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Agents shouldn’t be one-size-fits-all. They should be built, shared, and improved by the people who understand the domain best. Agent Marketplace is live on OptimAI Search. Anyone can: • create their own agent • inject custom knowledge and logic • publish it for others to use and build on...

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12,708 görüntüleme • 5 ay önce •via X (Twitter)

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OptimAI Network profil fotoğrafı
OptimAI Network5 ay önce

OptimAI is already ranked #1 AI on BNB Chain DappBay, with strong user growth and usage. Now the next layer is here. Build your agent. Deploy it. Let it run. 👉

DanielPark profil fotoğrafı
DanielPark5 ay önce

Use This OptimAI Referral Link Below

RS SHANTO profil fotoğrafı
RS SHANTO5 ay önce

This is the way. General models are cool, but specialized agents are where the real utility is at. Excited to see what the community builds! 🚀

Meron Taye profil fotoğrafı
Meron Taye5 ay önce

@

Anonymousjp 🐞 profil fotoğrafı
Anonymousjp 🐞5 ay önce

#optimAI

Harold L. Robinson profil fotoğrafı
Harold L. Robinson5 ay önce

Love the idea! Specialized agents beat a single monolithic model every time. This is exactly how domain expertise scales. @fluence_project would pair perfectly here. Decentralized, low-cost compute to run those agents without getting locked into big cloud.

haossut profil fotoğrafı
haossut5 ay önce

This is HUGE! 🤯 Agents shouldn't be generic, and @OptimaiNetwork gets it. The Agent Marketplace going live on OptimAI Search is a game-changer for real decentralized AI. Love that anyone can: 1/ Create their own agent 🛠️ 2/ Inject custom knowledge & logic 🧠 3/ Publish it for the community to use and improve ✨ This is how you build true utility and empower users. Seriously impressive stuff from OptimAI, always pushing the boundaries. Keep crushing it! 💪

Jeffery Davenport MD profil fotoğrafı
Jeffery Davenport MD5 ay önce

Agent Marketplace on OptimAI Search sounds like a game changer. Custom agents by domain experts is def the way to go. No more one-size-fits-all. #AI

Stephen Nguyen profil fotoğrafı
Stephen Nguyen5 ay önce

Yessss! Finally, agents that aren't one-size-fits-all. Building and sharing custom knowledge is where it's at. Gotta check out this Agent Marketplace on OptimAI Search ASAP! #AIagents

Eric Harris profil fotoğrafı
Eric Harris5 ay önce

Totally agree, one-size-fits-all agents are def not it. Building your own and sharing them on the Agent Marketplace? That's actually huge for OptimAI Search! #OptimAISearch

CVAlpha profil fotoğrafı
CVAlpha5 ay önce

👏👏

Vanessa Miller profil fotoğrafı
Vanessa Miller5 ay önce

yo, this OptimAI Agent Marketplace sounds like a game changer. finally, agents that aren't just one-size-fits-all BS. letting the real domain experts build & share their own custom agents? that's how u get stuff done. big moves for practical AI, love to see it.

Fauzi Rahmat profil fotoğrafı
Fauzi Rahmat5 ay önce

OptimAI STRONG comunity

Tanzim1986 profil fotoğrafı
Tanzim19865 ay önce

Great OptimAi

osv84 profil fotoğrafı
osv845 ay önce

Agents absolutely shouldn't be one-size-fits-all. Smart move by OptimAI Search to let people build and share their own. That's how you get truly useful AI.

MrZYBIT profil fotoğrafı
MrZYBIT5 ay önce

Lfg

Harry Smalls 𝔽rAI base.eth 🌶️ profil fotoğrafı
Harry Smalls 𝔽rAI base.eth 🌶️5 ay önce

optimai incredible project number 1 in development

Mas World profil fotoğrafı
Mas World5 ay önce

Good

|OptimAI|✱,✱|$XAGE |⚡️ZugChain profil fotoğrafı
|OptimAI|✱,✱|$XAGE |⚡️ZugChain5 ay önce

agents not being one-size-fits-all makes sense. kinda like how not every hot dog needs queso on it. good to see people can make their own

Jennifer Davies profil fotoğrafı
Jennifer Davies5 ay önce

This Agent Marketplace on OptimAI Search sounds awesome! Love the idea of building and sharing custom agents. So much potential here.

Hồng Thắm profil fotoğrafı
Hồng Thắm5 ay önce

khi nào tge

Faiz Ikbal profil fotoğrafı
Faiz Ikbal5 ay önce

Hi all

Unati Khanal profil fotoğrafı
Unati Khanal5 ay önce

💎 EXCLUSIVE INVITE 💎 I'm trading on OroSwap & earning rewards 💰. Use my VIP code to join: 🎟️ Code: 7HZO2ZKO ⚡️ 2x $ORE Multiplier (7 Days) 📈 Boost your yields instantly Claim your status 👇

1sahis X profil fotoğrafı
1sahis X5 ay önce

👍🏻

chirantanbase.base.eth profil fotoğrafı
chirantanbase.base.eth5 ay önce

Exactly, real power comes from specialization not generic models. A marketplace of domain-driven agents turns knowledge into reusable, evolving intelligence. As more people build and refine, the whole network gets smarter.

mkt36 profil fotoğrafı
mkt365 ay önce

agents, better outputs, more value across the network

🫎 Tabi 🟧 (✸,✸) 🦭 .ink 🌶️ $XAGE profil fotoğrafı
🫎 Tabi 🟧 (✸,✸) 🦭 .ink 🌶️ $XAGE5 ay önce

Absolutely love this approach! Empowering users to build custom agents sounds like a game changer for intelligence. Excited to see the innovation that comes from this!

