This Gist contains the system prompt for Claude Code, Anthropic's CLI for Claude. It details the tool's purpose, instructions for use, tone, proactive behavior, code style guidelines, task management, and references.
A new study by MIT CSAIL researchers maps the challenges of AI in software development, identifying bottlenecks and highlighting research directions to move the field forward, aiming to allow humans to focus on high-level design while automating routine tasks.
This article discusses the impact of Large Language Models (LLMs) on the field of software engineering, arguing that while LLMs can increase efficiency, it's crucial to maintain a pipeline of junior engineers who learn through practical experience and problem-solving, rather than solely relying on AI-generated code.
SWE-agent is an open-source tool that utilizes large language models (LLMs) like GPT-4o and Claude Sonnet 3.5 to autonomously fix bugs in GitHub repositories, solve cybersecurity challenges, and perform complex tasks. It features a mode called EnIGMA for offensive cybersecurity and prioritizes simplicity and adaptability.
Qodo releases Qodo-Embed-1-1.5B, an open-source code embedding model that outperforms competitors from OpenAI and Salesforce, enhancing code search, retrieval, and understanding for enterprise development teams.
Does Good Software Engineering Improve the Performance of Large Language Models with Software Tasks?The article discusses how structured, modular software engineering practices enhance the effectiveness of large language models (LLMs) in software development tasks. It emphasizes the importance of clear and coherent code, which allows LLMs to better understand, extend functionality, and debug. The author shares experiences from the Bad Science Fiction project, illustrating how well-engineered code improves AI collaboration.
Key takeaways:
1. **Modular Code**: Use small, well-documented code blocks to aid LLM performance.
2. **Effective Prompts**: Design clear, structured prompts by defining context and refining iteratively.
3. **Chain-of-Thought Models**: Provide precise inputs to leverage structured problem-solving abilities.
4. **Prompt Literacy**: Master expressing computational intent clearly in natural language.
5. **Iterative Refinement**: Utilize AI consultants for continuous code improvement.
6. **Separation of Concerns**: Organize code into server and client roles for better AI interaction.
Key takeaways:
1. **Modular Code**: Use small, well-documented code blocks to aid LLM performance.
2. **Effective Prompts**: Design clear, structured prompts by defining context and refining iteratively.
3. **Chain-of-Thought Models**: Provide precise inputs to leverage structured problem-solving abilities.
4. **Prompt Literacy**: Master expressing computational intent clearly in natural language.
5. **Iterative Refinement**: Utilize AI consultants for continuous code improvement.
6. **Separation of Concerns**: Organize code into server and client roles for better AI interaction.
