6d ago · 6 min read · AWS · Amazon Bedrock · April 2026 · 🕒 7 min read You've been fine-tuning models the hard way. Collecting thousands of labeled examples. Running expensive annotation pipelines. Managing GPU infras
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Mar 29 · 7 min read · My trading bot lost $176 in its first real backtest. Not because of a bug. Not because of bad data. The algorithm was working exactly as designed it just couldn't figure out when to exit trades. The b
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Mar 29 · 15 min read · TLDR: Reinforcement Learning trains agents to make sequences of decisions by learning from rewards and penalties. Unlike supervised learning, RL learns through trial and error rather than labeled examples. Use it for sequential decision problems wher...
Join discussionMar 22 · 5 min read · Two things happened this week that belong in the same conversation but nobody's connecting yet. Meta released OpenEnv, a framework for RL post-training of language model agents. And a Peking University State Key Laboratory paper on "Meta Context Engi...
Join discussionMar 21 · 7 min read · The RL Renaissance: Why Three Papers in One Day Signal the Death of Imitation Learning for AI Agents Reinforcement learning is the future of agent training, and imitation learning is a dead end. Three independent papers all converge on the same thesi...
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