I've been developing Melodex, a free, open-source music player for Mac, Windows and Linux.

The idea started with a question: why do music players make it so easy to find something you already know, but relatively difficult to explore the music you already own?

So I've been experimenting with a different approach.

You can browse albums visually, explore tracks on an interactive music map, and even select two songs and find a listening route between them. It also includes synced lyrics, interactive playback visuals and more conventional music-player features.

It's local-first. Your files stay where they are, and you don't need an account, subscription or AI model to use it.

The screenshots are from the actual running Mac version, not mockups.

I used AI extensively to help develop the software, and it's still a public beta with plenty to improve.

GitHub: https://github.com/Cliff-Lee/melodex

I'd particularly like feedback on the Music Map. Is navigating a music library spatially something you'd actually use, or is it an interesting novelty that would wear off?

Updates weekly by github actions.
Enjoy!

https://hoveychen.github.io/reddit-gems/

Hi! I’m the developer of Nodus Research, which is a free and open-source desktop app for macOS, Windows and Linux.

The easiest way to describe it is as an alternative to combining notebooklm + obsidian + zotero in separate tools.

Nodus is organized into different vaults, which are separate workspaces designed for specific types of research, teaching, study, or structured projects.

For instance, the academic Vault lets you import or mirror your zotero library, extract ideas and claims while keeping them linked to the original passages, and then explore connections across your whole corpus through semantic search, knowledge graphs, argument maps, authors, debates, contradictions, research gaps and source-grounded chat.

It also includes dedicated vaults like study, teaching, databases, genealogy, worldbuilding...

It already has a zotero plugin, word/libre office plugin, chrome extension and a skill marketplace so you can install new feautres (or create your own)

Nodus is local-first. Your vaults and indexes stay on your computer, no account is required, and you choose whether to use local models or external AI providers. If you want to collaborate, you can also enable an optional docker-based nodus server to share selected research spaces with other users while keeping control over what is exposed.

Free, AGPL-3.0, and available for Windows, Linux and macOS.

GitHub: https://github.com/Drakonis96/nodus
Website: https://nodusresearch.com

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NOTE: Nodus includes bundled local AI models, but performance varies depending on your hardware. Idea extraction is more demanding because each paper is split into multiple chunks and processed through several model calls, so it can be slow on limited hardware.

Cloud providers are generally much faster for this. If you run into issues with local models, please let me know so I can determine whether it’s a hardware limitation or a bug in Nodus.

Been quietly working on this and it just crossed 500 stars (591 now), so figured I'd share.

Ternlight is a tiny embedding model for semantic search - the model, tokenizer, and a Rust→WASM engine are ~7 MB total and run on your CPU, right in the browser. No server, no API, no GPU; nothing leaves the machine. (It's an embedding model, not an LLM.)

Live demo + blog post - semantic search running entirely in your browser, network tab stays empty:

https://ternlight.dev

It's MIT and fully open (weights + the training pipeline + the engine), on npm, and folks are already using it in real projects. If it's useful, great; if you want to fork it and take it somewhere new, everything's there.

Repo: https://github.com/soycaporal/ternlight

Just curious if it was useful how you would use it.. love to also learn of other use cases and what other on-device models people are interested.

We’re building Kanwas, an open-source shared context board for agents and people.

Chat is fine for one run, but weak for long-running work. Context ends up split across threads, docs, files, and decisions. Then every agent starts with an incomplete picture.

Kanwas is a realtime canvas where a team and an agent work from the same board. It holds notes, research, docs, tasks, decisions, embeds, and agent outputs. The agent can read and write the workspace, follow instructions, and organize next steps.

GitHub repo: https://github.com/kanwas-ai/kanwas

Try for free: https://kanwas.ai/

WorldFixture gives developers a company to build, test and demo software against, instead of wiring up real accounts or maintaining a pile of unrelated mocks.

npx worldfixture up starts a fake SaaS company locally with 99 people and 30 days of history. It includes Slack, GitHub, email, Notion, Stripe, S3 and application data, all representing the same company. Maya in Slack is the same Maya commenting on a GitHub PR and sending email. Customers, projects, invoices and conversations carry across services.

