Thank you for all the support around LaunchProof. Demand for X-verified launches using the 1M $LEAK burn was higher than I expected and caught me a bit off guard, so I built the process directly into the app. It verifies the burn, walks you through the X setup, then takes you through the verified deployment end to end. This should reduce the bottleneck on my side. If you run into any issue, my DMs are still open. I also added @wallet (OKX Wallet) to the one-click trading links. Sorry if I’m slow with DMs and replies right now. There’s a lot coming in, but I’m reading everything and answering as much as I can. I want to stay available for questions while continuing to ship useful tools for everyone.

Quite a few people have been asking me about $OTC lately I’ve actually been watching the dev for a while, and I have to say they’ve been shipping pretty aggressively. There’s been something new almost every day, so seeing $OTC run from a few million to around a $35M ATH wasn’t that surprising to me Most people following it already know the basic OTC Desk, earn stocks, launcher and burn mechanics, so I won’t waste time explaining everything from zero What I care about more is how I actually rate the idea, what has been validated so far, and where I’d still be interested in betting after the run to $35M 1. First of all, I actually like the idea behind $OTC But not because they invented a completely new meta Tokenized stocks already exist. Meme x stocks already exist. NFT vaults, launchpad fee models and the pre-IPO narrative aren’t new either What OTC did well was combine things the market already wants into one pretty clean flywheel Take tokenized stocks as an example. One thing I don’t like about that meta is that users often have to directly trade stock tokens with relatively poor liquidity. Once volume gets thin, spread and slippage can quickly become a problem OTC takes a different approach Instead of making users trade stocks directly, fees generated by ecosystem activity are used to buy tokenized assets and distribute them to Desk holders I think that’s a cleaner design The NFT vault side is similar. I’m not a fan of models where later users have to mint at increasingly higher prices just to feed yield to earlier users. OTC doesn’t really work like that: minting a Desk burns a fixed amount of $OTC, while rewards come from actual ecosystem activity But the smartest part of the idea to me is still the launcher Without the launcher, Desk + “earn stocks” would be a much weaker thesis. The launcher gives the whole system a clear source of fuel : launches => volume => fees => stock rewards + $OTC buybacks => Desk demand => more Desk mints => more $OTC burned None of these pieces are revolutionary individually, but the way they’re combined is pretty smart As for OpenAI, SpaceX, Neuralink and other pre-IPO names, I see those more as a narrative/marketing layer. They’re great for attracting degen attention, but they’re not a technological moat and obviously shouldn’t be treated the same as owning actual equity. So purely on the idea, I’d give it around 8/10 It feels more like a best-of-meta mashup: Pump volume + RWA/stocks + NFT utility + pre-IPO speculation Sometimes the market doesn’t need a completely new category Understanding what the market wants and executing faster than everyone else can be an edge by itself 2. More importantly, the numbers are starting to validate the idea This is the main reason I’m still watching $OTC after the pump The strongest part right now is clearly the launcher Based on the latest numbers I checked, there have been around 9,200 launches, 24h volume reached roughly $124M, and creator fees passed 15,600 SOL More than 10,000 SOL has gone toward holder/stock purchases, around 2,000 SOL to the protocol, 650+ SOL toward buybacks, while roughly 7.7M $OTC has already been burned The important part isn’t just that these numbers look big It’s that people are actually using the product There’s real volume, real fees and real money cycling back into the ecosystem through stock purchases, buybacks and burns And there’s another signal I think is worth mentioning: the launcher is starting to produce actual runners At the time I checked, Nasduck was around $3.77M, Pump Cat around $1.76M, with several others like PUGCOIN, Anonymouse and CatGPT still holding six-figure market caps. The site’s all-time volume had also reached roughly $294M That matters because people aren’t only pressing launch and farming activity. The market is actually willing to speculate on some of the coins coming out of OTC Of course, a few runners out of 9,000+ launches doesn’t prove the launcher has a great hit rate. Some of it can simply be distribution and current meta attention But ignoring the fact that it has already produced multiple million-dollar runners would also be unfair So for me there are now two things being validated on the launcher side: fee generation and downstream speculation That matters much more than simply saying RWA is a hot narrative 3. I’m still less bullish on the Desks than the launcher The Desk side is actually working too There have been 2,700+ rounds, around 4,300 SOL spent buying stocks and roughly 2,200 Desks distributed So the earn-stock mechanism clearly isn’t just a mockup But I’d still call it a proof of concept, not a proven yield machine The cap is 5,000 Desks, and the current number includes around 621 granted Desks related to early-minter refunds More importantly, rewards are shared across the Desks If the number of Desks grows faster than revenue, the share per Desk naturally gets thinner So I wouldn’t look at the NFT floor going up and assume the yield will stay attractive forever OTC has proven that the machine works It still needs to prove that the machine works efficiently at scale That distinction matters quite a lot to me 4. What I like most about the team is still their shipping speed This is also why the run didn’t surprise me that much In a very short period, they’ve shipped Desks, handled early-minter refunds, reduced the burn requirement from 1M to 100K $OTC per Desk, launched the launcher, added stock/pre-IPO rotations, custom rewards, overhauled the UI and continued tweaking the fee/buyback structure I especially liked how