Journey of Curiosity
The Notebooks 5 volumes

Notebooks

One question, one week, one hour a day. I write down what I learn while I learn it.

Why I work this way

Transfer learning works in brains too. The prefrontal cortex builds schema, mental frameworks that compress patterns from one domain and apply them to another. The more domains you feed it, the richer the schema. Convolutions in V1 and convolutions in CNNs are the same idea discovered independently. Attention in the parietal cortex and attention in transformers solve the same resource allocation problem. These are not metaphors. They are convergent solutions.

Leonardo studied anatomy to paint better and painted to understand anatomy better. Feynman learned to pick locks to understand information theory. Ramanujan found number theory in temple floor patterns. Cross-domain learning is not a distraction from depth. It is how depth actually works.

The time box is the point. Retrieval under pressure consolidates memory faster than passive review, and the hippocampus encodes better when a session has a clear boundary. You cannot passively read for sixty minutes and pretend you understood it. You have to write what you know, find the gaps, and come back the next day to fill them.

I am a Leonardo fanboy, not because I think I am smart like him because I am really not, but because the man had no formal education past age 14 and still taught himself anatomy and optics and geology and engineering from whatever books and mentors he could get his hands on, and that is the only way I have ever been able to learn anything either. He kept notebooks his entire life, thousands of pages, anatomy next to fluid dynamics next to machine designs, not organized by subject but by date, and he called himself "discepolo della esperienza" which means disciple of experience, and I think the notebook was never documentation for him, it was the actual thinking tool, the place where looking at something carefully enough turns into understanding it.

Days written
25
Questions chased
25
Notes taken
556
Volumes
5
Open
01

SLAM from Scratch

Building a full SLAM pipeline from scratch. Camera models, feature extraction, matching, motion estimation, mapping, loop closure, graph optimization. Theory and code, no shortcuts.

0 entries from May 13, 2026

The pipeline, stage by stage

  1. 01 Camera models
  2. 02 Feature extraction
  3. 03 Matching
  4. 04 Motion estimation
  5. 05 Mapping
  6. 06 Loop closure
  7. 07 Graph optimization

Running over 6 months, whenever it happens. Entries land here as each stage gets built.

  • computer vision
  • robotics
  • geometry
Closed
02

Overview of Modern Nets

Revisiting transformers, tokenizers, attention, and the GPT family. Intuitive understanding for interviews and curiosity.

7 days May 5, 2026

What I asked, day by day

  1. 01 The encoder block, BPE, and how tokens are made
  2. 02 Multi-head attention and why we use dot products
  3. 03 Masking, padding, and BERT vs GPT
  4. 04 The transformer architecture, drawn from scratch
  5. 05 Pretraining, feedforward, residual connections, and layer norm
  6. 06 LLaMA, Mistral, and the road to reasoning models
  7. 07 RAG, LangChain, LangGraph, and the LLM tooling landscape

Where it landed

Everything builds on the same transformer attention mechanism. From BPE tokenization to multi-head attention to the full GPT family. The ecosystem on top (RAG, LangChain, agents) is plumbing to make it useful. Still want to go deeper on RoPE, SwiGLU, the chain of thought paper, and diffusion models.

  • deep learning
  • transformers
  • LLMs
103 notes, 32 sections, 1 hr / day
03

Search Images with Words

From neuroscience to CLIP to building a working on-device search prototype. How do VLMs bind words to pixels?

4 days Apr 4, 2026

What I asked, day by day

  1. 01 CLIP: the paper that married text and images
  2. 02 CLIP's contrastive loss: how two encoders learn one space
  3. 03 Building CLIP from scratch: reading the paper properly
  4. 04 Zero-shot classification: from embeddings to probabilities

Where it landed

Went from the CLIP paper to a working implementation from scratch. Two encoders, one shared embedding space, contrastive loss. The key insight is that aligning text and image representations lets you do zero-shot classification and text-to-image search without task-specific training.

  • computer vision
  • machine learning
  • multimodal
57 notes, 13 sections, 1 hr / day, weekdays
04

Understanding Emotion

What are emotions, really? The neuroscience of feeling, and why an autistic brain might process them differently.

7 days Apr 9, 2026

What I asked, day by day

  1. 01 What even is an emotion?
  2. 02 Are guilt and love just layered explanations?
  3. 03 Why did evolution bother building emotions?
  4. 04 Guilt, cheating, and why monogamy won
  5. 05 What is happiness and where does emotional pressure go?
  6. 06 Beyond language: the other tools that shape how we feel
  7. 07 How emotions control what you think, who you trust, and what you remember

Where it landed

Nobody agrees on what emotions are, but evolution built them for a reason. Basic emotions are universal biology. Higher cognitive emotions like guilt are trust signals. Happiness comes from relationships, not money. Emotions actively distort memory, attention, and judgement. Empathy is not mirroring, it is feeling what others feel. The people we need are those who can manage it.

  • neuroscience
  • psychology
  • autism
221 notes, 38 sections, 1 hr / day