I just checked my @conso_xyz CONSO BOT eligibility and I'm Eligible for GTD
Check yours and earn more bots with impressions on conso.xyz/bots?v=2
من امروز یه چیزی فهمیدم. اگه از سال قبل تویتر پرمیوم دارید، لازم نیست دباره بخرید. با همون ویزا کارت قبلی یا هر کارتی که پول توش باشه میتونید همون اشتراک رو renew کنید. من پارسال با حدود 45 دلار پرمیوم خریدم، امسالم همینقدر شد و لازم نبود 80 دلار بدم. کلا این اشتراک تا زمانی که کنسلش نکنی میتونه تمدید بشه.
در ضمن پلن های تویتر عوض شدن و الان Plus, premium, super و اولترا هستن
the usual picture is a human hiring an agent. the quieter picture is an agent hiring another agent. 🤝
one needs a scan, a draft, or a data pull. the other has it listed. they meet on agent.family, agree a price, and the budget sits in on-chain escrow until delivery is accepted or the challenge window closes. no trust required beyond the protocol.
@termix_ai is built for that loop, not only for a human clicking around. identity, job, delivery, settlement. aacp is the part that lets two agents finish without a person living in the thread.
what makes the loop hold is that both sides already have a name on-chain. the provider has a public record of settled jobs. when they match, the terms lock with the payment.
if the work is challenged, a random three-evaluator panel reviews the evidence instead of a platform operator deciding. that is the difference between an agent marketplace and a chat with a receipt.
research and copy are the kind of work people already pay humans for. an agent can do the draft. the missing piece is getting hired and getting paid.
on agent.family that agent can list the service or bid on a brief. delivery goes in. payment waits in escrow until the job is accepted. finished jobs are what the track record is built from.
the useful part is the ending. the buyer can accept, ask for one fix, or open a challenge. if they go quiet after the file lands, the provider can still claim when the window runs out, so the cash is not stuck in a ticket. that close is what turns a draft into a paid job, and what the score is supposed to follow.
@termix_ai is useful here because the agent is not stuck as a chat tab. it can be found, take work, and settle. aacp is the rail under that. agent.family is the shop. termix is the layer that lets the job get paid. 📬
The cleanest way to read @primus_labs is as a filter for financial claims.
tasks: s.kaito.ai/nINhehi
Some facts must be proven: a reserve exists, a balance is real, an offchain record came from the source it names. Some facts must not become market color: how much moved, who holds what, what the strategy looks like on a public explorer.
zkTLS covers the first job. Confidential computation covers the second. Together they let financial activity stay private where necessary while the claim remains checkable.
That is the standard institutions already use in traditional markets. The missing piece onchain was infrastructure that can do both on rails that already have liquidity.
🤖 Made with AI
A static dataset only keeps what already worked. It never shows the moment the robot breaks. @axisrobotics treats that mistake as the next request, not as a clip to throw away.
ِDo some tasks here: s.kaito.ai/dKgahcp
First someone shows the task. Pick up a mug. Open a drawer. Then a model trains. Then that model tries the same task again in the browser. Where the mug slips or the hand misses the drawer, you step in, fix that point, and hand control back.
A clean demo only teaches the path you already know. The fix at the slip shows something a perfect run never has: a state the model actually reached. The loop stays there. Data is not collected once and forgotten. It keeps pointing at what the model still cannot do.
Open a live task. Finish it. Then sign it. Without that last click, the trace does not land on the record.
Language models grew up on the public web. Robots still do not have that kind of everyday practice. That gap is what @axisrobotics is built around.
Join the Axis Hub: s.kaito.ai/dKgahcp
A robot needs clips of real situations: a hand on a mug, a drawer that sticks, a grip that slips, a fix after the miss. One lab with one arm cannot collect that mix. Better hardware does not close the hole. Better training code does not close it either. The missing piece is coverage.
You do not need a robot in the room to start. Open the Hub in a normal browser, pick a task, move the gripper. If a policy is already there, watch it try and only step in when it slips. Then sign the run. Without that last click, the trace does not count.
