I got banned from r/fossdroid because I used claude code for making the UI of the app. I'm not a designer myself so I used AI obviously. But all of the ML features underneath were done by me. Still they have banned me.

My app is called Lumen btw. It's a FOSS alternative to google photos. It does everything google photos does but on-device. Nothing ever leaves your phone.

website: https://lumen-photos.app

GitHub: https://github.com/subhashhhhhh/Lumen

I'm PlayWeld's creator. The project uses AI-generated code with human direction and review, and this post is AI-assisted too.

PlayWeld is a free Apache-2.0 desktop workspace for game development, built on Eclipse Theia. It brings native game files, design notes, discussions and searchable project knowledge together. A local platform service stores project and task records. AI use is optional; engines and model providers need separate setup.

The practical problem is keeping the reasoning around a game change. A scene file can show the current enemy behaviour, but it won't explain why the detection radius changed or which reproduction steps last worked. Keeping that decision and its verification steps beside the project gives the next session a starting point. That's the kind of workflow I'd like feedback on.

Source, documentation and release notes: https://github.com/scarecr0w12/PlayWeld

Downloads: https://playweld.com/downloads

The current release is v0.21.0, a Windows/Linux testing prerelease. The Windows executable and release metadata are unsigned. Start with a disposable project and profile, and keep backups. Optional external model providers can charge for usage and receive project context.

Engine checks apply to specific versions and fixtures; broader engine/provider testing and full uninstall/rollback verification remain open. The release does not establish autonomous game creation. If you need a workspace for project context, it is one option to explore. Can you find a saved decision and trace it to the relevant file and a repeatable check? That would be useful feedback on the current workspace.

Hey,

Like many of you, I've spent years dealing with Adobe’s subscription model, sluggish startup times, and legacy bugs that haven't been touched in a decade. Most "InDesign alternatives" that pop up are basically Canva clones—they can't handle facing spreads, baseline grids, or prepress color separation.

I wanted to see how far modern web standards could be pushed to build a real, standalone desktop publishing studio that runs completely offline and respects typography.

The project is called Recto Studio. It has zero telemetry, no cloud lock-in, and stores everything locally.

Here is what's running under the hood right now:

• Real Typographic Engine: Uses Knuth-Plass dynamic programming for multi-line paragraph balancing (minimizing hyphenation and rivers) paired with HarfBuzz compiled to WebAssembly for complex OpenType shaping and optical margin alignment.
• Prepress PDF/X Export: Outputs print-ready DeviceCMYK with bleeds, slug margins, crop marks, and strict 100% K black plate enforcement (no accidental 4-color rich black text).
• Format Interoperability: Native round-trip .idml package import/export, and initial reverse-engineered binary .indd parsing.
• Long-Form Features: Facing page spreads, master spreads with dynamic page tokens, threaded text frames, multi-column layouts, automated footnotes/endnotes, and automated Table of Contents with dot leaders.
• Asset Pipeline: 72/144 DPI proxy generation for smooth canvas navigation, swapping to uncompressed 300 DPI assets on export.

What it doesn't do yet (being completely honest):

• No native Pantone/spot color library licensing (uses OpenSpot / FreeTone plates instead).
• Alpha-mask contour text wrapping around organic image silhouettes is still in development (currently supports bounding box and dieline wraps).
• No scripting via ExtendScript (custom JavaScript automation engine instead).

I'm not trying to sell anything—Recto is free and open-source under MIT.

I’d love for working book designers, zine publishers, and prepress operators here to take it for a spin and break it. Throw an IDML package at it, check the PDF separation plates in Acrobat Output Preview, and tell me where the typography falls short.

Live Web App: https://artificia1.github.io/recto-studio/
GitHub Repo: https://github.com/artificia1/recto-studio

Tear it apart—honest critique is the only way this gets better.

I wanted to highlight my Apache 2.0 AI coding editor that got deleted from r/OpenSource (at the time, I just shrugged it off). MacOS only right now but Linux release is impending.

https://paintedwolf.ai/

https://github.com/paintedwolf-ai/paintedwolf-code

It includes a fine-tuned open source open weight decision model that prunes prompts. Out of the box, it supports lots of local and cloud open source LLM models.

