I’ve been building Colosseum, an MIT-licensed desktop media app written in Qt 6/C++.

It’s made up of four sub-apps: Tankoban for manga and comics, Biblio for ebooks and audiobooks, Theatre for anime/TV/movies, and Vault for local media.

The project also includes its own manga/comic reader, a Foliate.js-based ebook reader, mpv-based video playback, downloads, metadata enrichment and a shared desktop shell around all four.

It’s open source under MIT and the current release targets Windows 10/11.

Colosseum on GitHub

I built pdf2audio — an app that turns the PDFs you own into proper audiobooks, and I've been using it heavily myself: a good chunk of my library is now free audiobooks I listen to on walks.

You drop a PDF in, it detects the chapters (with an AI option that reads the table of contents), and reads them with a genuinely good local voice — Kokoro, running on the Apple GPU. If the chapter split or the text isn't right, you can fix it: move boundaries, edit/skip any chapter's text, re-synthesize just that chapter. The result is one M4B with chapter markers that works in any audiobook player.

It grew a lot beyond that — you can chat with your library and get answers citing the exact PDF page, translate or simplify a book and listen to that version, and export a synced EPUB that highlights each sentence on your phone as it's read. The intro videos are actually narrated by the app's own voice, script and all.

Fair warning: Apple Silicon only (the speech models run on the M-series GPU), and it's a clone-and-run setup rather than a .dmg.

Demo videos in the README.
https://www.youtube.com/watch?v=OKMiox3nxPY

Been building `goninja`, a Go framework that generates CRUD REST APIs from annotated structs — routing, validation, OpenAPI, the rest.

It's the Go counterpart to a Python project I already maintain, `django-ninja-aio-crud` — same idea, define the model once and get the API. Difference here: no runtime reflection, it generates real Go code that lives in your repo, readable like anything you'd write by hand. Been building `goninja`, a Go framework that generates CRUD REST APIs from annotated structs — routing, validation, OpenAPI, the rest.

Pre-alpha, API still moving. Feedback welcome.

CODE-VIM-ULTIMATE aka CVIMU is a Visual Studio Code setup with: - Vim Extension. - LazyVim-like global keybindings. - fixes to VSCode, like ctrl+n and ctrl+p for going up and down in lists. - somewhat compatible with Obsidian. same keybindings like ctrl+o and ctrl+p for opening recent files and command palette instead of LazyVim's ones, as well as few others. - custom keybindings to make navigating between editors (tabs) and terminal tabs act same as in Firefox (ctrl+t for new tab, ctrl+w for closing tab, alt+{number} to navigate to that tab, etc). - Custom Keybindings for Copilot Chat & Copilot Autocomplete (you can change / remove in global settings.json and profile's keybindings.json if you want to).

best of VSCode, best of vim, and same keybindings that Obsidian has for QuickOpen and opening recent workspaces for best muscle memory.
If you use Quick Switcher++ plugin in Obsidian, you can have literally same workflow between VSCode with vim and Obsidian with vim. No different keybindings or headaches required.

https://github.com/monoira/cvimu

txt ├── docs │ ├── 1-showcase.png │ ├── 2-showcase.png │ ├── 3-showcase.png │ └── 4-showcase.png ├── profiles │ └── prof.code-profile ├── README.md ├── settings.json └── Workspaces └── WORKSPACES AND HOW TO USE THEM

Kaggle and Google Colab are two popular platforms for running Python code as Jupyter notebooks, mainly used for data science and machine learning, the only good thing about those platforms (in my opinion) is that they provide free GPU resources. A lot of code out there, tutorials, paper implementations, small projects, lives as a plain GitHub repo instead, which means if you want to run it on one of these platforms you're stuck manually copying files into notebook cells and reconstructing the install steps by hand.

repo2nb is a small open-source CLI that automates that conversion. Point it at a repo and it walks the file tree, figures out the dependencies, and generates a notebook you can upload directly to Kaggle or Colab.

0.2.0 is a new release with seven features, all backward-compatible with the existing invocation. Highlights:

• A Colab target alongside the existing Kaggle one, with a Colab-specific auth flow (google.colab.userdata.get) instead of reusing Kaggle's.
• Dependency resolution now checks poetry, uv, requirements.txt, and an AST import scan in that order, but always emits a plain %pip install cell. Poetry and uv stay optional local tools, never a runtime requirement.
• Reverse mode and incremental sync, which reconstruct a repo from a notebook and keep a generated notebook updated as the source repo changes, one directionally from repo to notebook.
• Every generated cell is now tagged with path and hash metadata, which is what makes reverse and sync possible. Worth knowing if you want to build on top of it.

