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CodingBrain – acquire skills faster

Hacker News

CodingBrain – acquire skills faster

CodingBrain is a knowledge-management, research and productivity app aimed at developers, technical bloggers and anyone who uses the internet to learn something. The purpose of CodingBrain is to make your learning, writing and remembering of technical facts more efficient. It serves as your central app that records what you do, helps you to generate new content, enables you to search or remember and tries to automate as much as possible to increase your productivity. This applies to grabbing content from websites, creating new files, moving content around, finding similar notes etc. CodingBrain is not yet-another-note-taking-app. In fact, it doesn't even have an editor. You point CodingBrain to the location of your plain-text markdown files and then it indexes and analyses them. It integrates with your preferred tools, most of all your editor, but also your (though not fully yet) browser and search engine (Google only at this time, more coming). * A markdown viewer of your notes (similar to Marked) * A markdown analyser that gives you info about: similar notes, files you linked to, websites you bookmarked or referenced, local & online searches you performed * It has a kind of automated tagging system, by using your search query as a tag * A searchable activity log of your file changes, browsing history and so on * A browser component that enables you to grab content from websites and optionally pasting it into your notes in markdown syntax * Every website you visit will be indexed and cached, and CodingBrain tries to generate a summary about the site that you can edit and annotate * A local search engine that allows you to search for notes, websites, bookmarks. * A large set of index fields you can search for, for example, you can specifically search for content of markdown headers or code blocks I have basically built CodingBrain for myself because I was unhappy with existing solutions and always longed for a tool like that.

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Actual performance

5points
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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, activity · Missing: mac, agents, macos
95%95% 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 NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
75%75% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: google, way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, 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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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.

Incorrect prediction on native model

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