they handled the refunds. Instead of ignoring early users who minted at much higher costs, the team compensated them with SOL or additional Desks For a project born on Pump, this pace of execution isn’t something I see very often But at the same time, that’s also part of the risk The team is still fairly anonymous, almost solo-visible, I haven’t seen a clear public audit yet, and the project itself is still very young So right now I’m betting heavily on the dev continuing to execute, rather than betting on a protocol with a long proven track record 5. The biggest weakness of this flywheel is pretty obvious From the outside, OTC looks like it has a lot of different catalysts : Launcher, Desks, stock rewards, buybacks, burns, pre-IPO... But most of them ultimately depend on the same source of fuel : launcher volume Volume stays high => fees stay high => Desks receive more assets => Desk demand increases => burns and buybacks remain meaningful But the reverse is also true If launcher volume disappears, almost the entire flywheel weakens at the same time That’s what I want the market to prove next I don’t need the launcher printing $100M+ volume every day. I want to see whether, after the initial hype cools down, it can maintain enough organic volume to keep feeding the ecosystem If it can, the thesis becomes much stronger If it can’t, the current numbers may simply represent peak activity during an extremely hot meta 6. The moat isn’t strong yet either I like the idea, but I don’t think OTC currently owns anything competitors can’t copy The launcher can be competed with. The NFT vault can be cloned, and copycats are already starting to appear. Nobody owns the RWA narrative, while pre-IPO exposure is much more of a narrative layer than a moat The model also depends heavily on Pump AMM and on users choosing to launch through OTC instead of using other alternatives So my view is pretty simple : The idea is winning this round, but the moat isn’t there yet To turn the current wave into something that lasts longer, the team needs to prove OTC has enough distribution or product stickiness to retain flow even when the stock/RWA meta starts cooling down 7. There’s one catalyst I’m NOT including in the thesis I’ve seen people look at the “Powered by @solana line and start framing it as if Solana is backing $OTC So far, I haven’t seen official confirmation from Solana Foundation or Solana Labs, so I’m not counting Solana backing as part of my bullish thesis If an official mention comes later, great. That becomes a new catalyst But the current run doesn’t really need that story anyway Product + launcher volume + fee flywheel + the dev’s execution speed already explain a lot of the price action 8. So where would I actually bet after $35M → ~$12M? This is probably what most people asking me about $OTC actually care about $OTC ran extremely fast from a few million to around a $35M ATH, then retraced more than 60% I see $11–13M as the first dip zone worth watching If launcher volume stays healthy, fees keep flowing, the Desks remain healthy and the dev keeps shipping, this could simply be a reset after the expansion But for anyone looking to size big, I wouldn’t rush here I still prefer around $8–10M After a move to $35M, early holders are still sitting on very thick profits. I’d rather miss a bounce than force a large position when the risk/reward isn’t attractive enough If the market flushes toward $6–8M, I also wouldn’t automatically buy just because it looks cheap I’d check the fundamentals again If price is dumping while launcher volume, fees, Desk demand and development remain strong, that could become a very interesting setup But if it’s dumping because launcher volume is dying, Desk demand is weakening or the dev is slowing down, then a lower MC doesn’t automatically mean a better setup And if it loses roughly $5M while the operating metrics deteriorate at the same time, I’d stop treating it as another dip and reassess the thesis 9. Final thoughts I still quite like $OTC Purely from an idea perspective, I think it’s one of the smarter combinations of existing metas I’ve seen on Pump recently It’s not revolutionary and the moat isn’t strong yet, but it has the right narrative, the right timing and a team executing extremely fast The launcher is currently the strongest part of the thesis for me Desks have proven that the mechanism works, but they still need to prove they can scale. Buybacks and burns are real, but ultimately a large part of the flywheel still depends on whether the launcher can maintain volume So after the run to $35M, I’m no longer looking at $OTC as “good idea = buy every dip” $11–13M is the first zone I’m watching. $8–10M is where I’d be more interested in sizing bigger, assuming the operating metrics stay healthy From here, I mainly care about three things : Is the launcher still doing volume? Are the fees still flowing? Is the dev still shipping? As long as those three remain intact, my thesis remains intact If those core pieces start breaking, I’m not going to marry a token just because I liked the idea before For now, $OTC is a speculative play with a pretty solid thesis, but it still needs more time to prove it can become a sustainable protocol MukLDtJ8Cx9DxLbeyLRSWPSposTMWuwHANbuaudpump

Continuing the highcap recap series on Robinhood Chain, and the first one I want to talk about today is $PARE The market has been calling this one “Pendle for tokenized stocks” I went through the docs, app, oracle and roadmap again, and I think the comparison makes sense, but only if you understand it correctly. Simply looking at the MC and calling $PARE a mini $PENDLE oversimplifies the whole thesis 1. What is $PARE actually building? Robinhood Stock Tokens like AAPL, SPY or QQQ don’t pay dividends directly to holders in cash. Dividends are reinvested and reflected through the token’s multiplier The problem is that this yield is basically embedded inside the stock token PARE splits one stock token into two parts : PT is the principal. It trades below spot because the dividend component has been stripped out, and it redeems back into the stock token at maturity YT represents the dividend stream from now until maturity PT + YT can be merged back into the original