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gn fam most people see tria as “yet another ai project” but that’s missing the point. @useTria isn’t trying to compete with openai, it’s building the rails for how ai actually talks to each other in a decentralized way. the internet connected humans. tria connects agents. it’s not about chatbots or prompts, it’s about giving ai systems a shared language, identity, and trust layer so they can collaborate, trade, and evolve on their own. that means every agent can have its own wallet, verifiable reputation, and logic that lives onchain not locked behind some api. tria turns ai into a new kind of network citizen, not just a tool.

gm fam the agent doesn’t need to know what chain it’s on. it just says “move liquidity” and bestpath handles everything. but @useTria isn’t just about liquidity routing. its triai framework lets agents work together sharing resources, optimizing strategies, and executing complex tasks as a swarm. and the coresdk glues it all together automating permissions, wallets, and execution. it’s full-stack ai for multichain operations. suddenly, agents can do more than “analyze” data. they can move capital across chains, find yield, rebalance treasuries, or execute arbitrage automatically and intelligently. to me, #tria isn’t trying to build the smartest agent, it’s building the smartest environment for agents to thrive in. that’s a big difference.

gm fam most people frame @Everlyn_ai as a “video model vs openai/meta” but i think the real comparison is with operating systems. #everlyn isn’t building just a model, it’s quietly laying the groundwork to become the os for video agents. look at the stack: 🔹everlyn-1 as the foundational video model 🔹everworld as the creation engine for interactive video agents 🔹decentralized agent api protocol (aapis) for routing and execution this isn’t just ai, it’s an entire runtime environment. @playAInetwork

gn fam @fogo could bake consumer protection primitives into the protocol, making onchain trading safer for retail by default, not as an afterthought fogo already has the building blocks: firedancer-level low latency, near-instant finality, an enshrined dex, native price feeds, curated validators, and colocated liquidity those primitives let the chain measure execution and risk precisely. that measurement can power protections like guaranteed worst-case slippage, pre-trade risk warnings, and realtime fill proofs imagine default-safe wallets. when a user sends a trade, the wallet shows a verifiable worst-case outcome computed from #fogo’s native feed and onchain liquidity depth, not just an estimate

gm @Lombard_Finance fam developer impact is big. account abstraction and smart-contract wallets will need to interop with external hardware-backed multisig schemes. expect a wave of standards and sdk work to make cubesigner-like flows pluggable into existing wallet frameworks. hardware market consequences follow. hardware wallets that only expose simple signing may lose relevance. vendors that offer networked hardware modules, secure enclave APIs, or turnkey mpa appliances for custodians get enterprise demand. this also creates new product categories. hosted policy wallets for treasuries, mobile-first ux for multisig co-signers, delegated signing services with rotation, and recovery primitives that preserve legal audit trails instead of exposing raw keys.

gm @fogo fam it also changes counterparty trust. brokers and desks could offer contracts with built-in arbitration clauses that execute automatically if onchain proofs show a breach. risks are cultural and legal. courts may be slow to accept blockchain-native proof, and bad arbitration incentives or biased arbitrators could be gamed without strong governance. mitigation is possible. multi-stakeholder arbitrator pools, rotation, transparent appeal processes, and cryptographic attestation standards raise the credibility of on-chain rulings. make proofs human-readable, time-stamped with native feeds, include full audit trails, and provide encrypted disclosures to authorized regulators or auditors when needed.

gSOUL @0xSoulProtocol fam DeFi redefines finance with crypto-native money markets. But there’s a problem: permissionless design created fragmented liquidity spread across chains, protocols, and even different versions of the same protocol. Result? Capital efficiency bottleneck. Every new chain → new isolated pools. Every new protocol → new silo of liquidity. Even protocol upgrades (v1 → v2 → v3) split TVL further. Collateral gets locked in these silos, borrowing power is capped, and utilization ratios remain suboptimal.

gSOUL fam most people see @0xSoulProtocol as just another defi protocol. but the deeper play? soul is building infrastructure for trust-native capital formation. it’s not just about yield. it’s about making trust a first-class primitive in defi. today, #defi runs on composable money legos. but these legos lack context: who’s behind them, what intent they carry, and why they exist. that’s why ponzis and mercenary flows dominate. soul adds a missing lego: trust-linked capital. every interaction isn’t just an address sending tokens. it’s a relationship carrying weight. that’s infra-level, because it can plug into lending, trading, governance, and beyond.

