Daytona

Evals Are Broken, Use Them Anyway — Ara Khan, Cline

Jun 6, 2026 · 19:04

Ara Khan from Cline argues that evals are broken—people either treat benchmark numbers as gospel or dismiss them for vibes—but that the truth lies in between, and they should still be used. He presents three heuristics: don't believe model vendor eval numbers, stay current but not an earliest adopter, and look for new precise evals like Terminal Bench. Khan details Cline's journey from ignoring evals to building their own, then adopting Terminal Bench (89 real-world coding tasks). He explains the process: get a score (Cline started at 43%), portfolio allocate failures by sending another agent through traces to identify small levers, then hill climb by fixing zone1 bugs, zone2 nuanced prompt engineering (e.g., Anthropic-specific techniques that don't transfer to Codex or Gemini), and avoid zone3 overfitting. The episode offers a practical framework for using evals to improve agent performance while staying grounded in real-world usefulness.

AX is the only Experience that Matters - Ivan Burazin, Daytona

Jul 24, 2025 · 15:25

Ivan Burazin, co-founder of Daytona, argues that agent experience (AX) is the only experience that matters, as AI agents will soon outnumber human developers and tools must be built for agents to autonomously operate. He cites that 25% of YC startups say AI writes 95% of their code and 37% of the latest YC batch build agents as products. Burazin outlines three AX pillars—seamless authentication, agent-readable docs (like Stripe's .md and LLMs.txt), and API-first design—then introduces Daytona's agent-native runtime, which spins up sandboxes in 27 milliseconds and includes features like a declarative image builder, network-mounted volumes for large datasets, and parallel execution for agents to fork environments. He concludes that any tool requiring a human in the loop is built for the past, and if agents cannot use a product, no one will.