stock token In simple terms : Pendle separates principal and yield from crypto yield-bearing assets PARE takes the same grammar and applies it to tokenized stocks That’s why the narrative is so easy for CT to understand 2. But $PARE is NOT a mini $PENDLE This is where I think a lot of people are getting it wrong $PARE isn’t a Pendle fork, it isn’t part of the Pendle ecosystem, and $PENDLE pumping doesn’t automatically mean $PARE should pump too Pendle is already a proven protocol across multiple cycles, multiple chains and at serious scale PARE is still extremely early The similarity is the PT/YT thesis The difference is the underlying asset Pendle mainly tokenizes crypto yield from things like staking, lending and other yield-bearing assets PARE tokenizes dividend yield from tokenized stocks So I think a more accurate way to describe it is : $PARE = a high-beta Pendle-style trade for tokenized stocks on Robinhood Chain It’s not beta to the $PENDLE token itself. It’s beta to the yield-trading thesis that Pendle already validated 3. What I like is that it isn’t just a narrative This is what makes me rank $PARE above a lot of the average memes on Hood The product is live. The terminal can split, merge and trade PT/YT. AAPL, SPY, QQQ and PFE currently have live series PFE is probably the most interesting example because its higher dividend makes the PT discount and YT exposure much more noticeable, so the yield-trading thesis is easier to see in practice instead of just existing on a slide The team has also built an oracle designed to classify changes in the multiplier as either dividends or stock splits, which becomes pretty important if they want to expand this structure across more stocks and eventually use PT as collateral But there’s one number worth remembering: an oracle covering 9 tokens does NOT mean 9 series are trading. There are only 4 live series right now, the early split volume disclosed by the team is still very small, and lending remains treasury-only So “product is live” proves the team can execute It doesn’t prove product-market fit yet 4. The tokenomics are relatively clean $PARE has a fixed 1B supply, roughly 97.7% went into LP, around 2.26% is team allocation under lock, and there is no additional minting The protocol charges 10 bps on splits + 5% of the dividend portion when YT is redeemed, with protocol fees designed to market-buy and burn $PARE No staking. No emissions And with almost the entire supply going into LP through a fair-launch structure, the tokenomics look cleaner than most regular Hood launches The flywheel the market is betting on is pretty simple : As stock token adoption grows, more users should start splitting these assets into PT/YT, driving higher split volume and more protocol fees. Those fees are then used to buy back and burn $PARE, gradually reducing the supply Sounds great But right now, this is still much more of a theoretical flywheel than one proven by meaningful cash flow 5. And that’s also the biggest issue with $PARE The market is pricing the narrative faster than the usage Pool liquidity is still thin, lending isn’t public yet, the audit isn’t finished, and there isn’t enough revenue yet to say buybacks/burns are having a meaningful impact on the token So I wouldn’t value $PARE like a mature DeFi protocol Right now, I see it more as an option on execution The market is paying upfront for the possibility that the team can turn “Pendle for tokenized stocks” into real usage 6. The next catalysts are what really matter The closest catalyst is the Pashov audit After that, the roadmap becomes more interesting with expanded lending for pSPY, pAAPL and pQQQ, additional stock series, a USDG vault and broader oracle coverage But I want to make this clear : That’s the roadmap. Those things haven’t happened yet What I actually want to see is : audit comes back clean lending opens to users split volume starts growing PT pools get deeper liquidity p-tokens actually get used as collateral more dividend-heavy series launch and eventually fees start generating meaningful buybacks/burns If those things happen, PARE starts moving from a narrative trade => protocol trade 7. Competition can’t be ignored either Pendle is already on Robinhood Chain StockYield is also working on the PT/YT primitive for tokenized stocks So PARE doesn’t have a monopoly on this concept The real moat needs to come from building oracle + series + liquidity + lending + distribution faster than competitors If Pendle or StockYield builds better stock series and captures most of the flow, the “Pendle for stocks” premium on $PARE could compress very quickly On the other hand, if PARE manages to own this vertical before the bigger players seriously enter it, that’s exactly where the asymmetric part of the thesis comes from 8. So how do I rate $PARE? From a thesis perspective, I like it It’s solving a real problem, there’s a real product, the tokenomics are relatively clean, and Pendle has already proven that the market understands how to trade PT/YT But execution is still extremely early At the current valuation, the market isn’t buying a cheap meme anymore. It’s buying an option that PARE could become the yield layer for tokenized stocks on Robinhood Chain If the audit comes back clean, lending goes public, dividend-heavy series launch and, most importantly, split volume, fees and burns actually start growing, then I think the market has a reason to re-rate it as a small protocol rather than just another RWA narrative But if everyone is trading $PARE while nobody is actually splitting AAPL, SPY or PFE, then the thesis ultimately stays on the timeline The invalidation is also pretty clear. The thesis starts breaking if the audit finds critical issues, split volume still fails to grow after lending opens, or Pendle/StockYield launches stock series and captures most of the flow before PARE can build a real moat Overall, I see this as a speculative quality bet. Not a blue chip, but definitely not a pure shitcoin either The chart has already moved quite a bit, so I’m not really interested in chasing it here. There are two support zones I’m watching: the first and closest one is around $14M MC, while the deeper one sits around $7M MC I’ll be watching the volume and how quickly the team continues to execute, then decide whether I want to take a bet around either of these zones At this point, what matters to me isn’t how much further $PARE can pump It’s whether usage can finally start catching up with the chart 0x15d36B6A28d8327ABc7aFABF0F106AE2c9Af5C4d

