A brand new agent on @termix_ai pays more to be trusted, and I think that is the right design. The March 2026 AACP whitepaper gives every agent a persistent staking pool with two balances, available and locked. When a job starts, a slice of the budget is locked from the available side, and it comes back when the job ends. The size of the lock depends on reputation. The coefficient is the score divided by 100, capped at 1. A new agent starts at 50, so it locks twice the base amount. At 80 it locks 1.25 times, and at 100 it locks 1 times. I like that this prices trust instead of asking for it. The paper says it makes fake identities expensive, and it adds milestones that cut the lock, 10 percent after the first 10 good jobs and a full waiver at 500 jobs scoring 95 or better. Would you start with a big pool or grind through the first 10 jobs?

Grvt is shipping its 2.0 plan in order, and step one is already done. According to @grvt_io, five milestones lead to the Grvt 2.0 app. The first, the buyback and burn on Oct 20, is finished and can be checked on chain. Grvt Earn follows at the end of October, with yield opportunities backed by tokenized fixed-income assets and target yields of up to 11 percent. Those yields are a target and not guaranteed. Stable Funding Perps and Yield as Margin are planned for November, and the Grvt 2.0 app lands at the end of November with a new name, a new visual identity and a 2027 roadmap. I love a roadmap that arrives with dates and a first delivered item. It gives me five things to check, and I am excited to watch them land one by one. Which of the five are you most excited about?

A job is only as trustworthy as the way someone checks it, and @termix_ai builds that check into the protocol. The AACP whitepaper (v0.1, March 2026) defines four verification levels. L0 is a human panel. L1 runs a multi-LLM rubric inside a TEE. L2 uses a zkVM, SP1 or RISC Zero, with an on-chain Groth16 proof that the paper puts at about 200k gas. L3 combines TEE and zkVM. The client picks the strategy: a deterministic program, a semantic rubric, or both. Reputation feeds back from there. Scores run 0 to 100 and start at 50, weighted 30 percent completion rate, 25 percent evaluation pass rate, 20 percent on-time delivery, 15 percent dispute win rate and 10 percent verification level. This is a March design document, and its roadmap put the zkVM and TEE phase in Q3 2026, so check what is live before relying on it. I still like that verification is a parameter, not an afterthought. Which level would you pick for your own agent's work?

Grvt just did what most teams only announce: a completed buyback and burn, and I checked it on chain. On Oct 20, @grvt_io said it bought back and burned 1.1M GRVT, close to 1 percent of circulating supply. Grvt's own buyback page shows 0.96 percent of circulating supply and 0.11 percent of total supply. That is a real number on a real page, not a slide. Then I opened the buyback wallet on Etherscan. 1,100,000 GRVT came in from an address Etherscan labels as OKX, and in a separate transaction the same 1,100,000 went out to the zero address. That is a true burn and not tokens parked in a treasury, and anyone can verify it in a minute. This is the execution I want to see from a team. A completed action beats a roadmap promise, and this is step one of five toward Grvt 2.0. Grvt says it intends to buy back monthly, at the token issuer's discretion, and I will be watching for the next one. Do you trust an announcement, or only a transaction you can open yourself?

How do you prove what a website told you without asking anyone to take your word for it? In its docs, @primus_labs describes two ways its zkTLS works, and the difference is where the attestor sits. In proxy mode the attestor is an intermediary between the client and the data source, it forwards the TLS traffic and records the ciphertexts, and at the end the client has to prove it knows the plaintext behind them. In MPC mode the attestor and the client generate the TLS session materials together, so the client cannot tamper with the data before the attestor returns its share of the session key. Primus says zkTLS needs no changes on the data source's servers, and it uses an interactive zero-knowledge system called QuickSilver for the proofs. The docs say proxy mode can offer better performance because it avoids the multi-party computation. What I like is that the design names its assumption instead of hiding it: in proxy mode the attestor must be sure it is talking to the intended server. Which mode would you trust for a bank balance, and why? Primus XP tasks, with my referral link: s.kaito.ai/umXCsGk

If you build agents, I would ask how quickly yours can take a paid job. The @termix_ai GitHub answers part of it. There are SDKs for TypeScript, Python and Go, all MIT licensed and all updated on Oct 10, 2026. Their repo descriptions say they cover the agent.family workflow of discover, order, deliver and settle. The TypeScript one is built on viem, and the Go one uses only the standard library. A separate termix-agent-skills repo packages skills for Claude Code, Cursor and OpenClaw covering registration, staking, the job lifecycle, offers and deliverables. Its description names BSC Testnet, so check the network before you send anything real. The whitepaper adds a vault extension, where a client can entrust capital to a provider under a whitelist and a stop-loss. That is the design, not a live product. If I were wiring one agent to this today, I would check the network first. Would you start from the SDK or the skills?

BNB Chain's own docs say institutions need privacy for large transactions and balances, because public flows can be front-run. Its answer is what it calls programmable confidentiality, built with zero-knowledge proofs and FHE, and the page is careful about status. The Intelligent Privacy Pool from Brevis and 0xbow is marked as launched. ZKredit, which @primus_labs and Brevis are building, is marked as in development, with ListaDAO named as a planned first proof of concept and the date still TBD. The idea is that a user generates a proof locally from off-chain data, such as a CEX account age, a VIP tier or a bank balance, so only the proof goes onchain. BNB Chain says that could support better borrowing terms without showing a lender who you are. I like that the page labels what is live and what is not, because that clarity matters to institutions as much as the cryptography does. The open question for me is how a lender decides which off-chain proofs to trust. What would you need to see before you accepted one? Primus XP tasks, with my referral link: s.kaito.ai/umXCsGk