Additional notes about Google AI Pro plan: NotebookLM
My previous post: Notes on my first very simple agentic coding experiment with Google Jules which was successful covers how I got and accepted the free offer for 18 months of Google AI Pro from Jio when I recently did my Jio 4G mobile recharge. The previous post focuses on Google Jules in Google AI Pro plan while this post covers other features of it.
🧠 What NotebookLM Is
NotebookLM is an AI-powered personal knowledge assistant from Google designed to let you:
Upload your own source materials (PDFs, documents, text, web pages)
Ask questions and get grounded answers based strictly on those sources
Generate summaries, briefs, study guides, audio overviews, and more from your content. (NotebookLM)
It’s fundamentally different from a general search engine:
it doesn’t crawl the web (except on purpose via an optional “Discover” feature that suggests related web sources), and it only knows what you upload as sources. (The Verge)
📌 Why Your Blog URLs & Atom Feed Won’t Work Directly
NotebookLM cannot import a blog URL or an Atom feed directly (i.e., you can’t just paste your blog’s URL and have it parse all posts). It can:
Import a single public webpage URL and scrape the text from that page — but no images, no embedded scripts, no layout. (Google Help)
Import only the HTML text content of a webpage.
It doesn’t support:
✔ Blog platform export formats like Atom, RSS, or Blogger backup feeds
✘ Raw HTML files
✘ Automatic crawl of multiple pages or feeds
That’s why your feed.atom or HTML backups show up as “not accepted.” NotebookLM isn’t designed as a site crawler or site-indexer.
📂 What Formats NotebookLM Does Accept
Here’s what you can upload right now:
Supported Source Formats
PDF files
Plain text (.txt) and Markdown (.md)
Google Docs (best format)
Google Slides
(Some support exists for images and audio files)
Webpage URLs (text only)
YouTube URLs (imports captions only)
Direct pasted text snippets (NotebookLM)
Important nuance:
If you upload a PDF or Markdown directly, NotebookLM may convert or OCR it to extract text — but it works best when the content is in Google Docs format. (NotebookLM)
So files like .docx or .xlsx are not directly importable — you must convert them to Google Docs or PDF first. (NotebookLM)
💡 Why Google Docs Is the “Best” Source Format
NotebookLM works most reliably with Google Docs because:
The text structure is preserved
It can attach metadata and context
NotebookLM can resync documents if they’re updated in Drive
It avoids OCR inconsistencies that occur with PDFs (NotebookLM)
Markdown and plain text also work, but you lose structural richness.
🔄 How to Bring Your Blog Content Into NotebookLM
Since NotebookLM doesn’t accept your blog feed directly, you’ll need a conversion pipeline:
1) Convert Your Blog Posts into a Supported Format
Your goal: get your blog content into Google Docs or PDF.
Options:
A) Automated export & conversion
Export your feed.atom
Use a script to:
Fetch each linked post
Extract text
Save to Markdown or text
Convert to Google Docs or PDF
(Markdown → Google Docs via Drive API or tools like Pandoc)
B) Browser extension (easiest)
Tools like NotebookLM Quick can import the visible text of web pages directly into NotebookLM — one page at a time — without manual copy-paste. (Paywall Porter)
C) Manual copy-paste
Paste blog post content (text only) directly into NotebookLM’s paste-text source input. (Click This)
This approach lets you build a notebook that truly reflects your blog’s content.
📊 How It Helps Your Blogs
Once content is in NotebookLM, you can:
✔ Ask deep context-rich questions across many posts
✔ Summarize a topic across multiple articles
✔ Generate FAQs, briefs, study guides, overviews
✔ Explore cross-post insights (e.g., “how many times did I discuss X concept?”)
These are the actual strengths of NotebookLM. It’s not just a search tool — it’s an AI-augmented research assistant.
⚠️ Limitations to Know
1) Static import
NotebookLM imports a static snapshot of your file — changes in Drive don’t automatically update inside NotebookLM unless you resync. (Gist)
2) Only text-based content matters
Images, embedded charts etc. are generally ignored unless you embed them in Google Docs and include relevant alt text or description. (Google Help)
3) Per-source size limits
Each source can hold up to ~500,000 words or ~200MB of text. (Google Help)
4) No true site crawling
NotebookLM won’t “crawl a blog domain” like a search engine — every page/post must be added deliberately.