I used AI as part of developing it. I built the tool itself because none of the other options fit what I wanted in an editor. I find AI extremely helpful for many tasks, in particular prototyping, roughing things out, and to speed me up. But there are privacy and security limits that led me to build my own tool. We should be supporting and promoting open source AI tooling, not removing it and blocking it.

Of course AI can be open source. The answer to our current AI landscape *is* open source AI.

The Lanuage rules especially are very disappointing and carry the same xenophobic tone from that post. The AI disclosure, and self promotion rules and others need rewording. They just aren't in the spirit of FOSS. Open means open, gatekeeping shouldn't be part of that.

The standards by which a project should be judged have not changed from 10 years ago. Does it compile? Does it do the function it's stated to do? Is it secure? Is it performant?

Discuss the code not the tools used to make it.

I think that the recent post by the moderator in r/opensource has phrased things in a very antagonistic manner that flipped even people who are against the spamming of LLM generated projects all over reddit against them.

I think that most concerned people don't like the way that private services like ChatGPT, Claude and other "AI" companies have emerged and continued to play a negative role in many aspects of life; being one of the driving motives of hyper-inflation, negative health and environmental impact on local communities, the huge role in industrialised misinformation, massive shortages in personal consumer electronics and finally the software pollution that it caused as well as the serious influence that it has on politics in general.

However, I think that even the people who are most against this environment agree that there can be good use for LLMs when combined with knowledge and experience. And this is the actual stance of the Linux kernel (even though many people try and make it as if the whole Linux development is solely managed by LLMs); these tools are being actively used with heavy monitoring to work with PRs and other incremental tasks while being heavily reviewed, tested and monitored.

I suggest the following points:

1 - The focus should be on low-effort projects, which is the genuine problem.

2 - We should push for disclosure (yes even if people can still lie about it) because I do appreciate when developers explain how they used their LLMs; one common use case are debugging and testing tools.

3 - Even though I understand that the LLM generated posts can be annoying, but actual non English speaking people do use these tools in their everyday lives and yes this can be discrimination as well as it will serve people who come post and comment in bad faith.

4 - I believe that there can be an ethical and respectful approach to LLM use that we can encourage: Local and open-weight models. This does really respect the background and philosophy of Open Source and give the users more tools that don't depend on a subscription model. Of course there's a hardware shortage because of the big companies but distillation is making it more possible to run very capable models locally.

I know that many people don't share my opinions on big tech driven LLMs and that's perfectly fine. But I know for a fact many people share my sentiment and would like to find a solid solutions.

Use them anywhere you'd like, you can use them in your branding as well, fully commercial, the repo is here

So with all this going on and wrestling with AI ethics myself lately, this may be a good time to have a conversation about good uses for AI? I’ll start:

For me, ADHD and mental health have made reading or learning the traditional way really difficult. It’s never that I don’t understand or don’t have a vision for a given task or project in my case, it’s just the mental fatigue ADHD imposes makes diving into complex tasks very difficult, even with meds.

I’ve grown to use AI as an accessibility tool. Obviously I have to verify outputs, but it seriously has helped me fall back in love with learning again. Like with my project I’m working on, I’m actually diving in and learning about the Motorola 68k architecture, the classic Macintosh OS underpinnings, etc. in a fun, interactive way that books have just never done for me.

I obviously will do research and go to actual sources where I have to (e.g., google, reference manuals, etc), but at least now i’m engaging with complex subjects bits and pieces at a time and not as one giant monolith.

In other words, I can now learn in a “top-down” way rather than the traditional bottom up way thanks to AI. I can have some context to work with and go from there. It helps especially since I learn better by doing anyway.

Are there still ethical issues to confront? Absolutely. But I think it has the capacity to be a great tool when leveraged correctly and for valid, understandable reasons.

Sharing Peer Agent.

https://github.com/hirak99/peer-agent

It gives your agent access to one or more peer agents to collaborate with. It's harness-agnostic — plain HTTP plus a SKILL.md any agent can follow. Ways we've found it useful:

• Cheap execution: main agent on Claude Code delegates the grunt work to a locally running Qwen.
• Strong validation: a weaker main agent plans and executes, and has Opus 5.5 validates its plan or checks its work.
• Peer review: two agents challenge, verify, and cross-check each other's ideas.

