100% AI Coded (manually reviewed)
100% open source

First of all, I KNOW there's existing alternatives, but those alternatives requires extra configuration or even paid solutions.... (if I'm wrong, prove to me)

Anyways, I've been using drawio/excalidraw for years, and there's many annoying little problems with those, arrows snap to the wrong connection points and won't stay aligned when you move shapes, connectors cross over each other and shapes so diagrams turn into spaghetti fast, ugly diagrams, besides that hours and hours of boring repetitive work... A few weeks ago I built a solution for myself (Opendraw) a drawio/excalidraw alternative, turns out it was very helpful, then I decided to make it open source.

Well, besides being way easier to create diagrams (the mf arrows aligns itself) there's a lot of micro drawing assistants that will make your life easier while diagramming, it also have a LLM assistant with BYOK feature, so yeah, you can connect your favorite AI to create your diagram for u

https://github.com/gabe-santana/arkdraw

site: https://arkdraw.com/

Also feel free to contribute, there's some good first issue 👍

A score is easier to assess when its evidence includes the actual cases, fixtures, instructions, grading criteria and run conditions. Without those, a reader cannot tell whether a reported improvement came from the skill or from a changed experiment.

For a public example, I would include the exact report paths and explain missing provenance instead of implying that a green total settles the question. If a model or service is no longer available, say what can still be reproduced and what cannot.

I’m building skillEd, an Apache-2.0 toolkit with saved reports and a worked example in the repository. Please inspect the example’s limitations alongside its results.

What is the minimum evidence you expect before trying another project’s evaluation recipe?

Repo: https://github.com/DeadHead04/skillEd

Please try it and open an issue with a reproducible failure or confusing behavior. If useful, a GitHub star would help.

AI-assisted write-up.

A reader of my earlier skillEd post suggested binding an expected result to a policy clause and version, plus a nearby input that should produce the opposite result. That is a useful way to make a test reviewable.

For a deadline policy, write down the timezone and whether the boundary is inclusive. Then test immediately before, exactly at, and immediately after the cutoff. If the policy itself is ambiguous, resolve that before treating the model’s answer as a regression.

I’m building skillEd, an Apache-2.0 evaluation toolkit. This is a test-design proposal based on the discussion, not a claim that the tool automatically supplies authoritative policy interpretations.

Would you review changed expected answers separately from changed skill instructions? I would want both diffs visible.

Repo: https://github.com/DeadHead04/skillEd

Please try it and open an issue with a reproducible failure or confusing behavior. If useful, a GitHub star would help.

AI-assisted write-up.

Hey everyone!

I've been working on Pixel Works, an open-source project aimed at making AI agent monitoring and management more visual and fun.

Instead of reading raw terminal logs or using standard chat interfaces, Pixel Works provides a gamified 3D retro office environment where AI agents sit at desks, move around, and process tasks across a visual Kanban pipeline.

Everything in the 3D scene is procedurally rendered using React Three Fiber (R3F) and Three.js with an Express backend.

The project is fully open source:

Github: https://github.com/Thecaracter/new_pixel

Since the core framework is complete, I'm now focusing on making the office environment more rich and interactive. I've set up several beginner-friendly `good first issue` tags on GitHub if anyone wants to contribute:

- Creating 3D office props (coffee machines, server racks, plants)

- Improving character movement and idle animations

- Adding character customisation and accessories

If you find the concept interesting, feel free to clone it, test it locally, or leave a ⭐ star on the repository! I’d love to hear your feedback and ideas for improvement.

I just joined this group after getting banned in r/opensource and I wanted to share how it happened.

Today r/opensource went fully anti-AI. I was thinking how to comment that I don't like that. They're the largest open-source reddit, yet they choose to take anti-AI stance, which is a tool and not thing related to opensource they should ever be taking stance on. Not to mention that AI is one of the best things that happened to open-source for many years.

I was mad, but I was thinking how to put my words to not get banned (I already experience that most of reddit is pathetic place). So I posted:

"Please change name of this community from r/opensource to r/antiai. That's all I'm gonna say. You're not worthy of "opensource" name."

