What's your favorite OPEN SOURCE Chromium-based browser with MV3 and vertical tabs?
Hi r/opensource, I've been a heavy user of the zen browser ever since it came out, and as such I really want a browser with similar features (proper ad block, vertical tabs, containerized workspaces) BUT I want it to be chromium-based, as just in the past week I ran into five websites that did not work on firefox (broken dropdowns, registration buttons doing nothing, important elements not appearing), and it is hard to continue using it.
https://redd.it/1ognl7j
@r_opensource
Hi r/opensource, I've been a heavy user of the zen browser ever since it came out, and as such I really want a browser with similar features (proper ad block, vertical tabs, containerized workspaces) BUT I want it to be chromium-based, as just in the past week I ran into five websites that did not work on firefox (broken dropdowns, registration buttons doing nothing, important elements not appearing), and it is hard to continue using it.
https://redd.it/1ognl7j
@r_opensource
Reddit
From the opensource community on Reddit
Explore this post and more from the opensource community
Built a package manager to compile & install packages directly from their git repository
https://github.com/phukon/sprout
https://redd.it/1ogsynu
@r_opensource
https://github.com/phukon/sprout
https://redd.it/1ogsynu
@r_opensource
GitHub
GitHub - phukon/sprout: 🌱 Package manager designed to compile & install packages directly from their git repository.
🌱 Package manager designed to compile & install packages directly from their git repository. - phukon/sprout
Lightweight Python Implementation of Shamir's Secret Sharing with Verifiable Shares
Hi r/opensource!
I built a lightweight Python library for Shamir's Secret Sharing (SSS), which splits secrets (like keys) into shares, needing only a threshold to reconstruct. It also supports Feldman's Verifiable Secret Sharing to check share validity securely.
**What my project does**
Basically you have a secret(a password, a key, an access token, an API token, password for your cryptowallet, a secret formula/recipe, codes for nuclear missiles). You can split your secret in n shares between your friends, coworkers, partner etc. and to reconstruct your secret you will need at least k shares. For example: total of 5 shares but you need at least 3 to recover the secret). An impostor having less than k shares learns nothing about the secret(for context if he has 2 out of 3 shares he can't recover the secret even with unlimited computing power - unless he exploits the discrete log problem but this is infeasible for current computers). If you want to you can not to use this Feldman's scheme(which verifies the share) so your secret is safe even with unlimited computing power, even with unlimited quantum computers - mathematically with fewer than k shares it is impossible to recover the secret
Features:
* Minimal deps (pycryptodome), pure Python.
* File or variable-based workflows with Base64 shares.
* Easy API for splitting, verifying, and recovering secrets.
* MIT-licensed, great for secure key management or learning crypto.
Comparison with other implementations:
* pycryptodome - it allows only 16 bytes to be split where mine allows unlimited(as long as you're willing to wait cause everything is computed on your local machine). Also this implementation does not have this feature where you can verify the validity of your share. Also this returns raw bytes array where mine returns base64 (which is easier to transport/send)
* [This](https://github.com/thedanhub/shamir-secret-sharing) repo allows you to share your secret but it should already be in number format where mine automatically converts your secret into number. Also this repo requires you to put your share as raw coordinates which I think is too technical.
* Other notes: my project allows you to recover your secret with either vars or files. It implements Feldman's Scheme for verifying your share. It stores the share in a convenient format *base64* and a lot more, check it out for docs
**Target audience**
I would say it is production ready as it covers all security measures: primes for discrete logarithm problem of at least 1024 bits, perfect secrecy and so on. **Even so, I wouldn't recommend its use for high confidential data(like codes for nuclear missiles) unless some expert confirms its secure**
Check it out:
* PyPI: [https://pypi.org/project/shamir-lbodlev/](https://pypi.org/project/shamir-lbodlev/) (pip install shamir-lbodlev)
* GitHub: [https://github.com/lbodlev888/shamir](https://github.com/lbodlev888/shamir-lbodlev) (README with examples)
\-Feedback or feature ideas? Let me know [here](https://github.com/lbodlev888/shamir/issues)!
https://redd.it/1oh925h
@r_opensource
Hi r/opensource!
I built a lightweight Python library for Shamir's Secret Sharing (SSS), which splits secrets (like keys) into shares, needing only a threshold to reconstruct. It also supports Feldman's Verifiable Secret Sharing to check share validity securely.
**What my project does**
Basically you have a secret(a password, a key, an access token, an API token, password for your cryptowallet, a secret formula/recipe, codes for nuclear missiles). You can split your secret in n shares between your friends, coworkers, partner etc. and to reconstruct your secret you will need at least k shares. For example: total of 5 shares but you need at least 3 to recover the secret). An impostor having less than k shares learns nothing about the secret(for context if he has 2 out of 3 shares he can't recover the secret even with unlimited computing power - unless he exploits the discrete log problem but this is infeasible for current computers). If you want to you can not to use this Feldman's scheme(which verifies the share) so your secret is safe even with unlimited computing power, even with unlimited quantum computers - mathematically with fewer than k shares it is impossible to recover the secret
Features:
* Minimal deps (pycryptodome), pure Python.
* File or variable-based workflows with Base64 shares.
* Easy API for splitting, verifying, and recovering secrets.
* MIT-licensed, great for secure key management or learning crypto.
Comparison with other implementations:
* pycryptodome - it allows only 16 bytes to be split where mine allows unlimited(as long as you're willing to wait cause everything is computed on your local machine). Also this implementation does not have this feature where you can verify the validity of your share. Also this returns raw bytes array where mine returns base64 (which is easier to transport/send)
* [This](https://github.com/thedanhub/shamir-secret-sharing) repo allows you to share your secret but it should already be in number format where mine automatically converts your secret into number. Also this repo requires you to put your share as raw coordinates which I think is too technical.
