Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
The Problem (3 paragraphs) MuJoCo is the fastest-growing robotics simulator Converting URDF to MJCF is painful (./compile is buggy, urdf2mjcf ignores off-diagonal inertia, mesh paths break) You just want to convert and start training your RL agent The Solution (show curl + Python code) @robot.urdf" import roboinfra Real Example (use your preview_test_arm.urdf) Show the input URDF (6 links, 5 j
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E
[02] Stress Testing Your Life — What Happens at -30%, -50%, -60%? This is Part 2 of a 6-part series: Building Investment Systems with Python After the 2008 financial crisis, regulators required banks to run stress tests — hypothetical scenarios where markets crash 30%, 40%, 60% — and prove they could survive. Your personal balance sheet faces the same risks. If you hold a securities-backed loan,