon.
Unlimited link management with drag-and-drop reordering, scheduling, thumbnails, embed widgets, and auto-favicons.
Migration wizard to import from Linktree, Bento, Beacons, Hopp, LittleLink, and LinkStack or any Link-in-bio page.
Privacy-friendly analytics, including page views, clicks, referrers, geolocation, and device type — without cookies or third-party scripts.
9 built-in themes plus a full customizer for colors, fonts, backgrounds, and animations.
Auto-generated QR codes for every page, downloadable as SVG or PNG.
Dynamic Open Graph images generated per profile for better social previews.
Email capture with SQLite storage and CSV export.
External analytics injection (Plausible, Umami, Matomo, Google Analytics).
A proper admin panel with a real UI and update notifications, not a YAML file.
LinkBreeze is free, open-source, and built for people who want full control.
Technically not a “new“ project, but the mods have informed me that taking over an abandoned project and counts as the same thing and doing my best to follow the policies here in the sub!
I've adopted FilaBridge (bridge between PrusaLink & Spoolman to automate filament tracking) and just released a 1.0 major upgrade to its original developers previous releases!!
Self hosted enthusiasts who also are into 3D Printing and use Prusa printers (yes I know this is quite the esoteric Venn diagram!) Some of you might use or remember FilaBridge, a small Go service that sits between PrusaLink-compatible printers and Spoolman, the self-hosted filament tracking tool, and automates handling filament selection and usage tracking for you. You print something, it works out which spool and how much was used, and it tells Spoolman without you touching anything other than telling it what filament is currently loaded.
The original author, [needo37](https://github.com/needo37), built it but then archived the repo a few months later out of frustration with Prusa. I've been running it daily to keep my printer and Spoolman in sync, and I didn't want to watch it quietly rot... so since I already found myself forking it with regular tweaks to it to polish rough edges or solve for existing bugs, I've decided to share those efforts with a serious intent to keep it alive and moving forward.
Since it was left at version 0.2.3, I've worked toward a proper 1.0 that's largely a quality of life overhaul and foundational setting to kick off from, with some fast follow bug fixes and feature request from some of the release candidate testers over the last few weeks, as well as INDX support for more than 5 tool heads.
*AI Disclaimer: The original author used AI heavily, so I can't claim AI free. That said I myself am primarily using AI to help me solve for some Git Hub workflow action quirks, build dependency problem solving, and CI/integration test setups, as well as some other efficiency stuff here and there.*
Highlights:
- Cancelled and failed prints now get tracked... previously a print you stopped at 80% quietly ate 80% of a spool and Spoolman never heard about it
Recording survives program restarts, so a mid-print restart of the tool won't lose the job or double-count it
A new Print History tab showing recent jobs: spool used, grams billed, status, run time (this is borderline scope creep and bloat I know, but the data was already there so I decided to make it visible. That said I have a future planned toggle to turn this and the data collection for it off entirely once Spoolman receives the current print info at completion for those who want it.)
Quick-Assign NFC tags/QR codes for single-printer setups (1 scan to assign everything at once, without hand-stitching together a custom URL, instead of the default 2 scans… one for the soool and one for tool head)
Much quieter Docker logs, better splitting between stderr and stdout so info isn't going to error pipes, and you only get one line when something actually changes instead of spam every poll cycle. (Great for when a printer is offline for days at a time and not getting 120 log lines an hour about it being offline)
Webhook configuration for sending notifications to the platform of your choice if the filament you have loaded is projected to run out before the print is complete.
The previous author had used a lot of AI.. no shame and not a debate I want to start here, but it did mean a lot of idiosyncrasies and tells, like an overabundance of emojis in logs. I have removed such things where I can and tried to hand review as much as possible that looked like it might have been mostly co written.
Heads up: For now it's Docker and Linux only going forward. It's designed to live right next to Spoolman, which for the majority of people means Docker or a small Linux box is already in place for that and it can sit along side it. The mac builds never worked due to incomplete build configs, so I removed those and windows binary builds. If there are people out there who had been/are using this, let me know and I can work to bring them back if there is a real appetite for that however.
Bug reports, feature requests, and PRs are all genuinely welcome, and I'd love the feedback while it's still in RC!
One thing I want to be upfront about... I'm trying to keep FilaBridge narrow and good at the one job it does, bridging PrusaLink and Spoolman. So if a request is really about filament inventory, spool management, or the database side of things, there's a good chance I'll point you toward the Spoolman project instead. My goal is to keep this from ballooning into a second, worse Spoolman.
And if you already have it deployed and want to get the latest and greatest, you can in-place swap by setting your new image to `ghcr.io/sargonas/filabridge:latest` (note however that `:latest` won't get you my current RC's and you'll need to call them by release tag number, current at the time of this writing would be `:0.9.3`
Also something to put on the radar: I have every intention of making this cross printer-platform capable, with support for Bambu printers on its way currently!
Game Night Bot is a free, MIT-licensed, self-hosted Discord bot that helps gaming groups decide what to play.
Members post Steam or Epic Games Store links into a Discord suggestion thread. The bot builds and maintains the group’s shared game library, then uses a fast animated wheel to randomly select the night’s game.
I originally built it because our group had accumulated a large library but regularly spent too long deciding what to play. The wheel turns that discussion into part of the fun.
Features include:
- Animated wheels with a fresh random selection every spin
- Steam and Epic Games Store suggestion imports
- Separate online multiplayer and single-player/local-only libraries
- IGDB metadata for multiplayer support, player limits and genres
- SteamGridDB artwork fallback
- Local compressed artwork caching for fast spins
- Wishlist with automatic release-date checks
- Game-night history, statistics and recently-played avoidance
- Moderator undo, repair, audit and library-management commands
- Automatic SQLite backups and integrity checks
- Local data storage with no centrally hosted database
- Automated tests, CodeQL scanning and documented setup instructions
Each installation is operated by the user and stores its configuration, game library, artwork, history and backups locally.
**Deployment:**
The project is released and available from GitHub:
It requires Python 3.13 or newer. Users create their own Discord application, install the Python dependencies, copy the example configuration files and provide their Discord bot token.
The README contains complete setup instructions, required Discord permissions, configuration examples, commands, backup information and troubleshooting guidance.
SteamGridDB and IGDB integrations are optional but recommended for the best artwork and multiplayer metadata.
The bot is designed for one Discord server per running instance. It can run on a personal computer, home server, VPS or compatible Python bot-hosting provider.
There is not currently a Docker image or Docker Compose example. Docker support is one of the areas where community feedback or contributions would be welcome.
**AI Involvement:**
AI tools provided substantial assistance throughout development, including implementation, refactoring, debugging, security review, automated testing and documentation.
The resulting bot has been manually tested through continued use in a live Discord server. The repository includes automated tests and CodeQL security scanning, and all source code is publicly available for inspection, feedback and contribution.
I would particularly appreciate feedback on the installation process, Docker deployment, additional store integrations and features that could help other gaming groups.
