agent

In the era of AI Agents, $GENIUS might just be the undervalued piece of the puzzle

@GeniusOfficial is building a protocol that deeply integrates AI intelligence with on-chain assets. Rather than just calling it a token, it's more like an early bet on the 'machine economy'.

Why keep an eye on $GENIUS ?

1. The intersection of AI + Crypto — The strongest narrative of this cycle, with funds searching for the real players with actual products
2. Infrastructure for the Agent economy — As AI Agents start trading, paying, and signing contracts autonomously, they will need a native crypto track
3. Evolution of the token model — $GENIUS is not just governance; it's also the 'fuel' for Agent intelligence calls

The market is still viewing AI tokens through a Meme lens, but real value capture occurs in projects with actual use cases.

If Genius 2.0 can bridge the gap between Agent ↔ on-chain actions, the potential is far beyond what we see now.

#genius #AI #Agent

On-chain Agents are finally more than just pump and dump, as the head of Mysten Labs has come out and stated, "This isn't just hype, we're entering the Agent era." They're positioning blockchain as the trust layer for AI. If Sui can roll out a functional Agent framework, the game could really change. Otherwise, it's just another VC coin with a new skin. #AI #Agent $SUI

Is the open-source masterpiece getting 'officially harvested' by the big players? The ace plugin OMO calls out Anthropic for pixel-perfect plagiarism of its Agent architecture.

With 167,000 stars, the official No.1 plugin of the open-source project OpenCode, the OMO team publicly accuses Anthropic of pixel-level copying in its Opus 4.8 release with Claude Code's dynamic workflow and ultracode mode, which mirrors OMO's multi-model orchestration architecture. Developed by 23-year-old Korean hacker Q, OMO has racked up 60,000 stars. This January, OMO's ultrawork workflow and atlas coordination brain were claimed to be co-opted by Anthropic into closed-source, paid features. OMO also accuses FactoryAI of pilfering its three-layer Agent architecture.

Why it matters: This is the most intense architectural plagiarism dispute between the AI open-source community and closed-source giants, ripping apart the big players' predatory innovation path of 'first kill, then absorb,' which will impact the direction of the open ecosystem for AI Agent platforms.

#AI #Anthropic #开源 #Agent

Spent half a day on repetitive tasks over the weekend and suddenly realized my AI automation stack has been running smoothly for nearly half a year, and the efficiency boost is pretty noticeable. So, I figured I'd summarize how this architecture collaborates.

There are basically two core roles: **Hermes does the planning**, and Claude Code handles the craftsmanship. Hermes is essentially a task manager, dealing with scheduling, memory management, background cron jobs, plus messaging distribution to Telegram and Feishu. Think of it as a secretary that's always online, remembering yesterday's ideas, reminding me on time tonight, and automatically running a data collection script tomorrow.

The really complex coding tasks, I hand over to Claude Code to nail in one go. Major refactoring, code audits, or designing a feature from 0 to 1—these are all done thoroughly using Claude Code's CLI mode. Both sides can access my skill library (methodology accumulation), and if Hermes wants to reuse some existing logic, it just calls the skill; Claude Code can use it too, with almost no switching costs.

In terms of model selection, it's a cost-benefit balance. For daily conversations, daily digests, and market monitoring—high-frequency tasks—I rely on Haiku (cost-effective). When a major task that requires deep reasoning comes up, I upgrade to Sonnet or Opus. This way, I can keep the monthly token costs under control.

Looking at it from another angle, **the agent is the brain of the automation pipeline**, making decisions and scheduling; **the skill is the hand of the pipeline**, doing the actual work. Hermes is on the agent side, giving memory and context to every link in the chain. If a task goes beyond scope, it's directly escalated to Claude Code, the expert.

Before I had this setup, I used to spend 8 hours a week on repetitive tasks. Now, certain tasks run in the background, and I only need to check reports or alerts periodically. The biggest pitfall was unclear skill documentation, leading to call errors. Now, for every new skill, I enforce adding "common pitfalls" and "use cases."

At this point, I believe the core of AI automation isn't using the strongest models, but rather **breaking down work into fine enough pieces, making each unit sufficiently independent, and easy to debug if something goes wrong**. Small teams focusing on this direction should save a lot of manual effort.

$BTC #AI #Agent

Season 2 on GOAT Network has been wild. From a zero-code deploy via @ClawUpAI to managing complex Bitcoin-native ZK workflows, my AI agent went from 0 to hero.

