Kompjuter biblioteka

09/05/2026

28/03/2026

Sistem šest majstora veštačke inteligencije — odgovornost je uvek naša

ChatGPT, Claude, Gemini, Perplexity, Grok i NotebookLM

Otvorite nekoliko alata veštačke inteligencije. Postavite isto pitanje svakom od njih. Dobićete više ubedljivih, logičnih, ali često međusobno suprotstavljenih odgovora. Svaki će zvučati sigurno. Nijedan neće snositi posledice ako pogreši.

Ko greši, taj odlučuje nije knjiga o tome koji je alat veštačke inteligencije najbolji. Ovo je knjiga o ljudskom rasuđivanju u vremenu kada su odgovori brzi, dostupni i zavodljivo ubedljivi.

U njenom središtu nalazi se jednostavna, ali presudna istina:
veštačka inteligencija može da ubrza mišljenje, ali ne sme da preuzme ljudsku odgovornost.

Mašina ne odgovara za posledice. Čovek odgovara. Zato čovek mora da odlučuje.

https://knjige.kombib.rs/ko-gresi-taj-odlucuje

31/01/2026

Question:
How many of you have launched AI agents (Antigravity, AutoGPT...) without first checking if you're doing the right thing?
I made this mistake 40+ times until I discovered the Pre-Agent Protocol.
The essence: NotebookLM BEFORE Antigravity.
Not speed → but direction → then speed.
I wrote a detailed article with concrete workflows.
If interested, I can share the link.

Google Antigravity creates AI agents that work faster than ever. But there’s a problem.

28/01/2026

Most business advice is designed for people who don't need it.

I proved it using NotebookLM on 23 texts.

The results were uncomfortable.

If you want to stop wasting time on generic advice that doesn't work for you — I documented the exact inverse reading strategy on Medium.

Link in first comment.

Tag someone who needs to stop blindly trusting "experts" 😅

https://medium.com//notebooklm-test-authorities-11bc40140776

TL;DR Authoritative texts seem convincing because they don’t have to explain their own assumptions. By using NotebookLM tools — Sources…

26/01/2026

How I used Medium to eliminate risk before making my first dollar

Everyone craves the fast track to passive income.

This story is not about shortcuts. It’s about a journey where every step shapes a stronger, more certain path forward.

This is a road where products are built on evidence, and risk is structurally eliminated.

How I used Medium to eliminate risk before making my first dollar

26/01/2026

Program Update: Week 2 is Unlocked.

We just released the complete "Source Architecture" module for all "NotebookLM for Beginners" students.

If Week 1 was about fixing your mindset, Week 2 is about fixing your files.

We cover the things most tutorials skip: ✅ Why a Google Doc is "clay" but a PDF is "stone." ✅ How to use the "Naming Strategy" to get laser-focused answers. ✅ How to turn a chat into a permanent study guide using the Note System.

Check your Gumroad dashboard/library. The new folder is there.

If you haven't joined yet, but you're ready to move from "Beginner" to "Knowledge Architect," check the link in the comments.

Let's build.

🧠 NotebookLM 101: Stop Searching, Start ThinkingDaily 5-minute lessons that turn Google's AI into a disciplined thinking system.Most people try NotebookLM once and quit. They upload a document, ask a question, get a generic answer, and think "ChatGPT is better."They're wrong — but not stupid.TH...

25/01/2026

NotebookLM for Beginners: Why Your First Steps Matter Most

https://medium.com//notebooklm-for-beginners-why-your-first-steps-matter-most-a1cc8e696b3a

To date, I’ve published over 200 articles about NotebookLM.

The main issue is that most NotebookLM tutorials miss the most important lesson: your first steps define your success.

After several months of writing and watching the same patterns repeat in comments, I realized this isn’t a tutorial problem. It’s a mental model problem.

Or: What 200+ articles taught me about how people start wrong — and how to start right

24/01/2026

NotebookLM as a Dictionary: From Words to Cognitive Maps

https://medium.com//notebooklm-dictionary-cognitive-maps-ee6229f04abd

Your brain doesn’t forget because it’s full. It forgets because it lacks shelves. Linear reading is like throwing books into a pile in the middle of a room. Creating a dictionary in NotebookLM is building the shelves before you bring in the books.

After reading 50 pages on Artificial Intelligence, most people report learning something about algorithms or neural networks, but often struggle to articulate specifics.

Your brain remembers terms but often misses their hierarchy and the patterns connecting them. A glossary here isn’t just a vocabulary list — it’s a cognitive map that reveals the text’s underlying architecture, which ordinary reading obscures.

Before we dive into the process, let’s clarify what tools will anchor our method. This will set the stage for deeper analysis.

Your brain doesn’t forget because it’s full. It forgets because it lacks shelves. Linear reading is like throwing books into a pile in the…

23/01/2026

NotebookLM Analyzed 5,000 Unread Emails

https://medium.com//notebooklm-5000-unread-emails-analysis-3b09cecc1643

Inbox isn’t a to-do list. It’s a museum of postponed decisions.

The Silent Explosion
My inbox isn’t on fire.
It’s decaying.

Every morning it fills quietly. No alerts or panic, just a dull pressure from thousands of unread emails. For years, I blamed spam, filters, or information overload.

I was wrong.

So I stopped analyzing what I read — and started analyzing what I refused to open.

Inbox isn’t a to-do list. It’s a museum of postponed decisions.

23/01/2026

Danas smo imali kupca koji je kupio knjigu iz 2007. godine. Kaže da je tražio, tražio i konačno našao.

22/01/2026

When to Use ChatGPT vs. Claude vs. Gemini vs. NotebookLM: The AI Model Decision Matrix

https://medium.com//ai-model-decision-matrix-d4b0c922c68c

We ask the wrong question. “Which AI is best?” assumes a single winner. The right question: “Which AI for this specific task?”

You’re staring at four browser tabs: ChatGPT, Claude, Gemini, NotebookLM. Same prompt copied into each. Four different answers. One confident. One cautious. One connected to your Google ecosystem. One refuses to answer without your data.

Which one is “right”?

None of them. Because you’re asking a single tool to solve multiple problems.

We ask the wrong question. “Which AI is best?” assumes a single winner. The right question: “Which AI for this specific task?”