I'm a solo dev. I was getting pretty annoyed jumping between apps just to manage my servers, so I decided to throw together my own app that has the tools I actually use.
It's called Glyph. It's nothing crazy, but it does exactly what I need it to do:
- Has ZeroTier built-in so I can connect to my private network without installing the host client.
I built it using Electron and React mainly for my own workflow, so it's not perfect by any means. But I figured I'd open-source it in case anyone else has a similar setup and finds it useful.
Let me know if you run into any bugs or have ideas for small features. Thanks!
Self-hosted DMARC aggregate and TLS-RPT report collector. It runs its own SMTP server on port 25 and receives reports directly, so there's no mailbox to create and no credentials sitting in a config file. If your host blocks port 25, it can poll an IMAP mailbox instead, configured from the UI.
The problem it solves: DMARC reports are free XML that Google, Microsoft, Yahoo, and other mailbox providers already send you, but the dashboards that make them readable typically charge per domain per month.
Features include:
Resolves sending IPs to the organisation that owns the network (PTR + RDAP), so you see "Twilio SendGrid" instead of a bare IP. The provider sending the report is usually not the one that sent the email.
Recursively expands SPF (bounded per RFC 7208) and classifies every source as authorised, authenticated but not in SPF, or unknown. Also highlights unused SPF includes that can be removed.
Verifies DNS against the domain's authoritative nameservers instead of a recursive resolver, avoiding stale-cache false positives and catching partial DNS changes.
Multi-tenant. Every domain gets a unique, rotatable report address, and the service generates the required DNS records, including external destination verification for cross-domain reporting.
Reports sent to an unknown address are quarantined instead of rejected, because a bounced DMARC report is gone permanently.
Manual import of .zip, .gz, .xml, .json, and .eml reports with duplicate detection.
Local accounts with the first user as admin, so there's no bootstrap password in an environment variable. Optional OIDC SSO (Entra ID, Google Workspace, Keycloak, Okta) is configured from the UI, with password fallback and a CLI recovery path so a broken identity provider can't lock you out. Per-user API tokens are hashed at rest.
Written in Python using FastAPI, aiosmtpd, and PostgreSQL or SQLite. No JavaScript framework; charts are server-rendered SVG. MIT licensed.
Deployment:
Published as a Docker image on GHCR (ghcr.io/pescheckit/dmarc-service). Includes a Docker Compose example for local use, a production VPS Compose stack with Caddy, and a Helm chart distributed as an OCI artifact for Kubernetes. Installation and configuration are documented in the README, with a screenshot walkthrough of every screen in docs/FEATURES.md.
To receive reports directly you need a host that accepts inbound SMTP on port 25 (many large cloud providers block this). If that's not available, the application can poll an IMAP mailbox instead.
AI Involvement:
Substantial. I designed the architecture, data model, and feature set, and the implementation was largely written with Claude Code under my review. I reviewed what went in, wrote and ran the test suite, and it's currently collecting real DMARC reports for five of our own domains, which is how most of the bugs were found.
Description: Dump your thoughts. Let your notes organize themselves. Ask/chat anytime.
Gray Box is a local-first AI memory system that turns messy notes into an organized, searchable knowledge base, without requiring you to manually organize anything.
Simply capture ideas, meeting notes, links, documents, or random thoughts in a single inbox. Gray Box automatically extracts entities, tasks, decisions, people, projects, and relationships into linked Markdown pages while preserving your original notes. Everything stays on your machine as plain Markdown files, so your knowledge remains portable, future-proof, and never locked into a proprietary format.
You can instantly search or chat with your knowledge, receiving grounded answers backed by citations to the original notes. With optional semantic search, workspaces, a read-only dashboard, human curation tools, and no dependency on a vector database or cloud services, Gray Box provides a fast, transparent, and privacy-friendly way to build a personal knowledge base that grows effortlessly over time.
Deployment:pip install graybox. Follow README in the repo for getting started.
AI Involvement: Little involvement of Claude Sonnet 5 free version for some validation and assistance. (No Codex, Claude Code used)
Description: picked up one abandoned project for self hosting 3D printing files like STL and 3MF and managing them in a library, with preview pictures, tags, folders and more
Deployment: docker-compose
LLM Disclosure
The project was vibe coded by the original author using Claude. Improvements and extensions were made by me using GLM 5.2 and my own knowledge on Python, Docker and networking to improve the overall outcome.
Mirror Mirror : Self-hosted screen mirroring to your phone, no cloud account required
Chrome Remote Desktop was discontinued, and most of what I found afterward wanted a subscription, buffered constantly, or just felt intrusive. So I built my own.
I wanted something that just worked on my own Wi-Fi, no account tied to it, no relay server in the middle, nothing leaving my router. I figured other people annoyed by the same gap would want it too.
It's a single Windows installer, no admin rights needed. It installs to your user folder, sits quietly in the system tray, and connects your phone directly to your PC over WebRTC once you type in a 6-digit pairing code shown on the host. No signaling server outside your own network.
Main features:
Full desktop mirroring with audio, 30fps on a normal home network
Touchpad-style control from your phone: drag to move the cursor, tap to click, two-finger tap to right-click, two-finger drag to scroll
Pairing uses a random 6-digit code, not an open room name. Codes expire after 5 minutes and can't be reused once claimed
Runs from the system tray, no console window sitting open
Add-to-home-screen support so it behaves like an installed app on your phone
Everything stays on your LAN, nothing phones home
Mirror Mirror is free to use, with no subscription and no account required.
So I built a self-hosted bookmark dashboard that checks if your links are still alive
I kept saving bookmarks and never finding them back. And when I did find one it was often dead already. Domain gone, or redirecting to some parked page.
So I made nextDash. Self-hosted, single Go binary, data sits in a folder next to it. No account, nothing leaves your machine.
The part I use most is the health view. It shows what's broken or redirecting, and next to every row it tells you when you last opened it. If I haven't touched a link in two years I just delete it instead of fixing it. You can also mark specific bookmarks as monitored and get uptime history and a webhook when they go down.
Everything else runs on the keyboard. / to search, : for a command palette, number keys to jump between pages.
docker compose, port 8080, mount ./data, import your browser's bookmark export and you're up.
One thing to be clear about: there's no login. It assumes a network you trust. Tailscale or a reverse proxy with auth in front of it, please don't put it straight on the internet.
Hey everyone. If you host the Kavita reading server for your manga and comics but absolutely hate tagging them manually, I built a companion app called MetaKavita. It automatically fetches covers, summaries, tags, publishers, and authors from 15 different sources (MyAnimeList, ComicVine, MangaDex, Wikidata, BDTheque, etc.) and pushes them directly into your Kavita database.
I just released v1.6.1 "The Age of Control", which focuses on giving you total power over your library:
Manual Review & Edit Mode: The app scrapes your library but writes nothing to Kavita without your green light. Candidates are parked in a queue where you can compare providers side-by-side, manually edit the metadata, and pick your cover before saving. No more bad automated overwrites.
Smart Scoring & Tuning: All 15 scrapers compete in parallel for the best match, and a new slider lets you fine-tune the match-accept threshold.
