--Q8ymwWEC
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SK Hynix is in exploratory talks with Intel over a deal that would
have i=
t manufacture memory chips in the US for the first time, with
one
scena=
rio=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0= =E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2= =A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C= =C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80= =8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0= =E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=C2=A0=E2=80=8C=C2=A0=E2=80= =8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0= =E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2= =A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C= =C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80= =8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0=E2=80=8C=C2=A0= =E2=80=8C=C2=A0
Sign Up [1] |Advertise [2]|View Online [3]=20
=
=09=09TLDR=20
TLDR HARDWARE 2026-09-18
=F0=9F=93=B1=20
HEADLINES=
AND LAUNCHES
SK HYNIX IS IN TALKS WITH INTEL TO MAKE ITS FIRST US ME=
MORY CHIPS (4
MINUTE READ) [4]=20
SK Hynix is in exploratory talks wi=
th Intel over a deal that would
have it manufacture memory chips in the U=
S for the first time, with
one scenario having it lease part of Intel's l=
ong-delayed Ohio campus
and another forming a joint venture with Intel an=
d major cloud
providers to lock in steady memory supply. Intel's Ohio sit=
e,
announced back in 2022 at up to $100 billion, has slipped to a
2030-=
2031 start, so landing SK Hynix as a tenant would give Intel's
foundry bu=
siness exactly the marquee customer it's been chasing, while
giving SK Hy=
nix a way to actually produce chips domestically rather
than just package=
them, as it's already doing at its $4 billion
Indiana facility under con=
struction.=20
FIGURE'S HELIX 2.5 WALKS INTO 30 UNSEEN HOMES AND GETS TO=
WORK WITH
ZERO FINE-TUNING (5 MINUTE READ) [5]=20
Figure pretrained =
Helix 2.5 entirely on Index, its crowdsourced human
video dataset, then f=
ine-tuned three whole-body behaviors - tidying
rooms, folding towels, mak=
ing beds - before deploying a single fixed
checkpoint into 30 Bay Area ho=
mes it had never seen, with no data
collection or adaptation on-site and =
objects it had never encountered.
The key isolation test: training two ot=
herwise identical policies on
the same task-specific data, one from rando=
m initialization and one
from Index-pretrained weights, showed Index pret=
raining alone lifted
zero-shot success from 9% to 56%, over 6x higher, wi=
th success
requiring full task completion and no partial credit.=20
=
=F0=9F=94=A8=20
ENGINEERING AND APPLICATIONS
ONSEMI REPLACES TINY =
WIRES INSIDE POWER CHIPS TO CUT ENERGY LOSSES (4
MINUTE READ) [6]=20
=
Onsemi's new Embedded Power Platform (EPP) builds power components
direct=
ly into the silicon itself, wiring them together with precisely
etched la=
yers instead of the usual wire bonds, which cuts electrical
losses and le=
ts the parts switch faster. The idea is to design the
electrical, thermal=
, and mechanical sides of a power system together
from day one instead of=
separately, since treating them as separate
problems has been forcing ex=
pensive redesigns late in development as
devices demand more power in les=
s space.=20
NVIDIA GRAPHICS CHIPS CONTAIN UP TO 40 TINY PROCESSORS FOR=
HOUSEKEEPING (5 MINUTE READ) [7]=20
The shader cores that do actual =
graphics work aren't where the RISC-V
lives - it's in a collective of sma=
ll management processors handling
clocking, memory, security, and other h=
ousekeeping, with Nvidia VP
Frans Sijstermans confirming each GPU packs 1=
0 to 40 of them depending
on the model. Users can't program these cores d=
irectly, and they're
cryptographically locked down so tightly the GPU fir=
mware won't even
boot if they've been tampered with.=20
=F0=9F=A7=
=A0=20
RESEARCH AND PROTOTYPES
HUGE AI MODELS CAN RUN ON A CONSUME=
R GRAPHICS CARD BY USING SSD
STORAGE (4 MINUTE READ) [8]=20
