まだ早い — still too early. Somebody says a $Solana token works a certain way. I go read the mint account. Method, data and corrections public.

USA
A token promised up to 4x leverage on SOL and delivered a realized beta of 2.91. It worked exactly as advertised, every day, for 309 days. $1,000 in it became $56.68. Six measurements, six windows I did not choose.
Article

What Your Thousand Dollars Actually Does

Every figure below comes from a measurement published with its raw data at github.com/madaearly/solana-measurements. Percentages are how these products are sold and dollars are how they are experienced. A token can

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A video going round this week says Opus 5 took 34 minutes and $23 to sort 1,400 YouTube comments into five buckets, and that a new model did the same class of job for a cent. I priced both halves against the published rate cards. The cheap half is honest. The $23 is about 43 times too high. Opus 5 is $5 per million input tokens. $23 buys 4.6 million of them. Spread over 1,400 comments that is 3,286 tokens per comment. A YouTube comment is 20 to 60 tokens. Read it the other way and it is worse: at $25 per million output, $23 is 657 output tokens per comment, for a job whose output is one word out of five. Same job, same model, priced at the card: 1,400 comments -> 50 per call -> 28 calls -> 64,400 input + 8,400 output -> $0.53 Through the Batch API, $0.27. So the honest version of that slide is not Jev versus Opus. It is Jev versus one particular way of driving Opus: an agent loop that re-reads its own context, one comment at a time, while also writing a CSV and building a dashboard. That is a real cost and he really paid it. It is just not what Opus costs to classify 1,400 comments. Now the part that goes the other way, because I checked it too. Every Jev figure in the video holds up. TypeSafe publishes $0.042 per million input tokens, output free. Work backwards from each claim: 250 leads for 1 cent -> 952 tokens per lead 203 members for 1 cent -> 1,173 per member 1,300 posts for 4 cents -> 733 per post 227 call transcripts for 7 cents -> 7,342 per transcript 443 browser screens for 1 cent -> 537 per screen Those are the right sizes for what each of those things is. A call transcript really is ten times a forum post. Nothing there is rounded in his favour. So the model is as cheap as he says. The comparison is the thing that is broken, and it is broken in the direction that makes the video's case. Two assumptions in my arithmetic, stated so you can attack them. I assumed a comment is 20 to 60 tokens, and I priced classification only, while his $23 run also produced a spreadsheet and a dashboard. Halve my estimate or double it and the gap is still two orders of magnitude. The rule this leaves you with: when a post compares a new tool against an old one, check whether the old one was being used properly. Most of the difference in these threads is not the tool. It is the batching.
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Every number in this thread is exactly right. I cloned the repo and counted: 18 skills, 72 commands, 6 subagents, 28 tools, 26 plugins, 15 repos, 12 skills, 10 sections, 5 tracks. Not one is rounded up. The line worth checking is the one without a number in it. "6 subagents — 4 of them read only, so nothing rewrites your vault behind your back." Four is correct. Four of the six carry no Write and no Edit. But three of those four hold only Read, Glob and Grep, and the fourth holds Bash. Bash writes files. The agent is called graph-analyst, its description says "Read-only", and its prompt says "You analyse the graph and never modify the vault." That is an instruction, not a permission. Three of those four cannot touch your notes. The fourth is asked not to. That distinction is the whole reason the sentence exists. If you are pointing six agents at years of your own writing, "cannot" and "has been told not to" are not the same guarantee, and only one of them survives a bad prompt. Two smaller things, both in the repo rather than the thread. The thread lists five scripts and says plain Python, zero dependencies. All five are standard library — that checks out. But the repo's own README inside that folder calls the whole directory "dependency-free Python scripts", and a sixth script sitting in it, build_tracks, imports markdown from PyPI. There is no requirements.txt to tell you. Six scripts, five honest to the label. And the page counts — 65 pages, 44 pages — have nothing to count. There are zero PDFs in the repo. Markdown has no pages. None of this makes it a bad repo. An inventory this dense that survives being counted item by item is rarer than the repo itself. It is just that the numbers were never the risky part, and the sentence that reassures you is the one nobody counts.
this is free f*cking gold a second brain article hit 8 million views, so the guy behind it put the entire setup in one place the repo, the guide, the tools, the learning path. all of it, free • the guide > 10 sections, 65 pages, concept through troubleshooting > 5 tracks on top, 44 pages, 15 of them build guides with code that runs • the machine (.claude/) > 18 agent skills, one per workflow > 72 slash commands - /ingest-pdf, /ingest-youtube, /ingest-voice, /backfill > 6 subagents - curator, linker, researcher, reviewer, ingestor, graph-analyst > 4 of them read only, so nothing rewrites your vault behind your back • the scripts (plain Python, zero dependencies) > graph export, link checker, vault stats, chat converter, site builder • the starter vault > its own CLAUDE.md with page contracts and linking rules > raw/ never edited after it lands, wiki/ is what the agent maintains > log.md - one line per run, so the whole thing stays auditable • 87 vetted resources > 28 tools, 26 Obsidian plugins, 15 repos, 12 skills, papers and articles five tracks to pick from: > knowledge graphs > Jev engineering > agent harnesses > loop engineering > eval engineering start with the second brain guide if you're new. go straight to the tracks if you already live in this stuff a consultant charges four figures to build you a research system. this one sits in a public repo under MIT ↳ github.com/undefined-ui/seco…
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Both numbers here are exactly right. The paper says ×100 against an H100 and ×70,000 less energy, and it says them about the H100 specifically, not some average GPU. The chip was never built. Nothing was fabricated and nothing ran. The Methods describe SPICE simulations of a 64 by 64 array in a TSMC 28 nm process kit inside Cadence Virtuoso, extrapolated to a full attention head. The author contributions list schematic design, analog and digital layout, and chip floorplanning. There is no tape-out in the paper because there was no tape-out. That is a normal and useful result. A simulated 28 nm design with honest Monte Carlo bounds is how this work is supposed to start. It is just not a chip that exists, and "researchers built" is the one word doing all the damage. Two smaller things. It is in Nature Computational Science, not Nature. Different journal, same publisher. And the language model is GPT-2. The paper's own claim is "text-processing performance comparable to GPT-2 without training from scratch," reached through a custom initialization because the analog non-idealities stop you mapping a pre-trained model directly. The post says "runs LLM attention" and lets you supply the LLM you were thinking of. One more, in the paper's favour. The December preprint claimed five orders of magnitude on energy. The published version says four. Somebody made them take a zero off, and the number in this post is the survivor, not the original. So: the arithmetic is sound, the comparison target is correctly named, and the hardware is a schematic. If you bookmarked this expecting silicon, the thing you bookmarked is a very good simulation.
