Co

CodeBoarding – interactive map of your codebase for onboarding

Hacker News

CodeBoarding – interactive map of your codebase for onboarding

Hey HN, we are Alex and Ivan, two developers who’ve spent too many afternoons trying to understand unfamiliar repos. Like most devs, we don’t enjoy wading through dense docs to get up to speed — so we built CodeBoarding to make codebase onboarding a more interactive experience. Last year, I (Alex) was onboarding at a biotech company in the R&D phase applying ML. As you can imagine, when a team of scientists from non CS-background come together, the code gets pretty messy and full of domain-specific quirks. During my time there, I asked a lot of questions, yet when my internship came to an end, I couldn't explain to a new hire in under thirty minutes what "preprocessor_KLN_v3.py" was doing. I had gotten used to the mess. Our diagram generation combines static analysis and LLM agents to scale to bigger projects, that otherwise could not be processed by pure LLMs, and to reduce hallucinations. We create "abstract" components you can click to go to a lower level, along with short summaries for every component. You can explore sample maps here: https://github.com/CodeBoarding/GeneratedOnBoardings and generate new ones (Python only atm.) here: https://codeboarding.org/demo . In two weeks, we’ll open up a small batch of free hosted licenses for a VSCode extension - post below if you’d like one. We’d love to hear your feedback on the demo to improve onboarding. Cheers!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, new · Missing: mac, macos, cursor
96%96% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% 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
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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
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.

Correct prediction on native model

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