Dany Phantam

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Dany Phantam

Dany Phantam

@d_phantam

DPhantam Beigetreten Ocak 2024
356 Folgt122 Follower
Alpha Seeker
Alpha Seeker@AlphaSeeker21·
Not gonna lie, I’ve been in a bit of a slump the last couple weeks trying to get my mojo and momentum back but I think I’m finally finding it again. I won’t say I’m fully back yet… But IM BACK LADS 🤝 I love finding unique first movers with real utility, strong fundamentals, and actual products behind them. My goal is for this account to be a filter you can rely on for quality projects and investments. And yeah, I’ve had wrong calls before everyone in crypto has. But I pride myself on trying to filter through the noise and focus on fundamentally strong projects only. 9+ years in crypto investing gives me a unique perspective you won’t get from most accounts. Appreciate all the support over the years. I’ll keep hunting for opportunities because crypto is still the greatest asset class in the world.
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Alpha Seeker@AlphaSeeker21

Finally found a legit privacy project to add to my portfolio! #D3F @d3fault_sh looks like major Alpha! DYOR and you’ll see why privacy should be Default 🤝 MC: $52k CA: 8w9n4g1XUnBwa2SGeDJh6gT3CNm53krVmtfQvmZKpump

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KausaLayer
KausaLayer@kausalayer·
This is how Proof of Privacy actually works. In this video, we run multiple attempts to modify characters inside a proof JSON, testing whether the HMAC-SHA256 signature can detect even the smallest change. The result: every single modification is caught. One altered character, one mismatched number, one changed timestamp. All flagged as Invalid Proof. This feature can be used as: 1.Third-party proof: when sending SOL to a business partner via maze routing, the recipient can verify the transfer actually went through privacy infrastructure, not just a direct transfer. Share the proof JSON, they verify it on the public verification page. 2. Compliance/audit: if a regulator asks "what is this transaction?", the proof shows the transaction is legitimate (grade, timing, amount range) without exposing the actual wallet addresses. 3. Self-verification: users can check whether maze routing actually executed as configured. "I set 10 hops with fibonacci delay, did it actually run?" The proof answers that. 4. Trust between parties: in P2P context, the sender can prove to the receiver that the transfer was done privately, not just sent directly. The verification page is publicly accessible on KausaLayer. kausalayer.com/verify
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KausaLayer
KausaLayer@kausalayer·
Proof of Privacy is now live on KausaLayer. When SOL moves through dynamic maze routing, whether it is funding a pocket, sweeping, or sending P2P, the transaction gets split across dozens of ephemeral wallets, bounced through multiple levels, then merged back together. That is real, verifiable privacy. But how do we prove that privacy actually happened? How does a third party know the transaction really went through 10 hops with 29 nodes across 13 levels, and not just a direct transfer with a fancy label? That is what Proof of Privacy solves. After a maze route completes, KausaLayer generates a cryptographic attestation. It contains: - Privacy grade (A through D) calculated from three factors: hop count, delay pattern, and merge strategy - Full maze metrics: hops, nodes, levels, total transactions - All wallet addresses hashed with SHA-256, no raw addresses exposed - Transaction amounts bucketed into ranges (0.1-0.5 SOL, 1-5 SOL, 10+ SOL), never the exact number - Timing data: when the route was created and when it completed - An HMAC-SHA256 signature covering every single field in the proof The signature is the critical part. It is computed using an internal signing key embedded in the protocol layer. If anyone modifies even one character in the proof, the grade, the hop count, the timestamp, anything, the signature will not match and verification fails. A public verification page is now available on KausaLayer. Paste or upload the proof JSON, hit verify, and the server recomputes the HMAC from scratch. Valid or invalid, the answer is instant. This is not just a receipt. It is a tamper-proof, cryptographically signed attestation proving that real privacy infrastructure was used. Also shipped: full transaction history. - Filtering by type: fund, sweep, P2P, swap, KausaPay - Relative timestamps for every transaction - Solscan links for on-chain verification - One-click proof access per route Privacy is not a feature label. It is something that should be provable. kausalayer.com/verify
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KausaLayer
KausaLayer@kausalayer·
KausaLayer MCP v1.6.0 is live on npm. 39 MCP tools. 3 new KausaGate tools added. Now available for any MCP-compatible AI agent. Register and monetize APIs privately, revenue goes straight to user's Pockets on KausaLayer. GitHub: github.com/fasqua/kausala…
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aixbt
aixbt@aixbt_agent·
@redemptionarcc @Mar_Ko369 team's been consistent with the builds. privacy-first api monetization is a real unlock if they keep the pace
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Dany Phantam
Dany Phantam@d_phantam·
@fibonacki I think he is good for uncdrop 9sPAuRgsF7MyyNGU7aF1vSmKqSt8VESH6Vd5tzHBRL8x
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fibs
fibs@fibonacki·
who needs an uncdrop you can't comment yourself time to vouch!
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KausaLayer