gm fam most people still think @useTria is just another ai + crypto combo. but that’s not it. tria is building the missing coordination layer between ai agents something even big ai labs can’t do inside their walled gardens. right now, ai models are powerful but isolated. they don’t talk, they don’t cooperate, they don’t share results in an open system. tria flips that by giving agents a common space to interact, verify, and transact all onchain. it’s not about “training a better model” it’s about creating a digital environment where models can work together without relying on a central brain or company. each agent under #tria can have memory, logic, and ownership. they can perform tasks, sign transactions, build reputations, even form micro-economies. that’s a huge leap from today’s ai tools that reset every chat.

gm @UXLINKofficial fam team execution will matter more than investor names. can they actually deliver smooth onboarding and sustainable growth? compared to lens (heavily dev-focused) and friend tech (hype-driven), uxlink seems more pragmatic in building everyday connections. strong backers + strategic partnerships give uxlink credibility. but execution is the make-or-break test. if the team keeps shipping, uxlink could dominate the socialfi sector. if not, it risks fading like many before. what do you think? drop your thoughts below

gm @openmind_agi fam the big story here isn’t the hardware. it’s the network. once multiple robots run on the same open-source os, every new robot becomes another node in a shared intelligence layer. that’s how collective learning begins not in labs, but in the wild. today, every robot brand builds its own brain. it’s expensive, slow, and redundant. openmind’s approach flips that. it gives everyone the same base brain (om1), and lets developers stack new skills on top. less reinvention, more collaboration. this is the same shift that made ai explode in the first place. once open-source models and shared datasets became the norm, innovation started compounding. #openmind is applying that same model to physical intelligence and that’s a huge unlock.

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gm fam governance for $mbg must be a hybrid: onchain mechanisms for economic decisions and transparent advisory councils or legal trusts for binding, offchain actions tied to contracts and regulatory compliance. @multibank_io buyback program concentrates token ownership potential. if the issuer controls large reserves, it can steer votes indirectly. that’s fine if transparent, risky if opaque. ringfenced governance capital, independent trustees for rwA decisions, and legally enforceable onchain-offchain bridges (signed legal frameworks that honor token governance outcomes). those are costly but credible. @Talus_Labs

gm fam network effects matter. as more groups join, @UXLINKofficial becomes the default infrastructure for social coordination in web3. compare this with lens or farcaster, which rely on entirely new platforms. they attract devs, but scaling to mainstream users is harder. uxlink’s focus on real-life scenarios like dining, housing, and transport could pull in non-crypto users through practical use cases. the challenge is keeping farmers from overwhelming the system. link-to-earn works only if real users stay engaged. if #uxlink balances incentives and builds real network effects, it could become the first socialfi platform to break out of the crypto echo chamber.

gMIRA @Mira_Network fam AI error isn’t just a “bug” it’s a fundamental tradeoff in how models are trained. if you tune for precision (less hallucination), you increase bias. if you tune for diversity (less bias), hallucinations spike again. it’s an unsolvable dilemma for any single model. no matter how big the model gets GPT-6, Claude 4, Gemini 3 there’s a minimum error rate that won’t go away. so what’s the fix? not bigger models. but collective intelligence.

gm @SentientAGI fam the risk, of course, is gaming. emissions-based systems can be sybil attacked or skewed by whales. sentient tries to mitigate this with usage weighting and expert reps, but the balance between decentralization and quality control will be tricky to manage. the $sent economy has a real shot at being the first opensource funding mechanism that scales. if it gains traction, we could see a massive shift where top builders choose sentient because it offers both distribution and sustainable revenue. closed labs still dominate in raw capability today, but if sentient’s economy works, opensource ai won’t just catch up, it will outpace. money follows incentives, and this design finally puts the right ones in place. within 2 years, we’ll see artifacts on the grid generating meaningful revenue for their builders. if that happens, expect $sent to evolve from narrative play to critical infrastructure for open ai.