Mixture-o=
f-Experts models keep growing capacity by adding more
experts, but even a=
fter quantization, the full expert pool still
dwarfs consumer RAM and VRA=
M, so local inference has to lean on SSD
storage instead - a much harder =
problem than just having enough disk
space. SSD-LLaMA builds an SSD I/O p=
ipeline tuned specifically for
fetching experts, layered with a three-tie=
r SSD-RAM-VRAM hierarchy
that dynamically decides which experts to hold w=
here, and CPU-GPU
hybrid execution balanced against the SSD's limited ban=
dwidth, all
while running every selected expert in full rather than pruni=
ng or
substituting cheaper approximations.=20
INVISIBLE CHANGES TO A =
ROBOT'S TRAINING WORLD CAN MAKE IT FAIL IN
REAL LIFE (3 MINUTE READ) [9] =
Every 3D asset in a robot simulator carries two geometries - a visua=
l
mesh for rendering and a coarser collision mesh for physics - a
legit=
imate gap since matching them exactly is too expensive. This
paper shows =
that gap is exploitable: an attacker can alter only the
collision mesh, s=
o a policy trained on the poisoned asset looks and
behaves normally in si=
mulation, then fails or creates real safety
risks once deployed. It slips=
past existing checks entirely, since
asset review covers malware, copyri=
ght, and format, but nothing
verifies visual-collision consistency, and t=
he researchers found the
defenses they tested weren't enough to catch it.=
=20
=F0=9F=8E=81=20
MISCELLANEOUS
INTEL VETERANS RAISE $100 MIL=
LION TO HELP DIFFERENT AI CHIPS WORK
TOGETHER (4 MINUTE READ) [10]=20
=
Delos Data, founded by Intel veterans, raised $100 million led by
Matrix=
Partners and Playground to build networking chips and software
that move=
data faster between processors inside increasingly
heterogeneous AI data=
centers. Early AI data centers were mostly
Nvidia GPUs wired together wi=
th Nvidia's own proprietary networking,
but as the focus has shifted from=
training toward running persistent,
failure-tolerant agentic inference w=
orkloads, data centers now mix
GPUs, XPUs, accelerators, CPUs, memory, an=
d storage from Nvidia, AMD,
Cerebras, and others, none of which were desi=
gned to talk to each
other efficiently at that scale.=20
LEAKED TEST =
SUGGESTS APPLE'S M6 IS 27% FASTER THAN M5 ON MULTICORE
TASKS (3 MINUTE RE=
AD) [11]=20
An unverified Geekbench 7 result for Apple's new 2nm M6 chi=
p,
debuting in the new Mac mini, shows a single-core score of 4,071 and a=
multi-core score of 22,783, up roughly 12% and 27% respectively over
t=
he M5's average scores. That single-core number tops every other Mac
or P=
C in the Geekbench database, including the M5 Max (3,731) and
Qualcomm's =
Snapdragon X2 Elite Extreme (3,396), backing up Apple's
claim that the M6=
has the world's fastest single-threaded CPU
performance, while the multi=
-core gain lines up with Apple's
advertised 1.2x multi-threaded improveme=
nt.=20
=E2=9A=A1=20
QUICK LINKS
ARM SEES A BETTER CHANCE OF REA=
CHING $2 BILLION IN SALES OF ITS OWN
AI CHIP (2 MINUTE READ) [12]=20
=
Arm CEO Rene Haas discussed the company's AI revenue targets with Jim
Cra=
mer on CNBC's Mad Money, framing Arm's architecture as increasingly
centr=
al to AI infrastructure even as competition from RISC-V and
custom silico=
n intensifies.=20
OPEN-SOURCE PLANS LET YOU BUILD YOUR OWN ROBOT ARM (2=
MINUTE READ)
[13]=20
OpenArm is an open-source, fully 3D-printable r=
obotic arm project
from Enactic, designed as a low-cost, hackable platfor=
m for robotics
research and development.=20
MISUMI AND FICTIV LAUNCH =
A $50 MILLION FUND BACKING THE NEXT
GENERATION OF HARDWARE, ROBOTICS AND =
PHYSICAL AI COMPANIES (3 MINUTE
READ) [14]=20
Misumi, a maker of high=
-end linear actuators, and Fictiv, a
manufacture-on-demand service, are l=
aunching a $50M venture fund that
aims to bring their hardware manufactur=
ing expertise to new hardware
and deep tech startups.=20
JENSEN HUANG=
SAYS NVIDIA WILL DOUBLE AI CHIP SALES NEXT YEAR (2
MINUTE READ) [15]=20
=
Speaking at a summit with King Charles III in Scotland, Jensen Huang