* Other notes: my project allows you to recover your secret with either vars or files. It implements Feldman's Scheme for verifying your share. It stores the share in a convenient format *base64* and a lot more, check it out for docs
**Target audience**
I would say it is production ready as it covers all security measures: primes for discrete logarithm problem of at least 1024 bits, perfect secrecy and so on. **Even so, I wouldn't recommend its use for high confidential data(like codes for nuclear missiles) unless some expert confirms its secure**
Check it out:
* PyPI: [https://pypi.org/project/shamir-lbodlev/](https://pypi.org/project/shamir-lbodlev/) (pip install shamir-lbodlev)
* GitHub: [https://github.com/lbodlev888/shamir](https://github.com/lbodlev888/shamir-lbodlev) (README with examples)
\-Feedback or feature ideas? Let me know [here](https://github.com/lbodlev888/shamir/issues)!
https://redd.it/1oh925h
@r_opensource
GitHub
GitHub - thedanhub/shamir-secret-sharing: A Python implementation of the Shamir secret sharing scheme
A Python implementation of the Shamir secret sharing scheme - thedanhub/shamir-secret-sharing
Unlocking the Sony PSP's Second CPU
Hey all!
The PSP may be an old device, but it still holds plenty of mysteries and possibilities for tinkering!
So I started this open-source project earlier this year with the goal of taking advantage of the Sony PSP's Media Engine, specifically its second MIPS CPU core, which has essentially the same capabilities as the main one.
However, it has no direct access to main system functions. It runs its own 'factory' core with functions stored in a kernel memory space, which hasn't been fully reverse-engineered yet.
- This project comes as a library that maps as many functions as possible from the Media Engine's core to make them accessible to homebrew developers
- It provides a custom initialization system and utility functions to simplify working with the Media Engine.
- It handles interrupts, suspend events, stack and local memory optimization, and thread management which is in WIP.
It's designed to make it easier for PSP homebrew developers to ease the integration and communication with the Media Engine. It's a work in progress, and contributions are welcome!
Available on GitHub: mcidclan/psp-media-engine-custom-core
Enjoy !
https://redd.it/1oh9qzv
@r_opensource
Hey all!
The PSP may be an old device, but it still holds plenty of mysteries and possibilities for tinkering!
So I started this open-source project earlier this year with the goal of taking advantage of the Sony PSP's Media Engine, specifically its second MIPS CPU core, which has essentially the same capabilities as the main one.
However, it has no direct access to main system functions. It runs its own 'factory' core with functions stored in a kernel memory space, which hasn't been fully reverse-engineered yet.
- This project comes as a library that maps as many functions as possible from the Media Engine's core to make them accessible to homebrew developers
- It provides a custom initialization system and utility functions to simplify working with the Media Engine.
- It handles interrupts, suspend events, stack and local memory optimization, and thread management which is in WIP.
It's designed to make it easier for PSP homebrew developers to ease the integration and communication with the Media Engine. It's a work in progress, and contributions are welcome!
Available on GitHub: mcidclan/psp-media-engine-custom-core
Enjoy !
https://redd.it/1oh9qzv
@r_opensource
GitHub
GitHub - mcidclan/psp-media-engine-custom-core: Advanced PSP Media Engine library providing access to the ME's native core functions…
Advanced PSP Media Engine library providing access to the ME's native core functions, facilitating integration and usage in homebrew projects. - mcidclan/psp-media-engine-custom-core
I built Flowcraft, a lightweight, zero-dependency alternative to heavy workflow platforms like Temporal/Airflow/Vercel
https://github.com/gorango/flowcraft
https://redd.it/1ohbuza
@r_opensource
https://github.com/gorango/flowcraft
https://redd.it/1ohbuza
@r_opensource
GitHub
GitHub - gorango/flowcraft: A lightweight workflow framework
A lightweight workflow framework. Contribute to gorango/flowcraft development by creating an account on GitHub.
What if Goldman Sachs made Slang open source?
https://youtube.com/shorts/wm5-vujv0FQ
https://redd.it/1ohej5z
@r_opensource
https://youtube.com/shorts/wm5-vujv0FQ
https://redd.it/1ohej5z
@r_opensource
YouTube
TIL Goldman Sachs maintains its own proprietary language (Slang) known only to its employees
Travis Oliphant uses Goldman Sachs' Slang as a bad example of tech that should've been open sourced, but wasn't
[R] Adaptive Sparse Training on ImageNet-100: 92.1% Accuracy with 61% Energy Savings (Open-source, zero degradation)
**TL;DR**: Implemented **Adaptive Sparse Training (AST)** on ImageNet-100 with a pretrained ResNet-50. Trains on \~37–39% of samples per epoch, cuts energy by \~61–63%, gets **92.12%** top-1 (baseline **92.18%**) with **no meaningful drop**; a faster “efficiency” variant reaches **2.78×** speedup with \~1–2 pp accuracy drop. **Code + noscripts open-source** (links below).
# Key Results
**Production (best accuracy)**
* **Top-1:** 92.12% (baseline: 92.18%) → Δ = **+0.06 pp**
* **Energy:** **–61.49%**
* **Speed:** **1.92×** over baseline
* **Activation rate:** 38.51% of samples/epoch
**Efficiency (max speed)**
* **Top-1:** 91.92%
* **Energy:** **–63.36%**
* **Speed:** **2.78×**
* **Activation rate:** 36.64%
# Method: Adaptive Sparse Training (AST)
At each step, select only the most informative samples using a significance score combining **loss magnitude** and **prediction entropy**:
significance = 0.7 * loss_magnitude + 0.3 * prediction_entropy
active_mask = significance >= dynamic_threshold # selects top K%
* Trains on **\~10–40%** of samples per epoch after warmup.
* **PI controller** keeps the target activation rate stable over training.
