DocComment – Code understanding tool using structural analysis and LLM

Hacker News

I built DocComment to solve the challenge of understanding unfamiliar code quickly, whether it's legacy code, AI-generated, or poorly documented. The key technical difference from existing tools like Copilot's commenting feature is that DocComment analyzes code structure before generating explanations. It builds a structural representation of both the specific code snippet and its broader context, allowing the LLM to generate more precise and contextual documentation. It's not competing against Copilot or Cursor, but rather work with them. Technical details: 1. Operates alongside code without modifying source files 2. Uses structural analysis to determine appropriate detail level for comments 3. Focuses on explaining both local code behavior and broader business context 4. Integrates with git repos for full codebase context(Planning) Current results show improved accuracy compared to pure LLM-based commenting systems, particularly for: 1. Large functions with complex logic 2. Code with unclear variable names 3. AI-generated code 4. Business logic heavy sections Would love feedback from the HN community. Both business and techincal perspectives are welcomed.

Visit product View on Hacker NewsNov 22, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, context, using · Missing: mac, agents, macos

92%92% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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