Over the past few weeks we’ve been building an AI system that tries to understand what a developer is working on throughout the day. Going into it, I assumed the biggest limitation of today’s coding agents was context length. The idea seemed obvious: Bigger context window → better memory. Better memory → better assistance. But after watching real development sessions, that assumption started to fall apart. A coding session isn’t just a conversation. It’s jumping between VS Code, Chrome, Terminal, Slack, documentation, GitHub, Jira, database clients, logs, and design docs. The important context often isn’t in the chat with the AI. It’s in everything happening around it. For example: A compiler error that appeared 30 minutes ago. A Git diff that explains why a file changed. A design document opened in the browser. A Slack message that changed the priority of the work. A terminal command that failed three times before succeeding. None of these are part of the prompt, yet they’re often the reason a developer makes the next decision. That made us rethink the problem. Maybe AI assistants don’t primarily need larger context windows. Maybe they need a structured memory of what actually happened during the work session. We’re now experimenting with representing development as a timeline of events instead of treating every interaction as an isolated chat. I’m curious how others are thinking about this. If you’re building AI developer tools, where do you think the missing context actually comes from? IDE state? Browser activity? Terminal history? Something else entirely? submitted by /u/Akarsh_Hegde
Originally posted by u/Akarsh_Hegde on r/ClaudeCode
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