Guide Gecko – AI-Powered Codebase Understanding (macOS)

Hacker News

Hi HN As a security/cloud architect, I'm constantly handed new projects for review with tight deadlines. Reviewing thousands of lines of code in a short time is a huge pain. To solve this, I built Guide Gecko, a macOS app that uses Gemini to let you query your entire codebase. It connects to GitHub, uses your Gemini API key, and allows you to ask broad architecture questions, perform security reviews, identify potential improvements, and quickly understand any repo. You can also scope prompts to specific folders. I've been using this daily and found it incredibly helpful. I'd love to get feedback from the HN community.

Visit product View on Hacker NewsJan 27, 2025

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

90%90% 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: gemini · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

34%34% 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

27%27% 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

14%14% 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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