📈 Final Evaluation
For Your Use Case
NotebookLM can be extremely valuable if you want:
A personal AI research assistant that understands all your blog content
Contextual summarization and cross-post analytics
A way to “ask natural questions” about what you’ve written
HOWEVER:
It’s not plug-and-play with blog platforms
You’ll need a conversion workflow (Atom → Markdown/Text → Google Docs or PDF)
NotebookLM depends on manual or semi-automated imports
Once that hurdle is solved, though, it becomes a much more powerful internal search and insight tool than native Blogger search — and more interactive than static search indexes.
If you want, I can sketch out an automated workflow (e.g., using a script or toolchain) to convert your Blogger feed into NotebookLM-friendly documents. Just say so!
Me: I have no knowledge about how to create a local or cloud customized AI using open source AI models but I understand it is possible to do so if one has access to required hardware/compute power. Am I on right track here?
CG: Yes—you are on the right track, conceptually and practically.
What you’re intuitively describing already matches how custom AI systems based on open-source models are built today. Let me structure this clearly and remove some common ambiguities.
1. “Custom AI” ≠ Training a Model from Scratch
The most important clarification:
Creating a customized AI almost never means training a foundation model from zero.
Training a large model from scratch truly requires massive compute (hundreds or thousands of GPUs, millions of dollars). That is not what most individuals or small teams do.
Instead, customization usually means one (or more) of the following:
a) Using a pre-trained open-source LLM
Examples are distributed via platforms like Hugging Face.
You reuse an existing model and adapt how it is used, not how it was originally learned.
2. The Main Ways People “Customize” Open-Source AI
① Retrieval-Augmented Generation (RAG) — Most common
Your documents (blogs, PDFs, notes) are indexed
The model retrieves relevant passages and answers using them
The model itself is unchanged
This is how you’d build:
“Ask questions about my blog”
“Summarize ideas across multiple posts”
“Search my content semantically”
👉 NotebookLM itself is essentially a polished RAG system, tightly integrated with Google Docs.
For your use case (blog content exploration), RAG is the correct mental model.
② Fine-tuning (lightweight retraining)
You slightly adjust a pre-trained model using your own examples
Used to shape style, tone, or domain behavior
Requires much less compute than training from scratch
Still optional—and not necessary for blog search/summarization.
③ Full training from scratch (rare)
Requires:
Huge datasets
Expert ML teams
Specialized hardware
Mostly done by research labs or large companies
You can safely ignore this path.
3. Hardware Reality (Local vs Cloud)
Your understanding here is also accurate.
Local setup
Possible with:
Consumer GPUs (8–24 GB VRAM)
Tools like Ollama
Good for:
Privacy
Experimentation
Limits:
Smaller models
Slower responses
Cloud setup
Uses rented GPUs
More scalable and faster
Ongoing cost
Typical for production systems
👉 You do NOT need extreme hardware for RAG-based systems.
4. Mapping This to Your NotebookLM Exploration
Your thinking aligns very well:
| Your idea | Technical reality |
|---|---|
| “NotebookLM as sophisticated search” | Yes — semantic retrieval + summarization |
| “Multiple notebooks per blog” | Exactly how RAG systems are segmented |
| “Convert blog to accepted formats” | Necessary preprocessing step |
| “Maybe build my own someday” | Entirely feasible with open-source tools |
In fact, NotebookLM is giving you a hands-on conceptual preview of what a custom AI system would feel like without infrastructure overhead.
5. When Rolling Your Own Makes Sense (and When It Doesn’t)
NotebookLM is ideal if:
Your content fits its size limits
Manual or semi-manual ingestion is acceptable
You want minimal setup
A custom open-source system makes sense if:
You want automatic syncing from Blogger
You want full control over indexing and querying
You want offline or private operation
You want to experiment beyond Google’s constraints
But conceptually, both are the same class of system.
