CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies

A smart context filter that removes noise, improves responses, and reduces token usage up to 90%

Automatic prompt caching for Claude Code. Cuts token costs by up to 90% on repeated file reads, bug fix sessions, and long coding conversations - zero config.

The context intelligence layer for AI coding agents. Compressing noise, routing content to the right strategy, preserving session state across compactions, and surfacing the files that actually matter.

💰 Save money on AI API costs! 76% token reduction, Auto-Fix token limits, Universal AI compatibility. Cline • Copilot • Claude • Cursor

Stop overpaying to run your agents. Kalibr routes every request to lower-cost model and tool paths without degrading performance.

Just hook it in front of your public S3 bucket and enjoy reduction in bandwidth costs from your bucket

CLI proxy for coding agents that cuts noisy terminal output while preserving command behavior

Save 30-60% on Claude Code costs -- proven strategies, real benchmarks, copy-paste configs, and interactive tools

Minimize LLM tokens from Python objects, code, logs, diffs, and more. Zero deps. Ultra-Lightweight.

Biological code organization system with 1,029+ production-ready snippets - 95% token reduction for Claude/GPT with AI-powered discovery & offline packs

A Kubernetes resource recommender that extends the API server to provide native suggestions.

Small utility that polls RPC endpoints for Base / Optimism / Arbitrum, writes timestamped JSON reports into `reports/`, and can post to a webhook.

Pi extension that turns noisy CLI output into compact structured results - fewer tokens, full logs preserved.

Claude Code settings.json auto-config tool to quickly switch API_KEY, AUTH_TOKEN, and model configs across multi-model setups. Secure backup and desensitized previews. 🐙

🎯 Optimize LLM token usage by 70-90% with smart context ranking, reducing costs while maintaining quality and performance.

To build a predictive model using machine learning to predict the probability of a device failure. When building this model, be sure to minimize false positives and false negatives. The column you are trying to Predict is called failure with binary value 0 for non-failure and 1 for failure.

Cut your OpenClaw / ZeroClaw token bill. Find which model earns its cost. Prove whether optimizations actually work. Local, no upload.

Nyquest — Semantic Compression Proxy for LLMs. 350+ rules, local LLM stage, 15-75% token savings. Full Rust stack.

Smart Context Optimization for LLMs - Reduce tokens by 66%, save 40% on API costs. Intelligent ranking and selection of relevant context using embeddings, keywords, and semantic analysis.