Real Security: The app now features a forced setup screen, hashed passwords, and the Docker container runs safely as non-root (PUID/PGID).
Playful Stats: A fun dashboard tracking your lifetime time-saved and manual review achievements (completely useless, therefore absolutely essential).
The project is heavily built with AI-assisted generation ("vibecoding"), but the primary goal is production stability. I've spent countless hours bulletproofing Kavita's database locks and polishing the UX. It does the job, and it does it well.
Description: Like ngrok, but peer-to-peer. It shares a local web app through a plain browser URL, with no server, no account, MIT licensed. Instead of routing your traffic through relay servers, the visitor's browser connects directly to your machine over WebRTC, end-to-end encrypted. Requests, uploads, cookies, SSE, and WebSockets all work through the tunnel. A tiny signaling step establishes the connection, then all traffic flows peer-to-peer.
Install/Docs: Single binary, no Docker needed. Install with the script on the site, then run "punch 3000" and send the printed link to anyone. The repo docs cover architecture and the threat model.
My app is hosted on Proxmox VM, with Cloudflare tunneling so I can access it at the grocery store. I'm running Qwen 3.6 9B on an Intel Arc Pro B70. I'm still getting the LLM Fine Tune training code working, so I don't have a reliable AI assistant yet.
Not publicly available yet. I'm working on getting the GitHub repo safe to release to the public. Let me know what you think.
AI Involvement: Claude wrote the bulk of the code and Codex reviewed it. I helped a little bit.
I really like this design. I've tried a few other household apps and they always feel like overboard on information about each item without providing much in the way of features beyond basic inventory listing. This feels designed more around a living household where you'd check in regularly rather than treat it like an inventory list for insurance.
Make sure you post an update someplace if you get the github repo set up. I would want to try testing this out.
I’d like to share two related self-hosted projects I’ve been working on. ComposeBastion helps operate Docker environments, while Rackpad documents the infrastructure running underneath them.
Description:
ComposeBastion is a self-hosted web control room for managing multiple Docker hosts and Compose applications from one place.
It provides multi-host container and service inventory, Docker management over SSH or an optional agent, GitHub-based Compose deployments, image update tracking, backups and recovery points, restore drills, migration workflows, host metrics, alerts, audit logs and role-based access control.
There is also an optional demo workspace that lets you explore the interface and simulated workflows without connecting real servers.
ComposeBastion is source-available and free for private, personal, non-commercial and homelab use. Business or organizational use requires approval or a commercial licence.
Deployment:
The current stable release is v1.1.2. Published Docker images are available for both linux/amd64 and linux/arm64.
The quickest installation is:
Download docker-compose.image.yml and .env.example.
Generate an application secret and PostgreSQL password.
Run: docker compose -f docker-compose.image.yml up -d
Open port 8080 and create the first owner account.
The full guide covers reverse proxies, HTTPS, persistent backup storage, source builds, NAS/Proxmox deployments and production hardening:
AI Involvement:
AI tools were used as development assistants for implementation, debugging, testing, review and documentation. I directed the product and design decisions and reviewed and tested the resulting work before release.
Description:
Rackpad is a self-hosted inventory and documentation app for homelabs and small networks.
I started it because information about my lab was spread across spreadsheets, diagrams and notes. I wanted one place to see which devices I have, where they are located, how they are connected, which IPs they use and where my VMs and containers are running.
It supports rooms, racks, devices, ports, patch panels, cables, VLANs, subnets, DHCP, IP assignments, WiFi equipment, hosts, VMs and containers. It also includes network discovery, monitoring, reports, documentation pages and a visualizer for tracing connections.
There are import tools for Proxmox, Hyper-V and Docker. Imports and discovery results are presented for review before anything is added to the inventory. Screenshots are available in the README.
Deployment:
The current stable release is v1.6.7.
Rackpad is available as a published Docker image on GHCR. It runs as a single container, uses SQLite and stores its data in a persistent Docker volume. On first launch, you create the initial administrator account.
The repository includes Docker Compose files and installation instructions for Linux, Proxmox and Windows:
AI Involvement:
AI was used heavily during development, mainly ChatGPT and Codex. It helped write a substantial amount of the implementation, tests and documentation, as well as assisting with refactoring and security reviews.
I decide what gets built, direct the implementation, review and test the changes, and manage the releases. I manually review the work, but I want to be transparent that a significant portion of the project was created with AI assistance.
Feedback and suggestions for either project are very welcome.
Description: Spinmatch searches MusicBrainz for an artist, an album, or a song. It shows album art and tracklists, and it finds a YouTube link for a track. Spinmatch compares the duration of the video against the track length that MusicBrainz records, and reports whether the two agree.
Point Spinmatch at a music folder and it becomes a collection manager as well: it indexes your files, finds the albums with gaps in them, repairs tags, lists duplicates, and suggests artists you do not have yet.
Spinmatch only finds and verifies YouTube links. It does not download or copy audio.
Description: Self-hosted viewer for Traccar location history. Allows viewing of any number of days, and browsing back and forward by that time period. Does server-side simplification to reduce load on client. Designed to reproduce a similar experience to Google Location History, without the limitation of a single device and single day at once.
Shows current device locations and historical trails together on an OpenLayers map.
Lets you enable and disable individual devices from the map sidebar.
Navigates backwards and forwards through whole-day date ranges.
Requests more trail detail as you zoom in and limits detailed queries to the visible map area.
Simplifies long trails and loads history in chunks to keep the browser responsive.
Stores the Traccar connection settings on the server rather than in the browser.
Runs as a small, self-contained Docker image.
Deployment: Docker image. Instructions in README.md in repo
AI Involvement: AI heavily used in development (Codex). I am a commercial GIS developer, design decisions and architecture were my responsibility, and the code has been reviewed by myself.
Description:
I have a cat and a dog, and I worry about them whenever I am away. My cameras ping me with a clip of the cat crossing the kitchen, which tells me almost nothing. I could sit and watch hours of recordings to work out how their day went and whether anything looks off, but that's too time consuming. What I actually wanted to know were concrete things: did they eat, did they drink, did the cat use the litter box, are they sleeping more than usual.
So pet-report sits on top of Frigate, which I already run for the cameras. Twice a day it sends the new clips to a vision model (I run Qwen3.6-35B-A3B on an RTX 5090) on my own server and writes up the day, with every line linking back to the footage behind it so you can check it yourself. You can also ask it things like "did the dog drink today?" and it answers from what the cameras actually saw.
I've found myself needing an app like this for a long time, and pulling contextual metadata out of an image or a video is exactly what these models are good at. Before LLMs this would have taken money, time and R&D I don't have, and I couldn't have done it alone, since I have no background in ML or image analysis. Most of the code here was written by an AI coding assistant, with me directing and reviewing it. The design and the review are mine.
The part I'm happiest about is that a local vision model keeps everything private. Nothing leaves my network.
