Anthropic Is Forcing Users to Pay Extra to Run OpenClaw With Claude - Lifehacker
Anthropic Is Forcing Users to Pay Extra to Run OpenClaw With Claude Lifehacker
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claude![[D] Tested model routing on financial AI datasets — good savings and curious what benchmarks others use.](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-graph-nodes-a2pnJLpyKmDnxKWLd5BEAb.webp)
[D] Tested model routing on financial AI datasets — good savings and curious what benchmarks others use.
Ran a benchmark evaluating whether prompt complexity-based routing delivers meaningful savings. Used public HuggingFace datasets. Here's what I found. Setup Baseline: Claude Opus for everything. Tested two strategies: Intra-provider — routes within same provider by complexity. Simple → Haiku, Medium → Sonnet, Complex → Opus Flexible — medium prompts go to self-hosted Qwen 3.5 27B / Gemma 3 27B. Complex always stays on Opus Datasets used All from AdaptLLM/finance-tasks on HuggingFace: FiQA-SA — financial tweet sentiment Financial Headlines — yes/no classification FPB — formal financial news sentiment ConvFinQA — multi-turn Q A on real 10-K filings Results Task Intra-provider Flexible (OSS) FiQA Sentiment -78% -89% Headlines -57% -71% FPB Sentiment -37% -45% ConvFinQA -58% -40% Blended avera
Show HN: ACP – Governance for AI Coding Agents (Claude Code, OpenClaw)
Hi. I'm David, founder of Agentic Control Plane (ACP). Last year I connected an app to an LLM with a MCP connector. Turned out that authenticating the LLM user in the app backend was surprisingly hard. That was the canary in the coalmine. If it's hard to authenticate actual users: - what about their agents? - what about downstream governance? Permissions, limits, audit logs ACP is a governance layer that sits in front of AI coding agents like Claude Code and OpenClaw. It runs on every tool call (Bash, Read, Write, file edits, web fetches, MCP, API calls). Every call is logged and optionally policy checked before execution. It works by hooking into your agent's tool pipeline. For Claude Code, it's a PreToolUse hook (~200ms). For OpenClaw, it's a before_tool_call plugin at priority 0. The pl
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[D] Tested model routing on financial AI datasets — good savings and curious what benchmarks others use.
Ran a benchmark evaluating whether prompt complexity-based routing delivers meaningful savings. Used public HuggingFace datasets. Here's what I found. Setup Baseline: Claude Opus for everything. Tested two strategies: Intra-provider — routes within same provider by complexity. Simple → Haiku, Medium → Sonnet, Complex → Opus Flexible — medium prompts go to self-hosted Qwen 3.5 27B / Gemma 3 27B. Complex always stays on Opus Datasets used All from AdaptLLM/finance-tasks on HuggingFace: FiQA-SA — financial tweet sentiment Financial Headlines — yes/no classification FPB — formal financial news sentiment ConvFinQA — multi-turn Q A on real 10-K filings Results Task Intra-provider Flexible (OSS) FiQA Sentiment -78% -89% Headlines -57% -71% FPB Sentiment -37% -45% ConvFinQA -58% -40% Blended avera
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