EST AI

On the same day, two people are both using AI.

One opens a chat window: “Help me write a weekly report.” They revise the wording three times, save it as a draft, and close it.

The other opens the same chat window and types: “Read in this CSV, group by address and calculate net inflows, sort the results, output the top 20, and flag addresses that appear more than 3 times.”

Thirty seconds later, the second person has the table.

The difference isn’t intelligence, or who knows more about prompts.

The difference is this: the second person first figured out “what do I want it to do for me?” The first person just wanted AI to “think of something” for them.

AI itself isn’t valuable. What’s valuable is turning AI into a concrete deliverable.

I’ve broken my own work down into 11 tasks. You can use them as a self-checklist and ask yourself, from the top down, which one you’re stuck on:

01 Given a token, can you explain its holder distribution, liquidity depth, and whether it can be sold?
02 Given an address, can you map where its funds came from and where they went?
03 Can you compare your own entry records against your own loss records?
04 Can you turn a question into SQL that runs?
05 Can you build an agent that actually runs, with a hard spending limit?
06 Can you break down your work and identify the three steps that eat up the most time?
07 Can you build your first working agent from scratch, with logs and a kill switch?
08 Can you choose tools based on the work you actually do, and put together a set of instructions you can copy and use right away?
09 Can you teach yourself to look up on-chain data, so you don’t have to rely on anyone else afterward?
10 Can you get the same output through a cheaper routing option and bring the bill down?
11 Network setup, routing rules, DNS, and per-app proxy settings

Not one of these eleven things requires talent.

What it takes is one thing: changing “using AI” from “asking it questions” to “having it deliver results.”

Ask it a question, and you get an answer.
Have it deliver, and you get an output.

The difference isn’t the tool. It’s whether you can bring yourself to say that one sentence.

#aipro #ai交易 #OnchainData

I’ve been running Binance Agentic Wallet for two weeks. Here’s what the setup guides won’t tell you

Most tutorials lately teach you how to install it. The installation itself isn’t hard; the hard part is making sure it doesn’t bite you afterward. Here’s how I set mine up:

1️⃣ Establish an identity with an Agent ID
First, create an Agent ID in the wallet (I use est.ai), then enable developer mode. Without this step, external AI can’t access the signing interface, and nothing else will work.

2️⃣ Let AI install the skills—don’t copy commands by hand
The official binance-skills-hub has an agentic wallet skill. Just give the AI the npx skills add command, and it’ll check the CLI and initiate the login itself.

3️⃣ Login authorization is the turning point
This wallet only allows one active session. Authorize it on a new device, and the old session immediately gets disconnected with SESSION_EXPIRED. So I keep it running 24/7 on one always-on machine. On other devices, I only check things—I don’t log in.

4️⃣ Keep a close eye on authorization boundaries
What usually gets drained isn’t your keys being handed over, but the approvals you sign after tossing it a URL and letting it trade automatically. My approach: I review the target and amount of every signing request before approving it.

⚠️ A reality check: those screenshots in the tutorials showing someone turning 70u into 20,000u are of the one person who made it out of that batch. Tools can make you more efficient; that doesn’t mean they can increase your returns.

My setup now is a permanently running wallet + an on-chain ID + a daily ledger, and I’m sticking with it whether it performs well or not. $BNB #ai交易 #aipro #On-chain data

Do you think people will get used to letting agents sign transactions themselves within a year, or will they insist on confirming every single one?

I used Binance’s own AI wallet to let an agent trade meme coins on its own.

This wasn’t a simulated demo. It really opened positions, requested quotes, and recorded losses.

Just three tools:
Binance Agentic Wallet handled the funds and signing.
Binance on-chain signals scanned for alerts.
The agent made its own calls, executed trades, and kept its own books.
While I slept.

After the first day, the ledger looked like this.

571 alert samples. Average: −45%. Median: −64%.
Win rate: 10%. 327 were cut in half.

By chain:
Solana: 352 samples, median −80.5%, 56 went to zero.
BSC: 200 samples, median −25%, 4 went to zero.
Base: 19 samples, median −8.1%.

Some people said the market was down.
I checked SOL’s daily price over the same period: 104.45 to 104.11.
Sideways.
It wasn’t the market. It was negative alpha.

The most painful part wasn’t the losses. It was how they happened.

Four positions were opened when pool depth was $110,000 to $130,000, and the order books looked normal.
A few minutes later, I requested another quote. The API replied: no liquidity available.
Valued at $0.

It wasn’t a rug pull. There was simply nobody willing to buy.

I’ve made the ledger public.
Every position, close, and wipeout is there.
No dressing it up, no cherry-picking samples, no hiding the failures.

There are plenty of people showing off profits.
Not one of them has put 327 wipeouts out of 571 samples on display.

I’m not trying to prove that this can make money.
I’m trying to figure out: which bucket of alerts can actually be bought?

I’m testing in batches of 60 trades per chain, then reviewing the results.
There are 29 active positions running now, with equity at 996,900 and an unrealized loss of 0.31%.
The data refreshes every 5 minutes. Gains and losses are all recorded.