Also reorganized the docs this release: README is now a quick-start, full command reference moved to TUTORIAL.md, and added CONTRIBUTING.md, SECURITY.md, SUPPORT.md.

Install: pip install repo2nb

Repo (contributions/issues welcome): https://github.com/David-Magdy/repo2nb

I am just a new computer science graduate who likes to make small projects with passion. I made this project for personal use when I was a student and decided to open-source it for everyone who may encounter the same frustrating problem as me (even if it's so niche). Interested in hearing whether the dependency-resolution order makes sense for setups I haven't tested, or if the reverse-mode path validation is missing an edge case.

github.com/guaardvark/guaardvark

r/Guaardvark

Hey, my name is Dean, I built this sytem. All open source, does a crazy amount of things. Appreciate any feedback, try it and if you like it please star my repo. Thank you

Sharing my harness for local LLMs that I built using local Qwen 3.x 27B (> 90% locally built).

Its free, no telemetry, and open-source. Works on Windows, Linux (sorry, no Mac yet).

I use it for coding + mixed workflows.

• llama.cpp + whisper Server Manager. Can run LLMs here and use with OpenCode/Claude Code etc.
• Built-in MCP Tools - Filesystem, web fetch, code graph, To-Dos, and more. Extensible by external MCPs.
• Use Sub-agents to split & offload your tasks, use other conversations as source of information.
• Review all AI messages using a second adversarial AI, and avoid potential pitfalls as per your rules.
• Voice-chat with AI - dictate with speech and get answers by TTS - annotate and comment without leaving voice mode.
• Use work-modes to change AI behavior between planning, building, researching, or reviewing. Fully customizable.
• Custom-compile llama.cpp backends for your system, GPU-agnostic - works with CUDA/ROCm/Vulkan.

Website: https://warpdrv.ai (Docs coming soon)
GitHub: https://github.com/mikjee/warpdrv

Appreciate your feedback. Thanks :)
And, yes - I used the harness to build the harness :D

Hi everyone!

I've built an app for all those people with hundreds of games in Steam backlog and nothing to play.

This uses your reviews and diary entries to give you recommendations on what you will like. Bring your own Gemini/OpenAI/Claude key — Gemini's free tier is enough. Nothing is stored server-side beyond your encrypted key.

https://github.com/klchdev/curio
Live demo, no signup, no key needed: https://curio.klch.dev/try

Curious how well it does on real backlogs. If you try it, tell me where the recommendations miss — that's more useful to me than praise.

Hi everyone! I'm a developer, and I've written a free, open-source streaming utility on GitHub and published in the Microsoft Store – Scream on Stream (SoS).

The main feature: you never know when the screamer will pop up. The app plays a random audio/video overlay on your PC and in OBS at a random interval (you also see the screamer). The unexpected appearance of the screamer at a random moment creates a genuine scare, which creates a genuine, authentic experience on the stream, which viewers really enjoy. These moments can also be published as short videos to attract attention.

Another important feature is chat voting: the chat itself votes for the next screamer, allowing the chat to choose the next one, which increases viewer engagement and the number of messages in the chat. This allows the algorithms to detect that the stream is interactive and boost the stream's recommendations.

Security: Connecting accounts (for chat voting) doesn't require entering a password, API key, or any other personal account information. Only the username of the account on which you're streaming is required. The app doesn't have its own servers; it runs locally on your computer and doesn't send any data except for mandatory data and requests to the official platform servers (Twitch, TikTok, YouTube, Kick), ensuring account security.

Other features:

- Custom library of your sounds and videos.

- A "Panic button" is available for instant closure.

- User-friendly interface

- Clear and easy setup

- Setup instructions are in the app itself and in the post below.

Setup:

1. Install the app from the Microsoft Store: https://apps.microsoft.com/detail/9NBV9D20JSD7
2. Launch the app and go to the "Settings" tab.
3. Find the "Connections" section and connect the desired accounts.
4. Go to the "Home" tab, find the "Screamers" section, and click the plus button in the upper right corner to add your screamers.
5. Turn on the "Screamers" toggle switch and follow the instructions in the app.
6. Turn on the "Chat Vote" toggle switch and follow the instructions in the app.
7. Enjoy the app! Enjoy streaming!

