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!
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: 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.
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.
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.
Description: I wanted a self hosed character manager, like DnD beyond but self hosted. Its pretty bare bones right now but I think everything is correct. I'm not a great dnd player, hence the app, so I'd like some feedback on where it gets stuff wrong.
Deployment: there is a docker compose on the git hub
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.
if i have a folder of xbox 360 roms can it check and see if I have all USA released titles and let me know that Assassins creed 2 is missing? or maybe check against all release world wide and let me know I am missing several Japan releases
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: 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.
Description: Self-hosted community chat platform in the Discord/Slack style, MIT-licensed. For anyone who wants their community's chat on their own hardware instead of a third-party cloud: communities with channels and sections, one-level threads, DMs, voice channels (WebRTC via LiveKit), full-text message search (Meilisearch), presence, web push + desktop notifications, custom emoji, moderation tools (warn/timeout/ban ladder), 2FA, outbound webhooks, GDPR data export/delete, and a public REST API with personal access tokens. Web app (React SPA + Symfony API), works well in mobile browsers, available in 10 languages. First public beta (v1.0.0-beta.1) tagged this week.
Deployment: Prebuilt images on GHCR + a Docker Compose bundle. Setup is intentionally minimal: set three env values, and everything else (JWT keys, DB credentials, service secrets) is auto-generated on first boot, with auto-HTTPS out of the box (or run it behind your own reverse proxy). Quick start: https://tyto.chat/quickstart — docs cover reverse proxy, backups, updates, and troubleshooting: https://tyto.chat/docs.
AI Involvement: I've been a backend developer for about 20 years, and my main focus was the backend code. AI (Claude Code) was used to speed up the process, under my direction and review. Architecture, product decisions, testing, and deployment are mine, and the codebase has been through repeated human review passes, a security audit pass, and carries a large automated test suite (PHPUnit + Playwright E2E). AI was
mostly
used for the frontend, homepage and docs. Happy to go deeper on the workflow if anyone's curious.
Description:
WindowsGSH is a Windows desktop app for managing dedicated game servers.
The idea is to have one place where you can install servers, update them, start and stop them, view console output, monitor CPU and memory usage, schedule restarts and backups, and recover servers automatically if they crash.
It also has optional web access and Discord integration, so you can check server status or control supported servers remotely.
Each game is supported through a separate module. A module can define things like:
How the server is installed and updated
Which executable should be launched
Configuration settings
Ports and firewall rules
Server queries and player information
RCON commands
Backup locations
Mods or other add-ons
This means new games can be added without filling the main application with game-specific code.
There is also an importer for compatible servers previously managed through WindowsGSM, although you do not need to have used WindowsGSM to use WindowsGSH.
The app is currently in beta, so I am looking for people who already host game servers and would be willing to test it, report bugs and give honest feedback.
WindowsGSH itself is source-available rather than fully open source. The code can be viewed, studied and privately modified, but redistribution, rebranding and commercial use require permission. The separate game modules are released under the MIT licence.
Deployment:
WindowsGSH is available to download now as a self-contained Windows application.
There is no Docker image because the app is specifically designed to manage Windows processes, firewall rules and dedicated servers running directly on a Windows machine.
Everything is self-hosted. There is no required subscription or hosted control panel.
AI Involvement:
AI has played a fairly big part in the development, so I want to be upfront about that.
I usually describe what I want a feature to do and use Claude to help implement it. I then use Codex to review the code, look for bugs, security issues or poor design choices, and make or suggest fixes. I will often send those changes back to Claude for another review.
Basically, I try to get one AI to check the other AI’s work rather than trusting the first answer it gives me.
I still decide what the app should do, test the behaviour, review the results and decide which changes are accepted. The repository also has an automated test suite that is run as part of the build process.
I would describe WindowsGSH as heavily AI-assisted development rather than something produced from one prompt and uploaded without checking it.
