Search Results for "doc"

...The idea is that AGENTS.md acts as a “README for agents”: a predictable, structured place where humans can put instructions, conventions, build/test commands, environment setup, and other guidance that generative agents (e.g. code-writing, code-assisting tools) should consult when operating in the repo. Instead of putting everything in README or doc files (which are more human-oriented and might mix high-level narrative), AGENTS.md is intended to surface agent-relevant details that help them “do the right thing” (tests, style, project structure, tooling).

TML has moved to http://www.villalon.cl/tml.html and the code to https://github.com/villalon/tml

It provides an interactive speech to text application that uses sphinx 4. With this you can use pre-recorded audio, record your own voice and convert incompatible audio/video to be compatible with sphinx 4. It currently supports U.S English by using hub4 acoustic and language model.

EDocIAS (Electronic Document Index And Search) is a PHP-based tool for indexing and searching files of various types. Third-party tools (tesseract, xpdf, etc.) can be configured to support any type of file.

ANts P2P realizes a third generation P2P net. It protects your privacy while you are connected and makes you not trackable, hiding your identity (ip) and crypting everything you are sending/receiving from others.

PIQLE is a Platform Implementing Q-LEarning (and other Reinforcement Learning) algorithms in JAVA. Version 2 is a major refactoring. The core data structures and algorithms are in piqle-coreVersion2. Examples are in piqle-examplesVersion2. A complete doc

NuMarkdown-8B-Thinking is the first reasoning OCR vision-language model (VLM) designed to convert documents into clean Markdown optimized for retrieval-augmented generation (RAG). Built on Qwen 2.5-VL-7B and fine-tuned with synthetic Doc → Reasoning → Markdown examples, it generates thinking tokens before producing the final Markdown to better handle complex layouts and tables. It uses a two-phase training process: supervised fine-tuning (SFT) followed by reinforcement learning (GRPO) with a layout-centric reward for accuracy on challenging documents. The model excels at non-standard layouts and complex table structures, outperforming non-reasoning OCR systems like GPT-4o and OCRFlux, and competing with large closed-source reasoning models like Gemini 2.5. ...