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Automated Software Documentation for GitHub Codebases

Hacker News

Automated Software Documentation for GitHub Codebases

Hey Hackers, My team and I have been working on an automated software documentation and impact analysis platform for the last 3 years. Our long-term goal is to enter safety/mission-critical applications, where improper documentation can lead to disastrous outcomes, e.g., costly reworks/overruns or endangering human lives. But, in an effort to recognize revenue in the near term with our existing functionality, we have found initial traction with use cases focused on reverse engineering legacy systems. Where getting up to speed with an existing system requires a team of engineers to manually review large amounts of code, taking weeks or months to come to grips with. ______________________________________________ Our Self-Service release is a no-frills offering to leverage a subset of our document generation capabilities. Using only the code, SAFA is able to: -Summarize Code Files -Generate an overall project summary -Generate Upstream Documentation, like Features and Functional Requirements -Map relationships between all code and generated documentation with explanations Our approach leverages our own LLM pipeline, which applies a variety of clustering/refinement techniques, embedding models, and LLMs to keep your entire system within context when generating documentation, change summaries, api flow, and more. We do not use customer data to train or refine our models. We currently only support Github integrations for self-service but will implement flat-file support in the near term. When using self-service, you will receive Code Summaries and a Project Overview for free, but we charge for generating documentation and relationships: 20 cents per code file and generated document (100 File Codebase = $35). Currently, self-service has a 1000 code file limit. ________________________________________________ If you want to see the quality of the documents SAFA generates before trying it with your code, feel free to check out our public codebases page ( https://www.safa.ai/codebases ). We have serious ones like Autoware's AV Control Module, and more fun ones, like Super Mario 64. Otherwise, our app is directly accessible via https://app.safa.ai (apologies, we do require an account to be made). I very much look forward to your feedback and insights. Feel free to email me directly at aarik@safa.ai.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
92%92% 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: model, models, email · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, pipe · Missing: https docs, excited, just released
72%72% 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: month · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue · Missing: arr, mrr, profit
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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