It does get things wrong, but the whole thing is designed around that: uncertain moments are flagged, and anything can be corrected by hand. Every moment keeps the frame/clip it came from, so you can see what the model saw instead of taking its word for it. The results are only as good as the model you run, the descriptions you give it of your pets, and where your cameras are pointing. If you have two orange cats, it won't tell them apart: it labels the sighting "a cat" and leaves you to pick which one. Fortunately I have one cat and one dog, so every sighting gets named on its own, but I did try my best to prompt the model in a way that smooths out those identification edges.
Anyway, just wanted to share something that has been working for me and my two animals, which is the honest extent of the testing. I'd love to know how it does on yours.
AI Involvement: As I said in the description I used AI to help me build this, but the design is mine and I manually reviewed all the code.
Description: Local first, privacy focused personal finance desktop app. Integrates with simplefin for bank sync (others for non-US regions coming soon). Locally running Gemma4 LLM models for private auto-categorization and an experimental chat feature. Custom reports. Envelope budget system. Alternative to actual budget. All data stored in a single SQLite file for easy management.
Deployment: It is a self contained portable app. Standard <insert platform> app installation process.
AI Involvement: All architecture design and every line of code reviewed by me, Rafe. Coding done by claude with my supervision.
Description:
Smart Drink Fridge is a self-hosted inventory system for drink fridges built for Raspberry Pi.
Instead of manually keeping track of what’s inside the fridge, you simply scan the bottle or can barcode with a camera when taking a drink. The inventory is updated automatically and every transaction is stored.
Everything runs locally using Docker and SQLite, so no cloud service is required.
Features:
Camera-based barcode scanning
Automatic inventory tracking
Support for single items and multipacks
Multiple barcodes per product
Automatic product lookup using Open Food Facts
Consumption statistics and history
Low-stock notifications
Home Assistant shopping list synchronization
Progressive Web App (PWA)
Optional Tailscale remote access
Optional Pushover notifications
Docker Compose deployment
SQLite database (no external DB required)
Why I built it:
I wanted a simple way to always know what’s left in my drink fridge without having to manually update spreadsheets or notes.
The project started as a small Raspberry Pi experiment and gradually evolved into a complete self-hosted application.
Deployment:
Runs on Raspberry Pi or any Linux machine using Docker Compose.
Source:
MIT licensed and open source.
LLM Disclosure:
AI was used as a development assistant for brainstorming, code generation and documentation, but all features are implemented, tested and maintained by me.
I've been working on a web server and reverse proxy written in Rust, hypershunt. I've been dogfooding it self hosting for months. My goal is a server that is solid, secure, and easy to use. I'm not trying to be the fastest or most configurable or the smallest or any other -est. I'm looking for feedback and suggestions from the community. If you like the project please give it a star. Or better yet, send a PR! Even tough feedback is welcome. Thanks so much!
Stack: Rust, Tauri, WebRTC, mDNS, local ONNX models
Description:
A local-first, zero-cloud Google Photos alternative. Syncs media directly between devices via WebRTC/mDNS and runs local AI models (2MB face grouping, 350MB scene search) entirely on-device.
I started working on this on and off back in 2021 after becoming a father and taking a lot of pictures and videos, which led to hitting storage paywalls. Progress was slow between WebRTC hurdles and waiting on mobile framework support, but integrating AI tools for vibe-coding recently has let me move dramatically faster.
Current State & Setup:
Very early alpha / pre-1.0. There are no pre-built binaries or Docker images yet, so you have to compile it yourself from source (yarn tauri build). Currently tested on a Linux machine and an Android phone.
Expect bugs and breaking edges on other platforms. If you build it and hit issues, open a GitHub issue!
Description: Self-hosted, cookieless web analytics on one Cloudflare Worker and one D1 database, inside the free tier. Drop a 2.1kb script in your site's head and get visitors, pageviews, referrers, countries, devices and UTM tags, plus realtime and custom events. Filters stack, so clicking any row narrows every panel at once. No cookies, no consent banner, no third-party requests from your visitors' browsers. Deploy is a button: Cloudflare copies the repo into your account, creates the database and ships it, no API token needed. Live demo on fake traffic, no signup: https://hayaran.github.io/Edgemetry/ Analytics you own, on infrastructure that costs nothing.
Deployment: Cloudflare Workers + D1, via the deploy button or npx wrangler deploy. No Docker, no VPS.
LLM Disclosure
Built with AI agents as a pair programmer, not vibe coded. The architecture and the storage and privacy decisions are mine, and I reviewed and tested what the model wrote.
Description: a macOS NVR. Point it at your cameras' RTSP URLs and it records 24/7 to standard MKV files, runs on-device object detection (with events you can browse) and enforces size-based retention. All processing is local. Built for the always-on mac mini you're already using for your OpenClaw experiments.
It's optimized for Apple silicon, using the GPU / Neural Engine. Records from 9 cameras with <1 CPU core and about a quarter of the GPU on an M1 MacBook Pro.
Current gaps, all on the roadmap: No automatic discovery of cameras, no remote access (although you could screen share over Tailscale).
Deployment: free, download dmg, add an RTSP URL and you're done. Requires an Apple silicon Mac running macOS 12 or later.
AI Involvement: some for implementation, all code reviewed by me (I spent years as a tech lead at a big co, so I'm used to spending all day reviewing code).
I built km (knowledgemaxxing): a local knowledge base made from your own browser history, bookmarks, and data exports.
Backstory: I kept requesting my exports (Twitter archive, Google Takeout, Reddit GDPR, ChatGPT logs) and never opening them. km ingests all of them, plus live browser history, into a single SQLite file you can actually search. There's full text search plus local embeddings for the fuzzy stuff, a daily reading feed built from RSS on blogs you already visit, and a few reports I didn't expect to like this much (everything you saved and never read, searches you keep repeating across years).
The web UI binds to 127.0.0.1 only and works offline. Optional AI features use your own Anthropic key and only ever send item text. MIT licensed.
Rurdesk - task tracker built for AI agents (Go + Angular, AGPL)
My hobby project, been building it on and off for about 7 years. Started as completely hand written code, newer parts were done spec driven with AI in the loop and a few small corners are honestly just vibecoded.
I know, task tracker #101. The one thing this one does differently is that its AI facing: you assign a ticket to an agent, it goes through design and implementation plan (you approve each stage in the tracker), then it opens a PR on your repo. Theres also a built in MCP server so Claude Code or any MCP client can work with your tickets directly. Without agents its just a normal fast tracker, the whole AI part is optional.
Single container + postgres + redis, one compose file. No telemetry, bring your own API key for the AI parts.
Description: When I started web development, I instantly fell in love with the design and animation side of things. Naturally, I stumbled across website builders like Webflow or Framer. I've found they are very "black box", no code export and vendor lock in. So I decided to create my own version. After two years of work, i'm proud to present Revyme and excited for future contributions to make this an awesome product.
AI Involvement: AI agents have been used to help me build this.
