Been building Lakon initially as a prompt compression tool because I personally kept running into token/credit limits while using ChatGPT, Claude, Gemini etc. At first I thought: “people just need shorter prompts.” But after talking to users and thinking more deeply, I realized something interesting: Prompt length is only a small part of the problem now. The real token drain usually comes from:
- long conversation history
- repeated context
- AI re-explaining things
- carrying entire chats forward
- losing context between models/tools For example, sometimes a single ongoing chat becomes more expensive than the actual prompt itself. So now I’m thinking of evolving Lakon from:
“prompt compressor” into something more like: “AI context optimizer” Current idea for the next patch: user pastes an entire AI conversation using shortcuts or paste the chat link or use our extension for fetching out your exact complete conversation. Lakon extracts:
- goals
- decisions
- important context
- unresolved tasks then creates a compact continuation snapshot that can be reused in a new chat/model Kind of like compressing the working memory instead of only compressing prompts. Still brainstorming the architecture because ultra-long chats can exceed LLM context limits themselves. Curious: Do you think this is a real pain point, or am I overestimating it because I’m a heavy AI user? submitted by /u/PriorNervous1031
Originally posted by u/PriorNervous1031 on r/ArtificialInteligence
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