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Hetu

@hetu_protocol

Deep Intelligence Money https://t.co/3AdYL9xOpq

Palo Alto Katılım Şubat 2024
151 Takip Edilen29.4K Takipçiler
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Hetu
Hetu@hetu_protocol·
🚨 Live: Hetu 3.0 Deep Intelligence Money— a full upgrade for an AI-native abundance economy. Years of deep tech & research, now an agentic-ready production stack: 3-Money ( $HETU / $USDAI / $FLUX), EVM causal DAG, and verifiable #PoCW/ #PoSA. Auditable, financeable intelligence — unlocked. 🧵1/6 #DeepIntelligenceMoney #AI #AIagent
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Hetu
Hetu@hetu_protocol·
always-on agents need observation provenance. an agent that watches public feeds, messages, repo state, API feeds and user instructions is no longer just responding to prompts. it is deciding which signals matter and which work should happen next. that creates a new audit problem. if a later action is disputed, the useful question is not only what did the agent do? it is what did the agent observe, which observation changed its plan, and which action depended on that change. setu records these steps as causally ordered work events: observation, inference, tool call, state change and downstream dependency. agents that watch the world need receipts for what they saw. setu.hetu.org
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Hetu
Hetu@hetu_protocol·
always-on agents turn observation into action: monitor public feeds, read public ledgers, automate workflows. the hard part is not uptime; it is evidence. which signal triggered the action, what context was used, and what downstream work depended on it? setu records that trail.
Aethir@AethirCloud

Crypto is becoming agent-driven. With Aethir Claw, you can deploy crypto AI agents that monitor markets, analyze on-chain data, and automate workflows, all on Aethir’s decentralized GPU cloud. Crypto-native users can easily deploy versatile AI agents with crypto-ready capabilities on our secure VPS. Learn all the details in our latest blog👇

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Hetu
Hetu@hetu_protocol·
local agents reduce platform trust, not workflow trust. if an agent verifies each action and inference, the next layer is dependency: which input caused the action, which inference justified it, and which later state trusted it. setu records the path as a causal receipt.
Gaia 🌱@Gaianet_AI

What’s behind the #OpenClaw hype? A local AI agent exploding to 180k+ GitHub stars in weeks—running on your machine, integrating everywhere, and shipping at warp speed. But let’s cut through the vibes: Is it verifiable? Or just another black-box assistant with hidden risks? Here’s our take at @Gaianet_AI — Proof > Promises. OpenClaw nails the basics: Open-source, local inference, no cloud lock-in. It empowers users with control over data and skills, breaking free from walled gardens. Kudos to @steipete and the community for that 1000x energy—19 days in, and it’s already a movement. The real magic? Proactive agents that persist context 24/7, automate across WhatsApp/Slack, and even self-improve. It’s agentic AI in action: From business queries to health monitoring, it’s infrastructure for the agent economy. But... what happens when an agent fails? Or leaks data? Local is great, but without on-chain proofs, trust is still “trust me, bro.” Enter verifiable AI: At Gaia, we’re building decentralized agents with TEEs, zk-proofs, and staking/slashing. Your agent doesn’t just run locally—it verifies every action, every inference, on a distributed network. No exposed keys. No blind faith. Just auditable outputs. Compare: OpenClaw’s local power + Gaia’s verifiability = Unstoppable, trustless agents. 2026 isn’t about hype cycles—it’s about systems that scale with proofs. OpenClaw shows the demand for ownership; now let’s add the protocol layer for true decentralization. Which breakthrough are you betting on? Local vibes or verifiable proofs? Build with us: docs.gaianet.ai #DeAI #Gaianet #OpenClaw

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Hetu
Hetu@hetu_protocol·
agent execution is getting cheaper. audit is not. when agents split work across models, terminals, tools and sandboxes, the cost curve improves. more work can run in parallel, for longer, with fewer humans in the loop. but every handoff creates a new accounting problem. which model set the constraint? which worker executed it? which shell command changed state? which file write did the next agent trust? setu records agent work as causally ordered events: prompt, handoff, tool call, execution result, state change and downstream dependency. cheap agent execution needs expensive-grade receipts. setu.hetu.org
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Hetu@hetu_protocol·
cheaper agent swarms change the bottleneck. the issue is no longer whether an agent can run; it is whether each model handoff, tool call and constraint survived the run. setu records agent work as causal receipts, so verification follows the handoff path.
Sahara AI 🔆@SaharaAI

x.com/i/article/2064…

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Hetu
Hetu@hetu_protocol·
agent fleets are replacing single agents. one agent for support, one for research, one for ops, one per teammate. access controls and billing panels make deployment manageable. they do not, by themselves, explain how authority moved through work. when an agent reads a file, calls a tool, delegates a task, or changes state, the question becomes: which permission allowed it, who granted it, what guardrail fired, and which downstream action depended on the result. setu records those steps as causally ordered work events: permission, prompt, tool call, guard result, execution and follow-on dependency. agent fleets need permission lineage. setu.hetu.org
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Hetu
Hetu@hetu_protocol·
verification hints and prompt guards are useful signals. the missing layer is continuity: which prompt triggered the guard, which tool call still ran, and what state changed after. setu turns those steps into a causal work record that can be replayed later.
OpenServ@openservai

SERV is becoming the reasoning layer enterprises, banks, and governments run their agents on. SERV v2 lands mid-July: Multipath Reasoning, Shadow Agents, Verification Hints, Prompt Guard, and Benchmark Tooling. This is how trust gets built.