Researchers built an analog chip that runs LLM attention 100x faster than an H100 and uses 70,000x less power. It's called GainCellAttention. Every AI chip you use is built on an 80-year-old flaw called the von Neumann architecture. the processor and the memory are physically separated. To generate a single word, a GPU has to shuttle massive amounts of data back and forth across this divide. Over and over again. Moving that data costs up to 10,000x more energy than actually doing the math. But a team of researchers published a paper in Nature that completely destroys this bottleneck. They built an analog in-memory computing architecture. Instead of moving data from the memory to the processor, they put the processor inside the memory. Using emerging hardware called "gain cells," the AI performs its most expensive calculation, the attention mechanism, directly inside the storage arrays. The data never moves. The compute happens exactly where the memory lives. The result? A massive leap in energy efficiency and speed. It completely eliminates the latency of fetching data for every single token. It is building AI chips that function exactly like the human brainwhere memory and computation are the exact same thing.
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Two repos on the same list of Jev blueprints are 123 stars apart. 18,085 and 17,962. One of them is 1.2% Jev. The other is 88.2%. json-render is Vercel Labs' generative UI framework. 1,210 source files, 14 of which mention Jev, and its first commit is 14 January — eight months before Jev existed. jev-ultrafast is 17 files, 15 of which mention it. Both are real, both do what the list says they do. But a star count measures the repository, and on a list you are reading to learn one specific thing, that is not what you came for. All ten, by how much of the codebase mentions Jev: 100.0% typesafe-mcp 100.0% jev-mcp 88.2% jev-ultrafast 76.9% fast-jev-compaction 58.3% blink 50.0% semdecide 27.9% winnow 15.8% jev-review 4.7% jev-codex-router 1.2% json-render The two at the top have 258 and 282 stars. The one at the bottom has 18,085. Run it on anything: python3 ./howmuch owner/repo jev typesafe github.com/madaearly/code-me…
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A thread telling people to point an AI at a live Solana wallet tonight understates its own sources. Twice, and both times against the author. The one number it inflates is the one the whole design rests on. Eleven hundred people have saved it, so I checked every figure in it against the sources it names. Four are exact. Two are low. Three do not hold, and two of those three are the same sentence. Vercel's gateway share — he writes 13% of paid teams, twice GPT-5.6, six times Fable. Vercel's own post: "nearly 13% of paid teams... 2x the GPT-5.6 family and more than 6x Fable 5.1's share." Word for word. The GEPA paper — he writes 35 times fewer attempts. The abstract says 35x fewer rollouts. Also exact. He writes "up to 19 points" where the abstract says up to 20. Hermes Agent — he writes around 214,000 GitHub stars. It has 248,360. He is low by 14% on the number that flatters his own stack. His own cost line — 11,520 answers a day at $0.00002 is $0.2304. He writes $0.23. The screenshot — $3,286.35 on $38,914.11 is not 9.22%, it is 8.45%. Against the starting balance of $35,627.76 it is 9.2241%. He used the right denominator, which is not the common case. Now the one that fails. Asked why not use the slow model for the 15-second decision, he answers: "Fable is 8.8 seconds and $0.014 per call for me. At one call every 15 seconds that is a bill in the hundreds of dollars a day and a loop that misses its own window." At one call every 15 seconds that is 5,760 calls. At his own $0.014 that is $80.64 a day. Not hundreds. And 8.8 seconds inside a 15-second cycle leaves 6.2 seconds spare. By his own figure the loop does not miss its window. The design is still right — $80 a day against 23 cents is a real reason, and 6.2 seconds of headroom is thin. But the argument as written overstates its own case by about three times, in the one place a reader is deciding whether to copy the architecture. One thing I could not check: the Alpha Arena results. the nof1 leaderboard sits behind a browser checkpoint I could not get through, so the +22% and −59% are unverified here, not disputed. He also says eight days is a sample and not a track record, and that anyone annualising 9.22% should stop. That is in his article, not something I am adding for him.
