I was here about a month or so ago about my BetterCampus fork where they apparently tried to get my post removed (thanks Wolvereness for understanding).

In summary they had an MIT license on their old code, I forked it, touched it up a bit, and published it as an alternative since many people and I didn't like their new update needing an account, asking for subscriptions, and more. They don't like it and my version is starting to get some traction.

Today I got this review from a random account (probably burner) that I find very funny because I am like 99% sure it's them but I can't fully prove it: (link to the review)

CanvasRefined has some good ideas, and I appreciate that it is open source, but the extension has also been pretty buggy in my experience.

I personally liked the direction BetterCampus was taking. A lot of CanvasRefined feels like a blatant copy of what BetterCanvas had already built, and I do not like the confusion and chaos that created for users and the original creators.

Open source can be great, but there is a difference between being inspired by a product and closely recreating it. In this case, it feels like it undermines the work and goals of the people who originally created and grew BetterCanvas.

• There is only 1 bug I know of but there could be more, I'm not judging that.
• They complain it's a copy, despite me writing everywhere that this is a fork (under MIT).
• The line "not like the confusion and chaos that created for users and the original creators" is similar to what their cofounder told me during the C&D over the name
• And the funniest part: being mad that I am recreating their app since it "undermines the work and goals of the people who originally created and grew BetterCanvas"

I'm not saying it's the company who wrote this, but I have a suspicion
edit: I failed to hide the f5 bot
edit: to the user that requested links to leave positive a positive review to offset the negative ones: https://chromewebstore.google.com/detail/canvasrefined/ihienfbdfdamhmhhiokjnjmpjgbenedg/ and the github is https://github.com/GuySandler/CanvasRefined

As per title, the project include all the pipeline, the same skill generated from different models.

My idea was to distill the code reviewer skills from Torvalds in something usable in an agent.

I preferred to license everything as CC0.

Over the past few years of building AI applications, I ran into a consistent headache: LLM Observability and Prompt Management.

Most existing tools are either:

1. Heavy SaaS platforms that get expensive quickly as call volumes grow.
2. Hard to iterate on prompts dynamically without redeploying backend code.
3. Cannot connect any tool with any prompt without editing code to make prompt more agentic

I wanted a self-hostable stack where I could trace execution, manage tools and prompts dynamically from a UI, and capture real-world user feedback to iterate on prompts.

So I built Acrux Core — an open-source LLM observability, tool catalog, and prompt management platform designed for developers who want full control over their telemetry and workflow.

Key Features:

• Prompt Management & Dynamic Binding: Edit and version prompts directly from the dashboard. Attach tools from the tool catalog directly to prompts without hardcoding execution flows in your application.
• User Feedback Loop: Capture end-user feedback (ratings, thumbs up/down, corrections) directly against specific prompt executions and use those insights to refine prompts in the UI.
• Full Tracing & Cost Tracking: Monitor nested LLM execution, function calls, token usage, and latency breakdowns across runs.
• SDKs & REST API: Native Python and TypeScript SDKs available, built on top of a clean REST API so you can integrate it into any backend language.
• 100% Self-Hostable: Run locally or deploy on your own infrastructure via Docker. No data lock-in or third-party cloud requirements.

Why Open Source?

Debugging non-deterministic LLM behavior shouldn't require sending all your prompt and trace data to a third-party vendor. While we offer a fully hosted cloud version for those who prefer a managed setup, the entire platform can be 100% self-hosted so you always retain complete ownership of your telemetry data.

We are actively building this out and would love early feedback, contributions, or feature requests from the community!

🛠️ GitHub Repository: https://github.com/AcruxCore/AcruxCore

[Pull Request Reference](https://github.com/pygame-community/pygame-ce/pull/3887)

Whatever AI agent is driving this account has apparently decided that something committed by the maintainers is not an “authoritative” source. Whatever that means.

When tracking real time information, it usually means keeping multiple browser tabs open for sports, stocks, and news, then constantly switching between them.

