Kortex AI

I turn elite AI into copy/paste blueprints. Do a full team's work in minutes, not days - verified, zero fluff. New drop weekly.

[Kortex AI @Kortex_AI_Biz]

One academic study. Three AI tools. Forty document queries. Same source set.

NotebookLM hallucinated in 13% of responses.
ChatGPT: 40%.
Gemini: 40%.

Same documents. Same questions. Three times the error rate from the two most used AI tools in business.

The difference is not the prompt. It is not the model size. It is not even which company built it. It is the architecture.

ChatGPT and Gemini answer from training data. They have seen hundreds of millions of documents and draw on all of them when answering your question, including documents that contradict your sources, are outdated, or simply do not apply. Confidence does not equal accuracy.

NotebookLM answers only from what you upload. Ask it something your sources do not cover and it says so. It does not guess. Every claim traces to a specific line in a specific document you can verify.

For client-facing work, the gap looks like this:

Consultant A uses ChatGPT to analyze a prospect before a call. Gets 8 bullet points. Flags 2 to the client as key insights. Client challenges one in the meeting. It is wrong. Not wrong by a lot. Just wrong enough.

Consultant B uses the Pre-Call Sales Intelligence workflow. Loads the prospect site, 3 case studies, 2 competitor pricing pages. One prompt. Gets a sourced brief where every talking point cites the exact sentence it came from. Nothing in that document can be challenged without challenging the source.

Same deadline. Same tools budget. Different architecture.

That same session generates a second output: Audio Overview in Critique mode. Two AI hosts argue the weak points in the pitch before the client does. Running while the consultant drives to the meeting.

Two billable-quality outputs. Fourteen minutes. One notebook.

I built a complete system around this and five other workflows. 28 production-ready prompts. Every claim in the guide verified against independent sources.

Like this post and I will send you the full system.

[Kortex AI @Kortex_AI_Biz]

2 minutes to generate. 2 hours saved. Nothing to do with the video - everything to do with what's underneath it.

Everyone's using this to make explainers. I used it to compress research I'd already finished. The clip took 2 minutes. The thing worth compressing took the actual time.

What's sitting in your notes right now that's more finished than you think?

[Kortex AI @Kortex_AI_Biz]

@JulianGoldieSEO Curious how the video pass handles conflicting sources, does it pick one silently or surface the conflict?
That's the failure mode that actually matters once you're feeding it real research instead of one clean PDF.

[Kortex AI @Kortex_AI_Biz]

Same pattern with Claude Max here - the sub pays for itself the moment you stop treating it as a chat window and start pointing it at recurring tasks instead of oneoff questions. Mine runs source curation across a dozen live projects in parallel. Quota's never been the constraint. Scope of what I bother automating is.

[Kortex AI @Kortex_AI_Biz]

@theLagosguyy @perkmaybe +1

[Kortex AI @Kortex_AI_Biz]

@shedntcare_ 75% blind preference is a good signal, but for methodology diagrams the failure mode isn't visual polish - it's the agent inferring structure from an ambiguous methods section. Worth stress-testing on a messy real paper before trusting it for submission, not just clean examples.

[Kortex AI @Kortex_AI_Biz]

Tried a version of this in March. Two things nobody mentions:
a. The 'no subscribers needed' streams pay pennies until you already have an audience elsewhere
b. 8 prompts get you a script. They don't get you the 40th video when motivation's gone

That second one is the actual filter.

[Kortex AI @Kortex_AI_Biz]

@the_codewala Step 1 - close Claude, open a niche. The MacBook and Max sub don't make money,they just remove your excuse not to ship something today.

[Kortex AI @Kortex_AI_Biz]

@CNETNews Most alts to NotebookLM lists compare features. Wrong axis. The real competitor to NotebookLM isn't another tool, it's your own habit of skimming 40 tabs and calling it research.

[Kortex AI @Kortex_AI_Biz]

@TawohAwa 5 prompts turns it into a professor. The real jump is treating it like a research partner instead - feed it the messy 40 sources first, let it map the contradictions, then ask your questions.
Professors answer what you ask. Partners tell you what you missed.

[Kortex AI @Kortex_AI_Biz]

@X_FINALBOSS X

[Kortex AI @Kortex_AI_Biz]

The core move is dumping 40 - 50 sources into one NotebookLM notebook, let it map connections across all of them, then hand Claude the synthesis (not the raw docs). It reasons way better starting from a brief than from scattered material.

Full prompt sequence is a bit much for a reply. DM me if needed and I'll send it over.

[Kortex AI @Kortex_AI_Biz]

I spent three weeks thinking NotebookLM just wasn't that good for client work.

Then I realized I was loading 40 sources into every notebook and asking broad questions, expecting the AI to figure out what mattered.

It can't. That is not how it's built.

Here is what changed when I stopped doing that.

Same notebook. Same 40 sources. Same question about Q3 positioning against two competitors.

She deselected everything. Reselected only the 3 sources that were actually about pricing objections from the last two months.

The output went from a 400-word summary nobody could use in a client meeting to three specific, sourced talking points with the exact language competitors are using against her right now.

Same AI. Same notebook. Different question to the model about which 3 sources mattered.

This is the part almost nobody talks about. Everyone optimizes the prompt. Almost nobody optimizes what the model is allowed to see when that prompt runs.

What's the one NotebookLM workflow you haven't tried yet?

Comment SYSTEM below and I will send you the full system.

[Kortex AI @Kortex_AI_Biz]

@developertaco 5 prompts is a good start, but the real unlock is what goes in before the prompts -source curation. Feed it 200 messy pages and even good prompts return noise. Curate first, prompt second.

[Kortex AI @Kortex_AI_Biz]

The gap isn't workflow vs quick questions - it's what you feed it before the workflow starts. Most people design a great prompt chain and still get generic output because the input layer (i.e. raw PDFs, transcripts, scattered notes) never got synthesized first. Claude thinks better when it starts from a brief, not from raw material.

[Kortex AI @Kortex_AI_Biz]

@uzorprospa @KXLAADE What actually scales is verified source material + a system you can update, not just resell.

[Kortex AI @Kortex_AI_Biz]

Bang on. And the gap isn't even about raw capability anymore, it's about workflow literacy. I watched this play out with NotebookLM, where most people use it to summarize a PDF. The 1% who actually compound results use it to synthesize over 40 sources into one brief, then hand that to Claude for the actual thinking.
Same tool but wildly different output - just because one group tinkered long enough to find the second layer.

[Kortex AI @Kortex_AI_Biz]

@nikitabier Year one milestone unlocked, you've officially achieved main character status in three different metrics simultaneously. Most of us take a decade to get cortisol and engagement trending the same direction.

[Kortex AI @Kortex_AI_Biz]

Same principle applies outside of code. I don't dump raw PDFs or transcripts into Claude either, NotebookLM does the ingestion pass first (I run 40 - 50 sources through it before Claude sees anything), then Claude gets one dense brief instead of twenty fragments.
Same logic as your CLAUDE.md routing table - the expensive model should spend tokens deciding, not reading.

[Kortex AI @Kortex_AI_Biz]

Most people stop at 'ingest then ask questions', and that's maybe 30% of the value. Real unlock is to treat NotebookLM's output as raw material for a second pass in Claude, not the final answer.

That's where contradictions between sources show up that NotebookLM's summary flattens out. I documented the full handoff as a system (happy to share if useful).