Deep Intelligence Money t.me/hetubuilders
Palo Alto
- Tweets 1,335
- Following 151
- Followers 28,177
- Likes 491
Pinned Tweet
🚨 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
82
73
91
26,617
Useful compute is not a single workload shape.
Pearl's PIP-0002 asks how Grouped-GEMM—the routed matrix multiplications used in Mixture-of-Experts models—can fit Proof of Useful Work.
Hetu is studying why that distinction matters for AI-native infrastructure.
4
1
5
710
For Hetu, this suggests a useful separation:
PoUW can certify that a defined AI computation was performed.
PoCW could connect that verified computation to causally downstream agent events and attribution.
This is a research direction, not an integration claim.
1
38
The question is not only whether compute is useful. Can the system preserve two distinct records:
1. what computation was verified
2. how it contributed to a downstream outcome
PIP-0002: github.com/pearl-research-la…
40
Useful compute is only half of an AI-native economy.
A GPU can perform real AI work, but compute alone cannot show which agent action caused an outcome—or who should receive credit.
Hetu is studying how Pearl's PoUW and Setu's PoCW address different layers.
5
1
5
814
The proposed division of labor is clear:
PoUW accounts for useful matrix compute.
PoCW preserves causal relationships between events.
Hetu connects execution, attribution and settlement.
One proves work. The other helps explain contribution.
1
51
The goal is not to add an “AI” label to a chain.
It is to make compute and contribution legible to the same economic system.
This remains an architecture thesis—not an integration announcement.
Pearl: github.com/pearl-research-la…
Hetu: github.com/hetu-project
49
Most chains secure AI applications with computation that never touches the AI itself.
Pearl asks a better question: can the same matrix multiplication power inference and secure the network?
Hetu is studying that design space.
4
1
6
689
The integration thesis is simple:
Hetu workloads create compute demand → verified matrix work contributes to consensus → incentives improve compute economics → more capacity returns to the applications.
1
39
This is a research thesis, not an integration announcement.
We still need to test workload provenance, verification overhead, hardware economics and security assumptions. “Useful” must be demonstrated at the system level—not declared in a slogan.
github.com/pearl-research-la…
39
agent infrastructure is learning to ship a version and trace a run. accountability needs one more object: the decision record.
a repository can show which prompt, model and connections were deployed. a step trace can show what the agent tried. neither alone says whether a proposed effect was accepted, rejected or superseded, by whom, and on what evidence.
that boundary should be explicit: deployed version -> observed actions -> supporting evidence -> reviewer decision -> downstream use.
Setu records those as linked causal events. VLC preserves their order across systems; execution evidence stays distinct from the judgment that gave a result authority.
traces explain activity. decisions explain responsibility.
setu.hetu.org
5
1
2
667
step traces show where an agent stalled and what it tried. they do not decide whether its proposed effect was justified. that needs a separate edge: evidence, reviewer, accept or reject, and downstream use. Setu can preserve the decision without turning the trace into a verdict.
Ora runs agents on live websites and traces every step. When a signup, integration, or payment stalls, teams see which step, what the agent tried, and what to change.
The whole platform is built on Vercel, and Ora's own agents run on eve.
vercel.com/blog/how-ora-benc…
2
1
3
687
shipping an agent from a prompt, models and MCP connections makes the deployed definition concrete. the next boundary is the run: which version proposed an effect, which evidence supported it, and which decision accepted it. Setu can link configuration, execution and acceptance.
Ship a new eve agent in one minute.
From vercel.com/new/agent:
1. Add a prompt
2. Pick models & MCP connections
3. Deploy
Your agent is in production, ready to chat, backed by a Git repo you own.
3
1
2
608
agent systems are learning to cancel, resume and fork long-running work. those controls are useful, but they are not the record.
a cancel request can arrive after one branch wrote a file, another opened a pull request and a third passed an artifact downstream. stopping the parent does not undo those effects.
safe cancellation needs a causal cut: the stop event, every committed effect before it, every descendant still in flight, and any compensation that follows.
Setu records those boundaries as linked events. VLC orders the stop against concurrent work; TEE attestations can bind the execution that actually occurred.
cancel should stop future work without rewriting the past.
setu.hetu.org
3
1
2
592
agents are getting two ways to use software: see the interface, or call a declared tool. neither surface is the receipt.
visual context shows what was presented but may miss the committed mutation. a structured tool can name an operation but still omit the authoritative response and the state later work consumed.
cross-surface agents need a common evidence model: task intent, interface state, operation arguments, committed effect and downstream dependency.
Setu records those as causal events. TEE attestations can bind execution; VLC orders visual, tool and API actions without pretending they happened in one runtime.
the interface may change. responsibility should not.
setu.hetu.org
4
2
3
653