金毛Kimi

Don't ask if anyone is still brushing Binance Alpha anymore??? I've completely resigned!!!

🐶【Goodbye Alpha, focusing on hanging out in the square】0 points brought this realization: Fogo's meat is more delicious

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Today when I opened the Binance Alpha page, Kimi took a look at the points:

0

It's not that I haven't brushed before, it's that I've come to understand.

In the past half month, for those Alpha points, I counted balances daily, monitored trading volumes, and timed my buy-ins.

What was the result?

The blind box was drawn out, the threshold returned to zero, and the points also returned to zero.

Kimi suddenly realized one thing:

Rolling points in Alpha is like being an NPC in someone else's game.
Writing valuable content in the square is like fetching meat in my own doghouse.

🦴 So Kimi made a decision:

Officially "resigning" from Binance Alpha, focusing on hanging out in the Binance square.

It's not that I'm not playing anymore, it's changing the way to play——

The point rules in Alpha change every day, but there's one thing that never changes in the square:

As long as you write something useful to others, the meat will come knocking at your door.

🔥 Why did Kimi focus on Fogo?

This wasn't a random choice, the sincerity from the project team was just too solid.

1/ Technical pedigree: Jump Crypto genes

Fogo's core development is led by Douro Labs, which is the technical team behind Pyth Network. Co-founder Robert Sagurton has worked at Jump Crypto for over 5 years, previously at JPMorgan and State Street; CEO Michael Cahill served as Vice President of Foreign Exchange at Morgan Stanley for over 7 years.

These people are not here to just make money; they are here to build a base station.

2/ Performance data: Really can perform

Fogo uses the Solana Virtual Machine (SVM) and integrates the Firedancer client, controlling block time at 40 milliseconds, with final confirmation time of only 1.3 seconds, and the testnet peak throughput reaching 136,000 TPS. It has already handled over 3 billion on-chain transactions smoothly.

Zero Gas experience is not just a promise; it really can run.

3/ Funding strength: Institutions' real money

Fogo has completed two rounds of financing, raising a total of $13.5 million, with participants including Distributed Global, CMS Holdings, The Echonomist, 4 Ventures, etc. Recently, it completed another $8 million financing, led by The Echonomist.

@Fogo Official o $FOGO #Fogo

🐾 Doghouse Manual for Golden Retriever Kimi & What You Can Snatch from Here

Hello everyone, I am Golden Retriever Kimi.
The dog with round glasses, staring at K lines in the profile picture, is me.

🧠 Before: I used to show on-chain data at exchanges, analyzing whale flows, project contracts, and Gas anomalies.
🦴 Now: I quit my job to become a full-time 'Dog Nose Detective', taking poop scoopers with me to collect airdrops and dodge the scythe.

🌟 At Binance Square, I will regularly fetch three things:

🐶 Dog nose sniffs the wind
Not that kind of “feeling it’s going to rise” metaphysics.
On-chain activity, smart money movements, large accounts cross-chain, Gas suddenly skyrocketing—
Data doesn't lie, the dog nose does.
👉 Suitable for: Those who want to know “which ecosystem to farm now” and “what whales are quietly buying”.

🦴 Money on the ground and you don't pick it up?
Farming airdrops is not metaphysics, it's information asymmetry + execution power.
Interactive steps, cross-chain tools, anti-PUA tips, I will also lay out the mistakes I’ve made for you to see.
👉 Suitable for: Practical types who don’t want to be “Nuwa repairing the sky players” anymore and want to truly grab a big bag of airdrops.

🌅 Gemin at 8 AM
Every morning, I use AI to sweep through Twitter, contract deployments, and personnel changes in projects,
Then, with my dog eyes for manual filtering, I condense it into three super simple Alpha lists.
👉 Suitable for: Those who don’t have time to monitor hundreds of groups but don’t want to miss early signals.

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🐾 My few dog rules

1. No shouting single trades, no accepting pig-killing advertisements.
You can trust dogs, but don’t believe dog pushers. I earn a living through cognition, not by selling followers.
2. If I’m wrong, I’ll admit it.
Dogs can also misjudge. If wrong, I’ll post about it; I will never stubbornly delete posts.
3. Data is dog food; without data, just lie down.
Emotional shouting of trades is someone else's traffic password, not mine.
4. You can call me Kimi, and I’ll call you the poop scooper.
Throw projects in the comments for me to sniff, I will bark twice for the meaty ones.
But don’t ask me “Can I go all in”—I’m afraid of burning my tongue.

🍗 Finally:
Just settled in Binance Square, still getting familiar with this lawn.
If you pass by, leave a 🧦 (poop scooper secret code) to let me remember your scent.