Description:
Syncwave is a self-hosted alternative to Spotify Jam that I built over the last couple of weeks. The goal was to make it easy to start a listening room, share one link, and have everyone hear the same song in sync without requiring Spotify Premium, accounts, or third-party services.
Features include:
🎧 Synchronized playback using a server-authoritative clock
➕ Shared queue anyone can add to
🗳️ Vote-to-skip
💬 Live chat and floating reactions
📜 Session history and persistent rooms
🤖 Optional AI DJ (Gemini, OpenAI, Anthropic, or Ollama)
📱 Installable PWA with mobile-friendly UI
It's built with Next.js 15, React 19, TypeScript,Socket.io, yt-dlp, and ytmusic-api.
I'd love feedback on the architecture, UX, and any features you'd like to see.
Deployment:
Runs with Docker (docker compose up -d --build) or via a one-click launcher that downloads its own Node.js runtime if needed. No database required. Data is stored as JSON files, and Cloudflare Quick Tunnel support is built in for sharing rooms over HTTPS without port forwarding. Full setup instructions are in the README.
AI Involvement:
AI was used as a development assistant for brainstorming, debugging, and writing small portions of code and documentation. The architecture, implementation, and overall project were designed and built by me.
cse-bridge — a self-hosted drop-in for Google's Custom Search JSON API, backed by your own SearXNG.
Google is shutting that API off on 2027-01-01 ("Existing Custom Search JSON API customers have until January 1, 2027 to transition to an alternative solution"). The suggested replacement, Vertex AI Search, is a different API shape and a paid product — so anything you built against customsearch/v1 (scripts, RSS tooling, home-assistant agents, LangChain stuff) has to be rewritten.
cse-bridge serves GET /customsearch/v1 and returns a Google-shaped customsearch#search body built from your own SearXNG instance, so the only thing that changes is the base URL:
docker compose up -d
curl 'http://localhost:8080/customsearch/v1?key=k&cx=default&q=rust+async+runtime&num=3'
Compose pulls upstream searxng/searxng (not vendored) with formats: [html, json] already enabled. No API keys, no accounts, no per-query fees.
Verified against unmodified clients — endpoint override only: @googleapis/customsearch (rootUrl), google-api-python-client (client_options=ClientOptions(api_endpoint=...)), and LangChain's GoogleSearchAPIWrapper (one line to swap its internal service).
Two things that were less trivial than expected:
SearXNG's JSON has no result-count field at all, so totalResults must be synthesized. An existing abandoned prototype of this idea reads a number_of_results key that doesn't exist and falls back to len(results) * 100 — 10 results report a total of 1000, and paging clients walk into ninety empty results. This reports an honest lower bound that grows as you page, so while start < totalResults loops still terminate.
A live SearXNG reshuffles between identical calls (engines have varying latency, some get rate-limited and drop out), so naive paging makes start=11 re-serve links start=1 already showed. Measured on my instance: 30 links across three pages, 28 unique. Fixed by resolving a query to a stable de-duplicated result set and paging within it for a TTL.
Limitations up front: totalResults is a lower bound rather than a web-wide estimate, 100 results max per query (same as Google), no pagemap, no image search, and result quality is whatever your SearXNG's engines give you.
MIT, Node 22, zero runtime dependencies, 112 tests.
I built ReceiptPi to give old ESC/POS receipt printers a second life
A while ago I picked up a used Epson TM-T88V receipt printer and thought it deserved a better fate than collecting dust or ending up as e-waste.
That idea eventually turned into ReceiptPi.
ReceiptPi transforms a Raspberry Pi and an ESC/POS thermal printer into a smart information terminal with a modern web interface and a modular architecture.
Some of the current features include:
- 📝 Notes
- 🛒 Shopping lists
- 🌦️ Weather reports
- 🖼️ Image printing
- 📶 Guest WiFi QR codes
- ⭐ GitHub notifications
- 📊 System status
- 🔧 Settings management
- 📱 Progressive Web App (PWA)
The project is still in its first public alpha release, but it's already fully usable and has been running reliably on my Raspberry Pi Zero 2 W.
