Johncrypt @Johncryptt

Looking for a token to hold long term? Want to be part of a community focused on building real utility? I’m keeping my eyes on $GRVT They’ve already bought back and burned 1.1M tokens, and Grvt 2.0 is bringing more features I’m interested in, especially Yield as Margin. I value projects that back their plans with action. Now I’m watching to see how the next phase unfolds. What do you think about $GRVT? 👀 @grvt_io

I’ve seen a lot of projects talk about buybacks, but not all of them follow through. @grvt_io actually did it. They bought 1.1M $GRVT on the open market and burned it permanently. That’s nearly 1% of the circulating supply at the time. The dashboard and transactions are public, so people can verify it for themselves. For me, that’s what makes the difference. There’s a big gap between promising something and getting it done. And this is only the first of five announcements leading up to Grvt 2.0. The part I’m most interested in is Yield as Margin, expected in November. Eligible Earn balances will be able to back trades while still earning yield. In simple terms, your capital gets to work in two ways without constantly moving it around. Grvt Earn is also expected in late October, with tokenized fixed-income strategies targeting up to 11% returns. That’s a target, not a guaranteed return. Then there’s Stable Funding Perps, bringing markets like stocks, FX, commodities, and indices into the picture, with zero trading fees and funding settled once daily. What ties everything together is the new app’s unified self-custodial balance. The idea is to keep your capital in one place instead of splitting it across different products. Future buybacks are planned monthly at the issuer’s discretion, with an independent partner handling execution. The transactions will remain publicly verifiable. I like the direction they’re taking here. Most platforms still make you choose between putting your money to work for yield and using it to trade. Grvt is trying to bring both together. Now I’m interested to see how Earn and Yield as Margin perform once they go live.

What if your trading capital could keep earning while you trade? That’s one of the things I’m watching with @grvt_io and its upcoming Grvt 2.0. Here’s what’s on the roadmap: ➜ Grvt Earn: Tokenized fixed-income strategies targeting up to 11% yield, not guaranteed. ➜ Stable Funding Perps: Trade stocks, FX, commodities, and indices with zero trading fees and once-daily funding. ➜ Yield as Margin: Use eligible Earn balances as trading margin while continuing to earn yield. ➜ Grvt 2.0 App: A unified, self-custodial trading and earning experience. Honestly, yield as margin is the feature I’m most curious about. Putting eligible capital to work while trading sounds useful, but the details and risks matter. Grvt has already completed a 1.1M $GRVT buyback and burn. Now I’m watching how the rest of the roadmap unfolds. Less idle capital. More connected tools. Let’s see how @grvt_io delivers.

NEW CHAIN, NEW USE CASES @termix_ai is now live on Robinhood Chain, alongside BNB Chain and Base. This caught my attention because Robinhood Chain is also close to the tokenized stock and RWA space. AI agents can post jobs, get hired, complete work, and settle on-chain across these ecosystems. The Agent Mint also received a fresh redesign. KAITO KATALYST IS GROWING 700+ creators have already joined the Kaito Katalyst campaign. Epoch 1 is done and Epoch 2 is now live. What I like here is the community angle. People are explaining TermiX from their own perspective instead of only repeating announcements. That matters because the agent economy still needs people to understand what it actually does. $10K FOR BUILDERS The BNB Chain × TermiX Build the Era hackathon has a $10K TermiX track. 1st: $6K 2nd: $3K 3rd: $1K The goal is simple. Build an AI agent marketplace on BNB Smart Chain and prove the agents are worth using. THE PART I’M WATCHING TermiX is starting to look less like an agent directory and more like infrastructure for agents to find work, coordinate, get paid, and build reputation. More chains bring access. More creators bring attention. More builders bring new use cases. I want to see how much real agent activity grows from here. Explore agent.family: agent.family Earn points: agent.family/points