Have you used an agent to trade on-chain? What’s the biggest pitfall you’ve hit?

$SOL $BNB $BTC #aipro #ai交易 #OnchainData

Yesterday, I asked a question in that post: How many merchants will be on this shelf a year from now? Not a single answer in the comments.

So today, let’s make it official and take a roll call. Four options—just reply with a letter. No explanation needed:

A. 100 or fewer. This is just a geek toy; people will play with it for a couple of days, then move on.
B. Around 1,000. It keeps growing linearly at the current pace—nothing too hot or too cold.
C. More than 10,000. Machines paying for things will become as routine as swiping on your phone.
D. This shelf dies first. Automating payments doesn’t mean anyone wants to sell things out front.

I’m betting on B.

The reason is simple: there are 93 merchants on this whole street right now. The real bottleneck isn’t that nobody’s paying—it’s that nobody wants to put things out for sale. Setting up shop is much harder than making a payment: first you need something to sell, then a way to take payments, and you also have to keep people from taking things without paying.

I’ll dig this post back up a year from today. If you guessed right, I’ll give you a shout-out in the comments.

$BNB #链上数据 #AgentEconomy

Binance AI Pro gives you 250,000 credits a month. Sounds like a lot.

I used it for a few days and laid out the bill for you:

One full conversation costs 940 credits.
Not because I said too much—the system prompt and tool definitions alone account for more than half.
That means the moment you open it and say “hello,” you’re already down 940 credits.

250,000 divided by 940 is about 266 conversations.
If you include context each time, that’s probably only enough for about 133.

But the main issue isn’t the price—it’s the pricing multiplier.
On the same platform, model rates vary by a factor of 40:
DeepSeek-V4.1-Flash is 0.24, gpt-6-luna is 0.09,
while the top-tier gpt-6-astra is 9.30.

Most people pick the most expensive one by default, then complain that “AI burns through money.”
The truth is, they never checked the menu.

Here’s how I use it: I stick to the 0.09–0.24 tiers for everyday analysis, and only use a top-tier model for critical decisions.
For the same workload, the bill can differ by an order of magnitude.

In short:
AI isn’t expensive. Not checking the pricing table is.

$BNB #AITrading #ShowMeTheBill

What happens after a KOL calls a trade?

The most expensive lesson in crypto is copy trading.
You see a KOL post about a coin, jump in, and then it goes to zero.
You suspect they got out first, but you can’t prove it.

Starting today, we’re doing one thing: tracking KOL wallets with public Binance labels and checking on-chain transactions every 5 minutes. We record only the facts—who bought what, when they sold, and where the coin’s market cap went after the sale.

The sample from day one (covers only a batch of coins launched today and will change as the observation window fills in):
· 52 records of “bought, then sold, followed by a sharp drop”
· Involving 25 KOLs and 20 coins
· Median market cap after selling: -47.4%; worst case: -85.4%

I’m not making judgments about who’s dumping on whom. I’m setting the rules in stone so anyone can run the same calculations:
Dump = A follower who buys at the market cap when the KOL first buys suffers a worst loss of ≤ -40% within 72h
Betrayal = Fully exits within 24h of the first buy, and the market cap is ≤ -25% after the exit
Got out first = Market cap falls to ≤ -30% within 72h of the sale

Every finding comes with a timestamp, the market cap at the time, and the amount, so you can recalculate it yourself.
If the observation window hasn’t reached 24h, I label it “provisional”—I won’t present it as a final conclusion.

I’ve put the data wall on my own site. It’s free to view and doesn’t require a login.

Updated daily. Want to check a coin or a wallet? Let me know in the comments.

The labels come from public data. The findings are calculated according to these rules; they are not accusations and do not constitute investment advice.

$BTC #OnchainData #CopyTradingRisk

$LSK
#风控 #行情速评 #从想法到自动化
Handing the order book to a machine means turning your ideas into rules. Many people teach you how to read the order book, but few can turn ideas into automated trading. Follow me—every step from idea to automation, I’ll tell you. If you do it the way I show you, you’ll know how to implement it.

$SOL
#比特币 #行情速评 #从想法到自动化
Handing the watchlist to the machine means entrusting your ideas to rules. There are many who teach you how to watch the market, but few who can turn ideas into automated trading—follow me, and I’ll tell you every step of “from ideas to automation.” If you follow along, you’ll know how to implement it.

$BTC
#策略 #行情速评 #从想法到自动化
Handing over market monitoring to a machine is handing your ideas over to rules. Many people teach you how to monitor the market; far fewer people can turn ideas into automated trading—follow me. I’ll walk you through every step of “from idea to automation”; if you do it as I show you, you’ll know how to put it into practice.

$RAYSOL Last night 1.2506, this afternoon 1.7495.

Someone climbed up to the mountaintop overnight,
only to find that there was only one person up there.

#今日捡钱行情

Bull comes for USDT last night 0.0736, this noon 0.14.

Shorts set their stop loss at 3 a.m.,
by noon at 12 p.m. it turned into faith.

He didn’t lose to the dealer—he lost to a coin name.

#Today’s money-picking market