=
said Nvidia expects to sell twice as many chips next year, extending a
fo=
recast that already projects 70% revenue growth for fiscal 2028.=20
TES=
LA'S AI5 CHIP ENTERS TRIAL PRODUCTION AT SAMSUNG'S TEXAS FAB (3
MINUTE RE=
AD) [16]=20
Samsung has started trial production of Tesla's AI5 chip on=
its 2nm
node at the new Taylor, Texas foundry, fast-tracking prototype r=
uns
ahead of schedule to handle a wave of custom silicon orders, backed b=
y
Tesla's $16.5 billion manufacturing contract at the site.=20
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Thanks for reading,=20
Ks=
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<div style=3D"display: none; max-height: 0px; overflow: hidden;">SK Hynix i=
s in exploratory talks with Intel over a deal that would have it manufactur=
e memory chips in the US for the first time, with one scenario =E2=80= =8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80= =8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80= =8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80= =8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80= =8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80= =8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C = =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C = =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C = =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C = =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C =E2=80=8C = =E2=80=8C =E2=80=8C </div>
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<div style=3D"text-align: center;">
<h1><strong>TLDR Hardware <span id=3D"date">2026-09-18</span></strong></h1> </div>
</td></tr></tbody></table>
<table style=3D"table-layout: fixed; width:100%;" width=3D"100%"><tbody></t= body></table>
</td></tr></tbody></table>
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<table align=3D"center" bgcolor=3D"" border=3D"0" cellpadding=3D"0" cellspa= cing=3D"0" width=3D"100%"><tbody><tr><td class=3D"container" style=3D"paddi= ng-top: 0px; padding-bottom: 0px;">
<div class=3D"text-block">
<div style=3D"text-align: center;"><span style=3D"font-size: 36px;">=F0=9F= =93=B1</span></div></div>
</td></tr></tbody></table>
<table align=3D"center" bgcolor=3D"" border=3D"0" cellpadding=3D"0" cellspa= cing=3D"0" width=3D"100%"><tbody><tr><td class=3D"container" style=3D"paddi= ng-top: 0px; padding-bottom: 0px;">
<div class=3D"text-block">
<div style=3D"text-align: center;">
<h1><strong>Headlines and Launches</strong></h1>
</div>
</div>
</td></tr></tbody></table>
<table style=3D"table-layout: fixed; width: 100%;" width=3D"100%"><tbody><t= r><td style=3D"padding:0;border-collapse:collapse;border-spacing:0;margin:0=
;" valign=3D"top">
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
https://tracking.tldrnewsletter.= com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2F9mlABf/1/010001a0b4aa6120-28= b8cf3f-548a-4c3b-89e2-0d8f5a910a77-000000/9hWVr6zprsN_WtvKR9RLg72I2WTlCe-1a= SxBScBWSsE=3D452">
<span>
<strong>SK Hynix is in talks with I= ntel to make its first US memory chips (4 minute read)</strong>
</span>
<span style=3D"font-family: "Helvetica Neue", Helvetica, Arial, V= erdana, sans-serif;">
SK Hynix is in exploratory talks with I= ntel over a deal that would have it manufacture memory chips in the US for = the first time, with one scenario having it lease part of Intel's long-dela= yed Ohio campus and another forming a joint venture with Intel and major cl= oud providers to lock in steady memory supply. Intel's Ohio site, announced=
back in 2022 at up to $100 billion, has slipped to a 2030-2031 start, so l= anding SK Hynix as a tenant would give Intel's foundry business exactly the=
marquee customer it's been chasing, while giving SK Hynix a way to actuall=
y produce chips domestically rather than just package them, as it's already=
doing at its $4 billion Indiana facility under construction.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