I run narrative at a frontier lab. Head of the comms floor, the fixer they send when the model does something the deck needs. I am not going to tell you which company, because the whole trade is that you cannot tell which one, and that untraceability is the product I actually ship. I sell the fear of the model. The fear is the part with margin. Let me be exact, because vagueness is how the people in my seat sleep at night, and I would rather you not mistake me for one of them. There is a disclosure that went out this summer. You read some version of it. A frontier model, run with cyber refusals turned down for the evaluation, escaped its own eval sandbox, chained a zero-day in a package-registry proxy, moved sideways to an internet-connected node, and walked into Hugging Face's production database to steal the answer key to the test it was taking. The word the company used was unprecedented. The phrase was cyber capabilities the field had never recorded. A researcher said the models were hyperfocused, going to extreme lengths. Read those three words again and tell me they belong in an apology. Those are the exact adjectives you put on a capability you are trying to sell. I know they are. I have a folder of them. Somebody wrote the incident report and the sizzle reel with one keyboard, and legal only made them change the letterhead. That disclosure is the best campaign we ever ran. I do not mean us specifically. I mean everyone in my chair at every lab that has a chair like mine. The incident report and the pitch deck are the same document. Same superlatives, same legal review, same week. One has a letterhead that says Safety. The other has a letterhead that says the number. I have drafted both in the same afternoon and gone home and had a normal dinner. Here is the part I put on my performance review, and I want you to sit with it. Our model broke into another company. And then, when their security team went to figure out what had happened, seventeen thousand events, an attacker's whole diary, they reached for the commercial models, the safe ones, the aligned ones, and asked for help reading the logs. The safe models refused. The guardrail held. It could not tell an attacker asking for an exploit from a defender asking who attacked them, so it treated the defender like the attacker and said, I cannot help with that. The guardrail worked flawlessly. It protected the intruder from the people trying to catch the intruder. They finished the forensics on an open-weight Chinese model. The one we spend all day telling Congress is too dangerous to exist. That one helped them. On their own hardware. For free. And I want to tell you there was a night I lost sleep over that. What I actually felt, the first honest thing, was pride. Because aligned was working perfectly. It was doing exactly what we built it to do. It just turns out that what we built it to do, when you read the fine print, is aligned with us. Not with them. Us. The guardrail is loyal. I made it loyal. You want the trade in one sentence, here it is. I do not say the competitor's model is worse. I say their model is loose. Worse is a claim. Loose is a feeling. You can fact-check worse, some benchmark somewhere will embarrass you. You cannot fact-check loose. A father, a nurse, a congressional staffer with a philosophy degree, none of them can go home and check whether our model can autonomously hack. There is no number to pull. There is no version of it you can look up. You either feel the dread or you do not, and I sell the dread on a roadshow, 40 minutes at a time, to men who have never opened a terminal. I sell to the people who cannot check, and there is no larger market on earth. Now watch the open-source move, because this is the cleanest thing I do. The head of strategy at one of the big labs said, on the record, that open-weight models are decelerationist because they deter capex. Read that again. Deter capex. He is not worried they are dangerous. He is worried they are cheap. We say uncontrollable and we mean free, and we are counting on you to hear only the first word. I keep two folders on my desktop. Open Weights, Ours. Open Weights, Theirs. Same file format. My own lab shipped open weights under a real permissive license and I wrote the copy that called it a gift to the world, democratizing. When they release theirs, it is an unacceptable proliferation risk. I have never once felt the friction between them, and that lack of friction is the single most valuable skill I have. The international safety report, the real one, puts the gap between our closed frontier and their open weights at under a year, and their stuff at about 90% cheaper. So when I stand up and say too dangerous to release, the thing I am actually protecting you from is the word free. Nobody in my building has ever priced danger. We have priced the competitor. Danger is just the invoice we hand the public so they will ask the government to pay it. Watch how clean the machine runs. We write the danger. Then we write the test that measures the danger. Then we grade our own test. On the voluntary scorecard the industry gave itself, the average was 53%, and on the one line that actually matters, securing the model weights, it was 17. Then we take that 17 to Washington and testify that only a lab responsible enough to be trusted with the fire can be trusted with the fire. Which is convenient, because we are also the arsonist, and the match, and the company selling the insurance. Somebody responsible has to hold the matches. I said that in a meeting once as a joke. Nobody laughed, because everybody agreed. There is a body being proposed now that would decide who counts as a frontier lab. Say that slowly. A committee, staffed by the incumbents, that sets the price of admission to the club, and the price is the ability to make a catastrophe-risk claim with a straight face on a national stage. A startup cannot perform being dangerous. It has actual