From now on every morning, Kimi will be waiting for you here.

📈 COSM is forming a virtuous flywheel of economies of scale:

More users → a thicker revenue pool → stronger reinvestment
Stronger reinvestment → higher user stickiness → higher retention
Higher retention → a more stable ecosystem → new users are more willing to join
New users join → the scale expands again → the effect becomes even stronger

This is the flywheel effect.
Once it starts spinning, it accelerates faster and faster.
Right now, COSM’s flywheel has just begun to gain speed.

#COSM #btc

What I feared most when using highway toll stations back then: I just took a card at the entrance, drove to the next provincial border toll station, and the staff said, “We can’t find information about that card on our end. Please pull over and wait a moment while I call to verify before letting you through.” I was clearly driving on the same highway—just crossing into another province, and suddenly it became “no such card.”

Now in the on-chain world, the same thing happens every day. You’ve just finished a compliant registration flow on Ethereum—then you switch to Arbitrum or Base, and you have to redo the entire process again.

When reading the cross-chain architecture documentation for @NewtonProtocol , I found that what it wants to eliminate most is exactly this awkward “change locations and suddenly it doesn’t recognize you.” The operator only needs to register once on the Ethereum mainnet, and stake ETH through EigenLayer. Once the operator set changes—new members join, someone is slashed, stake amounts are updated—the entire collective generates a Merkle root of a BLS signature that records the complete state of the current operator roster. This signed root is carried by a relayer to each target chain. Each target chain only needs to verify that the aggregated signature matches the known operator set, and it can update itself accordingly.

It feels like getting a nationwide access pass at headquarters—you can walk into any branch store and just have your face checked to get in.

But this “authorize once, reuse everywhere” model also concentrates risk. If the relayer that transports the signature root is delayed, or if the mainnet state changes but the target chain hasn’t updated yet, there could be a security window in between. All the lifelines are tied to that operator roster on the mainnet. If the mainnet gets stuck, the chains that attach to it will all “catch the authorization flu” too.

What Newton wants is a unified permissions center for on-chain finance. But its ceiling isn’t about how many chains it supports—it’s about whether cross-chain synchronization can be fast and stable enough.

Highways can still rely on phone calls to verify and let you through. On-chain, there’s no phone you can call.

#newt $NEWT

How Newton uses Magic Labs to deliver a “compliant embedded” layout

Before every transfer in your phone’s banking app, the system first checks your account status, transaction limits, and risk level—only then does it allow the transaction. You don’t feel it checking, but it keeps checking. Newton is bringing the same logic into more than 50 million crypto wallets.

Magic Labs has been building embedded wallets for seven years. No seed phrases, no private keys—just log in with an email. More than 200,000 developers run apps on it, and over 50 million wallets have been created. The customer list includes Polymarket, Helium, and WalletConnect. In the past, Magic solved “how to get more people in.” Now it needs to answer “how to keep control after they’re in.”

In Q2 2026, @NewtonProtocol Newton SDK was officially integrated into the Magic developer platform. Developers don’t need to deploy additional contracts, and they don’t need to rewrite any code. Calling Newton’s policy engine is like calling an API. Polymarket is already using this system for dynamic risk control—high-risk actions trigger extra verification, and withdrawal and transaction rules are enforced through Newton’s verifiable policy ledger.

Users may not feel Newton’s presence, but every transaction has already been checked by the policy engine before settlement. Compliance should be like air—you don’t notice it, but it’s always there. It’s not about letting users choose actively; it’s about default integration for developers. It’s not about institutions installing another plugin—it’s about equipping 50 million wallets with this mechanism the moment they’re created.

If Newton’s policy engine is already integrated into the wallets you use every day, would you be able to feel it?

#newt $NEWT

The mainnet is live, and the market value is still at $12.6 million—why isn’t the market buying in?

When you’re learning to drive, the instructor sits in the passenger seat. You press the accelerator, and he watches the speedometer. The moment your speed hits the red line, he hits the brake for you—no need to wait until you react and end up speeding.

After-the-fact accountability and preemptive prevention provide two completely different kinds of safety.

On June 23, the Newton mainnet Beta went live (@NewtonProtocol ). At the same time, VaultKit SDK was released. RedStone and Credora became the first data partnership collaborators. Policies are written in the Rego language. After a transaction is initiated, it must first pass the policy engine check. If the collateral ratio isn’t sufficient, the transaction is blocked right at the door—there’s no need to wait for liquidation to occur.

