Zinc, a systems programming language prototype
8 by birdculture | 0 comments on Hacker News.
Wednesday, March 12, 2025
New top story on Hacker News: Show HN: Nuanced – Help AI understand code structure, not just text
Show HN: Nuanced – Help AI understand code structure, not just text
12 by aymandfire | 3 comments on Hacker News.
Hi HN! We built Nuanced ( https://ift.tt/j1J5e47 ), an open-source Python library that makes AI coding tools smarter about code structure. The problem: current AI coding assistants see code as just text. They don't understand which functions call which, or how code components depend on each other. This is why they often suggest changes that break dependencies they should have known about. Nuanced solves this by generating call graphs that map these relationships. When you ask "What would break if I change this function?", instead of guessing, the AI can see the actual dependencies. How it works: 1. Run `nuanced init .` to analyze a Python module in your codebase 2. Use `nuanced enrich app/file.py function_name` to get relationship data 3. Include this data in your AI prompts or integrate it into tools We're already working with teams building AI coding assistants and security review tools to integrate this capability. Our initial release supports Python with plans for JavaScript/TypeScript next. I'd love your feedback, especially if you're building dev tools that could benefit from better code structure understanding!
12 by aymandfire | 3 comments on Hacker News.
Hi HN! We built Nuanced ( https://ift.tt/j1J5e47 ), an open-source Python library that makes AI coding tools smarter about code structure. The problem: current AI coding assistants see code as just text. They don't understand which functions call which, or how code components depend on each other. This is why they often suggest changes that break dependencies they should have known about. Nuanced solves this by generating call graphs that map these relationships. When you ask "What would break if I change this function?", instead of guessing, the AI can see the actual dependencies. How it works: 1. Run `nuanced init .` to analyze a Python module in your codebase 2. Use `nuanced enrich app/file.py function_name` to get relationship data 3. Include this data in your AI prompts or integrate it into tools We're already working with teams building AI coding assistants and security review tools to integrate this capability. Our initial release supports Python with plans for JavaScript/TypeScript next. I'd love your feedback, especially if you're building dev tools that could benefit from better code structure understanding!
Tuesday, March 11, 2025
Monday, March 10, 2025
Sunday, March 9, 2025
New top story on Hacker News: Show HN: Evolving Agents Framework
Show HN: Evolving Agents Framework
27 by matiasmolinas | 3 comments on Hacker News.
Hey HN, I've been working on an open-source framework for creating AI agents that evolve, communicate, and collaborate to solve complex tasks. The Evolving Agents Framework allows agents to: Reuse, evolve, or create new agents dynamically based on semantic similarity Communicate and delegate tasks to other specialized agents Continuously improve by learning from past executions Define workflows in YAML, making it easy to orchestrate agent interactions Search for relevant tools and agents using OpenAI embeddings Support multiple AI frameworks (BeeAI, etc.) Current Status & Roadmap This is still a draft and a proof of concept (POC). Right now, I’m focused on validating it in real-world scenarios to refine and improve it. Next week, I'm adding a new feature to make it useful for distributed multi-agent systems. This will allow agents to work across different environments, improving scalability and coordination. Why? Most agent-based AI frameworks today require manual orchestration. This project takes a different approach by allowing agents to decide and adapt based on the task at hand. Instead of always creating new agents, it determines if existing ones can be reused or evolved. Example Use Case: Let’s say you need an invoice analysis agent. Instead of manually configuring one, our framework: Checks if a similar agent exists (e.g., a document analyzer) Decides whether to reuse, evolve, or create a new agent Runs the best agent and returns the extracted information Here's a simple example in Python: import asyncio from evolving_agents.smart_library.smart_library import SmartLibrary from evolving_agents.core.llm_service import LLMService from evolving_agents.core.system_agent import SystemAgent async def main(): library = SmartLibrary("agent_library.json") llm = LLMService(provider="openai", model="gpt-4o") system = SystemAgent(library, llm) result = await system.decide_and_act( request="I need an agent that can analyze invoices and extract the total amount", domain="document_processing", record_type="AGENT" ) print(f"Decision: {result['action']}") # 'reuse', 'evolve', or 'create' print(f"Agent: {result['record']['name']}") if __name__ == "__main__": asyncio.run(main()) Next Steps Validating in real-world use cases and improving agent evolution strategies Adding distributed multi-agent support for better scalability Full integration with BeeAI Agent Communication Protocol (ACP) Better visualization tools for debugging Would love feedback from the HN community! What features would you like to see? Repo: https://ift.tt/WDfXTZw
27 by matiasmolinas | 3 comments on Hacker News.
Hey HN, I've been working on an open-source framework for creating AI agents that evolve, communicate, and collaborate to solve complex tasks. The Evolving Agents Framework allows agents to: Reuse, evolve, or create new agents dynamically based on semantic similarity Communicate and delegate tasks to other specialized agents Continuously improve by learning from past executions Define workflows in YAML, making it easy to orchestrate agent interactions Search for relevant tools and agents using OpenAI embeddings Support multiple AI frameworks (BeeAI, etc.) Current Status & Roadmap This is still a draft and a proof of concept (POC). Right now, I’m focused on validating it in real-world scenarios to refine and improve it. Next week, I'm adding a new feature to make it useful for distributed multi-agent systems. This will allow agents to work across different environments, improving scalability and coordination. Why? Most agent-based AI frameworks today require manual orchestration. This project takes a different approach by allowing agents to decide and adapt based on the task at hand. Instead of always creating new agents, it determines if existing ones can be reused or evolved. Example Use Case: Let’s say you need an invoice analysis agent. Instead of manually configuring one, our framework: Checks if a similar agent exists (e.g., a document analyzer) Decides whether to reuse, evolve, or create a new agent Runs the best agent and returns the extracted information Here's a simple example in Python: import asyncio from evolving_agents.smart_library.smart_library import SmartLibrary from evolving_agents.core.llm_service import LLMService from evolving_agents.core.system_agent import SystemAgent async def main(): library = SmartLibrary("agent_library.json") llm = LLMService(provider="openai", model="gpt-4o") system = SystemAgent(library, llm) result = await system.decide_and_act( request="I need an agent that can analyze invoices and extract the total amount", domain="document_processing", record_type="AGENT" ) print(f"Decision: {result['action']}") # 'reuse', 'evolve', or 'create' print(f"Agent: {result['record']['name']}") if __name__ == "__main__": asyncio.run(main()) Next Steps Validating in real-world use cases and improving agent evolution strategies Adding distributed multi-agent support for better scalability Full integration with BeeAI Agent Communication Protocol (ACP) Better visualization tools for debugging Would love feedback from the HN community! What features would you like to see? Repo: https://ift.tt/WDfXTZw