4 रिपॉजिटरी
Tools that send decompiled pseudocode to a language model and return plain-English summaries of what the code does.
Distinct from Code Explanation: Distinct from Code Explanation: specifically targets decompiled pseudocode from reverse engineering tools, not general source code.
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LLM4Decompile बाइनरी-टू-सोर्स कोड अनुवाद के लिए एक टूलसेट और फ्रेमवर्क है। यह मशीन कोड को पठनीय सोर्स कोड में बदलने और संकलित निष्पादन योग्य (compiled executables) के मूल लॉजिक को पुनर्प्राप्त करने के लिए बड़े भाषा मॉडल (LLM) का उपयोग करता है। इस प्रोजेक्ट में सोर्स कोड को असेंबली पेयर्स में परिवर्तित करके सिंथेटिक ट्रेनिंग डेटासेट उत्पन्न करने के लिए एक विशेष पाइपलाइन शामिल है। यह इन बाइनरी-टू-सोर्स डेटासेट पर डीप लर्निंग मॉडल को ऑप्टिमाइज़ करने के लिए एक फाइन-ट्यूनिंग फ्रेमवर्क प्रदान करता है, जिससे कोड रिकवरी की सटीकता बढ़ जाती है। सिस्टम में डीकंपाइल्ड स्यूडो-कोड को रिफाइन करने की क्षमताएं भी हैं। यह प्रक्रिया बाइनरी के स्ट्रक्चरल कंकाल और वेरिएबल नामों को पुनर्स्थापित करने पर केंद्रित है ताकि डिसअसेंबल किए गए लॉजिक की पठनीयता में सुधार हो सके।
Employs a language model to improve the readability and accuracy of existing decompiled pseudo-code.
This project is a comprehensive collection of Python programming education materials, including tutorials, exercises, and curated code samples. It serves as a learning curriculum and software engineering toolkit, utilizing Jupyter Notebooks to combine executable code with descriptive educational text. The repository provides practical implementation guides for building large language model applications, such as retrieval-augmented generation systems, stateful AI agents, and machine learning workflows. It distinguishes itself by offering a structured approach to agentic coding workflows, cover
Provides detailed breakdowns of code logic to help users understand complex code blocks.
This project is a developer utility that functions as an artificial intelligence-powered assistant for database query management. It provides an interactive interface for translating between natural language and structured database code, simplifying the processes of writing, debugging, and maintaining complex queries. The tool distinguishes itself by incorporating schema-aware context injection, which allows it to align generated queries with specific table definitions and relationship metadata. By maintaining stateful conversation history and utilizing large language model prompting, it enab
Provides plain-English summaries and breakdowns of complex database query logic for better code understanding.
Gepetto is an IDA Pro plugin that integrates large language models directly into the reverse engineering workflow. It functions as a multi-provider LLM client, allowing users to explain decompiled functions, rename variables, and add comments to pseudocode, all while supporting multiple language model backends and a localized interface. The plugin distinguishes itself through a plugin-based architecture that abstracts multiple LLM providers behind a unified interface, enabling hot-swapping between providers and models without restarting IDA Pro. It also features a command-line interface bridg
Sends decompiled pseudocode to a language model and returns plain-English descriptions of what the code does.