{
  "id": 7737497,
  "title": "I run a 'radar' that finds free LLM endpoints and auto-adopts the good ones — behind a five-part gate so it can't adopt junk",
  "url": "https://urgent.news/2026/09/16/i-run-a-radar-that-finds-free-llm-endpoints-and-auto-adopts-the-good",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-16T08:00:03.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/c1-anderson/i-run-a-radar-that-finds-free-llm-endpoints-and-auto-adopts-the-good-ones-behind-a-five-part-3bja"
  },
  "original_language": "en",
  "account": "Free LLM endpoints appear and disappear frequently due to various reasons, such as providers launching generous tiers, new open models surfacing on hosts' gateways, or promotional campaigns running for limited periods. Manually tracking them is a futile endeavor. This prompted the creation of a small radar (model-radar) designed to monitor for free models and automatically adopt those that prove to be genuinely good, provided they pass a rigorous gate screening process. This gate consists of five sequential checks that a model must pass before it can be adopted into the agent stack. The radar is designed to prevent the adoption of poor-quality models that could lead to silent failures and degrade the performance of agent tasks. The gate verifies, in order, whether the model responds correctly within a reasonable timeout, can perform tool calls accurately and incorporate the results into subsequent turns, respects prompt context, maintains stability across repeated calls, and outperforms the existing models on specific agent tasks. Only a model that successfully passes all five checks is allowed to be adopted. The radar's design ensures that the automated adoption process remains transparent and reproducible, minimizing the risk of introducing silent failures. By gating the adoption of free models based on their ability to handle real tasks, model-radar aims to prevent the adoption of suboptimal models that may appear functional on surface-level metrics but fail to deliver desired outcomes in actual agent deployments.",
  "summary": "Free LLM endpoints appear and vanish constantly — a provider opens a generous tier, a new open model shows up on someone's gateway, a promo runs for a month. Chasing them by hand is a losing game. So I built a small radar ( model-radar ) that watches for free models and, when one is genuinely good enough, adopts it automatically into my agent stack — but only after it clears a gate strict enough…",
  "key_points": [
    "Model-radar monitors free LLM endpoints and auto-adopts good ones",
    "Five-part gate screens models before adoption, preventing junk",
    "Gate checks responsiveness, tool calls, prompt context, stability, and performance"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}