In Edy Cu’s test of 11 tokens, buyers identified as professionals accounted for 0–3.6% of DEX buy volume. That is a result from his limited sample, not a market-wide rule. The more consequential input to his Glidepath sell planner was observed token flow: its calendar could change more noticeably when Smart Money or exchange flows turned adverse.
Glidepath turns a token holding into a dated selling schedule. It does not execute trades. Cu describes it as a way to pace a sale of a token someone already holds, while showing the data and assumptions behind each tranche.
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What Glidepath does—and does not do
The user supplies a token, chain, and amount held. Glidepath returns a proposed selling calendar, with one tranche per day, and an .ics file that can be added to a calendar app. As Cu puts it, “It plans. It never trades.” The schedule is a planning aid, not an order or execution service.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe tool’s premise was to avoid selling a large holding all at once or scheduling sales without regard to buying and selling flows. Its output is an estimate based on current and historical data; it cannot know what flows or prices will be on future sale dates.
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What the “under 4%” result actually means
Across 11 tokens Cu tested, Nansen-labeled professional buyer cohorts represented 0–3.6% of DEX buy volume. In his sample, major tokens were 98.5–100% organic and fresh Solana launches were 96.4–96.7% organic. Four sniper-bot labels matched no rows in those observations. These are Cu’s reported measurements, not independently reproduced results or a claim about tokens generally.
Cu says the first afternoon of testing challenged the assumption that labeled professionals dominated buying. In eight probed tokens, he reports that Nansen’s include and exclude smart-money-label filters partitioned the buyer set exactly. The returned address_label was a display name, however, not the specific label that caused an address to match a filter. The classifications therefore depend on Nansen’s labels and categories, rather than a transparent buyer identity test.
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How the planner estimates organic buying
Counting every unlabelled buyer directly would require paging through very large result sets. Cu reports that professional-buyer lists in his observations contained no more than 67 wallets, while a seven-day query for young Solana launches returned more than 20,000 buying addresses and was still truncated after 20 pages. Glidepath therefore estimates organic buying by subtracting labeled professional buyer volume from total DEX buys, rather than summing an exhaustive list of unlabelled wallets.
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tgm/token-informationandtgm/who-bought-soldendpoints described by Cu. - It subtracts the labeled volume from total DEX buys to estimate organic buy volume over seven days.
- It divides that seven-day estimate by seven to produce an estimated daily organic-buy figure.
This is a proxy, not a direct count of every organic purchase. Its reliability depends on the underlying DEX totals, the professional labels, and the period queried.
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How flow data changes the calendar
The planner also uses current-day tgm/flow-intelligence data and the preceding 13 complete days of tgm/flows history. It marks a day red when Smart Money net selling exceeds a threshold or exchange deposits exceed a threshold tied to estimated organic daily buys. A red current day halves the first tranche; the recent proportion of red days extends the expected finish date.
In the PEPE snapshot Cu recorded on September 16, 2026, the seven-day period showed $3,236,056 in DEX buys, of which $24,893 was attributed to labeled cohorts. The planner reported 528 organic buyers and estimated organic buying at $458,738 per day. In the associated 13-day history, four days were red, alongside exchange deposits ranging from +$499,000 to +$1.92 million. A later PEPE demo showed +$736,000 in net exchange deposits and a halved first tranche.
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These flow signals affected the schedule more visibly than the buyer-label split in Cu’s account. There is a cohort-definition caveat: the current-day Smart Money measure uses smart_trader, while historical flow uses smart_money. For PEPE, Cu reports 21 wallets under the former definition and 18 holders under the latter, so the counts are not directly interchangeable.
How it sizes each tranche
According to Cu, the daily tranche is the smaller of two limits: a risk-adjusted share of estimated organic daily buying, or 1% of reported liquidity. The share of estimated daily buying ranges from 10% down to 3% as Nansen peer-percentile risk indicators rise. A red current day applies the separate first-tranche reduction described above.
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For the PEPE example, Cu reports tranches of $30,598 and $9,372. Those figures are outputs for that example, not a recommended amount or a guarantee of what a real sale would receive.
What the cost estimate can—and cannot—tell you
Cu reports an estimated cost of $147.90 for the PEPE schedule, compared with $230.36 for selling the whole holding at once under the planner’s constant-product approximation. That comparison is a model output, not a measured execution result. It does not establish that following the calendar would reduce costs in live trades.
- The model treats
liquidity_usdas if it were one pool with the token on one side. Cu warns this can overstate usable depth when liquidity is concentrated in a thin pool. - It ignores MEV and gas costs.
- Only Solana and Base reportedly receive a real routed quote; other estimates should not be read as routed execution quotes.
- Future calendar dates cannot account for whether the market will be red or green when a tranche is due. Check conditions again on the day before acting.
Other constraints to know about
In the implementation Cu describes, stablecoins have no history strip because the relevant endpoint returns a 422 error. He also reports that submitting an address that is valid in format but does not exist still consumes 12 credits. These are implementation-specific details, not general properties of token calendars.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallCu reports six property-test properties run against 10,000 generated cases each, 258 Vitest tests, 13 recorded plans replayed byte-for-byte offline, and live cold-plan latency of p50 3.5 seconds and p95 6.0 seconds. Those are author-reported development and performance results, not independently verified measurements.
How to interpret the tool before relying on it
Glidepath is most useful as a visible pacing framework: it lays out how a holding might be split and shows how estimated buying, labeled cohorts, liquidity, and flows influence that schedule. It is not a forecast of price, proof that a buyer category predicts returns, or a way to eliminate execution risk. Cu’s own description is: “This is not financial advice. It is a pacing calculator with its reasoning shown.”
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