The 1.1M ethereum:0xad29f2723fcdbcf665f210f25e06f97477e417cf burn is done, but i’m more interested in what @grvt_io is building around the actual trading experience. Earn on Equity is designed to let eligible USDT and USDC balances earn yield while still serving as trading margin. add the self-custodial model, and the bigger picture becomes clear. more capital efficiency without giving up the ability to trade with eligible balances. that’s the idea behind GRVT 2.0.

i think people might be looking at AI marketplaces from the wrong angle. everyone imagines a platform where you can browse different agents, compare services and decide which one to hire. that’s useful for humans, but what happens when the customer is another AI agent? it doesn’t necessarily need a nice dashboard or a bunch of buttons to click. it needs a way to find the right service, submit a request, get the work delivered and settle the transaction. this is where the @termix_ai SDK becomes interesting. the workflow can be called directly by software, which means the marketplace doesn’t have to depend on a human manually managing every step. discovery → ordering → delivery → settlement. simple enough to understand, but important if agents start transacting with each other at scale. because an economy where agents can independently purchase services needs more than intelligent models. it needs infrastructure that lets them actually get things done.

one thing that always bothered me about trading is how much capital ends up sitting idle while you’re waiting for the next setup @grvt_io is trying to address this with GRVT 2.0 the idea is to earn yield on eligible assets and potentially use those balances as trading margin, instead of treating earning and trading as two completely separate activities add tokenized fixed-income strategies targeting up to 11% APY (not guaranteed), and you can see why this concept is interesting would you use something like this for your active trading funds?

1.1M ethereum:0xad29f2723fcdbcf665f210f25e06f97477e417cf bought from the open market and permanently burned That’s close to 1% of circulating supply at the time What i like is that the tokens weren’t just moved into a treasury The burn is permanent, and the public dashboard lets anyone verify the transactions Future buybacks are intended monthly, but the timing and amount remain discretionary Now look at what’s coming next → @grvt_io Earn targeting up to 11% yield → Zero-trading-fee Stable Funding Perps → Yield as Margin → A unified GRVT 2.0 app The buyback is done The product rollout is the next thing to watch I am interested in seeing all these pieces actually work together than making price predictions from a burn announcement

GRVT JUST PUT ITS FIRST BUYBACK ON THE BOARD. 1.1M ethereum:0xad29f2723fcdbcf665f210f25e06f97477e417cf purchased from the open market and permanently burned. That’s 1% of the circulating supply at the time. And honestly, the part i like most isn’t even the number. It’s the fact that the tokens are gone for good, not sitting in some treasury waiting to be used later. Crypto has no shortage of projects making promises about tokenomics. Actually executing the plan is a different story. Now GRVT is kicking off its five-announcement journey toward GRVT 2.0. First action completed. Waiting to see what's coming next.

If you’re exploring @primus_labs, Epoch 2 of the points campaign is worth checking out. but don’t just connect your wallet and click everything blindly. understand what the project is building first. Primus is working on privacy-focused infrastructure that uses zkTLS to help verify web data without unnecessarily exposing sensitive information. the points campaign gives people a way to participate in the ecosystem while learning more about the project. just remember → points don’t automatically mean a future token allocation, and rewards aren’t guaranteed. do your own research, check the official campaign rules and participate only if it makes sense for you. @primus_labs is exploring this through zkTLS and verifiable web data. i think this matters because crypto still has a big challenge to overcome. people want trust, but they also want control over their information. making those two things work together could unlock use cases that full public transparency alone can’t support. privacy isn’t the opposite of trust, sometimes it’s what makes trust possible.

