Tokenless

Model selection

Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean

Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean

This talk argues against the common practice of picking LLMs based solely on leaderboards, emphasizing that there's no single best model, only the right one for a given request. It introduces Digital Ocean's Inference Router, a customizable, open-source solution that intelligently selects models based on user-defined preferences (cost, latency, task, quality) rather than benchmarks, demonstrating significant cost savings and performance improvements in live demos.

LLM vs. SLM vs. FM: Choosing the Right AI Model

LLM vs. SLM vs. FM: Choosing the Right AI Model

A guide to understanding the differences between Large Language Models (LLMs), Small Language Models (SLMs), and Frontier Models (FMs). Learn the unique strengths of each model type and see practical use cases for document classification, customer support, and incident response to help you choose the right model for your AI project.

912: In Case You Missed It in July 2025 — with Jon Krohn (@JonKrohnLearns)

912: In Case You Missed It in July 2025 — with Jon Krohn (@JonKrohnLearns)

A review of five key interviews covering the importance of data-centric AI (DMLR) in specialized fields like law, the challenges of AI benchmarking, strategies for domain-specific model selection using red teaming, the power of AI in predicting human behavior, and the shift towards building causal AI models.