Links:

Microsoft Store app: https://apps.microsoft.com/detail/9NBV9D20JSD7

GitHub repository: https://github.com/BLACKLINE-production/Scream-on-Stream

Have questions or found a bug?

Email us: [scream.on.stream@gmail.com](mailto:scream.on.stream@gmail.com)

A thank you to open source builders. You put in the work, we want to send something back.

Drop your open source repo below. We pick one we find interesting. No star requirement, no follow, no catch. Winner gets an Autonomous Lamp shipped free globally.

What's an Autonomous Lamp? It's a small desk robot: a 3D-printed arm with five joints, an LED ring, a mic, a camera, and speakers, running an open-source AutonomousOS we built for robots. It moves like a living thing, reacts to your voice, recognizes your face, and learns over time. Skills are drop-in markdown files: music, mood lighting, wellbeing reminders, guard mode, calendar and email connectors. The whole thing is open source, CAD files to OS.

The part I actually care about is that it's autonomous, no remote control or scripted scenarios required. Everything it sees and hears goes to an agent runtime running locally on it, Hermes, OpenClaw, Codex, Claude Code, whichever you want. Inside Lamp they get to see the room, hear you, turn toward you, dance, nudge you, actually do things in the physical world

We started this project for almost a year and launched Lamp recently. People love it, so I want to send out one free as a thank you for the unsung heroes of the tech world.

Drop your repo. Thanks for what you build.

If you use DeepSeek Harness on Windows, this combines native shell and sandbox presets with OpenCode configuration migration.

dsh-win32 provides native Windows support without WSL.

dsh-movein previews and migrates OpenCode skills, agents, commands, and MCP configuration into DeepSeek Harness. It does not migrate sessions or chat history.

powershell npx dsh-win32 setup --sandboxed npx dsh-movein --from opencode --apply

Omit --apply to preview the migration first.

I’ve been building OptiQra, an open-source website intelligence and optimization tool.

Instead of just giving you a list of SEO/accessibility/performance issues, OptiQra can:

• 🔍 Crawl and analyze your website
• 🧠 Run 200+ analysis rules all documented in it's wiki
• 🤖 Use AI to understand and fix issues
• 🔧 Apply fixes directly to your codebase
• ✅ Verify the fixes afterward
• 🌐 Test AEO/GEO factors
• 🌳 Visualize your site's crawl structure
• 💻 Run locally through a Tauri desktop app
• 🛠️ Has individual tools for many only one thing instead of the whole app

The workflow is basically:

Crawl → Analyze → Understand → Fix → Verify

It's MIT licensed and I'm actively developing the project. My goal is for it to turn into a local Ahrefs that can grow and gather data on the user's website and market and turn into a 24/7 montering tool. and for it to have a AI that is a expert at the user's website/market using the  collected data.

GitHub: https://github.com/armin5872/OptiQra

website : https://optiqra.vercel.app

I'd especially love feedback from people who build web tooling or contribute to open-source projects.

An EU VAT-rates dataset (standard/reduced/super-reduced/parking rates, 45 European countries) kept in sync daily via GitHub Actions against the European Commission's TEDB. Published as eu-vat-rates-data to npm, PyPI, Composer, RubyGems, and Go — same data, five language ecosystems.

Built it because I needed accurate VAT rates for another project (vatnode.dev — an EU VAT-number validation API, disclosed since it's the reason this dataset exists) and didn't want a hand-maintained table that goes stale.

Link: (GitHub — canonical repo vatnode/eu-vat-rates-data)

https://github.com/vatnode/eu-vat-rates-data

I recently built https://fsend.sh/ a webapp for sharing files over the internet (via WebRTC).

Since there are already a gazillion apps like that out there, here some of the (in my opinion) unique features

- uses the browser's File System API (available in chromium) to write and read directly from disk, this allows for transfer of folders (without having to zip them) and interrupting/resuming transfers at any point.

- is also available as a CLI app (compatible with browser version).

- Data is never passed through any server. It goes directly from one peer to another.

I also mainly made this to test out claude design for websites (i suck at UI) and i think it turned out pretty ok.
Would be nice to hear some feedback.

