#python #ai #ocr
Chandra OCR 2 is a top OCR model that turns images and PDFs into structured Markdown, HTML, or JSON, keeping layout, tables, math, handwriting, and 90+ languages accurate—it leads benchmarks like olmOCR (85.9% overall) and multilingual tests (77.8% average). Install easily with `pip install chandra-ocr` for CLI use, local HuggingFace, or fast vLLM server; try the free playground first. You benefit by quickly digitizing complex docs with high precision, saving time on extraction and enabling easy editing or analysis without manual fixes.
https://github.com/datalab-to/chandra
Chandra OCR 2 is a top OCR model that turns images and PDFs into structured Markdown, HTML, or JSON, keeping layout, tables, math, handwriting, and 90+ languages accurate—it leads benchmarks like olmOCR (85.9% overall) and multilingual tests (77.8% average). Install easily with `pip install chandra-ocr` for CLI use, local HuggingFace, or fast vLLM server; try the free playground first. You benefit by quickly digitizing complex docs with high precision, saving time on extraction and enabling easy editing or analysis without manual fixes.
https://github.com/datalab-to/chandra
GitHub
GitHub - datalab-to/chandra: OCR model that handles complex tables, forms, handwriting with full layout.
OCR model that handles complex tables, forms, handwriting with full layout. - datalab-to/chandra
#python
AI Scientist-v2 is an autonomous AI system that generates research ideas, runs experiments, analyzes data, and writes full scientific papers using agentic tree search—no human templates needed. It produced the first entirely AI-written paper accepted via peer review at an ICLR workshop. You benefit by quickly exploring ML topics, automating discovery to save time and costs (about $20 per run on Linux with GPU), and scaling your research productivity for faster breakthroughs. Install via conda, set API keys, ideate with a Markdown file, then launch experiments. Run in a safe sandbox due to code risks.
https://github.com/SakanaAI/AI-Scientist-v2
AI Scientist-v2 is an autonomous AI system that generates research ideas, runs experiments, analyzes data, and writes full scientific papers using agentic tree search—no human templates needed. It produced the first entirely AI-written paper accepted via peer review at an ICLR workshop. You benefit by quickly exploring ML topics, automating discovery to save time and costs (about $20 per run on Linux with GPU), and scaling your research productivity for faster breakthroughs. Install via conda, set API keys, ideate with a Markdown file, then launch experiments. Run in a safe sandbox due to code risks.
https://github.com/SakanaAI/AI-Scientist-v2
GitHub
GitHub - SakanaAI/AI-Scientist-v2: The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search - SakanaAI/AI-Scientist-v2
#python #claude_code #guide #tutorial
Claude How To is a free GitHub guide (3,900+ stars) with visual tutorials, copy-paste templates, and a 11-13 hour learning path to master Claude Code features like slash commands, memory, skills, subagents, hooks, MCP, and plugins. Start in 15 minutes by cloning the repo and copying a command—build automated code reviews, deployments, and docs. You gain 10x productivity by combining features into real workflows, skipping basic docs for production-ready setups that save hours daily on grunt work.
https://github.com/luongnv89/claude-howto
Claude How To is a free GitHub guide (3,900+ stars) with visual tutorials, copy-paste templates, and a 11-13 hour learning path to master Claude Code features like slash commands, memory, skills, subagents, hooks, MCP, and plugins. Start in 15 minutes by cloning the repo and copying a command—build automated code reviews, deployments, and docs. You gain 10x productivity by combining features into real workflows, skipping basic docs for production-ready setups that save hours daily on grunt work.
https://github.com/luongnv89/claude-howto
GitHub
GitHub - luongnv89/claude-howto: A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy…
A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy-paste templates that bring immediate value. - luongnv89/claude-howto
#python #glm #image2text #ocr
GLM-OCR is a top 0.9B-parameter model for accurate OCR on complex documents like tables, code, formulas, seals, and receipts, scoring 94.62 on OmniDocBench V1.5. Install via `pip install glmocr`, use cloud API (no GPU needed) or self-host with vLLM/SGLang for fast, low-cost inference, and get JSON/Markdown outputs easily via CLI or Python. You benefit from quick, robust document parsing that saves time, cuts compute costs, and integrates simply into your apps for real-world tasks.
https://github.com/zai-org/GLM-OCR
GLM-OCR is a top 0.9B-parameter model for accurate OCR on complex documents like tables, code, formulas, seals, and receipts, scoring 94.62 on OmniDocBench V1.5. Install via `pip install glmocr`, use cloud API (no GPU needed) or self-host with vLLM/SGLang for fast, low-cost inference, and get JSON/Markdown outputs easily via CLI or Python. You benefit from quick, robust document parsing that saves time, cuts compute costs, and integrates simply into your apps for real-world tasks.
https://github.com/zai-org/GLM-OCR
GitHub
GitHub - zai-org/GLM-OCR: GLM-OCR: Accurate × Fast × Comprehensive
GLM-OCR: Accurate × Fast × Comprehensive. Contribute to zai-org/GLM-OCR development by creating an account on GitHub.