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You're already good at your work. But you want to become great and don't know how. Practiced will get you there.
You're already good at your work. But you want to become great and don't know how. Practiced will get you there.
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WHAT WE DO
We help you build a special layer of knowledge
We help you build a special layer of knowledge
We help you build a special layer of knowledge
Essential to being great at work, but rarely covered in other courses
Intuition
Frameworks
Taste
Principles
Pattern Matching
Rules
Judgment
Procedures
Practiced:
Contextualized Knowledge
Elsewhere:
General Knowledge
Intuition
Frameworks
Taste
Principles
Pattern Matching
Rules
Judgment
Procedures
Practiced:
Contextualized Knowledge
Elsewhere:
General Knowledge
WHY WE DO IT
So you can rule the world
In the age of AI, workers stretching from good to great will gain massive leverage

Great
Good
Impact
WHY WE DO IT
So you can rule the world
In the age of AI, workers stretching from good to great will gain massive leverage
HOW WE DO IT
HOW WE DO IT
Comparing your intuition to the best in the business
We share problems, compare your thinking to experts, and find your gaps
✔︎
✔︎
✔︎
✔︎
?
✔︎
✔︎
Perceiving situational cues
Perceiving situational cues
Spotting points of leverage
Spotting points of leverage
Spotting points of leverage
Generating accrurate simulations
Generating accrurate simulations
Using the right mental model
Using the right mental model
INAUGURAL COURSE SERIES
AI Native Product Intuition
INAUGURAL COURSE SERIES
AI Native Product Intuition
Based on product knowledge from:












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HOW WE'RE DIFFERENT
We source insider knowledge that other courses never touch
1
Find exemplary practice
We find instances of exceptional work
2
Conduct special interviews
To see how experts doing that work think
3
Elicit tacit knowledge
The know-how that's not in the books
What did you think would happen?
What did you notice?
What was behind that decision?
Feeling that product lacks soul
Knowing workflow has hidden layer
Sensing what will impact retention

Cursor's autocomplete

OpenEvidence's semantic search
Gamma's design building blocks
1
Find exemplary practice
We find instances of exceptional work
2
Conduct special interviews
To see how experts doing that work think
3
Elicit tacit knowledge
The know-how that's not in the books
What did you think would happen?
What did you notice?
What was behind that decision?
Feeling that product lacks soul
Knowing workflow has hidden layer
Sensing what will impact retention

Cursor's autocomplete

OpenEvidence's semantic search
Gamma's design building blocks
1
Find exemplary practice
We find instances of exceptional work
2
Conduct special interviews
To see how experts doing that work think
3
Elicit tacit knowledge
The know-how that's not in the books
What did you think would happen?
What did you notice?
What was behind that decision?
Feeling that product lacks soul
Knowing workflow has hidden layer
Sensing what will impact retention

Cursor's autocomplete

OpenEvidence's semantic search
Gamma's design building blocks
1
Find exemplary practice
We find instances of exceptional work
2
Conduct special interviews
To see how experts doing that work think
3
Elicit tacit knowledge
The know-how that's not in the books
What did you think would happen?
What did you notice?
What was behind that decision?
Feeling that product lacks soul
Knowing workflow has hidden layer
Sensing what will impact retention

Cursor's autocomplete

OpenEvidence's semantic search
Gamma's design building blocks
And we deliver that knowledge in a special way so it becomes sticky
1
See How You Think
About tough problems in your domain
2
Compare and spot gaps
Are you missing knowledge experts use?
3
Nudge you to think different
So you build new mental models yourself
Context is expensive - we should look back just one year
Not valuing the emotional impact of long memory
How do users feel when the product triggers memories?
1
See How You Think
About tough problems in your domain
2
Compare and spot gaps
Are you missing knowledge experts use?
3
Nudge you to think different
So you build new mental models yourself
Context is expensive - we should look back just one year
Not valuing the emotional impact of long memory
How do users feel when the product triggers memories?
1
See How You Think
About tough problems in your domain
2
Compare and spot gaps
Are you missing knowledge experts use?
3
Nudge you to think different
So you build new mental models yourself
Context is expensive - we should look back just one year
Not valuing the emotional impact of long memory
How do users feel when the product triggers memories?
1
See How You Think
About tough problems in your domain
2
Compare and spot gaps
Are you missing knowledge experts use?
3
Nudge you to think different
So you build new mental models yourself
Context is expensive - we should look back just one year
Not valuing the emotional impact of long memory
How do users feel when the product triggers memories?
OUTCOMES
What you'll find and how you'll change
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Missing Mental Models
Before:
The value of automation is time and money
Users have strong preferences about what to automate
After:
The value users place on automation has many dimensions
Wrong model applied
Before:
Priorization based on functional needs
Perceiving situations where emotional needs must be met
After:
Prioritization based on emotional needs
Missed cues
Before:
Retention rates too low
Smile curve--retention uptick
After:
Strong retention
Conflation
Before:
Code gen tools allows code edits
Missed no-coder user base segment
After:
Code gen tool needs to have drag/drop edit system
Anchoring to existing paradigms
Before:
Browsers serves web pages
Code is the new content
After:
Browser creates web pages
Finally, a better way to learn
Finally, a better way to learn
Finally, a better way to learn
You've been drinking from the AI info torrent, but it's not really making you better. Practiced will change that.
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