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In this episode of Inference Time Tactics, Cooper sits down with Yash Sharma, Head of AI Research at Neurometric AI, to break down the Pioneer Agent paper from Fastino Labs—a system that uses Claude Sonnet as an ML engineer in a box to build and improve small language models end-to-end. From cold start data curation to production failure diagnosis, the paper argues the real bottleneck in SLM creation isn't training—it's everything around it.
We talked about:
- What Pioneer Agent actually is and why Fastino built a 25-page system paper instead of just a training paper.
- How Neurometric is already tackling the same problems—and where the paper maps onto our approach.
- Why naive retraining on production data quietly degrades your model—and how an agentic loop fixes it.
- What the benchmarks reveal about where SLMs work out of the box versus where they need intervention.
- Where Pioneer Agent hits its limits—and how Neurometric is pushing SLMs toward harder agentic tasks.
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
Host/s:
Calvin Cooper
https://www.linkedin.com/in/coopernyc
Guest/s:
Yash Sharma
Bahasa Inggeris
Amerika Syarikat
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Penafian: Podcast dan karya seni yang dibenamkan pada halaman ini adalah daripada NeuroMetric AI, yang merupakan hak milik pemiliknya dan tidak bergabung dengan atau disokong oleh Listen Notes, Inc.
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