# Setup
* **Model:** ResNet-50 (pretrained on ImageNet-1K, 23.7M params)
* **Data:** ImageNet-100 (126,689 train / 5,000 val; 100 classes)
* **Hardware:** Kaggle P100 GPU (free tier) — fully reproducible
**Two-stage schedule**
1. **Warmup (10 epochs):** 100% samples (adapts features to 100-class subset)
2. **AST (90 epochs):** adaptive selection, 10–40% active
**Optimizations**
* **Gradient masking** → single forward pass (vs double) for \~3× reduction in overhead
* **AMP (FP16/FP32)** on both baseline and AST
* Dataloader tuning (prefetch, 8 workers)
# Why it matters
* **Sustainability**: \~61–63% less training energy
* **Iteration speed**: **1.9–2.8×** faster ⇒ more experiments per GPU-hour
* **Accuracy**: Production variant matches/slightly outperforms baseline (transfer setting)
* **Drop-in**: Works with standard pretrained pipelines; no exotic components
# Notes & comparisons
* **Baseline parity**: Same ResNet-50, optimizer (SGD+momentum), LR schedule, and aug as AST; only sample selection differs.
* **Overhead**: Significance scoring reuses loss/entropy; <1% compute overhead.
* **Relation to prior ideas**:
* Random sampling: no model-aware selection
* Curriculum learning: AST is **fully automatic**, no manual ordering
* Active learning: selection **per epoch** during training, not one-shot dataset pruning
* **From scratch?** Not tested (this work targets transfer setups most common in practice).
# Code & Repro
* **Repo**: [https://github.com/oluwafemidiakhoa/adaptive-sparse-training](https://github.com/oluwafemidiakhoa/adaptive-sparse-training)
* **Production noscript (best acc)**: `KAGGLE_IMAGENET100_AST_PRODUCTION.py`
* **Efficiency noscript (max speed)**: `KAGGLE_IMAGENET100_AST_TWO_STAGE_Prod.py`
* **Guide**: `FILE_GUIDE.md` (which version to use)
* **README**: complete docs and commands
# Discussion
1. Experiences with **adaptive sample selection** at larger scales (ImageNet-1K / beyond)?
2. Thoughts on **warmup→AST** vs training from scratch?
3. Interested in collaborating on **ImageNet-1K** or **LLM fine-tuning** evaluations?
4. Suggested **ablations** (e.g., different entropy/loss weights, alternative uncertainty metrics)?
**Planned next steps**: full ImageNet-1K runs, extensions to BERT/GPT-style fine-tuning, foundation-model trials, and curriculum-learning comparisons.
https://redd.it/1ohj0tf
@r_opensource
**TL;DR**: Implemented **Adaptive Sparse Training (AST)** on ImageNet-100 with a pretrained ResNet-50. Trains on \~37–39% of samples per epoch, cuts energy by \~61–63%, gets **92.12%** top-1 (baseline **92.18%**) with **no meaningful drop**; a faster “efficiency” variant reaches **2.78×** speedup with \~1–2 pp accuracy drop. **Code + noscripts open-source** (links below).
# Key Results
**Production (best accuracy)**
* **Top-1:** 92.12% (baseline: 92.18%) → Δ = **+0.06 pp**
* **Energy:** **–61.49%**
* **Speed:** **1.92×** over baseline
* **Activation rate:** 38.51% of samples/epoch
**Efficiency (max speed)**
* **Top-1:** 91.92%
* **Energy:** **–63.36%**
* **Speed:** **2.78×**
* **Activation rate:** 36.64%
# Method: Adaptive Sparse Training (AST)
At each step, select only the most informative samples using a significance score combining **loss magnitude** and **prediction entropy**:
significance = 0.7 * loss_magnitude + 0.3 * prediction_entropy
active_mask = significance >= dynamic_threshold # selects top K%
* Trains on **\~10–40%** of samples per epoch after warmup.
* **PI controller** keeps the target activation rate stable over training.
# Setup
* **Model:** ResNet-50 (pretrained on ImageNet-1K, 23.7M params)
* **Data:** ImageNet-100 (126,689 train / 5,000 val; 100 classes)
* **Hardware:** Kaggle P100 GPU (free tier) — fully reproducible
**Two-stage schedule**
1. **Warmup (10 epochs):** 100% samples (adapts features to 100-class subset)
2. **AST (90 epochs):** adaptive selection, 10–40% active
**Optimizations**
* **Gradient masking** → single forward pass (vs double) for \~3× reduction in overhead
* **AMP (FP16/FP32)** on both baseline and AST
* Dataloader tuning (prefetch, 8 workers)
# Why it matters
* **Sustainability**: \~61–63% less training energy
* **Iteration speed**: **1.9–2.8×** faster ⇒ more experiments per GPU-hour
* **Accuracy**: Production variant matches/slightly outperforms baseline (transfer setting)
* **Drop-in**: Works with standard pretrained pipelines; no exotic components
# Notes & comparisons
* **Baseline parity**: Same ResNet-50, optimizer (SGD+momentum), LR schedule, and aug as AST; only sample selection differs.
* **Overhead**: Significance scoring reuses loss/entropy; <1% compute overhead.
* **Relation to prior ideas**:
* Random sampling: no model-aware selection
* Curriculum learning: AST is **fully automatic**, no manual ordering
* Active learning: selection **per epoch** during training, not one-shot dataset pruning
* **From scratch?** Not tested (this work targets transfer setups most common in practice).
