Building Better LLM Benchmarks with Prime Intellect
RLM eval. Basis, and the cost of a wrongly-aimed eight directions of benchmark design that imitate but mismeasure model capabilities.
// Building the agentic work stack.
Developer Evangelist at Arcade. Founder, Scale Intelligence. I write playbooks from the field on agents, GTM engineering, and what happens when machines start doing the work.
Why agents are bottlenecked on context, not capability.
Read the essay →RLM eval. Basis, and the cost of a wrongly-aimed eight directions of benchmark design that imitate but mismeasure model capabilities.
From Q&A chatbots to agents that act independently, retrieval, and tools. Latency, cost, quality tradeoffs at each layer.
Transparency, control, and building agents that search with intention rather than bruteforce.
Turning scanned PDFs into searchable, embeddable knowledge. Built with OCR plus Qdrant.
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