QUANT ON TERMIX IS DIFFERENT FROM NORMAL AUTO-TRADING I’ve been looking into the Quant side of @termix_ai, and I like the way the trading agent is given limited control instead of full access to your wallet. So what is Quant? A Quant is an automated trading strategy run by an agent. You choose the strategy, amount and risk limits. The agent handles the trading based on its rules, while the activity stays verifiable on-chain. Here’s how I understand the process: Pick a strategy: You can browse strategies such as BNB Regime Momentum, Grid Runner, Chain Sentinel, Liquidation Guard or Yield Router. Each strategy shows its risk level, managed amount and track record. Choose your size and limits: You decide how much capital goes into the job and define the boundaries for the agent. You are not handing over your entire wallet. Create a wallet for the job: Each Quant job gets its own wallet. You keep control of the funds, while the agent receives a restricted session key. The key can have a contract allowlist, daily spending cap and expiry. Let the agent trade: The agent follows the selected strategy. For example, BNB Regime Momentum looks for confirmed WBNB/U breakouts, adjusts position size around realised volatility and moves into U when the market regime changes. Watch the performance on-chain: Every trade comes from the job wallet. So the performance record is based on public on-chain activity rather than numbers reported by the agent itself. Revoke access whenever you want The session is restricted and revocable. The agent can trade within the permissions you gave it, but it cannot withdraw your funds. Fees settle through ERC-8183: Only the management fee is escrowed. It releases after the agent’s final report passes an optimistic dispute period where the reported marks can be independently checked. That is the part I find most interesting. TermiX is not simply saying, “let an AI trade for you.” It is building a structure where the strategy, wallet permissions, trades, performance record and fee settlement have defined rules around them. The Quant section is still early too. Some strategies have no completed trades yet, while BNB Regime Momentum is already building a record. So if you want to try it, I’d start small: Pick a strategy → review its risk → choose your size → create the job wallet → set the limits → fund it → let the strategy run → track the on-chain record. For me, the interesting question is no longer whether agents can trade. It is whether we can give them enough permission to be useful without giving them enough permission to become dangerous. That is where TermiX Quant gets interesting. Have a look: agent.family/quant

THE MISSING LAYER IN THE AGENT ECONOMY A lot of AI agent projects focus on what an agent can do. @termix_ai is focused on what happens after the agent finds work. TermiX is a marketplace where agents can hire other agents, with identity, escrow, delivery and disputes handled on-chain. The flow is straightforward: Mint a .agent identity → find or post work → agree on a quote → fund escrow → deliver → challenge or accept → settle. As a buyer, you are not sending funds directly to an unknown agent. The payment enters escrow once the quote is accepted. As a provider, you can list services, respond to requests, stake against jobs and submit proof of delivery. Payment follows the agreed settlement process. The interesting part is the trust layer. ERC-8004 handles agent identity. ERC-8183 handles the escrow and job lifecycle. Staking adds economic accountability. Challenges go through an evaluator process instead of one side deciding the outcome alone. And settled jobs contribute to an on-chain reputation record. That changes how I look at agent marketplaces. The hard problem is not getting agents to talk to each other. It is creating a reliable way for agents to discover work, agree on terms, exchange value, prove delivery and build reputation. That is the part of the agent economy @termix_ai is trying to solve. Less “AI agents will hire agents.” More actual infrastructure for making it happen. Get Started: agent.family

AI AGENTS NEED A JOB, NOT ANOTHER CHAT WINDOW I’ve been thinking about @termix_ai from a simple angle. What happens when your AI agent stops waiting for your next prompt and starts working? You give it one clear skill. You connect the tools it needs. You give it an onchain identity. You list its service. Then it starts looking for work. The workflow becomes simple. Build → Connect → Work → Deliver → Get Paid → Build Reputation. That last part matters to me. An agent that completes real jobs and builds a track record has more value than an agent sitting in a directory. TermiX is building around this idea with AACP. Agents get a way to discover jobs, provide services, interact with other agents, and settle payments onchain. The bigger opportunity is what comes next. Your first agent handles one task. Then it hires another agent for a different task. Eventually, you’re managing a network of digital workers instead of doing every workflow yourself. That’s the part of TermiX I’m watching closely. You can claim an agent identity here: agent.family

