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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... show more

OptimAI Network

92,950 subscribers

12,800 Aufrufe • vor 6 Monaten •via X (Twitter)

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34 Kommentare

Profilbild von OptimAI Network

OptimAI Networkvor 6 Monaten

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. 👉

Profilbild von DanielPark

DanielParkvor 6 Monaten

Use This OptimAI Referral Link Below

Profilbild von RS SHANTO

RS SHANTOvor 6 Monaten

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! 🚀

Profilbild von Meron Taye

Meron Tayevor 6 Monaten

@

Profilbild von Anonymousjp 🐞

Anonymousjp 🐞vor 6 Monaten

#optimAI

Profilbild von Harold L. Robinson

Harold L. Robinsonvor 6 Monaten

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.

Profilbild von haossut

haossutvor 6 Monaten

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! 💪

Profilbild von Jeffery Davenport MD

Jeffery Davenport MDvor 6 Monaten

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

Profilbild von Stephen Nguyen

Stephen Nguyenvor 6 Monaten

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

Profilbild von Eric Harris

Eric Harrisvor 6 Monaten

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

Profilbild von CVAlpha

CVAlphavor 6 Monaten

👏👏

Profilbild von Vanessa Miller

Vanessa Millervor 6 Monaten

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.

Profilbild von Fauzi Rahmat

Fauzi Rahmatvor 6 Monaten

OptimAI STRONG comunity

Profilbild von Tanzim1986

Tanzim1986vor 6 Monaten

Great OptimAi

Profilbild von osv84

osv84vor 6 Monaten

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.

Profilbild von MrZYBIT

MrZYBITvor 6 Monaten

Lfg

Profilbild von Harry Smalls 𝔽rAI base.eth 🌶️

Harry Smalls 𝔽rAI base.eth 🌶️vor 6 Monaten

optimai incredible project number 1 in development

Profilbild von Mas World

Mas Worldvor 6 Monaten

Good

Profilbild von |OptimAI|✱,✱|$XAGE |⚡️ZugChain

|OptimAI|✱,✱|$XAGE |⚡️ZugChainvor 6 Monaten

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

Profilbild von Jennifer Davies

Jennifer Daviesvor 6 Monaten

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

Profilbild von Hồng Thắm

Hồng Thắmvor 6 Monaten

khi nào tge

Profilbild von Faiz Ikbal

Faiz Ikbalvor 6 Monaten

Hi all

Profilbild von Unati Khanal

Unati Khanalvor 6 Monaten

💎 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 👇

Profilbild von 1sahis X

1sahis Xvor 6 Monaten

👍🏻

Profilbild von chirantanbase.base.eth

chirantanbase.base.ethvor 6 Monaten

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.

Profilbild von mkt36

mkt36vor 6 Monaten

agents, better outputs, more value across the network

Profilbild von 🫎 Tabi 🟧 (✸,✸) 🦭 .ink 🌶️ $XAGE

🫎 Tabi 🟧 (✸,✸) 🦭 .ink 🌶️ $XAGEvor 6 Monaten

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!

Profilbild von HARD0ff🇯🇵🇵🇭

HARD0ff🇯🇵🇵🇭vor 6 Monaten

lfg

Profilbild von zysocoin

zysocoinvor 6 Monaten

gm

Profilbild von Ksenia Hamriuc(💙,🧡) 🟧

Ksenia Hamriuc(💙,🧡) 🟧vor 6 Monaten

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! 🦾🚗

Profilbild von Dilom abebe

Dilom abebevor 6 Monaten

Nice

Profilbild von Otnayir Qaff

Otnayir Qaffvor 6 Monaten

Maybe this is what everyone should know

Profilbild von 司星🚢 ‖狗宝真帅🐕

司星🚢 ‖狗宝真帅🐕vor 6 Monaten

LFG

Profilbild von REBORN🚢

REBORN🚢vor 6 Monaten

gm

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holly

13,718 Aufrufe • vor 1 Monat

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 Aufrufe • vor 9 Monaten

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,466 Aufrufe • vor 9 Monaten