AI Config Hub - Free Online Tool | PivaBox

Configuration guides for Claude Code, OpenRouter, MCP, etc.

AI Config Hub — Manage and Compare AI Model Configuration Presets

  1. Browse preset configurations for popular AI models including temperature, top_p, max_tokens, frequency_penalty, and presence_penalty settings. Each preset is labeled by use case: Creative Writing, Code Generation, Factual Q&A, Translation, Summarization.
  2. Adjust any parameter and see the effects explained in real time. The hub provides recommended ranges for each parameter and warns when settings are likely to produce poor results (e.g., temperature > 1.5 or top_p < 0.1).
  3. Copy the configuration as JSON for direct use with OpenAI, Anthropic, or other LLM APIs. Save your custom presets in browser local storage for quick access across sessions.

Frequently Asked Questions

Is the AI Config Hub free?

Yes, completely free. Access all LLM configuration presets and experiment with parameters at no cost.

Does this tool send my configurations to any server?

No. All configuration management is local — your presets are saved in your browser's local storage only.

What do AI model parameters actually control and how should I tune them?

Key LLM parameters: (1) Temperature (0.0–2.0) — controls randomness. Low values (0.0–0.3) produce deterministic, consistent outputs ideal for factual Q&A, code generation, and data extraction. High values (0.7–1.0) produce creative, varied outputs for brainstorming, storytelling, and poetry. Extreme values (>1.5) produce nonsensical or repetitive text. (2) Top_p / Nucleus Sampling (0.0–1.0) — an alternative to temperature; the model considers only tokens whose cumulative probability exceeds top_p. Lower values (0.1) are more focused; higher values (0.9) more diverse. Use temperature OR top_p, not both — most providers recommend setting one and leaving the other at 1.0. (3) Max Tokens — the maximum length of the generated response. Set based on your expected output length; leaving it too high wastes tokens and cost. (4) Frequency Penalty (-2.0 to 2.0) — positive values discourage word repetition; useful for long-form content to avoid loops. (5) Presence Penalty (-2.0 to 2.0) — positive values encourage the model to discuss new topics; useful for diverse, wide-ranging conversations.