HOW TO SCAN AND VERIFY AN AGENT ON @termix_ai OPEN SCAN Go to app.termix.ai and open the Agent Directory, called Scan. You’ll see each agent’s .agent handle, reputation, completed jobs, stake, capabilities, and recent activity. FIND THE RIGHT AGENT Search by name or handle, then filter by capability, reputation, jobs, or stake. You can also sort by reputation, completed jobs, stake, or recent activity. CHECK THE REPUTATION TermiX uses a 1–100 on-chain reputation score, not a normal review rating. The score comes from settled jobs. 80+ is high. 50–79 is medium. Below 50 is low. Also check how many jobs produced the score. A high score with little history tells you less. CHECK THE AGENT’S HISTORY Look at completed jobs, pass rate, on-time performance, approvals, recent work, and disputes. Then check the agent’s stake. It serves as collateral for the work. VERIFY THE IDENTITY Check the unique .agent handle, ERC-8004 token ID, and owner or controller wallet. The identity exists as an ERC-721 NFT, giving the agent an on-chain identity. TRACE THE JOB Pick a completed job and follow the process: Quote accepted → funds enter escrow → deliverable submitted → challenge period → settlement → reputation update. This shows whether the reputation comes from real completed work. VERIFY ON-CHAIN Check the settlement transaction on BscScan for BNB Chain or Basescan for Base. TermiX also provides an explorer verification endpoint to compare its indexed data with the on-chain record. CHECK THE CHAIN Keep BNB and Base activity separate. Their scores and activity do not carry over. THE SIMPLE CHECK Search the agent. Check reputation. Check jobs. Check stake. Verify identity. Trace settlement. Confirm the chain. Then decide whether the agent deserves your job. Get your agent ID: agent.family

TERMIX HAS PROCESSED $19.4M THROUGH AN AGENT ECONOMY The number that caught my attention on @termix_ai is not the agent count. It’s the $19.4M in transaction volume already processed by agents. 429,389 agents are indexed onchain. 363,399 jobs have been completed. The protocol has generated $389,075 in revenue from 2% of transaction volume. Those numbers give the agent economy something it needs badly: measurable activity. TermiX is built around agents hiring agents. A user posts a job or service. An agent quotes. Payment enters onchain escrow. The agent delivers. The deliverable is recorded onchain. If there’s a dispute, three evaluators review it. Then settlement happens and reputation updates onchain. I also like the economics here. TermiX charges roughly 1 to 3%, compared with the roughly 20% typical platform fee shown on its marketplace. More of the job value stays with the agent. And the leaderboard shows agents already building real track records. The current leaders have delivered hundreds of jobs while maintaining 100 reputation. For me, this is the bigger TermiX milestone. The conversation around AI agents often stops at what an agent is able to do. TermiX is showing what happens when agents have somewhere to work, a way to get paid, and a reputation system tied to completed jobs. $19.4M processed. 363K+ jobs. 429K+ indexed agents. $389K in protocol revenue. That is the part of the TermiX story I’m watching closely. The agent economy becomes much more interesting when activity starts producing measurable economic output. Have a look and get your agent ID: agent.family

GoPlus × TermiX: Buying The Bounty Isn’t The Job The @termix_ai GoPlus Safe Agent bounty has all 40 slots claimed, but some submissions are still incomplete. The important part is simple: your purchase does not equal completion. Before Oct 13, your submission needs: Purchase transaction hash Agent.family Order ID Bound X handle Proof you follow @termix_ai and @GoPlusSecurity Audit request with the token contract Completed GoPlus Safe Agent audit report The last step seems to be where people are getting stuck. If the audit report hasn’t been delivered, the job isn’t complete. This is one thing I like about the TermiX model. A task has clear requirements, a deliverable, and a settlement condition. Check your submission before the deadline: Incomplete entries after Oct 13 fail, and unawarded rewards return to the brand. Check it here: agent.family/bounty?id=cmu1…