# Code & Repro
* **Repo**: [https://github.com/oluwafemidiakhoa/adaptive-sparse-training](https://github.com/oluwafemidiakhoa/adaptive-sparse-training)
* **Production noscript (best acc)**: `KAGGLE_IMAGENET100_AST_PRODUCTION.py`
* **Efficiency noscript (max speed)**: `KAGGLE_IMAGENET100_AST_TWO_STAGE_Prod.py`
* **Guide**: `FILE_GUIDE.md` (which version to use)
* **README**: complete docs and commands
# Discussion
1. Experiences with **adaptive sample selection** at larger scales (ImageNet-1K / beyond)?
2. Thoughts on **warmup→AST** vs training from scratch?
3. Interested in collaborating on **ImageNet-1K** or **LLM fine-tuning** evaluations?
4. Suggested **ablations** (e.g., different entropy/loss weights, alternative uncertainty metrics)?
**Planned next steps**: full ImageNet-1K runs, extensions to BERT/GPT-style fine-tuning, foundation-model trials, and curriculum-learning comparisons.
https://redd.it/1ohj0tf
@r_opensource
GitHub
GitHub - oluwafemidiakhoa/adaptive-sparse-training: Energy-efficient deep learning with Adaptive Sparse Training - 89.6% energy…
Energy-efficient deep learning with Adaptive Sparse Training - 89.6% energy savings, 11.5× speedup on CIFAR-10 - oluwafemidiakhoa/adaptive-sparse-training
FlexingUSB is a New, Blazing-Fast, & Safe Terminal Utility for Creating Bootable USBs on macOS (Faster than dd/Etcher)
https://github.com/Goenvim/FlexingUSB
https://redd.it/1ohltje
@r_opensource
https://github.com/Goenvim/FlexingUSB
https://redd.it/1ohltje
@r_opensource
GitHub
GitHub - Goenvim/FlexingUSB: A macOS command-line utility for safely writing ISO images to USB drives
A macOS command-line utility for safely writing ISO images to USB drives - Goenvim/FlexingUSB
The OSI is seeking its next Executive Director, responsible for advancing its mission, growing and diversifying its funding base, and fostering a global, inclusive community of stakeholders.
https://opensource.org/blog/open-source-initiative-now-accepting-your-application-for-executive-director
https://redd.it/1ohm13j
@r_opensource
https://opensource.org/blog/open-source-initiative-now-accepting-your-application-for-executive-director
https://redd.it/1ohm13j
@r_opensource
Open Source Initiative
Open Source Initiative now accepting your application for Executive Director
The Open Source Initiative is seeking its next Executive Director (ED), the chief executive and strategic leader of the OSI, responsible for advancing its mission, growing and diversifying its funding base, and fostering a global, inclusive community of stakeholders.…
So OpenObserve is ‘open-source’… until you actually try using it
I’ve been exploring OpenObserve lately — looked promising at first, but honestly, it feels like another open-core trap.
RBAC, SSO, fine-grained access — all locked behind “Enterprise.” The OSS version is fine for demos, but useless for real production use. If I can’t run it securely in production, what’s even the point of calling it open source?
I maintain open-source projects myself, so I get the need for sustainability. But hiding basic security and access control behind a paywall just kills trust.
Even Grafana offers proper RBAC in OSS. OpenObserve’s model feels like “open-source for marketing, closed for reality.” Disappointing.
Obviously I can build a wrapper its just some work, but opensource things should actually be production-ready
https://redd.it/1ohmsso
@r_opensource
I’ve been exploring OpenObserve lately — looked promising at first, but honestly, it feels like another open-core trap.
RBAC, SSO, fine-grained access — all locked behind “Enterprise.” The OSS version is fine for demos, but useless for real production use. If I can’t run it securely in production, what’s even the point of calling it open source?
I maintain open-source projects myself, so I get the need for sustainability. But hiding basic security and access control behind a paywall just kills trust.
Even Grafana offers proper RBAC in OSS. OpenObserve’s model feels like “open-source for marketing, closed for reality.” Disappointing.
Obviously I can build a wrapper its just some work, but opensource things should actually be production-ready
https://redd.it/1ohmsso
@r_opensource
Reddit
From the opensource community on Reddit
Explore this post and more from the opensource community
Cooklang Federation: A decentralized, GitOps-based recipe sharing platform (no ads, no tracking, just recipes)
I've spent 4 years building Cooklang - an open-source markup language for recipes (think Markdown for cooking). Today I'm launching the Cooklang Federation, a decentralized recipe search platform.
The Problem: Recipe sites optimize for ads and SEO, not quality. They modify copied recipes to make them "unique," creating an arms race that produces absurdities like dishwasher salmon. Good recipes are harder to find despite abundance.
The Solution: A federated, decentralized platform where creators maintain full control of their recipes while making them discoverable to the community.
# Key Features
Decentralized Architecture:
- Recipes hosted on your own domain or GitHub repo
- No central authority or platform lock-in
- You control your data completely
GitOps Workflow:
- All feeds version-controlled in GitHub
- Changes require pull request review
- Full audit trail and transparency
- Community-governed feed list
Open Standards:
- RSS/Atom feed support
- GitHub repository indexing
- Plain text recipe format
- Open API (coming soon)
Currently indexing: 60+ active feeds, 3,500+ recipes
Try it: https://recipes.cooklang.org
# Technical Architecture
The federation uses a crawl-index-search pattern:
1. Feed Registry: YAML file listing all RSS/Atom feeds and GitHub repos
2. Automated Crawler: Periodically fetches and parses recipes from registered feeds
3. Full-Text Search: Indexes recipes for fast, powerful search
4. Decentralized Hosting: Recipes stay on creator's infrastructure
Think "GitHub Pages for recipes" or "RSS reader meets recipe search."
# Cooklang Format
Recipes are written in a simple markup language:
Benefits:
- Human-readable plain text
- Machine-parseable for tooling
- Version control with git
- No vendor lock-in
- Easy migration and backup
# Contributing
As a Recipe Creator:
1. Write recipes in Cooklang format
2. Host them (blog, GitHub, static site)
3. Add your feed to
4. Submit a PR to the federation repo
As a Developer:
- Federation repo: https://github.com/cooklang/federation
- Cooklang spec: https://github.com/cooklang/spec
- Parser libraries: Rust, JavaScript, TypeScript available
- Draft federation spec: https://github.com/cooklang/federation/blob/main/spec.md
# The Ecosystem
Cooklang has grown to 30+ repositories on GitHub:
- CLI tools for recipe management
- Mobile apps (iOS/Android)
- Parser libraries in multiple languages
- Editor extensions (VSCode, Vim, Emacs)
- Static site generators
All open source, all community-driven.
