AI & Tools desk — breakout report
Why Nobody Has Solved Continual Learning
LangChain's upload has reached 11.4K views — about 3.7× the ≈3.1K this channel typically draws at the same age.
Key facts
- •3.7x LangChain's own 90-day median — this channel normally draws about 3.1K views at the same age.
- •11.4K views so far, published 6 days ago.
- •Category: Science & Technology · Language: en · Quality: HD · Captions: yes
- •"Breakout" is measured against this exact channel's trailing 90-day median at matching video age — not a generic niche average.
Updated 2026-10-16 21:50 UTC
View trajectory
Views this upload has gathered since it published, last measured about 96h in — from our own tracking.
Published Oct 16, 2026 · 6 days ago. How breakouts are measured →
From the creator
Charlie O'Neill, Co-Head of Model Training at Baseten, explains why the train step in the own-your-intelligence loop is still unsolved once you update a model continually instead of once. He walks through the trade-offs: SFT can install new knowledge but is off-policy, and repeated SFT causes catastrophic forgetting…
Category: Science & Technology · Language: en · Quality: HD · Captions: yes
Topics: Knowledge
Tags: The Learning Loop, LangChain, continual learning, AI agents, Charlie O'Neill, Baseten, reinforcement learning, RL, SFT, supervised fine-tuning, fine-tuning, distillation
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