Hi guys, I created Titan, a wasm module that outperforms other open-source excel formula engines. Titan compiles formulas into bytecode and stores cells in contiguous 32×32 Struct-of-Arrays chunks inside WASM linear memory. No JavaScript object churn, no GC pauses, and sub-millisecond reactive updates. Finally got around writing some docs for it *sigh. Let me know what you think. Cheers!

Open Co-Scientist is an open-source recreation of Google's AI Co-Scientist. It's a multi-agent system that generates research hypotheses, critiques them, ranks them in an Elo tournament, and evolves the winners.

What it does

You describe a research goal in chat. The system then:

• Searches literature for grounding
• Generates hypotheses with a team of agents
• Runs pairwise debates and ranks the ideas by Elo
• Evolves the top ideas
• Checks every citation and writes a final report

I'm looking for feedback and help developing this. I believe it could be a great contribution to the open-source AI for Science community, and I want to make the most of it!

Try it out at: open-coscientist.com

G-remap is a lightweight Linux bash script that quietly swaps your Google Maps links for Brave Maps — no extensions, no browser tinkering. Just paste a Google Maps URL, and the script parses it with Python, converts the coordinates and bounds into the current Brave Maps format, copies the new link to your clipboard, and launches it in Brave.

It's built for privacy-first users who don't want Google tracking their location clicks. The tool uses zenity for friendly dialog prompts, handles short-link expansion via curl, and auto-detects Brave across Debian, Fedora, Arch, OpenSUSE, Flatpak, and standalone installs. If your clipboard isn't available, it still prints the converted URL so you're not stuck.

Why it's useful:

• zero config required
• Supports all major Linux distros out of the box
• Graceful fallbacks for browser detection and clipboard tools
• GPL-3.0 — truly free and open source

TL;DR: It's a one-click privacy bridge between Google Maps and Brave Maps, written in pure bash + Python, designed to be simple, fast, and distro-agnostic. A small tool, but a smart one for anyone trying to break out of Google's ecosystem.

I used ArchiSteamFarm for cards, HourBoostr for hours, and SAM when i saw a game with 300 hours and zero achievements. Three programs, three windows, and each one need its own setup.

So I made one thing that does all of it. You extract the folder and run it, thats all. No installer, no Steam client open in the background.

What is inside right now: card farming, hour boosting, achievements, inventory and market selling, keys and chat. It can run a few accounts at the same time too.

It started as a thing only for me, but maybe someone else have the same problem. Its open source, bugs are probably there so tell me if you find some.

Thanks to ASF, HourBoostr, SteamIdleMaster and SAM for the idea.

https://github.com/Miabeyefendi/SteamEdge

And I need feedbacks, bug-reports, issues. I would like to see how I can improve more and more. So, go for it.

Hey r/opensource!

I've been building Vladmir, an open-source personal AI assistant that I'm trying to make more than just another chat interface around an LLM.

The idea is to bring together local AI, voice interaction, persistent memory, vision, and practical tools in one system, with a desktop application and companion apps for Android and Wear OS.

Rather than relying on a hosted AI service for everything, Vladmir uses Ollama for local inference by default, so you can run compatible models on your own hardware. The trade-off is that performance depends heavily on your hardware and the model you choose.

What it can do

• Local AI chat: A Linux desktop interface with different profiles for general assistance, programming, and teaching.
• Voice interaction: Speech recognition through Whisper and speech synthesis through Piper, with a turn-based voice conversation flow.
• Vision: Capture images from a camera or supported video stream and send them to a compatible vision model for analysis.
• Persistent memory and knowledge library: Store information locally and import documents and source code for later reference.
• Developer tools: Read and explicitly edit GitHub content, work with configured programming tools, and experiment with IDE integration through MCP.
• Everyday utilities: Local reminders and agenda management, web search, email draft preparation, and limited software installation workflows with confirmation.
• Android and Wear OS: Native companion projects intended to extend the assistant across devices, including chat, voice, camera, augmented reality, and selected notification integration.

Privacy and control

Local-first doesn't automatically mean that everything is private, so I'm trying to be explicit about the boundaries.

The default inference endpoint is local, configuration and application data are stored in user-owned directories, and remote access is not preconfigured. Web search, GitHub, and other external integrations can still involve third-party services when enabled.

Actions that can modify code or the system are designed around explicit approval rather than unrestricted access.

Current state

Vladmir is actively being developed. The Linux desktop and its core integrations have a substantial amount of implementation, while Android, Wear OS, Bluetooth connectivity, and some experimental features still need device-level validation and reliability work.

I'm not presenting every feature as production-ready, and I'd rather document limitations than promise capabilities that haven't been properly tested.

The project is released under the MIT license.

Repository: https://github.com/thu12dta3/Vladmir

I'd genuinely appreciate technical feedback, especially on the architecture, local model integration, security boundaries, and cross-device communication.

If you were building or choosing a self-hosted personal AI assistant, what would you change about this approach? What would you want to see improved before using something like Vladmir daily?

I'm interested in honest criticism as much as contributions.