ex @IBM, @N26, @Accenture, @thomsonreuters, @Inditex
They reduce risk by testing them.
How do you validate your Big Bang Decisions?
They reduce risk by testing them.
How do you validate your Big Bang Decisions?
• Core workflows configured and tested
• Custom analytics widget built
• Discovered early that the landing-page widget model wouldn’t scale
Total time: 1 day.
Risk removed.
Stakeholder confidence gained.
• Core workflows configured and tested
• Custom analytics widget built
• Discovered early that the landing-page widget model wouldn’t scale
Total time: 1 day.
Risk removed.
Stakeholder confidence gained.
For a nonprofit project, one Big Bang Decision was: Where will staff and volunteers run core business processes?
Hypotheses:
• AmoCRM can serve as the backbone
• It can be extended for nonprofit needs
I prototyped both.
For a nonprofit project, one Big Bang Decision was: Where will staff and volunteers run core business processes?
Hypotheses:
• AmoCRM can serve as the backbone
• It can be extended for nonprofit needs
I prototyped both.
Does it?
With cloud, serverless, and AI-assisted coding, most critical hypotheses can be validated in 4–8 hours.
If a decision can destroy your system, isn’t that worth a day?
Does it?
With cloud, serverless, and AI-assisted coding, most critical hypotheses can be validated in 4–8 hours.
If a decision can destroy your system, isn’t that worth a day?
Yes. Who else fully owns the risk?
Prototyping forces you to confront reality instead of trusting your intuition.
It grounds the architecture in execution.
Yes. Who else fully owns the risk?
Prototyping forces you to confront reality instead of trusting your intuition.
It grounds the architecture in execution.
Anything minimally functional:
• A bash script
• A CLI tool
• A plugin
• A small deployed system
It doesn’t need to be pretty.
It needs to prove something.
Anything minimally functional:
• A bash script
• A CLI tool
• A plugin
• A small deployed system
It doesn’t need to be pretty.
It needs to prove something.
It’s saved money. Saved time. Saved reputation.
In my experience:
• Experienced architects get ~30% of assumptions wrong
• Inexperienced ones can be wrong up to 80%
Unchecked assumptions are expensive.
It’s saved money. Saved time. Saved reputation.
In my experience:
• Experienced architects get ~30% of assumptions wrong
• Inexperienced ones can be wrong up to 80%
Unchecked assumptions are expensive.
For every critical assumption → build a prototype.
Not slides.
Not diagrams.
Something that actually runs.
Validate before you commit.
For every critical assumption → build a prototype.
Not slides.
Not diagrams.
Something that actually runs.
Validate before you commit.
– Architect defines assumptions.
– Logs them.
– Finalizes the design.
– Moves on.
– Six months later everything explodes.
Response? “That was an assumption.”
Technically true. Practically useless.
– Architect defines assumptions.
– Logs them.
– Finalizes the design.
– Moves on.
– Six months later everything explodes.
Response? “That was an assumption.”
Technically true. Practically useless.
If you get one wrong, you don’t just refactor.
You rebuild. Or you fail.
They are high-risk by definition.
If you get one wrong, you don’t just refactor.
You rebuild. Or you fail.
They are high-risk by definition.
t.me/prod_ideas
No hype. No “idea generators”.
Just structured, real-world pain.
Founders, consider this a free database of ideas for your next product 🎁
t.me/prod_ideas
No hype. No “idea generators”.
Just structured, real-world pain.
Founders, consider this a free database of ideas for your next product 🎁
– Is this actually a real problem?
– What’s the root cause?
– How hard is it to solve?
– How popular is it?
Building for ultra-niche audiences is a very specific kind of pleasure 🙂
– Is this actually a real problem?
– What’s the root cause?
– How hard is it to solve?
– How popular is it?
Building for ultra-niche audiences is a very specific kind of pleasure 🙂
Collect real user pain from Reddit and run AI analysis on top of those complaints.
Not opinions.
Not guesses.
Actual problems people already care about.
Collect real user pain from Reddit and run AI analysis on top of those complaints.
Not opinions.
Not guesses.
Actual problems people already care about.
A lot.
And for some reason, they especially love doing this on Reddit:
– Ugly design.
– Constant bugs.
– Random bans.
– Questionable product decisions.
Yet people still go there to share what’s bothering them.
A lot.
And for some reason, they especially love doing this on Reddit:
– Ugly design.
– Constant bugs.
– Random bans.
– Questionable product decisions.
Yet people still go there to share what’s bothering them.
That still works. But we live in the 21st century.
When something truly hurts, people don’t stay quiet about it anymore.
That still works. But we live in the 21st century.
When something truly hurts, people don’t stay quiet about it anymore.