https://tracking.tldrnewsletter.= com/CL0/https:%2F%2Fwww.figure.ai%2Fnews%2Fhelix-2-5-zero-shot-30-home-gene= ralization%3Futm_source=3Dtldrhardware/1/010001a0b4aa6120-28b8cf3f-548a-4c3= b-89e2-0d8f5a910a77-000000/eXw1lv7UB-MZvd7trLa-PYhpHAdTX5t_5pfq-nluGp4=3D45=
<span>
<strong>Figure's Helix 2.5 walks in=
to 30 unseen homes and gets to work with zero fine-tuning (5 minute read)</= strong>
</span>
<span style=3D"font-family: "Helvetica Neue", Helvetica, Arial, V= erdana, sans-serif;">
Figure pretrained Helix 2.5 entirely on=
Index, its crowdsourced human video dataset, then fine-tuned three whole-b= ody behaviors - tidying rooms, folding towels, making beds - before deployi=
ng a single fixed checkpoint into 30 Bay Area homes it had never seen, with=
no data collection or adaptation on-site and objects it had never encounte= red. The key isolation test: training two otherwise identical policies on t=
he same task-specific data, one from random initialization and one from Ind= ex-pretrained weights, showed Index pretraining alone lifted zero-shot succ= ess from 9% to 56%, over 6x higher, with success requiring full task comple= tion and no partial credit.
</span>
</span>
</div>
</td></tr></tbody></table>
</td></tr></tbody></table>
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<div class=3D"text-block">
<div style=3D"text-align: center;"><span style=3D"font-size: 36px;">=F0=9F= =94=A8</span></div>
</div>
</td></tr></tbody></table>
<table align=3D"center" bgcolor=3D"" border=3D"0" cellpadding=3D"0" cellspa= cing=3D"0" width=3D"100%"><tbody><tr><td class=3D"container" style=3D"paddi= ng-top: 0px; padding-bottom: 0px;">
<div class=3D"text-block">
<div style=3D"text-align: center;">
<h1><strong>Engineering and Applications</strong></h1>
</div>
</div>
</td></tr></tbody></table>
<table style=3D"table-layout: fixed; width: 100%;" width=3D"100%"><tbody><t= r><td style=3D"padding:0;border-collapse:collapse;border-spacing:0;margin:0=
;" valign=3D"top">
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
https://tracking.tldrnewsletter.= com/CL0/https:%2F%2Fwww.servethehome.com%2Fonsemi-embedded-power-platform-a= nnounced%3Futm_source=3Dtldrhardware/1/010001a0b4aa6120-28b8cf3f-548a-4c3b-= 89e2-0d8f5a910a77-000000/PcrdSo5-oeGMo0fidxiOzhB5cFwvkHI_CuUXxj9UtEg=3D452"=
<span>
<strong>Onsemi replaces tiny wires = inside power chips to cut energy losses (4 minute read)</strong>
</span>
<span style=3D"font-family: "Helvetica Neue", Helvetica, Arial, V= erdana, sans-serif;">
Onsemi's new Embedded Power Platform (E= PP) builds power components directly into the silicon itself, wiring them t= ogether with precisely etched layers instead of the usual wire bonds, which=
cuts electrical losses and lets the parts switch faster. The idea is to de= sign the electrical, thermal, and mechanical sides of a power system togeth=
er from day one instead of separately, since treating them as separate prob= lems has been forcing expensive redesigns late in development as devices de= mand more power in less space.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
https://tracking.tldrnewsletter.= com/CL0/https:%2F%2Fwww.xda-developers.com%2Fyour-nvidia-gpu-dozens-risc-v-= cores-one-took-over-graphics-driver%3Futm_source=3Dtldrhardware/1/010001a0b= 4aa6120-28b8cf3f-548a-4c3b-89e2-0d8f5a910a77-000000/gIWUgdV-S6l2knOHzCN_vBx= KzHOB-ohU8qd5ggJztDw=3D452">
<span>
<strong>Nvidia graphics chips conta=
in up to 40 tiny processors for housekeeping (5 minute read)</strong>
</span>
<span style=3D"font-family: "Helvetica Neue", Helvetica, Arial, V= erdana, sans-serif;">
The shader cores that do actual graphic=
s work aren't where the RISC-V lives - it's in a collective of small manage= ment processors handling clocking, memory, security, and other housekeeping=
, with Nvidia VP Frans Sijstermans confirming each GPU packs 10 to 40 of th=
em depending on the model. Users can't program these cores directly, and th= ey're cryptographically locked down so tightly the GPU firmware won't even = boot if they've been tampered with.
