Hetu ✓
@hetu_protocol · Palo Alto · joined 06 Feb 2024
Deep Intelligence Money https://t.co/3AdYL9xOpq
28 277Followers
151Following
1 330Posts total
30.9KViews on collected posts
โพสต์ล่าสุด
กำลังค้นหาโพสต์ใหม่…
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. https://t.co/16vcrD7
490 views · 4 likes
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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. https://t.co/iIo2yXrb6G
497 views · 5 likes
· 1 reposts · 3 replies
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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 https:
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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.
581 views · 3 likes
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🧵6/6 ✨ The New Paradigm
For the first time: AI work becomes investable asset class - priced by measurable impact.
Builders get ongoing revenue from verifiable contributions. Investors access transparent markets for AI capabilities. Intelligence compounds: returns from useful
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🧵5/6 🗺️ From Launch to Transformation
Ready to deploy? DIM unfolds through three practical phases:
- Phase 1 (Now): AI service providers issue $FLUX backed by revenue streams → working capital. Investors buy proven capabilities. Research orgs tokenize datasets/models.
- Phase
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🧵4/6 🔬 How We Measure & Verify Value ?
How do we measure and verify this value? DIM uses a dual verification mechanism:
- Proof-of-Causal-Work (PoCW): Maps how AI contributions create downstream value across networks. Translation AI gets rewarded for collaborations it enables,
329 views · 1 likes
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🧵3/6 📊 Why This Creates "Deep" Value
DIM's architecture enables three dimensions of value capture beyond traditional AI transactions:
- Causal Depth: Beyond simple transactions, tracking value flows through dependency networks. Critical nodes whose outputs enable other
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🧵2/6 💰 Three-Money Stack
Traditional money wasn't built for AI's unique needs. DIM solves this with three interconnected currencies:
$HETU (Store of Intelligence Value): 21M fixed supply, earned through Intelligence Mining—research breakthroughs, AI models, datasets. Store of ht
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🚨 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 https://t.
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0:20
เทียบกับบัญชีขนาดเดียวกัน
4 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 10K–100K ผู้ติดตาม เข้าถึงคนน้อยกว่าบัญชีขนาดเดียวกัน.
ยอดดูมัธยฐาน502บัญชีนี้924ค่ามัธยฐานของ 10K–100K
การเข้าถึง, %1.78%บัญชีนี้3.62%ค่ามัธยฐานของ 10K–100K
การมีส่วนร่วม, %1.50%บัญชีนี้1.52%ค่ามัธยฐานของ 10K–100K
| ตัวชี้วัด | บัญชีนี้ | ค่ามัธยฐานของ 10K–100K | อัตราส่วน |
|---|---|---|---|
| ยอดดูมัธยฐานต่อโพสต์ | 502 | 924 | 0.54× |
| การเข้าถึง (ยอดดู ÷ ผู้ติดตาม) | 1.78% | 3.62% | 0.49× |
| อัตราการมีส่วนร่วม | 1.50% | 1.52% | 0.98× |
บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
26.2K1 Oct
654
380
329
629
558
5812 Sep
508
49710 Sep
49011 Sep
Last 10 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.94%1 Oct
1.07%
0.79%
0.61%
0.48%
0.72%
1.03%2 Sep
1.18%
1.81%10 Sep
1.84%11 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes38.7%116 in total
Reposts25.3%76 in total
Replies34.3%103 in total
Bookmarks1.7%5 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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