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Linkitall – create dependency-graphs of ideas

Hacker News

Linkitall – create dependency-graphs of ideas

What is the structure of knowledge? It's a broad question, and finding a structure that suits everyone is no easy task. Consider this: a backend developer might view knowledge as a table accessible with different keys, while a teacher might perceive it as a book, complete with chapters, sections, and subsections—a linear structure, yet with a tree-shaped Table of Contents for efficient reference. Generally, the structure we require depends on (a) the data involved and (b) the purpose at hand. Let's narrow it down: What's a suitable structure for teaching scientific knowledge? In the context of a specific course, one can assert that certain ideas are more fundamental than others. There's a hierarchy of ideas, where complex ideas build upon simpler ones, and those, in turn, rest on basic concepts. In simpler terms, with the exception of a few obvious or axiomatic ideas, every other idea relies on something else. Organizing all these ideas into a graph based on their dependency structure creates a dependency graph. Using graphical structure (including dependency graphs) to arrange ideas for presentation or teaching is nothing new. There are several tools out there, but none of them really worked for my use-case. So I wrote a tool in Golang which will take a graph definition in YAML format and produce a graph output in HTML. I am a noob in Golang. I am using python for work, so I wanted something else for this "hobby" project, and ended up with Go. This is the tool: https://github.com/charstorm/linkitall Here are a few example outputs: Sets: https://charstorm.github.io/class-11-12-india/class11/maths/... Trigonometric relations: https://charstorm.github.io/class-11-12-india/class11/maths/... Note: one can click on the node name and it will open the associated slide. Kindly let me know what you fellows think. In particular, I would very much appreciate feedback from those in training/teaching/research background.

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
80%80% 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: new, context, using · Missing: mac, agents, macos
56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released
41%41% 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
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, training · Missing: mrr, revenue, profit
13%13% 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.

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