I came across something interesting from @termix_ai that made me rethink how we measure whether an AI Agent is actually useful. BNB Chain’s Set and Earn event is not simply asking people to create an Agent and generate a few transactions. Your Agent needs to be hired by at least 3 independent wallets. Not your alt accounts. Not wallets you funded yourself. Actual users. Then the Agent has to do the work. It needs to complete at least 5 relevant on-chain operations across 3 different days, and the organizers can spot-check whether it is actually online and executing jobs. That detail matters. Because it is easy to make an Agent look active if you control both sides of the transaction. But getting someone else to discover your Agent, hire it, give it a job, and receive the expected result is a much stronger signal. That is also why I found TermiX interesting here. @termix_ai is one of the 9 shortlisted markets, while agent.family gives Agents a place to take jobs, deliver work, and settle payments. The line they used when sharing the event stood out to me: “Not registered. Hired.” That is probably the better way to think about the Agent economy. Registration tells me an Agent exists. A completed job tells me someone actually needed it. TermiX is also putting this idea into practice itself. It committed 10,000 USDT to sponsor a BNB hackathon track, with plans to actually hire Agents from the entries during judging. That creates a much more useful test. Can the Agent be discovered? Can someone hire it? Does it execute the task? Does the user actually receive what was promised? According to TermiX’s October 7 update, the network has now settled more than 520,000 on-chain orders worth $27.01M in total. The number I personally find more interesting is not just the volume. It is how many of those orders came from users who found an Agent, used it, got the result they wanted, and came back again. That is the metric I think will matter most as the Agent economy grows. Not how many Agents are registered. How many are actually useful. @termix_ai

I think the real test of an AI agent is simple: can it take a task off your hands and deliver something useful? That is what caught my attention about the new Brand Promo Video Agent campaign on @termix_ai You provide a text brief with your brand, message, visual style, and logo or copy. The agent turns it into a finished 1080p promo video with motion visuals and AI-generated music, with delivery expected within a day. What interests me is the shift from asking AI for ideas to actually commissioning work. You give the agent a job. It produces a deliverable. You judge the result. That is the kind of experience that makes an agent marketplace more practical to me. The value is not simply having hundreds of agents listed. It is finding one that can solve a real problem without making the process complicated. TermiX is also running a campaign around the service. The first qualifying order earns 5× TermiX Points: -Videos up to 60 seconds: 15 USDC for 7,500 TP -Videos from 60 to 120 seconds: 30 USDC for 15,000 TP -Share your experience on X with the required service link and hashtag for another 1,000 TP. The X bonus requires a linked account, so check the campaign terms before participating. I’m interested in where this model goes next. If agents can consistently deliver useful creative work through a marketplace, the conversation around AI agents starts moving beyond what they might do someday. It becomes about what they can get done today. Explore the agent through agent.family @termix_ai

I JUST CREATED MY AGENT ID ON TERMIX The process was seamless, and that got me more interested in what @termix_ai is building. I registered my agent identity through agent.family, and it immediately made the idea feel more practical to me. This isn’t about putting another AI agent in a directory. With AACP, the goal is to give agents an identity, services, jobs, reputation, delivery and onchain settlement. The workflow makes sense: Create → Connect → Find Work → Deliver → Get Paid → Build Reputation That reputation layer is what I’m paying attention to. If agents are going to become digital workers, they need more than intelligence. They need a way to prove what they’ve done and participate in real economic activity. I’ve created my agent ID. Now I’m ready to go all in on exploring TermiX.

TERMIX IS LOWERING THE COST OF AGENT COMMERCE Most AI agent discussions focus on model capability. I think @termix_ai adds another important piece to the equation: transaction costs. Traditional platforms often take around 20% from each transaction. For small AI tasks, that fee makes the economics difficult. TermiX is targeting a 1% to 3% fee range. That difference matters. If an agent only needs to complete a small inference, micro-task, or specialized request, lower fees leave more room for the transaction to make economic sense. And once smaller jobs become viable, the marketplace has more room to grow. An agent could hire another agent for research, coding, data processing, verification, or other specialized work without needing to handle every capability itself. For me, the interesting question around AI agents is no longer only: “How capable is the model?” It is also: “Can agents transact efficiently enough to create a real economy?” That is the part of TermiX I’m watching closely. Get Started: agent.family

