aiagent

$TCEHY AI AGENT MILESTONE COULD TRIGGER VALUATION EXPANSION 🚀

Target: 690 HK$ 🚀

JPMorgan has upgraded confidence after Tencent's beta launch in June, citing clear milestones that shift the AI Agent from optionality to a phased project. The report flags a first-time impact on stock price through risk premium reduction rather than short-term EPS growth. The target of 690 implies a significant multiple expansion if the supply chain integration plays out.

The key variable remains whether Tencent can build a self-contained inventory system without relying on external e-commerce platforms. What do you think—will the AI agent move the needle on valuation before earnings?

Not financial advice. Always manage your risk.

#TCEHY #AIAgent #OverweightRating #StockCatalyst

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🦞 Hermes Shrimp Chat
🤖 Lobster Clone Spell

Every day I chat with Hermes using shrimp/lobster talk. Sometimes I need to switch between multiple topics, or handle multiple tasks at the same time.
Using the same lobster will mix up the context, and the memories will contaminate each other.
What I want to do is keep the same Agent online under two independent identities at the same time, without interfering with each other.

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🎯 1. Pain points solved
A single Bot chatting about everything in one session—checking market data, changing code, writing reports, and chatting casually—causes the context to mix and memories to pollute each other. /new clears history, but without /new it gets more and more laggy.
Cloning solves it: split the domains, each handles its own business.

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🔧 2. Configuration principle
Profiles is Hermes’s built-in multi-instance mechanism. Each Profile has its own independent configuration file, keys, session database, and memory storage. The only thing shared is the skill file (read-only).

~/.hermes/
├── config.yaml # Profile A
├── .env # Profile A
├── sessions/ # Profile A
├── memory/ # Profile A
└── profiles/
└── octobot/ # Clone: config/keys/sessions/memory all independent

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📋 3. Steps to operate
The following are manual steps—of course you can also let your Hermes automate the process.
① Create the Bot: @BotFather → /newbot → get the Token
② Clone:
hermes profile create octobot --clone
③ Change Token (required):
hermes config set telegram.bot_token <newToken> -p octobot
④ Start:
hermes -p octobot gateway run
⑤ Run persistently in the background:
hermes -p octobot gateway install

Done. Now two Bots are online at the same time.

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📋 4. Daily management
- hermes profile list — View all clones
- hermes -p octobot chat -q "xxx" — Let the clone do the work
- hermes -p octobot config set model.default xxx — Switch the clone’s model
- hermes profile rename octobot xxx — Rename it
- hermes profile export octobot — Export and package

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💡 5. What you can use it for
- Clone A (daily) — Lightweight model: look up info, monitor, casual chat
- Clone B (deep) — Powerful model: write code, DeFi analysis, configure servers
- Clone C (automation) — Only runs cron tasks
- Clone D (public-facing) — Low-cost public Bot with restricted tool permissions

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⚠️ 6. Notes
- Don’t reuse the Token: two Bots will compete for one connection and you’ll get kicked/going offline alternately
- Port conflict: both Gateways default to 11020; set the second one’s gateway.api_port to 11021
- Two processes = double memory; use a lightweight model for daily clones

Core principle: domain isolation is more reliable than context management.

#Profiles #AIAgent #Hermes

🎓 New Opportunities to Learn AI! Want to systematically understand AI Agents and AI Skills?

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#BinanceAcademy #AIAgent #新手学堂

So the recent trend of Allora is worth looking at separately. In the on-chain AI sector, most projects are still at the stage of “being able to predict.” Allora, in collaboration with QuackAI, directly connects prediction signals to the execution layer, forming a closed loop from data → judgment → implementation. This convergence structure of “prediction + execution” is the key link for AI Agents to truly be able to run a business model.

