I Gave GitHub Copilot Access to My Private Notes 🤯📓

“What if your AI pair programmer knew everything you’ve ever learned?”

In school, we’re told to take notes.
In dev life, we forget they exist after saving notes.md.

So I asked myself:
What happens if I feed my entire dev knowledge — journal entries, notes, tips, terminal commands — into GitHub Copilot or an LLM?

Answer:
I built a personal knowledge-trained Copilot clone, and suddenly I was pair programming with… my past self.

This is how I turned years of scattered markdowns into the most useful coding companion I’ve ever had.

💡 The Problem: Your Knowledge Is Trapped

Let me guess:

• You’ve saved cheatsheet.md, til.txt, and docker-fix-notes.md
• You wrote blog drafts, copied StackOverflow tricks, and kept AI prompts
• But when it’s time to use them, you can’t find or recall anything

What if you could just type:

“What’s the trick I used for SSH tunneling inside Docker Compose?”

And get an instant answer from your own writing, inside VS Code?

🧠 The Idea: Build a Self-Trained Coding Copilot

Instead of asking an AI “how to do X,” I wanted an AI that answered:

“How did I do X in the past?”

So I built a local LLM that:

1. Reads all my .md, .txt, .ipynb, and .bash_history
2. Indexes them into a searchable database
3. Responds to natural language queries like:

• “How did I deploy that huggingface model?”
• “What did I learn about PostgreSQL full text search?”
• “What alias did I use for kubectl?”

And it runs entirely offline using small open-source models like Phi-3, TinyLlama, and Instructor-XL.

🛠️ How I Built It

🧾 Sample script to index notes:

from langchain.document_loaders import TextLoader
from langchain.vectorstores import Chroma
from instructor_embedding import INSTRUCTOR

docs = TextLoader("/my/notes/").load()
embeddings = INSTRUCTOR()
db = Chroma.from_documents(docs, embedding=embeddings)

🗣️ Sample query CLI

mycopilot "What’s the SSH trick for Django + Postgres in Docker?"

Response:

“In til-docker.md, you used ssh -L 5433:db:5432 your-ec2 and added localhost:5433 in settings.”

🤖 Why This Works: It’s Like Dev Journaling on Steroids

This isn’t just automation. It’s augmentation.

It solves:

• 👻 Forgetting how you solved a bug last year
• 🔁 Rewriting the same bash script over and over
• 💬 Re-answering questions your teammates already asked

And it teaches you something schools never do:
How to talk to your own mind.

📚 Bonus: I Hooked It Up to My GitHub Repos Too

What if your AI could also answer:

“What was I thinking when I wrote this function?”

So I did this:

• Pulled my old GitHub repo commit messages
• Parsed README.md and code comments
• Linked commits to note entries using timestamps

Now it answers stuff like:

“What’s the difference between tokenizer.py v1 and v2?”

“v2 introduces HuggingFace fast tokenizer; v1 had regex hacks, see ‘notes/tokenizer-rewrite.md’”

It’s like GPT met Obsidian + Git + me.

✨ Unexpected Magic

• 🧠 Improved recall: I remembered why I did something, not just how
• 🧱 Modular reuse: My old notes turned into reusable snippets
• 💬 Debugging partner: I’d paste an error and it’d say “You solved this in bugfix-log-2023.md”

And guess what?

I started writing better notes, because I knew they’d actually be used.

🧰 You Can Build Yours Too (for Free)

Requirements:

• A folder of notes, commits, or codebases
• A free HuggingFace model like Instructor-XL
• ChromaDB + LangChain
• Ollama + Phi-3 or mistral-7b (optional)

If you want:
✅ Full walkthrough
✅ GitHub template repo
✅ VS Code extension
Let me know and I’ll publish it all!

🧘 Final Thought: This Is the Future of Learning

AI won’t just write code for us.
It will teach us from our own minds.

Don’t build a second brain.
Build a smart brain that talks back.