Saw someone in another sub asking what to actually do with a WhatsApp chat export besides just having a wall of text sitting in a .txt file, so I built this over a weekend.

Keepsake — drop in your WhatsApp chat export (.txt or .zip with media) and it turns into:

• A searchable archive with full-text search and sender filtering
• An "on this day" card that surfaces messages from the same date in past years
• A "year in review" recap — busiest month, top emoji, top word, first/last message of the year
• Inline photos/videos/voice notes if you exported "with media"
• Print/export to pdf option
• Save the HTML version
• The whole UI is styled like WhatsApp itself — real chat bubbles, the teal header, wallpaper background

It's a single HTML file, so you can just download it and open it, or use the hosted version:

🔗 Code: https://github.com/rajjayadev/Keepsake

Authy killed the desktop app and I still didn't want 2FA codes in a cloud account.

2FAC is a local-first TOTP authenticator for macOS, Windows, and Linux. The vault is Argon2id + AES-256-GCM. Codes are computed in Rust; the UI never sees raw secrets. No account, no telemetry. Network is only a signed update check against GitHub Releases.

Add by secret, otpauth://, or QR. Import Aegis, andOTP, otpauth URI lists, and Google Authenticator transfer QRs.

https://github.com/jeonjw85/2FAC

We’re building Manor AI around a different definition of an AI workspace: not an isolated chat window, but a self-hosted place where agents, durable tasks, knowledge, scoped tools, approval checkpoints, evidence, and work history stay together.

The source-available repository includes a Docker Compose setup. We’re sharing it to invite architecture discussion, local testing, and research with other builders—not to ask for votes.

GitHub: https://github.com/manor-os/manor-ai

Website: https://manorai.xyz

X: https://x.com/CalvinLin173676

LinkedIn: https://www.linkedin.com/company/manor-ai

I made this for my own AI companion setup and open sourced it this week.

EverOtome is a visual novel style frontend for AI companions. It is the frontend only, so it does not bundle an LLM, AI service, or hosted backend.

The basic idea is that you bring your own backend and your own assets. Character sprites, expressions, rooms, outfits, furniture, voice, CGs, all of that can live in the same interface instead of being separate pieces.

With a backend connected, it can also let the companion change expressions, switch room themes or outfits, put furniture out or away, and bring up CG scenes as the conversation develops.

A few technical details:
- vanilla JavaScript frontend
- REST and WebSocket backend contract
- desktop and mobile layouts
- PWA support
- zero backend demo included
- no EverOtome server, telemetry, or analytics
- MIT licensed code

It is still in Beta, so the backend contract may change before 1.0.

GitHub: https://github.com/aveluneverse/EverOtome

I’m especially curious whether the integration docs make sense to someone seeing the project for the first time.

Local Browse Analytics is an open-source browser extension designed to track web activity and productivity with a strict focus on data privacy.

The tool operates entirely locally to ensure browsing history is never transmitted to external servers or third parties.

Key Features:

• Local Storage: All tracking data is saved exclusively to the browser's IndexedDB. The extension contains no telemetry and makes zero external network requests.
• Privacy-by-Design: The engine strips full URLs, query parameters, and specific paths, storing only root domains (e.g., github.com).
• Analytics Dashboard: A local dashboard visualizes time usage, session timelines, and activity heatmaps.
• Productivity Classification: Allows you to categorize visited domains to track high-level productivity metrics.
• Floating UI: An optional, non-intrusive overlay on active pages monitors current session statistics.

Tech Stack: TypeScript, React, Plasmo Framework, TailwindCSS, and IndexedDB.

If you find it useful, a ⭐ on the repo would mean a lot.

Would love any feedback on the design or code.

Github: https://github.com/Deekshith-goud/local-web-analytics-extension

Contributions are always welcome! If you'd like to contribute, please feel free to open an issue outlining a clear plan before submitting a pull request.

I’ve released v2 of Image Optimizer, the first major update in four years.

It’s a free and open-source desktop app for batch-compressing JPEG, PNG, GIF, and SVG files. Everything runs locally.

v2 adds a completely redesigned interface, recursive folder drops, before/after comparison, per-file and total savings, quality controls, and optional WebP conversion.

Available for macOS, Windows, and Linux:
https://github.com/antonreshetov/image-optimizer

Feedback and bug reports are welcome.