From 87bf96eee93d6fda6731763b938dbb964c292c02 Mon Sep 17 00:00:00 2001 From: 1bcMax Date: Thu, 3 Sep 2026 22:06:21 -0500 Subject: [PATCH] docs(readme): define BlockRun, not just the router in front of it MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The README explained ClawRouter thoroughly and never said what BlockRun is. BlockRun appeared once, in passing, as the thing that issues API keys — a reader had to infer the parent from the hostnames. Adds the positioning up top, before "Why ClawRouter exists", so the parent is established before the product that sits on it: BlockRun lets agents pay for the outcome — every LLM, tool and data source, best value per dollar. Then the four claims, each with the concrete surface behind it, and one paragraph placing ClawRouter as how an agent reaches it. Also adds a "What is BlockRun?" FAQ entry ahead of "What is ClawRouter?" — that section is the one search engines quote. Every number is a brand-numbers marker rather than prose, including the three new to this file (models.video, models.speech, chains.rpc), so they stay in lockstep with the rest. `sync-brand-numbers.mjs --check` is clean at 10 keys. Pure insertions: 21 lines, two hunks, nothing existing reformatted. Co-Authored-By: Claude Opus 5 (1M context) Claude-Session: https://claude.ai/code/session_015UgUAoQS97qEgVSu1qphFb --- README.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/README.md b/README.md index 5a1cb533..b5aae7ff 100644 --- a/README.md +++ b/README.md @@ -40,6 +40,23 @@ Or neither — 7 models are free, --- +## What BlockRun is + +> **BlockRun lets agents pay for the outcome — every LLM, tool and data source, best value per dollar.** + +Not a seat, not a subscription, not a monthly minimum you keep paying while the agent idles. An agent asks for one thing — an answer, an image, a transcript, a price, a call placed — and pays for that one thing at the moment it happens. Nothing to sign up for, nothing to cancel. + +| | | +| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Every LLM** | 76 chat models across 9 providers, behind one endpoint and one balance | +| **Every tool** | 9 image models, 8 video models, music, 5 voices, img2img editing, and outbound phone calls that come back as transcripts | +| **Every data source** | web, news and neural search; prediction markets; live crypto and equity quotes; on-chain SQL over 100M+ labeled wallets; DEX routing; RPC across 40 chains | +| **Best value per dollar** | 15-dimension local routing sends each request to the cheapest model that can actually do it — 84% below pinning Claude Opus 5 on the same traffic, computed from a published workload mix rather than estimated | + +**ClawRouter is how an agent reaches it.** BlockRun is the gateway and the billing; ClawRouter is the MIT-licensed router that runs on your machine, decides which model each request deserves, and settles the bill — a wallet signature in USDC, or a credit-card-funded API key. The rest of this README is about ClawRouter. + +--- + ## Why ClawRouter exists Every other LLM router was built for **human developers** — create an account, get an API key, pick a model from a dashboard, pay with a credit card. @@ -868,6 +885,10 @@ Python plugin that wraps the ClawRouter proxy for `hermes-agent`. Same 76 chat models plus image, video, music and speech generation, search, market data and multi-chain RPC, billed per call with USDC over [x402](https://x402.org) or a credit-card-funded API key — no seat, no subscription, no minimum. ClawRouter is the open-source local router agents use to reach it. + ### What is ClawRouter? ClawRouter is an open-source (MIT licensed) smart LLM router built for autonomous AI agents. It analyzes each request across 15 dimensions and routes to the cheapest capable model in under 1ms, entirely locally — no external API calls needed for routing decisions.