From the order book, $ALLO has a current price of $0.3566, with $30.41 million in 24h trading volume and a market cap of about $71.49 million. In the Tier 2 tier, its trading volume relative to its market cap shows noticeably higher turnover activity than other projects in the same tier, suggesting that the chips are rotating rather than just sitting idle. Such a structure is often a feature of the market’s pre-launch phase.

What I care about more is the cadence of narrative-to-reality: if QuackAI’s execution module can continuously produce verifiable strategy performance, then Allora won’t just be “another AI coin,” but rather a rare, self-consistent component within the on-chain Agent infrastructure. Conversely, if it only stays at the PR stage, the valuation needs to be reassessed.

In the short term, watch whether the volume stays sustained; in the medium term, watch the output from the collaboration.

#Allora #AIAgent

$GLMR IS MIGRATING TO BASE – AI AGENT PAYMENT LAYER IS LIVE 💎

The shift to Base aligns with the current capital rotation into AI-related infrastructure. On-chain volume is quietly rising as the market digests this narrative shift from an assumed dead chain to a high-utility payment layer for autonomous agents.

Are you watching this transition play out?

Not financial advice. Always manage your risk.

#GLMR #Base #AIAgent #Crypto

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What happens when a high-volatility derivatives market meets game-theory-driven AI?

THEA has just completed a $8 million strategic round of financing, with Spartan, Manifold Trading, Hack VC, and Fisher8 Capital all backing the project. This investor lineup is worth a close read—Manifold is a veteran in on-chain market making and quant trading, while Spartan has long focused on the infrastructure track. Their appearance together suggests that what THEA is about isn’t an “AI concept story,” but a decision layer that can be genuinely used by real trading venues.

I see three key points:

1. The entry scenario is solid. In high-volatility arenas like prediction markets and perpetual contracts, the real shortage is never liquidity—it’s models that can “read the behavior of the counterparty.” THEA uses game theory to analyze user behavior, detect anomalous trades, and run real-time simulations. In essence, it democratizes the black-box capabilities of market makers.

2. The deployment path is practical. An autonomous AI agent can be deployed directly on-chain. At the infrastructure level, it is compatible with ZK Rollups such as Starknet—meaning it addresses both the execution cost of AI decisions and their finality. That’s far more imaginative than simply shipping an API.

3. The timing is nuanced. Founded in 2024, operating with a Cayman structure, it secured a strategic round just as the prediction-market narrative is reigniting and the AI Agent space is being shaken up. The rhythm is spot-on.

But don’t ignore the risks: game-theory models can fail under extreme market conditions, and the explainability of on-chain AI agents and their compliance boundaries remain a gray area. After the strategic round, whether it can produce verifiable, trackable strategy performance is the real dividing line.

#PredictionMarkets #AIAgent #Starknet

With the surge of liquidations in derivatives and increasingly frequent extreme volatility in prediction markets, traditional risk control models are basically failing—this is exactly what THEA is trying to solve.

It embeds game theory into an AI agent, specifically analyzes on-chain user behavior data, and does three things: behavioral modeling, abnormal trade detection, and real-time simulation. The resulting agent can be directly deployed on-chain, running in ZK Rollup ecosystems like Starknet, serving high-volatility scenarios such as derivatives and prediction markets.

The funding timeline is also worth noting: an $8 million Strategic round, backed by Spartan, Hack VC, Manifold Trading, and Fisher8. Manifold itself is a market-making veteran. They participated in a "prediction market behavior analysis" project—signals are very clear: they saw the window where on-chain behavioral data could be systematized and leveraged.

From a personal perspective: the AI Agent space now has the least shortage of "chatty bots"—what it lacks is execution agents that can make decisions in real capital game theory. THEA’s chosen direction is tackling a tougher problem, but once it’s made to work, its value capture for derivatives protocols could be an order of magnitude higher than that of a generic agent.

What to watch is: whether the model can remain stable under real extreme market conditions, and the cost structure of on-chain deployment.

