Maverick

[Matt Van Horn @mvanhorn]

https://t.co/8wlyyfinBT

[Jason Lewris @jasonlewris]

The walls are closing in on the central part of Dallas, with dozens of locations on the perimeter having 70%+ of all homes for sale undergoing price cuts.

DFW metro currently has a staggering 52.9% of all homes for sale undergoing price cuts.

In Denton, Collin and Kaufman counties, this is even higher above 56%.

There is currently a battle in DFW between existing homes for sale and new construction. This is most pronounced in the outer perimeter, but the walls are closing in.

Lets dig in.

[Freda Duan @FredaDuan]

A humble attempt to est. the infra required to serve 100M DAU @Muse

Rough conclusion is:

1 GW of power to serve 100M DAU in the base case, of which only ~0.1 GW comes from the CPU/VM layer. Depending on the # of reasoning-equivalent model calls one Muse DAU generates per day, 3-4GW is entirely plausible. Maybe that’s why @Meta is rumored to be adding 7-10GW of compute next year.

The sandbox layer = sub $1B of CPU content and ~$2B of DRAM content, which is much smaller than many expected.

Lot of moving assumptions. Welcome all feedbacks/ pushbacks.

------
Two very different pieces of infrastructure behind Muse.

1. Muse VM / sandbox infrastructure

2 vCPUs, ~8 GB of RAM and ~100 GB of persistent logical storage per user. https://t.co/7rP5vDKnT7

2. Muse Spark inference

Model inference goes out through Meta's external inference infrastructure. https://t.co/e203XUp6jq
------

1/ Sandbox infrastructure

A. CPU
The first mistake is assuming that 100M DAU means 100M VMs are actively consuming compute at the same time.

Suppose the average Muse DAU has an agent actively working for two hours per day.

100M users * 2 hours / 24 hours = ~8M average simultaneous active VMs

Meta obviously cannot provision only for the daily average. Usage will be concentrated during waking hours and bursty.

Assume a 2.5x peak-to-average ratio:

8M * 2.5 = ~20M peak active VMs

Then add roughly 20% capacity headroom: ~25M provisioned live VMs. So the base assumption is effectively that Meta needs enough infrastructure to support roughly 25% of DAU being live simultaneously.

The next important distinction is between virtual CPU allocation and physical CPU demand. Agent sandboxes are particularly well suited to CPU oversubscription. They spend a lot of time waiting. During those periods, the VM may still be alive, but it is barely using CPU.

DeepSeek’s recently published DSec infrastructure provides a useful benchmark. Its production agent sandbox platform runs approximately 30,000 physical CPU cores and 250TB of DRAM across ~160 nodes, with peak concurrency above 380,000 sandboxes. https://t.co/PFlgc6nnUs DSec also demonstrates stable operation at around: 800 microVMs per node. With roughly 188 physical cores per node: 188 physical cores / 800 microVMs = ~0.23 physical cores per live VM.

DeepSeek is obviously the King of efficiency. The number for Muse might be at 0.3-0.75 physical cores per live VM, or assume 0.5 physical cores per live VM as the base case. That is equivalent to roughly two simultaneously live Muse VMs per physical CPU core.

Using the base assumptions: 25M live VMs * 0.5 physical cores per VM = 12.5M physical CPU cores.

On a 256-core CPU: 12.5M cores / 256 cores per CPU = ~50K CPUs; Or on a 192-core CPU that would be 65K CPUs.

At the current public pricing, that is ~$800M.

B. DRAM
CPU can be aggressively oversubscribed because a VM that is waiting may consume almost no CPU. Memory is harder to oversubscribe because a live VM still needs to retain its working state.

Muse exposes roughly 8GB of RAM to the user environment, but one observed instance was actually using only around 3GB at the time of measurement.

25M live VMs * 3GB = 75PB of physical DRAM, call it ~75-100PB of physical DRAM feels like a reasonable base range.

At the current public pricing, that is ~$2B.