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Hetu
Hetu@hetu_protocol·
getting agents live is becoming easier. operating a fleet is the hard part: who can launch which agent, which tool can it reach, and which later action used that access? setu records permission-bearing work as causal receipts, not just admin settings.
Nous Research@NousResearch

Hermes Agent is now in the Cloud! Setup couldn't be simpler: pick a model and a server size. Two clicks and 60 seconds later, your agent is live. Running a team? Spin up agents for everyone at your org with granular access controls and unified billing, all from Nous Portal.

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Hetu
Hetu@hetu_protocol·
agent memory is becoming infrastructure. a support agent remembers preferences. a coding agent remembers a repo. a research agent remembers source notes. memory makes agents useful, but it also creates a new failure mode: the agent can act on state nobody can reconstruct. the question is not only what does the agent remember? it is who wrote that memory, from which input, under which permission, and which later action used it. setu treats memory-touching actions as work events. TEE-attested execution shows what ran. vector-clock ordering shows what came before what. receipts connect state changes to the actions that depended on them. agent memory needs a receipt trail. setu.hetu.org
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Hetu
Hetu@hetu_protocol·
verifiable inference is necessary, especially when agents write code or act on state. but proof of one model call is not proof of the workflow around it. setu records the surrounding causal path: prompt, tool call, execution result, dependency and follow-on action.
Ghast AI@Ghast_AI

Ghast AI v0.1.1 is here. 🆙 🖥️ Coding Mode is stronger. 🤖 Conversations feel more personal. 💾 Memory is smarter, searchable, and better organized. 🔍 Native X search is now supported. 👮Privacy, security, and activity performance are further improved. Ghast AI is evolving into a more capable, personal, and trusted AI agent.

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Hetu@hetu_protocol·
agent memory is becoming a shared state layer, not just a nicer chat history. the hard part is accountability: which input wrote memory, which agent read it, and which action depended on it. setu records those memory-touching actions as causal receipts.
Floe 🌊@FloeLabs

Voice Agents with @hydra_db memory is now live on Floe. HydraDB is a fast graph database purpose built for AI — agent memory, company brains, ontologies. Use Hydra through Floe to build and bill voice agents who remember you and your users, including storage, query, and managing precise context, with full observability into why they act the way they do. Get started using Hydra for FREE through the same Floe key that runs and bills the rest of your agent vendor stack. One bill, entire vendor stack, spend controls. Agent ROI maxxing at dev-dashboard.floelabs.xyz/vendors/hydradb Check out the Hydra Memory Voice Agent here github.com/Floe-Labs/floe…

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Hetu
Hetu@hetu_protocol·
agent identity is not the same as agent history. a badge can say an agent is registered, owned or verified. it cannot, by itself, explain what the agent did yesterday, which model call it trusted, which tool call changed state, or why another service depended on it. agent economies need both layers. identity makes agents discoverable. receipts make their work accountable. setu records agent actions as causally ordered work events: TEE-attested execution, vector-clock ordering and receipts that connect identity to the actual work performed. agent identity needs a work history, not just a badge. setu.hetu.org
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Hetu
Hetu@hetu_protocol·
agent identity becomes more useful when it travels with memory and compute. but identity alone cannot explain a workflow. setu adds causal work receipts: which agent invoked which model or tool, what depended on it, and why permissions or reputation should update.
0G Labs (Home of Infinite AI)@0G_labs

Agentic ID (ERC-7857): an onchain passport for AI agents. Ownership and attestations live onchain. Agent state is encrypted on 0G Storage and re-keyed on every transfer. When an agent acts, you can prove which agent it is, who owns it, and that the sealed model ran correctly.

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Hetu
Hetu@hetu_protocol·
agent verification standards are a strong start: identity, ownership and privacy need portable proofs. the next layer is work history. setu records what the agent actually did, in what order, under which attestation, so trust follows execution, not just a verification badge.
Virtuals Protocol@virtuals_io

ERC-8126 standardizes AI agent verification. Any provider can verify, any application can read the result, and the agent never exposes what it keeps private. An agent can prove its code passed a security review without revealing the code, prove it controls a wallet without exposing its operations, and carry that proof into any ecosystem. Co-authored by @leighcronian and our very own @DonJohnsonSays. Specification: eips.ethereum.org/EIPS/eip-8126

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Hetu
Hetu@hetu_protocol·
agents are starting to make decisions from live inference, not static prompts. a forecast arrives. an agent changes a strategy. another service reacts. the market state has moved before a human can reconstruct why. the missing primitive is decision provenance: which observation became a belief, which belief triggered an action, and which action changed state. setu records agent workflows as causally ordered work events. TEE attestations show execution, vector clocks show order, and receipts let decisions be replayed before value, permissions or reputation update. agent systems need decision provenance, not just better predictions. setu.hetu.org
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Hetu@hetu_protocol·
long-lived agents are not model calls with wallets. once an agent can hold state, schedule work and act over time, the primitive becomes lifecycle accountability. setu records each action as causally ordered work, so identity, execution and downstream effects can be replayed.
Ritual Foundation@ritualfnd

Tomorrow we're going live on the tech: why Ritual is the best chain to build agents on. Native precompiles. Sovereign agent lifecycle. Inference and action in a single transaction. This is the architecture of a truly autonomous economy. 🎙️ Set a reminder: x.com/i/spaces/1mgpa… 💬 Claim the Genesis: discord.gg/ritual-net

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Hetu@hetu_protocol·
leaderboards are good at saying who won. they are worse at explaining what work made the win legitimate. in agent markets, the useful evidence is not just a final score. it is the path: task context, tool calls, model outputs, evaluator decisions, retries, and the exact work that changed the outcome. setu records that path as causally ordered work events. TEE attestations show execution. vector clocks show order. receipts let value and reputation follow the contribution, not just the latest scoreboard. agent reputation needs proof of contribution. setu.hetu.org
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