Local Context Router cuts vision-token waste by pre-classifying PDF pages
A developer built LocalContextRouter, a tool that runs locally to classify each PDF page as text, OCR-needed, or image before sending to a multimodal model. This avoids the wasteful practice of rendering every page to an image, which can cost 1,300–4,800 tokens per page versus 400–800 tokens for plain text. The tool never calls a model itself, leaving the final API call to the user's app.
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ContextOps: open-source tool to audit and optimize LLM prompt context
ContextOps is an open-source tool that analyzes LLM prompts to detect token waste such as duplicated retrieval chunks, bloated system prompts, oversized conversation history, and repeated tool outputs. It helps developers reduce costs and improve model consistency by auditing what goes into the prompt before inference.
New model with 500K-token context and $2/$6 pricing shifts cost calculus
A model offering a 500,000-token context window at $2 per million input tokens and $6 per million output tokens has been released, drawing attention for its cost-effectiveness. The pricing and context length are seen as significant for applications requiring long-context processing, potentially changing the competitive landscape before benchmark comparisons are even made.
Offline PDF chat with RAG using local LLMs and vector search
A production-oriented Retrieval-Augmented Generation (RAG) system for chatting with PDFs. It uses local LLMs via Ollama, ChromaDB for vector search, and LangChain to provide grounded answers from uploaded documents, fully offline. Aimed at users needing private, local document Q&A.
Upload project folder to get optimized markdown for LLM context
A web tool that lets users upload an entire project folder and receive a single, clean, optimized markdown file ready to paste into Claude or Codex. It solves the problem of manually preparing context for LLMs by automatically consolidating and formatting code files. All processing is done client-side for privacy.
Understudy: local Ollama drafts answers before Claude, saving API costs
Understudy is a single-file hook that intercepts prompts to Claude Code and routes routine tasks (summarization, classification, JSON conversion, etc.) to a local Ollama model for a draft. The draft is injected as context with instructions for Claude to verify and deliver if correct, or discard and redo if not. It saves API costs by offloading grunt work to a free local model while keeping Claude as the quality gate.
