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INTRODUCING THE MONSTER MODEL 🚨📈📊 The laboratory-grade corporate treasury and Bitcoin simulator. The Monster Model is a fully parameterized discrete non-linear dynamical system that models the complex reflexive interactions between @Strategy, @Strive, and #Bitcoin in a unified framework. Key features: → 345 input parameters. Everything from assumed weekly $STRC / $SATA net proceeds to target amplification range, mNAV variant, and Bitcoin hodler flows is included. Parameters that don't currently apply to your configuration disable or disappear on their own. → 159 plottable series. This includes the basics like $MSTR and $ASST share price, Bitcoin price, shares outstanding, BTC reserve, USD assets, Duration, mNAV, and amplification, as well as metrics that are novel to the Monster Model such as $STRK effective in-the-money value, effective sats per share, expected break-even time horizon (EBETH), and Bitcoin's expected one-year forward return. Expected-return premiums for MSTR and ASST, based on work by @stonychambers, are also provided. → 36 charts. MSTR and BTC price, BTC reserve, BTC flows, shares outstanding, modeled BTC liquid/illiquid supply, net proceeds, market cap, USD and months of coverage, reserve valuations, mNAV, amplification, sats per share, convertible bond tracking, and more. → Powered by a bespoke supply-and-demand price clearing model of the Bitcoin ecosystem. The featured method for modeling Bitcoin tracks projected mining emissions, lost coins, Strategy and Strive's own purchases, hodler withdrawals from liquid float into illiquid storage, and releases from illiquid storage back into the liquid float — cleared against a demand trajectory, week by week, into a price. Switch it on and the treasury companies' own buying pressure feeds back into the prices they pay. → 6 Bitcoin price paths, or compose your own. If you prefer a static price path instead of a dynamic one, the Monster Model offers a menu of choices: @Giovann35084111 Santostasi's classic power law; the Bitcoin. com and BitcoinFairPrice variants; @Saylor, @Shirishjajodia and @CJ_Bitcoin's Bitcoin24; @TheRealPlanC's Q1 power law floor; Stephen Perrenod's (@moneyordebt) 8-parameter log-periodic curve. Or build a custom path out of normalized variants of a power law, an S or Gompertz adoption curve, a linear incline, a periodic wave, a decaying exponential, and Perrenod's 8-parameter curve. → Full convertible bond accounting. Every bond either company has ever issued or held, retired ones included, with Black-Scholes option deltas producing a novel share accounting method alongside BSO, ADSO and FDSO: *effective* shares outstanding. Parameters govern how eagerly holders exercise puts and conversions, and whether Strategy calls as soon as the bonds' 30% premium condition permits. → 5 mNAV variants. Multiple of enterprise value (mEV), plus three variants of price-to-book (P/B) that use basic, effective, and assumed-diluted treatments, plus the recently introduced fully diluted multiple of Net Reserve (mNR) in which out-of-the-money convertibles count as debt rather than as future shares. Picking a variant automatically selects the matching share count *and* liability treatment, so the numerator and denominator can never quietly disagree. → 5 ways to drive mNAV forward in time. A constant expansion scalar that gaps and holds; a multiple of break-even mNAV; a parameterized oscillating sinusoid (with or without a decaying amplitude); a linear rise (or fall) towards a terminal mNAV; or an experimental sentiment algorithm built on fast and slow SMAs plus week-over-week Bitcoin moves. → Weekly time steps, aligned to the filings. Strategy and Strive announce by 8-K, so the model breathes at that cadence. Quarterly bond interest and monthly preferred dividends land where they actually land, and the rhythm between capital raises is visible instead of averaged away. → Real history back to 2024. Common share issuance, BSO, ADSO and BTC holdings, taken from Strategy's 8-Ks — used exactly from mid-November 2024 forward, when the filings became regular. The earlier filings are sparser, so a few of those weeks are interpolated to smooth the gaps between them. (Strive's history starts in September 2025, with weekly 8-K coverage from June 2026.) → An experimental credit collateral subsystem. Adoption curves for Bitcoin pledged against credit, with carry spreads, eligible and pledged fractions, and lender LTV — and some of that credit recycling back into Bitcoin demand. → STRC volume cycle modeling. If you expect STRC to trade at or above par into its record dates on rising volume, this parameterized sub-model reproduces that rhythm in its projected capital raises. Otherwise, index either an initial weekly net proceeds estimate or a fraction of the total notional value to a growth curve of your choice. → One-off equity events. Drop a hypothetical issuance or buyback — any share count, any price, any future week — and watch the proceeds, or the funding deficit, propagate through the same allocation rules as everything else. Try using it to explore the potential impacts of various levels of exercise on Strive's outstanding warrants. → 472 modeled series across 8 interconnected systems: convertible debt, preferred equities, common equity, Bitcoin price dynamics, USD assets, the BTC reserve, mNAV, and amplification. — And the model is only half of it. The other half is a premium-grade visualization panel built for tweaking and customization. → Automatic or manual recalculation. Change any assumption and the entire system re-solves in a fraction of a second. Or, switch to Manual mode and queue up several changes to see their combined effect in a single run. → A chart grid you arrange yourself. Drag to reorder, insert a slot anywhere, delete several at once with undo, star your favorites. Stretch charts to fit, or size them to the pixel. Embiggen one to the full pane and the rest stop recomputing behind it. → Charts that follow your parameters. Set Bitcoin to a static curve and the supply series leave the reserve charts, because supply plays no part in that model. Switch amplification from a ratio to a multiple and the axis, the tick format, the series and its target band all change together, in one step. → Pin a reading and watch it move. Hover a chart to read every visible series at that date; press to pin it in place. It holds while you change parameters, with a column reporting how far each series moved since the last run — so you watch a number move instead of remembering it. Synchronize the grid and one press freezes every chart at a single date. → Six auto-saving workspaces: five yours, plus a sandbox that catches links other people send you. Keep the bull, base, and bear cases side by side. Give them custom names, copy between them, reset a single tab, export them to JSON and carry them to another machine. → Shareable links. Your entire parameter set, encoded into the URL. A scenario that differs from the defaults in a dozen places travels in about 50 characters — it records only what you changed and packs each value into the fewest bits its own range allows. It arrives in a reserved sandbox workspace, so opening someone else's scenario never disturbs yours. → Copy any chart as an image. Not a screenshot — composed for the purpose, with its own title, legend and margins, none of the surrounding interface, your hidden series still hidden, your log axes still logarithmic, and your pinned readout included. → Live prices. History ends on the last completed week, so the week you're actually in is a live row: mNAV, amplification and reserve value reflect where things stand right now rather than at last Monday's close. Switch it off for the neutral "what if nothing else happens" baseline, or when conducting controlled parameter impact analysis. → Operable entirely from the keyboard. Not most of it — every control, every menu, every modal. Including lifting a chart off the grid and walking it to a new slot with the arrow keys, or even repositioning a readout within a single chart. → It even works on your phone. The model was built for a big screen and that is still where it shines, but the mobile version keeps the core of it: tap a chart and the marker lands on that date; drag to sweep it with every value updating as it goes; tap again to pin. Tap anywhere else you would normally click on desktop. → ~42,000 words of documentation, delivered where you need it. Every one of the parameters, every plottable series and every chart carries a written description on hover — not parked on a docs page you have to go and find. — A word on how this was built. The framework began as a Google Sheets workbook in late summer 2025, built by hand. I had two rules: no formula went in unless I understood how it worked and believed it earned its place; and AI was never allowed to operate on the spreadsheet itself. The site is that workbook translated into TypeScript and given a thoughtful interface. AI was used heavily and deliberately throughout that process. I spent 18 years in high-performance computing. That work teaches you one thing above all others: numerical integrity comes first — even before performance — and a number nobody checked is not a result. AI is very good at producing numbers nobody checked. So every step of the way, the TypeScript output was validated against the source workbook row by row, series by series. Not a spot check. I've invested over 1,600 hours in the project, the vast majority of that in the last five months. I've never worked harder on anything in my life. — The Monster Model is live at monstermodels.live. No account, no signup — and your scenarios never leave your browser unless you want them to. For the best experience, I recommend using a desktop or laptop browser. Please give it a try and tell me what you think! And if you like the tool, please consider reposting this announcement. This has been an incredible journey, but my hope is that this is just the beginning. Thank you all for your support! 🧡