# Why This Matters
Recipe sites are the poster child for how ad-driven incentives corrupt content quality. The federation provides a sustainable alternative:
- Creators maintain control and ownership
- No ads, no tracking, no paywalls
- Community-curated, not algorithm-driven
- Built on open standards and protocols
# Get Involved
Search recipes: https://recipes.cooklang.org
Contribute: https://github.com/cooklang/federation
Learn more: https://cooklang.org
Spec: https://github.com/cooklang/federation/blob/main/spec.md
I'm happy to answer questions about the architecture, the format, or the roadmap. Looking forward to your feedback!
https://redd.it/1ohpn7w
@r_opensource
I've spent 4 years building Cooklang - an open-source markup language for recipes (think Markdown for cooking). Today I'm launching the Cooklang Federation, a decentralized recipe search platform.
The Problem: Recipe sites optimize for ads and SEO, not quality. They modify copied recipes to make them "unique," creating an arms race that produces absurdities like dishwasher salmon. Good recipes are harder to find despite abundance.
The Solution: A federated, decentralized platform where creators maintain full control of their recipes while making them discoverable to the community.
# Key Features
Decentralized Architecture:
- Recipes hosted on your own domain or GitHub repo
- No central authority or platform lock-in
- You control your data completely
GitOps Workflow:
- All feeds version-controlled in GitHub
- Changes require pull request review
- Full audit trail and transparency
- Community-governed feed list
Open Standards:
- RSS/Atom feed support
- GitHub repository indexing
- Plain text recipe format
- Open API (coming soon)
Currently indexing: 60+ active feeds, 3,500+ recipes
Try it: https://recipes.cooklang.org
# Technical Architecture
The federation uses a crawl-index-search pattern:
1. Feed Registry: YAML file listing all RSS/Atom feeds and GitHub repos
2. Automated Crawler: Periodically fetches and parses recipes from registered feeds
3. Full-Text Search: Indexes recipes for fast, powerful search
4. Decentralized Hosting: Recipes stay on creator's infrastructure
Think "GitHub Pages for recipes" or "RSS reader meets recipe search."
# Cooklang Format
Recipes are written in a simple markup language:
---
servings: 4
time: 30 minutes
---
Add @bacon{200%g} to a pan and fry until crispy.
Add @onions{2} and cook until soft.
Mix in @tomatoes{400%g} and simmer for ~{15%minutes}.
Serve with @pasta{400%g}.
Benefits:
- Human-readable plain text
- Machine-parseable for tooling
- Version control with git
- No vendor lock-in
- Easy migration and backup
# Contributing
As a Recipe Creator:
1. Write recipes in Cooklang format
2. Host them (blog, GitHub, static site)
3. Add your feed to
feeds.yaml4. Submit a PR to the federation repo
As a Developer:
- Federation repo: https://github.com/cooklang/federation
- Cooklang spec: https://github.com/cooklang/spec
- Parser libraries: Rust, JavaScript, TypeScript available
- Draft federation spec: https://github.com/cooklang/federation/blob/main/spec.md
# The Ecosystem
Cooklang has grown to 30+ repositories on GitHub:
- CLI tools for recipe management
- Mobile apps (iOS/Android)
- Parser libraries in multiple languages
- Editor extensions (VSCode, Vim, Emacs)
- Static site generators
All open source, all community-driven.
# Why This Matters
Recipe sites are the poster child for how ad-driven incentives corrupt content quality. The federation provides a sustainable alternative:
- Creators maintain control and ownership
- No ads, no tracking, no paywalls
- Community-curated, not algorithm-driven
- Built on open standards and protocols
# Get Involved
Search recipes: https://recipes.cooklang.org
Contribute: https://github.com/cooklang/federation
Learn more: https://cooklang.org
Spec: https://github.com/cooklang/federation/blob/main/spec.md
I'm happy to answer questions about the architecture, the format, or the roadmap. Looking forward to your feedback!
https://redd.it/1ohpn7w
@r_opensource
Cooklang: recipe markup language
Cooklang – Recipe Markup Language
Cooklang is a markup language for recipes.
My Spotify student deal is expiring
Hi r/opensource, I've been building this project just for myself, but then thought it might be cool to share if anyone is interested or has a similar problem as myself. It's pretty much an audio archival tool, and it's completely self-hosted and hopefully easily installable (only requires Python) so the only cost is leaving my laptop plugged in at home when I'm out, and the storage audio files take up.
I've been using it when I drive and go to the gym now so I feel a bit more comfortable talking about it, and it has a lot of stuff that makes it baseline functional at this point:
Search and download
Regular audio controls (play, pause, skip, scrub, queue, shuffle, loop, loop one)
Queue and queue to front via one swipe
Playlists, and importing from the green app
Renaming playlists and audio metadata
Backgrounded playback (yes, even on iOS Safari!!)
Obviously I'm no UI designer and there is still quite some work to make this what I envisioned, but I think I would need way more time and money to commit to that (audio editing, recommendations, listening stats, moutning existing audio folders).
I've been contributing to a few projects like free code camp on my personal accounts, and it would honestly be incredible if I could one day contribute to something goated like ffmpeg.
I'm grateful for any advice or feedback from the open source community, as it's my first project that I feel kind of proud of and want to share with others. If you decide to check it out or drop a star, thank you!
Link to project: https://github.com/whimsypingu/scuttle
Screenshots:
1. https://imgur.com/a/6lcYP1w
2. https://imgur.com/a/EVM4SrW
https://redd.it/1ohjlcr
@r_opensource
Hi r/opensource, I've been building this project just for myself, but then thought it might be cool to share if anyone is interested or has a similar problem as myself. It's pretty much an audio archival tool, and it's completely self-hosted and hopefully easily installable (only requires Python) so the only cost is leaving my laptop plugged in at home when I'm out, and the storage audio files take up.
I've been using it when I drive and go to the gym now so I feel a bit more comfortable talking about it, and it has a lot of stuff that makes it baseline functional at this point:
Search and download
Regular audio controls (play, pause, skip, scrub, queue, shuffle, loop, loop one)
Queue and queue to front via one swipe
Playlists, and importing from the green app
Renaming playlists and audio metadata
Backgrounded playback (yes, even on iOS Safari!!)