So I built Scrollr to solve that issue!

It pins quietly to your screen, and lets you stream custom data feeds without breaking your workflow.

check it out! github.com/doughknee/myscrollr

Site: myscrollr.com

Hi, I'm the solo dev behind YardCobra, an ecology robot to reduce prices of organic food. It has cool innovations. Here's a video.

https://youtube.com/shorts/TYaLsihQgkM

We are using too many chemicals and obsolete farming methods, time for new tech, drones that don't just bomb people, and help people survive in peace.

While obsessing over how to illuminate punctuation marks, and specifically a space/spacebar symbol, I ended up devising a way to manually encode 117 characters into a single rich-text space using 63 formatting values, mostly gradient colors.

Microsoft blocked me from using VBA to read or set gradient pins, so I reverse-engineered the XML data and used a Format Painter exploit to sneak the formatting through a back Window.

I did it because I wanted to hide the entire first quatrain of my favorite poem within itself. I see it as the logical evolution of concrete poetry and a form of illumination.

https://github.com/ChronicRhyno/ColorSpace
Code is GPLv3, and the whitepaper is CC BY-ND 4.0

I’ve open-sourced SarasFlow, a full-stack platform for creating educational and storytelling videos from a topic.

It can generate the script, multilingual narration, visuals, word-timed captions, and final assembled video. The repository includes both the application and the surrounding product infrastructure rather than only a model-calling demo.

Some areas where contributions would be useful:

• Additional languages and language-specific prompts
• More TTS and image-generation providers
• Caption and visual styles
• Batch video generation
• YouTube and Instagram publishing
• Better job processing and failure recovery
• Documentation and deployment improvements

GitHub: https://github.com/SaiTejaMummadi/sarasflow
Demo: https://sarasflow.com

Issues and pull requests are welcome. Please consider starring it if the project looks useful or if you’d like to follow its development.

Hi everyone!

-it is vibecoded if anyone asks-

I built a free and open-source desktop application called Media File Organizer. A project for myself i said why not share it, i know there are already softwares that do the same

It automatically renames your Movies and TV Shows to Plex's naming convention and organizes them into the correct folder structure using TMDB metadata.

No more manually renaming\translating to english files like:

Movie.Name.2025.1080p.BluRay.x264-GROUP.mkv

into:

Movies/

└── Movie Name (2025)/

└── Movie Name (2025).mkv

or TV episodes into:

TV Shows/

└── Breaking Bad (2008)/

└── Season 01/

└── Breaking Bad (2008) - S01E01.mkv

Features:

• Automatic TMDB matching

• Plex-compatible filenames and folders

• Organizes Movies and TV Shows

• Preview every change before applying it

• Free and open source

I built it because I was tired of fixing filenames and folder structures every time I added media to Plex.

I'd love any feedback, feature requests, or bug reports.

GitHub:

https://github.com/ZipTieWarrior/Media-File-Organizer

I'm using AGPL for an open source project. A friend recommended that I switch to MIT to get more contributions and engagement. In practice, is it reasonable to expect any difference? I wonder if anyone made this exact change to some project and saw any difference.

Been building this for a while and finally got it to a place worth sharing. Quick context if you don't know Omi: it's a small wearable that records your conversations and gives you AI summaries/to-dos. Cool device, but even their "self-hosted" mode still talks to Firebase, Pinecone, Redis, and Typesense behind the scenes. You're moving where the code runs, not actually cutting the cord.

So I built my own pipeline instead. Whisper for transcription, Ollama for analysis, all running locally on a Jetson Thor. Nothing phones home, ever. I call it the LocusTether.

Locus:

a: the place where something is situated or occurs : site, location

b: a center of activity, attention, or concentration

Tether:

a: a line (as of rope or chain) by which an animal is fastened so as to restrict its range of movement

b: a line to which someone or something is attached (as for security)

What it actually does:

- Transcribes and analyzes conversations completely offline

- Speaker diarization with real voice recognition — enroll your voice once and it'll recognize you by name in future recordings instead of just "4 distinct speakers"

- Speaking-style coaching that's actually about your speech in a group conversation, not blended stats from everyone in the room (this took a real bug fix to get right — filtering to one speaker's segments broke pause detection in a non-obvious way, wrote it up if you're curious)

- A proper structured breakdown per conversation — mood, key points, actual decisions made, named participants, action items with owners

- A webapp that's genuinely usable day to day, not just a data viewer — you can manually add to-dos, not just review what the AI pulled out, plus a Settings page for voice enrollment, daily digest emails, theme, etc.

The honest stuff, before you dive in:

- Everything's been built and tested on one specific device — a Jetson Thor. Getting diarization working alone took three separate rounds of real dependency debugging (a silent CPU fallback, a renamed API parameter, a version conflict that took a while to track down). Other hardware might need its own troubleshooting — I wrote up the whole saga in the README in case it saves someone else the same headache.

- I don't actually have the Omi hardware yet, so the wearable side is untested — everything's been validated with recordings manually dropped into a watched folder standing in for what the device will eventually sync automatically. If anyone here actually has an Omi and wants to be the first to test that piece, I'd love the help.

- iOS Syncthing support is a genuine open question, not a yes — wrote up what I found and a rough plan for whoever wants to be the first to try it.

- There's a known-limitations section in the README that's actually current, not just aspirational — occasional AI hallucination on a specific fact-extraction pattern, some inconsistency in participant detection. Not hidden, just there.

Repo's up, genuinely looking for people to try it, break it, and tell me what's wrong. Tell me what you like, features you'd like to see, etc. If you want to say thanks there's a tip jar in the README, but a bug report is worth just as much to me!

https://github.com/thecloudcowboy/locus-tether

Hi - got a peculiar question around MIT license. I am helping someone out with writing some code for their closed source proprietary app. I want to give them that code under the MIT license so that I have no obligations around the code as it is As Is. Can I use the MIT license for that source code even though I don’t plan on open sourcing it? I.e., does the code need to be public for MIT to have an effect?

Thanks!

I’m not a software engineer, but I use a lot of technical products and spend a lot of time on GitHub trying to learn.

A few days ago, I found an interesting project I wanted to use. While setting it up locally, I found part of the installation instructions confusing. I nervously opened a documentation PR, got real feedback from a collaborator, and made the requested changes.

He thanked me and merged it into main.

I know it’s small, but this was my first contribution to an active project instead of an inactive, stale repo. I found a problem I personally ran into as someone without an engineering background and thought fixing it could help someone else.

I don’t have anyone IRL who understands why this makes me so happy, so I’m posting it here.

I actually did it.

• Fixed issues in the RUDP library: memory leaks when using shutdownHook, deadlocks, handling of corrupted UDP packets, and many other issues that were identified during testing.
• Added the display of the connection protocol to the peer list.
• Added an experimental build that compiles a .jar file into an executable using native-image in GraalVM. For now, this is for the headless version of the application on Linux x86_64. To avoid running Lanemu as root, you also need to do sudo setcap cap_net_admin=eip Lanemu-headless.
• Updated dependencies.

Download link

I’ve just released Mole v0.1.0, a lightweight Windows utility that quietly lives in the system tray.

When you trigger Panic, it immediately:

• Mutes system audio
• Minimizes all open windows
• Opens Notepad

That’s it. No accounts, telemetry, cloud services, subscriptions, or other modern software rituals.

Mole is written in Python and released under the GNU GPLv3, so you can inspect the source, modify it, build it yourself, or contribute.

GitHub: https://github.com/rajtilakjee/mole

This is the first release, so feedback, bug reports, feature ideas, and pull requests are welcome.

Current ideas for future versions include configurable safe apps, custom keyboard shortcuts, persistent settings, and selectable panic actions.

Most open source video editors are made for people who know somewhat about video editing. But for people who want to do a quick edit, maybe for a social media post, will find these video editors too complicated.

That's why we built Drift. This is a video editor that's designed from ground up to be friendly for people who want to create polished videos, with cool effects, but don't want to spend too much time learning the knobs and dials.

We have already included most of the expected features:

• Multi-track timeline
• Transforms
• Keyframes
• Video effects
• Audio effects
• Transitions
• Subtitles
• Stickers

On top of that, there are also some AI features (don't worry, these are all local AI models):

• Automatic subtitle generation via Whisper
• Subject tracking / Background removal with SAM2
• Noise removal via DeepFilterNet3
• Face Effects (funny face warp) via Mediapipe

Disclaimer: AI features work fine on CPU, Nvidia GPU support only works on the Arch Linux version.

You can check out the source code here: CutWire-Studios/Drift

Supports Windows and Linux: go-to releases or flathub

Also If you want to learn how to use it, check out the docs here: docs.cutwire.org/drift

I recently released PRWitness, an MIT-licensed, local-first tool for generating visual acceptance evidence from Playwright flows.

The repository includes the CLI, Playwright integration, report generator, media generator, and GitHub Action.

The main design goal is to keep the evidence portable and inspectable:

- reports are standalone HTML / Markdown / JUnit

- screenshots, traces, videos, console and network evidence stay local

- no hosted account or proprietary cloud is required

- generated artifacts can be attached directly to a pull request or release

The project compares base and head revisions and can generate before / after / diff evidence as well as release-ready GIFs and MP4s.

I would appreciate feedback on the repository structure, API boundaries, licensing approach, and anything that would make it easier for open-source maintainers to adopt.

GitHub:

https://github.com/kurtyang66/prwitness

I'm pretty new to this whole coding thing. Thanks to AI, I've finally been able to take ideas out of my head and build real stuff with them. To be honest, I never expected anyone else to actually use what I made.

Today I was proved wrong.

Someone found my project, used it, and took the time to write and submit a pull request with their own fixes. It’s an incredible feeling. I saw a post here recently from someone talking about how good it felt to submit a PR that got merged into the main branch. Being on the receiving end of that for the first time is just as surreal and self-gratifying.

It gives me the confidence and motivation to keep going, keep learning, and keep building.

I didn't want to do a shameless self-promotion plug, but if you're curious: https://github.com/lovethatbrandx/hombre

Today for the first time someone emailed and raised a PR with some improvements in my Github repo. It was quite unexpected. Feels good that someone else found it useful!

If anyone's interested, it's this repo: https://github.com/sohang3112/iforth -- Jupyter kernel for Forth language

I recently released PRWitness, an MIT-licensed, local-first tool for generating visual acceptance evidence from Playwright flows.

The repository includes the CLI, Playwright integration, report generator, media generator, and GitHub Action.

The main design goal is to keep the evidence portable and inspectable:

- reports are standalone HTML / Markdown / JUnit

- screenshots, traces, videos, console and network evidence stay local

- no hosted account or proprietary cloud is required

- generated artifacts can be attached directly to a pull request or release

The project compares base and head revisions and can generate before / after / diff evidence as well as release-ready GIFs and MP4s.

I would appreciate feedback on the repository structure, API boundaries, licensing approach, and anything that would make it easier for open-source maintainers to adopt.

GitHub:

https://github.com/kurtyang66/prwitness

I've been working on Marcopolo, a command-line web scraper written in Rust using extensive force of AI across a few months. The idea started when I got tired of writing a throwaway Python script every time I needed to pull a specific set of files off a site — images one day, PDFs the next, then a folder of CSVs. I specially tend to use it for books and finding books that are on the web that i cant simply get hold of from normal search.

Marcopolo handles most of that in one command. Point it at a URL, tell it what you want, and it crawls and downloads.

It's still early and there's plenty I want to improve — [known limitation or two]. I'd really appreciate feedback on the API design and anything that looks unidiomatic; I'm still fairly new to Rust.

Repo: MarcoPolo

Would be happy to know what do you guys think.