Idea Acceleration Engine

A methodology for compounding ideas through structured batches.

Methodology → · Prompts → · Case Studies →

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The Idea Acceleration Engine (IAE) is a structured ideation framework. You run it in batches. Each batch generates 25 numbered vectors across five clusters. Vectors are numbered globally — Batch 001 starts at #1, Batch 002 continues from #26, and so on. The numbering creates a corpus that compounds over time.

Three things distinguish IAE from a generic brainstorming list:

1. Reactive, not predictive. Vectors anchor in evidence already visible. Speculation about what might happen is replaced with extrapolation of what's already in motion.
2. Conviction mining. Periodically, you audit the corpus for repeated patterns. Repeated ideas across batches signal an operating conviction worth naming. Repeated unbuilt ideas are stuck convictions that need scheduling, not more ideating.
3. Money Angle as a discipline. Every batch ends with a callout for monetisable vectors — but only if they genuinely exist. The forcing function keeps the corpus action-oriented.

Case studies

#	Date	Domain	Vectors	Notes
01	May 2026	AI Landscape (external corpus)	#101 – #110	First application of IAE to an external artefact rather than personal corpus

How to apply IAE

1. Read METHODOLOGY.md end to end. It's not long.
2. Decide your cluster scheme. The default five work for most domains; deviate only with reason.
3. Use prompts/batch_template.md to run your first batch.
4. After every 3–4 batches, run prompts/conviction_mining_template.md against your corpus.
5. Publish a case study back as a PR if you want — case studies strengthen the methodology.

License

Code (the runnable prompt templates): MIT. Methodology content (the docs, case studies, written analysis): CC-BY-4.0. Attribution requested when applying publicly.

Origin

This framework was developed by Ram across four batches of personal ideation (March – April 2026), reaching 100 vectors before being formalised here. Batch 005, applied externally to the AI landscape, was the first instance of IAE used as a tool for outward-facing analysis — and the genesis of this repo.