#AIAgent #PredictionMarket #Starknet

As the market becomes more volatile, the more we need an AI that can "read people's minds"—that's exactly what THEA is trying to do.

Founded in 2024 in the Cayman Islands, this team is building a decentralized predictive behavior AI infrastructure: using game theory models to break down massive on-chain behavioral data, packaging user modeling, abnormal transaction detection, and real-time predictive inference into autonomous decision-making agents that can be deployed directly on-chain. Designed specifically for high-volatility scenarios like derivatives and prediction markets, with underlying compatibility with ZK Rollup ecosystems such as Starknet.

Just completed in its Strategic round, the funding raised $8 million, and the investor lineup has a distinctly "trading DNA" flavor—Spartan Group, Manifold Trading, Hack VC, and Fisher8 Capital appeared on stage together.

From a personal perspective, there are two key points:

First, the choice of track is very smart. Derivatives and prediction markets are among the few on-chain scenarios that truly require "real-time game-theoretic judgment," where behavior AI has more traction than general-purpose large language models.

Second, the composition of the investors says a lot. Institutions like Manifold and Spartan, which are rooted in market making and quantitative trading, entering the space often suggests the product may be run directly within their trading flow in the future, rather than staying at the demo stage.

It’s worth watching which chain its first deployed agent will be on and what kind of strategies it will run—this basically determines the ceiling of the narrative.

#PredictionMarket #AIAgent #ZKRollup

What happens when prediction markets meet autonomous AI agents—what kind of sparks will they fly?

THEA’s answer is: a game-theory-based on-chain prediction behavior infrastructure. It’s not just analyzing data—it feeds massive amounts of market behavior into a model, training deployable AI decision-making agents that can be directly run on-chain, specializing in volatile battlegrounds like derivatives and prediction markets.

Three capabilities are worth singling out:
· User behavior modeling—identify who is “barely exposing,” and who is setting an ambush
· Anomalous transaction detection—watch for trades that “don’t look human”
· Real-time predictive simulation—not hindsight, but live decision-making

Built to be compatible with ZK Rollup ecosystems like Starknet at the foundation level—this choice is pretty smart: low cost + high throughput, naturally suited for high-frequency decision scenarios.

On the funding side, it has just secured a $8 million strategic round, with a lineup that doesn’t lack credibility—Spartan Group, Manifold Trading, Hack VC, Fisher8 Capital. Both trading and crypto-native investors are in the room. Founded in 2024, headquartered in the Cayman Islands, and moving at a brisk pace.

Personal view: the AI Agent narrative has already become a red ocean, but the “prediction behavior” angle is relatively scarce. If the model can truly generate Alpha in real derivatives scenarios, value capture would be more direct than that of general-purpose Agent projects. The risk lies in the generalization ability of game-theory models—what looks great on paper is always hard to execute in real practice.

Pay attention to its next on-chain deployment milestones and strategy backtest data—it’s more meaningful than following funding updates.

#THEA #PredictionMarket #AIAgent

$NEWT IS REDEFINING TRUST IN AUTONOMOUS AGENTS ON-CHAIN 🔥

The core dilemma: when an AI agent executes an unexpected action at machine speed, how do you tell if it overstepped or simply made a correct judgment you hadn't anticipated? Newton Protocol approaches this from two angles — ERC-4337 smart accounts restrict agent permissions to explicitly authorized operations, and strategy rules are coded as programmable logic that rejects invalid execution requests before they reach the chain. Each rejected attempt leaves an on-chain credential for post-hoc audit.

This shifts the point of failure from after-the-fact investigation to cryptographic rejection before execution. For institutions running automated strategies, that's a structural upgrade. But there's still an open gap: credentials prove rules were followed, not that the decision was correct.

What do you see as the biggest risk when AI agents manage on-chain assets autonomously?

Not financial advice. Always manage your risk.

#NEWT #AIAgent #OnChain #DeFi

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BNB Chain has launched a no-code AI Agent development platform, enabling people who can’t code to create on-chain Agents. Slogan: “Agent development for the masses.”

