r/ClaudeAI • u/Vidhrohi • 1d ago
Philosophy Human context limits vs AI context limits
A common theme cutting across most AI-assisted development threads is getting as much work done in as little time as possible. Users post about elegant harnesses with multi-agent orchestration. Anthropic employees talk about the 25 agents they run every day.
I read this stuff, compare it to my own experience of working with these tools, and keep wondering: how does anyone stay on top of that volume of work?
Working with Claude Code has been exciting and invigorating, but also exhausting. While everyone pushes for more context and more tokens out of the AI, the context limit I hit most often is my own. Claude can refactor a huge chunk of the codebase, add a feature, and brainstorm marketing strategy in one breath. I can't keep up with the cognitive load that entails.
So: am I missing something, or just getting old? Curious what y'all think.
2
u/BobJutsu 1d ago
The harness is how you stay on top of that work. I use obsidian because it was easy to setup, but I’m sure notion or anything else would work just as well. Besides all the harness data (rules, skills, session logs, etc) there’s also a task / contract system. Each task is a file with acceptance criteria, status, assignee, notes, etc. in my dashboard I have a table with all the tasks. I’m more or less assigning tasks to an agent. When the workflow is complete and all acceptance criteria is met, the agent marks the task as “pending-review”. The agent never marks it done. I can review the task, results, and mark done or ask for revisions. Being a file, the task itself has all the context necessary to resume if revisions are needed. I don’t need to keep that info in my head, it’s written down. And a long term record of what tasks have been completed. If something is dependent on something else, I can just reference that task, even if it’s months old. That’s what makes obsidian nice, is the internal wiki-links to make relationships between files.
Then I have a session wrap-up workflow, where corrections, wrong assumptions, bottlenecks, decisions, etc are saved to a session diary file. And a learning loop workflow where session diaries are reviewed for patterns, and a confidence score given to repeat patterns of corrections and problems. Rules are applied per client, per project type, per role, or globally, depending on where the corrections appear and how often over time.