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Framework for modelling of Natural General Intelligence. This project aims at creation of open source AGI (Artificial General Intelligence) through modelling of natural thinking. See http://roland.pri.ee/bakalaureusetoo/ for theoretical details.

This is a mirror site for downloads and forks. There is no documentation or issue tracking here. Please see the code issue tracking system on Bitbucket at https://bitbucket.org/taraathan/oojdrew

Generic engine to filter information. We wish to show that the power of expression of a filter makes it possible to appreciably reduce the size of the code necessary to extract information and that it is possible in Python.

using simple cuboids and gjk algorithm, this general purpose algorithm gives a ready made code to work with swissranger 3D camera.

** IMPORTANT NOTICE ** 10 Feb 2006 Code is being moved to the SMI subversion repository (http://smi-protege.stanford.edu/svn/owl/trunk/) Project will continue to be open source. ProtegeOWL info at: http://protege.stanford.edu/overview/protege-owl.html

Nex-N2-mini is an open-source agentic model from Nex AGI designed for real-world productivity, coding, tool use, deep research, and terminal-based execution. Built on Qwen3.5-35B-A3B-Base, it offers a lighter latency and deployment profile than Nex-N2-Pro while preserving the core Nex-N2 “Agentic Thinking” framework. This framework unifies requirement understanding, planning, code implementation, environmental feedback, debugging, evaluation, and iteration into a closed loop. ...

Qwable-v1 is an open-weight agentic coding model created through a chained distillation process based on Qwen3.6-35B-A3B. The model combines two distinct training stages: first, it was fine-tuned on reasoning traces derived from Claude Opus 4.7 to improve structured reasoning, and then further trained on Claude Fable-5 agentic tool-use traces to develop autonomous coding and tool-calling behavior. The result is a 35B Mixture-of-Experts model with only 3B active parameters that can switch...

OpenVLA 7B is a multimodal vision-language-action model trained on 970,000 robot manipulation episodes from the Open X-Embodiment dataset. It takes camera images and natural language instructions as input and outputs normalized 7-DoF robot actions, enabling control of multiple robot types across various domains. Built on top of LLaMA-2 and DINOv2/SigLIP visual backbones, it allows both zero-shot inference for known robot setups and parameter-efficient fine-tuning for new domains. The model...

wav2vec2-large-xlsr-53-portuguese is an automatic speech recognition (ASR) model fine-tuned on Portuguese using the Common Voice 6.1 dataset. It is based on Facebook’s wav2vec2-large-xlsr-53, a multilingual self-supervised learning model, and is optimized to transcribe Portuguese speech sampled at 16kHz. The model performs well without a language model, though adding one can improve word error rate (WER) and character error rate (CER). It achieves a WER of 11.3% (or 9.01% with LM) on Common...