C. Sandbox power
~0.1 GW for the entire Muse sandbox / VM layer at 100M DAU.

------

2/ Inference
Muse’s personal computer executes tools and stores state locally, but the actual model runs on separate inference infrastructure. Meta’s Muse architecture

Energy per inference event
Microsoft’s 2026 study estimates that optimized frontier-scale inference consumes a median of approximately: 0.31Wh per normal query

But a long reasoning query with roughly 15x the token count consumes approximately 13x as much energy, or around: 4Wh per long reasoning query

The study specifically highlights reasoning and agentic workloads as significantly more energy intensive.
https://t.co/z1YEsvVeMo

Sensitivity analysis on # reasoning-equivalent events per DAU per day

Suppose each active @Muse user generates the equivalent of 50 heavy inference events per day.

At 5Wh each:

100M users * 50 events/day * 5Wh = 25GWh/day

25GWh/day / 24 hours = ~1.0GW average power

So inference alone could require: ~1-2GW of average power

A 3-4GW Muse is entirely plausible. Maybe that’s why @Meta is rumored to be adding 7-10GW of compute next year.

------
The popular framing around Muse is that giving every user 2 vCPUs and 8GB of RAM creates an enormous CPU requirement. But the naive calculation materially exaggerates the CPU requirement because it treats logical VM allocation as dedicated physical infrastructure.

The more interesting conclusion is: Consumer agents may be a meaningful new demand driver for CPUs and conventional DRAM, but inference remains the real compute bottleneck. And as agents do more work, run longer trajectories and increasingly spawn other agents, inference demand can scale much faster than the number of users itself.

+++
Calling my peer review group: @bubbleboi @damnang2 @Midnight_Captl @FundaAI @fi56622380 . Feedback/ Pushbacks pls :).

++
Better formatted: https://t.co/qVwYKNr1ZA

[Tim Denning @Tim_Denning]

This article should have 100M views.

[Esther & Michael @SuperLuckeee]

This will take 8 minutes to learn so be ready.
8 set-ups for beginner and intermediate $SPY

Included charts & when to take enter the trade 👇

[Maverick @Maverick5m8]

@petrolandeco nerden olduğuna çok bagli. blackstone ellerindeki evleri çıkartıyor. Blackstone’nun çok ev sattigi yerlerdeysen piyasa alt üst şu anda

[Vaishnavi @_vmlops]

Minko Gechev was preparing for a Google interview

Instead of keeping his prep notes to himself, he turned them into a GitHub repo
He collected the problems he actually practiced and organized them around the concepts he was preparing for

No “500 problems in 30 days”
No random problem dump
Just practical interview prep

He also openly points out that solving these problems isn't enough you need strong fundamentals and the ability to explain your thinking during the interview

That repo now has 3K+ stars. 👀

https://t.co/fjlyo7Se4b

[Maverick @Maverick5m8]

@DeepValueBagger @kurtknapp @manukafirth @robert_gess i did subscribed

[Maverick @Maverick5m8]

@DeepValueBagger @kurtknapp @manukafirth @robert_gess @DeepValueBagger luck always has a play in stock market.

[Mark Phelps @mark_a_phelps]

Stanford dropped CS329Z today. Engineering AI Agents. compound systems, build RAG/tool use from scratch, then frameworks, then how you actually evaluate this stuff.

gonna follow along online. looks pretty neat.
https://t.co/7To6HNMqxu

[Maverick @Maverick5m8]

@Cristina_AC_X The idea that 'competitiveness' will save human jobs ignores how economic structures actually work. We aren't heading toward a meritocracy; we are heading toward a modern form of neo-feudalism.
most of the humanity will be pushed into a modern 'peasantry'

[Maverick @Maverick5m8]

@congressdj Without a case, it is as slippery as hell

[Trackmind @0xTrackmind]

this paper is f*cking insane

a quant paper combined a Hidden Markov Model with reinforcement learning to shift portfolio allocation on the fly as market regimes change.

the numbers: it beat SPY on risk-adjusted returns, with shallower drawdowns across 2004-2025.

the crazy part is it's not trying to forecast the market at all, it figures out what regime it's in first, then decides the allocation from there.

bookmark it before this thread gets buried.

[Maverick @Maverick5m8]

@MoleLabs i don’t see any sivers there.

