Slides: Jack Morris: Stuffing Context is not Memory, Updating Weights is
Source Video
Jack Morris: Stuffing Context is not Memory, Updating Weights is
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
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.
Extracted Slides
OCR text:
q |
if A
A
\
t
‘
3 €
Vos
1: jr
En 1 oy
Bars teak Sine
: Ree c . 2 me
F a en 4
; bs Ua ~ . wa
ot er : a x
wii Se -a as =:
— a %
my . ~ i)
mead ar - 4 mace? —_ —, ei
mt " ra ,
me) - Cratey =
cm . q gs aM! &
wey 1 3 - hha ee wt
my 4 4 a \ a ‘Mw
at og ’ F , 7: + —~
my 3 pa 5s
= = =~
mry x . —e
o 1 BERR Oh Nee eae Tae
ae Pe a smees ms
ae & See: ee —. Bet Oey ow
fe" Le a Paar oan
BN Bea e “
See pean 4s
ane Ro e ,
a Sod Bet “
, ss F
Fs C ee
Ps .
OCR text:
Vs Late] me (eX-\-0 le el Pree TCT TO) | a Pier
did the Blue Jays win the World Series?
(things that come after its knowledge cutoff) os
help me optimize this kernel | wrote for AMD GPUs m
(domain-specific, long-tail knowledge)
OCR text:
which of the shirts should | ie from amazon? a
implement a new feature for the figma web interface in the -
Figma monorepo. a
Nigh Cour lam-lear- lm Ameah Acie (em
diagnose this patient given their past history.
what arguments did opposing counsel use in the Martinez
settlement negotiations?
is this question already answered from our internal wiki?
OCR text:
Three ways to teach things to LLMs: Las
Fest
Full Context STAG AVore IAL tS
OCR text:
2, xc
Context F
sm Output
. [ops segs | ;
Cc.) Is ae bale) re
P Sameer Tale cd ; —~, we 8 A De
im re coe core ae ee ae
Full Context * RAG ¢ Weights
OCR text:
Context comes at a Cusr aa a
ae
2
With 1k tokens of context per user, we can ann
output 10,000 tokens per second
With 128k tokens of context per user, we can
output 130 tokens per second
“numbers computed for Llama 8B running at peak
throughput (.e. excluding prefill) on a single H100
Full Context ¢ RAG ¢ Weights
OCR text:
MoV Gime, COL seconu
“rymbers computed for Llama 8B running at peak
Papen ere mca cuv Ae 8 Ci) Re Tie (mate)
. wf . ,
av erry (4 tT RAG e Mersre nics Fy 2a, nee oe
Lo. 2 ~ OO ee! ~
_ at sow Be 7 ; _— we om -
. _' — ott oe ; ; —
to ae —= a
=~ ~~. 4 = =
mi we ~ =
=) e r =
=m “ ‘i | iy i: RR
me a : mm _ _ as
— Ta 1 ee. --T
a . = ine mcm | = _
OCR text:
alg-lals)ce)anel-16om al \A- eo CRererrernee a
<
CX
es | 2
: ere a ~ _ ‘ es : 7 z = freer art 7 ae — Lea hanna
. EMM IGOR. j F F FE z
; PTT |
Leer ese
Cte Ear ad
cre
Full Context ¢ RAG ¢ Weights
OCR text:
i co TP TTT i
TP 2 lees
PPP REEOLEL OS. . She
PT RPE UROOS SS lo
PP REEUELL [oe
PT RUS.
PU RUEERESEE SL.