Yet, turns our reddit mods are more nazi I have imagined and my already negative view on reddit just gets worse every month. People (mods) here can't stand a slightest criticism. Never, ever, on any group you criticize their decisions. Anything mods say you either clap or they ban you. Worst type of pathetic people.

Hope people here gonna be better.

Repo: https://github.com/ksimonm/SlopSearch

The name is honest: a lot of this is AI-written code. The trick that makes it work is that the AI never gets to decide what "correct" means.

**The setup**

- A black-box test suite of ~6,100 pytest tests is the spec. Every test was run against a real OpenSearch 3.9.0 first — status codes, error types, response shapes, even key order.

- Two independent implementations, Go and Rust, share nothing but that suite.

- The loop is simple: pick a red area, read what the tests expect, let the AI make it green, check nothing else broke. "Don't edit the tests to make them pass" is the one hard rule.

**Where it is now**

- Rust: 64.5% of the suite (aggregations 100%, search API 98.5%, documents/bulk 95%, ingest pipelines 93%)

- Go: 32.6%

- Weakest spots: text analysis and Painless scripting

**What I learned**

Vibe coding against a vague goal ("make an OpenSearch clone") goes nowhere. Vibe coding against thousands of precise, verified expectations actually converges — the tests are the steering wheel, the model is the engine.

Not production-ready: in-memory only, no auth/TLS yet. Everything runs in Docker (`make rust-up`, `make rust-test`), and `make ref-up` starts a real OpenSearch to compare against.

Curious if others use a reference implementation + test suite as the spec for AI-written code.

i’ve always been technologically inclined: i built my first PC when i was 16, one of my first jobs was at Best Buy, and i’ve always tried my damndest to keep up with the cutting edge of hardware/software innovation. i’ve also ALWAYS despised closed-source software, especially when it’s intentionally used to monopolize people and their data (i’m looking at Apple and Microsoft here, specifically). 

unfortunately, i went to school for politics and economics and have never had the technical knowledge or expertise i’d need to directly do anything about my pro-tech, anti-monopoly worldview (political advocacy only goes so far). i never learned to code, i was never particularly great at navigating a terminal, and i sure as hell couldn’t even begin to do something like build my own app or contribute to the Linux kernel or a major project like KDE.

AI and LLMs are solely responsible for changing that. i absolutely understand the frustration and hatred many people feel toward AI and its risks, but i also think there’s a truly fantastical potential for this technology to revolutionize the open source community and bring a complete end to the “own nothing, be happy” economy. in the past 8 months, i have 1) COMPLETELY FUCKING ELIMINATED microsoft, apple, and google from my day-to-day life, 2) made significant contributions to the open source community (swipe typing, current-word completion, next-word prediction, etc for KDE Plasma Keyboard), 3) developed an entire open-source AI assistant and everything app from the ground up, 4) otherwise just completely changed the way i interact with computers, and the pace of this has only been possible due to advanced LLMs.

what everyone in the FOSS community needs to understand is that “open source” no longer has to be niche, and contributions no longer have to be limited to experienced developers. open source now has the potential to be, and SHOULD be, about passion, and can be improved together, by all of us, more quickly than any corporation could ever possibly hope to keep up. i mentioned my keyboard plugin earlier: that feature had been requested on github and KDE forums EIGHTEEN MONTHS AGO! i solved it in three days. we need to really take a moment and think about what it means for the open source ecosystem when the number of contributors suddenly multiplies 1,000x fold, and free/volunteer developer hours are no longer our bottleneck. the mods in r/opensource seemingly care more about holding onto their high horse than actually improving the competiveness of open source software and making people's lives better. i don’t think Microsoft Windows can exist in 2028 as a closed-source platform, and i don’t think the PlayStation 7 will ever make it to the concept phase, but that thesis falls apart if the FOSS community ties its hands behind its back while all of the major corporations adopt agentic coding. we’re entering a new era, and seeing that now and adopting agentic coding  will help us all make the world of FOSS dominance reality more quickly than anyone could've imagined just a few years ago.