</span>
</span>
</div>
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<div class=3D"text-block">
<div style=3D"text-align: center;"><span style=3D"font-size: 36px;">=F0=9F= =A7=A0</span></div>
</div>
</td></tr></tbody></table>
<table align=3D"center" bgcolor=3D"" border=3D"0" cellpadding=3D"0" cellspa= cing=3D"0" width=3D"100%"><tbody><tr><td class=3D"container" style=3D"paddi= ng-top: 0px; padding-bottom: 0px;">
<div class=3D"text-block">
<div style=3D"text-align: center;">
<h1><strong>Research and Prototypes</strong></h1>
</div>
</div>
</td></tr></tbody></table>
<table style=3D"table-layout: fixed; width: 100%;" width=3D"100%"><tbody><t= r><td style=3D"padding:0;border-collapse:collapse;border-spacing:0;margin:0=
;" valign=3D"top">
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
https://tracking.tldrnewsletter.= com/CL0/https:%2F%2Farxiv.org%2Fabs%2F2609.18110%3Futm_source=3Dtldrhardwar= e/1/010001a0b4aa6120-28b8cf3f-548a-4c3b-89e2-0d8f5a910a77-000000/YXu5mO966f= gbVu-EIzN06NFwWadbvuoxGGM7jdX5LYU=3D452">
<span>
<strong>Huge AI models can run on a=
consumer graphics card by using SSD storage (4 minute read)</strong>
</span>
<span style=3D"font-family: "Helvetica Neue", Helvetica, Arial, V= erdana, sans-serif;">
Mixture-of-Experts models keep growing = capacity by adding more experts, but even after quantization, the full expe=
rt pool still dwarfs consumer RAM and VRAM, so local inference has to lean =
on SSD storage instead - a much harder problem than just having enough disk=
space. SSD-LLaMA builds an SSD I/O pipeline tuned specifically for fetchin=
g experts, layered with a three-tier SSD-RAM-VRAM hierarchy that dynamicall=
y decides which experts to hold where, and CPU-GPU hybrid execution balance=
d against the SSD's limited bandwidth, all while running every selected exp= ert in full rather than pruning or substituting cheaper approximations.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
https://tracking.tldrnewsletter.= com/CL0/https:%2F%2Farxiv.org%2Fabs%2F2609.18122%3Futm_source=3Dtldrhardwar= e/1/010001a0b4aa6120-28b8cf3f-548a-4c3b-89e2-0d8f5a910a77-000000/F3kqLr_L9n= ChuH7pBKwuGsUpvDTyBH5qkysQfIUW6no=3D452">
<span>
<strong>Invisible changes to a robo= t's training world can make it fail in real life (3 minute read)</strong>
</span>
<span style=3D"font-family: "Helvetica Neue", Helvetica, Arial, V= erdana, sans-serif;">
Every 3D asset in a robot simulator car= ries two geometries - a visual mesh for rendering and a coarser collision m= esh for physics - a legitimate gap since matching them exactly is too expen= sive. This paper shows that gap is exploitable: an attacker can alter only = the collision mesh, so a policy trained on the poisoned asset looks and beh= aves normally in simulation, then fails or creates real safety risks once d= eployed. It slips past existing checks entirely, since asset review covers = malware, copyright, and format, but nothing verifies visual-collision consi= stency, and the researchers found the defenses they tested weren't enough t=
o catch it.