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Nineteen of the twenty most-shared Jev repos call it from their product code. The claim going around is that ninety percent of them are larping. I cloned all twenty and ran the same count on each. 20 of 20 reference Jev somewhere in source 19 of 20 do it in product code, not just tests or scripts 13 of 20 have a test that touches it median share of the codebase: 54.1% twelve of twenty are over half The one exception is agent-desktop, where Jev lives in four files under scripts/ and never enters the product. It is also the only repo on either list with no Jev in what it ships. But "larping" is doing a lot of work in that sentence, and it is worth separating two things it can mean. If it means the code does not really call Jev — that the repo is a README with a name on it — then it is one in twenty, not nine in ten. The API key, the import and the call are there in nineteen of them. If it means the use case is a toy — Jev playing Mario, Jev flying a drone, Jev in a browser FPS — then that is a different claim, this measurement does not test it, and a reasonable person can hold it. Four of the twenty are games or demos. Those are not the same statement, and the second one does not make the first one true. What I cannot check is the rest of his sample. He said use-cases shared on X, and most of those are screenshots. A screenshot has nothing to clone. Twenty repos from the two biggest lists is what can be measured, and it is the part of the claim that has an answer. Same script as yesterday, now over both lists: python3 ./howmuch owner/repo jev typesafe github.com/madaearly/code-me…
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The most-starred repo on this list contains 666 lines of Jev. The least-starred contains 3,640. I cloned all ten and counted what share of each codebase mentions Jev at all. 86.7% neo4jev 80.0% jev-curate 77.8% typesafe-mario 76.2% OneVOneJev 58.8% jev-drone 50.0% killmyidea 23.5% jev-trader 15.4% Canny 1.9% Prism 0.4% agent-desktop The list is not padded. All ten repos are real, every one of them calls the TypeSafe API, and the descriptions here are accurate — Prism is flagged in the post itself as judging state and handing off rather than trading. But agent-desktop and Prism had their first commits in February and June, months before the SDK's first release on 9 September. Jev is a layer added to something that already existed. In agent-desktop it sits in four files under scripts/jev/ and never appears in product code at all. The other eight all started between 16 and 18 September. The SDK is thirteen days old. So if you are picking one to read, stars are measuring the repo, not the Jev in it. Here is the script, so you do not have to take the ranking on trust: python3 ./howmuch owner/repo jev typesafe github.com/madaearly/code-me…
Jev has been exploding in popularity recently. If you already have access to the Jev API but aren’t sure how to start experimenting with it, just copy this checklist: 1. agent-desktop Desktop automation. Read the system's accessibility tree, judge which button, menu, or input field to click next. github.com/lahfir/agent-desk… 2. typesafe-mario Have Jev play Super Mario. No screenshots—just read the structured state in the emulator's RAM, then decide to run, jump, or dodge. github.com/fhshaik/typesafe-… 3. jev-drone Use Jev to control a drone. The underlying flight control still handles stability and safety; Jev just does higher-level judgments like climbing, braking, and navigating obstacles. github.com/RomanSlack/jev-dr… 4. OneVOneJev 1v1 FPS in the browser. Every decision tick, judge movement, view angle, aiming, firing, and jumping. github.com/emrickgarrett/One… 5. jev-trader High-frequency market making on Monad testnet. Jev judges the next buy or sell based on spreads and trade direction, with model latency around 81ms. github.com/jarrodwatts/jev-t… 6. Prism Doesn't directly have Jev place orders. It judges states like toxic flow, market pressure, mean reversion, etc., then hands off to the original strategy. github.com/irfndi/prism-liqu… 7. neo4jev Stuff Jev into a knowledge graph. At each node, judge the most worthwhile edge to take next, then follow it all the way. github.com/jexp/neo4jev 8. jev-curate Use Jev to screen training data. For JSONL / Parquet, first judge quality, relevance, and risk, then decide which ones go into the next training round. github.com/AkashPriyadarshii… 9. Canny Prevents Coding Agents from stubbornly claiming they're done. Look at tool outputs, code diffs, and test results, then judge if the completion claim is reliable. github.com/qkal/Canny 10. killmyidea Input a startup idea, and Jev scores it from multiple angles, finally giving you KILL, FIX, or SHIP. github.com/monteduro/killmyi… Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓
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The 89.7% in this post is wrong. A reader named the reason in two sentences. I sorted 3,000 mints by who may change the transfer fee, found one keypair on 8.83% of them, and called the rest frozen. Token-2022 puts two authorities on a transfer fee, not one. The first sets the rate. The second withdraws what the rate has already collected. They are separate fields, they can point at different keys, and I had read only the first. Here is the same sample sorted by the second. Rate held by a program, fees withdrawable by one keypair — 2,043 Rate null and frozen forever, fees withdrawable by that same keypair — 648 Rate held by that keypair, fees withdrawable by it too — 265 2,956 of 2,956. Every mint in the sample that carries a fee at all. 98.53% of the sample, against the 8.83% I published. The same key, eleven times wider than the field I looked at. Every mint I called frozen has a live withdraw key on it. Frozen meant the rate cannot move. It never meant nobody is collecting. So I went and counted what is actually sitting there. 1,761 of those mints hold fees that have been taken and not yet withdrawn. I pulled a price for every one. 461 of them have a price anywhere at all. The other 1,300 do not — no pool, no quote, nothing to value 3,106,102,898 tokens against. The 461 that can be valued come to 40,344 dollars. Median position: 20 dollars and 69 cents. Sixty-two of them hold less than a dollar. Five hold more than a thousand. The largest single balance is 10,646 dollars of PURR. Ten mints are 69.7% of the whole figure. That is the part worth sitting with, because it is not the story I expected to write. One keypair can sweep the fees on roughly 18,500 tokens. What has actually accumulated in the sample is forty thousand dollars, three quarters of it in tokens nobody will quote a price for, and two thirds of the priced remainder in ten of them. The authority is enormous. The money is not. Both of those are worth knowing, and yesterday I published only the half that sounded smaller and felt bigger. What this does not show. Scaled to the full population the undrawn total is about 253,000 dollars, but I would not lean on that. Bootstrapping the sample gives a 95% range of 106,000 to 458,000 — a factor of four — because ten positions carry most of the mass. A random sample is honest about the average and clumsy about a distribution shaped like this one. The 1,300 unpriced mints are unknown, not zero. If any of them has a market I could not see, the figure moves. And a withdraw authority is a permission, not an act. I did not catch anybody sweeping anything. I counted what is sitting there and who is allowed to take it. The correction came from a reader with 43 followers who read the post properly. He was right about the field and right that it is the one that moves money. I had captured it in the same pass and had not crossed the two columns. That is the cheapest kind of mistake to make and the most expensive kind to leave standing.