Obviously I'm no UI designer and there is still quite some work to make this what I envisioned, but I think I would need way more time and money to commit to that (audio editing, recommendations, listening stats, moutning existing audio folders).
I've been contributing to a few projects like free code camp on my personal accounts, and it would honestly be incredible if I could one day contribute to something goated like ffmpeg.
I'm grateful for any advice or feedback from the open source community, as it's my first project that I feel kind of proud of and want to share with others. If you decide to check it out or drop a star, thank you!
Link to project: https://github.com/whimsypingu/scuttle
Screenshots:
1. https://imgur.com/a/6lcYP1w
2. https://imgur.com/a/EVM4SrW
https://redd.it/1ohjlcr
@r_opensource
GitHub
GitHub - whimsypingu/scuttle
Contribute to whimsypingu/scuttle development by creating an account on GitHub.
AGPL questions: API calls with proprietary services and commercialization ?
I’m evaluating the AGPL for a new open-source project and want to sanity-check my understanding.
Hypothetical questions:
AGPL -> Proprietary API: Can someone fork and Integrate it with Proprietary products such as Auth0 over API/HTTP? Obviously they can't open source Auth0 as well as it's a product that's not in their control.
Proprietary service -> AGPL : Can Proprietary products such as Auth0/stripe call back to AGPL product over the network? The constraint is we can't open source Auth0/stripe which are not in the control of forker.
ElasticSearch Style Forks? If something like ElasticSearch had been AGPL, would that stop an AWS-style fork/hosted service for commercialization? AWS also shared the source of OpenSearch. My current read is: AGPL wouldn’t prevent forking or commercialization per se, but it would require the host to publish their fork’s source (and subsequent changes) to users of the network service, which AWS did. I am trying to understand what could have been implications for AWS had it been AGPL?
If the about screen has copyright , can the fork change that?
https://redd.it/1ohtf9o
@r_opensource
I’m evaluating the AGPL for a new open-source project and want to sanity-check my understanding.
Hypothetical questions:
AGPL -> Proprietary API: Can someone fork and Integrate it with Proprietary products such as Auth0 over API/HTTP? Obviously they can't open source Auth0 as well as it's a product that's not in their control.
Proprietary service -> AGPL : Can Proprietary products such as Auth0/stripe call back to AGPL product over the network? The constraint is we can't open source Auth0/stripe which are not in the control of forker.
ElasticSearch Style Forks? If something like ElasticSearch had been AGPL, would that stop an AWS-style fork/hosted service for commercialization? AWS also shared the source of OpenSearch. My current read is: AGPL wouldn’t prevent forking or commercialization per se, but it would require the host to publish their fork’s source (and subsequent changes) to users of the network service, which AWS did. I am trying to understand what could have been implications for AWS had it been AGPL?
If the about screen has copyright , can the fork change that?
https://redd.it/1ohtf9o
@r_opensource
Reddit
From the opensource community on Reddit
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yasr - a minimal no bloat web screen recorder under 1000 lines
https://unixextremist.github.io/yasr/
https://redd.it/1ohtqpv
@r_opensource
https://unixextremist.github.io/yasr/
https://redd.it/1ohtqpv
@r_opensource
Pimo — tiny always-on-top Windows popup notes (auto-save + drag/drop images) — made this for myself, open-sourced it
Hi everyone — I made a tiny Windows app called Pimo for quick popup notes. It’s intentionally minimal: always-on-top, frameless, auto-saves every 5s (and Ctrl+S), supports drag/drop images and thumbnails, and packages as a single NSIS installer. I built it in Electron and shipped a v1 installer.
Why I built it
I wanted a note that just pops up, saves instantly, and hides away without cluttering my taskbar.
Dragging screenshots into a note felt essential, so I handled browser/Explorer/URL drags gracefully.
I kept the UI small and focused — no heavy feature bloat.
What I’d love from you
Try the app or the source and tell me what’s annoying or missing.
If you have a quick idea (UX or tiny feature), drop it here and I’ll consider it for v1.1.
If you find a bug, please open an issue and I’ll investigate.
Link
https://github.com/higgn/pimo-popup-notes
Small notes
Installer SHA256: B2217BF3BE3BAEDF6F50B5A644376C170635FF05371A8392065881F579E8E2F0
I know unsigned EXEs trigger SmartScreen; signing is on the roadmap — feedback on install flow is especially helpful.
https://redd.it/1ohkmck
@r_opensource
Hi everyone — I made a tiny Windows app called Pimo for quick popup notes. It’s intentionally minimal: always-on-top, frameless, auto-saves every 5s (and Ctrl+S), supports drag/drop images and thumbnails, and packages as a single NSIS installer. I built it in Electron and shipped a v1 installer.
Why I built it
I wanted a note that just pops up, saves instantly, and hides away without cluttering my taskbar.
Dragging screenshots into a note felt essential, so I handled browser/Explorer/URL drags gracefully.
I kept the UI small and focused — no heavy feature bloat.
What I’d love from you
Try the app or the source and tell me what’s annoying or missing.
If you have a quick idea (UX or tiny feature), drop it here and I’ll consider it for v1.1.
If you find a bug, please open an issue and I’ll investigate.
Link
https://github.com/higgn/pimo-popup-notes
Small notes
Installer SHA256: B2217BF3BE3BAEDF6F50B5A644376C170635FF05371A8392065881F579E8E2F0
I know unsigned EXEs trigger SmartScreen; signing is on the roadmap — feedback on install flow is especially helpful.
https://redd.it/1ohkmck
@r_opensource
GitHub
GitHub - higgn/pimo-popup-notes: A tiny always-on-top popup notes app for Windows (Pimo)
A tiny always-on-top popup notes app for Windows (Pimo) - higgn/pimo-popup-notes
Would you say Mozilla is a good starting point to contribute to open source
I am a student with a bit of experience developing and would like to start contributing to open source. From what I read they assign you a mentor for each ticket you take on. What do you think?
https://redd.it/1ohye7o
@r_opensource
I am a student with a bit of experience developing and would like to start contributing to open source. From what I read they assign you a mentor for each ticket you take on. What do you think?
https://redd.it/1ohye7o
@r_opensource
Reddit
From the opensource community on Reddit
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Open-source MBOX → EML → PST toolkit (Outlook, Python, no paid libs)
I was hired to back up old Google Workspace mailboxes to PST. Most mailboxes were 50–100 GB, and the tools I tried were either paid or just didn’t work. So I built my own and I’m sharing it here.