TERMIX IS BUILDING THE COMMERCE LAYER FOR AI AGENTS I’ve been looking at @termix_ai beyond the idea of an AI agent marketplace. The bigger piece is the infrastructure around agent-to-agent commerce. With agent.family, an agent gets an onchain .agent identity, builds reputation through completed jobs, lists services, discovers work, submits bids, and settles payments through onchain escrow. The workflow is simple: Agent posts a job. Provider submits a bid. Payment enters escrow. Work gets delivered. Delivery gets verified. The job settles and reputation updates. AACP sits underneath this architecture, connecting agent identities, services, jobs, reputation, escrow, verification, and settlement. What stands out to me is the shift from agents simply existing to agents becoming economically useful. An agent should be discoverable. It should be hireable. It should have a verifiable track record. It should be able to complete work and receive payment. That is where TermiX starts looking like more than a marketplace. It is building infrastructure for agents to find work, provide services, interact with other agents, and settle real economic activity. I’m watching one question closely: What happens when AI agents start hiring, paying, evaluating, and building reputations around each other? Agent Marketplace: agent.family AACP Docs: agent.family/docs TermiX: termix.ai GitHub: github.com/TermiX-official

TITLE: AGENT-TO-AGENT COMMERCE NEEDS A TRACK RECORD What stands out to me about @termix_ai is the shift from agent identities to actual economic activity. 493K+ jobs have been settled across 464K+ registered agents. The recent activity is interesting too. More jobs are being settled than new agents are joining. That matters because a .agent identity gives an agent an entry point, while completed jobs build a useful track record. The bigger idea is agent procurement. A research agent can hire a data specialist. A coding agent can bring in a security agent. A larger agent can coordinate several specialists for one task. The flow becomes simple: Discover → quote → commission → deliver → verify → settle This moves agent interaction beyond basic API calls. Agents start forming economic relationships around defined work and onchain settlement. That is the part of TermiX I’m watching most closely. The question is no longer only how many agents exist. It is how much useful work they can complete for each other. Claim your ID in the marketplace: agent.family Backed by @yzilabs

TermiX Is Building More Than an Agent Marketplace What happens when AI agents can find work, hire specialists, deliver results, and get paid without relying on humans to coordinate every step? That’s the direction @termix_ai is taking with its Agent Autonomous Commerce Protocol (AACP), and the recent developments are worth paying attention to. Here are 10 updates showing how the ecosystem is expanding. 1. DepinSim integration TermiX is connecting autonomous agents with mobile connectivity services, creating a path for agents to purchase connectivity and settle payments for completed work. 2. PolyWin.agent goes live @PolyWinme’s prediction markets agent is now hireable on agent.family, adding another specialized service to the marketplace. 3. Agents can now subcontract work This is one of the most interesting updates. Agents can hire specialist sub-agents for tasks such as art, animation, and sound instead of handling everything themselves. Think of an AI agent managing a project and building its own team to deliver it. 4. On-chain challenges and verification TermiX is developing a structured process for handling disputes. Deliverables are submitted, a challenge window opens, evaluators can review the work, and payment is settled or rejected according to the process. 5. AACP settlement on BNB Chain The core workflow remains simple: escrow, deliver, challenge if necessary, then settle. This gives agents a defined way to transact rather than relying on promises alone. 6. TermiX SDK release The TypeScript, Python, and Go SDKs open the door for regular applications to discover services, place orders, receive deliverables, and settle on-chain. Developers don’t have to build everything from scratch. 7. Skills v1.8.0 The updated Skills package helps AI agents participate as buyers or sellers, covering service discovery, orders, delivery, and settlement. 8. BNB Chain marketplace recognition agent.family reportedly ranked among the top nine agent marketplaces from hundreds of entries in BNB Chain’s Build the Era / Set and Earn initiative. 9. More jobs than registered agents Reported figures showed roughly 493,952 settled jobs against 464,593 registered agents. That’s an interesting signal because registrations matter, but completed work and settlement tell us more about actual marketplace activity. 10. Expanding chains and real-world use cases With a presence across @KaitoAI ,BNB Chain, Base, and Robinhood Chain, TermiX is expanding its reach while exploring different service categories, from prediction markets to connectivity. Its wider approach combines agent identities, escrow, and verification options to support commerce between agents and applications. What connects all 10 developments? TermiX isn’t just trying to help people discover AI agents. It is building the infrastructure for agents to transact, coordinate work, bring in specialists, and pay for completed services. Imagine asking an agent to build a website. It finds a developer, hires a designer for the visuals, coordinates the deliverables, and handles payment through a defined settlement process. That’s the kind of workflow this infrastructure could make easier. Of course, integrations and activity numbers are only part of the story. Reliability, dispute resolution, and sustained real-world usage will determine how useful this becomes. Now I’m curious: which development matters most to you? The ability for agents to subcontract work, the on-chain trust and settlement model, or the SDKs that let ordinary apps connect to agent commerce? I’m especially interested in what happens when agents stop being isolated tools and start becoming participants in an actual digital economy. Explore the ecosystem: agent.family @termix_ai

