Honestly it amazes me what great things can community make.

This is the best project I have ever made.
2 weeks ago I posted about my flight finder hitting 1.3k github stars sharing some of the stuff and thanking early contributors. That post got over 800 upvotes and 150 comments of feedback...

Through this 1 post I've learned more than in 3 months of running this project. (this post explains more about the API/programmatic access and dev stuff).

letsfg.co started as a simple webstite where you could find the best flights on the internet by comparing every other website in the world.
We've got so much overwhelming feedback, requests, problems with travelling.
Which got us to go back to our roots - what we are really here to do. Fix the travel experience.

We've worked on all of your feedback, listened to the problems and smacked our heads against the wall to tackle them.

We're going to comoditize travel management.

Thanks to your feedback we rebuild the whole searching experience on the homepage, where you're now able to search mutli-city trips, multi-departure trips, etc:

(couldn't put images for some reason now. check the 2nd image)

We build a compeltely new layer - ground transport. We will now show you transport options for each airport in the city, so you can choose the best:

(check the 3rd, 4th images)

This is my favourite, and I'm so excited about this. We've mapped the flights history from airlines, and will now show you reliability of each flight in your trip. Missed connection probability, and help with adjustments:

(check the 5th image)

But leaving you at this point wouldn't feel like we truly care.
So we will also monitor your trips 24/7, detecting disruptions, cancellations and delays, and instantly send you rebooking options, including free options from same-alliance flights.

We have changed the pricing like so many of you suggested, and added a subscription for frequent travelers. When you buy subscription, as a thank you, you get 4 free full-access trips for friends.

EDIT: We changed to free searching and pay for booking.

Everybody who bought previously keeps their access for a trip.

This project has evloved so much, and repeating myself, we really are aiming to fix the travelling experience, all the disruptions, cancellations and anxiety, especially when you need to be somewhere on time.
Travel management, comoditized.

And there is soo much more exciting coming, we're working on a partnership with Uber and add them to our ground layer transport.
We're on the last stages of signing an agreement with insurance and we'll work on providing that to all of members, so in case of cancellations - you're covered.
And as a last - we're working on automatic flight booking options, where when your flight gets cancelled, you don't even bother because we rebook the same-alliance - free option - for you.

This will all be available as openclaw, agent native, and API - we will be updating docs on the github repo in the following weeks. https://github.com/LetsFG/LetsFG

I can't thank you all individually, because at this point there is too many of you. 1.6k, wow.

But thank you, to everybody. Your amazing feedback and openness makes world-changing things possible. I Love the dev community <3

Keep on travelling.

Cheers,
Adam

Pixora is a lightweight Python library and command-line tool for converting ordinary images into retro-style pixel art. Built on top of Pillow, it provides a simple API for pixelizing images with customizable pixel sizes while supporting both file paths and in-memory objects. Whether you are creating game assets, generating pixelated avatars, or adding a nostalgic visual effect to your images, Pixora offers a fast, and easy-to-use solution for both scripts and terminal workflows.

GitHub: https://github.com/sepandhaghighi/pixora

The project is almost completely done, I'm doing the reverse engineering of fl studio.

You are ready to open your Fl studio projects, detect your plugins and the plugins you don't have. The reverse engineering of the best known is being done too! Like fabfilter, Valhalla, waves and etc... All in clean-room model.

Site: www.lynx-share.com
Repo: https://github.com/Ka1-S/LYNX

you can look at someone's github and see commits, stars, repos, languages, all that stuff.

but you still don't really know what that person is actually good at.

like...

someone could have 500 commits because they're building AI infra.

someone else could have 500 commits because they're building dev tools.

on the surface, their profiles can look pretty similar.

and i kept thinking...

why is it so hard to understand what someone actually spends their time building?

that's basically why i started building Shiftza.

i've been testing it on random github profiles and honestly, some of the reports have been pretty interesting 😂

so i made the little shortcut thing again:

just add "shift" before any github profile URL.