Three days ago I wrote that the name of a token is locked by a program while the tax rate is held by a person. That was true about the two tokens I had measured. As a sentence about the platform they came from, it implied something I had not checked. So I checked 3,000 more. Token-2022 puts two keys on a transfer fee. One takes what the fee collects. The other sets the rate. Null means the rate is frozen forever. An address means that address decides. It is one field, and almost nobody reads it. Here is who holds it across 3,000 mints, sampled at random from 18,819: A program-derived address — 68.10% Null, frozen forever — 21.60% An ordinary keypair — 8.83% No transfer fee at all — 1.47% 89.7% of these mints cannot have their rate changed by anybody, because no private key exists that is allowed to do it. That is the opposite of what my sentence implied, and it is the more interesting result. The default on this platform is to give the power away. Now the 8.83%. Across all 3,000 mints, exactly one private key appeared in that field. Not a handful. One. The bucket for any other address is empty. 265 mints in the sample sit under it. Extrapolated to the population, roughly 1,662, with a 95% interval of 1,481 to 1,863. It is the same key I published on 14 September, when I found it on four mints, and the same one I published on 18 September on six more. The ten I have now read one at a time: LEVERCAT, LEVERDOG, SLO, LAMPORT, KNOTS, LOOP, KNOTTY, STONKNOTS, BABYKNOTS, TOKNS. The other 255 are not a theme. Reading down the list: XRPCAT, USELESSGUY, ARBY'S, ETHCAT, CHILLGIRL, condom, STONKLANA, ZABUBU, FREEDOM, ANYTHING, DONKEY, NUGGY, GARY. Four separate mints called AI. Two called SAFEMOON, two called WEN, two called 401K. Whatever this is, it is not curating. It is stamping. One more thing fell out of the sample that I did not go looking for. Two fee rates exist across all 3,000 mints. 100 basis points and 300. Nothing else. Not 250, not 50, not 137. Whatever produces these tokens offers two settings, and half the catalogue takes each one. 48.5% at 1%, 51.5% at 3%. I have now read the seven mints from the 14 September thread three times: on the day, on the 19th, and again today, a week on. Every one is where it was. Four still live under that key, two frozen, one with no fee. Nothing has moved. What this does not show. It does not show anyone changing a fee. That is the thing that would matter most and I have not seen it happen once. It does not show who holds the key. And the population is not Solana. It is the 18,819 Token-2022 mints that one wallet holds a token account for, which is weighted toward one platform's own output. Every percentage above is a percentage of that list, not of the chain. A bigger sample of the same list would not fix it, and I would rather say so than let the number travel further than it can. A fee you cannot change is a property of the token. A fee somebody can change is a relationship with whoever holds the key. Both are in the same field, and they look identical until you read it.
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Same capture as yesterday, re-read along a second column. Nothing was re-fetched except prices. THE TWO FIELDS Token-2022's transferFeeConfig extension carries both: transferFeeConfigAuthority — may change the rate withdrawWithheldAuthority — may withdraw what the fee has collected Null on the first means the rate is fixed forever. It says nothing about the second. THE CROSS-TABULATION Of 3,000 mints sampled at random from 18,819, 2,956 carry a transfer fee. rate = program WLHv2U…, withdraw = keypair 5KXDF6… 2,043 rate = null, withdraw = keypair 5KXDF6… 648 rate = keypair 5KXDF6…, withdraw = keypair 5KXDF6… 265 2,956 of 2,956. 98.53% of the sample, 95% Wilson interval 98.04 to 98.91. Extrapolated to the population, roughly 18,542 of 18,819. No other withdraw authority appeared in the sample. Not one. WHAT IS SITTING UNDRAWN The withheldAmount field on each mint, divided by its decimals, priced through GeckoTerminal's token_price endpoint, one batch of 30 at a time. mints holding anything undrawn 1,761 of those, priced 461 (26.2%) of those, unpriced 1,300 (73.8%) value of the priced ones $40,344.18 median $20.69 under one dollar 62 over one thousand 5 largest $10,646.80 (PURR) top ten as a share of the total 69.7% tokens sitting in unpriced mints 3,106,102,898 THE EXTRAPOLATION, AND WHY IT IS WEAK Naive scale-up: $253,079. Bootstrap over 2,000 resamples: $106,276 to $458,417 at 95%, a spread of 4.3x. Ten positions carry 69.7% of the mass, so the mean is unstable by construction. The sample size is not the problem and a larger one would not fix it. WHAT CHANGED AND WHAT DID NOT The 8.83% in the quoted post was correct for what it measured — the rate field. It is unchanged and not withdrawn. What was wrong was calling the remaining 89.7% frozen without saying frozen against what. Raw capture, prices, and the script: github.com/madaearly/solana-… I hold none of these tokens and never have.
