A Minimal PHP Wiki

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A Minimal PHP Wiki

First, I'm by no mean a programmer. Second, I am reading HN for years, just registered to make this public and see if it can be useful for someone. I have nothing to gain monetary. A while ago, I decided that no blogging system was simple enough and I looked for a small wiki to use as a CMS/blog. Found WikWiki on C2.com and I mixed it with some basic Markdown syntax, a minimal template and added password protection. The result is a single php file, no database and no dependencies. Code is a mess, not modulable, probably wrong in so many ways, but it can probably used by some as personal notepad, wiki, CMS, blog, etc. Any comments appreciated.

Visit product View on Hacker NewsAug 26, 2022

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% 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.

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: single, code · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

49%49% 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

32%32% 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

18%18% 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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