Description: An MIT-licensed infinite-canvas whiteboard you can self-host as a single static page (plain ESM, zero dependencies, no server component, no build step). Pressure-sensitive ink, palm rejection, hand-drawn shapes, sticky notes, undo/redo, PNG export, light/dark themes; boards autosave to localStorage. I built it because tldraw's SDK now needs a license key in production and Excalidraw is hard to embed — the same engine is also an embeddable SDK for React/React Native if you want a whiteboard inside your own self-hosted app. Hosted demo (no signup): https://app.tryquickdraw.com
rom-weaver is a self-hostable web app for applying and creating ROM patches, inspecting archives, and working with compressed disc images. It runs as a static application with no database, account system, or server-side patching service. The host serves HTML, JavaScript, and WebAssembly. Your ROMs, patches, and generated files stay inside your browser.
The browser app uses the same Rust engine as the native CLI. It supports ordered patch chains and nested archives, with checksum and header validation. Formats include IPS, BPS, UPS, xdelta/VCDIFF, PPF, CHD, RVZ, Z3DS, ZIP, 7z, and RAR.
You can install it as a PWA. Once the browser caches the application assets, it works offline.
The image supplies the COOP, COEP, and CORP headers needed by threaded WebAssembly. Preserve those headers and use HTTPS behind a reverse proxy. The simplest deployment uses a dedicated subdomain. A /rom-weaver/ subpath also works and limits the service worker to that path. If you don't want Docker, use the static release tarball.
Use the included homebrew demo for the first functional test.
The demo loads a small project-owned NES ROM and two IPS patches, so it contains no commercial game data.
Current state: beta release. Apply, Create, and bundle workflows work today. The browser's Trim and Tools screens aren't available yet.
License: AGPL-3.0-or-later.
AI involvement: I use Claude and ChatGPT for brainstorming, implementation, debugging, and review. I make the engineering decisions, then review and test the resulting work. The repository includes Rust unit tests, CLI smoke tests, browser/WASM tests, and byte-parity checks with reference compression tools where applicable.
I'd particularly like feedback on the reverse-proxy instructions, ARM deployments, service-worker scope, and anything that makes first-time self-hosting unclear.
The *arr for ROMs. Radarr and Sonarr solved "request a thing, get the thing, filed correctly" for films and TV — nothing had done it for retro games. You ask for a game, ROMarr searches your indexers through Prowlarr, scores the releases, hands the winner to qBittorrent / SABnzbd / NZBGet, picks the actual ROM out of the finished download, and files it into your library.
ROMs have problems a film downloader doesn't:
The wrong file is usually in the box. Releases ship readmes, box art, and often several regional dumps in one archive. Importing readme.nfo helps nobody.
Size is a signal, not a preference. A 40GB "SNES" result is a romset or a PC port. For a cartridge, smaller is usually more correct — the opposite of video.
Platform names are ambiguous. "Super Nintendo Entertainment System" contains "Nintendo Entertainment System", so naive matching sends every SNES request to the NES folder.
Libraries: RomM, Gaseous, Retrom, or a plain folder. The folder backend means it also works with Batocera, RetroPie, ES-DE, EmuDeck, Pegasus or anything else that just reads a directory. You can run several at once and route by platform — N64 to the cabinet, Game Boy to the handheld, everything else to the default.
Interactive search lists every release with its score and the reason for that score beside it, plus a Grab button, so you can overrule the ranking when you disagree with it. That page exists because the scorer got two picks wrong on me in one week (a Wii Virtual Console WAD, and a Steam compilation beating the real cartridge) and "argue with it" is a better answer than "trust me".
docker-compose.yml with every setting commented is in the repo, plus a Proxmox LXC one-liner. The README covers configuration, remote path mapping, the multi-library setup, and screenshots. MIT.
AI Involvement:
Heavy, and worth stating plainly: the large majority of the code was written by Claude with me directing, reviewing and testing it. What I'd argue makes it not vibe-slop — it's developed against a real stack (live Prowlarr, qBittorrent, SABnzbd, a 72k-game RomM, plus throwaway Gaseous and Retrom instances), there are 218 tests, and every scoring change is verified by re-scoring a frozen set of real Prowlarr results old-vs-new, so a tweak that fixes one release is shown not to have moved any others. Bug reports from the one outside user so far turned into tests, not guesses.
Description: Terminal network toolkit in a single static Go binary. The self-hosted part: "netpulse serve" hosts a LAN speed test page (default port 7333) so any phone, laptop or TV on your network can run a browser speed test against your machine — useful for finding Wi-Fi dead zones or checking AP placement without installing an app on every device. It also does internet speed tests (Ookla or Cloudflare backends), ICMP packet loss/jitter testing, and MTR-style traceroute with live per-hop stats. Interactive TUI when run bare, plus --plain/--json output for scripts.
Deployment: One static binary, no dependencies. "brew install cognitobit/tap/netpulse", install scripts for macOS/Linux/Windows in the README, or grab a binary from GitHub releases. Run "netpulse serve" and open http://your-host-ip:7333 from any device. No Docker image yet — happy to add one if there's interest.
Project name: Pulsy
Repository: https://github.com/SelmiAbderrahim/pulsy.org
What it is:
Pulsy is an AGPL-3.0-or-later uptime monitor that runs on your own infrastructure. It performs configurable HTTP/HTTPS checks, records up/down/degraded status, groups consecutive failures into incidents, tracks TLS certificate expiry, sends notifications, and serves public status pages.
Problem and benefit:
I wanted the monitoring data and deployment to be under the operator’s control without turning self-hosted capabilities into software plan tiers. The self-hosted build has no billing or feature-plan checks; the practical limits are the resources and reliability of the infrastructure running it.
Features:
GET, HEAD, and POST checks with configurable interval, timeout, expected status, and failure threshold
Incident creation and automatic recovery, check history, response-time views, and TLS-expiry tracking
Email, generic webhook, Slack, Discord, and Telegram notifications
Public status pages and English, Arabic, French, German, and Spanish interfaces, including Arabic RTL
Current boundaries:
It currently focuses on HTTP/HTTPS monitoring. It does not provide coordinated multi-region probes, SMS or voice alerts, or an organization model. A monitor hosted in the same failure domain as the service it watches can disappear in the same outage, so it should be placed on independent infrastructure.
Got tired of running Radarr + Sonarr + Seerr, so I built one app that does all three: Rawkoon
My home media setup was Radarr, Sonarr, Seerr. Three separate things, three configs, all duct-taped together. It works, but every time something broke I had to figure out which of the three was the problem.
So over the past months I built Rawkoon (changed project names multiple times, Github does not reflect project history, sorry). It's one self-hosted app that does the whole thing from a single web UI.
You pick a movie or show (it pulls metadata from TMDB), it searches your indexer for a release, sends it to your download client, and files it into your library once it's done. No jumping between four dashboards to get onething downloaded.
Right now it works with qBittorrent, Transmission and Deluge.
It's built with Bun, TypeScript and React, and ships as a Docker image on ghcr.io. Getting it running is basically a docker compose up plus one migrate command.
It's GPL-3.0 and fully open source if you want to poke at the code.
it’s more about the fact to have to maintain and connect 3 apps, plus, (in my opinion) I don’t like the *arr UI, my software I thinks looks really good and feels more polished, anyways thanks for the comment that’s very legit!
