Documentation Generator SaaS (Looking for Feedback & Signups)

Hacker News

Hi, we created Code2Docs, a SaaS which helps developers generate documentation for their codebase using GenAI, thus allowing them to focus on delivering value for their software/projects. We also participated in Backdrop Build V5 and were among the finalists. It would be great if yall can pre-register and provide some feedback :) Links: - Our Demo Site: https://code2docs-demo.netlify.app/ - Our Youtube Demo: https://www.youtube.com/watch?v=yMbU3eq1vwQ&t=1s

Visit product View on Hacker NewsAug 18, 2024

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1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntUnlikely to reach the leaderboard · Strong signals: using, code · Missing: mac, agents, macos

48%48% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

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

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

33%33% 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

25%25% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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