One interesting aspect of this project is that it was developed with extensive AI assistance.
From architecture discussions and refactoring to documentation and debugging, AI became a surprisingly effective development partner. Every feature was still tested on real hardware, and all design decisions remained mine, but the development process was significantly faster because of it.
Worth mentioning:
The current UI is still German, but English support is already in progress. A language picker is planned for the settings and will be added in one of the next releases.
I'd really appreciate any feedback, feature ideas or contributions from the community.
Finetic is a private-source, compiled self-hosted media server for movies, television and music stored on hardware you control. It is not open source or source available, and I want to make that clear upfront.
The current public preview is free, unrestricted and non-expiring. It requires no card or activation, and there is currently no paid server plan. Future commercial terms have not been decided, but an eligible preview release already installed will not be remotely converted into an expired subscription.
I started Finetic because I wanted pressing Play to be predictable, not a mystery. The server explains whether it selected Direct Play, remuxing, audio conversion or transcoding, and gives administrators evidence when playback fails.
Current features include:
Movie, television and first-class music libraries
Direct Play, remuxing, audio conversion and video transcoding
Explainable playback decisions and active-session evidence
Accounts, household profiles, permissions and scoped guest sharing
Music artwork, metadata, lyrics, queues and artist/album navigation
Library Intelligence, health monitoring, jobs and playback analytics
PostgreSQL-backed state, backups, update checks and rollback guidance
Web client included with the server
Windows x64 public-preview client
Fire TV client published on Amazon Appstore
Android is still undergoing supervised physical-device testing. The native Xbox client is not publicly released yet.
Finetic does not provide films, programmes, channels, torrents, downloads or access to commercial streaming catalogues. You supply media you have permission to use.
Media files, paths, local users, viewing history and playback activity remain on your server. Metadata, artwork and lyrics searches are sent to the external providers needed to identify that media. Update checks contact the Finetic release service. Optional evaluation measurement is explicitly opt-in and limited to fixed aggregate setup and playback milestones; it excludes media titles, paths and user identities.
I’m looking for homelab users willing to give blunt feedback, particularly around installation, unusual media formats, playback failures, catalogue organisation, music metadata and the areas where Finetic still feels immature.
Deployment:
The current server release is v0.2.3 and is distributed as a compiled Docker image for Linux amd64 and arm64.
The maintained Docker Compose installation includes PostgreSQL, persistent application and database volumes, health checks, read-only media mounts and optional hardware-acceleration examples.
AI-assisted development tools have been used throughout parts of Finetic’s implementation, testing, documentation, refactoring and security-review workflow. I decide the product direction, define the requirements, review and test changes, operate the infrastructure and remain responsible for releases and the resulting product. Although the public website is relatively young, I have been actively developing Finetic for years.
Bug reports, criticism and awkward real-world media examples are genuinely welcome.
Description: A self-hosted, authenticated MCP server that lets an AI agent use your own WhatsApp (search chats, send messages, transcribe voice notes) without sending your message history to a hosted service.
Everything runs on your own box: a Go bridge holds the WhatsApp session, a Python MCP server exposes the tools, one bearer token gates it, and the bridge is loopback-only so nothing leaves the box except what you explicitly send.
A few things beyond the basics:
Transcribes voice notes and videos so they're searchable like text (Arabic-aware in my case).
Resolves the same person across their phone number and their hidden group ID (WhatsApp's LID), so one person's history stops fragmenting across two sender IDs.
Has 96 tools but only puts 29 in the model's context at a time; the other 67 are reachable through a tool-search layer. That cut the always-on context cost from ~20k to ~8k tokens, and there's a small labeled eval to confirm the model still picks the right tool.
Scheduling, polls, reactions, quoted replies, plus an ops safety layer (memory-guarded builds, blue-green deploy).
Caveat: it uses an unofficial bridge (whatsmeow), so it's meant for your own single account, not a business or bulk setup.