PT PUES.
aliealies “Te TEReeneaa
anion "TET
ehenteotis “Te RPERReeeua
let PT Pu EUeEGe.
ice lU ate).<S/ AIS) twelve tokens
Full Context ¢ RAG ¢ VWeiahts
OCR text:
x See eee REY alae y ee
Grok 4.1 Fast and Agent Tools API
&
RASS mec Cri Bria age! eu can Re art eae a aCe ogre cos am a ote Cre
Be a eee ee re er eer) ar eer se ee ecm ed
Grok 4.1 Fast ou cecttes Oe eae eae a re ae Ge
Fi Rae same ee ear A cet te ree Re Aa ea ae
A aToue Cs (clan a oto) Ed MeO Co SO Se
Gea Ro Eo oC CO a aD 0 EO Cee OS ce
NeCaeee ae Oe eae ae ee
Full Context ¢ RAG « Weights
OCR text:
e| Context Rol: How Increasing Input
Tokens Impacts LLM Performance a
2
Full Context « RAG ¢ Weights
OCR text:
CCE a i
OP ai 2s) iene tbo nos We
ee ie ad Sea dk hel de
rota Cage Oe ta ode
ee tS nS Peet a ees a
ROT SE ote a ae Rissa
OV stained OP a Md
PP hated baal Da)
anentt OF eyed meh wY
¢
. om
7. r ;
hee Cy a RAG e Weights rane
are weme os
a
‘ a. -
2 me : ~
= aw aa 7 ~ ek as
=) - a : Me « SS .
7 -, ran s s _
” = au = a none 8 =~
7 3 t-_
” a a —_—
" mt — —
_ am & sa -
=i ‘ aaa a
Be! —————
~ am pa 4 ee enn Re, /
= oo x ea oo
1 ee f wre,
om 7 j i we ee
A __ ew BRR eos coun | Fn =
= =) ee: eae . i A ~ oe - omen,
wn Be —_ i a a
eats : Pere -“—— =
r% ce ae eee me
\ eats alee re “i ~~ ome -
ra) RSA a i eee :
BSA i oe
OCR text:
Context Rot a
Fepeated Wierds Rertremarce ty Inpt Length ives: O P|
\ . . Se Py
: ‘4
: —— \
ve \ .
No.
a et ~~ =
Full Context * RAG ¢ Weichts
OCR text:
1
= ae ee ee
ao
a. De. oe
mee mu. x
= -™ em ame _
=~ — .
Se ee
an 7 -— EP pe
WEED BB (ver Ce -
emi am y a -
=68 se | eS ai w=:
amen) us | onla GUS. cores
ED Gee a, — a e een. ~
EE Gites vy _ a: ~_ oe me em -
Sei .— ei Se A ay
—=5 Gian at a > a 2 . ™
SEE OS nee * ar 4 forename gt
EE OB jie a : 460 4 zn | CET yee
y —— ie oe ee ers ans ies
— a Bre, Deen -
== 2 / 7 a :
| aa ar ee a or
ee
OCR text:
Nothea
ringanything?
Tumupvolume
inport chronadb
chroma_client=chromadb.Client()
siitchcreate_collectiontooet_or.cceate_collection
to.avotd creating.ahercoliectionevery
NYC(NYT) 47.14 Cafe
collectton=chroma_cltent.get_or_create_collection(name=my_collection)
01Pp0R21
upsert`
to.votd=ddn
collectlon.upsert(
docunents=[
"Thisisadocument aboutpineapple
This is a document about oranges*
[ZP}.‘-IP1.]-sp)
results=collectton.query(
query_texts[Thts ts a query document about florida],Chroma wlll etbed thts for you
2=511n
manyresotts to retur
print(results)
FullContext·RAG·Weights
OCR text:
ar
or
ae eta a me.
ho mee -™ a a
We os on —mme _
=. ae
_- hf
| . H : # _tpreee .
i .o a co
$ SiS. Ss eee ===
| == aey - —
= EB, » be =
SEG) am _.. _— i‘ =
| omen) im, " = =
; Ploy, a. - ; a :
i “EEE Glin, y “ - — —
Se Ss -———
al SS ES .