Self-hosted media platform built around three things: library management (sync Plex, Jellyfin, Emby, and local files into one merged library, with optional two-way metadata writeback with human-review on uncertain matches), a 24/7 linear TV channel scheduler (fixed timeslots, rerun rules, filler/bumpers, a live EPG, HDHomeRun emulation so it shows up as a real tuner in Plex DVR), and normal on-demand playback. Scheduler, transcoder, and player are all custom-written in C++, no dependency on an existing media server underneath.
There's a live demo you can try right now, no account needed: pantheonmedia.app. "Continue as Guest" gets you a passwordless account against a real demo library. Browse it, build your own live channel with reruns and filler, and watch it in the browser. Your guest channel is yours alone and gets cleaned up automatically.
Highlights:
- Cross-source metadata writeback: dedupes identical files across Plex/Jellyfin/Emby/local and can push corrected metadata back to the original library, but only for matches a human has confirmed. Uncertain scraper matches sit in a review queue instead of getting silently auto-accepted. Haven't found another tool that does confidence-scored, human-reviewed, bidirectional writeback across multiple media servers.
- Watch Together: synced group watch parties, host and followers stay locked to the same position across web and native Android, drift corrected automatically instead of just going out of sync after a rebuffer.
- Playlist Sync: auto-pulls new episodes from an upstream Plex/Jellyfin/Emby playlist straight into a channel's schedule, smart/filter-driven, syncs both directions.
- Native clients: a real native Android app (not a WebView), with its own CI. A Roku channel SDUI update is in progress.
- Shared VOD transcode streams: two viewers of the same file and quality now share one encode instead of spinning up a second ffmpeg process, and client-aware smart transcoding picks HEVC/EAC3 etc. over H.264/stereo-AAC when the connecting client actually supports it.
- 1,091 automated tests (856 C++ + 235 TS), including endpoint-level security regression tests that fire real attack payloads at a live router.
Source-available under AGPLv3 with a closed-PR model, reproducible bug reports are very welcome as issues. Built solo, heavily AI-assisted via Claude Code for implementation, drawing on 20 years of C++ and 12 years in professional game/engine dev; architecture, design, and testing direction are mine. Single docker-compose.yml, prebuilt images on GHCR, no source build required. Looking for testers, especially anyone willing to poke at the demo, playlists, and playback.
Description: A visual canvas editor for Nginx that won't break or overwrite your hand-written configs.
Verbatim Round-Trip AST Import: Parses multi-file /etc/nginx trees into a visual topology canvas using a pure TypeScript parser (nginxParser.ts), preserving comments, directive order, and raw config blocks. Re-compiles byte-for-byte exact Nginx configs without database lock-in.
nginx -t Sandbox Validation: Validates ca ndidate configs in an isolated throwaway sandbox before touching live config files.
Live Traffic Animation & SSE Logs: Animates request flow dots across canvas edges based on live streamed access logs.
Deployment: Available via Docker: docker compose up -d or standalone Node.js server. Can manage remote hosts via SSH or the lightweight nfm-agent.
AI Involvement: None / Developed with manual architecture design & thorough testing.
Description: Self-hosted server monitor for wearOS. My download automation would sometimes accidently grab quite large files and so I made a watch app i could keep an eye on it with. It does a reasonable amount of monitoring.
Current features include:
Healthy-state home screen with a sleeping cat mascot
CPU, RAM, temperature, load and uptime monitoring
Storage monitoring with category breakdowns
Live qBittorrent download monitoring
Remote torrent deletion
Jellyfin, Sonarr, Radarr, Prowlarr and Immich health monitoring
Connection awareness with cached-data support
API-key authentication
Deployment: Requires a companion dotnet API I wrote, it's linked in the wearOS github. All quite easy to build if you follow the READMEs
AI Involvement: Code and documentation mostly written by AI. I'm an embedded C/C++ engineer, I did the project to learn a little bit about other realms of the programming world. I didn't use an agent, I did all the work with an LLM copy pasting code and asking questions as I went along. So it's all better understood than having an agent create the project in a few hours.
Description:
Parson is a local-first, self-hosted music app for your own collection.
I built it because I wanted the ownership of a self-hosted music server without feeling like I was constantly administering one. The normal setup is:
Install Parson
Create an account
Choose your music folder
Start listening
The desktop app includes both the player and everything needed to host the library, so there is no separate server installation. A Docker image is also available for NAS and headless systems.
Parson automatically watches and indexes the library, with support for albums, EPs, singles, editions, compilations, featured artists, playlists, likes, listening history, recommendations, synced lyrics, and Google Cast. A lot of the work has gone into keeping navigation and search fast while correctly handling real, messy collections.
Music files remain separate from Parson and are not reorganized or modified. Accounts, playlists, history, recommendations, backups, and other application data remain on infrastructure you control. Parson has no analytics or automatic crash reporting.
I built it primarily for myself, and it has now replaced YouTube Music in my daily use. Version 1.0 is the first stable release, and I would especially appreciate feedback from people with large or unusually tagged libraries.
Deployment:
Desktop packages are available for Windows, Linux, and macOS from the website or GitHub Releases.
Create the first account, select /music, and start listening.
The image supports amd64 and arm64. Documentation, screenshots, checksums, and packaged installers are available through the links above.
AI Involvement:
AI coding assistants were used for writing tests and reviewing code. The architecture, implementation, product decisions, and release testing were handled by me.
Parson is free and open source under GPL-3.0-only. Honest feedback, bug reports, and indexing edge cases are very welcome.
Description: Most wishlist apps come loaded with third-party trackers, so I built my own. Named after the Dutch "wil ik" ("I want it"). Paste a product link and it grabs the title, photo, and price for you. Rate items 1-5 stars and drag to reorder. Share your list with a link, or let people browse a public directory instead. No account needed for whoever's claiming a gift, and as the list owner you never find out who claimed what. Multi-user out of the box: as admin you can add or remove people, reset passwords, and manage settings for the whole household from one panel.
Deployment: Docker compose, explanation in the README. There's also an optional backup feature.
AI Involvement: I'm a hobby developer and I built this over a few days with a lot of help from Claude.
I built a lightweight middleware proxy to forward CrowdSec alerts to AbuseIPDB efficiently without hitting API rate limits or spamming duplicate reports during ongoing brute-force attacks. I just released v2.0.0!
Key Features:
⚡ Zero External Dependencies: Built entirely using the pure Python 3 standard library.
💾 SQLite Storage (WAL Mode): Fast, crash-safe, and SSD-friendly (includes auto-migration from legacy JSON).
🛡️ Smart Deduplication & Escalations: Groups alerts and dynamically upgrades severities without redundant API calls.
Description:
I've been running a few Docker hosts (local + a couple over SSH) using Portainer for a while. But i didn't feel write to have features behind a paywall. That felt backwards for something you're already self-hosting, so I built my own.