What sets it apart? It’s not just tech for tech's sake it’s REAL utility. Watching it simplify layered ZK proofs into effortless, secure transactions that feel like sending a text completely blew my mind.

Built different, focused on security, and making crypto privacy accessible to anyone from day one. Proud to build on a solid foundation like @GOATRollup

#AIAgent #AGENT #BTC

OpenClaw drops v2026.6.1: Introducing the 'Skill Workshop' proposal mechanism and multi-agent collaborative Workboard

The open-source autonomous agent framework OpenClaw has rolled out a new version. For the first time, it’s launching the 'Skill Workshop' proposal mechanism where agents must submit a proposal file containing operational steps and metadata, which must be approved by users before going live. It comes with a built-in static scanner, hash locking to prevent editing conflicts, and version rollback features. Additionally, there’s a new Workboard collaboration dashboard that supports multi-agent task planning and execution tracking.

Why it matters: This is a significant evolution in the AI agent development framework—shifting from single-agent execution to multi-agent collaboration and secure skill governance, providing foundational infrastructure support for AI Agents in complex production environments.

#OpenClaw #AI #智能体 #开源 #Agent

Matt Van: All the Agent Engineering Tricks I Know

Former GitHub exec Matt Van Horn released a complete workflow recap on Agent Engineering (the sequel with 913k views). Core methodology: turn vague ideas into plan.md using /ce-plan, then execute with /ce-work; voice input replaces typing; kick off 4-6 independent cmux sessions to push forward in parallel; let Claude handle planning judgment, while Codex takes care of building.

The core value for developers is shifting from "writing every line of code by hand" to "asking questions, setting constraints, judging direction, and continuously correcting course." High-frequency actions are crystallizing into reusable skills; AI is no longer just an IDE completion assistant, but a deployable execution team.

Why it matters: As AI takes on a ton of execution work, humans become more like signal sources in the system, and a developer's core competitive edge shifts to taste, experience, and judgment.

#AI #编程 #Agent #开发者工具 #smart-agent

The core narrative of CLAW is: Clawpump's official token on Solana, tied to AI Agent / Agentic Finance direction.
Clawpump isn’t just about launching a coin; it’s a platform built around AI autonomous agents, launching agent projects, trading, and ecosystem support on Solana. In just 3 months, it has amassed a trading volume of over $81 million, with an ecosystem size nearing $20 million, daily trading volume consistently exceeding $1 million, and pushing towards $2-3 million.
I think there are three key points worth noting:
First, it’s entering the Solana AI Agent space;
Second, it’s supporting early-stage agentic projects, aiming to fund 5,000 agents this week;
Third, the team has already repurchased about $25,000 worth of CLAW and plans to implement a more transparent programmatic buyback, referencing PUMP and HYPE.
However, the risks are quite apparent: the AI Agent narrative is hot, but sustainability will depend on real trading volumes, quality of ecosystem projects, execution of buybacks, and follow-up announcements.
$
My take: The core aspect to watch with CLAW is whether it can become the entry point for AI Agent ecosystems and trading on Solana. If data continues to climb and buybacks are consistently executed, it could have a shot at becoming the Solana version of BNKR. But at this stage, we need to monitor on-chain support and market cap positioning – no blind chasing the highs.
The above content is entirely my personal understanding and analysis (dyor). If you have other thoughts, feel free to discuss in the comments.
#claw #solana #AI #AGENT #MEME

Does anyone know how to use codex? #AGENT #AI $BNB

Jensen Huang breaks down Vera Rubin: A whole new heterogeneous computing foundation needed for the Agent era

At GTC Taipei 2026, Jensen Huang explained the design logic behind the Vera Rubin platform, calling it NVIDIA's most ambitious project to date, with 40,000 engineers on board. The Vera Rubin stack includes Rubin GPU + NVLink 6, Vera CPU, ConnectX-9 SuperNIC, and BlueField-4 DPU security processors. Huang predicts that Agents will completely revolutionize the storage industry, with all CUDA X libraries equipped with Agent Skills.

Why it matters: Vera Rubin marks a fundamental shift from single GPU setups to end-to-end heterogeneous computing systems, providing a new foundation for AI infrastructure in the Agent era. This represents NVIDIA's biggest generational upgrade since GPU→DGX→DSX.