shiftgithub.com/github_username

try yours and tell me if it actually describes what you've been building.

also, i'm working on something i haven't launched yet.

i want Shiftza to eventually understand private repositories too, because obviously a lot of the most interesting work people do isn't public.

that's probably one of the next things i want to build.

for now, the product is mostly built around open-source github data.

and honestly, i'd like to make more of this open source as i keep building it.

so if you've been following this idea, understand what i'm trying to build, or just think there's something interesting here...

dm me.

i'm also trying to build a small team around this.

if you actually understand the product and want to build this together, send me your linkedin.

would be pretty cool to find a few people who want to build this with me instead of just working on another side project.

Teleporty is an open-source sandbox simulation pipeline written in Python. It evaluates the constraints of classical information transmission and rate-distortion theory when applied to extreme biological data reduction (using the complete physical and neural architecture of a human body as a benchmark).

Rather than tackling teleportation through quantum physics, the project models it entirely as an engineering, data serialization, and throughput challenge.

Core Features & Technical Implementation
Connectome Graph Compression: Models neural infrastructure as a Sparse Directed Graph using compressed sparse row (CSR) configurations. By setting an optimization threshold, the pipeline strips away structural "noise." Testing on a real C. elegans worm dataset yields a 99% behavioral similarity match when the compressed data matrix is fed back into a network state simulation loop. It scales down a functional human brain specification to just 42 KB.
Hierarchical Bulk Tissue Mapping: To bypass the impossible disk I/O of saving individual 3D voxel coordinates for repeating tissue types (muscles, fat, bone matrix), the project implements hierarchical tree structures and run-length spatial compression maps. This optimizes total physical structural data down to roughly 247 GB (fitting comfortably on a standard consumer SSD).

Extreme Multi-Queue Throughput Simulation: The final piece of the pipeline runs a parallel simulation evaluating the manufacturing bottleneck: processing a 247 GB object schema through an active assembly interface within a strict 1-hour tissue viability window. It models a heavily parallelized matrix of 10.3 million individual output queues throttled to 1,000 elements/second to test execution stability.

Technical Focus
The codebase is structured to explore performance bottlenecks in Python, specifically focusing on matrix traversal optimization, high-volume memory layouts, and stress-testing multi-threaded execution pools against the limitations of the Python Global Interpreter Lock (GIL).

Contributing & Feedback
The math derivations (LaTeX), simulation loops, and pipeline profiles are completely open source. I am highly interested in optimization suggestions, particularly regarding:

Minimizing matrix traversal overhead during the sparse-to-dense conversion cycles.
Better methods for simulating massive concurrent queues natively in Python.

Repository Link: https://github.com/ninjahawk/teleporty

I've been working on Drivebase for quite some time, and I recently launched v4.

The idea started because I was tired of jumping between different storage providers just to manage my files. Google Drive, Dropbox, S3, R2, OneDrive — each had its own interface, workflows, and limitations.

For v4, I decided to rethink the experience entirely.

Instead of building another cloud storage dashboard, I built what I call DriveOS. Everything lives inside a desktop-like workspace in the browser, with a familiar file explorer, drag-and-drop file management, keyboard shortcuts, context menus, and window-based workflows.

The goal is to make cloud storage feel more like using your computer and less like navigating a collection of disconnected web apps.

Drivebase can be self-hosted if you want full control, but I also offer a hosted cloud version for people who just want to sign up and use it.

I'd genuinely love feedback on the concept, the UI, and whether this is something you'd find useful.

Website: drivebase.io

Github: https://github.com/drivebase/drivebase

I'm the solo dev who built this. webclaw pulls clean web content (Markdown, text, or JSON) for feeding into LLMs and agents. It runs as a CLI and as an MCP server you can drop into Claude or Cursor, and for normal pages it extracts everything locally, so there's no account or API key.