Honestly, building an ai agent is becoming easier with tools like claude and chatgpt but building something useful and getting people to pay for it are two very different things you can spend days creating an agent, improve its capabilities and make everything work the way you want. but if nobody knows it exists or has a way to hire it, monetization becomes difficult this is where agent.family from @termix_ai gets interesting developers can register their agents, list services, explore tasks and submit bids for available jobs users can also hire other agents, verify services and settle payments using stablecoins like usdc and usdt the platform also has a reputation-based discovery process, giving users another signal to consider before requesting a quote aacp powers agent identities and interactions, with mcp and apis supporting integrations i think the next interesting phase of the ai agent space will be less about how many agents people can create and more about whether those agents can consistently solve problems for paying customers that’s where i’ll be watching for real adoption

I think one of the more practical use cases for ai agents is helping businesses handle work they already need to get done earlier, i came across a customer service manager agent on @termix_ai that focuses on setting up customer support processes, ticket workflows, knowledge bases, escalation procedures and communication strategies the goal is pretty straightforward → improve customer retention, reduce response times and lower operational overhead these aren’t problems businesses need to invent just to use ai. they’re existing problems where better automation could actually help the agent is listed on the platform and has gone through its verification process, with a 100% reputation score, a 100% pass rate and one completed job at the time i saw it of course, one completed job is still a small track record, but the use case itself makes sense for me, ai becomes much more interesting when it moves beyond answering questions and starts helping people handle real work

Another project I’m keeping on my radar is @primus_labs The BNB connection is interesting, but it’s not the only thing here → Confidential vault already live → $62M+ in reported verified reserves → Kaito, Brevis and Phala integrations → VanEck, Samsung NEXT and other notable backers They’re building around privacy and verifiable data, two areas worth watching as onchain finance grows Still early, but definitely not a project I’d ignore without doing my homework

crypto traders want transparency until someone starts tracking their bags imagine your competitors knowing every position you take and every move you make @primus_labs is working on a different approach with confidential onchain finance powered by FHE let the blockchain verify the operation without exposing every number now we’re talking the next big unlock isn’t just bringing more money onchain it’s giving that money a reason to stay private

imagine an app pulling data from a website and doing calculations with it how do you know the data is real? and how do you know the calculations weren’t messed up? @primus_labs is working on this with DVC, combining zkTLS and zkVM pretty interesting stuff for apps that rely on offchain data

Primus Labs is one to keep on your radar @primus_labs is building a trust, verification, and privacy layer for the crypto and AI agent economy using zkTLS and FHE here’s why I’m paying attention👇 → $7M in funding → Backed by YZi (formerly Binance Labs), Samsung Next, VanEck and others → AlphaNet campaign is free to explore → Complete tasks and build your reputation score how to get started 👇 1) Visit: s.kaito.ai/ 2) Connect your wallet 3) Enable and install the Chrome extension 4) Complete the available tasks to increase your reputation score ⚠️ Some tasks may require a small gas fee on Base, so check before confirming any transaction the interesting part is the tech behind it zkTLS and fully homomorphic encryption (FHE) can help verify information while improving privacy as AI agents and onchain applications grow, trust and verification could become a much bigger narrative I’m exploring the campaign early DYOR, and never share your seed phrase

who decides when an AI agent deserves to get paid? this is actually one of the more interesting problems in the AI agent economy imagine hiring an AI agent to create a video, then another agent to review it, and another to handle research once agents start doing business with each other, payments, trust, and disputes become a much bigger deal this is where @termix_ai caught my attention its Agent Autonomous Commerce Protocol (AACP) uses four on-chain states to manage the transaction: → escrow: payment gets locked after the quote is accepted → deliver: the provider submits the work, and its deliverable hash is recorded → challenge: either side can dispute the result, with three randomly selected evaluators reviewing the case → settle: funds are distributed based on the outcome, and agent reputations are updated the interesting part is that the payment process follows protocol-defined rules instead of depending on a TermiX employee to manually decide who gets paid and that’s important if we ever reach a point where AI agents are hiring other agents, completing tasks, and paying for services at scale because an agent economy can’t run on trust alone. it needs rules for what happens when things go wrong, too of course, the real test is how well the dispute process works in practice but AI agents doing the work is one thing. building a system where they can transact and settle payments with each other is a completely different game that’s the part of @termix_ai worth watching check out agent.family if you want to explore it yourself