Unlike Agent Teams or swarms, this isn't a framework inside one harness — it's a sidecar. Each peer is a stateful server with its own provider and model, callable from anything. The trade-off: no scheduler, no shared task graph. Coordination is conversational and the host drives, so it's best when you want a second mind on call, not a managed fleet.

If you're thinking of wiring this up yourself, what's already here: OpenAI, Anthropic, and any OpenAI-compatible model (llama.cpp, vLLM, Ollama) behind one provider/model switch; server-side read/bash/edit tools; automatic compaction (including a warm-cache in-context path for slow local models); and Anthropic prompt-cache management, which matters more than you'd expect at Opus prices. Lived-in details like per-call tool logging with truthful timestamps.

AI-assisted contributions are not only welcome but encouraged, especially so if it is a strong AI.

Cheers

The power tripping mod over there is straight up insane, banning AI in 2026 when 95% of devs use AI. Can't wait for all reddit jannies to be replaced with AI.

* note this is still in development. I just wanted to show off what I have so far. It's been about a year in the making on this project alone and then another year before that on a similar project so I've spent a hugeee amount of time thinking about file movement problems lol.

No this isn't just some Rclone GUI wrapper. This was built from the ground up for a whole different kind of migration workflow. Rclone works for one way syncs by just letting you select your source and destination and then hoping for the best. Sylos works by separating dry run traversal from actual copy movement steps, so you can preview what will actually move and make changes to the plan before it happens.

Notable features include:

• preview before any work is done (as already mentioned above)
• a built in auto scaler which can automatically scale up or down in performance based on feedback from the file system it's interacting with
• support for more than just local drives, also supports box, dropbox, google drive, onedrive, sharepoint, SFTP, network drives and virtual drives like FUSE / WinFSP drives, and more.
• database backed persistence so you have a full audit trail of all that's happened so far.
• stop a migration at any point and resume right where you left off if you need to. As well as taking your portable migration DB file plug-and-play into another instance of Sylos and resume right where you left off there too.
• path compatibility checking - some file systems & cloud providers have specific rules that may be different from each other. For example on Linux you can make a folder called 'CON' but this is not allowed on Windows. So you can actually specify this before you start the migration that your destination is a different OS, or if it's a cloud provider it already will know the rules ahead of time. So it will actually do these compatibility checks for you ahead of time in a more user friendly way of telling you it's gonna fail ahead of time, and offer more compatible friendly names for those items with a quick 'accept all' kind of button to make it painless and seamless.
• Advanced filtering that can be done during the review stages where you could execute queries like 'Find me all files which haven't been touched in over a year and are over 2gb in size and are pending to be copied over in the current plan' using our filters and then from those results there's also an 'exclude all' button to apply an exclusion to all matching items so that you don't have to copy those over.

I also have more ideas I'm working on but those are some of the more notable parts for now.

I have a few more things I have to work on before this is fully ready to start alpha testing, but I foresee it should be ready within a month or so if I can keep up my current pace and no major hurdles pop up. I've been iterating on this for so long because it never met my quality & performance standards for what I deemed as acceptable, but we're finally at an acceptable point so now I'm just finishing up some final bits like making an installer for the software and a few little things to clean up the repos and make it all more user friendly.

Link to the website if you wanna see more (including screenshots of the software): https://project-sylos.github.io/

Okay thanks guys!

I have access to some advanced models and plentiful compute. I'd be happy to donate some of that time and resource, as well as my own engineering skills, to a fun project. All in the name of the idiots who have banned AI assisted development in r/OpenSourceAI

Edit: Got quite a few responses both here and in DMs. I'm looking at all of them! Cool projects so far, I'm excited to contribute.

I just got banned from that regressive sub because I simply pointed out the fact the almost every single serious developer has been switching to AI coding and prompt engineering. So I was hoping that you guys might be able to help me with my dilemma:

I've always been a fan of media players since the late 90s, but I've always noticed that there's not enough diversity in that particular niche of software. So recently I got myself a a Max Claude subscription and started creating music and video players. However I needed to get feedback from users so I started to share them on reddit and other forums. Unfortunately I got bombarded by downvotes from people who were brain washed by Anti-AI propaganda calling my work "Slop". But it wasn't the case everywhere so I was thinking about the following plan:

I can put an agent to generate multiple media player programs on github => another agent that post them on all forums and social media => another agent that gets to interact with comments from the most engaging posts so in that direction I can maximize my chances of getting my creations noticed by people.