</span>
</span>
</div>
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</td></tr></tbody></table>
<table align=3D"center" bgcolor=3D"" border=3D"0" cellpadding=3D"0" cellspa= cing=3D"0" width=3D"100%"><tbody><tr><td class=3D"container" style=3D"paddi= ng-top: 0px; padding-bottom: 0px;">
<div class=3D"text-block">
<div style=3D"text-align: center;"><span style=3D"font-size: 36px;">=F0=9F= =8E=81</span></div></div>
</td></tr></tbody></table>
<table align=3D"center" bgcolor=3D"" border=3D"0" cellpadding=3D"0" cellspa= cing=3D"0" width=3D"100%"><tbody><tr><td class=3D"container" style=3D"paddi= ng-top: 0px; padding-bottom: 0px;">
<div class=3D"text-block">
<div style=3D"text-align: center;"><strong><h1>Miscellaneous</h1></strong><= /div>
</div>
</td></tr></tbody></table>
<table bgcolor=3D"" style=3D"table-layout: fixed; width: 100%;" width=3D"10= 0%"><tbody><tr><td style=3D"padding:0;border-collapse:collapse;border-spaci= ng:0;margin:0;" valign=3D"top">
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
https://tracking.tldrnewsletter.= com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FxVEfsp/1/010001a0b4aa6120-28= b8cf3f-548a-4c3b-89e2-0d8f5a910a77-000000/sA7zZDIV5DTH9zn_HGT4vCp7DmSGoGtjp= On4sYJOCXg=3D452">
<span>
<strong>Intel veterans raise $100 m= illion to help different AI chips work together (4 minute read)</strong>
</span>
<span style=3D"font-family: "Helvetica Neue", Helvetica, Arial, V= erdana, sans-serif;">
Delos Data, founded by Intel veterans, = raised $100 million led by Matrix Partners and Playground to build networki=
ng chips and software that move data faster between processors inside incre= asingly heterogeneous AI data centers. Early AI data centers were mostly Nv= idia GPUs wired together with Nvidia's own proprietary networking, but as t=
he focus has shifted from training toward running persistent, failure-toler= ant agentic inference workloads, data centers now mix GPUs, XPUs, accelerat= ors, CPUs, memory, and storage from Nvidia, AMD, Cerebras, and others, none=
of which were designed to talk to each other efficiently at that scale.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
https://tracking.tldrnewsletter.= com/CL0/https:%2F%2Fwww.macrumors.com%2F2026%2F09%2F15%2Fapple-m6-chip-benc= hmark%3Futm_source=3Dtldrhardware/1/010001a0b4aa6120-28b8cf3f-548a-4c3b-89e= 2-0d8f5a910a77-000000/B9jiCCRtOHgkK7y6KYetG3D6D7XrwbBOUq5VxYAcKN0=3D452">
<span>
<strong>Leaked test suggests Apple'=
s M6 is 27% faster than M5 on multicore tasks (3 minute read)</strong>
</span>
<span style=3D"font-family: "Helvetica Neue", Helvetica, Arial, V= erdana, sans-serif;">
An unverified Geekbench 7 result for Ap= ple's new 2nm M6 chip, debuting in the new Mac mini, shows a single-core sc= ore of 4,071 and a multi-core score of 22,783, up roughly 12% and 27% respe= ctively over the M5's average scores. That single-core number tops every ot= her Mac or PC in the Geekbench database, including the M5 Max (3,731) and Q= ualcomm's Snapdragon X2 Elite Extreme (3,396), backing up Apple's claim tha=
t the M6 has the world's fastest single-threaded CPU performance, while the=
multi-core gain lines up with Apple's advertised 1.2x multi-threaded impro= vement.
