Show Notes
In this episode of Inference Time Tactics, Cooper and Byron break down NeuroMetric's Thinking Algorithm Leaderboard and what it reveals about building production-ready AI agents. They share why prompt engineering with a single model won't cut it for enterprise use cases, explore the impact of inference-time compute strategies, and discuss what they learned from testing 10 models across real CRM tasks—from surprising token inefficiency to catastrophic failures in SQL generation.
We talked about:
Why NeuroMetric built the first leaderboard combining models with inference-time compute strategies. How Salesforce's CRMArena-Pro reflects real multi-step business tasks better than pure reasoning benchmarks. The jagged frontier: no single model or technique dominates across all tasks. Why GPT 20B was surprisingly token inefficient—twice as slow as GPT 120B for similar accuracy. How GPT-5 nano's conversational style broke SQL generation tasks completely. Trading accuracy for speed: two-model ensembles versus five, and saving 20+ seconds per task. Throughput constraints as a hidden bottleneck when scaling to production volumes. Future directions: LLM-guided search, task clustering, and compression to specialized small models.
Resources Mentioned: CRMArena-Pro from Saleforce: https://www.salesforce.com/blog/crmarena-pro/ Thinking Algorithm Leaderboard: https://leaderboard.neurometric.ai/
Connect with Neurometric:Website: https://www.neurometric.ai/ Substack: https://neurometric.substack.com/ X: https://x.com/neurometricai/ Bluesky: https://bsky.app/profile/neurometric.bsky.social
Hosts: Calvin Cooper https://x.com/cooper_nyc_ https://www.linkedin.com/in/coopernyc
Guest/s: Byron Galbraith https://x.com/bgalbraith https://www.linkedin.com/in/byrongalbraith
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