I’m working on a system called Presence. It’s an experiment in distributed inference using multiple ESP32-class devices, not a cloud multi-agent framework and not a theoretical model.
Each node runs a lightweight AI process locally (ESP32 / microcontroller constraints). Nodes are specialized by role (e.g. physics, math, engineering, control logic). A coordinator aggregates their outputs over the network and produces a final response along with hard metrics.
The core question is simple:
What happens when you spread reasoning across many cheap, low-power devices instead of one large model?
What Presence actually does
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Nodes are physically separate devices (ESP32 or equivalent)
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Each node runs independently and asynchronously
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No shared memory, no hidden state
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Communication is explicit and lossy by design
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Aggregation is deterministic and observable
What gets measured per query
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total latency (network + inference)
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token / compute usage per node
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which nodes participated
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confidence (bounded, scalar)
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field coherence (agreement vs complementarity)
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optional per-node reasoning trace
Observed behavior (example)
A complex engineering prompt was run as:
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single-node baseline
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distributed across 3 ESP32 nodes
Results:
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confidence improved ~15%
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responses covered more constraints and failure modes
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latency and energy cost increased significantly
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coherence was medium (expected for heterogeneous domains)
This isn’t “emergent intelligence” or anything mystical. It’s tradeoffs:
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better coverage vs slower response
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redundancy vs power consumption
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robustness vs bandwidth
Why ESP32 matters
This is not about scaling up models.
It’s about scaling out intelligence into environments where:
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cloud access is unreliable or impossible
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power is limited
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devices fail
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intelligence must exist at the edge
Think sensor networks, robotics, autonomous infrastructure, field systems… not chatbots.
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PRESENCE SWARM INTELLIGENCE TEST
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Initializing PRESENCE swarm…
Creating specialized nodes…
✓ Physics specialist (ID: 06eb482d)
✓ Mathematics specialist (ID: 17879c3d)
✓ Engineering specialist (ID: a02de710)
Network formed:
Nodes: 3
Entanglements: 3
Query: Design a rocket propulsion system for a Mars mission
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TEST 1: Single Node (Baseline)
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Latency: 10.13s
Tokens: 813
Confidence: 0.700
Answer:
Designing a rocket propulsion system for a Mars mission involves several considerations, including the mission profile, the payload, the type of propulsion system, and the specific challenges posed by the Martian environment. Below is an outline of a propulsion system design that could be suitable for a crewed Mars mission.
### Mission Profile
1. **Mission Objectives**:
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Transport crew and cargo to Mars and return to Earth.
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Conduct surface operations on Mars.
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Ensure safety and r…
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TEST 2: Swarm Intelligence (3 Nodes)
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Latency: 38.30s
Tokens: 3748
Confidence: 0.852
Field Coherence: 0.649
Contributors: a02de710, 17879c3d, 06eb482d
Answer:
Designing a rocket propulsion system for a Mars mission is a multifaceted challenge that combines insights from engineering, mathematics, and physics. Each discipline contributes unique perspectives and methodologies that are essential for developing a robust, efficient, and reliable propulsion system.
### 1. Propellant Selection and Performance Efficiency
The choice of propellant is critical to the mission’s success, influencing both performance and mass efficiency. Analyzing the specific impu…
Reasoning Steps:
[engineering]
Designing a rocket propulsion system for a Mars mission is a complex challenge that requires a multidisciplinary approach, combining mechanical, electrical, and systems engineering principles. Here ar…
[mathematics]
Designing a rocket propulsion system for a Mars mission involves intricate calculations and modeling across various mathematical domains. Here are 2-3 key insights from my mathematics expertise that c…
[physics]
Designing a rocket propulsion system for a Mars mission involves several critical considerations rooted in physics. Here are three key insights:
1. **Propellant Efficiency and Specific Impulse (Isp):…
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COLLECTIVE INTELLIGENCE ANALYSIS
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Confidence improvement: +15.2%
Single: 0.700
Swarm: 0.852
Field Coherence: 0.649
→ Medium coherence: Complementary viewpoints
Token usage:
Single node: 813
Swarm: 3748
Overhead: 2935 tokens
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SWARM STATUS
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Nodes: 3
Entanglements: 3
Total queries: 1
Swarm queries: 1
Total cost: $0.0015
TEST COMPLETE
KEY INSIGHT:
Swarm intelligence combines multiple specialized perspectives into a more comprehensive answer than any single node could provide.