hivemind
Distributed AI worker system
Run autonomous AI workflows from the terminal.
Demo
terminal
$ hivemind run "analyze diffusion model papers"
> spawn worker[1]
> spawn worker[2]
> spawn worker[3]
>
> worker[1] gathering sources
> worker[2] summarizing architecture
> worker[3] extracting math
>
> aggregating results...
>
✓ synthesis complete
Problem
Single-agent systems hit limits quickly. Complex tasks need multiple agents, shared memory, and orchestration.
What it is
hivemind is a distributed AI worker system where:
- tasks are decomposed
- agents collaborate
- memory is shared
- results converge
Features
Distributed Workers
Multiple agents run in parallel.
Shared Memory
Agents read and write shared state.
Task Pipelines
Decompose and orchestrate workflows.
Tool Usage
Agents call tools and APIs.
CLI Control
Run from the terminal.
Architecture
User Task
Orchestrator
worker[1]
worker[2]
worker[3]
Shared Memory
Final Output
Example result
Task
analyze diffusion model papers
→
Synthesis (agents)
## Diffusion model papers — synthesis
**Architecture (worker[2])**
Common pattern: noise schedule → UNet backbone → conditioning embedding.
DDPM vs DDIM: discrete vs continuous steps.
**Math (worker[3])**
Key equations: forward process q(x_t|x_0), reverse process p_θ(x_{t-1}|x_t).
Score matching objective.
**Sources (worker[1])**
Ho et al. (DDPM), Song et al. (DDIM), Rombach (LDM).Why I Built This
I wanted multi-agent systems that behave like distributed computation instead of simple chat loops.
hivemind lets agents collaborate, spawn work, and converge on results.