Shell script for AI-powered code reviews using local LLMs

Hacker News

Here is a shell script that performs automated code reviews using locally hosted Large Language Models (LLMs). This tool to address the need for privacy-conscious, customizable, and efficient code review processes. Key features: - Uses locally hosted LLMs (compatible with models like GPT-J, BLOOM, etc.) - Analyzes pull requests and provides detailed feedback - Customizable review criteria and output format - Language-agnostic (works with multiple programming languages) - Fast execution, suitable for CI/CD pipelines The script is designed to be easily integrated into existing workflows and can be customized based on team preferences and project requirements.

Visit product View on Hacker NewsOct 18, 2024

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

86%86% 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: compatible · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: reviews, host, efficient · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% 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

30%30% 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

12%12% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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