<h1> How LLMs Work: A Journey Through Tokens, Attention, and Transformers </h1> <p>“My favourite rock band is…”</p> <p>How does a large language model take that unfinished
<p>About a year ago, I was learning PyTorch.</p> <p>I was learning about model training, transformers, language models, tokenization, architectures, and all the other things that
<p>GPT-5.4 shipped on March 5. Seven weeks later, on April 23, 2026, OpenAI released GPT-5.5 — and the pace alone tells you something about where this race is headed. This isn't
<p>"The Reasoning Trap." The line the authors won't quite say out loud is that the smarter your model gets at reasoning, the more likely it is to fabricate a tool that doesn't
<!-- SC_OFF --><div class="md"><p><strong>Submitted by:</strong> Adam Kruger <strong>Date:</strong> March 23, 2026 <strong>Models Solved:</strong> 3/3 (M1, M2, M3) + Warmup</p>
<p>Last week I was stitching together Serper for web + Tavily for synthesis + a YouTube transcript API just to answer one chat question. Every category was a new API contract, a
<h1> Anthropic Managed Agents Architecture: Decoupling Brain from Hands </h1> <p>Anthropic published <a href="https://www.anthropic.com/engineering/managed-agents" rel="noopener
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