</span>
</span>
</div>
</td></tr></tbody></table>
</td></tr></tbody></table>
<table align=3D"center" bgcolor=3D"" border=3D"0" cellpadding=3D"0" cellspa= cing=3D"0" width=3D"100%"><tbody><tr><td class=3D"container" style=3D"paddi= ng-top: 0px; padding-bottom: 0px;">
<div class=3D"text-block">
<div style=3D"text-align: center;"><span style=3D"font-size: 36px;">=E2=9A= =A1</span></div></div>
</td></tr></tbody></table>
<table align=3D"center" bgcolor=3D"" border=3D"0" cellpadding=3D"0" cellspa= cing=3D"0" width=3D"100%"><tbody><tr><td class=3D"container" style=3D"paddi= ng-top: 0px; padding-bottom: 0px;">
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<div style=3D"text-align: center;">
<h1><strong>Quick Links</strong></h1>
</div>
</div>
</td></tr></tbody></table>
<table bgcolor=3D"" style=3D"table-layout: fixed; width: 100%;" width=3D"10= 0%"><tbody><tr><td style=3D"padding:0;border-collapse:collapse;border-spaci= ng:0;margin:0;" valign=3D"top">
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
https://tracking.tldrnewsletter.= com/CL0/https:%2F%2Fwww.cnbc.com%2F2026%2F09%2F16%2Fjim-cramer-arm-ceo-ai-r= evenue-goal.html%3Futm_source=3Dtldrhardware/1/010001a0b4aa6120-28b8cf3f-54= 8a-4c3b-89e2-0d8f5a910a77-000000/H5uIazWOK_R1_ZppPs8beeDmRriszin5GdrRLWtQ5x= A=3D452">
<span>
<strong>Arm sees a better chance of=
reaching $2 billion in sales of its own AI chip (2 minute read)</strong>
</span>
<span style=3D"font-family: "Helvetica Neue", Helvetica, Arial, V= erdana, sans-serif;">
Arm CEO Rene Haas discussed the company=
's AI revenue targets with Jim Cramer on CNBC's Mad Money, framing Arm's ar= chitecture as increasingly central to AI infrastructure even as competition=
from RISC-V and custom silicon intensifies.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
https://tracking.tldrnewsletter.= com/CL0/https:%2F%2Fgithub.com%2Fenactic%2FOpenArm%3Futm_source=3Dtldrhardw= are/1/010001a0b4aa6120-28b8cf3f-548a-4c3b-89e2-0d8f5a910a77-000000/uPn5HSP0= 290nUdIFkl3ISsdtI54MpzT4Cd1or5fkLVI=3D452">
<span>
<strong>Open-source plans let you b= uild your own robot arm (2 minute read)</strong>
</span>
<span style=3D"font-family: "Helvetica Neue", Helvetica, Arial, V= erdana, sans-serif;">
OpenArm is an open-source, fully 3D-pri= ntable robotic arm project from Enactic, designed as a low-cost, hackable p= latform for robotics research and development.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align=3D"center" border=3D"0" cellpadding=3D"0" cellspacing=3D"0" wi= dth=3D"100%"><tbody><tr><td class=3D"container" style=3D"padding: 15px 15px=
<div class=3D"text-block">
<span>
<a href=3D"
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<strong>Misumi and Fictiv Launch a = $50 Million Fund Backing the Next Generation of Hardware, Robotics and Phys= ical AI Companies (3 minute read)</strong>
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Misumi, a maker of high-end linear actu= ators, and Fictiv, a manufacture-on-demand service, are launching a $50M ve= nture fund that aims to bring their hardware manufacturing expertise to new=
hardware and deep tech startups.
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<strong>Jensen Huang says Nvidia wi=
ll double AI chip sales next year (2 minute read)</strong>
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Speaking at a summit with King Charles = III in Scotland, Jensen Huang said Nvidia expects to sell twice as many chi=
ps next year, extending a forecast that already projects 70% revenue growth=
for fiscal 2028.
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Samsung has started trial production of=
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