From 3dffe890b1c3de92034f1d202a55f35a2b415a35 Mon Sep 17 00:00:00 2001
From: wassname <1103714+wassname@users.noreply.github.com>
Date: Tue, 10 Mar 2026 05:40:31 +0800
Subject: [PATCH] docs(ml_debug): annotate sanh outbound links with content
 summaries

---
 docs/evidence/sanh_simple_considerations_hf_2021.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/evidence/sanh_simple_considerations_hf_2021.md b/docs/evidence/sanh_simple_considerations_hf_2021.md
index 4a7d2af..c13bf66 100644
--- a/docs/evidence/sanh_simple_considerations_hf_2021.md
+++ b/docs/evidence/sanh_simple_considerations_hf_2021.md
@@ -57,5 +57,7 @@ Credence ~65-70% -- specific domain claim, lacks ablation study reference.
 
 ## External links from this post
 
-- "Checklist for debugging neural networks" -- Cecelia Shao (Towards Data Science)
-- "A recipe for Training Neural Networks" -- Karpathy
+- **Cecelia Shao, "Checklist for Debugging Neural Networks"** (2019, KDnuggets/Towards Data Science): 5-section checklist (start simple, confirm loss, check intermediate outputs, diagnose parameters, track work). Thin; largely overlaps with Karpathy recipe and Slavv. Not captured separately -- see those sources instead.
+- **Chase Roberts, "How to unit test machine learning code"** (2017, Medium, 4 min): Focuses on software unit testing practices applied to ML models -- testing gradient flow, output shapes, that outputs change when weights change. Spawned `mltest` library. Not a full debugging guide. Main insight: "The code never crashes, the loss still goes down, it just converges to poor results."
+- **Joel Grus, "Reproducibility in ML as engineering best practices"** (Google Slides): Experiment reproducibility / engineering hygiene. Not fetched.
+- **A recipe for Training Neural Networks** -- Karpathy (captured in full: karpathy_recipe_training_nn_2019.md)