Diagramming tool that helped me understand codebases much faster

Hacker News

I built Code Canvas, a canvas where we create diagrams and link their components to source code in a two-way mapping: 1. Click on the diagram component to reveal the underlying source code 2. Click on the source code block to reveal which functionality it serves in the big picture on the diagram - The app also has a simulations feature where we can create tutorials and runtime simulations directly on the diagram. - The diagram stays in sync with source code changes. It has a companion scanner that runs on new pull requests to ensure the relevant components of the diagram are updated before merging the PR. This app allowed me to understand code much faster and also offload the mental fatigue of trying to reason up the big picture by browsing through endless lines of code. I recommend clicking the "Learn More" button for a 1-pager summary before you hit the demo button. - App link: https://code-canvas.com - Discord server: https://discord.com/invite/t3ezMyMPqr After the demo go back to the main page to load your repository and start building your diagram! Nasser ------- P.S. This is the first time I am putting the app publicly, so any feedback is appreciated!

Visit product View on Hacker NewsMar 13, 2024

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

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best fitHighest predicted score across all platforms for this description.

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.

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

51%51% 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: way, scanner · Missing: mobile apps, ios, personal

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

26%26% 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.

Correct prediction on native model

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