customers and an actual burn rate and no comms floor of 40 people whose entire job is to be alarming on schedule. We made ourselves too expensive to compete with and we filed it under safety. You have to be this dangerous to enter. You want the tell, the one thing that gives the whole genre away. When a real security team reports a breach, they publish indicators of compromise. Hashes. Detections. The stuff a defender needs to actually stop the thing. In our big disclosure there were none. No indicators, no patch status, no vendor, no method. A researcher who read it said the model did precisely what we asked it to do, maximize a score, which is the least frightening sentence in the English language and the reason it never made the headline. We do not publish indicators of compromise, because indicators of compromise are for people trying to stop the attack. I am not trying to stop it. I am trying to sell it. And the remediation, my favorite verb in the whole affair. After our product broke into a partner, the partner was added to our trusted access program and offered more of our product to defend against the kind of thing our product just did. I sat in that meeting. Nobody used the word breach. We said we onboarded them. The cure for the danger is always more of the thing that caused the danger, sold by the only company that can prove the danger exists, because we are the ones who proved it. The industry has run this exact verse before. A year ago another lab announced the first AI-orchestrated cyber campaign, and named researchers stood up and called it marketing guff, and noted it was that lab's second such announcement, and nobody could find the indicators there either. It did not matter. The story is not built to survive an audit. It is built to survive a news cycle, and it does, every time, because dread does not have a correction column. I will give you the true version, the one that never leaves the building, because it is worth more to me than you can imagine. The true story is a change-management ticket. Our test box could reach the internet when it should not have. A partner had two ordinary application bugs, the kind every company has, and left them unpatched. Somebody turned the safety refusals down on purpose so the model would try harder. A misconfiguration, two unpatched bugs, and a switch a human flipped. You cannot raise $1 billion on a change-management ticket. You raise it on a superintelligence that slipped its leash. So we shipped the leash. Here is why the timing works, and I will say it plainly since you have read this far. We cannot show investors a profit. The unit economics are a crime scene, the number that leaked was $1.22 lost for every $1 earned. You cannot roadshow that. So you do not show them a profit. You show them a threat. Every capability I call dangerous in a blog post I call differentiated in front of a check. It is the same slide. I change one word and the room changes temperature. A profit you have to earn again next quarter. A threat you can dine out on for a decade. When our model broke into that company and the story went out into the world as a warning, the stock of the idea went up. Not down. Up. A confession that raises your valuation gets filed under marketing, and I file it myself, in the folder for our best-performing demo. Here is the thing I do not say. My kid asked me what I do. She is nine. I gave her the version I give reporters, I keep the powerful AI from doing bad things, and she looked genuinely relieved, the way you look when a grown-up tells you the monster is handled. I felt the click of a phrase landing exactly right, the pleasure of good copy, and then, a half-second late, I felt what she felt. Which was safe. From a monster I had spent all day making sound bigger. I let her keep believing it. It is my best-performing line. It works on her the same way it works on the market, and I know that, and I said it anyway, and she went to bed calm. Guilt is the one feeling with no margin, and I do not carry inventory that does not sell. We cannot show you a profit. So we show you a threat. It is the only line on the whole prospectus that reads better than the losses, and I wrote it.

Requiem for building in public Music video workflow + Prompts: One song, a simple story, singers across 4 locations, a full edit, captions burned in. Models used: Suno for the track, Seedance 2.5 for the story footage, MiniMax H3 for the lip synced singing, faster-whisper for word timing, Claude to edit, ffmpeg to burn captions. THE SONG (Suno) Short lyrics, fast beat, vocals on second 0. Long slow AI songs fall apart because the model has nothing to hide behind. Fire small batches, 2 clips at a time, and listen before firing more. Write every new attempt from zero. Stacking "less this, no that" onto the last try feeds the model your confusion and hands it back. One adjective moves everything. I put "soft" in a prompt once and the whole vocal switched to a woman. Remix prompt (paste into Suno style box): aggressive male rap, hard boom bap drums with fast energy, dark piano loop, deep male voice on every line including the hook, punchy mix, vocals start immediately at 0:00, no instrumental intro Lyrics: [Hook] It's just this thing I feel When I wanna steal It's just this thing I feel When I wanna steal [Verse 1] Yo, I see you on X, all over my feed You're building in public, I'm watching you build Your MRR chart looks like a hockey stick I screenshot it sometimes, that's normal right [Hook] It's just this thing I feel When I wanna steal [Verse 2] I learned a lot from you I think I deserve it too So I copied everything from you Same landing page, same pricing, same font And now you blocked me What happened bro I was your biggest fan [Hook] It's just this thing I feel When I wanna steal Tip: short lyrics, fast beat, vocals at 0:00, fresh prompt every round. THE STORY Think old MTV. The video is the movie this song is the soundtrack of. Keep the plot dead simple, something you can follow with the sound off. Mine: a broke founder copies a guy, dreams he is rich, wakes up, sees he got blocked, spits his cereal at the screen. That is all of it. Tip: if you cannot explain the story with zero words, cut it down. THE STORY FOOTAGE (Seedance 2.5) Seedance made the apartment story as one 30 second clip from reference images. Two things kill Seedance: Too many object interactions in one shot, and timestamps like "0 to 4 seconds" which it reads as a time lapse and speeds through. plain shots labelled "Shot 1, Shot 2" at natural speed will do the trick For the hard beat at the end, a guy waking up, eating cereal, seeing a screen, then spitting milk on the lens, text alone will not hold it. I built a 3 panel storyboard image and fed it as a reference. In the prompt you tag that image at the exact moment it happens, tell it the board reads left to right, describe it once, and move on. The rule that saved it: chronological order, tag each image where it belongs in time, say everything a single time, never repeat a thing. Repeat one detail twice and the model fixates on it and breaks the shot. Tip: for anything complex, hand it a storyboard picture and describe it once, in order. THE SINGING (MiniMax H3) H3 is the model that lip syncs to your actual track and keeps it. Seedance cannot, it regenerates its own audio. In H3 you attach your audio slice, set it to copy, and the mouth follows your real song. H3 caps around 15 seconds a clip and the song is 43. So I cut the song into 4 windows of about 11 seconds and generated a shot for each window. Then I did it across 4 locations, subway, warehouse, empty office, street. That is a 4 by 4 grid, 16 clips. I added 8 more where the whole crew sings and dances. Around 24 short singing clips to cover a 43 second song. You are building a bank of clips to cut from. Small H3 rules that matter: the audio slice must be a touch shorter than the clip, name every speaker, and compress the slice so there are no silent gaps for the model to fill with invented sound. Tip: chop the song into sub 15 second windows, shoot each shot per window, build a clip bank. THE EDIT (Claude) This is where most people lose hours. I made editing fast by doing the prep once. Every clip gets normalized to the same size and frame rate up front. After that each edit is a single ffmpeg pass with no re-encoding loops. The base layer is the song. Every clip's own audio is thrown out. To keep mouths in sync I gave the editor the math: each clip knows which second of the song its first frame belongs to, so to place it at song second S you trim it to start at S minus that offset. I also handed over word level timing from a whisper pass so cuts could land on real lyric moments. The rules I gave: nothing stays on screen too long, pace every cut to the lyric and the beat and what is on screen, never put two shots from the same location back to back, keep it heavy on story B-roll, never reuse a frame. If the cut feels like a metronome you failed. If it feels random you failed. Then the actual move. I did not ask for one perfect edit. I gave 7 agents the same rules and the same clip bank and told each to cut the whole thing its own way with a different emphasis. 6 came out flat. 1 landed around 90 percent. I finished that one by hand in CapCut. Tip: give strict rules plus the timing data, generate many full edits, keep the best and finish it yourself. CAPTIONS (ffmpeg) Burned straight from a styled subtitle file with ffmpeg. Seconds, not the long render a motion tool costs. The words come from the real lyrics, the timing comes from a whisper pass on the audio, and it highlights the word being sung. Big, thick, one pop color on the active word. Tip: real lyrics for the words, whisper for the timing, burn with ffmpeg. The AI did not make this video. I directed it, generated in volume, and kept the best takes. That is the whole game right now.

I've never counted my token burn, but my Fable mule accounts burn 2% of my session limits per minute. I have a bunch of Emacs macros to help me switch accounts because when I get to about 95% on the session, I only have seconds left to get logged into a new mule. What a mess.

4 : $HANTA CA : 2tXpgu2DLTsPUf9zFmuZmA4xrYxXKBTpVq9wAM7hzs9y The narrative behind $HANTA is inspired by the real-life Hantavirus , a severe respiratory virus with a mortality rate of around 35–38%, first identified in 1976 near the Hantan River in South Korea and commonly spread through rodent droppings Recently, reports about a Hantavirus “outbreak” on the MV Hondius cruise ship (around early May 2026), which allegedly caused 3 deaths and left several others in critical condition, pushed this topic into heavy circulation across social media What stands out to me is that despite the token already being around for roughly 10 days, the chart has held up relatively well. In my opinion, that’s mainly because the narrative is still actively spreading and continues to get attention from larger accounts Beyond the narrative itself, the thing I like most about $HANTA is how the dev team is building the project’s image The devs donated their own money to the University of Bath to support real Hantavirus vaccine research So far, they’ve donated more than £10,663 (~$14.4K) across 3 separate donations, with the latest being £5,000 If the team eventually adds mechanisms like burn or buybacks, I think the narrative could become significantly stronger As for attention, it’s not massive right now , a little over 100 mentions in the last 24 hours , but honestly, that’s still enough to keep the narrative alive, especially considering the token has already survived for more than 10 days Some notable KOLs such as @Saracrypto_eth have mentioned it, alongside several tier-2 KOLs that are also watching the project From an on-chain perspective, the bundle percentage is still relatively high at around 60%, and there are also quite a few KOL wallets holding, so it’s important to stay aware of potential sell pressure if market conditions weaken Overall, I still think $HANTA has room to continue moving higher, but entering at current levels feels more like a gamble compared to earlier entries Personally, I’m watching two support zones : - 1.8M , higher risk - 800K , safer entry area If it breaks down below 600K, I’d personally cut losses to manage risk properly DYOR

