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Latest Articles
Explore our most recent posts and stay updated with the latest insights.
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Building a Tokenizer From Scratch: Byte-Pair Encoding, Verified on Real Data (Ep:00.02)
Whole-word tokenization hits a wall the first time it meets a word it’s never seen.…
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9–13 minutes -
Colab Setup + TinyStories — Picking the Right Dataset to Train an LLM From Scratch (Ep:00.01)
Set up a free Google Colab environment for training an LLM from scratch, and learn…
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5–8 minutes -
From Scratch to Agents — Build a Real LLM, Fine-Tune It, Add RAG, Make It an Agent (Ep:00.00)
A hands-on, code-only course: build a real GPT-style LLM from scratch on Google Colab’s free…
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4–6 minutes -
Multi-Agent Systems Explained: A Sub-Agent Is Just a Tool That Runs Its Own Loop (Ep:06.08)
An orchestrator agent invoking a specialist sub-agent is structurally identical to a single agent calling…
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5–8 minutes -
Why “More Training” Didn’t Fix the Undertrained Tool — a Deeper Diagnosis (Ep:06.07)
Oversampling a tool’s training data 4x more didn’t fix it — because the real problem…
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4–6 minutes -
Why Self-Correction (Reflexion) Doesn’t Fix an Undertrained Model (Ep:06.06)
Reflexion lets an agent verbally reflect on its own mistakes and retry, entirely in-context, with…
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5–8 minutes -
Fixing a Broken Evaluation Metric: Comparable Positions, Not Comparable Sequences (Ep:06.03)
The fix for a per-tool evaluation metric confounded by trace length isn’t more data —…
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4–6 minutes -
Catching an Undertrained Tool Before It Fails: Two Signals, and a Real Evaluation Trap (Ep:06.02)
Per-tool held-out loss and label-free generation confidence both aim to catch an undertrained agent tool…
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4–7 minutes -
Multi-Tool Agents and Chained Tool Calls: Extending the ReAct Loop (Ep:06.01)
Choosing between tools is just next-token prediction over a bigger action vocabulary, and chaining tool…
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7–11 minutes -
Building a Tool-Using Agent From Scratch: The ReAct Loop Explained (Ep:06.00)
An agent isn’t a new model architecture — it’s a loop, built entirely on top…
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8–12 minutes
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