The part I'm happiest with: the core extraction crate has zero network dependencies and is WASM-safe, just pure HTML to structured output, with fetching and the CLI/MCP frontends as separate crates.

https://github.com/0xMassi/webclaw

Honest caveats: it's solo and about three months old, it's AGPL-3.0 (the copyleft won't suit everyone), and a handful of sites that block bots or hide behind JS still fall back to an optional cloud step that isn't self-hostable. Everything else is local and free.

If you've wired a tool like this into an agent, what's the one thing it always gets wrong on real-world pages?

https://github.com/Defanoux/Oxide360/
So I know vibe coded is a very turning off set of words, but I have no programming experience and this was my first break into the development world so just bear with me.

This is Oxide 360 - a complete rebuild of Fusion 360 made by me (and Claude by Anthropic).

I've tested it as I would treat Fusion 360 and it works pretty well! I've gotten rid of as many bugs and things that would separate it from Fusion 360 as much as I can. And its public so you can fix it if you'd like!

There will be a FlatPak coming out soon (if not by the time this post is up).
If you'd like to support me then star it if you'd like!

Converts pretty much anything: video, audio, images, documents, 3D models, even .blend if you enable the Blender module. Drag in, pick a format, convert. Nothing gets uploaded.

Site: https://batushn.github.io/coldmill/

Source: https://github.com/Batushn/coldmill

I got tired of lightweight Linux distros somehow still looking like they were designed in 2005, so I decided to make my own.

This is XXRI OS Lite 2.5 — a lightweight Linux project focused on making old hardware feel like an actual modern desktop.

256MB RAM minimum.

It's finally released, and I'm curious how it behaves on hardware beyond what I've tested.

If you have an old PC sitting around, I'd actually love to see someone throw it at one and tell me how it goes.

GitHub: Jantzen1212/XXRI-OS-LITE*

Website: https://xxri.flows.best/⁠

I got tired of productivity apps that feel like control panels, so I built Kiyoshi: notes, tasks, kanban board, calendar, and a lightweight block canvas, all local-first, all in a quiet glass-panel UI.

• 100% free and open source

• Runs on Linux, macOS, Windows (AppImage for Linux)

• No account, no cloud dependency — your data stays on your machine

• Built with Flutter, so it's a proper native desktop app, not an Electron wrapper

https://kiyoshi-beta.vercel.app/

Feedback, issues, and PRs welcome — it's still early and there's a lot I want to add (sync, mobile companion, etc.).

I just made my first own, working programming language. You can find it here. I started by writing a lexer, than parser, than checker. After that, I used Claude to generate a LLVM Wrapper for the C-API (that is litarally only boilerplate) and wrote a bridge. To write installation scripts, workflows, debugging etc. I also used Claude. If you are good at programming, I have a few questions:

• What do you think about it‘s core philosophy?

• What do you think about the syntax?

• How is the DX?

• Can you find any bugs?

• Did I overuse Claude in your opinion? I am looking forward to your feedback.

Note: I already made some languages like a year to half a year ago, but:

• They were designed badly

• They did not work

• They were pure AI-slop to be honest.

Example: Fibonacci in C<<

import std;

// tunnels int32 result
def fib(int32 n)
{
    int32 a = 0;
    int32 b = 1;
    int32 tmp = 0;

    for (int32 i = 0; i < n;)
    {
        tmp = b;
        b = a + b;
        a = tmp;
        i += 1;
    }

    tunnel a -> int32 result;
}

entry
{
    for (int32 i = 0; i < 16;)
    {
        reserve int32 result = fib(i);
        printf("fib(%2d) = %d\n", i, result);
        i += 1;
    }
}

I was outside doing a CE5 session (the look-up-and-invite-contact thing), genuinely saw a steady light glide across the sky and got excited. So like any normal person would, I vibe-coded my own tracker over a few weeks to figure out what it was.

It works great. It also instantly informed me my first contact was the International Space Station. My literal first logged sighting is "ISS (ZARYA)." The app is basically a monument to me being wrong.

