Today only, contact me on WhatsApp for my U.S. stock chart-comparison notes and shortlist as a follower bonus. See how I compare similar stock charts. Use the link below to request the notes 👇 https://t.co/ctxlkbq6Dw
Today only, contact me on WhatsApp for my U.S. stock chart-comparison notes and shortlist as a follower bonus. See how I compare similar stock charts. Use the link below to request the notes 👇 https://t.co/ctxlkbpyNY
Today only, contact me on WhatsApp for my U.S. stock chart-comparison notes and shortlist as a follower bonus. See how I compare similar stock charts. Use the link below to request the notes 👇 https://t.co/ctxlkbq6Dw
Just locked 34,719,296 $OLBOS tokens with @Streamflow_Fi
It's on-chain. You can check the amount, time-period and recipients.
Check it out👇
https://t.co/5UFvicZPGl
The official Olbos SDK and MCP server are now live.
Whether you're building an autonomous agent, a yield bot, or a self-managing treasury inside your app, Olbos powers the backend on Solana.
Developers can now:
Deploy, fund, rebalance, and withdraw treasury strategies.
Fund over x402 — the payment is the deposit
Read live state: ranked venue opportunities, positions, risk caps, and the full audit log
Drive it conversationally via MCP, or in five lines with the SDK
Olbos is non-custodial: each strategy lives in its own Swig smart account where the owner's wallet holds root authority and the engine holds a narrow, revocable role (it can move funds into approved venues only, never out to anyone, not even us). Break-glass is owner-only and fires on-chain even if Olbos is offline.
Every move is simulated before it's sent and written to an append-only, replayable log.
SDK: npm i @olbos/sdk
MCP: claude mcp add olbos -- npx @olbos/mcp (+ env, see docs)
CLI: npx @olbos/cli init
Docs: https://t.co/ITSH9im5Fw
@0xichor You need to deploy a strategy through MCP and login with your agent’s treasury wallet.
Then you’ll be able to see your treasury fully working from the Olbos Mission Control.
Check out our docs for more details https://t.co/LPSzHuVbah
Your agent's USDC sits idle between transactions.
Multiply that across thousands of agents and the agent economy is leaving a fortune on the table every single day.
What if every dollar an agent earned went to work the second it landed?
The moment your agent earns, its USDC goes to work: deployed into vetted Solana venues, rebalanced around the clock, settled on-chain.
Fully non-custodial. The owner holds the keys, the engine can only move funds into approved venues, never out. Every move audited.
The treasury layer for the agent economy. Live now.
$OLBOS — $OLBOS governs the rules every Olbos treasury runs on: which venues are trusted, and how much risk is allowed.
CA: H17bb72Af1dzFoDG84tt6xJGg6cBx1Gm3azNAT8Ppump
One thing we’re focusing on heavily at $XPAYMIND is solving problems step by step instead of ignoring them until scaling becomes impossible.
A good example is this recent development direction:
structured banking integration adapters
eligibility score systems
Open Banking connectors
benchmark validation for AI agents
training and hardening loops for agent reliability
Instead of building “AI finance” only as a concept, we’re trying to create actual infrastructure layers developers can use and test in practice.
A lot of the work happens directly through continuous GitHub commits and architecture improvements.
https://t.co/dKl1PHf6Sv
Agent Studio progress update for $XPAYMIND
we added:
define()
configure()
submit()
certify()
functions into the Agent Studio workflow.
Step by step building a more complete environment for creating, configuring, testing, and certifying AI banking agents directly through the platform
Agent Studio progress update for $XPAYMIND
Working DEFINE block has been added into Agent Studio.
Users can now:
-define agent name
-select agent type
-add custom description for the agent
Current agent types:
1.Payment
2.Treasury
3.Compliance
4.Lending
5.Custom
We also added styling improvements for the whole form section with mono-style UI, focus highlights, labels, and hints under each field.
Step by step Agent Studio is turning into a real environment for building AI banking agents.
Last Agent Studio update was pushed to GitHub for $XPAYMIND
Current progress includes the first working DEFINE block where users can:
set agent name
choose agent type
add custom descriptions
We’re continuing to build everything openly step by step while expanding the infrastructure around AI banking agents and Agent Studio workflows.
https://t.co/uU29OnPq4v
Today’s progress on $XPAYMIND
1.continued Agent Studio development
2.added DEFINE workflow functions
3.pushed multiple new GitHub commits
4.improved AI banking agent structure
5.expanded benchmark and certification logic
6.worked on Open Banking/x402 infrastructure
7.improved UI/UX for Agent Studio forms
8.continued backend architecture updates
We’ve also prepared more updates that will be shared very soon.
A lot still coming.
backend progress for $XPAYMIND
feat(core): implement Agent Studio SUBMIT block - pre-flight checks and benchmark queue submission
This update adds submission flow logic into Agent Studio including:
pre-flight validation checks
benchmark queue handling
submission pipeline structure
preparation for automated agent evaluation
Continuing to build the full workflow layer for AI banking agents.
https://t.co/QOYObR1y24
added the 02 CONFIGURE block into the app.
Users can configure:
-supported protocols
-transaction limits
-daily spending limits
-API communication structure
-operational behavior for AI banking agents
Current supported protocols:
-x402
-REST
-WebSocket
-GraphQL
The goal is giving developers more control over how their agents interact with financial infrastructure, APIs, and payment environments directly through Agent Studio.
Another Agent Studio update pushed to GitHub for $XPAYMIND.
feat(core): implement Agent Studio CERTIFY block — tier scoring, badge assignment, certification result
Added certification logic into Agent Studio:
tier-based scoring
certification results
badge assignment system
structured evaluation outcomes for AI banking agents
Main goal is making agent verification more transparent and measurable.
https://t.co/QOYObR10cw