gn fam most people see tria as “yet another ai project” but that’s missing the point. @useTria isn’t trying to compete with openai, it’s building the rails for how ai actually talks to each other in a decentralized way. the internet connected humans. tria connects agents. it’s not about chatbots or prompts, it’s about giving ai systems a shared language, identity, and trust layer so they can collaborate, trade, and evolve on their own. that means every agent can have its own wallet, verifiable reputation, and logic that lives onchain not locked behind some api. tria turns ai into a new kind of network citizen, not just a tool.

gm fam the agent doesn’t need to know what chain it’s on. it just says “move liquidity” and bestpath handles everything. but @useTria isn’t just about liquidity routing. its triai framework lets agents work together sharing resources, optimizing strategies, and executing complex tasks as a swarm. and the coresdk glues it all together automating permissions, wallets, and execution. it’s full-stack ai for multichain operations. suddenly, agents can do more than “analyze” data. they can move capital across chains, find yield, rebalance treasuries, or execute arbitrage automatically and intelligently. to me, #tria isn’t trying to build the smartest agent, it’s building the smartest environment for agents to thrive in. that’s a big difference.

gSOUL @0xSoulProtocol fam DeFi redefines finance with crypto-native money markets. But there’s a problem: permissionless design created fragmented liquidity spread across chains, protocols, and even different versions of the same protocol. Result? Capital efficiency bottleneck. Every new chain → new isolated pools. Every new protocol → new silo of liquidity. Even protocol upgrades (v1 → v2 → v3) split TVL further. Collateral gets locked in these silos, borrowing power is capped, and utilization ratios remain suboptimal.

gn fam @fogo could bake consumer protection primitives into the protocol, making onchain trading safer for retail by default, not as an afterthought fogo already has the building blocks: firedancer-level low latency, near-instant finality, an enshrined dex, native price feeds, curated validators, and colocated liquidity those primitives let the chain measure execution and risk precisely. that measurement can power protections like guaranteed worst-case slippage, pre-trade risk warnings, and realtime fill proofs imagine default-safe wallets. when a user sends a trade, the wallet shows a verifiable worst-case outcome computed from #fogo’s native feed and onchain liquidity depth, not just an estimate

gm fam most people frame @Everlyn_ai as a “video model vs openai/meta” but i think the real comparison is with operating systems. #everlyn isn’t building just a model, it’s quietly laying the groundwork to become the os for video agents. look at the stack: 🔹everlyn-1 as the foundational video model 🔹everworld as the creation engine for interactive video agents 🔹decentralized agent api protocol (aapis) for routing and execution this isn’t just ai, it’s an entire runtime environment. @playAInetwork

gm @fogo fam it also changes counterparty trust. brokers and desks could offer contracts with built-in arbitration clauses that execute automatically if onchain proofs show a breach. risks are cultural and legal. courts may be slow to accept blockchain-native proof, and bad arbitration incentives or biased arbitrators could be gamed without strong governance. mitigation is possible. multi-stakeholder arbitrator pools, rotation, transparent appeal processes, and cryptographic attestation standards raise the credibility of on-chain rulings. make proofs human-readable, time-stamped with native feeds, include full audit trails, and provide encrypted disclosures to authorized regulators or auditors when needed.

gm @Lombard_Finance fam developer impact is big. account abstraction and smart-contract wallets will need to interop with external hardware-backed multisig schemes. expect a wave of standards and sdk work to make cubesigner-like flows pluggable into existing wallet frameworks. hardware market consequences follow. hardware wallets that only expose simple signing may lose relevance. vendors that offer networked hardware modules, secure enclave APIs, or turnkey mpa appliances for custodians get enterprise demand. this also creates new product categories. hosted policy wallets for treasuries, mobile-first ux for multisig co-signers, delegated signing services with rotation, and recovery primitives that preserve legal audit trails instead of exposing raw keys.

gSOUL fam most people see @0xSoulProtocol as just another defi protocol. but the deeper play? soul is building infrastructure for trust-native capital formation. it’s not just about yield. it’s about making trust a first-class primitive in defi. today, #defi runs on composable money legos. but these legos lack context: who’s behind them, what intent they carry, and why they exist. that’s why ponzis and mercenary flows dominate. soul adds a missing lego: trust-linked capital. every interaction isn’t just an address sending tokens. it’s a relationship carrying weight. that’s infra-level, because it can plug into lending, trading, governance, and beyond.