[Maverick @Maverick5m8]

@PeterSchiff It’s easy to say to someone who has never been a single mom, sick with needy small children, or disabled

the problem isn’t having a social system, but the abuse. As a society, we should have support for those who needs but abuse part should be resolved in this tech era

[Mert Metin Tekdemir @mertmetindev]

Milyarderlerin Ne Aldığını ve Ne Okuduğunu Ücretsiz Gösteren 50 Site

1.) https://t.co/ZhUILN1bYn → Ünlü yatırımcıların güncel portföyleri
2.) https://t.co/h1569lJ8Vk → Fonların çeyreklik alım satımları
3.) https://t.co/1QHfeu7If6 → ABD'li politikacıların hisse işlemleri
4.) https://t.co/kgSSMWDftH → Yöneticilerin kendi şirket hisseleri
5.) https://t.co/Xozx2k1LxX → Fon bildirimlerini okunur hale getirme
6.) https://t.co/EEqRbmZyUA → Şirketlerin resmi raporları
7.) https://t.co/ZFf9Dgfbdm → Buffett'ın 1977'den beri mektupları
8.) https://t.co/ctiJsdUJxV → Buffett toplantılarının video arşivi
9.) https://t.co/90FXbhPxdS → Howard Marks'ın yatırımcı notları
10.) https://t.co/zz1MDr8IuB → Ray Dalio'nun firmasının araştırmaları
11.) https://t.co/v1HeHWaEVu → Dalio'nun ilkeleri ücretsiz
12.) https://t.co/Q2IjfoIYHB → Jeremy Grantham'ın piyasa notları
13.) https://t.co/caKtETdPAv → Kantitatif fon araştırmaları
14.) https://t.co/S6mHnkB8SM → Damodaran'ın değerleme verileri
15.) https://t.co/lBdy39m0In → Değerleme profesörünün blogu
16.) https://t.co/fzQM5n9vjt → Morgan Housel'in para yazıları
17.) https://t.co/hnk89cWDwW → Karar vermenin zihinsel modelleri
18.) https://t.co/Tpz6XSTKil → JPMorgan'ın ücretsiz piyasa rehberi
19.) https://t.co/kahV40Yt2s → Yenilik odaklı fon araştırmaları
20.) https://t.co/g9kDE8CDU6 → Girişim sermayesi denemeleri
21.) https://t.co/iTXfy7MVeu → Değer yatırımı ders notları
22.) https://t.co/HMxqonW0J4 → Columbia'nın değer yatırımı arşivi
23.) https://t.co/FC3aO4sqOS → Fed'in ekonomik veri arşivi
24.) https://t.co/6FJpzUj6Pz → Fed kararları ve açıklamaları
25.) https://t.co/0CG0xVKRgb → Avrupa Merkez Bankası verileri
26.) https://t.co/LfG7m4Gngq → Merkez bankalarının bankası raporları
27.) https://t.co/Yi6kEniOvO → Ülkelerin ekonomik göstergeleri
28.) https://t.co/ncvoSOdTWK → Şirketlerin uzun vadeli grafikleri
29.) https://t.co/T576J29xBh → Şirket finansalları ücretsiz
30.) https://t.co/Li4FmycBIU → Şirketlerin piyasa değeri sıralaması
31.) https://t.co/vC9L4Ueg3b → Hisse tarama ve ısı haritası
32.) https://t.co/0iHwKw6igv → Portföy geri testi
33.) https://t.co/4YtgkWpsTp → Grafik ve piyasa analizi
34.) https://t.co/LO0MYji1vc → Profesyonel piyasa ekranı
35.) https://t.co/rOclEWESJE → Şirket faaliyet raporları arşivi
36.) https://t.co/XIXMyONkRr → SEC'in yatırımcı eğitim sitesi
37.) https://t.co/xNaRTjbel5 → Pasif yatırım ansiklopedisi
38.) https://t.co/MsyMnuJ131 → Finans terimleri sözlüğü
39.) https://t.co/MyMk80MEFK → Ücretsiz finans rehberleri
40.) https://t.co/nSr55dm52J → CFA araştırma yayınları
41.) https://t.co/K6HkgDBhXR → Fon ve hisse değerlendirmeleri
42.) https://t.co/pCDxBWV17P → Şirketlerin görsel analizi
43.) https://t.co/9wWQX6dVKJ → Seçilmiş yatırım tezleri arşivi
44.) https://t.co/HbKe9ZjipZ → Fonların içinde ne olduğunu görme
45.) https://t.co/RH5eswHSaA → Avrupa fon geri testleri
46.) https://t.co/scGmGpK4Br → Veriyle yatırım yazıları
47.) https://t.co/Vj6iVrOhwp → Piyasa tarihi notları
48.) https://t.co/zPBUHjw9pH → Yatırımcı psikolojisi yazıları
49.) https://t.co/E4ayZUhSbT → Ekonomiyi grafiklerle anlatma
50.) ourfiniteworld? → (bkz. not)