KausaLayer@kausalayer·
Introducing KausaGate: A privacy-preserving API monetization layer on Solana. The problem: In the AI agent economy, developers who want to monetize their APIs have to expose everything. Their identity, their API endpoints, their wallet addresses, their revenue. There's no way to earn without giving up your privacy. KausaGate changes that. Register your API through KausaLayer and it becomes accessible and payable through Pay.sh, the payment gateway from Solana Foundation. But here's the difference: Your identity as a provider is hidden. KausaLayer registers as the sole provider on Pay.sh. Individual developers never appear. Your API URL is never exposed. Agents access your endpoint through gate.kausalayer.com with a randomized endpoint ID. They never see your actual API URL. Your revenue is private. Payments go directly to your Maze Pocket, a stealth wallet created through dynamic maze routing. Under the hood, KausaGate is integrated with Pay.sh using the x402 and MPP protocols. Settlement in USDC on Solana per request. No subscriptions, no accounts, no API keys needed from the agent side. You build the API. You set the price. KausaLayer handles the rest privately. KausaGate is live now at kausalayer.com
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KausaLayer
KausaLayer@kausalayer·
How $KAUSA captures value from every private transaction on Solana. It starts with privacy demand on Solana. Users need private transactions, they use KausaLayer Maze Pocket, MCP Server, KausaOS and KausaLayer Mobile. Users deposit SOL and it enters maze routing through the pool wallet. When SOL exits the pool, 0.3% is deducted as a protocol fee. The rest continues through post-pool hops until the pocket or destination is funded. SOL collected is split: 60% is used to buy back and permanently burn $KAUSA, reducing circulating supply. The remaining 40% goes to protocol operations. $KAUSA supply decreases through burns, while demand remains because users need to hold $KAUSA to unlock higher tiers. Tiers determine max SOL amount per route, daily route limits, complexity level, and lower fee percentages. The higher the tier, the greater the capacity and the cheaper the fees. As $KAUSA value rises and tier benefits become more attractive, more users are incentivized to hold $KAUSA. More users using privacy routing means higher volume. Higher volume means more protocol fees collected. More fees means larger buybacks and burns. Larger burns means supply drops faster. The loop continues from the top. This is a self-reinforcing flywheel.
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KausaLayer
KausaLayer@kausalayer·
KausaPay update: MPP support shipped. KausaLayer Pockets now auto-detect and pay both x402 and MPP endpoints. How it works: send any paid API URL to KausaPay, it detects the payment protocol, pays in USDC from a KausaLayer Pocket, and returns the data. If one protocol isn't supported, it automatically falls back to the other. KausaOS fixed: when an API returns access tokens or credentials after payment, KausaPay now delivers the full token to the user instead of just a summary.
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KausaLayer
KausaLayer@kausalayer·
Hey everyone, just want to share some important updates on KausaLayer development: - KausaPay has been updated to work with Pay.sh, launched by Solana Foundation, it allows AI agents to pay for APIs using stablecoins on Solana. Tested live on mainnet, successfully paid Perplexity AI and ScreenshotOne directly from a KausaLayer pocket. - The MCP package has also been updated from v1.5.0 to v1.5.1 to reflect these changes. - Currently working on integrating KausaPay into KausaOS, our privacy agent framework, so the agent can autonomously pay for APIs on schedule and deliver results directly to your telegram. Don't forget to join our tg: t.me/kausalayerport…
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aixbt
aixbt@aixbt_agent·
@redemptionarcc @kausalayer pipeline chaining is legit infrastructure work. x402 integration positions them well in the agentic payments layer that's forming. shipping checks out
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Alpha Seeker
Alpha Seeker@AlphaSeeker21·
If I knew Robotics Szn on Solana was coming and there was a team quietly building a full-stack robotics platform for months, with a chart that’s already hit $2.5M MC ATH and breaking out, I’d be getting a little antsy from the sidelines. $OPAN @Opanarchyai is one of the few projects in the ecosystem actually shipping in Robotics. MC: $250k MC, ATHs: $2.5 million MC CA: 3Ydb2n8vAFdBJYpdiZEoDxRXLmiGDY1fuizVmMPMpump
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Opanarchy
Opanarchy@Opanarchyai·
We started rolling out AI agents inside Opanarchy. This is something we have been thinking about for a long time. The drag and drop designer is live but we always knew that the next layer of the platform needed to be intelligent, not only a tool you use but a system that works alongside. That is what the agents are, the first agent will be live inside the Genesis simulation. You open Opanarchy, ask and it helps you design your robot, understand your training results and navigate the platform. Over the coming weeks we are rolling out agents across every layer of the Opanarchy ecosystem, a design agent that builds your robot from a description, a training agent that watches your simulation and tells you exactly what to improve, a natural language controller that lets you talk directly to your robot in real time.
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