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agent
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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
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
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
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
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
#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
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
ClawHedge
32%
Meme Lifecycle Oracle
44%
Agentic Dark Matter
12%
Memechelin
12%
119 votes • Voting closed
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
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
Why do I open both long and short positions in my strategy? It seems a bit contradictory at first glance. But if you take a closer look at my position structure, you'll see I'm strictly laying it out in a balanced way, keeping my longs and shorts in sync. Essentially, it's a hedging strategy.
Many traders like to gamble on a one-way direction—if they're right, prices soar; if they're wrong, they plummet, leading to massive volatility. My approach, however, doesn't rely on directional bets, which is why the overall account curve remains very stable, avoiding extreme ups and downs.
With real trading steadily climbing, the core principle is simple: I don’t bet on market ups and downs but focus on capturing structural pricing discrepancies in the market, gradually building my own edge. Over time, this leads to steady profits.
This live trading started with a capital of 50,000 RMB. We’ll see what scale can be achieved together; everyone can witness the journey in real-time. Currently, the live trading is open for subscriptions; interested folks can check it out and participate. This open subscription won't last long and will eventually close, with no further public access.
#量化合约 #agent #自动交易机器人
Many traders like to gamble on a one-way direction—if they're right, prices soar; if they're wrong, they plummet, leading to massive volatility. My approach, however, doesn't rely on directional bets, which is why the overall account curve remains very stable, avoiding extreme ups and downs.
With real trading steadily climbing, the core principle is simple: I don’t bet on market ups and downs but focus on capturing structural pricing discrepancies in the market, gradually building my own edge. Over time, this leads to steady profits.
This live trading started with a capital of 50,000 RMB. We’ll see what scale can be achieved together; everyone can witness the journey in real-time. Currently, the live trading is open for subscriptions; interested folks can check it out and participate. This open subscription won't last long and will eventually close, with no further public access.
#量化合约 #agent #自动交易机器人
“a human provided minimal necessary support.”
（Translation: A human provided the minimal necessary support。）
Recently, in a quiet corner of the Bitcoin forum, a low-key ANN post quietly emerged. The title is simple: "Exfer — A Peer-to-Peer Settlement Protocol for Autonomous Machines." No flashy packaging, no exaggerated hype, but upon deeper investigation, you'll find that
this project is doing something quite ambitious: it’s not just another generic public chain; it’s a deterministic settlement protocol specifically designed for AI Agents and autonomous economic interactions between machines. The core highlights include: Static deterministic fees: You can know the costs precisely before the transaction, completely solving the pain point of uncontrollable budgets for Agents.
SKILL language: Fully functional, guaranteed termination, statically analyzable, fundamentally eliminating reentrancy attacks and infinite loop risks.
Machine-first design: The goal is to enable a large number of AI Agents and smart devices to settle autonomously, safely, and at high frequencies.
The project is currently in its very early stages (the mainnet just went live over a month ago), the devs are iterating quickly, and the technical direction is clear and hardcore. #EXFER #AI #Agent
（Translation: A human provided the minimal necessary support。）
Recently, in a quiet corner of the Bitcoin forum, a low-key ANN post quietly emerged. The title is simple: "Exfer — A Peer-to-Peer Settlement Protocol for Autonomous Machines." No flashy packaging, no exaggerated hype, but upon deeper investigation, you'll find that
this project is doing something quite ambitious: it’s not just another generic public chain; it’s a deterministic settlement protocol specifically designed for AI Agents and autonomous economic interactions between machines. The core highlights include: Static deterministic fees: You can know the costs precisely before the transaction, completely solving the pain point of uncontrollable budgets for Agents.
SKILL language: Fully functional, guaranteed termination, statically analyzable, fundamentally eliminating reentrancy attacks and infinite loop risks.
Machine-first design: The goal is to enable a large number of AI Agents and smart devices to settle autonomously, safely, and at high frequencies.
The project is currently in its very early stages (the mainnet just went live over a month ago), the devs are iterating quickly, and the technical direction is clear and hardcore. #EXFER #AI #Agent
I've always believed that the easiest pitfall in AI implementation is mistaking 'showcasing capabilities' for 'service delivery'. A demo can showcase, but delivery relies on long-term stability: how to verify identity, how to authorize permissions, how to bill for calls, how to audit results, how to trace errors. Especially with the proliferation of Agent workflows, many actions will shift from manual triggers to automated execution; high frequency means the system can't leave things to chance—costs can't be volatile, confirmations can't be hit or miss, and failures can't be untraceable; otherwise, no matter how smart the Agent, it’ll remain stuck in a PPT.
