Markdown source

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

Relationship To World's Fair 2026

These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

Related Scheduled Sessions

Extracted Slides

slide-001.jpg

OCR text:

My Second Brain -

5,489 notes in Obsidian aaa Fr 1

rh \

5,505 files in Readwise ie — |

Plus, Notion, Google Drive. ona

=

Growing ~250 files per month = a

.

slide-002.jpg

OCR text:

ca

en ae eee)

a Or “

Pa Oe ae .

ea a

‘te * as °

es co

5 5 °

a: - oe 5 a y =

Sy

. ay . 5 7 .

. a 7 :

: oY

. 5 » . . °

a p * . ,

e a a ee s . ° ° °

. i

. j Oo

5 : a ee . ao . A

F . - « .

, ar -

: o » Co ” Oy . A .

, an A ao

5 A ns

te - 4

a . E acer 7 . bs . co

Per a oO og ,

.

. .

E : a

a 7, . .

Ps 7 o cd

ao ee s : . x

cry an S fee

4 ' ae

slide-003.jpg

OCR text:

a UGS

Sha” : FA. \

: a an aa SENAY

: a)

yin

mY

; a,

slide-004.jpg

OCR text:

My personal notes!

slide-005.jpg

OCR text:

Hi, I'm Paul lea <_

Sie LLM Engineer's

co coal 25 a ne?

oo eS Handbook

th a wees Founder & CEO @ Decoding Al t

a Content and courses on shipping Al products :

i a cates

/ Co-Author of the ” on

LLM Engineer’s Handbook Bestseller

Pont taetin | Mewtme Letenne <packt>

¥ a. i

slide-006.jpg

OCR text:

\

49 >

he

le

Vs

re,

slide-007.jpg

OCR text:

; 7 ao

: =v = Hi, I'm Louis-Francois

oe = | — Co-founder & CTO @ Towards Al

PF

n — “What's Al’ on YouTube - author of Building LLMs for Production

Va . a — Previously PhD @ Mila

— Build courses, videos, and trainings for a living

TOWARDS A

o Which all starts from good research!

slide-008.jpg

OCR text:

TOWARDS AI

slide-009.jpg

OCR text:

(problems) Every research session starts from zero

Right now: give Codex 5 links, 3 repos, PDFs, two videos, my notes — it answers

WH PaO

w7

ie

slide-010.jpg

OCR text:

TOWARDS AI

slide-011.jpg

OCR text:

4° agree My OS didn't start from zero

. j co a bm

: ‘~ 4

i : ye — Years of notes — every video and course I've made, all moved into Obsidian

- ; | — Meeting recaps | save after every call -> Obsidian

i,

al — Highlights from posts, tweets, and articles -> Obsidian

1" eka ; — Even my agent skills -> Obsidian

TOWARDS NA

slide-012.jpg

OCR text:

One long-term research companion

for everything | teach.

and it compounds — every note, meeting, source. and question feeds it Ww BK

ww

a " &

slide-013.jpg

OCR text:

v1: a topic in, research.md out

Topic

+ golden links

slide-014.jpg

OCR text:

GHA

ee Ba! iZ

Se oe | tw

in by A eee NN

rat cag . a NN ho

cial

lt £

‘ ‘ : in

: ; F

slide-015.jpg

OCR text:

e : _ Forget the infrastructure you think

_ eae you need

as

——~ . aw No vector database. No knowledge graph.

\\" No semantic search. No text search.

slide-016.jpg

OCR text:

No database,justtheindex

total_wik.pages:38 total_sources:10 SOUTCES:

Agent readsfirst catalog+summaries index.yaml uri.source_page:wiki/sources/agent-mory.d publfshed_date:2026-85-17 publfcation:null uri_ful1:iki/repos/agent-msory/AROHETECTuRE.nd original_path:github://eeo4j-1abs/agent-nemory89ae7Bc7994 origin:github title:agent-menory authors: -neo4j-labs

applied to agent memory.cite mermaid flowchart LRsubgr

slide-017.jpg

OCR text:

No database, just the index

Lg

ig -\.

rN

i al a By “ Ni @ roads first index.yaml aa manees

. im a b yy catalog + summaries ee

H ayer 4z] wiki derivatives

the index is the retrieval layer N

=| raw source

~awW ga

no vector OB

slide-018.jpg

OCR text:

How does the wiki look like?

research-coding-agent-architectures

raw

wiki

comparisons

concepts

entities

notes

repos

sources

open-questions

overview

synthesis

index

log

ARCHITECTURE

slide-019.jpg

OCR text:

One immutable PARA snapshot

‘ 6K i AS. Projects

‘ / a Si Obsidian snapshot

i “ ra oe Areas immutable

| ,

|

slide-020.jpg

OCR text:

lusztinpaul al-research-os-worikshop QTypeto search

(>Code Issues IPullrequests G:Agents Actions Projects wiki Security and quality Insights Settings

Pmain ai-research-os-workshop/plugins/ai-research-os/skills/ QGotofile

lusztinpaul feat:Rename to al-research-os

Last commitmessage

nlm-skil feat:Rename to ai-research-os

obsidian-cll feat: Rename to al-research-os

readwise-cli feat:Rename to af-research-os

research-distill feat:Rename toai-research-os

feat:Rename toai-research-os

research-render feat:Rename toai-research-os

research feat:Rename to ai-research-os

slide-021.jpg

OCR text:

« ai-research-os-workshop a eS as

man f 0 Cees Coen becca

Pe ras re rae rl] ery

eee

ora

Do pap aR eke oa

Dra

ee een Tere)

CCS Ran

& abNk

Ter eames

eee

Lael eye hy

Limes Ls

of Contributors 4

cs a a Serr oo)

ere ai-research-os-workshop workshop s

a>

ii) ae ‘ ets

iL we? < booms Desa TCO HOI EV ae Tat Soe a onde ea eee ee Oe CTL oe ES - eunarnat

. 5 Cota ntes ery ue erat a y

NA meer CLt ee Cre test TED

Languages

Fors rp 2 qvesters, you sncu'd If you "ave one repo, one ca or ora Quica Question, open Codex or C-aude Cade

SIE EL Cen Oe Eee

slide-022.jpg

OCR text:

Second-Bain

erompcnd

Profects/Building Your Oen AI Research OS(Empty)/examgle_1_deep_research/proe

19.Coselalon-TFtteefms

1.Z

Osls

uplicetions,eillarthildinghares.

Dals

(1stinpjoo

d fce.ealie t girg tpt te sat t f it,cpled te

giligt rt fi peiae tg ts,p

D

verycatoe ut/oandas lsgit.

3.15w

D1-tamg

esnrtes

2-Bler

D3-Meda

1.yAAton r(://etllh.c/etig/ea-ogte-y)

2.(ffectie hrnfr long-rtng eta(ht://thrpe.c/egir

D4-Joumal

/etfectise-basem-fer-log-rsag-ageta)

S-Soures

3.(lding A Age riLangChrisa e(tt:/yt./tch

D6-Nos

4isagtyMel)

D7-A

Crt t r dectryts ietery is.

8-Poec

DAlEngeersHacbook

DBdngaCodngAgntFrom Seh

DudngYsur OnAL es

rhO5(

promp.md

Dep_2.gb

Deample3ges_inks

DCortent

DHo

D.9-Ac

anv.sh

Nen

AGENTSNd

CUAUDEmS

MP

ectio

wo

slide-023.jpg

OCR text:

Second-Bain Claude Code Cte Code (ly)/e./. 1reehst.Fitetchck th Paejacta/Biling Yor DnAI Rrch 0S /rsti/ocnts/ta/ecn-/ 1 iF h////a/ The scan clarifies tha zouting.Mote the ciectory iehulding Ya 1ies in Dn-(bty)itry),erhdir ist. Thbin dap crree]edes) - thr esch dir edy erists Rscai1ac 1soty (etx+ to pan) AL hos(ty)t(Ety)fix t ffereet lecatienfr thecieting ikieerenced inmeoryhich tis ie n iait fro catch. wthpie.(o/egieerirg/efectie-hesses-fer-]ong-nning-apets 3.Ae le(ngainid gnt b) PrichartieleinAgtie kames Engiezigtht acchitectass). Per til1'p-rhgte,dlacory ispt-1adyou F.2 --ptf 516114:2 tetel16 (pe +[)[+ Prejects/iling YerOnAIrhOs (Ety)/ee_h1-1aech-S Yt Proects/Buslding Your Oen AL eseazch OS (Eepty)/exa 5.Lat'sLook atOpenCiAzchatecte 14.Cocn.he fate ( 2.(cth r logrng ]t://.hc.c/gir /ffectin-arss-fer-long-riog-agesta) 3(idng ove AI Apett ihLonghtn](tt://yt.ce/tch-w Crte t earh diectey ithsn th sm diectry s thia l pronptnd 1.p-leal diagrs ef t architectre capents frs aben wk 3.225s 1t tb trct tp ftZ tng la.tht wplicetis,ll start hildg. sCladCodthepetcpocethy arond fince,ln ecalied cotart egiering te get the cst et it,couped te very caton t/ snd RAG logic 1.150 5-Sr D1-a D2-B D4-Jou D6-Noes D7-A DAp p. 8-Pojects (1sinpjon Dsk ALEro DBdngaCodngAgntF Stch Dsklls DAcAlEng DContent Caude Deme.2.gb Doa premptmd idng Your Oe AL Ra r'aHandbock chos(E

dtalled tlin.nda afh acher(lnt)Sn th(lty) 3 (2mb1Yvi) DMo

eiace Beteb 400

LgM indt. (1ight pet) [3.2} 1. hes edisesdin -10-20ain CUAUCEmd

slide-024.jpg

OCR text:

Newtab

Newtab

1-Templates

2-Buffer

3-Media

4-Jounal

5-Sources

6-Notes

7-Areas

8-Projects

Agentic Al Engineering Course

X

Buildinga CodingAgentFrom Scratch

Building Your OwnAl Research OS

example_1_deep_research

Create newnote (N)

research-agentic-harness

Gotofile(O)

raw

Close

wid

index

log

prompt

example_3_ingest_links

Building Your Own Al Research OS (Emp-.

Content

Moving

9-Archive

AGENTS

Second-Brain

Slide-Derived Subjects To Review

Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.