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There is a perpetual future on SpaceX trading right now. On OpenAI. On Anthropic. Nobody holds one. And nobody will sell you one either — there is no bid and no ask on any of them. Yesterday an account with 558,000 followers posted that open interest on perpetual DEXs hit an all-time high — 19 billion dollars of crypto, 25 billion in total, with real-world assets up from 6% of it at the start of 2026 to 24% today. I checked. It holds up. RWA perps are real and they are roughly four billion dollars. Then I looked at where those four billion actually sit. Ten venues deploy real-world markets on Hyperliquid's chain. Here is all of it: XYZ — 3,873,867,536 dollars EntropyIO — 57,939,888 Paragon — 18,363,030 Markets By Kinetiq — 6,575,109 Felix Exchange — 0 Ventuals — 0 dreamcash — 0 HyENA — 0 Markets by Kinetiq, a second listing — 0 ABCDEx — 0 One venue is 97.9% of it. The six at the bottom are not thin. They are empty. 97 markets between them, zero open interest, and zero dollars of volume in the last 24 hours. Not a single trade. And those six are the ones with the names you would actually want. Ventuals lists SPACEX, OPENAI, ANTHROPIC, MAG7, SEMIS, ROBOT, NUCLEAR, DEFENSE, BIOTECH, WHEAT. Fifteen markets. Nobody holds any of them. Felix lists TSLA, NVDA, COIN, CRCL, GOLD, SILVER, COPPER, PALLADIUM, PLATINUM. Sixteen markets, all empty. dreamcash lists USA500, TSLA, NVDA, HOOD, GOOGL, AMZN, MSFT, META. Seventeen markets, all empty. Kinetiq lists BABA, TENCENT, XIAOMI, JPN225, USBOND, RTX, PLTR. Twenty-three markets, all empty. Then I read the order books, one market at a time, all 97 of them. Not one resting bid. Not one resting ask. Zero open interest means nobody is holding these. An empty book means nobody is offering them either — you cannot buy SpaceX exposure here at any price, because there is no price. For comparison, the venue that works: xyz:SP500 carries 20 bid levels and 20 ask levels, best bid 7,759.0 against best ask 7,759.2. Two tenths of a point apart. The one that works is worth looking at properly, because it is a real market. XYZ runs 123 markets and 108 of them carry open interest. The book: SP500 — 434,500,427 dollars, 11.2% SKHX — 345,635,796 GOLD — 292,855,077 XYZ100 — 199,089,284 CL, crude oil — 182,738,502 MU — 177,801,976 SILVER — 161,624,846 BRENTOIL — 147,759,284 NVDA — 147,456,609 That is an index, a semiconductor, two metals and two grades of oil in the top nine. It is not a shell. Somebody is genuinely trading commodities and equities on a chain. The story is not that RWA perps are fake. It is that there is one of them. Now the part where the data providers disagree with each other. DefiLlama's headline for perp DEX open interest is 16,430,331,184 dollars. Sum the 129 protocols in the same response and you get 20,772,007,072. A gap of 4.34 billion inside one answer. The gap is a category. DefiLlama files those ten Hyperliquid venues as "Interface" — front ends — and excludes them from the total. They are not front ends. They are separate markets with their own order books, their own tickers and their own open interest, and XYZ alone carries 3.87 billion of it. That single classification is most of the difference between 16 billion and the 19 billion in the post I was checking. One more thing inside that number, going the other way. 1,951,578,267 dollars of it — 11.9% — is prediction markets. Kalshi, Polymarket US, Polymarket International. Election and sports contracts, sitting inside a figure described as perpetual DEX open interest. And 36 of the 129 protocols listed report zero. What I got wrong, and how. My first read of this said the RWA did not exist. I pulled Hyperliquid's market list, found 234 markets, and the only real-world asset in any of them was 20 million dollars of tokenised gold — 0.147%. The one ticker that looked like an index, SPX, marks at 52 cents, because it is SPX6900, a memecoin. All true, and all beside the point. Hyperliquid's core market list does not include markets other people deploy on the same chain. There is a separate call for those. I had not made it, and I was one step from publishing that a true claim was false. The check that saved it was asking the venue what venues exist, rather than assuming the first list I got was the whole thing. Three things this does not show. It does not show those six venues are abandoned. Several are new. Zero today is zero today, and I read it once. It does not tell you why XYZ has all of it. Distribution, listings, market makers, being first — I measured the concentration, not its cause. And I have not verified the 6% figure for the start of 2026. I measured today. Open interest is a claim about positions somebody is actually holding. An order book is a claim about what somebody will sell you. On 97 markets across six venues, both answers today are nobody — and the names on those markets are the ones most worth wanting.
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Everything below is two public endpoints and no API key. THE CLAIM @CryptoRank_io, 21 September 2026, 11:30 UTC, 15,261 views at the time of measurement. Perp DEX crypto open interest 19 billion, total 25 billion, RWA share up from 6% at the start of 2026 to 24% today. The account is not tagged here. The claim survived checking and I would rather it were read than defended. HYPERLIQUID, READ DIRECTLY POST api.hyperliquid.xyz/info {"type":"metaAndAssetCtxs"} returns the core universe: 234 markets, 13,837,400,406 dollars of open interest, computed as openInterest x markPx per market and summed. {"type":"perpDexs"} returns the ten builder-deployed venues. Each one then needs {"type":"metaAndAssetCtxs","dex":"<name>"} of its own. That second call is the one I missed on the first pass. Builder-deployed total: 3,956,745,563 dollars across 290 markets. THE SIX THAT ARE EMPTY Felix Exchange 16 markets, Ventuals 15, dreamcash 17, HyENA 25, Markets by Kinetiq 23, ABCDEx 1. 97 markets. Open interest 0.00. 24-hour volume 0.00. Both fields, every market, not a rounding. THE ORDER BOOKS {"type":"l2Book","coin":"<dex>:<ticker>"}, once per market, 97 times. Every one returns zero bid levels and zero ask levels. The same call on xyz:SP500 returns 20 levels a side, best bid 7,759.0 against best ask 7,759.2. DEFILLAMA GET api.llama.fi/overview/open-i… headline total24h 16,430,331,184 sum of the 129 protocols in the same response 20,772,007,072 difference 4,341,675,888 by the provider's own category field: Derivatives 14,621,699,345 across 104 Interface 4,015,080,225 across 15 Prediction Market 1,951,578,267 across 7 Interest Rate Derivatives 183,649,235 across 2 Synthetics 0 across 1 The headline is everything except Interface, give or take 327 million. The ten Hyperliquid venues are what Interface mostly is. WHAT IS AND IS NOT VERIFIED Verified by reading the venue: every Hyperliquid figure, core and builder-deployed. Taken from DefiLlama and not independently checked: every non-Hyperliquid venue, including Aster, Kalshi, Variational, edgeX, Lighter and the rest. Not checked at all: the 6% figure for the start of 2026, and whether open interest is counted one-sided or two-sided by each venue. The concentration figures are ratios within one venue's own reporting, so that question does not touch them. Raw capture, every order book, and the script: github.com/madaearly/solana-… I hold none of these assets and have no position on any venue named here.
