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Model Context Protocol

By Anthropic

0 recommend/use · 3 sourced episodes

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Productivity4 steps

Ambitious Retry (Pass@N) Tool Use

Get more from AI tools by asking for the ambitious change and fully restarting on failure instead of hammering the same attempt

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Innovation4 steps

Build for the Exponential (Skate to Where the Puck Is Going)

Build products for the model that arrives in 6-12 months, betting that today's 20%-working features hit 100%

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Strategy4 steps

Build Only at the Magic Intersection

Don't ship what anyone could build off the shelf — build only where model and product uniquely meet.

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Innovation5 steps

Constitutional AI Self-Critique Loop

Align a model to written principles by having it critique and rewrite its own outputs, then train on the fix

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Entrepreneurship4 steps

Defensible Moats for AI Startups

Four durable places to build in AI where foundation-model labs are least likely to squash you.

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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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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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Productivity5 steps

Hunting New Bottlenecks When AI Writes the Code

When AI removes the coding bottleneck, constraints shift up and downstream — go find them.

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Communication4 steps

Make the Other Mistake (Prompting for Brutal Feedback)

To get honest AI critique, over-correct toward brutal — the model won't actually overshoot.

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Mindset4 steps

Marginal-Impact Reasoning Under Tail Risk

Prioritize catastrophic low-probability risks by expected value and by how few people are already working on them

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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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Self-Mastery4 steps

Resting in Motion

Treat the busy, working state as your normal baseline so heavy long-term work stays sustainable

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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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Leadership4 steps

The 10-to-1 Input/Output Kill Test

When you pour 10 units of effort in for 1 unit of output, the project has run its course.

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Strategy4 steps

The Economic Turing Test

Measure transformative AI by whether you'd hire an agent for a real job without knowing it's a machine

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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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Innovation4 steps

The Three-Part AI Product Utility Equation

A useful AI product needs model intelligence, context/memory, and application/UI to all converge.

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Strategy4 steps

Three Worlds Theory of Change

Decide what to actually do about a hard problem by naming the pessimistic, optimistic, and pivotal worlds you might be in

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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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