Vijay Tank — Tech, Code & Game Dev

Overview This guide walks through setting up a self-hosted LiteLLM proxy that unifies multiple free-tier LLM providers (NVIDIA NIM and OpenRouter) behind a single OpenAI-compatible API endpoint. It also covers automated model-availability probing and integrating the resulting gateway with an AI coding CLI. No API keys, account names, personal identifiers, or real secrets are included anywhere in this guide. All example values are placeholders — replace them with your own credentials. Why build this Free-tier LLM providers often enforce low per-minute rate limits (e.g., 20-40 requests/minute). Model catalogs change frequently — models get added, deprecated, or temporarily rate-limited. A single unified gateway lets any OpenAI-compatible client (coding CLIs, IDE extensions, custom scripts) fail over automatically across multiple providers/models without code changes. Part 1: Probing Model Availability Before wiring a provider into a gateway, verify which models are actually ...

If you've ever worked with an AI coding agent like Antigravity across a real project, you've probably run into the same repeated problem: you keep re-typing the same instructions in every single prompt. "Don't hardcode values." "Cite the actual file and line." "Don't break existing tests." "Give me a table, not a wall of text." Eventually you realize these aren't one-off preferences — they're rules that should apply to every task, every time, without you having to restate them. Here's a practical set of global rules that came out of exactly that realization, refined across several real review and refactor tasks on a production codebase. Set these once as your agent's standing context, and your day-to-day prompts can shrink down to just the task itself. Why Global Rules Matter More Than Clever Prompts A single well-written prompt only helps once. The moment you start a new session, or hand off a task to a teammate u...