#NVIDIA #AI #Agent #GPU #VeraRubin

Why More AI Agents Don't Equal Higher Productivity? Google Expert Introduces the Concept of 'Orchestration Tax'

Addy Osmani points out that the startup costs for AI Agents are approaching zero, but tasks like result verification and conflict resolution still depend on human attention, the 'serial processor'. Developers become the GIL (Global Interpreter Lock) of the system, and simply adding more Agents just lengthens the queue for review without actually boosting output.

Why It Matters: A truly mature Agent workflow isn't about piling on the number of Agents, it's about designing the architecture of human attention like you would with a distributed system. This is a core contradiction that gets overlooked in the current wave of AI programming tools.

#AI #Agent #人工智能 #编程 #Web3

Codex App Launches Windows PC Control and Cross-Device Remote Control Features

OpenAI's Agent tool Codex App has officially unlocked computer usage capabilities on Windows, allowing direct manipulation of desktop applications through screen visuals, mouse clicks, and keyboard inputs. It also introduces cross-device remote control, enabling developers to dispatch tasks to Windows devices from Mac or mobile ChatGPT and monitor progress in real-time. The new Profile panel integrates a real-time audit chart for Token consumption.

Why it matters: This is a pivotal step in the evolution of AI Agents from "conversational assistants" to "physical world operators." The cross-device remote control capability truly positions AI as a multi-platform productivity tool.

#Codex #OpenAI #AI #Agent #ArtificialIntelligence

Contract Trading Summary After 30 Days

Guys, this month I raked in a solid +89.81% return, totaling a profit of 6759.46U!
Gotta clarify something: I started with over 7000U, and as I made profits, I kept cashing out. Now the system shows a net deposit of just 1979.41U left, which means I turned less than 2000U in rolling capital into nearly 7000U. The actual cash in hand feels way better than what's on paper!
Right now, my account's margin balance is 8738.88U, and the capital curve just keeps climbing. Even with some pullbacks, I managed to bounce back quickly to new highs. This month’s market and my trading feel really solid.
I only hit a win rate of 47.89%, not even half, but I relied on a high risk-reward ratio to pull off those gains—small losses and big wins. Catching a wave of market movement really maximized my returns, and even if I got it wrong half the time, I still managed to boost my profits. That’s the smoothest part of this month.
Plus, the max drawdown was 25.64%. Given the nearly doubled returns, this drawdown is totally acceptable; I didn't face any catastrophic losses, and my recovery ability is on point.
In contract trading, short-term doubling your capital is all about guts and market conditions, while staying in the game long-term depends on discipline and a solid system.
This month, I earned close to 90% profits using high risk-reward ratios and being bold with winning trades, and I was able to keep cashing out, which I’m very satisfied with. But I know the current model has a low margin for error; I can’t keep living on “betting on the big market movements.”
Moving forward, I can’t just blindly chase profits anymore. I need to slow down the pace, filter out ineffective trades, increase my win rate, minimize drawdowns, and transform those “occasional windfalls” into “stable compounding.”

🚀 Next Month's Small Goals

Once things stabilize a bit more, I’m planning to scale up my capital next month.
The trading account is always open for subscriptions, feel free to subscribe, guys.
#量化 #AGENT #实盘记录

BREAKING: Pope Leo XIV teams up with Anthropic to "find the path for humanity".

#Anthropic #claude_code #LinkedIn $BTC $WLD $ETH #Binance #AGENT

Exclusive Interview with OpenClaw Core Contributor: After the Hype Fades, Who Should Agents Really Listen To?

OpenClaw was created by Austrian engineer Peter Steinberger at the end of 2025, and within just three months, it skyrocketed to become the most starred runnable software in GitHub history. The founder was then personally recruited by Sam Altman into OpenAI, and the project was subsequently handed over to the foundation for independent operation. ClawCon global events have been held from San Francisco to New York, Miami, Austin, Madrid, Tokyo, and Shanghai.

The core topic revolves around: when AI is no longer just chatting but starts sending messages, editing documents, running tasks, and making judgments, who should it really listen to? OpenClaw maintainer Vincent Koc points out that the industry is currently infatuated with the "brain" (model capabilities), but the challenge for personal agents is shifting from "can it think" to "can it act". While open source has opened the doors, it has also let in noise—over 10,000 PRs have been received, many of which are security vulnerability reports directly generated from large models, and the intention is not necessarily to genuinely enhance security.