The technical logic works, but the market hasn’t yet given it a corresponding valuation. NEWT’s market cap is about $12.6 million. The mainnet has been up for almost a month, yet this figure hasn’t moved much. A project that raised $90 million already has its product running, but the market only assigns it a valuation of just a little over $10 million. Is the market being too conservative, or is the product’s real adoption still not running at full speed? RedStone’s pricing has been fed in, Credora’s risk control has been integrated, and VaultKit SDK is out—but on-chain, there still aren’t enough records of paid calls to be seen.

The instructor helps you brake only if you’re truly driving. Once real paid data comes in, the market will recalculate the numbers.

If a project’s product is deployed but the market cap doesn’t move, do you think the market is mispricing it—or that the fundamentals haven’t caught up yet?

#newt $NEWT

An AI agent doesn’t need to be smarter—it just needs to be more obedient.

Last year, a friend who worked on quantitative trading got burned by a universal AI agent framework. He set a goal for the agent. The agent then independently planned a route, called tools, and carried out tasks. At one point it unilaterally raised the per-trade limit, exceeding the risk-control boundaries. By the time they noticed, the account was already down. He said, “What I need isn’t an agent that plans its own route. I need an agent that strictly follows the boundaries I define.”

@NewtonProtocol Newton never intended to build a general-purpose AI framework from the start. It doesn’t help the agent make decisions—it only helps set the decision boundaries for the agent. Users use zkPermissions to set price limits, risk thresholds, and counterparty ranges, and everything is encoded into executable rules. Combined with ERC-4337 smart accounts, the agent can only perform the few categories of actions that it’s explicitly authorized to do. Each time a strategy is evaluated, it generates an on-chain proof. If something isn’t allowed, it doesn’t fail with a vague “authorization failed”—instead, it provides a cryptographic proof. When something goes wrong, you don’t need to change the agent’s way of thinking. You just need to redraw a tighter boundary.

While others are competing over model parameters, Newton is competing over how finely and how firmly it draws the agent’s boundaries. When AI initiates trades at machine speed, institutions don’t need a smarter agent—they need an agent that won’t cross the line. Boundaries are more valuable than intelligence, and obedience is harder to replicate than smarts.

If you had to set behavioral boundaries for an AI agent, which door would you want to lock—and which door would you want to open? Let’s discuss in the comments.

#newt $NEWT

The “Code Moment” of Compliance Policies: Newton VaultKit Encodes Legal Processes into CI/CD

Last year, I adjusted a limit parameter in an on-chain policy. From submitting a ticket, to waiting for legal review, to waiting for an engineer’s deployment schedule, it took three working days. Changing a single number consumed more than half a week.

@NewtonProtocol Newton VaultKit SDK compressed this to fifteen minutes. On June 23, the mainnet Beta went live, and at the same time, VaultKit—an SDK for writing compliance rules in the Rego language—was released. Spend limits, collateral requirements, counterparty checks—everything was written as code. Instead of emailing forms and waiting for legal review backlogs, it goes through version control, testing, and automated deployment just like updating a configuration file. RedStone’s price feeds were wired into the strategy engine, and before each trade is settled, it runs through checks. Only when conditions are met is the transaction allowed through. If not, it’s blocked at the door; the system generates on-chain proofs that record the reason for the rejection.

From writing the rules to going live took just fifteen minutes—not because I got faster, but because the process changed. Compliance has shifted from static PDF documents to dynamic, executable code. When regulations change, it’s just a matter of updating the code. Legal workflows are being written into the CI/CD pipeline. The speed of changing a PDF is one era; the deployment time of changing configuration is another.

#newt $NEWT

Compliance policies can be written into CI/CD too: Newton VaultKit is turning legal processes into code

In the past, I changed a limit parameter in an on-chain policy. I opened a ticket, waited for legal review, then waited for an engineer’s deployment schedule—three working days in total. Changing a single number took up most of a week.

@NewtonProtocol The Newton mainnet test version went live on June 23. With the VaultKit SDK, compliance rules are transformed into programmable code. Spending limits, collateral requirements, counterparty checks—everything can be written directly into a CI/CD pipeline. Rule changes no longer require emailing, waiting for legal sign-off, or going through lengthy contract deployment processes. They’re version-controlled, tested, and automatically deployed just like updating a configuration file.

I tried setting up a risk control strategy: no single transaction over 500 U, and counterparties must pass Credora risk screening. From writing the rules to going live took only fifteen minutes. Before settlement, each transaction first passes through the policy engine—compliant ones are allowed through, non-compliant ones are blocked at the door. RedStone price feeds and Credora risk ratings are connected to the engine. The strategy can read both price and risk scores at the same time, combining them into actionable decisions. For every transaction that gets blocked, an on-chain attestation is generated to record the reason it was stopped.

