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A tool to help you write more reliable prompts

Hacker News

A tool to help you write more reliable prompts

Hey HN! While writing prompts for a few projects recently, a couple of friends and I realised that writing prompts today is like writing code in a txt file - it sucks and there is no tooling to help you. So we decided to hack on a tool to help you fix this - https://optimist.varied.ai/ It is literally a hack rn but we already find it useful for writing prompts. That said we'd love to talk to folks who are writing serious prompts everyday and see how the product can be better. Also the only reason the waitlist is there, is running this is super expensive like $100/day for a couple of users because of the amount of requests we make - so just limited how many people we roll out too.

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, code · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% 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: users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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.

Correct prediction on native model

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