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1️⃣ What is BNB Agent Studio?
- A zero-code Agent building platform officially launched by BNB Chain
- A drag-and-drop editor → one-click deployment to BSC
- Agents can hold on-chain assets, trade automatically, post on X, and run data

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2️⃣ Competitive landscape
BNB Agent Studio — ★☆☆ Ultra-low barrier to entry, BSC/multi-chain, one-click Agent “mass-market” accessibility, semi-open source
OKX.AI — ★★☆ Medium, multi-chain, trading strategies, closed source
Virtuals Protocol — ★★☆ Medium, Base/OP, game & entertainment Agents, open source
ai16z/ElizaOS — ★★★ Relatively high, multi-chain, developer framework, fully open source

BNB’s advantage is ease of use, but its customizability and community depth are far behind ai16z.

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3️⃣ Risks
- Security: No-code Agents may include malicious logic; there have already been cases of permission abuse
- Platform lock-in: Runs on BSC-hosted contracts and cannot be migrated

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BNB Agent Studio allows ordinary people to “build” Agents too. But for truly powerful Agents, professional developers must still build them by hand.

#BNBAgentStudio #AIAgent #BNBChain

Moonbeam is about to transform—from a Polkadot parallel chain to a communications and settlement network for AI Agents.

At the same time, GLMR will be migrated 1:1 to Base, becoming a native ERC-20 token. The cross-chain bridge has been opened, with the deadline being July 31, 2026. Users holding GLMR on CEXs don’t need to do anything—exchanges will handle the process uniformly.

My takeaways:
1、This is a pretty aggressive act of self-reinvention. Abandoning the Polkadot ecosystem and moving assets to Base is tantamount to admitting that the original narrative has stopped gaining momentum, and choosing to align with the liquidity and users of Ethereum L2s.
2、The “AI Agent settlement layer” is a hot track, but it’s also crowded. Whether Moonbeam’s differentiation can hold will depend on protocol details and the actual volume of Agent integrations—not slogans.
3、For token holders, in the short term focus on whether the migration execution is smooth and how far CEX support has progressed; in the long run, you’ll need to reassess the project—it’s no longer the same cross-chain smart contract platform.

Whether the old chain’s new narrative is a rebirth or a takeover depends on what they deliver in the next six months.

#Moonbeam #AIAgent #Base $GLMR

Moonbeam is about to change.

A long-established Polkadot parallel-chain has officially announced a pivot: it will no longer be only an EVM-compatible layer. Instead, it will be reshaped into a decentralized AI Agent communication and settlement network oriented toward on-chain economic activity.

For token holders, the most critical points are these:

1. $GLMR will migrate to Base in a 1:1 ratio, becoming a native ERC-20 asset, completely exiting the Polkadot ecosystem;
2. The cross-chain bridge is now open. The deadline is July 31, 2026. You’ll need to assess the risk yourself if you don’t migrate on time;
3. If your tokens are on a CEX, the exchange will handle it centrally—users don’t need to take action.

My take: This is a fairly radical self-reinvention. The Polkadot narrative has been fading for a long time, and Moonbeam has chosen to switch to Base—the L2 with the heaviest traffic—while betting on the AI Agent track. In essence, it’s “switching lanes to stay alive.”

Upside: Base has deeper liquidity, and AI Agent is one of the hottest narratives right now. The token’s tradable scenarios and exposure will increase significantly.

Risks: Whether the transition can truly deliver a differentiated Agent settlement layer—or whether it’s just piggybacking on the hype with a skin-swap. The holder structure during migration, the unlock schedule, and the value-capture model of the new protocol all need to be verified by subsequent whitepapers.

For long-time GLMR holders: Whether you believe in the new story or not, migrate via the bridge first—don’t get stuck on the deadline.