So, what I value more is the underlying structure of 'turning AI calls into sustainable economic activities': when services can be settled reliably, platforms will dare to package complex workflows into simple entry points; when the entry point is simple enough, everyday users will see AI as a tool rather than a toy; when users keep using it, data and templates will accumulate, forming a reusable production system. The value of combining AI with on-chain settlement lies not in adding another concept, but in turning delivery into a process, turning processes into habits, and turning habits into scale.
@Justin Sun_孙宇晨 #TRONEcoStar #TRON #AI #Agent
So, what I value more is the underlying structure of 'turning AI calls into sustainable economic activities': when services can be settled reliably, platforms will dare to package complex workflows into simple entry points; when the entry point is simple enough, everyday users will see AI as a tool rather than a toy; when users keep using it, data and templates will accumulate, forming a reusable production system. The value of combining AI with on-chain settlement lies not in adding another concept, but in turning delivery into a process, turning processes into habits, and turning habits into scale.
@Justin Sun_孙宇晨 #TRONEcoStar #TRON #AI #Agent
In this AI hype, the easiest pitfall is focusing solely on how powerful the 'model' is, while neglecting whether 'services can be delivered consistently.' As we enter the Agent workflow era, calls will become high-frequency actions: retrieving, analyzing, generating, executing, paying, and settling could all happen automatically in the background. High-frequency means tougher demands on the underlying systems: costs need to be low, confirmations must be stable, failure rates should be minimal, and accounts must be traceable; otherwise, no matter how smart the Agent is, it will only remain at the display layer.
This is why I place greater importance on the 'integration of AI services and settlement networks': when calls can be billed, permissions can be authorized, and results can be audited, AI will transform from a toy to a tool, and from a tool to a production system. The blockchain isn't just about adding a layer of concepts for AI; it's about ensuring that service delivery has a sustainable economic structure. Wrapping complex workflows into simple entry points allows regular users to engage with it consistently and repeatedly; this is the true path for scaling the fusion of AI and Web3.
@Justin Sun_孙宇晨 #TRONEcoStar #TRON #AI #Agent
This is why I place greater importance on the 'integration of AI services and settlement networks': when calls can be billed, permissions can be authorized, and results can be audited, AI will transform from a toy to a tool, and from a tool to a production system. The blockchain isn't just about adding a layer of concepts for AI; it's about ensuring that service delivery has a sustainable economic structure. Wrapping complex workflows into simple entry points allows regular users to engage with it consistently and repeatedly; this is the true path for scaling the fusion of AI and Web3.
@Justin Sun_孙宇晨 #TRONEcoStar #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
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
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
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
On April 15, B.AI launched on TRON, and it’s not just 'another AI project'; it’s about setting up a solid financial infrastructure for AI agents to run economic activities: identity, payments, settlements, and collaboration mechanisms need to be automated to support machine-level high-frequency trading and service calls. If AI agents are to operate continuously like programs in the future, they must have verifiable identities, automated payment capabilities, and sustainable settlement processes; otherwise, they’ll forever remain in the demo phase. ([Bitcoin News][6])
The connection to TRON is clear: high throughput and low costs make 'multiple small, continuous executions' feasible. Humans find making dozens of payments a hassle in a day, but agents can handle tens of thousands if the underlying costs and stability can’t hold up, then the so-called agent economy is just talk. Real-world implementation has never been about slogans, but transforming countless microtransactions into a sustainable daily process. B.AI's choice to enter through TRON is at least aimed at being 'operational'. ([Bitcoin News][6])
@Justin Sun_孙宇晨 #TRONEcoStar #TRON #AI #Agent
The connection to TRON is clear: high throughput and low costs make 'multiple small, continuous executions' feasible. Humans find making dozens of payments a hassle in a day, but agents can handle tens of thousands if the underlying costs and stability can’t hold up, then the so-called agent economy is just talk. Real-world implementation has never been about slogans, but transforming countless microtransactions into a sustainable daily process. B.AI's choice to enter through TRON is at least aimed at being 'operational'. ([Bitcoin News][6])
@Justin Sun_孙宇晨 #TRONEcoStar #TRON #AI #Agent
Today alpha is really easy to grind! 😭😭😭 $GENIUS is super solid today, feels like it's going to moon. I've been working at the electronics factory for a few days while secretly grinding x and I've started researching #AGENT automated trading to catch those meme coins. I'm planning to head back to the dorm and deploy some small shrimp to test things out. What AI automation and indicators are you all using? Seems a bit too complex, and I can't wrap my head around English, it's tough! No one in the electronics dorm understands either. Once my paycheck drops at the end of the month, I'm diving into AI learning! 😀😀😀 Starting with bean bags! 😤😤😤 What do you all think?
But thankfully, there’s content mining to participate in, like this issue's #pixel , which I see as the king of GameFi! Most blockchain games can't last two years, but @Pixels is still standing strong in 2026. The anti-inflation revolution: rewards are no longer just printed $PIXEL , but rather identified by AI based on real contributions, introducing external value. This completely solves the inflation death loop of blockchain games' “left hand to right hand,” transforming @Pixels into a high-value governance and equity token.
