Two Years of a Knowledge Repo: What I Actually Learned

A data-driven retrospective on ~600 notebooks across 18 repos since Sep 2024 - how the work shifted from foundations to LLMs to real APIs to hardware.
Author

Benedict Thekkel

Published

July 6, 2026

I started the Knowledge mono-repo on 1 September 2024. Almost two years and ~600 notebooks later, the git history tells a clear story about how my focus moved. This is that story, from the data.

The arc, in one table

Period Theme What landed
2024 Q3-Q4 Foundation Django/backend, front-end, Linux/dev tooling, Docker
2025 H1 Depth First LLMs, math/physics notes, AWS, stock/finance
2025 H2 Real-world Client APIs (Cliniko/Nookal/Snapforms), AWS deep dive
2026 AI systems + hardware RAG course, LLM pipelines/agents, IaC, motors/ESCs

Notebooks added per quarter

The 2024-Q4 spike is the initial build-out - most of the reference library was seeded at once. After that, steady growth with a clear AI-and-hardware resurgence in 2026.

Where the work went (commits by repo)

Finances, Other, WEB_doc, and Back_End dominate - the repos tied to real projects and daily use, not just reference.

Topic evolution

flowchart LR
  F[Foundations 2024] --> L[LLMs 2025]
  L --> A[Real APIs 2025-H2]
  A --> R[RAG + agents 2026]
  R --> H[Hardware / embedded 2026]

Four things I actually learned

  1. The biggest AI value came late and fast. The RAG course and LLM pipelines (2026) are the single largest coherent build-out - see Building a production RAG system.
  2. Reference notes pay off when they meet a real project. The repos that grew most (Other, WEB_doc, Finances) are the ones wired to actual work.
  3. Tooling maturity compounds. Moving to uv/Ruff/Polars made every later notebook cheaper to write.
  4. Breadth then depth. 2024 was breadth (seed everything); 2026 is depth (go deep on RAG, on motors, on IaC).

The site that holds it all together is itself a project - how the federated docs site works.

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