Step 1: MBOX → EML (year/month/flat layout, year filters, folder size/file limits)
Step 2: EML → PST (Outlook via pywin32), split by year or evenly by size, PST cap (15–20 GB), progress + optional flush so Windows updates file size
GitHub: https://github.com/madsonrick/mbox-to-pst-toolkit
Tested on Windows + Outlook 2016/M365. Requires Python and
https://redd.it/1ohy0sg
@r_opensource
I was hired to back up old Google Workspace mailboxes to PST. Most mailboxes were 50–100 GB, and the tools I tried were either paid or just didn’t work. So I built my own and I’m sharing it here.
Step 1: MBOX → EML (year/month/flat layout, year filters, folder size/file limits)
Step 2: EML → PST (Outlook via pywin32), split by year or evenly by size, PST cap (15–20 GB), progress + optional flush so Windows updates file size
GitHub: https://github.com/madsonrick/mbox-to-pst-toolkit
Tested on Windows + Outlook 2016/M365. Requires Python and
pywin32https://redd.it/1ohy0sg
@r_opensource
GitHub
GitHub - madsonrick/mbox-to-pst-toolkit: Python tools to migrate email: MBOX → EML and EML → PST (Outlook), with year/size splitting…
Python tools to migrate email: MBOX → EML and EML → PST (Outlook), with year/size splitting and progress. - madsonrick/mbox-to-pst-toolkit
Good Java Backend heavy Open-Source Codebases
As the noscript. In case documentation is available for those stuff it would be great. Thought best way to learn is to read and contribute. In case discord exists for the community would be an icing on the cake :)
https://redd.it/1ohzap0
@r_opensource
As the noscript. In case documentation is available for those stuff it would be great. Thought best way to learn is to read and contribute. In case discord exists for the community would be an icing on the cake :)
https://redd.it/1ohzap0
@r_opensource
Reddit
From the opensource community on Reddit
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Synthalingua v1.2.5 - Open-Source, Self-Hosted Real-Time AI Translation & Trannoscription (100% Local, No Cloud)
Hey r/opensource! I'm the dev behind Synthalingua \-a fully open-source, privacy-first AI tool that transcribes and translates audio in real time, all on your own machine.
GitHub: github.com/cyberofficial/Synthalingua
License: AGPL v3
Built Windows Download: itch.io (Contains a useful GUI to use)
# What It Does
Real-time translation from 70+ languages → English (or any supported target via Whisper)
Works with live streams (YouTube, Twitch), microphones, or local files
Generates SRT subnoscripts, burns them into video, or embeds as tracks
AI vocal isolation \- strips background music/noise automatically
Outputs to console, Discord webhook, or local web server (so you can use on OBS for example.)
Silence detection, repetition suppression, blocklists, word-level timestamps
All processing happens locally. No data leaves your device.
# Latest: v1.2.5 (Oct 2025)
Adaptive batch processing \- smarter CPU/GPU load balancing for long videos for generating sub noscripts/captions.
Up to 3x faster subnoscript generation on mixed workloads, check out the new and improved batch mode processing for creating subnoscripts. https://streamable.com/7b2by2
Improved AMD GPU support on Linux (still experimental as I don't have an AMD device so stuff is dependent on if an AMD user submits a bug report or not.)
Portable GUI builds (Windows) - no Sys Python install needed
# Tech Stack
Python 3.12 + PyTorch
Whisper, SeamlessM4T, Demucs, FFmpeg
CUDA (NVIDIA), ROCm (AMD, Linux), CPU fallback
Minimal dependencies, full setup noscript included
# Why I Built It
The first public release dropped Mar 30, 2023, (just from a single noscript), and for the past two years, I've been perfecting it, tuning every detail, and crafting it with passion.
It started as a personal fix: I wanted to follow Japanese VTuber streams live, without waiting days for fan subs. Now it's used by language learners, meeting recorders, accessibility advocates, and global communities.
The mission remains: break language barriers - without ever sacrificing privacy.
For a long time, I kept it quiet, not out of secrecy, but insecurity. I advertised it twice but. I didn't want to keep "advertising" something i felt like it half-baked about a year ago. It spread slowly through word of mouth, and that felt safe and sane for me. But after two years of relentless iteration, hundreds of fixes, a poor 3090 getting abused daily , and features I'm genuinely proud of, I'm finally ready to share it openly. Not as a pitch - just as a tool I believe in, built for people who need it, or might find some use from it.
https://redd.it/1ohz3ww
@r_opensource
Hey r/opensource! I'm the dev behind Synthalingua \-a fully open-source, privacy-first AI tool that transcribes and translates audio in real time, all on your own machine.
GitHub: github.com/cyberofficial/Synthalingua
License: AGPL v3
Built Windows Download: itch.io (Contains a useful GUI to use)
# What It Does
Real-time translation from 70+ languages → English (or any supported target via Whisper)
Works with live streams (YouTube, Twitch), microphones, or local files
Generates SRT subnoscripts, burns them into video, or embeds as tracks
AI vocal isolation \- strips background music/noise automatically
Outputs to console, Discord webhook, or local web server (so you can use on OBS for example.)
Silence detection, repetition suppression, blocklists, word-level timestamps
All processing happens locally. No data leaves your device.
# Latest: v1.2.5 (Oct 2025)
Adaptive batch processing \- smarter CPU/GPU load balancing for long videos for generating sub noscripts/captions.