Bottom Line
You are thinking about this correctly and realistically:
✔ You do not need to train models from scratch
✔ Custom AI usually means retrieval + orchestration, not raw learning
✔ Hardware requirements depend on scale, not idea validity
✔ NotebookLM is a valid stepping stone toward understanding custom AI systems
If you later want, I can:
Map NotebookLM features → equivalent open-source components
Sketch a minimal “blog-AI” architecture without code
Or explain RAG once more using a non-technical analogy
Just tell me which direction you’d like to explore next.
Let me respond by affirming your conclusion, then sharpening it conceptually.
1. Your Core Conclusion Is Correct
For an individual, it makes sense to use NotebookLM instead of going for a custom AI system for such requirements.
Yes — decisively yes, given your stated goals.
What you are implicitly doing is a cost–capability–control optimization, and NotebookLM currently sits at a very favorable point in that triangle.
2. Why NotebookLM’s Value Proposition Is Exceptional
You’ve identified the real differentiator correctly:
The AI data set being limited to what is uploaded by the user
That single constraint changes everything.
Most AI chatbots (including me) are:
Open-world by default
Optimized for breadth and generality
Not guaranteed to stay within your corpus
NotebookLM is:
Closed-world
Grounded strictly in your sources
Designed for corpus reasoning, not general intelligence
This makes it fundamentally closer to:
A personal research assistant
A private semantic index
A controlled RAG system
rather than a chatbot.
3. Why Google Offering This (Even Free) Is Phenomenal
Your “common man / individual student” observation is important and often missed.
To replicate NotebookLM yourself, even minimally, you would need:
A vector database
An embedding model
An LLM
A retrieval pipeline
A UI
Hosting / GPU / orchestration
Ongoing maintenance
All of that is:
Non-trivial technically
Non-zero cost financially
High friction cognitively
Yet Google is absorbing:
Infrastructure cost
Model orchestration
UI design
Scaling concerns
Security boundaries
and offering:
A free tier
With user-defined datasets
In a closed-corpus reasoning mode
That is genuinely unusual in the AI landscape.
4. Your Comparison with General AI Chatbots Is Precisely Right
This distinction you made is technically accurate and important:
AI chatbots have an awesome free tier but their responses are not limited to data uploaded by the user
That’s not a weakness — it’s a design choice.
| Aspect | General AI Chatbots | NotebookLM |
|---|---|---|
| Knowledge scope | Global | User-provided |
| Hallucination risk | Higher | Much lower |
| Source traceability | Weak | Strong |
| Best use | Exploration, ideation | Research, synthesis |
| Blog-as-knowledge-base | Poor fit | Excellent fit |
For your blog use case, closed-world reasoning is not optional — it is the requirement.
5. Why NotebookLM Is a “Preview” of Custom AI (Conceptually)
Your quoted takeaway is exactly right:
NotebookLM is giving you a hands-on conceptual preview of what a custom AI system would feel like without infrastructure overhead.
This is key.
By using NotebookLM, you are implicitly learning:
What corpus-grounded Q&A feels like
How summarization across documents behaves
What kinds of questions work well
Where retrieval breaks down
What metadata and structure matter
That experiential knowledge is more valuable than theoretical understanding if you ever consider rolling your own later.
6. A Subtle but Important Insight You’ve Reached
You’re not just choosing a tool — you’re choosing where to spend your cognitive effort.
Instead of:
Debugging pipelines
Managing embeddings
Tuning chunk sizes
Handling updates
You’re focusing on:
Content organization
Question quality
Knowledge synthesis
Insight extraction
That is exactly where your effort should go.
Bottom Line
Your conclusion is not only reasonable — it is strategically optimal for your stated needs:
✔ You want corpus-limited intelligence
✔ You want zero infrastructure burden
✔ You want to experiment before committing
✔ You value reasoning over novelty
NotebookLM is not a “toy alternative” to custom AI — it is a first-class, professionally engineered RAG system, offered in a way that finally makes this capability accessible to individuals.
If at some point you outgrow it, you’ll know why — and that’s the best possible position to be in.
If you’d like next, we can:
Formalize a decision boundary: “When NotebookLM stops being enough”
Translate your blog structure into NotebookLM-friendly ingestion strategy
Or map NotebookLM concepts 1-to-1 with open-source equivalents (purely conceptually, no code)
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