

Specifically, I’d love to hear your thoughts on how to better optimize the context window limits when dealing with massive raw outputs (like huge DNS dumps or nmap scans) before feeding them back into the LLM’s memory.
Launch a sub-agent that reports its findings back to the main orchestration agent. If it’s even too long to fit in the sub-agent’s context, you can chunk it up, have a sub-sub-agent per chunk report back to the sub-agent for a shorter summary, just the interesting lines per chunk, or “no relevant lines found” or whatever. Can get even fancier by allowing the sub agent to use tools like grep, head, tail, etc on the text to search it instead of reading the whole thing directly.
Surprised you’re not using LangChain/LangGraph as it makes some of the things you’re doing easier. But it looks like you’re vibecoding this anyways, so it’s just doing whatever Claude Code or whatever decides to do. My suggestion would be to code it yourself with minimal AI assistance, as this will just turn into an unmaintainable mess as time goes on, and eventually, the AI coding agent will get stuck and be unable to really progress.
Now that I think if it, you could probably get Claude Code or OpenCode to do everything this project can do by just installing all the tools needed in your environment, creating a new empty project, telling it what tools are available in your environment, allow it to download any other tools necessary, and prompting it to do the recon (may need to use an abliterate, heretic, or otherwise uncensored model to do some things).















The authoritarianism is exercised differently in both countries. Mainly, the US state is openly more physically violent. In China, surveillance, media control, censorship, and control of their population in general is stronger (but the US is rapidly trying to catch up). As one example, the US wouldn’t have been able to effectively silence Naomi Wu and presumably remove her way of making a living at the time, if she was a US citizen in the US.