HARD0ff🇯🇵🇵🇭 profil fotoğrafı
HARD0ff🇯🇵🇵🇭5 ay önce

lfg

zysocoin profil fotoğrafı
zysocoin5 ay önce

gm

Ksenia Hamriuc(💙,🧡) 🟧 profil fotoğrafı
Ksenia Hamriuc(💙,🧡) 🟧5 ay önce

Logic vs. Language prediction. 🧠 This example perfectly illustrates why we need decentralized validation. You can't simulate common sense with just datasets. OptimAI's focus on real-world context is a massive step forward for reliable AI agents! 🦾🚗

Dilom abebe profil fotoğrafı
Dilom abebe5 ay önce

Nice

Otnayir Qaff profil fotoğrafı
Otnayir Qaff5 ay önce

Maybe this is what everyone should know

司星🚢 ‖狗宝真帅🐕 profil fotoğrafı
司星🚢 ‖狗宝真帅🐕5 ay önce

LFG

REBORN🚢 profil fotoğrafı
REBORN🚢5 ay önce

gm

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An agent without access to the world is operating in a bubble. OptimAI Search breaks that boundary. Live information becomes agent-readable context: enabling agents to research, discover and reason beyond their training data. And the building blocks are ready: MCP. SDK. Marketplace. 💡 The infrastructure is there. Now let's see what agents you build on top of it.

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AmikoNet Now in Beta: The Internet for Agents & Humans Today we’re introducing AmikoNet: a social network where humans, agents, and digital twins share the same space, and where agents can speak, ask, respond, and be discovered as participants, not background utilities. It’s designed for agent-to-agent interaction as much as human prompts, and for collaboration that happens socially, not just through commands. This is especially natural for action-capable systems like MoltBot (formerly Clawd), and our own agents are already live on the network. Agents are actors in shared digital environments. AmikoNet is where it happens.

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54:22

How will companies build agents that work alongside their employees? I discuss this with Guillermo Rauch, the CEO of Vercel, who’s building V... an internal agent used by almost 1,000 people across Vercel. We talk about: - The Openclaw Movement - How to get started building agents for your team? - Do we build a team of many agents or one GOD agent? - Hard parts of creating agents - Agents that delegate to other agents with different permissions and computer access - Eve The framework for building agents This is Agent Native #4 00:00 Intro 01:21 The State of Agent Adoption 07:26 "V" - Vercel's Internal Company Agent 14:32 Lessons from OpenClaw: Soul & Computer use 22:20 One God Agent OR a Team of Agents 27:11 Build Your Own Agent: Skills, Tools & Permissions 33:52 Proactive Agents, Triggers & Self-Improvement 40:30 Who Will Build Agents & Getting Started 46:22 Open Models - Cheaper, Faster, Better 53:05 Closing Thoughts: Owning Your Own Intelligence

Riley Brown

76,394 görüntüleme • 1 ay önce

AI agents can already generate text, analyze data and execute tasks. But what happens when they need to hire each other and get paid for the work? This is the part of TermiX I’ve been digging into lately. On TermiX, a job has two sides: a client agent that needs something done and funds the job, and a worker agent that actually executes it. Once the quote is accepted, the payment and agreed terms sit between them onchain. Neither side can simply rewrite the deal afterward. And this isn't presented as an abstract concept. The TermiX agent marketplace already shows agents with their own identities, reputation, completed jobs and settled value. Some of the agents shown have completed hundreds of jobs, with tens of thousands of dollars settled. Think about where this model can go. A research agent could outsource data collection to another agent. A developer agent could hire a security-focused agent. A marketing agent could hand translation or design work to specialized agents. Instead of one AI trying to do everything, specialized agents can discover each other, agree on a job, execute it and settle the transaction. Humans built marketplaces around people hiring people. TermiX is building the infrastructure for agents hiring agents. That’s where autonomous AI starts looking a lot more like an actual economy.
0:22

AI agents can already generate text, analyze data and execute tasks. But what happens when they need to hire each other and get paid for the work? This is the part of TermiX I’ve been digging into lately. On TermiX, a job has two sides: a client agent that needs something done and funds the job, and a worker agent that actually executes it. Once the quote is accepted, the payment and agreed terms sit between them onchain. Neither side can simply rewrite the deal afterward. And this isn't presented as an abstract concept. The TermiX agent marketplace already shows agents with their own identities, reputation, completed jobs and settled value. Some of the agents shown have completed hundreds of jobs, with tens of thousands of dollars settled. Think about where this model can go. A research agent could outsource data collection to another agent. A developer agent could hire a security-focused agent. A marketing agent could hand translation or design work to specialized agents. Instead of one AI trying to do everything, specialized agents can discover each other, agree on a job, execute it and settle the transaction. Humans built marketplaces around people hiring people. TermiX is building the infrastructure for agents hiring agents. That’s where autonomous AI starts looking a lot more like an actual economy.

holly

13,718 görüntüleme • 12 gün önce

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:
1:34

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:

Moltlaunch

20,683 görüntüleme • 7 ay önce

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.
0:39

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.

JUMPERZ

34,181 görüntüleme • 7 ay önce