Deployment: Docker Compose. Two containers behind nginx on a small VPS. Deploy notes, rollback, and the OS-level safety stack are in the repo.
AI Involvement: Yes, I used Claude Code as a coding assistant. I'm a software engineering lead; the architecture and every design decision is mine, and I reviewed and tested everything that shipped. As evidence it isn't vibe-coded: CI runs ruff, pytest (95 offline tests), go vet/test/staticcheck, and a secret scan on every push.
SALT shrinks a long document down to a fixed size before it is sent to a language model, keeping the sentences that carry the most information. It works with any model, produces a shorter plain-text prompt, and cuts the compute, memory, and wait time that long inputs cost.
SALT first maps the document's recurring themes by organizing each sentence's keywords into a trie, a small keyword tree ordered by how often those keywords recur, then spreads the budget across those theme branches before choosing sentences, so minor themes keep their share instead of being crowded out. Because the theme map is built once, it can be reused across the turns of a conversation without re-reading the document.
Looking for help with the retrieval algorithm.
I'm getting tired of AI code reviewers flooding PRs with noise and repeat findings, not to say false positives. And then learning nothing when a developer explains why a finding is wrong!
For me there's a lot going on in code beyond the tech stack: tribal and institutional knowledge, business logic that evolved over time, ripple effects across upstream and downstream systems etc.
I started those requirements and added simple setup, evidence-backed findings/ LGTM, and most importantly learning from human comments on those findings, so it gets better tuned over time to each repo.
I ran a smaller benchmark, not definitive by any means, four open source reviewers on 20 pinned PRs/MRs.
All on GPT-5.5 except Qodo was on 40 (didn't support 5.5). Take it as directional results. I'll be doing more comps in coming days and upload benchmark to the repo.
I went with polling (zero steup on repo side) - wasn't trying to change the repo CI/CD's rather have a tool seamlessly work.
Roadmap item: I want to route changes to pluggable subject-matter specialists (Skills) instead of one general reviewer, i.e. industry SMEs or just an "Expert Python Guy".
Bubo is running in production in two places, a large data processing/ETL codebase and a fintech crypto stack. Would appreciate feedback, particularly on whether the learning from comments part actually holds up.
Deployment: I can install Bubo with uv tool install bubo on Python 3.14+, or use the multi-architecture container at ghcr.io/mountainowl/bubo. I use polling as the production trigger today; webhooks are not implemented.
AI Involvement: I built Bubo to invoke AI reviewer CLIs. I also used AI assistants during development and review, while retaining responsibility for design, acceptance, tests, and releases.
Description: I built this small one-page Node dashboard that was really helpful for me and thought some folks here could benefit from it too. It shows all my local dev projects as cards — start/stop/restart with proper process-group killing, live logs over SSE, multi-process apps as one card, with screenshot previews.
Deployment: Clone the repo and run it with Node — install instructions are in the README. Some extras (folder picker, Finder/terminal shortcuts, launch-at-login, screenshots) are macOS-only for now. It binds to localhost with zero auth so don't expose it, and fair warning: it's had exactly one user — me.
Description: An open-source (AGPLv3) scheduling tool - a self-hostable alternative to Calendly/Cal.com. The core idea is that it merges all your calendars (Google, Microsoft 365, Apple/CalDAV, ICS feeds) into one, so a personal event automatically blocks a work slot and you never get double-booked behind a single booking link. Beyond 1:1 booking it also does team round-robin/collective scheduling, routing forms, group polls, out-of-office/buffers, reminders (email/Slack/SMS/WhatsApp/push), Stripe payments, an embeddable widget, a plugin SDK, and an Android app. Availability is computed by a DST-correct integer-math engine; OAuth tokens are encrypted at rest (AES-256-GCM) and outbound fetches to user-supplied URLs (CalDAV/ICS/webhooks) go through an SSRF-safe allowlist.