Few understand... #Bitcoin

⚡️The internet is about to stop being a human environment with machines inside it. It is becoming a machine environment with humans inside it. That is enormous. For thirty years, every website, app, dashboard, marketplace, social network, financial terminal, search engine, and enterprise system was ultimately designed around one assumption: a human being would be the final perceptual and decision-making node. The machine could calculate. The human had to interpret. The machine could display. The human had to navigate. The machine could store. The human had to decide. CAPTCHAs were almost a literal membrane enforcing that ontology. Human on this side. Machine on that side. Now the membrane is breaking. Once an AI can look at an arbitrary screen, infer what the interface means, understand the goal, manipulate the environment, observe the consequence, recover from mistakes, and continue, something much larger than “automation” has happened. The machine has acquired a general-purpose body inside civilization’s digital layer. And that means the first world AI colonizes is not the physical world. We already built its world for it. Every database is terrain. Every website is architecture. Every button is an actuator. Every API is infrastructure. Every financial market is an ecosystem. Every corporate software stack is a workplace. Every digital account is an identity. Every network is transportation. Human civilization accidentally spent fifty years constructing an artificial environment before producing agents capable of inhabiting it. That is the thing hiding inside this goofy “I’m not a robot” demo. And once you see that, the next step becomes obvious. The internet will increasingly contain machine-native economic actors. Not tools waiting for humans. Actors. Agents managing capital. Agents negotiating with other agents. Agents buying services from other agents. Agents writing software for other agents. Agents monitoring systems, making trades, comparing vendors, negotiating contracts, managing infrastructure, researching opportunities, running experiments, allocating compute, generating media, auditing transactions. Eventually an enormous amount of economic activity may occur without a human observing the intermediate steps at all. Humans specify ends. Machines negotiate the path. That creates a completely different economy. Today, a corporation is ultimately a network of humans coordinating through software. Tomorrow, many corporations may become software coordinating through a much smaller number of humans.

