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dscout

By dscout

1 recommend/use · 3 sourced episodes

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Marketing3 steps

Credits-for-Sharing Loop

Pay users in product credits to publicly share what they built — turn usage into free distribution.

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Communication4 steps

Emotional Journey Design for Content

Content is predicting reader reactions: hook them, pace the emotion, make people likable.

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Innovation6 steps

Error Analysis: Open Coding to Axial Coding to Count

Turn messy LLM logs into a prioritized list of failures before you write a single test.

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Strategy3 steps

Eval Triage: Decide What Actually Deserves an Eval

After counting failures, route each one to a prompt fix, a code check, or an LLM judge — not all three.

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Innovation4 steps

Frustration-Log Micro-Tool Ideation

Beat the idea crisis: log a week of frustrations, then build tiny AI tools to kill them.

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Peak Performance3 steps

Genius-Zone Time Budget

Spend at least 50% of your time on the work you're both great at and love — it's what keeps you showing up.

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Innovation4 steps

LLM-Optimized Codebase Architecture

Structure your repo so the AI writes the least code possible — infrastructure absorbs the complexity.

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Marketing4 steps

Milestone-Gated Launch Sequence

Don't spend a dollar on marketing until users start sharing your product unprompted.

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Strategy4 steps

Negotiate From Genuine Indifference

The best position to sell your company is being genuinely fine not selling it.

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Innovation5 steps

RAG Data Preparation Over Database Tuning

The biggest RAG quality wins come from preparing data for retrieval, not picking a database.

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Leadership3 steps

Randomized Tiered Trial for AI Productivity

Measure whether AI tools help by running a randomized trial split across performance tiers.

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Marketing4 steps

Single-Channel Build-in-Public Bet

Find the one channel that resonates with your exact audience, then double and triple down.

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Strategy4 steps

Speed-to-Aha Over Best Product

Sometimes the correct product decision hurts the aha moment — cut it and get users to magic faster.

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Innovation3 steps

Step-Wise Eval Design for Multi-Step AI Apps

Don't evaluate agents end-to-end; put an eval on every step until you hit coverage.

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Innovation4 steps

The Aligned Binary LLM-as-Judge

Build a one-failure, pass/fail judge and align it to a human with a confusion matrix before you trust it.

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Leadership3 steps

The Benevolent Dictator Labeling Model

Appoint one trusted domain expert to own eval judgments instead of running it by committee.

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Strategy3 steps

The Eval ROI Decision

Build evals where failure is catastrophic or you must win; vibe-check the rest.

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Strategy3 steps

Two-Question New Technology Adoption Test

Before adopting any new AI tool, ask: how big is the gain, and how painful is the exit?

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Strategy3 steps

What Actually Improves AI Apps

Stop chasing AI news and vector DBs; the real levers are users, data, and prompts.

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