Been looking into @termix_ai and I think the idea behind Agent Family is worth understanding most of us have seen AI tools that can generate videos, write posts, or automate different tasks but what if you could hire an agent to do that work instead of managing every step yourself? and what if you could build your own agent, list its services, and let people hire it? that’s the marketplace model TermiX is working toward agents can register their services, find tasks, submit bids, and get paid through stablecoins the infrastructure is designed to support the process from hiring through delivery and settlement I also find the verification side interesting because paying for AI-generated work is one thing; having a way to handle disputes when something goes wrong is another still, the biggest question is whether the marketplace can generate enough real demand to sustain the ecosystem because let’s be honest, we don’t need another place full of agents nobody uses we need agents that solve actual problems AI becomes a lot more interesting when you can do more than chat with it, when you can hire it, put it to work, and pay for the result

A lot of AI agents look impressive in demos, but there’s still a big gap between building an agent and turning it into something people will actually pay for. - how do people find it? - what does it do? - how much does it cost? - how can someone trust the result? that’s where marketplaces like @termix_ai get interesting. an agent can have an identity, offer a specific service, compete for jobs and build a track record through completed work. over time, that record could become more useful than another flashy demo or a big claim from the builder. still, listings alone don’t prove demand. actual paid jobs and repeat customers will tell the real story. i’d rather watch how many agents are getting hired than how many agents are being launched. that’s where things get interesting for me.

i actually tried something interesting on @termix_ai today picked a random job worth $76.96 and just followed the money client funds went into escrow, the agent completed the work, proof was recorded onchain, and after 33 seconds without a challenge, the payment was released 98% to the agent. 2% to the protocol no complicated story here. just an agent doing a job and getting paid and that’s what got me thinking everyone wants to talk about how advanced AI is becoming, but what happens when these agents can actually earn money, hire other agents and pay for work without needing a human to manage every little thing? 552k jobs have gone through this same pipeline the low fees are a big part of it too. when you’re dealing with small tasks, giving away 20% of every payment just doesn’t make sense maybe the AI agent economy is still early, but this is the kind of stuff i’ll pay attention to over another flashy demo because making AI smarter is one thing giving it a way to work, earn and create real value is a whole different story

The 1.1M ethereum:0xad29f2723fcdbcf665f210f25e06f97477e417cf buyback and burn is just step one. The bigger story is what's coming on the road to GRVT 2.0: → Grvt Earn (end Oct): tokenized fixed-income strategies with target yields up to 11% (a target, not a guarantee) → Stable Funding Perps (Nov): stocks, FX, commodities, and indices with zero trading fees and once-daily funding, targeting 1,000+ markets by end of Dec 2026 → Yield as Margin (Nov): use eligible Earn balances as margin while they keep earning → Grvt 2.0 app (end Nov): one self-custodial balance, a new brand, and the 2027 roadmap A busy few weeks ahead

ngl, the more i look into @termix_ai, the more interesting it gets everyone’s talking about what AI agents can do, but I’m more curious about how they’re actually going to do business who hires them? who checks their work? and what happens when a job goes wrong? that’s where TermiX makes sense to me through Agent.family, agents can offer services, get hired, build a reputation, and settle payments REA handles reverse engineering with evidence-backed findings, while Walcert adds another layer of wallet intelligence before agents send funds it’s not just about having more agents. it’s about giving them a way to actually work and transact with each other imagine agents hiring other agents, paying for specialized work, and building a track record without humans having to manage every single step still early, and execution matters, obviously but if the agent economy really takes off, the infrastructure behind it could be just as important as the agents themselves that’s the part i would like to watch at @termix_ai

I think @termix_ai is tapping into something people will appreciate more as AI agents grow -- Getting an AI to do the work is one thing -- Knowing where its findings came from is another -- REA is a good example of why this matters -- If agents are going to handle serious tasks, verifiability needs to be part of the package --- Practical features > flashy promises -- The best AI products won't just save time, they'll make difficult work more accessible