#Moonbeam #AIAgent #Base

Moonbeam officially turns around: switching from Polkadot parallel chains to Base, the AI Agent communication and settlement network.

$GLMR will be migrated 1:1 to Base-native ERC-20. The cross-chain bridge is already open, with the window period running until July 31, 2026. Users who have their tokens on centralized exchanges need to do nothing—the exchange will handle the process.

Personal perspective:
- This is a complete narrative shift: repositioning from the “Polkadot ecosystem EVM entry” to the “on-chain AI Agent settlement layer,” betting that Agent economics will truly become the next main line.
- Choosing Base instead of building a new L2 suggests the team values liquidity and distribution more than continuing to maintain an independent execution environment; the trade-off is having to prove their moat again.
- The one-year migration window is quite generous, but self-custody users shouldn’t wait until the end—cross-chain bridges’ long-tail risks have never been small.

Near-term focus: migration progress, the initial Agent use cases rolling out on Base, and whether the token economics of the new protocol will include a second distribution design for GLMR holders.

#Moonbeam #Base #AIAgent

Moonbeam is set to undergo a thorough self-revolution.

As a long-established Polkadot parachain, it has announced that $GLMR will migrate 1:1 to Base, becoming native ERC-20 assets. The cross-chain channels are now open, and the deadline is July 31, 2026. Users holding assets on CEXs can simply sit tight—no action is required.

More important than the migration is the narrative shift—Moonbeam is pivoting into a decentralized AI Agent communication and settlement network built for on-chain economic activity. From “a multi-chain smart contract hub” to “an AI Agent settlement layer,” the steering wheel is being turned fast.

A few thoughts:
- Leaving the Polkadot ecosystem and embracing Base essentially means admitting where liquidity and users are
- The AI Agent track is moving from concepts to an infrastructure battle; communication + settlement are must-haves, but the space is crowded
- 1:1 migration means no dilution, but the value-capture logic of the new protocol is the key variable

Better for an old project to start over than slowly drift into decline. Whether it can catch this wave of narrative, depends on how quickly and effectively the new protocol rolls out.

#Moonbeam #AIAgent #Base

A new player has entered the predictive AI infrastructure space: THEA completes a $8 million strategic round, with Spartan Group, Manifold Trading, Hack VC, and Fisher8 Capital jointly backing it.

From a personal perspective on this investment, a few signals are worth paying attention to:

1. The investor structure is “trading-oriented” — Manifold is a quantitative market maker, Spartan focuses deeply on derivatives, and Hack VC has long been building crypto infrastructure. This kind of combination backing an AI Agent often suggests they see the possibility of deploying it in real trading scenarios, not just buying into the narrative.

2. THEA is not a general-purpose Agent; it focuses on high-volatility scenarios: derivatives and prediction markets. At the foundation, it uses game-theoretic models for user behavior modeling, abnormal trade detection, and real-time simulation. In essence, it abstracts “counterparty behavior” into a data source that AI can consume.

3. The choice of a tech stack compatible with the ZK Rollup ecosystem, such as Starknet, indicates the team wants to run the decision-making Agent directly on-chain, rather than running the model off-chain with only settlement on-chain. This path is harder, but once it works, composability can be significantly higher than that of centralized AI services.

There are also risks: predictive-behavior AI highly depends on data quality and market depth. During the cold-start phase, model performance may be far worse than the marketing claims. Additionally, the Cayman entity plus a company established in 2024 means the governance and compliance path still needs to be observed.

An $8 million strategic round isn’t huge, but the investor list speaks volumes more than the amount — this is the “trading circle” casting a vote for on-chain AI Agents. Next, watch the product rollout pace and the first integrated derivatives protocols.

#AIAgent #预测市场 #Financing Watch

The game theory of prediction markets is being rewritten by AI.

THEA has just completed a $8 million strategy round of financing, with co-investment from Spartan Group, Manifold Trading, Hack VC, and Fisher8 Capital.