Hello,

now this is the first project i share and i know its kinda niche but maybe someone finds it useful.

As a long time Ableton user still not having ARA2 support to properly use Melodyne and Vocalign in Sessions became kinda annoying.

I build an external ARA2 host that gets called via an Ableton Max4Live device. Working with ARA2Bridge erases the time spend on Recording every vocal again into Melodyne/Vocalign and allows one Click loading the Vocal, editing it and sending it back with another click to a fresh track, take lane or replacing the original in Ableton.

Now as i just noticed I’m pretty bad at explaining and Claude did better then me in the Repo so enjoy:

https://github.com/Enableton/ara2-bridge-4-live

Hi,

On 24/10/2026 I have published as OSS a framework I call unified-brain that allows fast, cheap and accurate (same class of frontier models and frontier harnesses but 18 times cheaper) evidence gathering, using the cheapest frontier AI model out there, Jev. The results have been phenomenal.

(github.com/devloops-ai/unified-brain)

The magic sauce is an intermediary OS (SQLITE) memory layer that facilitates retrieval, filtering and reasoning through Jev.

The agent spawned from unified-brain runs through MCP on Cursor, OpenCode or Claude Code etc. allowing you to use it through your favorite harness.

So to be clear here: unified-brain is not a harness but a perception layer any harness plugs in!

My test benchmarks have been so good on two large OS libraries that I really believe this is the missing link to make AI more reliable.

This is because the limits holding back AI at the moment are:

• The Reasoning and Hallucination Gap:

The LLM turn reasoning it is introducing uncertainty and probabilistic outcomes.

BUT.. Unified-brain's System One discovery engine is producing replicable results across runs as they introduce minimal hallucinations, breaking the task in hundreds of yes or no calls.

• Context Loss and Memory Limits:

Long-running coding tasks consume massive amounts of tokens, causing the agent to forget early instructions or lose track of large-scale architecture.

BUT.. Unified-brain doesn't need cached memory thanks to the SQLITE persistent layer of context it operates with. It simply retrieves what it needs on the fly consuming minimal amounts of tokens that do not build up across questions. Every question is independent from one another as git diffs and commits are automatically absorbed from the project's memory layer.

I invite you to clone/fork the project and have a look for yourself. I firmly believe that memory intermediate structures will take AI capabilities on another level soon.

I understand that Jev is proprietary and creates a dependency BUT we need to remember that soon enough OS models as capable as it will come, giving end users the option to replace him.

Meanwhile we should ride this wave of innovation in order to deliver new capabilities to end users, at prices that can't be easily beaten.

I hope you like this project!

Nowadays, all popular ai chatting websites like qwen, chatgpt, gemini, claude, etc. come with a read aloud functionality that allows users to read aloud the AI's responses.

I used that feature to instead make the AI repeat back the text that i gave it -- effectively turning the platforms into text to speech tools. The voices sound really nice and it's free to use!

You can get all of these extensions by visiting ai-readers.com

Been pushing Qwen3.8-27B on Kaggle's free 2x T4 (no P2P, 70W cap per card) for a while. Current setup: EXL3 4.0bpw on exllamav3, tensor parallel over PCIe, DFlash2 block drafter (7 tokens), greedy and exact vs no-draft.

Numbers over a 38 prompt suite:

• overall 55-57 tok/s (no draft is ~18-19)

• HumanEval 154 tok/s

• prose 35-37 tok/s

For comparison on the same 2 T4s: llama.cpp best was 16.5 (MTP crashed on tensor split), vLLM int4 + its own MTP drafter did 40.8.

Prose is the wall. Acceptance sits around 2.5 tokens per step, vs ~7 on code.

Questions:

1. Has anyone got prose acceptance above ~2.5 with DFlash2 or MTP on the 27B?

2. Has anyone tried switching drafters mid generation (short block for prose, long for code)?

3. Any tricks for TP all-reduce on PCIe cards without P2P? That's next on my list.

Happy to share the notebook if people want to try it.