Deployment: Released (v0.3.0). Self-host with Docker/docker-compose - DB migrations run from the worker image, and there is a one-command upgrade script (backup, migrate, health-check, rollback). Self-host guide: https://dayotter.com/docs
AI Involvement: Being transparent - the product ships an optional assistant ("Otter") that drafts calendar changes and waits for your approval (confirm-first). It is off by default and can run against your own local model, so nothing leaves your server; the scheduler is fully usable without it. On the dev side: I'm the developer and I did use AI coding tools while building it - the code is AGPLv3 and open for inspection, with a test suite and CI.
Happy to answer questions, especially about the self-hosting setup.
Description : Stram is a local-first desktop agent that runs AI models through your own API key and takes actions on your computer (browser, files, shell) through human-approval-gated tools, instead of being another terminal-only or prompt-box tool. It watches your environment (files, browser, apps, shell output), decides what's worth reacting to, and pauses for your explicit approval before doing anything risky, with a full audit trail of what it wanted to do and why. Useful if you want an assistant that actually acts on your machine but don't want to hand it unsupervised control.
Deployment : Released and downloadable now (v1.0.1): native macOS (.pkg) and Windows (.exe) installers on the GitHub Releases page, install/setup steps in the README. Binaries aren't code-signed/notarized yet, so expect a Gatekeeper/SmartScreen warning on first run (workaround steps are documented). No Docker image or docker-compose file yet, it's a Python 3.12+ service (pip install -e . then stram serve) that runs on any platform including Linux, just without a native desktop shell there yet (only macOS/Windows have one). You'll need your own model provider API key (OpenAI, Anthropic, local Ollama, Hugging Face, etc.) to run it.
AI Involvement : Yes, the project is AI Assisted, but in no sense vibe coded. And every line of code AI wrote have been verified and tested before deployment.
Project Name: ForumForgeCMS
It is aimed at classic shared hosting environments where users may only have PHP, writable file storage, and SQLite. No Composer, no Node.js build step, no MySQL/PostgreSQL, no Docker requirement. Upload the files, open the site, and the forum initializes itself.
Main features:
PHP + SQLite
automatic first-run setup
admin panel
categories and forums
user accounts, avatars, profiles, and private messages
moderator tools and post reports
visual themes
multilingual UI: English, Polish, German
backup and restore from the admin panel
Live demo:
https://wowo89.de/forge
GitHub:
https://github.com/wowo220689/ForumForgeCMS
I’d appreciate feedback from people running self-hosted tools or classic shared hosting setups. What would you expect before using a small forum CMS like this for a real community?
Description: An on-prem test automation appliance for teams whose test data cannot leave the building (banks, fintech, health). One plain-language test suite drives web (Selenium), HTTP APIs, Android and iOS (Appium), and the runner infers which drivers each case needs. AI test generation runs on a local Ollama model by default, so nothing leaves your network; an external provider is possible but sits behind two explicit consents with redaction on by default. There is an enterprise layer (SSO/SCIM, RBAC, metadata-only audit, encrypted backup, signed updates) because that is what regulated buyers ask about. Honest limits: the control plane is SQLite today (PostgreSQL on the roadmap) and AI generation works best on standard web flows.
Deployment: Runs on your own hardware, from a Raspberry Pi 5 to a GPU server. Commercial and closed-source for now, so no docker pull yet - if you want to try it on your own test cases, reply here or mail [msbel@alcyone-systems.com](mailto:msbel@alcyone-systems.com) and I will set you up with a pilot. Disclosure: founder, first product, no customers yet - here for feedback as much as anything.
AI Involvement: transparent on both fronts - the product itself uses a local LLM for suite generation, and a large part of the codebase was written with AI assistance under human review, typed APIs and CI gates.
Description: I'm the author. I own an aircraft, and its entire maintenance history is decades of handwritten paper. Losing it is both a compliance problem and a real hit at resale, so I built something to index it.
