---
title: "Dense Slides: Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI"
category: "slides"
video_id: "ZRM_TfEZcIo"
sourceLabels: ["Captured video frames", "Local OpenCV slide-region detection"]
---

# Dense Slides: Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI

## Source Video
[Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI](https://www.youtube.com/watch?v=ZRM_TfEZcIo)

## Method
This deck is slide-only. The existing captured video frame set supplies candidate frames, then local OpenCV rejects sponsor/title/speaker-only frames, crops visible slide surfaces, deduplicates, and saves the cropped slide images.

## Cropped Visible Slides
![[assets/dense-slides/ZRM_TfEZcIo/slide-001.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/ZRM_TfEZcIo/slide-001.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> My Second Brain
> 5,489 notes in Obsidian
> 5,505 files in Readwise
> Plus, Notion, Google Drive.
> Growing ~250 files per month

![[assets/dense-slides/ZRM_TfEZcIo/slide-004.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/ZRM_TfEZcIo/slide-004.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> My OS didn't start from zero
> Years of notes — every video and course I've made, all moved into Obsidian
> Meeting recaps I save after every call -> Obsidian
> Highlights from posts, tweets, and articles -> Obsidian
> Even my agent skills -> Obsidian

![[assets/dense-slides/ZRM_TfEZcIo/slide-005.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/ZRM_TfEZcIo/slide-005.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: agent_vision.

Slide text:

> Inside v1: fan out, rank, scrape
> Topic
> - golden links
> Scrape links
> seed context
> Main agent
> Query rounds
> Gems grounded in Google
> subagent Q1
> subagent Q2
> subagent Q3
> subagent Q4
> subagent Q5
> subagent Q6
> x3 rounds

![[assets/dense-slides/ZRM_TfEZcIo/slide-006.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/ZRM_TfEZcIo/slide-006.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: agent_vision.

Slide text:

> Forget the infrastructure you think you need
> No vector database. No knowledge graph.
> No semantic search. No text search.

![[assets/dense-slides/ZRM_TfEZcIo/slide-007.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/ZRM_TfEZcIo/slide-007.html)
- AI slide classifier: `content_slide` confidence `0.95`
- Text source: advanced OCR `rapidocr-live/bright-screen/contrast`.
- OCR decision: ready — Small embedded code/screenshot text on the right is dense and better suited for OCR.

Slide text:

> No database, just the index
> cta 0
> 1
> tuIce s
> 1 1
> VT11k1/IeOs/agt·mry/AROHITECTE.d
> reads first index.yaml g1thub://re04).1abs/agn.e0s01y9ae78c79940 1k1/sovIces/agent·meory.o
> catalog + summaries origin:gitnub
> Agent _c0*2026-05-17 D.blicatio- acpliodtoaxeteoeoiy. cite 1MIy n004j.labs 1snA0a(0·2.0Pyton1ib1 meieaid flcachart tR suogi

![[assets/dense-slides/ZRM_TfEZcIo/slide-008.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/ZRM_TfEZcIo/slide-008.html)
- AI slide classifier: `content_slide` confidence `0.96`
- Text source: advanced OCR `rapidocr-live/bright-screen/contrast`.
- OCR decision: ready — The file-tree and graph labels are small and dense, so OCR is likely better than manual transcription.

Slide text:

> How does the wiki look like?
> research-coding-agent-architectures
> raw corteat-compaction ARCHTECTURIARCHITECTURE
> wiki
> comparisons
> concepts tool-rogistry
> entities do01-000 cvervow
> notes sposuado
> repos aqer!-ptrrtittion-low-opencoce-vs- -hermtn-agtns
> sources caude-code
> open-questions subogerts-architecture-cpencode iratruction-fles
> synthesis overview mcp sandboaing ARCHITECTURE
> log index TNTIO EYST quntiors

![[assets/dense-slides/ZRM_TfEZcIo/slide-009.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/ZRM_TfEZcIo/slide-009.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> Now, how do we query this wiki?

![[assets/dense-slides/ZRM_TfEZcIo/slide-010.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/ZRM_TfEZcIo/slide-010.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> Every question leaves a trace

![[assets/dense-slides/ZRM_TfEZcIo/slide-011.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/ZRM_TfEZcIo/slide-011.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> One immutable PARA snapshot

![[assets/dense-slides/ZRM_TfEZcIo/slide-012.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/ZRM_TfEZcIo/slide-012.html)
- AI slide classifier: `title_card` confidence `0.99`
- Text source: agent_vision.

Slide text:

> Want to build systems like this?
> Agent Engineering: Building Multi-Agent Systems
> Towards AI Academy
> - the engineering behind this repo
> - agent workflows, tool use, memory
> - multi-agent systems & orchestration
> - evals & production-grade agents


### Hidden Non-Slide Evidence
- [`slide-002.jpg`](/assets/dense-slides/ZRM_TfEZcIo/slide-002.jpg) — `title_card` confidence `0.97`; speaker intro card
- [`slide-003.jpg`](/assets/dense-slides/ZRM_TfEZcIo/slide-003.jpg) — `title_card` confidence `0.97`; speaker intro card

Classification audit: `raw/sources/slide-ai-classification/dense/ZRM_TfEZcIo/audit.json`