Scott has an income of $55,000 per year. Scott spends $75,000 per year ($10,000 on guns). Scott grows his debt by $20,000 per year. Scott has total debt right now of $400,000. Scott has fixed unfunded liabilities of $1,750,000 and is less than 10 years from retirement. Scott's own employees say that his 130% debt will be 690% by the year 2096. Scott needs to refinance roughly $100,000 in the next 12 months to stay solvent. What is happening now? Scott has a Japanese friend who is his biggest lender (despite being in even more debt) that just told Scott he might need to unwind $110,000 of Scott's debt to the open market. Therefore Scot sent his Japanese friend "$50-$100" this summer to keep him calm and ask him to not dump the debt. The credit card companies Scott borrows from are refusing to give him cheap debt anymore, and are giving him 19 year highs on interest rates. Scott said on August 19th that because nobody is buying his credit card debt that he will "at least double" the rate he is buying back his own credit card debt with new debt he takes out. The credit card companies didn't like that and offered him even higher rates 24 hours later. If Scott goes bankrupt we have WW3, because Scott has been buying lots of guns. Scott would rather dilute Dollars he is borrowing in and watch himself starve than go bankrupt. You can buy 100% of the insurance in the world against Scott's insolvency for $15,700. Scott himself has said publicly that he wants to buy insurance against himself and that people should consider buying it. Scott's friends say they want to back his debt with the insurance against his debt - because the insurance is that cheap and the debt is that bad. This insurance plan's cost to buy went up 30% over the last 3 days. Why? Because people are starting to doubt Scott. Scott is America. The insurance plan is Bitcoin.

There are no more sellers left. I’m still buying every single day

It's official. On July 31st, we called for US government intervention as long-term borrowing costs hit 2008 levels. Today, it happened. The US Treasury is DOUBLING buybacks to $4 billion per operation for "liquidity support." What comes next? Let us explain. (a thread)

BTC finally doing something after thousands of years! More activity to come. Stay tuned kids!

Bitcoin is 17 years old. Cars took 30+ years to become mainstream. Credit cards took decades. Electricity took even longer. The internet took two decades to become normal. Bitcoin at 63k will make sense when someone offers you a house in exchange for 1 Bitcoin in 2040.

When the money is broken and there feels like there is no hope in saving, this is what people resort to. Really sad and alarming stuff. Bitcoin is the escape valve.

this might be the best tweet ever written

Try this one.

Real talk? Don't worry about why that burrito costs $20 Go learn why your $20 bill is only worth a burrito

The @PubKey studio is ready to rip your pod. Grab a chair and pull up a beer.

I'm told Affordability isn't a Problem I'm told i can just cook at home Yet, i can go through my Purchase History, the price for the Exact same Product and Exact same Weight has Doubled since 2023.

Buying during a bear market is almost like locking in profits. It’s hard to do, but it’s a sure fire way to avoid kicking yourself later.

Never go full fiat maxi. It’s just not sustainable. Think about the long term vision and keep Bitcoin top of mind.

We made a short visual explainer to help people understand the Coldcard bug, and what it means for the security of affected wallets. Bitcoin's entire security model depends on a user's ability to select astronomically large numbers from a truly random set. If you imagine every possible Bitcoin private key as individual atoms, when a private key is generated properly, the pool of possibilities is so large that it's like an attacker is hunting for one specific atom hidden somewhere across 2 billion galaxies. No classical computer could ever search that many possibilities. Unfortunately, affected Coldcard firmware drew its seeds from a much smaller, more predictable pool. If every possible Coldcard private key was an atom, the possible set of keys would only be about the size of a large virus. In comparison, a standard computer could work through that space in just a few hours. Our video helps visualize what this difference looks like, we hope this is helpful for everyone to establish the importance of true entropy in key generation. We are deeply sorry for anyone who lost funds to this bug. We know several people that were personally affected, including members of the BPI team. Losing funds after working hard protect your wealth is extremely difficult. We are developing a set of policy reflections based on this vulnerability that we hope to publish as soon as possible.

If Bitcoin is the ultimate store of value, does owning MSTR give you the same thing? The answer depends on one question: Do you value exposure or ownership? Holding Bitcoin in cold storage minimizes trust. Holding MSTR means trusting a company, management, and the market. That's why this debate matters. Watch the full podcast now - piped.video/LJpduE738-o

This Coldcard hack cuts deeper than anything I have seen since MtGox. This isn't shitcoiners or yield chasers getting rekt on Celsius, BlockFi or FTX . This is bitcoin maxis following the rules and losing everything. This will go down as one of the worst days in Bitcoins history