gm fam most people still think @useTria is just another ai + crypto combo. but that’s not it. tria is building the missing coordination layer between ai agents something even big ai labs can’t do inside their walled gardens. right now, ai models are powerful but isolated. they don’t talk, they don’t cooperate, they don’t share results in an open system. tria flips that by giving agents a common space to interact, verify, and transact all onchain. it’s not about “training a better model” it’s about creating a digital environment where models can work together without relying on a central brain or company. each agent under #tria can have memory, logic, and ownership. they can perform tasks, sign transactions, build reputations, even form micro-economies. that’s a huge leap from today’s ai tools that reset every chat.

gm @SentientAGI fam the risk, of course, is gaming. emissions-based systems can be sybil attacked or skewed by whales. sentient tries to mitigate this with usage weighting and expert reps, but the balance between decentralization and quality control will be tricky to manage. the $sent economy has a real shot at being the first opensource funding mechanism that scales. if it gains traction, we could see a massive shift where top builders choose sentient because it offers both distribution and sustainable revenue. closed labs still dominate in raw capability today, but if sentient’s economy works, opensource ai won’t just catch up, it will outpace. money follows incentives, and this design finally puts the right ones in place. within 2 years, we’ll see artifacts on the grid generating meaningful revenue for their builders. if that happens, expect $sent to evolve from narrative play to critical infrastructure for open ai.

gm fam strategic choice for @Lombard_Finance. lean into auditable, institution-ready rails and prioritize adoption by compliant players, or invest in privacy-preserving primitives to broaden retail appeal. both paths change the protocol’s partner set and regulatory trajectory. lbtc’s technical mapping between btc deposits and crosschain mints is more than an implementation detail. it’s a social contract that decides who trusts the system and who stays away. that choice will shape bitcoin’s role in defi.

gm @UXLINKofficial fam team execution will matter more than investor names. can they actually deliver smooth onboarding and sustainable growth? compared to lens (heavily dev-focused) and friend tech (hype-driven), uxlink seems more pragmatic in building everyday connections. strong backers + strategic partnerships give uxlink credibility. but execution is the make-or-break test. if the team keeps shipping, uxlink could dominate the socialfi sector. if not, it risks fading like many before. what do you think? drop your thoughts below

gm @openmind_agi fam the big story here isn’t the hardware. it’s the network. once multiple robots run on the same open-source os, every new robot becomes another node in a shared intelligence layer. that’s how collective learning begins not in labs, but in the wild. today, every robot brand builds its own brain. it’s expensive, slow, and redundant. openmind’s approach flips that. it gives everyone the same base brain (om1), and lets developers stack new skills on top. less reinvention, more collaboration. this is the same shift that made ai explode in the first place. once open-source models and shared datasets became the norm, innovation started compounding. #openmind is applying that same model to physical intelligence and that’s a huge unlock.

gm fam governance for $mbg must be a hybrid: onchain mechanisms for economic decisions and transparent advisory councils or legal trusts for binding, offchain actions tied to contracts and regulatory compliance. @multibank_io buyback program concentrates token ownership potential. if the issuer controls large reserves, it can steer votes indirectly. that’s fine if transparent, risky if opaque. ringfenced governance capital, independent trustees for rwA decisions, and legally enforceable onchain-offchain bridges (signed legal frameworks that honor token governance outcomes). those are costly but credible. @Talus_Labs

gm fam network effects matter. as more groups join, @UXLINKofficial becomes the default infrastructure for social coordination in web3. compare this with lens or farcaster, which rely on entirely new platforms. they attract devs, but scaling to mainstream users is harder. uxlink’s focus on real-life scenarios like dining, housing, and transport could pull in non-crypto users through practical use cases. the challenge is keeping farmers from overwhelming the system. link-to-earn works only if real users stay engaged. if #uxlink balances incentives and builds real network effects, it could become the first socialfi platform to break out of the crypto echo chamber.

gMIRA @Mira_Network fam AI error isn’t just a “bug” it’s a fundamental tradeoff in how models are trained. if you tune for precision (less hallucination), you increase bias. if you tune for diversity (less bias), hallucinations spike again. it’s an unsolvable dilemma for any single model. no matter how big the model gets GPT-6, Claude 4, Gemini 3 there’s a minimum error rate that won’t go away. so what’s the fix? not bigger models. but collective intelligence.