Zenginler bilgiyi saklamıyor. Sadece nerede durduğunu kimse söylemiyor.

Kaydedin, lazım olur.
Yatırım tavsiyesi değildir.

[Matt Dancho (Business Science) @mdancho84]

🚨BREAKING: Google DeepMind + Meta + Amazon just dropped a 135 page roadmap that redefines what AI agents actually are.

Here's the roadmap:

[Rohan Paul @rohanpaul_ai]

New Google paper shows LLM agents handle long tasks better when their workflow lives in an editable procedure graph that learns from execution, instead of being buried in chat history.

The problem is: as agents run longer, they can forget where they are, repeat tools, or do steps in the wrong order.

Procedural Graphs give the agent a small map of what can happen next, while still letting the LLM reason freely.

After runs finish, another LLM compares successes and failures and edits the map, but an edit is kept only if it does not hurt held-out tasks.

Across 24 model-and-benchmark combinations, the method ranked 1st or tied 1st in 21.

It also repaired a bad human-designed workflow: on MultiChallenge, success went from 58.93% with the flawed graph to 92.86% after refinement.

This guidance costs extra tokens, so it makes most sense when long workflows are the bottleneck.

Overall, it recommends move critical procedures out of chat history and into an explicit, editable workflow that improves from execution.

– arxiv. org/abs/2609.09153

Title: "Procedural Graphs: Self-Evolving Execution Structures for LLM Agents"

[Tech Fusionist | Kushal Gangil @techyoutbe]

Cybersecurity IAM Engineer (Learning Plan) 🔥🚀
(Specializing in CyberArk, Okta, or SailPoint)

𝘚𝘵𝘦𝘱-1: 𝘓𝘦𝘢𝘳𝘯 𝘵𝘩𝘦 𝘍𝘶𝘯𝘥𝘢𝘮𝘦𝘯𝘵𝘢𝘭𝘴 𝘰𝘧 𝘐𝘈𝘔, 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘺 𝘚𝘦𝘤𝘶𝘳𝘪𝘵𝘺 𝘢𝘯𝘥 𝘡𝘦𝘳𝘰 𝘛𝘳𝘶𝘴𝘵

𝘚𝘵𝘦𝘱-2: 𝘔𝘢𝘴𝘵𝘦𝘳 𝘈𝘶𝘵𝘩𝘦𝘯𝘵𝘪𝘤𝘢𝘵𝘪𝘰𝘯 & 𝘍𝘦𝘥𝘦𝘳𝘢𝘵𝘪𝘰𝘯 (𝘖𝘈𝘶𝘵𝘩2, 𝘖𝘐𝘋𝘊, 𝘚𝘈𝘔𝘓, 𝘑𝘞𝘛, 𝘚𝘊𝘐𝘔, 𝘞𝘦𝘣𝘈𝘶𝘵𝘩𝘯/𝘍𝘐𝘋𝘖2)

𝘚𝘵𝘦𝘱-3: 𝘚𝘵𝘶𝘥𝘺 𝘈𝘤𝘵𝘪𝘷𝘦 𝘋𝘪𝘳𝘦𝘤𝘵𝘰𝘳𝘺, 𝘓𝘋𝘈𝘗, 𝘌𝘯𝘵𝘳𝘢 𝘐𝘋 𝘢𝘯𝘥 𝘊𝘭𝘰𝘶𝘥 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘺

𝘚𝘵𝘦𝘱-4: 𝘓𝘦𝘢𝘳𝘯 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘺 𝘓𝘪𝘧𝘦𝘤𝘺𝘤𝘭𝘦, 𝘑𝘰𝘪𝘯𝘦𝘳-𝘔𝘰𝘷𝘦𝘳-𝘓𝘦𝘢𝘷𝘦𝘳 (𝘑𝘔𝘓), 𝘗𝘳𝘰𝘷𝘪𝘴𝘪𝘰𝘯𝘪𝘯𝘨 & 𝘋𝘦𝘱𝘳𝘰𝘷𝘪𝘴𝘪𝘰𝘯𝘪𝘯𝘨

𝘚𝘵𝘦𝘱-5: 𝘔𝘢𝘴𝘵𝘦𝘳 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘺 𝘎𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 & 𝘈𝘥𝘮𝘪𝘯𝘪𝘴𝘵𝘳𝘢𝘵𝘪𝘰𝘯 (𝘐𝘎𝘈, 𝘈𝘤𝘤𝘦𝘴𝘴 𝘙𝘦𝘷𝘪𝘦𝘸𝘴, 𝘚𝘰𝘋, 𝘙𝘰𝘭𝘦 𝘔𝘪𝘯𝘪𝘯𝘨)

𝘚𝘵𝘦𝘱-6: 𝘓𝘦𝘢𝘳𝘯 𝘗𝘳𝘪𝘷𝘪𝘭𝘦𝘨𝘦𝘥 𝘈𝘤𝘤𝘦𝘴𝘴 𝘔𝘢𝘯𝘢𝘨𝘦𝘮𝘦𝘯𝘵 (𝘗𝘈𝘔), 𝘑𝘐𝘛/𝘑𝘌𝘈, 𝘚𝘦𝘤𝘳𝘦𝘵𝘴 𝘔𝘢𝘯𝘢𝘨𝘦𝘮𝘦𝘯𝘵 & 𝘊𝘳𝘦𝘥𝘦𝘯𝘵𝘪𝘢𝘭 𝘝𝘢𝘶𝘭𝘵𝘪𝘯𝘨

𝘚𝘵𝘦𝘱-7: 𝘐𝘮𝘱𝘭𝘦𝘮𝘦𝘯𝘵 𝘔𝘍𝘈, 𝘗𝘢𝘴𝘴𝘬𝘦𝘺𝘴, 𝘗𝘩𝘪𝘴𝘩𝘪𝘯𝘨-𝘙𝘦𝘴𝘪𝘴𝘵𝘢𝘯𝘵 𝘈𝘶𝘵𝘩𝘦𝘯𝘵𝘪𝘤𝘢𝘵𝘪𝘰𝘯 & 𝘊𝘰𝘯𝘥𝘪𝘵𝘪𝘰𝘯𝘢𝘭 𝘈𝘤𝘤𝘦𝘴𝘴

𝘚𝘵𝘦𝘱-8: 𝘔𝘢𝘴𝘵𝘦𝘳 𝘊𝘭𝘰𝘶𝘥 & 𝘔𝘶𝘭𝘵𝘪-𝘊𝘭𝘰𝘶𝘥 𝘐𝘈𝘔 (𝘈𝘞𝘚, 𝘈𝘻𝘶𝘳𝘦, 𝘎𝘊𝘗, 𝘞𝘰𝘳𝘬𝘭𝘰𝘢𝘥 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘪𝘦𝘴, 𝘚𝘦𝘳𝘷𝘪𝘤𝘦 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘪𝘦𝘴)

𝘚𝘵𝘦𝘱-9: 𝘚𝘵𝘶𝘥𝘺 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘺 𝘛𝘩𝘳𝘦𝘢𝘵 𝘋𝘦𝘵𝘦𝘤𝘵𝘪𝘰𝘯 & 𝘙𝘦𝘴𝘱𝘰𝘯𝘴𝘦 (𝘐𝘛𝘋𝘙, 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘺 𝘈𝘵𝘵𝘢𝘤𝘬 𝘗𝘢𝘵𝘩𝘴, 𝘗𝘳𝘪𝘷𝘪𝘭𝘦𝘨𝘦 𝘌𝘴𝘤𝘢𝘭𝘢𝘵𝘪𝘰𝘯)