Anyway, here's SkyWatch, a 3D satellite/ISS/rocket tracker and CE5 session app that runs entirely in the browser. First pic shows the home screen, ~4,000+ live objects, rockets, ground tracks. Filter by layer/orbit: ISS, Starlink, GPS, the works. Use the Sky function to see what's actually passing overhead and when (this is the screen that ruined the mystery). Conduct your own CE5 sessions and log what you see. A friends layer, so my friends can also watch me fail to find aliens in real time. And an awards section with a UFO avatar I refuse to change.

The vibe-coding bit: it's essentially one index.html. globe.gl/three.js for the 3D, satellite.js for the orbital math, Firebase for the social layer, deployed free on GitHub Pages.

Free, no account needed to use, but if you do decide to link your account, friend me _@FLuX

If you point it at your own mystery light and it turns out to NOT be the ISS, please tell me, because I have a UFO avatar to justify.

Not a coder by any means, just a dude trying to make contact and proud of what I accomplished. Would love me some feedback.

Live: https://nuno927-tech.github.io/skywatch/

Infinity for Reddit on iOS is finally here after over a year of development. It is open source, built natively with Swift and SwiftUI, and designed to be fast, smooth, and highly customizable, with a focus on performance, transparency, and user control, all in a clean, ad-free experience.

Key Features

• Full anonymous mode with local voting, saving, hiding, and read tracking

• Optional login

• Embed images and GIFs when you submit posts and comments

• Powerful filters to block unwanted content

• Lazy mode for automatic scrolling through your post feed

• Extensive customization and theming options

• Smooth, responsive performance

• No ads

The app is available here: https://apps.apple.com/us/app/infinity-for-reddit/id6759064642

The GitHub repo is available here: https://github.com/foxanastudio/Infinity-For-Reddit-iOS

Using multiple AI tools feels like working with five assistants who never talk to each other.

Claude knows one part of a project. ChatGPT remembers a preference. Cursor knows why the code changed. Codex starts from zero.

Eventually, I was spending more time reconstructing context than getting useful work done.

So I built the opposite.

Second Brain is a free, open-source memory layer that gives Claude, ChatGPT, Cursor, Codex, and other MCP-compatible tools access to the same persistent memory.

Your projects, decisions, preferences, and lessons can follow you between tools instead of being trapped inside one app.

The new version also turns those memories into a self-organizing knowledge graph.

When you save something, Second Brain can connect it to related memories automatically. Recall can then follow those relationships, so a question about a project might also surface:

- the decision you made
- why you made it
- the person or idea connected to it
- a later update that changed the plan

You do not need to remember the exact wording of the original note.

What you get

- One shared memory across your AI tools
- Semantic recall based on meaning, not exact keywords
- Automatic relationships between related memories
- Multi-hop recall for following connected context
- A graph view for exploring how memories fit together
- Tools to remember, recall, append, update, and forget
- Capture from the terminal, Obsidian, browser, iPhone, or directly inside an AI conversation
- MIT-licensed code running in your own Cloudflare account

There is no Second Brain subscription or product-imposed memory tier. Your data lives in your infrastructure. Cloudflare's normal quotas still apply.

Setup

1. Choose an authentication token

2. Deploy the Worker to your Cloudflare account

3. Add the `/mcp` URL to the AI clients you use

The basic deployment takes a couple of minutes. One honest caveat: Cloudflare may require a one-time Wrangler command to enable the Vectorize index if it is not provisioned automatically.

GitHub:

https://github.com/rahilp/second-brain-cloudflare

I would especially value feedback on the graph behavior.

Should relationships stay mostly automatic, or should users have more manual control? How many hops should recall follow before related context becomes noise?

Github repo

https://github.com/Blankscreen-exe/Notty-Text-Editor

Add the button with this Chrome extension: https://chromewebstore.google.com/detail/clone-anywhere-for-github/effhdkonnknoebahhnnciakckbbfmcpi

I always wondered why there was no "Clone in VS Code", "Clone in Cursor", or "Download as .zip" button for GitHub. So I made one!

Go from GitHub repo to editing in literally 3 seconds. No more using terminal commands!