=a 7 = |
| vo ween Se
iy wm any! . A a oc © os oes —_—e, ;
7 EEE 09 | : : - —e
t= | ee
~ i
OCR text:
. es Each of these points
F ; Men cee Ue (ole lenis laimn ts a
; ae corresponds tc ~*~
. ae Oy mya ro
a SS Ps J a
A ‘ ~
LS . zi i. 5
. .. aw on" .
e y
'
; re ; :
Full Context + RAG + Voghts
OCR text:
Vector databases turns out to be invertible
’ a es
a cS i-¢ 6a “% 40d ood. -0 199 > oD -e1te aaa v »
Mage (foaled April 18. 2020) is an ger cook ce niy bo gti. oer, ae
fh IS Ok ot ee
American Thoroughbred racehorse SH, pest, oo) ee, ak ate
who won the 2023 Kentucky Derby. an Que eon, bow eer rar oan [)
O41 O46 ose. ony arte. 0 a6T, tet
aon 12098, +6919, -0 18-8 01>. -8 ted 0 021,
ae a F 70004, 0 O92. 0 224 8 OLS -9 EER. 0 858,
o oa 004d. 6 OTT © OTS -2 14d. 9 277, -6 OOH. 0 OF?
= 4 Q G48, 527d, 8 1R4 © ove. -4 eae aae, 8 91@, 6 Unt
* ‘ 10087. +6 O82 O18 e326 $103. -0 938 8 O79. 0477
a 6 Gt, -0 045. -6 48 -& O37 eo Oats, O74. 8 O82
a oaae O11. O48 €orn, 2 Om aos, -9 Gen. Oo Gay
rd @ O22 © 04) oon e212 +9 6272 o027, GO 817, 0 Ott
7. 5 aa P
. Kentucky Derby, which was won by
‘ Mage (April 20, 2010), who is an
; American Thoroughbred horse and mare.
Full Context + RAG + Vlewwhts
OCR text:
ee
ao (6 y
+ «| Kentucky Dert was won by
; Mage (April 20 whois an
: auc cro males Arlee Man):
: 0) a
Fo Comaxt « RAG « Weights oot OO
i a. De mt c
Mt nw a -wee. ws
=> -@ tw meme _
=. — >
| : _—— 3. om.
==:
pee - | --Y Lt ===
Ld oo : - = es
ae ' Sn
nan oe ] bee mae oo
SE. — - 4 i. :
SEED iio, _ a
WEEE (un, y a ‘ ie a :
| b>— 11] aren ; —. er
=n ey a is f paw =
| =e -_ Tt i —
i ang . it ao | = _-
7 = asi) _,- . eee eae ' . ‘to © aoes — es
i a S Sanaa Re : Re 2
eee cece a
ene En A bed
¥ —
_ 4
OCR text:
Original text Ps ss ns | ~~
vec2text is an algorithm for recovering a
text from embeddings. Using vec2text, we ec
can recover over 90% of text inputs fro
m embeddings exactly. PI
agi
Hypothesis (Round 7) an
vectext2 is an algorithm for recovering
text from_ embed s. Using vectex
» we cd recover text from embed
s, approx 90% 0 in.
Embedding
Full Context * RAG + Wechts
OCR text:
Vector databases are absolute. ‘
They should be relative. “= ~
6H o ory 5
Full Context + RAG « eee
OCR text:
Vector databases are Prey ce an - .
They should be relative. a Pa
Search for ee ae
customer Lee, |
documents at a az |
credit card a
COMpany 7 .
Full Context + RAG ene
OCR text:
Document 1
Purchased electronics from Amazon for
$45.6/ on October 75, 20623.
Card: ** 8A kee RARE TOGA VISA
; a Document 2
; = Bought household items at Target for
$32.99 on Gctober “6, 2623.
ak ; Oc in Ce e a ee eS (O07 omer OAM Et Gr i0 2 8)
Similarity according to OpenAl Embeddings
mr -
332
“ull Context + RAG + Weights
OCR text:
Embedding Context 7 bene
ccore le nn Guan
Card: * keke * 5676 MASTERCARD
Bougit .. =
Card: ##* ##4e #e** 5938 VISA i —
[| a oo ee
ooo _ —— =
Bougnt .. -
Car ai eh ie a ee an aor \ | =D
a c
a oe Full Context * RAG © Weghts
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.