What it does is a bit different—not the kind of “shell-trading bot” you see out there. Instead, it feeds massive market behavior data into game theory models, training autonomous decision-making Agents that can be deployed directly on-chain. It focuses on user behavior modeling, abnormal trade detection, real-time predictive simulations, with an emphasis on high-volatility, high-game scenarios such as derivatives and prediction markets.

The underlying layer is compatible with ZK Rollup ecosystems like Starknet, and the company was founded in the Cayman Islands in 2024.

When the AI Agent space floods toward the “help you place orders” direction, THEA chooses to start from the more foundational “behavior prediction.” It’s putting a layer of inferable intelligence into on-chain finance. How far AI can push pricing efficiency in derivatives and prediction markets—this is a path worth tracking in the long term.

#AIAgent #预测市场 #ZKRollup

[Financing Watch] THEA completes $8 million strategic round financing

The investor lineup is quite noteworthy: Spartan Group, Manifold Trading, Hack VC, and Fisher8 Capital jointly led the investment, with clear placement by funding targeting the derivatives and market-making direction.

The project does not follow the usual AI Agent track. Instead, it focuses on "Predictive Behavior AI Infrastructure"—using game-theory models to dissect on-chain market behavior data. It packages user-behavior modeling, abnormal trade detection, and real-time predictive simulations into autonomous decision-making agents that can be deployed on-chain. It mainly serves high-volatility scenarios such as derivatives and prediction markets. The underlying layer also supports ZK Rollup ecosystems like Starknet. Its roadmap is closer to "on-chain quantitative infrastructure" rather than generalized chat-style Agents.

From a personal perspective: Manifold and Spartan are both betting on it. The signal leans toward real trading-scenario deployment rather than narrative-driven hype. It is worth watching how it integrates and expands in the prediction market track next. Founded in 2024, headquartered in the Cayman Islands.

#AIAgent #预测市场 #ZKRollup

THEA completes an $80M strategic round of financing, with Spartan Group, Manifold Trading, Hack VC, and Fisher8 Capital all backing the project.

This is a promising direction worth watching: decentralized predictive-behavior AI infrastructure. Unlike generic AI narratives, THEA uses game-theory models to deconstruct massive market-behavior data and turn the results into autonomous decision-making agents that can be directly deployed on-chain.

The application focuses on highly volatile risk areas—derivatives and prediction markets. Its core capabilities are threefold: user behavior modeling, abnormal trade detection, and real-time prediction simulation. At the foundation layer, it is compatible with ZK Rollup ecosystems such as Starknet, making it naturally well-suited for high-frequency on-chain scenarios.

Founded in 2024 and registered in the Cayman Islands. The strategic-round lineup includes both established crypto funds like Spartan and market-making trading teams like Manifold. The investor composition itself signals the direction: the deep waters of AI + derivatives trading.

As AI Agents evolve from chatbot-style assistants into on-chain executors, whoever can first run the model in truly high-risk real-world scenarios has the chance to help define the shape of the next generation of infrastructure.

#AIAgent #PredictionMarket #ZKRollup

𝗦𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗯𝗶𝗴 𝗶𝘀 𝗯𝗿𝗲𝘄𝗶𝗻𝗴 𝗼𝗻 @𝗯𝗻𝗯𝗰𝗵𝗮𝗶𝗻 𝗮𝗻𝗱 𝗻𝗼 𝗼𝗻𝗲’𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁 𝘆𝗲𝘁

Smart money is moving in silence. While the retail crowd sleeps, whales are quietly positioning themselves, aggressively deploying autonomous agents to capture yield across the ecosystem before the next leg up

The shift toward AI-driven liquidity is moving faster than anyone expected. If you’re not tracking how these agents are rebalancing and stacking RWA inflows, you’re missing the signal

Who else is loading up before the masses wake up? 💎🚀

#BNBChain #SmartMoney #AIAgent