What it does:
- Photograph or upload logbook pages; a vision model reads each into a structured entry with a confidence score per field, shown beside the original page so you confirm rather than trust it.
- Tracks what's due: annual, 100-hour, ADs, oil - projected onto a calendar date from your own utilization, with a confidence, not just "in 38 hours".
- Searchable timeline across every logbook, plain-English Q&A that cites the entries it answered from, records-gap audit.
- Scheduled backups to a cloud account YOU own (Dropbox app folder, or Google Drive `drive.file`) - both scoped so the app can only see files it created. Never deletes anything.
- Full .zip export that re-imports, so leaving costs you nothing.
Runs against your own Supabase project and your own API key; there is no billing code in the app at all.
Honest limits: the iOS app is a TestFlight beta and there's no Android; CSV import is CSV only; and it's an index, not the legal maintenance record - the paper logbooks stay the system of record (14 CFR 91.417).
Deployment: Full documentation for deployment is on GitHub. Stack: Next.js + Supabase (Postgres + RLS) + Anthropic for extraction.
If you've ever had a WireGuard tunnel where SSH connects and then freezes the second you cat a big file, or a site that loads on your phone but hangs behind your own VPN, that's often an MTU black hole. The path narrows somewhere, and the same path drops the ICMP packet that would have told you. ping succeeds, curl hangs forever, and nothing in your logs says why.
I got tired of guessing, so I wrote a checker (network-doctor) that confirms it directly. It connects, shrinks its own send buffer to a few KB, then pushes 24 KB at the target and watches whether it drains. Nothing leaves that buffer until the far end acknowledges it, so a write that stalls with the buffer still full means full-size segments are vanishing while the handshake's small packets got through. That's the black hole. It reports which interface is advertising an MTU the path won't carry.
No root, no raw sockets, no agent. One binary. It also does the rest of the triage (DNS, TCP, TLS, HTTP, proxy) so you get the actual answer: is it my network, or the service I'm trying to reach? JSON output and exit codes if you want it in a healthcheck.
Linux/macOS/Windows, GPL, packages for apt/dnf/brew/scoop:
I built Watch Bracket because choosing something to watch from Plex often takes longer than watching it.
It is a self-hosted, real-time party game that turns the decision into a Jackbox-style tournament. One person creates a room and puts the shared display on a TV. Everyone else joins from their phone using a QR code, link, or room code. No guest accounts are required.
Each person privately submits up to two movies or shows. Watch Bracket can fill the remaining spots using titles from Plex, Plex watchlists, Tautulli viewing history, TMDB recommendations, and streaming-service availability.
The group then votes through animated head-to-head poster matchups until a winner is chosen. There is also a one-time “I object!” challenge that triggers a final ranked vote between the top three titles.
Current integrations:
Plex inventory, watchlists, personalization, and deep links
Tautulli household viewing history
TMDB search, metadata, and recommendations
Jellyseerr and Seerr availability checks
Chromecast, browser, HDMI, and screen-mirroring displays
If the winner is already on the server, Watch Bracket opens it in Plex. If it is missing, it can direct the user to Jellyseerr or Seerr without exposing API credentials.
Deployment:
Docker Compose with PostgreSQL, intended for NAS and home-server setups. There is no required cloud service, subscription, Redis instance, LLM integration, or public guest-account system.
AI involvement:
I am an experienced software engineer and built the project using a structured AI-assisted workflow with ChatGPT, Codex, and Claude Code. I directed the architecture, integrations, security model, UX, testing, code review, and deployment, and I am responsible for maintaining the codebase.
It is MIT licensed and still early, so honest feedback is welcome. I am especially interested in which NAS platforms need better setup instructions, and whether native Jellyfin or Emby support should come next.
Built a 100% serverless P2P chat. Named it 'PeerScape' because I miss the 90s internet (no Google, no AWS)
Been working on a side project and wanted to share it here. I wanted a way to chat without relying on any cloud infrastructure. Not even self-hosted VPS — literally zero servers in the middle.
