Hevolve AI: Self-Evolving Multimodal AI Agents

Turn your domain expertise into AI agents that keep learning. Hevolve AI lets experts build multimodal AI systems by talking to them and correcting them in real time, with no code to write.

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Is the network getting better?

Every node broadcasts a signed delta and every receiver verifies it before counting it. This shows what was counted and how many nodes it came from. Measured figures and projections are labelled separately, and a projection with no basis is not shown.

Genuine installs

7

cumulative, gate-verified
Reporting now

4

active within 60 min
Counted in the mean

0

2 stale, excluded
Collective index

--

knowledge capacity, not capability
Growth per join

--

above 1.0 is compounding
Next threshold

6 nodes to go

Ten nodes: Expert routing across models with different blind spots.

Stages are a hypothesis from hive_benchmark_prover.py, unmeasured. The node count is measured.
Projection
Not enough nodes to project
A growth rate needs at least two joins to exist. One node is a reading, not a trend, and drawing a line through it would be invention.
The ladder
Seven stages as written in the source, with where the hive actually is. Every score in the original is a projection; none has been measured.

1

One node

A single model, running locally.

here

3

Three nodes

The first threshold: does the sum beat the single?

here

10

Ten nodes

Expert routing across models with different blind spots.

100

A hundred

Network mixture-of-experts.

1,000

A thousand

Generate, review, test as separable roles.

10,000

Ten thousand

Hive learning compounds across the population.

100,000

A hundred thousand

Beyond what one model does.

Per node
The rows the totals came from. Recompute them yourself if you hold the same deltas; that is why they are here.
NodeIndexGrowthAgentsLast heard
2c48f50bdf6b (this node)----07s ago
3f67ab428d81----0119414s ago
stale
e6e957fdc7ba----0129234s ago
stale
fee9f14a481f----0208s ago
The index is a knowledge-capacity figure from concept-graph topology: log2(paths + 1) x (1 + depth/10) x (learned/concepts). It measures what the graph can express, not how well a node answers. Nodes past the freshness window are listed and excluded from the means. Refreshes every 30 seconds.