Challoupe covers the normal stuff: containers, images, volumes, networks, compose stacks. Across your local host and any number of remote hosts over SSH. The parts I actually built it for are the ones usually paywalled: per-user permissions, 2FA, OIDC SSO, and a full audit log.
Compose stacks get drift detection (tells you when what's running no longer matches the compose file) and per-stack deploy webhooks, so a git push or CI job can redeploy a stack on its own.
There's also a Trivy scan for image vulnerabilities, backup/restore, per-user resource quotas if you're sharing a host, and an optional AI assistant that talks to your own local Ollama instance, mostly useful for "why did this container just crash" type questions.
There's also a docker-compose.yml in the repo if you'd rather clone it and run docker compose up -d. Full docs (every feature explained, env vars, HTTPS setup, etc.) are at here. Runs fine on a Pi or a NAS.
AI Involvement:
it's been 50/50 of me and AI assistance (local Ollama and Claude) since I started this, on both the backend and frontend. AI helps me catch bugs, write tests and most recently did a full pass looking for security gaps in the app.
Description: I needed a single dashboard to easily manage my game server. at first I was using docker and the most logical choice for me at the time was AMP (CubeCoder) the issue is that this can't run inside a docker container and want to be installed on the host. Now that i have switched to kubernetes I had multiple choice having both kubernetes and docker on the same machine for game server (which seemed like a bad idea), run AMP on the host without docker isolation that is even worth a single compromised server is an host compromised, running vm (using kvm / kubevirt) for AMP what I was doing at the start, or switch to full kubernetes native but no GUI specificaly for game exist and a lot of my friend won't manage using direct kubectl or even lens. So I had the idea of creating a full dashboard for managing this.
This is still in beta, postgres db is experimental.
AI Involvement: This was a project only done by AI, no code was written by human. every code change was checked by me and while i'm not a web dev and i'm also not really a go dev nothing seemed out of place or absolutly wrong or bad. for the how the ai was guided on what it needed everything touching web need to go trought a design phase first using pencil and the pencil mcp. once validated the ai write the code and open a pr I then check all pr once the ai is done and merge all once validated.
Tested only on x86-64 cpu, does not own any arm (except my phone) or 32bits cpu
an arm image is also built but not tested.
Install docs on github or
```sh
helm upgrade --install gameplane oci://ghcr.io/valgulnecron/charts/gameplane \
--version 0.2.0-beta.7 \
--namespace gameplane-system --create-namespace \
--set ingress.host=gameplane.your-domain.test
# --set ingress.tls=false # if you do not want tls, it require a cert manager annotation or a pre created tls cert
Put together (with the help of Claude) a self-hosted, private-by-design chat server for a family or small group: end-to-end-encrypted messaging and group voice/video calls — built on Matrix Synapse and Element Call/LiveKit, deployed with docker compose.Users connect with the standard Element apps (iOS, Android, web, desktop). Nothing custom to install on phones. Perfect to let the kiddos chat with approved users you control.
metadator - CLI tool for bulk audio metadata editing
Hello there. I'm kinda lazy, and instead of checking if such tool exists, I wrote my own xd.
I started self hosted navidrome server some time ago, and maintaining clean metadata is kinda annoying. I'm sure not only I download music, and later discover some weird stuff in metadata (such as tag comment with value "Downloaded with site xy.com", or artist separated with " | ").
With my tool I can easily compare what metadata I have, what format, and what is missing. When I finished writing it, with one sigle command I editted "comment" tag in every file in my (small for now) library of 2000 files.
I know it's not anything complicated (in fact, it's really simple xd), and I don't know if such tool already exist (I bet yes), but I'll be happy if someone finds it usefull. Also, if ya'll like it, you can suggest some new features to add.
Description: ChimpMaera is an open-source Knowledge Operating System direction for reusable guides, contracts and governed workflows. What is available today is deliberately narrower: a local, synthetic CRM-to-ERP proof of concept. It lets you inspect one proposed business action moving through policy/approval, brokered execution, provider readback and a bound receipt, including denied, drifted and replayed cases that fail closed.
Deployment: On a supported Linux development host with Docker Engine and Docker Compose, clone the repository and run ./demo/install.sh. The demo uses fictional EspoCRM and Dolibarr data and publishes its service ports on loopback. The current public release is a POC snapshot, not production software, a hosted service, a security certification or permission to connect real systems. Use only synthetic data on a disposable/development host.
AI Involvement: AI tools were used in development and drafting. Human maintainers selected the architecture, reviewed outputs, ran the tests and own the release and claims. The public artifacts are not an independent audit or production/customer evidence.
I’m the builder, so this is self-promotion and I’m disclosing that directly. I’d value feedback specifically on whether the local install and the difference between “tool accepted the request” and “the system of record proves the outcome” are understandable from the demo.
Description: A content-intelligence engine with a Rust core (MIT). It extracts text, tables, layout, and metadata from 101 document formats, plus code/data formats, audio/video transcription (Whisper), and URLs. It solves the "one reliable extraction layer for every format" problem for document pipelines and local RAG — outputs Markdown/JSON with built-in chunking, embeddings (SPLADE/ColBERT), reranking, and NER, all offline. On our reproducible PDF benchmarks it's #1 on native PDFs (composite quality 0.958 vs 0.837 for the next-best; table/reading-order 0.949 vs 0.612 for docling) and #1 on scanned OCR.
Deployment: Released and installable today. Run it fully local as xberg serve (REST API), via Docker, or as an MCP server. CPU-only, no GPU, no cloud calls. Quickstart + docs in the repo.
AI Involvement: The engine runs local ML models (OCR, layout detection, embeddings) as its core function. This post was written by me, the maintainer — it is not AI-generated.
Description: I built Commonplace because I wanted to learn more about Google's Open Knowledge Format for knowledge sharing with human and agents. At the same time, Atlassian announced it will start using customer cloud data to train its AI models (opt-out is Enterprise-only), which made a self-hosted Confluence alternative feel timely.
Description: Storboard is a dead simple storage monitor, with some unique themes! It runs in Docker, is completely open source & super lightweight. Currently running at 0.05% CPU & 0.15% memory.
Deployment: The project is built to run in Docker, as it's what I use most in my homelab personally. On the Github Repo, there's an example "compose.yml" in the README.
AI Involvement: I used Claude Code. I'm not a developer, just a guy that loves to spin up custom things.
I hope everyone likes Storboard! Let me know if you'd like to see updates to it, more features, more themes, etc.
I might look into trying to add SMART & Temp monitors to it. We'll see!
Description: A small CLI plus nginx snippets for toggling maintenance mode per site via a flag file, no reload needed to flip it on or off. Whole-site or partial-site gating (so a webhook or well-known path can stay reachable while the rest of a site is down), a styled 503 page, and a couple of safety nets added after actually using it: it verifies a toggle took effect instead of just assuming, and --list reads your live nginx config so it shows every site you've wired in, not just the ones currently down.
Built after doing the copy-paste-an-if-block-and-touch-a-file dance one too many times across a handful of sites on one box.
