LocalEyes gives blind LLMs vision via local Ollama models
LocalEyes is a new tool that enables text-only LLMs like DeepSeek, CodeLlama, and Qwen-Coder to process images locally using an Ollama vision model. It supports screen capture, clipboard reading, and image file analysis without cloud uploads or API keys, offering a private, fast, and free solution for developers using Claude Code.
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llm-eyes tool lets any blind LLM see via tiny local VLM
A GitHub project called llm-eyes bolts a tiny vision-language model (Qwen3.5-0.8B) onto any machine as a local vision service, enabling text-only models like DeepSeek V4 Flash, Qwen, and Llama to caption images, watch webcams, or read video. It runs on Mac (MLX), PC+NVIDIA (llama.cpp), and DGX Spark, and exposes an OpenAI-compatible API.
Local-first CLI that masks PII and secrets before sending to LLMs
LocalMask is a command-line tool that runs locally to detect and mask personally identifiable information (PII) and secrets before data is sent to large language models. It solves the privacy and compliance problem for developers and organizations that need to use LLMs without exposing sensitive data.
AI-powered desktop overlay for coding assessments and interviews
HintLens is an AI-powered desktop overlay that stays on top of the screen to provide answers during coding online assessments and interviews. It uses LLMs to help users avoid switching between apps or browser tabs, reducing stress from strict time limits and challenging questions.
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.
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.