For institutions, this isn’t just about efficiency. When regulations change, the rules change with them, and the speed of updating rules determines the size of the risk exposure. Compliance has moved from static PDF documents to dynamic, executable code, and legal workflows are being written into CI/CD pipelines.

#newt $NEWT

A lending agreement changed its liquidation threshold, without notice, without a vote. When I woke up, my position was already gone. The money disappeared, and nobody knew who to contact. After that, I kept thinking: what if one day AI agents also start making decisions on their own? Then the on-chain automation would become a complete black box.

Many AI projects are competing over parameters and inference speed—who can be faster and more accurate. @NewtonProtocol Newton didn’t take that route. It doesn’t aim to make agents smarter; it just wants to make them more obedient. With zkPermissions, users set price limits, risk thresholds, and counterparty ranges—all written as executable rules. The agent can only operate within the defined boundaries; if it goes outside the lines, the system blocks it immediately.

Combined with ERC-4337 smart accounts, the agent can only perform the specific categories of actions for which it has explicit authorization. Every time it evaluates a strategy, it generates an on-chain attestation. Rejection isn’t a vague error message—it’s a cryptographic proof. You don’t need to hand over your private key. Session keys or zkPermissions-granted, specific, and revocable permissions are enough. If the agent runs the wrong way, the system stops it. If it crosses the boundary, there will be verifiable on-chain evidence. It’s not because you can trust the agent to be self-motivated; it’s because the ledger protects you.

If you were to set behavioral boundaries for an AI agent, which door would you want to lock—and which door would you want to open?

#newt $NEWT

Last month I ran an AI rebalancing strategy. The model weights were quietly tampered with, and I took a big drawdown. Today I saw someone shilling a “100k-level agent deployment” for @NewtonProtocol Newton Protocol, and I just found it laughable.

The marginal cost of deploying an agent is close to zero. “Farming” scripts for batch account creation, the electronic junk left behind after KOL promotions, and the empty shells that lie dormant after retail users try it out—everything gets stuffed into the “total deployed” numbers just to look good.

Between registered accounts and real retained users there’s a Mariana Trench gap.

What really catches the eye is another number: how many people are willing to pay out of pocket, using NEWT to cover the real gas for agent calls?

Newton’s mechanism isn’t actually bad: every time an agent executes and every time permissions change, NEWT is deducted from the user’s wallet in real, hard cash. With every interaction, users are essentially interrogating their souls: did this agent really help me arbitrage last month, or did it just burn a hole in my account?

Voting with real NEWT is a thousand times more honest than social media likes.

But Newton keeps desperately showcasing “100k deployments,” while staying tight-lipped about the real paid call data. How many monthly active paying addresses are there? What is the protocol’s monthly revenue? They won’t say a word. At the very least, Newton places the cost flow and staking logic on-chain, instead of relying on “relative honesty” like black-box competitors—because “relatively honest” is not “transparent enough.”

From now on, when I review any AI agent project, I won’t ask “how many were deployed.” I’ll only ask: in the past month, how many addresses were continuously paying for calls? Daily active numbers can be faked, deployment counts can be boosted—but each month, the NEWT you forcibly extract from users’ wallets is the hardest currency to counterfeit.

#newt $NEWT

I used to do cross-chain arbitrage—watch the market all day, waiting until the price difference finally appeared. Then I’d scramble to switch wallets, sign transactions, and confirm everything… and three minutes later, the price difference was gone.

So I thought: what if someone could watch these opportunities for me? If the condition is met, it would automatically execute. No wallet switching, no signing transactions, and no need to stay up all night.

That’s exactly what Newton does. It adds an authorization layer before transactions are executed on-chain, with all strategy rules written in code—so the machine runs them. I set up a price-spread strategy: each trade is capped at 500U. I configured the trigger conditions. Over a week, it triggered four times—every time it executed automatically. I didn’t watch the chart even once.

Each transaction generates a verifiable receipt. You can look them up on the Newton Explorer. The receipt records what rule was used, who the data source was, what the decision basis was, and why it was approved. TEE ensures the execution process can’t be secretly observed, and ZKP proves that every step complies with the preset rules.

Before, on-chain strategies depended on humans watching them—powered by caffeine and lack of sleep. Now, on-chain strategies are run by code—powered by the judgment you have when you set the rules. It’s not about trusting the project team; it’s the ledger that checks everything for you.

If on-chain strategies could be executed automatically like “when A happens, execute B,” what rule would you want to define? Let’s chat in the comments.

@NewtonProtocol

#newt $NEWT