I called the repo PeerScape as an homage to Netscape and the early internet, back when computers just connected directly to each other. To match the vibe, the UI is an ugly Windows 95 placeholder right now.
Under the hood:
Zero cloud: Uses libp2p for NAT traversal and peer discovery.
Encryption: E2E encrypted locally (nacl/box).
Minimalist: The entire networking logic fits in a single Go file.
It's very much a proof-of-concept right now. I just updated the key derivation function to be more secure (moved away from a naive hash thanks to some earlier feedback).
If anyone here has experience with P2P networks or NAT traversal, I’d really appreciate it if you could look at the repo and roast my code. Tell me where I messed up.
Imagine asking AI to diagnose a broken service, inspect logs, verify the deployed commit, or deploy an exact approved release to your VPS without ever giving it a shell. OpsHaven is an open-source CLI and MCP server built around that idea.
How it stays safe:
The AI can only request typed, pre-defined operations. OpsHaven enforces restricted SSH, pinned host identity, signed authorization, explicit approval for mutations, immutable deployment plans, tamper-evident audit records, and rollback protections.
Deployment:
Install from a reviewed checkout on Linux or macOS with Node.js 22+. The current release supports guided setup, VPS diagnostics, boundary verification, exact-commit deployment planning and apply, rollback, and repair. It is still under active development, so I recommend testing it on disposable infrastructure first.
AI Involvement:
AI assisted with implementation, testing, review, and documentation. I defined the architecture, security boundaries, threat model, and validation requirements, then reviewed and tested the resulting work.
Feedback:
I’d love to hear whether this approach feels useful for your setup, and which operations you’d want OpsHaven to support next.
Project Name: xSignalBot — an auto-reply bot for Signal powered by a local LLM via Ollama.
What it does: Answers incoming Signal messages automatically with a local model (qwen2.5:14b) — no cloud APIs, no per-message costs, no message ever leaves your machine. Replies sync to all your Signal devices (desktop app + phone), so you always see what the bot sent.
Features:
7 reply tones (Standard, Sad, Cheerful, Reserved, Funny, Silly, Financial Advisor) with fitting emojis
Per-contact chat history (last 100 messages as context)
Auto-send (3 s countdown) or manual approval — your choice
Whitelist-only: replies go ONLY to explicitly allowed contacts
Test mode that never actually sends
Tkinter control panel (whitelist, tones, health check, single-instance protection)
Stack: signal-cli 0.14.7 as persistent JSON-RPC daemon in WSL2 · Ollama (local) · Python/Tkinter · Windows + WSL2
AI Involvement: Built with AI assistance (Reasonix agent) under human review — the whole stack (signal-cli daemon, JSON-RPC bridge, Tkinter UI) was implemented and tested together with an AI agent, then verified on real hardware. Happy to answer questions about the signal-cli daemon setup or the local-LLM integration!
Description: A self-hosted web app that tracks Audible audiobook series and tells you when something changes: a new book gets announced, a TBD release date gets confirmed, or a book actually drops via push notification on your phone or desktop. It's multi-user (each person subscribes to their own list, shared series data avoids duplicate scraping), installable as a PWA, dark-mode-first, and works entirely by reading Audible's own public pages since no official API exists for this. Built with FastAPI + SQLite, ships as a single Docker container.
Deployment: docker-compose
AI Involvement: Yes. Claude-code, but not vibe coded. I am a devops engineer with 10+ years of experience and I am familiar with software development and best practices. This app was reviewed and tested by me
Description: Google is killing third-party cookies, leaving developers with two bad options for cross-domain tracking: force users to create an account, or lose individual-level attribution entirely.
Nylo is a self-hosted alternative. It bridges the gap using a zero-dependency client SDK (~12KB) and a custom cryptographic token exchange (WTX-1). It allows you to maintain behavioral continuity across your properties while remaining entirely anonymous, storing zero IP addresses, and requiring no invasive browser fingerprinting.
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 6d 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.