Up to 3x faster subnoscript generation on mixed workloads, check out the new and improved batch mode processing for creating subnoscripts. https://streamable.com/7b2by2
Improved AMD GPU support on Linux (still experimental as I don't have an AMD device so stuff is dependent on if an AMD user submits a bug report or not.)
Portable GUI builds (Windows) - no Sys Python install needed
# Tech Stack
Python 3.12 + PyTorch
Whisper, SeamlessM4T, Demucs, FFmpeg
CUDA (NVIDIA), ROCm (AMD, Linux), CPU fallback
Minimal dependencies, full setup noscript included
# Why I Built It
The first public release dropped Mar 30, 2023, (just from a single noscript), and for the past two years, I've been perfecting it, tuning every detail, and crafting it with passion.
It started as a personal fix: I wanted to follow Japanese VTuber streams live, without waiting days for fan subs. Now it's used by language learners, meeting recorders, accessibility advocates, and global communities.
The mission remains: break language barriers - without ever sacrificing privacy.
For a long time, I kept it quiet, not out of secrecy, but insecurity. I advertised it twice but. I didn't want to keep "advertising" something i felt like it half-baked about a year ago. It spread slowly through word of mouth, and that felt safe and sane for me. But after two years of relentless iteration, hundreds of fixes, a poor 3090 getting abused daily , and features I'm genuinely proud of, I'm finally ready to share it openly. Not as a pitch - just as a tool I believe in, built for people who need it, or might find some use from it.
https://redd.it/1ohz3ww
@r_opensource
GitHub
GitHub - cyberofficial/Synthalingua: Synthalingua - Real Time Translation
Synthalingua - Real Time Translation. Contribute to cyberofficial/Synthalingua development by creating an account on GitHub.
Our open source agent
Hey everyone,
I wanted to share what happened when we built Droidrun, our open-source framework for automating real Android apps.
We started this because honestly, we were frustrated. Everything in automation seemed stuck in browsers, but people actually live on their phones. Apps are walled gardens and nobody had cracked how to make agents work inside them. So we built something that could tap, scroll, and interact with real mobile apps like a human would.
A few weeks back, we posted a short demo. No pitch deck, no fancy landing page, just an agent running through a real Android UI. What happened next caught us off guard. Within 48 hours we hit over 3000 stars on GitHub. Devs started flooding into our Discord asking questions and wanting to contribute. We got on the radar of investors we'd been trying to reach for months. And we closed a $2M+ funding round shortly after.
Looking back, a few things made the difference. We led with a real working demo, not a roadmap of what we planned to build. We posted in developer communities where people cared about solving real problems, not product launch forums chasing upvotes. We genuinely asked for feedback instead of begging for attention. And we open-sourced everything from day one, which gave us instant credibility and momentum we couldn't have bought.
We're still figuring out a ton of stuff. The framework breaks in weird ways, there are edge cases everywhere, and we're learning as we go. But the biggest lesson so far is this: don't wait to polish everything. Ship the weird, broken, raw thing. If the core idea is strong enough, people will get it.
If you're working on something with agents, mobile automation, or just something bold that doesn't fit the usual mold, I'd genuinely love to hear what you're building.
Happy to answer questions if that's helpful!
Github- https://github.com/droidrun/droidrun
https://redd.it/1oi29tr
@r_opensource
Hey everyone,
I wanted to share what happened when we built Droidrun, our open-source framework for automating real Android apps.
We started this because honestly, we were frustrated. Everything in automation seemed stuck in browsers, but people actually live on their phones. Apps are walled gardens and nobody had cracked how to make agents work inside them. So we built something that could tap, scroll, and interact with real mobile apps like a human would.
A few weeks back, we posted a short demo. No pitch deck, no fancy landing page, just an agent running through a real Android UI. What happened next caught us off guard. Within 48 hours we hit over 3000 stars on GitHub. Devs started flooding into our Discord asking questions and wanting to contribute. We got on the radar of investors we'd been trying to reach for months. And we closed a $2M+ funding round shortly after.
Looking back, a few things made the difference. We led with a real working demo, not a roadmap of what we planned to build. We posted in developer communities where people cared about solving real problems, not product launch forums chasing upvotes. We genuinely asked for feedback instead of begging for attention. And we open-sourced everything from day one, which gave us instant credibility and momentum we couldn't have bought.
We're still figuring out a ton of stuff. The framework breaks in weird ways, there are edge cases everywhere, and we're learning as we go. But the biggest lesson so far is this: don't wait to polish everything. Ship the weird, broken, raw thing. If the core idea is strong enough, people will get it.
If you're working on something with agents, mobile automation, or just something bold that doesn't fit the usual mold, I'd genuinely love to hear what you're building.
Happy to answer questions if that's helpful!
Github- https://github.com/droidrun/droidrun
https://redd.it/1oi29tr
@r_opensource
GitHub
GitHub - droidrun/droidrun: Automate your mobile devices with natural language commands - an LLM agnostic mobile Agent 🤖
Automate your mobile devices with natural language commands - an LLM agnostic mobile Agent 🤖 - droidrun/droidrun
Looking for Some Good Open source projects to contribute to!
I'm a Student and starting my open source journey and I'm looking for some repos to contribute to.
My tech stack is MERN, C++, React Native and Python.
My main aim to start with this is to learn how to understand and navigate through large codebase.
I want a community which is active so my PR's can be accepted as I make them.
All suggestions are welcome, if you have a open source project you can DM me.
https://redd.it/1oi3roa
@r_opensource
I'm a Student and starting my open source journey and I'm looking for some repos to contribute to.
My tech stack is MERN, C++, React Native and Python.
My main aim to start with this is to learn how to understand and navigate through large codebase.
I want a community which is active so my PR's can be accepted as I make them.
All suggestions are welcome, if you have a open source project you can DM me.
https://redd.it/1oi3roa
@r_opensource
Reddit
From the opensource community on Reddit
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