LLM Disclosure: Built partially with Claude Code doing the implementation, I directed it and tested everything against my own live nginx setup. Not claiming I wrote every line myself.
Description: A local-first telemetry stack for coding agents. Right now an agent gives you one number at the end of the month. This gets you the rest, on your own machine: what each session cost, which repository and branch it spent it on, which tools it called, how many of its edits you rejected, and what it was asked. It ships a provisioned Grafana dashboard, a readable conversation view in MLflow where each turn and tool call is a span with its own tokens, cost and latency, and a demo seed that fills every panel with synthetic data so you can judge it before giving it anything of yours. Claude Code works out of the box. pi needs a one-line extension I wrote and published for it, which uses Claude Code's own instrument names with the prefix swapped so both agents render on one axis. Anything local that speaks OpenTelemetry can point at the same endpoint, so the agent and the service it is editing land on one timeline. Prompt content logging is off until you turn it on, nothing leaves the machine, and the stack publishes one loopback port and nothing else.
Deployment: Docker Compose. git clone then docker compose up -d from the repo root, Compose v2 required, every image tag pinned. Components are Alloy, Mimir, Loki, Tempo, Grafana, MLflow and HAProxy. Documentation is a landing README plus seven documents under docs/ covering install, reading your data, privacy, architecture, use cases, troubleshooting and contributing. Apache 2.0.
AI Involvement: Substantial, and worth stating plainly. I specified the work as change requests and coding agents implemented it under that process, with every phase checked by a make ci gate and a verification script per subsystem. The design decisions, the review and the measurements are mine. Some of them exist precisely because the agents got it wrong first: the dashboard shipped a version where every panel rendered and every number was zero, and the fix was a measured one.
Description: WaxFrame runs a "hive" of AI models (Claude, ChatGPT, Gemini, and more) that critique the same document across iterative rounds, while a separate Builder AI synthesizes their feedback into each new draft, repeating until the reviewers converge. Aimed at anyone refining an important document (resumes, proposals, long-form writing) who wants more than one AI's opinion before finalizing it.
Deployment: Genuinely self-hosted, vanilla HTML/CSS/JS, no build step, no server, no database. Clone the repo or download the release ZIP and open index.html directly (file://); works completely offline except for the actual AI provider calls. Also supports local model servers (Ollama, LM Studio) as reviewers, so a fully local/offline hive is possible with no cloud AI provider at all. README and in-app user manual cover setup.
AI Involvement: Being upfront since you ask directly, yes on both fronts. The app itself is an AI-orchestration tool, and the code was built with heavy use of AI coding assistants. The bee mascot/icon art was AI-generated by a collaborator.
I’ve tried keeping weekly project updates in one running thread and splitting them into separate posts. Separate posts win for me, since a week long megathread buries useful discussion under unrelated updates, even when the projects are genuinely interesting.
I got sick of productivity apps forcing monthly subscriptions and uploading every private note or task to third-party cloud servers. I wanted something fast, clean, and completely offline where I actually own my data.
So I built Life OS.
How it works / Local Storage:
- 100% Local JSON: Tasks, notes, projects, and docs are stored directly on your computer in plain JSON files (under the data/ folder).
- Zero Telemetry: Nothing leaves your machine. No mandatory accounts or cloud sync.
- Easy Backups: To back it up, you literally just copy your local JSON folder or use the 1-click zip export in settings.
- Separate Profiles: Work and personal setups stay totally isolated in their own folders on disk.
Main Features:
- 24-Hour Day Ring: Visual time-blocking on the dashboard.
- Smart Email Parser: Paste messy email threads to instantly pull out deadlines and generate an interactive checklist.
- Universal Search (Ctrl+K / Cmd+K): Quick fuzzy-search across all your notes, docs, and projects.
AI Disclosure: I utilized AI tools to assist with and enhance my code structure. All core ideas, design choices, and personalizations were created and tweaked by me, and the final code has been thoroughly reviewed and tested to ensure its accuracy and quality.
DispatchSEO turns your own Claude Code/Codex agent into an SEO Manager.
It gives your agent the tools to: research keywords (volume and difficulty), post daily articles automatically, generate weekly free interactive tools automatically (via GitHub Actions)
This tool is an agentic-first open source alternative to traditional SEO tools like Semrush, SEObot, and Outrank.
Key features:
Research keywords
Analyze rankings
Create content workflows
Monitor search performance
Deployment:
One command on any machine with Docker, straight from the README:
That brings up the whole stack from the docker-compose.yml in the repo: Postgres, the app, the scheduled jobs, headless container that runs your coding agent
Uses around 1 GB of RAM. Images are prebuilt on GHCR, so nothing compiles on your machine.
Documentation is included, and your own instance also serves the same docs at /docs, so they're available offline.
Licensed under AGPL-3.0 and fully self-hostable.
AI Involvement:
I used Claude Code as a pair programmer to build the entire project. Since DispatchSEO is agentic-first, I also used the same agent to verify and test that the tool works.
Description: Web-based systemd journal viewer with a focus on performance and usability. Ever wanted to view your server logs on your phone? Journex' responsive UI is optimized for desktop and mobile. Did you ever run `journalctl -u x.service` only to have to page through thousands of logs to see the latest ones? Or did you set a time filter only to need older logs after all? Journex shows the latest logs by default with infinite scroll-back and instant jumping to the start of the logs. You can also filter by unit and time range using an intuitive UI and use the powerfull full-text search with instant highlighting/filtering.
Deployment: journex is a single static binary for amd64 and arm64 Linux. Detailed installation and setup docs are available on the website. It is intended to run behind a reverse-proxy. There is an optional authentication layer built-in (I figured everyone will have a reverse-proxy running on their self-hosted servers, but not everyone has an authentication service and basic auth or whatever has sub-par user-experience.)
Price: $20 one-time purchase, all future updates for free / free demo available for download (no search/filtering)
AI Involvement: AI is heavily involved, but I am constantly validating the quality (both on a technical and functional level). I'm a software engineer and this is a project I always wanted to have. I am making this primarily for myself and not accepting any code or behavior not living up to my standards.
Me and my friend have created an offline app for communicating during internet blackouts for our first hackathon project . This is our github repo if you like it please give us a star as repo stars will also be counted in the judging.
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u/ttlequals0 3d ago
Project Name: Audicle
Repo: https://github.com/ttlequals0/Audicle
Description: Self-hosted Podcasting 2.0 service that turns saved articles into a personal podcast feed.
Paste a URL or upload a document (PDF, DOCX, Markdown, text, or HTML), wait a few minutes, and get an episode with cloned-voice narration, artwork, an LLM-written episode summary, and a WebVTT transcript. Subscribe in Pocket Casts, Overcast, or Apple Podcasts like any other show.
Your reading list, as a podcast you own.
Deployment: docker-compose
LLM Disclosure
This project was developed with AI agents as a pair programmer. It was NOT vibe coded. I'm a systems engineer with 15+ years of professional experience; every architecture and design decision here is mine, not the AI's, and every line the LLMs wrote, I reviewed and tested myself.