manifest · ecosystem

AI Mindset
Manifest

Build an AI operating system: prompt → attention → mind → life {engineering}. Start with a Context Engine — then scale into automation and teams.

ai tools

attention

creative

community

teams

// AI MINDSET MANIFEST (v1)
prompt = "make my week clearer";
return model(prompt);

Product map

Simple human language. Clear entry points. Links to everything.

Stack

Frames

Legend: community · sprint · foundation · automation · partner · teams · premium

Base (catalog)

One place with the full ecosystem: programs, one-pagers, links.

Open Base

AI Mindset Stacks

In development: a living library of practitioner stacks — tools + story + workflows + video demos. Turns Space demos into permanent assets and a discovery layer for the ecosystem.

Format: tools + story + workflows + video demos
Loop: Space demo → Stack profile → discovery → Space

Circle

2 months after labs: accountability + peer learning + support.

Base

Coaching Lab

6-week premium program: personalized workflow per participant. Ship outcomes, not knowledge.

Limited cohort

Personal Consulting

Deep work on strategy, context engineering, and systems.

Base

Loyalty Program

Alumni discounts + referrals.

Base

What is AI Mindset

Not a course about tools. A practice environment where you build an AI operating system for your work — one loop at a time.

Core craft: attention systems (context engines) that make prompts, workflows, and agents reliable.

A Context Engine turns messy signals into structured context + queryable memory — so automation is not a lottery.

You leave with artifacts: a dataset, operating principles, and one repeatable loop.

Attention > Prompts

A prompt without attention is a lottery. Reliability starts when AI sees your decisions, calendar, and constraints.

Systems > Hacks

We build stable loops: input → context → action. Prompts are the UI; memory + agents are the system.

Meaning > Noise

AI should amplify focus, not produce more chaos. The purpose is human. The machine is the amplifier.

attention · engines

Context engines

A Context Engine is your attention system: it turns messy signals into structured context, repeatable decisions, and agent-ready memory.

4 layers

Capture — signals → inbox
Distill — summary → decisions
Store — KB + dataset
Retrieve & act — agents → outcomes

Why it matters

Prompts are fragile. Context engines are upgradeable. Agents become safe and useful only when they are grounded in your engine.

// CONTEXT ENGINE (v1)
capture  = inbox.pull("signals");
distill  = model.summarize(capture);
store    = kb.write(distill);
retrieve = memory.query(store);

agents.attach(retrieve);
return decisions.make(retrieve);

creative · art/music

Creative track

Use art and music as an attention engine: ship artifacts, not content. (Lamp of music.)

What you build

1–3 creative artifacts (music / visuals / narrative demos)
A repeatable loop: idea → prompt → artifact → critique → iterate
A small library of prompts + templates

Stack + frames

Stack: Suno/ElevenLabs · Midjourney/Runway · Obsidian (ideas + datasets) · Cursor (demos).

Frames: constraints → iteration → publish. Make it small, then make it real.

// CREATIVE (v1)
prompt = "make a song";
return model(prompt);

for teams

B2B is teams mode

Not a separate corporate course. Pilot-first teams programs layered on labs: shared context engine, reusable workflows, working prototypes.

// PILOT-FIRST
+pilot(team = 3..10)
+  → deliverables (processes, agents, templates)
+  → scale(org)
+
+focus = "AI Operating System"  // not "AI tools training"

Team Track

3–5+ people go through the lab together. Extra team sessions. Shared artifacts.

Pilot

Start small. Prove ROI. Then scale.

Contact

Email: info@aimindset.org

Contact founder →

AI Mindset {space}

{space} is the weekly practice hub: demos, co-working, give-get. This is where the ecosystem becomes visible.

Give-get principle: you come to contribute, not to consume content.

Trajectory: {space} → {society} → {state}. Start with weekly demos, evolve into a case library, then scale into teams/founder OS.

Join {space}

// {space} LOOP (v1)
demo()
co_work()
rule = "give-get"

How to start

Pick your entry: media → {space} → 2-week sprint → Winter Lab. Then build the next layer.

New to AI Mindset

Start with {space} → then a sprint → then Winter Lab.

Technical

Winter Lab → Agents Lab.

Founder / Team lead

Foundation → Team Track / Premium.

Media layer

Demos, announcements, and drops.

Telegram YouTube

Start / Contact

Join {space}, pick a lab, or talk to the founder. For teams: info@aimindset.org.

Join {space} Winter Lab W26 Context Agents Lab Contact founder

AI MINDSET {manifest} v2 · source-of-truth in Obsidian

AI Mindset Manifest