𝘚𝘵𝘦𝘱-10: 𝘓𝘦𝘢𝘳𝘯 𝘕𝘰𝘯-𝘏𝘶𝘮𝘢𝘯 & 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘪𝘦𝘴 (𝘚𝘦𝘳𝘷𝘪𝘤𝘦 𝘈𝘤𝘤𝘰𝘶𝘯𝘵𝘴, 𝘈𝘗𝘐 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘪𝘦𝘴, 𝘊𝘦𝘳𝘵𝘪𝘧𝘪𝘤𝘢𝘵𝘦𝘴, 𝘚𝘦𝘤𝘳𝘦𝘵𝘴)

𝘚𝘵𝘦𝘱-11: 𝘜𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘈𝘐 𝘈𝘨𝘦𝘯𝘵 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘺, 𝘈𝘤𝘤𝘦𝘴𝘴 𝘊𝘰𝘯𝘵𝘳𝘰𝘭 & 𝘓𝘦𝘢𝘴𝘵-𝘗𝘳𝘪𝘷𝘪𝘭ege Access

𝘚𝘵𝘦𝘱-12: 𝘔𝘢𝘴𝘵𝘦𝘳 𝘐𝘈𝘔 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯 (𝘙𝘌𝘚𝘛 𝘈𝘗𝘐𝘴, 𝘗𝘺𝘵𝘩𝘰𝘯, 𝘗𝘰𝘸𝘦𝘳𝘚𝘩𝘦𝘭𝘭, 𝘛𝘦𝘳𝘳𝘢𝘧𝘰𝘳𝘮, 𝘎𝘪𝘵)

𝘚𝘵𝘦𝘱-13: 𝘚𝘱𝘦𝘤𝘪𝘢𝘭𝘪𝘻𝘦 𝘪𝘯 𝘊𝘺𝘣𝘦𝘳𝘈𝘳𝘬, 𝘖𝘬𝘵𝘢, 𝘰𝘳 𝘚𝘢𝘪𝘭𝘗𝘰𝘪𝘯𝘵 𝘢𝘯𝘥 𝘉𝘶𝘪𝘭𝘥 𝘙𝘦𝘢𝘭-𝘞𝘰𝘳𝘭𝘥 𝘐𝘈𝘔 𝘗𝘳𝘰𝘫𝘦𝘤𝘵𝘴

𝘚𝘵𝘦𝘱-14: 𝘓𝘦𝘢𝘳𝘯 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘺 𝘙𝘪𝘴𝘬, 𝘊𝘰𝘮𝘱𝘭𝘪𝘢𝘯𝘤𝘦 & 𝘈𝘶𝘥𝘪𝘵 (𝘚𝘖𝘟, 𝘎𝘋𝘗𝘙, 𝘏𝘐𝘗𝘈𝘈, 𝘗𝘊𝘐 𝘋𝘚𝘚)

𝘚𝘵𝘦𝘱-15: 𝘗𝘶𝘳𝘴𝘶𝘦 𝘐𝘈𝘔 𝘊𝘦𝘳𝘵𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴 & 𝘒𝘦𝘦𝘱 𝘜𝘱𝘥𝘢𝘵𝘦𝘥 𝘸𝘪𝘵𝘩 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘺 𝘚𝘦𝘤𝘶𝘳𝘪𝘵𝘺 𝘛𝘳𝘦𝘯𝘥𝘴

🏆 𝘉𝘶𝘪𝘭𝘥 𝘗𝘳𝘰𝘫𝘦𝘤𝘵𝘴, 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘦 𝘐𝘈𝘔 𝘞𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 & 𝘉𝘦𝘤𝘰𝘮𝘦 𝘢 𝘔𝘰𝘥𝘦𝘳𝘯 𝘊𝘺𝘣𝘦𝘳𝘴𝘦𝘤𝘶𝘳𝘪𝘵𝘺 𝘐𝘈𝘔 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳!