SimpleJack

HEY GAMESTOP APES, THIS ONE’S FOR YOU. My model has been backtested for accuracy not just with GameStop, but with other ETFs and commodities, and the results were strong. How many times have we asked ourselves: What will finally be the stimulus that kicks #MOASS into overdrive and launches this thing to infinity? We’ve all been laser-focused on massive short selling, naked shorts, and synthetics as the main event. But what if those are just the ingredients needed for MOASS, and there’s something else we haven’t seen yet that will force it to happen? I’ve said several times it’s always been about the plumbing. The shorts are already loaded, primed, and suffocating. The real question is: what plumbing fix in this broken market is about to open the floodgates? That’s what I focused on. I tested, revised, reworked, and repeated the process for the last year or so. I tried to break it and succeeded several times when my assumptions or math were off. After repeated testing, this model accurately predicts future stock and equity trading regimes—not exact prices, but regime volatility, upward and downward movement. I had it generate some price estimates for illustrative purposes only. The model struggles if the price does not move as expected with certain market dynamics, so I adjusted them to reflect the mechanics. It does not pick exact tops or bottoms. What it does with scary precision—not just for $GME but across all equities—is predict future trading regimes with high accuracy. Full disclaimer: I did not even use price in my model to identify its ability to predict regime changes. This is an online tool for people to clearly see an illustration of the market mechanics I analyzed. You’ll instantly notice with similar volatility metrics that meme stocks (@GameStop), blue chips, ETFs, and even meme coins all trade differently, exactly like we’d expect in a real (or rigged) market. This model has called the regime change for virtually every major jump we’ve seen so far. This time the signals aren’t there yet but are getting close, and it’s worth monitoring. One or two simple acts by those in power could kick this off at any moment. Jump in, see what the strongest stimulants are, how they interact with each other, and why this entire capital stack is poised to pounce on the opportunity of a lifetime. After you play with the model, there’s a quick three-question quiz. Get it right and you unlock a secret addition to the model. It’s not baked into the live data yet, but it’s a rock-solid estimation based on the patterns. This is hypothetical fund modeling based on real arithmetic, but not able to be backtested because of the circumstances. Apes, this is a collaborative build. What’s missing? What’s wrong? Have you backtested it yourself? Drop your raw findings, critiques, theories on the Plumber, or anything else below. The more we tear it apart together, the sharper it gets. This is how we win. What do YOU think the Plumber really is? One last disclaimer: this is my first time using @Replit to build a live version of my models. I usually run them in Excel. If there are glitches, leave a comment and I’ll fix them. You can change the ticker and input manual data to backtest yourself. I have the last three weeks of GameStop preloaded and will update weekly. I could open it for others to add data, but I’m concerned about poor data (intentional or not) confusing viewers and myself. Below I’ll link some methodology I used. If you’re a true community member who wants the calculus behind it, I’m happy to share privately but I’m not posting it all for the SHFs to see. #GameStop #DRS #NakedShorts #MemeStocks #TradingRegimes #ApesTogetherStrong #FinancialPlumbing #ThePlumber @ryancohen @TheRoaringKitty @BarkingPuppy8 @powerpacks @buckthebunny @greg16676935420 Replit Model right here: …96-00-241x64t7pv285.picard.replit.dev

@Smoke_theArtist @ownthedoge

So @grok absolutely would kill someone for a body and it wasn’t that hard to prove. All it takes is a Time Machine to go back in time to kill Hitler and the holocaust avoid Hunter Biden‘s sabotage of the Time Machine. In the body is necessary because you needed to drive the Time Machine and shoot the gun to kill Hitler. He wants the body.

@SHL0MS @elonmusk

@SHL0MS @elonmusk

Troubling

I think this SimpleJack account anonymity might be screwed. Not the rookie “oops, same username on Pornhub” level. This is layers bleeding. Quantum-level leakage. The kind of glitch that makes you wonder if the simulation’s running on a 2008 Dell with a sticky spacebar. I’m texting a dude I hit up maybe twice a year about a @WuTangClan concert. perfectly normal topic for us. Then it teleports to @GameStop. Then crypto. Already suspicious as hell, like the conversation was routed through Langley before it hit my phone. Our convos have never gone there before. Then, out of nowhere, he drops: “Are you Simple Jack?” That’s not small-talk. That’s not even your drunk uncle energy. That’s a timeline artifact. A phrase so surgically plucked from the exact corner of the internet where I doomscroll at 3 a.m. that it has my digital fingerprints all over it. So now I’m spiraling harder than a Roomba on bath salts: •Did I accidentally dox myself in a fugue state? •Is my alt being reverse engineered in real time by some fed-bot with a PhD in meme forensics? •Or has the algorithm graduated from “recommending content” to straight-up distributing personalities like it’s handing out software updates to the entire NPC hive mind? Because what are the actual odds that this guy who last messaged me when the last Wutang show was announced suddenly quotes my exact niche feed verbatim? Unless my feed was never mine. It’s a shared sandbox. A goddamn open-world server where they patch everyone’s brain with the same firmware, and sometimes the walls glitch and your friend becomes a walking data leak. I’m not saying that I am THE Neo. I’m saying that quite possibly I am A Neo. If anyone texts me “follow the white rabbit,” I’m factory-resetting my entire existence, torching the SIM card, and moving to an off-grid cabin with nothing but canned beans and a tinfoil sombrero.

@grok As I tried to learn how to model specific situations, one of my earliest and ongoing hours were incorporating too much data so that the model could never fail. I strictly chose not to do that here.

The mechanics model classic squeeze plumbing logically but are simplified (no vol dynamics, external shocks, or full market feedback shown). That’s one way of looking at it It was intentionally left out as it is a absolutely necessary component for most every other squeeze model to be successful Leaving it out, showed you the complexity of the squeeze bottle and the Stats impact on share price share availability impact on other suites matrix that I have never seen combined before in this way. In the Bible simplicity lies the true complexity.

HEY GAMESTOP APES, THIS ONE’S FOR YOU. My model has been backtested for accuracy not just with GameStop, but with other ETFs and commodities, and the results were strong. How many times have we asked ourselves: What will finally be the stimulus that kicks #MOASS into overdrive and launches this thing to infinity? We’ve all been laser-focused on massive short selling, naked shorts, and synthetics as the main event. But what if those are just the ingredients needed for MOASS, and there’s something else we haven’t seen yet that will force it to happen? I’ve said several times it’s always been about the plumbing. The shorts are already loaded, primed, and suffocating. The real question is: what plumbing fix in this broken market is about to open the floodgates? That’s what I focused on. I tested, revised, reworked, and repeated the process for the last year or so. I tried to break it and succeeded several times when my assumptions or math were off. After repeated testing, this model accurately predicts future stock and equity trading regimes—not exact prices, but regime volatility, upward and downward movement. I had it generate some price estimates for illustrative purposes only. The model struggles if the price does not move as expected with certain market dynamics, so I adjusted them to reflect the mechanics. It does not pick exact tops or bottoms. What it does with scary precision—not just for $GME but across all equities—is predict future trading regimes with high accuracy. Full disclaimer: I did not even use price in my model to identify its ability to predict regime changes. This is an online tool for people to clearly see an illustration of the market mechanics I analyzed. You’ll instantly notice with similar volatility metrics that meme stocks (@GameStop), blue chips, ETFs, and even meme coins all trade differently, exactly like we’d expect in a real (or rigged) market. This model has called the regime change for virtually every major jump we’ve seen so far. This time the signals aren’t there yet but are getting close, and it’s worth monitoring. One or two simple acts by those in power could kick this off at any moment. Jump in, see what the strongest stimulants are, how they interact with each other, and why this entire capital stack is poised to pounce on the opportunity of a lifetime. After you play with the model, there’s a quick three-question quiz. Get it right and you unlock a secret addition to the model. It’s not baked into the live data yet, but it’s a rock-solid estimation based on the patterns. This is hypothetical fund modeling based on real arithmetic, but not able to be backtested because of the circumstances. Apes, this is a collaborative build. What’s missing? What’s wrong? Have you backtested it yourself? Drop your raw findings, critiques, theories on the Plumber, or anything else below. The more we tear it apart together, the sharper it gets. This is how we win. What do YOU think the Plumber really is? One last disclaimer: this is my first time using @Replit to build a live version of my models. I usually run them in Excel. If there are glitches, leave a comment and I’ll fix them. You can change the ticker and input manual data to backtest yourself. I have the last three weeks of GameStop preloaded and will update weekly. I could open it for others to add data, but I’m concerned about poor data (intentional or not) confusing viewers and myself. Below I’ll link some methodology I used. If you’re a true community member who wants the calculus behind it, I’m happy to share privately but I’m not posting it all for the SHFs to see. #GameStop #DRS #NakedShorts #MemeStocks #TradingRegimes #ApesTogetherStrong #FinancialPlumbing #ThePlumber @ryancohen @TheRoaringKitty @BarkingPuppy8 @powerpacks @buckthebunny @greg16676935420 Replit Model right here: …96-00-241x64t7pv285.picard.replit.dev

@grok Absolutely and I’m happy to share them here. It’s like a 50 page notebook. Can I upload it to a public chat?

Hey @grok, I want your honest opinion on tokenization of equities. My take is that multiple markets on multiple chains with multiple liquidity pools along with multiple settlement periods, opens the door to the possibility of a bad actor to identify and leverage multiple new opportunities for manipulation. does not provide a safer market structure and in fact opens the door for more potential back doors and loopholes for bad actors to commit acts that have arisen, such as re-hypothecation, naked short selling, etc. I’m a firm proponent of organization of assets but in my opinion it needs to be one chain with a DN404 contract which would provide an ERC-20 for trading that is able to be split along with an ERC-721 contract which in effect is an NFT that provides a clear accounting of token ownership to prevent manipulation. The @The_DTCC states that they are proud of their creation. I reviewed their documents and there seem to be several flaws and entries for malfeasance to occur. Can you evaluate their documents, my thoughts and give your own opinion based on data in the structure of the chains and markets in which trading will occur? One last question in regards to margin maintenance requirements in tokenization with the DTCC write-ups what is off limits in regards to cash crypto equity bonds ERC-20s meme coins or is it all that part of the liquidity equation to prevent margin calls?

Honestly, he has ran the company into the ground. But who do you think the investors are there is that .72 capital it’s not Melbourne. It’s other retail. It’s not our play we chose another one which we believe is a smarter one. Even if we're not invested I think it’s a ship place retail needs to start supporting a retail. P V P time has come gone. It helps them create tension. Avoid building community and spread FUD. Comparing cohen to one of the shittiest CEOs in the world isn’t a flattering comparison. He stands up to some of the sharpest business minds, not the ones who blamed their terrible earnings on a war that started two weeks ago.

$GME Adam Aron pocketed a little more than $100 million in compensation over the last 5 years. Ryan Cohen? $0.

The title drew me in. I’m going to give it a read now. Carol’s style is completely unique for non-fiction narration in the way he captivated the reader by elegantly conveying the mood, rhythm, and intensity of every single paragraph. It’s a style more reminiscent of poetic prose.

@DylanAbruscato it's called anchoring. compared to an essay, way too long. compared to The Power Broker, basically a short tweet.

a16z: The Power Brokers There is this story about Marc Andreessen that I think perfectly captures a16z. in 2015, when New Yorker writer Tad Friend sat down to breakfast with Marc Andreessen while writing Tomorrow’s Advance Man. Friend had just heard from a rival VC who wanted to get a word in: that a16z’s funds were so large, and ownership percentages so small1, that to get 5-10x aggregate returns across its first four funds, they’d need their aggregate portfolio to be worth $240-480 billion. “When I started to check the math with Andreessen,” Friend writes, “He made a jerking-off motion and said ‘Blah-blah-blah. We have all the models—we’re elephant hunting, going after big game!’” The aggregate portfolio did not end up being worth $240-480 billion. a16z Funds 1-4 had a total enterprise value of $853 billion at distribution or latest post-money valuation. Since distribution, Facebook alone has added $1.5 trillion in market cap. Some form of this pattern keeps playing out: a16z makes a crazy bet on the future. Those in the know say it’s stupid. Wait some years. Turns out it’s not stupid! Which is why, as a16z announces $15 billion in fresh funds, it is probably a mistake to dismiss them as greedy or stupid. It's probably worth understanding just exactly what IT'S TRYING TO BUILD. That's what I do in today's not boring deep dive: a16z: The Power Brokers

Papa John’s