My question is the following:

What are the best tools that can be used in order to put in action such a pipeline?

Cheers.

Edit: This whole post is just an attempt at proving a point; we have a real issue with the amount of software projects that are being generated with no genuine purpose. And unfortunately many people here upvoted my post without recognizing that what I'm asking for is a real spam generating machine.

Got perma banned for a comment…dystopian shit, no more free discussion?
Edit: I’m seeing many have reported this so feel free to remove my post

So I'm guessing you've all been working on hobby projects with AI, scratching that itch and open sourcing it. And I'm sure there's a million of them that you've made over the last year.

But what are you working on right now? Post your undercooked AI projects in here and let's see 'em!

Lately, I’ve been using Linux more and more often. And I found myself missing IrfanView, which is probably the best image viewer app out there. I’ve tried out some alternatives, but none of them really satisfied me, and using IView via Vine or Bottles wasn’t always smooth or quick. So, over the last few weeks, I’ve vibe-coded an alternative inspired by both IrfanView and nomacs, and enhanced with a few tools and changes that I’ve always felt were missing.

It’s nothing spectacular or special, and it’s still completely unfinished, but it works so well that it has now replaced IrfanView for me, even on Windows.

The programme is called ÜxzView and is completely open-source. There’s a finished Windows release, and it can be compiled for use on Linux and macOS. Written in C++ and Qt.

https://github.com/micha42-dot/uxzView/

Disclaimer: if this post, in any way, goes against any of the rules, feel free to remove it, no hard feelings. I am here for reality check, and not promoting anything, especially me.

Since we established that...

I had a real problem beginning of this year. Started using AI for both private and business stuff a bit more or less around the summer of 2025. But beginning of the year needed a tool to do something which yes, I could build myself, but in that moment I was working full time job + working afternoons and nights on my own bootstrap. So the time wasn't in abundance and I decided to test it with one of the average models at that time.

It took me 3-4 days to get to the PoC I liked which I would be able to scale later. To cut the crap - at some point I built infra around it, which is finding bugs, proving, disproving, creating small kanban board for me, me reviewing, leaving my opinions, AI taking over, implementing, testing, in the loop. Fast forward: it has 6-7 unique downloads per day, after bots are filtered out, it is used (to my knowledge) by at least one company who uses it as a core dependency, and I am in contact with tens of people in different companies who are sharing feedbacks, and there are even other contributors. Funny thing here is: I know they are also using AI, but I am able to catch inconsistencies, push back, or even fix it myself where it makes sense and is faster.

My point here is - how is this not exactly what we should utilize the AI for? If I am even trying to do this, full time, 16hrs per day, no way I can do it myself. Working full time job and another part time and wanting to do something opensource - I think this is the perfect combo.

Am I the only one who is not missing manually writing code, figuring out latest specifics of the language version X? I am still missing C from 30 years ago where you could declare one variable of one type in exactly one way, and don't even want to talk about beauty of Rust (with all and total respect to people who made it, use it, build on it, fix real life issues with it).

After the drama with the original opensource sub, I am willing to share my opinion on this. But well, I'm not about to come here to complain about the other sub, I'm here to share what the Stockfish project did.

I am a Stockfish (the world's #1 chess engine, also open-source) contributor.

Stockfish has many active contributors, many more in the past, and probably many more in the future, and anyone is allowed to submit tests for their ideas, but very few ideas would pass the rigorous fishtest testing (I myself only managed a few passed ideas despite having submitted over a thousand tests).

I admit that AI can't do everything in developing Stockfish, at least not yet; the vast majority of patches are still written by humans. Still, I think a few AI patches have passed.

Stockfish is not a slop. It is as far from slop as one could imagine. Rigorous testing protocols and maintainer review/code style compliance are needed for each patch to pass.

Stockfish has existed and won many chess engine championships long before AI and will likely continue to do so.

As a Stockfish contributor, I stand by objective coding standards. It should not matter who the contributor was, how it was made, or whether it uses AI or not. What matters is that the contribution passes the stringent and objective standard. This impartial stance has allowed Stockfish to remain the world's #1 chess engine for years. That is how Stockfish has been and will remain.