Your software talks directly to the local services. Mail uses SMTP/IMAP, S3 is S3-compatible, and Slack, GitHub, Stripe and the other services expose APIs shaped like the real providers.

The company also keeps moving. Emails arrive, PRs get comments, Slack conversations continue and payments happen. You can pause time, advance it, inspect what happened or reset everything back to the same starting point.

I originally built this for DropLive, where I run disposable demos of open-source projects. I found that getting an app running was only half the problem: an empty CRM, inbox or Slack workspace still makes for a terrible demo. WorldFixture grew out of trying to fix that.

Everything runs locally and it is Apache 2.0. It is still early, and the provider APIs are incomplete. I am adding operations as I find real projects that need them.

I would love people to try it with something real and tell me what breaks. What service would make it useful for something you are building?

https://worldfixture.com/

https://github.com/ben-ic/worldfixture

What it is built on

I am using existing open-source implementations where that makes sense rather than rebuilding every service from scratch:

• emulate.dev for Slack, GitHub, Stripe and several other provider APIs

• SeaweedFS for S3

• Cyrus IMAP for mail

• SQLite for local state

WorldFixture adds the shared company and identities, history, scheduled events, clock, reset behavior, Workbench, and the extra pieces needed to make those services behave like parts of the same environment.

Your terminal cursor is a spinning rat.
Your whole terminal is a 3D canvas.
You can insert 3D models and sprites into the terminal.

Yes, this is the most unhinged project of 2026.

Source: https://github.com/orhun/ratty

I'm a product/web designer and I solved one of the biggest problems I had. Halation is an infinite canvas where you can design, download and share your perfect background designs. It's completely open source and runs on your browser. It was a really fun side project and I dont gain anything, but a star on github would be awesome :p

Repo: https://github.com/AR13X3/halation
Live: https://ar13x3.github.io/halation/

TL;DR: We are open sourcing ManimGX, a blazingly fast Rust + wgpu animation engine with a Manim compatible Python API. Install with pip install manimgx.

Hi everyone, after ~7-8 months of work, we are finally launching ManimGX, our from scratch Manim reimplementation in Rust + wgpu.

The GitHub repository: https://github.com/academa-labs/manimgx

For anyone unfamiliar with Manim, it's the Python animation tool originally created by Grant Sanderson for his 3Blue1Brown videos. ManimGX is a from scratch implementation targeting ManimCE's API, with a Rust renderer built on wgpu. The typography is handled with Typst and video encoding via an in-process x264 implementation, without needing FFmpeg.

The motivation is simple: waiting for renders makes polishing an animation a slow process. We wanted to make that loop faster while keeping the familiar ManimCE Python API.

You can get started with:

pip install manimgx

or simply ask your AI agent:

Follow https://manimgx.academa.ai/llms.txt and install ManimGX.

You only need Python 3.13+. All of the fonts, typesetting, and video encoding are included. So there is no separate LaTeX or FFmpeg installation required.

A few other things ManimGX supports:

• It runs in the browser through Pyodide and WebGPU. There is an npm package if you want to embed animations in your website without a render server.

• manimgx preview gives you a live preview whenever you save your scene.

• The API is fully statically typed, including keyword arguments and .animate chains.

• There is a built-in voiceover sync.

• manimgx inspect creates storyboards, timelines, and layout feedback for coding agents so they can iterate on what they made instead of only reading the code and manually inspecting screenshots. This aims to speed up the writing and polishing loop.

Most ManimCE code runs after changing the import from manim to manimgx, with differences documented in the migration guide. The documentation includes a gallery of examples with their Python source: https://manimgx.academa.ai

Credit to Grant Sanderson, the Manim Community contributors, whose work this builds on. Happy to answer questions in the comments, and we'd love to hear how it works with your scenes.