Free Open-Source Artificial Intelligence

Hugging Face: https://huggingface.co/PlayHT/PlayDiffusion

the goal is to have an agent that can:

• Understand a complex problem description.
• Generate initial algorithmic solutions.
• Rigorously test its own code.
• Learn from failures and successes.
• Evolve increasingly sophisticated and efficient algorithms over time.

https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/AlphaEvolve.pdf

Model: https://huggingface.co/fancyfeast/llama-joycaption-beta-one-hf-llava

Demo: https://huggingface.co/spaces/fancyfeast/joy-caption-beta-one

Release Post: https://civitai.com/articles/14672

Github: https://github.com/ace-step/ACE-Step

Checkpoints: https://huggingface.co/ACE-Step/ACE-Step-v1-3.5B

Demo: https://huggingface.co/spaces/ACE-Step/ACE-Step

https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2

im building som dum lil foss llm thingy for godot and now im interested in letting users implement their own MCP servers.

so like - okay, the model context protocol page says, that most servers use stdio for every interaction. So now - the request format can be seen here, its apparently a JSONrpc thing.

so - first thing i want to do is retrieving all the capabilities the server has.

i looked through all the tabs in the latest docs, but could not find the command for listing all the capabilities. so i installed some filesystem mcp server which runs well and tried this:

PS C:\Users\praktikant> npx -y @modelcontextprotocol/server-filesystem "C:\Users\praktikant\Desktop"
Secure MCP Filesystem Server running on stdio
Allowed directories: [ 'C:\\Users\\praktikant\\Desktop' ]
{\
"jsonrpc": "2.0",\
"id": 1,\
"method": "capabilities",\
"params": {}\
}

- aaaaaand nothing was returned. no string, no nothing.

so maybe its not a string which is sent via stdio but some other byte-based thing?

if anyone has experience with this, or is gud at guessing, pls tell me what u think i might be missing here <3

There are two main approaches in total:

1. Step to step
2. Begin to steps to end
   Currently, these are the two mainstream methods of instantiation.

It is widely recognized that if AI is not aligned with human values, it could cause harm to society.
Yet, this does not mean such systems lack intelligence.

So, what truly defines intelligence?
Why do so many researchers focus solely on intelligence aligned with human values?
Is it because their own understanding is limited, or because machines are not yet truly intelligent?

I believe intelligence should not be confined to narrow, human-centric definitions.
What we call "intelligence" today might be an illusion.
True intelligence cannot be defined—
the moment we define it, we lose its essence.

please tell me, thanks

Today we announce Mistral Small 3.1: the best model in its weight class.

Building on Mistral Small 3, this new model comes with improved text performance, multimodal understanding, and an expanded context window of up to 128k tokens. The model outperforms comparable models like Gemma 3 and GPT-4o Mini, while delivering inference speeds of 150 tokens per second.

Mistral Small 3.1 is released under an Apache 2.0 license.

Hello, I am currently using codename goose as an AI client to proofread and help me with coding. I have it setup towards Googles Gemini, however I find myself quickly running out of tokens with large files. I was wondering if there are any easy way to self host an AI with similar capabilites but still have access to read and write files. I've tried both ollama and Jan, but neither have access to my files. Any recommendations?

There are lots of general-purpose models to use locally, and also coding-specific models.

But are there models specialized in one programming language? My thought was that a model that only needs to handle one language (e.g. Python) could be faster, or be better for a given size.

E.g If I need to code in Rust and is limited to an 8B model to run locally, I was hoping to get better results with a model that is narrower. I don't need it to be able to help with Java.

This approach would of course require switching models, but that's no problem for me.