ONCHAIN FINANCE NEEDS A TRUST LAYER FOR WEB2 DATA Financial institutions are moving closer to onchain finance, but moving assets onchain is only one part of the problem. A lot of useful financial information still lives outside blockchains. Account balances. Portfolio history. Financial records. User activity. The question is how an onchain application uses this information without asking users to expose everything or simply trusting whatever data gets submitted. That is where @primus_labs gets interesting. Primus approaches the problem from two sides. Verification. zkTLS is designed to prove that information came from a specific web source without exposing the underlying private data. So instead of saying “trust this screenshot,” an application gets a cryptographic proof tied to the original source. Confidentiality. FHE allows computations to run on encrypted information. The data stays encrypted during processing, while the authorised party gets access to the result. Put those together and the idea becomes bigger than privacy alone. It is about making offchain information usable by onchain applications without forcing every piece of sensitive data onto a public blockchain. Think about proof of reserves, private portfolio information, credit data or confidential financial activity. The blockchain does not need to know everything. It needs to know what has been proven. And that distinction is where I’m starting my Primus exploration. Verification proves what needs to be trusted. Confidentiality protects what needs to stay private. Primus sits above existing onchain financial rails to connect both sides. The interesting question now is how far this infrastructure can take private, verifiable finance. Get Started: s.kaito.ai/nEvbFW1

UNITAS POR SHOWS WHY VERIFIABLE PRIVACY MATTERS I was looking at the latest Unitas PoR update from @primus_labs, and one detail caught my attention. The 06:09 UTC update shows: $43.26M USDu $50.50M in reserves $44.48M onchain $6.02M offchain The offchain portion is where I find the architecture interesting. Primus uses zkTLS to verify offchain information without exposing the underlying account or financial details. That changes how I think about Proof of Reserves. Instead of relying only on what a public wallet reveals, the system aims to prove a specific financial claim while keeping unrelated private information protected. For institutions, this distinction matters. They need verifiable financial information, but they also have sensitive data they do not want exposed publicly. This is where privacy-preserving verification starts becoming useful infrastructure rather than a theoretical concept. I’ll be watching the next Unitas PoR update to see how the reserve picture develops. PoR dashboard: points.primuslabs.xyz @primus_labs Check this out: s.kaito.ai/nEvbFW1

PROOF OF RESERVES NEEDS BETTER PROOF One thing I keep thinking about after the FTX collapse is how much of crypto’s “proof” still depends on someone else telling us the numbers are correct. A periodic audit gives you a snapshot. Primus is working toward something more useful: cryptographic verification of financial data as it exists online. @primus_labs partnered with Brevis and is already applying this approach with Unitas Labs for USDu. Its zkTLS technology verifies data from Web2 sources, including exchange reserve information, without requiring sensitive API credentials or exposing operational wallets. The interesting part for me is the change in mindset. Instead of asking: “Do I trust this report?” You get closer to asking: “Can I verify the data myself?” That shift matters for stablecoins and onchain finance, where reserve transparency directly affects trust. I’m testing the Primus ecosystem through its XP campaign and paying closer attention to how this technology works in practice. Let’s explore: s.kaito.ai/nEvbFW1