The SaaS Prince

Hacker News

The SaaS Prince

I spent my 42nd birthday generating 100% pure AI slop. I adapted Machiavelli The Prince to the Silicon Valley context, using Sonnet. It took a 6-hour+ non-strop spree to write a 81 pages, and it's kind of an instructive read! Rather than making any line edits, I was able to share high level feedback over the first few chapters and keep regenerating. This process was awesome - I could be extremely ruthlessly critical for hours on end and it didn't hurt Claude's feelings, they just improved, until it was effectively "trained". We're all used to that with objective contexs like coding, but in a subjective context it was a different and really good feeling, like working with an incredibly mature friend in your band. Because philosophical texts are short, you can write adaptations that maintain your feedback to the end, making it easier and easier to generate later chapters with no intervention. One of the better collaborative experiences I've had! Once we have agents ready, should kind of just adapt all classic philosophical texts to allegories about DHH, AMD, and uh, Quibi. Enjoy!

Visit product View on Hacker NewsDec 20, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, model

94%94% 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 · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

55%55% 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 · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

16%16% 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.

Incorrect prediction on native model

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