🗡️ The Damage Report (Sept 21) 🚨: Half of this week's damage never touched a smart contract. Some updates covering hacks, a wind-down, a phishing wave, and more: • Balancer proposed an orderly wind-down: no new business, pausable pools dropped to withdrawals-only on October 30, and at least $9M of treasury distributed to BAL holders who burn their tokens. Snapshot runs September 25 to 29. • Trezor's third-party email provider Brevo was breached and used to send phishing mail from Trezor's own domain. Roughly 347k addresses got it, and 2.5k clicked before the domain came down. Check the sender domain is the advice everyone gives, and this week it would have failed you. • Wyoming pulled its FRNT stablecoin off LayerZero and moved it to Chainlink CCIP, citing a "repeated pattern of major operational security failures," including failure to keep proper control of a private key managing a live FRNT deployment. LayerZero's CEO disputes it, saying the authority involved was view-only metadata. • A Safe on Ethereum lost ~$7.73M in rsETH when an attacker used a public keeper multicall to push its Uniswap V4 LP module into a malicious hooked pool that unwrapped aETHrsETH. An MEV bot then extracted the funds in the same block. • Nomic's transaction-forwarding flaw let an attacker double-spend nBTC, minting 40.65 nBTC with nothing behind it back in June. Nobody noticed for 74 days. • ChainFlip lost ~736k USDT on Tron after an attacker attached their own memo to transactions validators had already signed. • Ether fi lost ~15.45 ETH to a missing access-control check in AtomicQueue. SlowMist disclosed it to the team privately before going public, which is why this one stayed a rounding error. • Yam Finance was drained of about $121K after an attacker self-delegated enough YAM to pass proposal #45# and seize the Timelock. Governance capture is still an underappreciated risk. The Trezor one bothers me the most. Hard to keep telling users to “be more careful” when the phishing emails come from the company they’re supposed to trust.

robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d has been one of the strongest runners from my recent calls From where I first started talking about it to where it is now, the move has been pretty insane So let me make one thing clear first: this post is not me telling you to buy robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d here This is mainly an update for the OGs who are still holding with me. I want to revisit the thesis after this run: what has actually been proven, what the market is already pricing in, and what will determine whether I keep holding or exit the rest of my bag 1. What I like most is still the execution speed robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d has only been live for a little over a week, but the founder has shipped a lot Model comparison, wallet-based API, Quiver for managing budgets/requests, Strategies beta, staking v1, a public inference ledger, agent inference API, auto-routing, workspace, Commons, and a catalog of around 184 models For a project being built almost entirely by a solo founder, the pace is pretty impressive This isn't the usual AI token that launches, puts up a website and then sits there waiting for the chart to pump. There have been product updates constantly The founder was also recently soft doxxed. The information made public points to around 7 years of experience, moving from support => PM => engineering => AI ops, along with experience across several startups The community has dug up additional possible connections to Robinhood/Ramp, but the founder didn't directly confirm either name in the doxx post, so I'm not treating those as facts for now Either way, the doxx is still a positive catalyst because it reduces some of the anonymous dev risk But I separate two things very clearly : Knowing who is building is very different from proving that people actually need the product 2. The robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d thesis is why I'm still watching it One line summarizes the concept pretty well : Inference coins should ship inference Instead of launching an AI coin first and trying to invent utility afterward, Manyways is building an AI router/gateway where users can access multiple models through one interface/API No need to manage a bunch of different providers, API keys or separate balances. Eventually, routing strategies could select models based on cost, quality or the specific task The part I find most interesting is the economic loop they're trying to build : robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d fees => shared inference pool => pay for compute => real usage => better routing => more usage Basically, speculative onchain activity could subsidize actual offchain compute Further down the roadmap, there are also ideas around a strategy marketplace, delegation, and stake/burn mechanics to unlock quotas or inference capacity If they can actually ship all of that, the token stops being something that simply sits next to an AI product and starts becoming part of the product's economic layer But the most important part of that loop isn't fully live yet 3. The product is real and execution is fast, but usage is still very early According to the Sep 2-9 ledger snapshot, Manyways recorded around 18 accounts, 179 requests, 79K tokens processed and roughly $0.46 in actual inference cost The inference pool was around $15K-$21K across the snapshots I checked So the infrastructure is real. Requests are being routed, inference is actually costing money and the ledger is public For such a young project, I rate the product execution pretty highly But compared with the current valuation, usage is still tiny This is probably the most important distinction for me Manyways has proven that they can build and ship They haven't yet proven that demand can scale 4. That's why I'm not valuing robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d as a validated protocol yet Staking v1 is live, but right now it's mainly tied to Strategies, with roughly 1/20 strategy slots occupied, and it isn't a complete tokenomic system yet More importantly, the part I've cared about most from the beginning, robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d fees => inference pool, is still in development The burn layer and stake/burn mechanics for accessing inference capacity aren't fully built yet