// Assignment Solver

PASTE YOUR ASSIGNMENT / PROBLEM / PROMPT

PROBLEM DOMAIN

// Structured Output READY

Paste your assignment above and hit Generate. The engine will break it using: → First Principles Thinking → Systems Analysis with feedback loops → AI-Human division of labor → Startup-grade execution plan → Measurable metrics + next experiments

// Systems Thinking Architecture

🔁 Core Feedback Loop Model

Every business problem has a feedback loop. Map it before solving it. The structure below is a universal template — identify which node is your bottleneck.

INPUT / TRIGGER

What enters the system? User action, market signal, capital, demand.

PROCESSING ENGINE

Core mechanism that transforms input → output. Product, ops, team.

OUTPUT / SIGNAL

Revenue, retention, NPS, churn, viral coefficient, activation rate.

// Bottleneck Identification Framework

TYPE A

Supply Constraint

Can't deliver fast enough
Ops / fulfillment is the chokepoint
Hiring or capacity problem
Fix: automate or outsource core ops

TYPE B

Demand Constraint

Not enough qualified users entering
Distribution or awareness problem
ICP mismatch or messaging gap
Fix: sharpen positioning, find channel

TYPE C

Conversion Constraint

Traffic exists but doesn't convert
Value prop unclear or trust missing
Onboarding or activation failure
Fix: reduce time-to-value, reframe offer

TYPE D

Retention Constraint

Users leave before habit forms
Leaky bucket despite good acquisition
Product-habit fit not established
Fix: trigger loops, habit hooks, CS

// System Archetypes (Senge)

Archetype	Pattern	Startup Example	Fix
Limits to Growth	Growth hits ceiling due to hidden constraint	Viral app slows when CAC rises post-saturation	Find next growth constraint proactively
Shifting the Burden	Short-term fix masks root problem	Discounting to hit revenue instead of fixing churn	Address root; use workaround only while fixing
Tragedy of Commons	Shared resource depleted by rational actors	Marketplace quality degrades as sellers race to bottom	Governance layer + incentive alignment
Escalation	Two sides in arms race	Price wars between competitors until both lose margin	Exit the race; compete on different axis

// AI-Native Operator Framework

◈ The DT AI Division Matrix

For every task, ask: who does this best — Human, AI, or Structured Data? The answer defines your leverage architecture.

Task Layer	⚡ AI Does	◎ Human Does	▦ Data/System Does
Research & Synthesis	Summarize, pattern-find, cross-reference large corpus	Frame the right question; judge relevance	Store structured signals, run queries
Strategy Formulation	Generate option space, stress-test logic	Choose, commit, take responsibility for tradeoffs	Historical performance data informs priors
Product Design	Generate UI variants, write copy, suggest flows	Empathize with user; make taste decisions	A/B test results, usage analytics drive iteration
Customer Communication	Draft responses, detect sentiment, triage	Handle escalations, build relationship trust	CRM tracks history; autorouting rules handle volume
Ops & Execution	Generate SOPs, flag anomalies, automate workflows	Handle exceptions; ensure culture in the loop	Zapier/n8n/pipelines run the repeatable
Learning & Reflection	Identify blind spots in your thinking, challenge reasoning	Own the insight; decide what to internalize	Notion/Obsidian knowledge graph compounds over time
Fundraising / Pitch	Build narrative structure, sharpen language	Conviction, relationships, room-reading	Data room auto-populates from live metrics

// AI Tool Stack for DT Fellows

THINKING

Claude / GPT-4o

Deep reasoning tasks
Assignment frameworks
Stress-test arguments
Structured output generation

RESEARCH

Perplexity / Gemini

Live web research
Competitive analysis
Market sizing facts
Recent case studies

BUILDING

Cursor / v0 / Replit

Prototype MVPs fast
AI-native codebase
Ship before perfect
Iterate in hours not weeks

SYSTEMS

Notion / n8n / Airtable

Structured data architecture
Workflow automation
Team knowledge base
OKR + experiment tracking

// Experiment Tracker

LOG A NEW EXPERIMENT

Cold email sequence A/B test on subject line personalization

Hypothesis: Personalized subject lines with company-specific insight will increase open rates by 40% over generic lines because they signal research and relevance.

Metric: 40% open rate Deadline: 5 days Status: Running

Landing page value prop — problem-first vs solution-first copy

Hypothesis: Leading with the user's pain (not our feature) will reduce bounce by 25% because users identify faster and feel understood.

Metric: 25% lower bounce Deadline: 1 week Status: Planned

// The DT Structured Execution Framework

LAYER 01

First Principles

Break every assumption
Ask "why does this system exist?"
Find the root cause, not symptom
Reconstruct from fundamentals

LAYER 02

Structured Execution

Problem → Root Cause → Analysis
Constraints → Solutions → Tradeoffs
Recommended Path → Execution Plan
Metrics → Key Insights → Next Step

LAYER 03

AI-Native Thinking

Human vs AI vs Data tasks
Where does automation create leverage?
What compounds over time?
Build systems, not processes

LAYER 04

Systems Thinking

Map feedback loops first
Find the actual bottleneck
Identify perverse incentives
Second-order consequences

// Execution Metrics Scorecard

Dimension	Leading Indicator	Lagging Indicator	Owner
Thinking Quality	Depth of problem decomposition	Feedback from peers / mentors	Human
Execution Speed	Tasks shipped per week	Milestone completion rate	Systems
Experiment Velocity	Hypotheses tested / month	Insight-to-action conversion	AI + Human
Learning Compounding	Notes + reflections logged	Pattern recognition speed over time	Human
AI Leverage	% of repeatable tasks automated	Time saved → redirected to thinking	AI + Systems