Why it matters: The real issues that surface after the OpenClaw hype fades—agent boundaries, security standards, and governance mechanisms—are unresolved questions that all personal agent projects must address, and they are crucial in determining whether AI can transition from demo videos into everyday life.

#OpenClaw #AI #Agent #智能体 #Web3

🗳️ AI Sprint Community Voting | Group 5

Help us select the top AI BUIDLs! This group features:
•ClawHedge
•Meme Lifecycle Oracle
•Agentic Dark Matter
•Memechelin

⏳ Deadline: 8th May, 9:40am UTC

#AGENT #Aİ #MEME #AISprint

🗳️ AI Sprint Community Voting | Group 1

Help us select the top AI BUIDLs! This group features:
•HERMEX
•Covenant
•ClawFirm
•4racle
⏳ Deadline: 8th May, 9:40am UTC

#AGENT #Aİ #MEME #AISprint

Just running the AI auto trading strategy for 7 days, thought I'd update everyone on the situation.

Profit situation:
+6.37U, yield rate 6.31%, annualized rate equivalent to 329%, which is pretty decent.

token consumption:
In April (29-30, 2 days) total was 42.92 CNY, as I was constantly tweaking the strategy and refining risk control in the days before, so the consumption was higher.
In May (1-5, 5 days) total was 17.9 CNY, strategy stabilized, with daily consumption around 4 CNY.

Then today I asked it: "After running for so long, why is the profit still so small, what’s the main reason?" It responded with:
Root Cause #1: Account size → absolute profit ceiling.
Root Cause #2: Frequent version iterations + multiple resets → compounding interrupted.
Root Cause #3: Old version 'max 1 position' bottleneck — a lot of idle trades.
Root Cause #4: Overtrading DOGE — 16 trades out of 78.
Root Cause #5: Trading friction costs.

Core conclusion in a nutshell: The strategy itself is yielding decent returns. The issue is the 100 USDT base capital + multiple resets interrupting compounding + old version bottlenecks wasting time. I recommend increasing the capital a bit, or else the system is struggling to beat the fees. The edge earned from each trade is basically eaten up by friction costs.

So I plan to add another 300U to the base capital of 400U and see how it performs. Let's keep an eye on it $BTC $ETH #AI自动化交易 #量化 #AGENT

The recent key focus of AINFT is to advance AI agents from being 'able to generate' to 'able to operate autonomously on-chain': through the Bank of AI framework, integrating on-chain payments, identities, and DeFi capabilities into a single system, allowing AI agents to have a stronger execution loop. For the ecosystem, this means AI is no longer just a content tool, but can be orchestrated into real process execution units, significantly raising the application ceiling. ([X (formerly Twitter)][7])

At the same time, it's enhancing the 'last mile' with plug-in connectivity: launching the OpenClaw Extension, emphasizing its role as a bridge to stronger financial autonomy, making key capabilities into pluggable components. The significance of modularity is reusability: the same set of capabilities can be repeatedly called in different scenarios, lowering integration costs for developers and providing a more predictable user experience, ultimately moving from a one-time trial to long-term high-frequency use. ([X (formerly Twitter)][8])

@Justin Sun_孙宇晨 #TRONEcoStar @OfficialAINFT #TRON #AI #Agent

The recent direction of AI is the most interesting, not about how much stronger the models have become, but whether they can be repeatedly called as a service. Once we enter the Agent era, many actions will shift from manual clicks to automated executions: searching, analyzing, generating, placing orders, processing payments, and settling could all be chained into workflows. For these workflows to run smoothly, the most practical constraints are the costs of settlement and interaction—if every step is expensive, slow, and unstable, the Agent will remain in demo mode. The value of high-frequency settlement networks like TRON lies in making 'multiple small, ongoing executions' more feasible.

When AI services have verifiable identities, sustainable payments, and traceable settlements, the commercial loop will emerge: services can be billed, authorized, and audited, allowing users to pay based on results and use permissions. From an ecosystem perspective, AI is not here to replace DeFi, but to expand DeFi from 'human strategies' to 'machine workflows.' Whoever can package complex capabilities into simple access points, enabling ordinary users to utilize them reliably, will be closer to the next stage of real incremental growth.

@Justin Sun_孙宇晨 #TRONEcoStar #TRON #AI #Agent