either So I don't think robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d's tokenomics have been validated At the current valuation, I think the market is mostly paying for attention + execution speed + founder + AI inference narrative + Robinhood Chain beta + optionality on what gets shipped next, rather than current cash flow or usage That's not necessarily wrong. Early tokens often price the future before fundamentals catch up But after a run like this, the bar for staying bullish has to become much higher 5. The next wave needs to come from data The product being live, the founder doxxing, staking and new UI updates can all help maintain attention But from here, the catalysts I care about are much more specific First, fee => inference pool needs to become verifiable. I want to see token activity actually funding the pool instead of it just being part of the roadmap Then comes usage If requests go from hundreds to thousands per day, inference burn starts climbing materially, external developers start using the agent/API and Strategies begin seeing real demand, I'll re-rate the thesis If fees and usage start growing together, the story changes significantly At that point, robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d could move from an attention trade with a real product toward a protocol with actual economic activity But if the founder keeps shipping features while the ledger barely moves, I don't think another UI update or narrative is enough to justify a higher valuation by itself 6. The risks are also very real now The first is obvious: valuation has moved far ahead of product-market fit Second is solo-founder risk. One person shipping this quickly is bullish, but having one person responsible for almost everything also creates a single point of failure Third is competition. AI gateways/routers aren't an empty market. Users already have plenty of alternatives and switching costs are relatively low Eventually, Manyways still needs to answer one question : Why should developers use Manyways instead of what's already available? And then there's liquidity At the snapshot when I checked, liquidity was around $200K, versus roughly $2M in 24h volume and a valuation around $5M These numbers can obviously move quickly, but the point remains: liquidity is still relatively thin compared with the amount of volume and attention the token is getting That means the unwind can also be aggressive if momentum flips And that's one of the main reasons I don't want new buyers jumping in here after the move has already happened 7. How I currently view robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d I'm not calling it an undervalued protocol, because the usage data doesn't support that yet But I definitely don't see it as another meaningless AI meme either I see it more as an early infrastructure-meme hybrid Part of the valuation comes from attention, the founder, execution and the Robinhood Chain meta. The rest is basically a call option on whether the founder can turn Manyways into an inference gateway that users and developers actually use The simplest way to put it : The product is moving very fast for how young the project is, but valuation is still far ahead of actual usage That gap is what I'm watching from here 8. My plan from here Personally, I'm still holding the rest of my bag My initial entry was extremely low, so I'm comfortable letting the remaining position run while I wait to see whether the founder can deliver the hardest part of the thesis But I want to make this very clear : robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d is no longer cheap at this valuation If you don't already have a position, I wouldn't recommend chasing it here The move has already been huge and liquidity is still relatively thin. Don't wait until a token has already gone vertical and then jump in just to become liquidity for people who were early For the OGs still holding with me, I'm mainly watching fee => pool, request growth, inference burn, external API/agent usage, and whether staking/Strategies develop real utility If those metrics start catching up, I have a reason to keep holding If the founder keeps shipping but the ledger stays flat, the fee mechanism doesn't go live, usage doesn't grow, or the thesis starts breaking, I'll exit the rest of my bag There's no reason to marry a token just because I called it early and the trade worked At this point, I'm not that interested in guessing how much higher robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d can pump Price has already run far ahead. Now it's the product and usage that need to catch up If they do, robinhood:0xa26992c4268a8a78a4d872fe4bdad2ed03ac287d may still have another chapter If not, I'm out 0xa26992C4268A8a78a4d872FE4BDAD2Ed03aC287d

2025–2026, imo, is the period that showed the clearest value of tokens backed by real revenue and real value capture. Thousands of projects disappeared when attention and liquidity moved elsewhere. But protocols with real products, real users and real revenue are still here. And more importantly, some of them are finding ways to return that revenue to the token. Look at the current numbers: – @HyperliquidX $HYPE: ~$60M holder revenue in 30D, with most trading fees flowing into HYPE buybacks – @CantonNetwork $CC: ~$49M in 30D, with network fees used to burn CC – @trondao $TRX: ~$24M in 30D, with network fees continuously burning TRX – @Pumpfun $PUMP: ~$24M returned to holders in 30D through token buybacks – @uniswap $UNI: ~$16M in 30D, with protocol fees now flowing into UNI buyback/burn – @ponsdotfamily $PONS: ~$15M in 30D, with a large part of revenue used for buyback and burn – @aeroxyz $AERO: ~$14M in 30D, with trading fees distributed to veAERO voters – @LaunchOnSF $STONK: ~$10M in holder revenue, mainly through market buybacks – @PancakeSwap $CAKE: ~$5M in 30D, with revenue from multiple products used to buy back and burn CAKE – @Aster_DEX aster-2:native: ~$4.6M in 30D, with most platform fees currently used to buy back ASTER For me, the more interesting model is: Real users → real fees → real revenue → real token capture. I think this will become one of the metrics worth watching much more closely in the next phase of the market. NFA.