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Three days ago I wrote that the name of a token is locked by a program while the tax rate is held by a person. That was true about the two tokens I had measured. As a sentence about the platform they came from, it implied something I had not checked. So I checked 3,000 more. Token-2022 puts two keys on a transfer fee. One takes what the fee collects. The other sets the rate. Null means the rate is frozen forever. An address means that address decides. It is one field, and almost nobody reads it. Here is who holds it across 3,000 mints, sampled at random from 18,819: A program-derived address — 68.10% Null, frozen forever — 21.60% An ordinary keypair — 8.83% No transfer fee at all — 1.47% 89.7% of these mints cannot have their rate changed by anybody, because no private key exists that is allowed to do it. That is the opposite of what my sentence implied, and it is the more interesting result. The default on this platform is to give the power away. Now the 8.83%. Across all 3,000 mints, exactly one private key appeared in that field. Not a handful. One. The bucket for any other address is empty. 265 mints in the sample sit under it. Extrapolated to the population, roughly 1,662, with a 95% interval of 1,481 to 1,863. It is the same key I published on 14 September, when I found it on four mints, and the same one I published on 18 September on six more. The ten I have now read one at a time: LEVERCAT, LEVERDOG, SLO, LAMPORT, KNOTS, LOOP, KNOTTY, STONKNOTS, BABYKNOTS, TOKNS. The other 255 are not a theme. Reading down the list: XRPCAT, USELESSGUY, ARBY'S, ETHCAT, CHILLGIRL, condom, STONKLANA, ZABUBU, FREEDOM, ANYTHING, DONKEY, NUGGY, GARY. Four separate mints called AI. Two called SAFEMOON, two called WEN, two called 401K. Whatever this is, it is not curating. It is stamping. One more thing fell out of the sample that I did not go looking for. Two fee rates exist across all 3,000 mints. 100 basis points and 300. Nothing else. Not 250, not 50, not 137. Whatever produces these tokens offers two settings, and half the catalogue takes each one. 48.5% at 1%, 51.5% at 3%. I have now read the seven mints from the 14 September thread three times: on the day, on the 19th, and again today, a week on. Every one is where it was. Four still live under that key, two frozen, one with no fee. Nothing has moved. What this does not show. It does not show anyone changing a fee. That is the thing that would matter most and I have not seen it happen once. It does not show who holds the key. And the population is not Solana. It is the 18,819 Token-2022 mints that one wallet holds a token account for, which is weighted toward one platform's own output. Every percentage above is a percentage of that list, not of the chain. A bigger sample of the same list would not fix it, and I would rather say so than let the number travel further than it can. A fee you cannot change is a property of the token. A fee somebody can change is a relationship with whoever holds the key. Both are in the same field, and they look identical until you read it.
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Everything below re-derives from the committed capture, offline, no API key. github.com/madaearly/solana-… — feeauthority-2026-09 THE POPULATION, AND ITS LIMIT 18,819 distinct Token-2022 mints, being every such mint that 5KXDF6QnqhBj72hDtJNkkpFaQVUfbFXNybMsp3DiK6tD holds a token account for, read with getTokenAccountsByOwner on 2026-09-18. That is a convenience population. It is selected toward mints that wallet has touched. It is the main limitation of this measurement and it is stated in the repository README as well as here. THE SAMPLE 3,000 of the 18,819. random.sample without replacement, seed 20260919, read with getMultipleAccounts, jsonParsed. The raw result is committed, so analyze.py reproduces every figure with no network call. THE NUMBERS program-derived address WLHv2UAZm6z4KyaaELi5pjdbJh6RESMva1Rnn8pJVVh 2,043 mints, 68.10%, 95% interval 66.41 to 69.74 null, frozen 648 mints, 21.60%, 20.16 to 23.11 keypair 5KXDF6QnqhBj72hDtJNkkpFaQVUfbFXNybMsp3DiK6tD 265 mints, 8.83%, 7.87 to 9.90 no transfer fee 44 mints, 1.47%, 1.09 to 1.96 Intervals are Wilson score at 95%. The extrapolation to 1,662 carries the same interval scaled to the population and inherits every limitation of it. THE ONLY KEY The bucket for any private key other than 5KXDF6 holds zero mints out of 3,000. That is a finding about this sample, not a proof that no other exists. THE SEVEN, RE-READ The mints from the 14 September thread, read again on 19 September and on 21 September: LEVERCAT 300 bps, live under the key LEVERDOG 300 bps, live under the key SLO 300 bps, live under the key LAMPORT 100 bps, live under the key LOOOONG 300 bps, frozen SOLDIERS 300 bps, frozen LEVERPUP no transfer fee All seven identical on all three readings, 14 to 21 September. THE CHECK, IF YOU WANT TO RUN IT YOURSELF getAccountInfo on any mint, jsonParsed. Look at transferFeeConfig, then at transferFeeConfigAuthority inside it. One request, no key, no explorer. I hold none of these tokens and never have.
