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Oh My Senior – AI-powered codebase analysis

Hacker News

Oh My Senior – AI-powered codebase analysis

https://127-0-0-1.io/oh-my-senior Hi HN, I created this toy project after seeing developers on HN and Reddit leveraging Gemini's large context window to analyze entire codebases. Inspired by their insights, I wanted to streamline this process. While I've seen similar attempts, most tools like the @codebase feature in Continue VSCode extension are editor-bound or CLI-based. I wanted a simple web-based tool that integrates with my GitHub account directly. This tool uses Gemini's API to analyze codebases, providing insights and answering questions about the code. It's particularly useful for: - Developers who've inherited projects without documentation or knowledgeable colleagues - New engineers during onboarding - Getting a fresh perspective on complex codebases Key features: - Web-based interface for easy use - Supports multi-turn conversations - Markdown formatting in responses Currently using Gemini API (free quota available), but plan to add support for other models with free quotas like Codestral. This is a PoC with basic functionality. I've tested it on several of my own repositories with promising results. Feedback and suggestions welcome! Disclaimer: The quality of responses is inherently limited by Gemini's capabilities. This tool streamlines the process but cannot exceed Gemini's underlying performance.

Visit productView on Hacker NewsJul 23, 2024
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · 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 · Strong signals: supports, created, gemini · Missing: reddit linkedin, podcasting, latex
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive
65%65% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
46%46% 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
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% 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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