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What story are you telling? (narrative archetypes tool)

Hacker News

What story are you telling? (narrative archetypes tool)

Two weeks ago we posted our narrative archetypes framework here - thanks for the discussion. (We've published a whitepaper on the same on the website, for those who are interested) Curiosity seems to be getting the better of me still. The question is, if someone has a general premise for a story, can we suggest the narrative archetypes that best tell that story. For a lot of storytellers, that itself would give them plenty of ideas, and even a sense of direction to take the story into. We've since shipped a tool that applies the framework directly: enter a premise, optionally pick a genre, and it matches your story to the archetypes that best fit its structural logic - with a sketch of what the story would look like under each one. https://arc.quanten.co/tools/logline The same premise reads differently under different genre lenses - the archetype shifts, and so does the story. Curious what you make of it. One of the intriguing questions I've heard so far: can we use narrative archetypes to tell founder stories? You often hear in the world of startups that storytelling is everything - could this enable founders to find their story? Curious to explore that next.

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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