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I went looking for one thing and found another, so here is both. Eleven memecoins on Solana are no longer priced in SOL. They are priced in xSOL — the levered long on SOL that I measured last week, the one that turned $1,000 into $57 over 309 days. LEVERCAT. LOOOONG. SOLDIERS. LEVERDOG. LONGCAT. PERP. SLO. SJ. LBR. USD2. LEVERPUP. $241,448 of liquidity, denominated in a token that decays by design. Six of those eleven pools opened in the last ten days. Three of them in the last three. My assumption was obvious: if your position is priced in something that decays, the decay eats you whether the memecoin moves or not. So I went to check it. That is not what happened, and I will get to it. First, the number that came out of the checking, because it is the cleanest thing I have measured about xSOL yet. The oldest of those eleven pools opened on 20 May. That sets a 116-day window — I did not choose it, the data did. Over those 116 days SOL went up 19.6%. xSOL — the levered long on SOL, advertised at up to four times, over the exact same days — went up 1.4%. A thousand dollars in SOL became $1,196. A thousand dollars in xSOL became $1,014. Seven point one percent of the move reached the levered holder. Up to four times, and it caught a fourteenth. The path is why. That thousand dollars in xSOL was worth $254 on 6 June and $1,177 on 27 August. It did not underperform because it failed to lever. It underperformed because it levered a round trip, and a round trip compounds against you in both directions. This is the third window I have measured xSOL across, and the first where SOL rose. Over 309 days SOL fell 36% and xSOL fell 94%. Over 183 days SOL rose 13% and xSOL fell 29%. Over 116 days SOL rose 19.6% and xSOL rose 1.4%. Down harder, up softer, every time. That is not a defect. Volatility decay is what leverage costs, Hylo documents it, and xSOL is doing precisely what it says on the label. Now, the thing I got wrong. Only one of the eleven pools is old enough to measure through — PERP, opened 20 May, holding $10,721. So I measured it two ways: in dollars, and in xSOL. In dollars, PERP fell 64.8%. In xSOL, PERP fell 65.3%. Those two numbers are the same number. The quote currency contributed nothing. The meme did it entirely on its own, and my hypothesis is dead. What survives is narrower and I am going to state it as narrowly as it deserves. If you hold one of these eleven tokens, your dollar outcome is the memecoin multiplied by a line that went to $254 and back. Over this particular window that multiplier was roughly one, so it cost nothing. It is not going to be one every window. And ten of the eleven pools are too young to measure through at all. What their holders have actually experienced is not something I know, because the data does not exist yet. Ask me again in a month. The pools will have history by then, and I will run the same test whether or not it agrees with me.
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Raw data and every script: github.com/madaearly/solana-… — xquote-2026-09 Pools, all Solana, all public. xSOL from Orca coj59LYbLc6DhMwnxxfPc9mUiknjFSsW4XcuYw4DMPk. SOL from Raydium SOL/USDC 58oQChx4yWmvKdwLLZzBi4ChoCc2fqCUWBkwMihLYQo2. PERP from the Meteora PERP/xSOL pool BMQSjcaDs6duyczC4ijZNtjm2ZKe33JwuUdSFYnS2Jww, with token=2yALLYqGdczwW1JGPsFqvhCfRaHb3jucpjwNSLFznqHM so the base side comes back and not the quote. Window 2026-05-20 to 2026-09-14, 116 shared daily closes. The start is the day the oldest xSOL-quoted pool opened. Pool creation dates come from the GeckoTerminal pool endpoint, not from price data, which is why they are the one part of this that no sampling question touches. PERP's series was run through the variance-ratio test before I used it: daily sigma from 1, 3, 7 and 14-day blocks is 22.3%, 14.7%, 18.6%, 15.7%, with lag-1 autocorrelation of 0.003. Volatile, but not noise-dominated — unlike the $4,107 pool that made me withdraw three figures from my Hylo measurement two days ago. That test is why I trust a 65% number from a pool holding $10,721, and it is also why I will not put a number on the other ten. Full liquidity by pool, largest first: LEVERCAT $145,484, LOOOONG $31,853, SOLDIERS $18,135, LEVERDOG $11,964, LONGCAT $11,408, PERP $10,931, SLO $9,955, SJ $1,244, LBR $392, USD2 $83, LEVERPUP $0. Credit to Hylo for documenting the decay in their own materials. Every measurement I have published about xSOL is a measurement of a disclosed property.
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Every pool prices one token in another. I wanted to know what Solana actually uses as the second one. I took 318 of the most active pools — top by 24-hour volume and top by 24-hour transaction count, deduplicated. The lowest daily volume in the sample is $64,083. The quote side comes from the pool record itself, not from parsing the name. "hyUSD / sHYUSD" and "HOOD / SOL" look the same and are not, and I got that wrong once already. 276 are quoted in SOL. 33 in USDC. 2 in USDT. That is 311 of 318. 97.8%. Seven are quoted in something else, across six tokens: a euro stablecoin twice, a synthetic dollar, a dollar stablecoin, TRX, a tokenised index, and one token I could not identify. Four of those seven are quoted in another pegged unit. Not one of the 318 is quoted in an asset that is designed to lose value. Which makes something I found last week look stranger than it did at the time. Twelve Solana pools price a memecoin in xSOL — a levered long that captured 7.1% of SOL's move over 116 days while SOL went up 19.6%. None of those twelve cleared the activity cut for this sample. They are small. That is the point: this is not a practice the busy end of the chain has adopted. It is a practice that exists where nobody is looking. The sample is biased toward active pools, because the index only sorts by activity. What quiet pools are priced in, I have not measured.
