author
AI Advocate
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Snorkel AI
Jonathan Schlosser is an AI Advocate at Snorkel.ai. He has previously worked as an AI Engineer, an Educator, and Founder. He has taught graduate-level AI and Data Science courses at UNC Chapel Hill for the last few years, and has taught hundreds of data science professionals through programs with Correlation One, Amazon, Google, and others. With 10+ years across the data and AI spectrum, from data science roles to building AI-driven applications, he writes about deep learning, LLMs, and AI agents with a direct and simplified instructional approach.
The latest from Jonathan Schlosser
Blog
RL environments for LLM agents: Design, rewards, and validation
TLDR: An agent, by definition, can take actions and is more than just a language model. The model is only one part of the system, and is only one element that you are training. The environment determines what the agent can observe, what it can change, which actions are available, and what behavior receives a reward. For a coding agent,…
Blog
Opus 5.5 vs Opus 5 vs Fable 5.1: Coding Benchmark Results
We’ve now run three generations of frontier models through the same expert-created terminal-bench style task set: Fable 5.1 in September, Opus 5 in July, and, now Opus 5.5. For this analysis, the task set contained 200 trajectories, of 24 tasks, with every failure traced to a judge-confirmed root cause. A generational climb On the task set, pass@1 is 61.5% for…
Blog
Grok 4.7 on Senior SWE-Bench: Strong pass@3, Cheaper Cost per Trial
Grok 4.7 was evaluated on Senior SWE-Bench, our benchmark for measuring whether coding agents work like senior engineers. It reaches a tasteful pass@3 of 40.0%, up from 38.9% for Grok 4.6, ranking sixth overall at $0.24 per trial, which is a fraction of the cost compared to Opus 4.8. Benchmark Senior SWE-Bench evaluates agents as compared to a senior engineer….
Blog
From Foundational Competency to Expert Performance: A Curriculum Approach to Model Development
A student does not go from 1st to 12th grade in a single step. Each grade builds on a specific set of skills, and each one assumes the previous skills have already been mastered. Nobody learns calculus without algebra. When a student skips ahead anyway, what they end up with is memorization rather than understanding. The gaps show up later, usually in ways that are harder to trace back and fix.
Blog
Fable 5.1 on Frontier Coding Tasks: Efficient Successes, Distinct Failure Modes
We evaluated Fable 5.1 on a series of frontier coding tasks from our proprietary Terminal-Bench+ dataset and compared the results against Opus 5. Fable remained competitive across most categories and was materially more efficient on successful runs, while its gap was concentrated in a small set of terminal-heavy and build/dependency tasks. Because category sizes are small and uneven, we treat…