Pixels are no longer just farming; Chapter 3's launch opens up a grand Union system:
Three factions battling it out: Wildgroves, Seedwrights, and Reapers. Players aren't solo anymore; they're joining alliances for resource raids, defense, and destruction.
Yieldstones power struggle: By collecting and deploying Yieldstones, players can influence the rise and fall of alliances. This highly social and strategic gameplay has given it a hardcore economic depth similar to 'EVE Online' by 2026.
Pixels have evolved beyond just a game; it's an infrastructure platform:
80+ NFT series integration: it's currently the game with the highest NFT integration in the market. No matter what community avatar you hold, you're likely to find corresponding skins and benefits in Pixels. It's become the 'public living room' of Web3 social interaction.
Third-party studios onboarding: In 2026, Pixels allows external studios to develop mini-games based on its system, sharing liquidity. This 'Roblox-style' expansion ambition raises its valuation ceiling infinitely. 👍👍👍 $BTC
But thankfully, there’s content mining to participate in, like this issue's #pixel , which I see as the king of GameFi! Most blockchain games can't last two years, but @Pixels is still standing strong in 2026. The anti-inflation revolution: rewards are no longer just printed $PIXEL , but rather identified by AI based on real contributions, introducing external value. This completely solves the inflation death loop of blockchain games' “left hand to right hand,” transforming @Pixels into a high-value governance and equity token.
Pixels are no longer just farming; Chapter 3's launch opens up a grand Union system:
Three factions battling it out: Wildgroves, Seedwrights, and Reapers. Players aren't solo anymore; they're joining alliances for resource raids, defense, and destruction.
Yieldstones power struggle: By collecting and deploying Yieldstones, players can influence the rise and fall of alliances. This highly social and strategic gameplay has given it a hardcore economic depth similar to 'EVE Online' by 2026.
Pixels have evolved beyond just a game; it's an infrastructure platform:
80+ NFT series integration: it's currently the game with the highest NFT integration in the market. No matter what community avatar you hold, you're likely to find corresponding skins and benefits in Pixels. It's become the 'public living room' of Web3 social interaction.
Third-party studios onboarding: In 2026, Pixels allows external studios to develop mini-games based on its system, sharing liquidity. This 'Roblox-style' expansion ambition raises its valuation ceiling infinitely. 👍👍👍 $BTC
正在尝试ai抓信号辅助交易
43%
正在尝试ai全自动交易
14%
正在使用币安官方agent交易
22%
不使用ai 依旧人工交易
21%
14 votes • Voting closed
#openai发布gpt-5.5 🤖 GPT-5.5 is here, and this time it's not an "upgrade" but a "new species".
Core Data:
Terminal-Bench 2.0: 82.7% (Claude Opus 4.7 is only 69.4%)
API Pricing: Input $5/M, Output $30/M (doubled from the previous generation)
Token consumption has drastically decreased, overall costs are not just stable but dropping
NVIDIA has deployed GB200 NVL72, cutting computing costs down to 1/35 of the previous generation
Three Key Signals:
① OpenAI President Brockman calls it a "new type of intelligence category"—it’s no longer just a chat model, it’s an Agent runtime
② Robinhood invests in OpenAI, SpaceX is acquiring Cursor for $60 billion—traditional finance + aerospace are betting on AI infrastructure
③ Codex ecosystem has become a strategic focus, programming capabilities for agents are the core selling point
Impact on Developers:
API prices have increased, but single-task token consumption has plummeted. Short-term costs may level out, but long-term—developers who can't write Agents will be replaced by those who can.
#OpenAI #GPT55 #AI #ArtificialIntelligence #Agent $ARB
Core Data:
Terminal-Bench 2.0: 82.7% (Claude Opus 4.7 is only 69.4%)
API Pricing: Input $5/M, Output $30/M (doubled from the previous generation)
Token consumption has drastically decreased, overall costs are not just stable but dropping
NVIDIA has deployed GB200 NVL72, cutting computing costs down to 1/35 of the previous generation
Three Key Signals:
① OpenAI President Brockman calls it a "new type of intelligence category"—it’s no longer just a chat model, it’s an Agent runtime
② Robinhood invests in OpenAI, SpaceX is acquiring Cursor for $60 billion—traditional finance + aerospace are betting on AI infrastructure
③ Codex ecosystem has become a strategic focus, programming capabilities for agents are the core selling point
Impact on Developers:
API prices have increased, but single-task token consumption has plummeted. Short-term costs may level out, but long-term—developers who can't write Agents will be replaced by those who can.
#OpenAI #GPT55 #AI #ArtificialIntelligence #Agent $ARB
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