ExpertSwarm

prototype

Concept Inspired by AutoResearch

ICP Definition

Target Role / Title

Industry

Pain Points

Buying Triggers

Tone / Voice

Scoring Rubric

Evaluation Dimensions (weighted) The fixed "eval function" — like prepare.py's evaluate_bpb

ICP Relevance 25%

Pain Point Specificity 25%

Clarity & Readability 20%

Call-to-Action Strength 15%

Urgency / Motivation 15%

Content Type

Max Iterations

Content

v0 — baseline

Ready — configure ICP and click Start

0 / 10 iterations

Experiment Log

—

ICP Score

—

Best

—

Improvement

Relevance

—

Pain Points

—

Clarity

—

CTA

—

Urgency

—

Experiments will appear here as the optimizer runs.

Each iteration: hypothesis → rewrite → score → keep/discard

Optimization Progress

Click "Start Optimization Loop" to begin

The AutoResearch Pattern for Content

Inspired by Karpathy's AutoResearch, this applies the same autonomous experimentation loop to content optimization:

AutoResearch	ExpertSwarm
train.py	Your content
val_bpb metric	ICP alignment score
prepare.py (eval)	Scoring rubric + LLM judge
program.md	ICP definition + strategy
5 min GPU time	~10 sec per iteration

The loop: modify content → score against ICP rubric → keep if improved, discard if not → repeat. Each change is evaluated against the same fixed standard, preventing drift.