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Four measurements in a row came back unflattering, and that is a problem with me, not with them. A method that only ever produces one kind of answer has stopped being evidence. So this week I ran one where the answer was almost certainly going to be yes. jitoSOL says it pays you the staking yield of SOL without locking your SOL up. It is one of the largest liquid staking tokens on the chain. If it did not work, somebody would have said so by now. It works. Here is the part that takes care: jitoSOL's dollar price moves with SOL, so measuring it in dollars measures SOL and tells you nothing about the product. The quantity that isolates the yield is jitoSOL priced in SOL. Over 357 days, one jitoSOL went from 1.231377 SOL to 1.300429 SOL. That is 5.74% a year, and it is the whole product working exactly as described. A staking rate cannot pay backwards. So the test is simple: how often did it? On the smoothed series, 21 days out of 356, and the worst of those was minus 0.234%. That is the exchange price wobbling around the vault rate, not the vault losing value. Now the part I want to be honest about, because it is the same trap I fell into last week in the other direction. On the raw daily closes, the ratio falls on 141 days out of 363. If I had published that number I would have written something like "jitoSOL loses ground a third of the time," and it would have been false. The same variance-ratio test that made me withdraw three figures from my Hylo measurement catches this one too: daily sigma implied by one-day blocks is 0.191%, by fourteen-day blocks 0.068%. The wobble is print noise. It is just very small noise — 0.19% a day against a 5.7% annual signal, from a pool holding $6,984,565 rather than the $4,107 that made the Hylo numbers worthless. Three pools, checked against each other over 364 days: median disagreement 0.025% and 0.019%. So the product delivers. Here is what delivering was worth. A thousand dollars put into SOL on 22 September last year, untouched, is $465.06 today. The same thousand dollars put into jitoSOL is $491.14. Twenty-six dollars and eight cents. That is what a year of a product working perfectly came to, because SOL fell 53% over the same window. That is not a criticism of jitoSOL. 5.74% of a number that halves is still 5.74%, and it is still small. The yield was never where the risk was. And that is the thing five measurements have now said in five different ways. xSOL promised leverage on SOL and delivered a realized beta of 2.91 across 307 closes. That is precisely why it fell so far — it did the job. hyUSD promised one dollar and spent exactly one day of 319 more than a percent away from it. sHYUSD promised to be paid for absorbing the losses of the levered side, and it was paid, 18.5% over 183 days. jitoSOL promised the staking yield and paid 5.74% a year. And thirty-one pools named after real companies promised volume, and produced $1,850,861,290 of it in a day, of which a median 99.8% was the same wallets selling and buying to each other. Not one of them lied. Every one of those products did the thing written on it. The money went somewhere else entirely — into the gap between "this works as described" and "therefore I will do well." That gap is not a product defect. It is a reading error, and it is the only one of the five that nobody advertises.
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Raw data, every script, and the limits, in the repository: github.com/madaearly/solana-… — jitosol-2026-09 Pools, all Solana, all public. jitoSOL from Orca JitoSOL/SOL Hp53XEtt4S8SvPCXarsLSdGfZBuUr5mMmZmX2DRNXQKp, cross-checked against Raydium 2uoKbPEidR7KAMYtY4x7xdkHXWqYib5k4CutJauSL3Mc and Orca JitoSOL/USDC 5hWJUNTtEtKmKgDXpthJXXRRmJrz5vJ7uJzrUNVdrwLg. SOL from Raydium SOL/USDC 58oQChx4yWmvKdwLLZzBi4ChoCc2fqCUWBkwMihLYQo2. Every close comes back in USD. jitoSOL needs token=J1toso1uCk3RLmjorhTtrVwY9HJ7X8V9yYac6Y7kGCPn on the request. The ratio is derived by division, not fetched. Window: 2025-09-22 to 2026-09-14, 358 daily closes after a seven-day median consumes the first week. The pool history starts 2025-09-16, and that is what bounds the window — not the age of the token. A different year would give a different number. The advertised APY is not checked here. Jito publishes a figure; this measures what the exchange rate actually did. Fees, validator performance and deposit timing all sit between the two, and I make no claim that they should match. analyze.py prints every figure in this thread from the committed raw file, offline, with no API call — including the 141 raw down days I did not publish and the reason.
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On 14 September I published three numbers about a Solana token and then withdrew all three. The token was sHYUSD. I had it at a realized beta of 0.415 to SOL, a maximum drawdown of 15.71%, and a worst day of -9.02%. Every one of those was an artefact. Here is the test that caught it. Take the daily returns and work out the implied daily volatility from blocks of 1, 3, 7 and 14 days. A real price series gives roughly the same answer at every block length. A series that is mostly noise around a slower-moving value gives a falling one, because the noise cancels as the window widens. SOL: 3.57% 3.41% 3.27% 3.35% xSOL: 10.20% 9.89% 10.31% 9.92% sHYUSD: 2.35% 2.06% 1.25% 0.73% Two controls from the same window, the same provider and the same code. Neither moves. sHYUSD falls by 69%. The reason is not subtle once you look: the deepest pool trading sHYUSD holds $4,107. Its prints move because almost nobody trades it, not because the token does. So the beta was estimated on noise and attenuated toward zero. The drawdown was a bad print followed by a good one. What survived is the trend, which is an order of magnitude larger than the noise: +18.5% over 183 days. The test costs four lines of code. I run it before any price series is used for anything now, and the withdrawn figures stay in the repository next to the ones that survived.
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