Slojka: AI-First WebGL2 Graphics Editor for Advanced Image Editing
Explore Slojka, an AI-first Photoshop-like graphics editor built in TypeScript with WebGL2 and integrations like SAM 2.1, polza ai, and Claude Code/MCP.
#open-source×
Shepherd: Stanford’s Git-Like Rollback System for AI Agents
Stanford’s open-source Shepherd lets AI agents roll back to the last correct step, reuse up to 95% of prior compute, and auto-recover from errors.
5 Open-Source No-Code Platforms for LLMs, RAG, and AI Agents
Explore 5 open-source no-code platforms to build LLM apps, RAG pipelines, and AI agents—AutoAgent, AnythingLLM, LangChain OAP, Sim, and Dify.
Cut Claude Code Costs by 70% with OCR Context Images (pxpipe)
Turn Claude Code context into an image and use OCR to reduce Fable 5 costs by ~70%. Beware lossy compression and misread IDs, hashes, secrets, numbers.
Build a Second Brain Assistant: RAG, Agents, and LLMOps Course
Open-source Decoding AI course repo to build a personal knowledge base assistant with RAG, agents, ETL, fine-tuning, deployment, and LLMOps tools.
OpenHuman Hits 33K GitHub Stars with Super Context AI Feature
OpenHuman, an open-source OS-level AI assistant, surged past 33K GitHub stars thanks to Super Context, local data use, and 118 app integrations.
NVIDIA Open-Sources LocateAnything-3B for Faster Visual Localization
LocateAnything-3B uses parallel decoding to predict bounding boxes at once, improving detection in dense scenes and supporting UI, OCR and documents.
Free Open-Source Tool to Convert PDFs, Docs & Scans to Markdown
Convert PDFs, Word, Excel, and scanned images into clean Markdown with OCR, LaTeX formulas, HTML tables, 109 languages—local and private.
PixelRAG: Open-Source Visual Web Scraping & RAG via Page Screenshots
PixelRAG is an open-source retriever that indexes rendered page screenshots, not HTML. Uses Qwen3-VL embeddings, FAISS, and beats text RAG on QA.
Datalab Open-Sources Lift: 9B Model for Document Data Extraction
Lift is a 9B open-source model for extracting structured data from documents via JSON Schema, reaching 90.2% accuracy. Install with pip install lift-pdf.