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ReadPaths – A Dependency Graph for Knowledge

Hacker News

ReadPaths – A Dependency Graph for Knowledge

Typically if articles need to be read in some order, the author chooses one order, so that the articles form a series or a tree, but some people recognize the superiority of a graph structure: - Some textbooks have dependency graphs in the introduction. - Some blog articles have links to other articles that they are a follow-up to. ReadPaths (readpaths.com) lets you set dependents and dependencies for articles. When you set article A as dependency of article B, on article B's page A is shown as dependent. Everyone can add dependents and dependencies, dependents and dependencies added by the author are shown separately, dependents and dependencies shown by other people are shown sorted by how many people added them. On the front page the articles with the highest pagerank are shown in a graph, where a dependency is counted as a link. (In a sense these are the most important articles, if you started at a random article and tried to read a random dependency you would land on them.) You can mark articles as "want to understand" and "have understood" so that you can find articles you need to understand (that several articles you want to understand depend on) and articles you can understand (that several articles you have understood depend on).

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
55%55% 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 · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
42%42% 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: para · Missing: mobile apps, ios, personal
31%31% 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
27%27% 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
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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