Language models read text as tokens, small chunks of characters. Paste anything to estimate its token count and see how much of different context windows it would fill.
Each color is one estimated token. Real tokenizers differ by model, but most English averages about 4 characters per token.
The context window is everything a model can consider at once: instructions, documents and the conversation so far.
API pricing and rate limits are counted in tokens, in and out.
Long chats and big documents eventually crowd out earlier details.
Retrieve the right passages (RAG) instead of pasting everything.
Symbols and indentation often cost more tokens than prose.
Lead a software project from kickoff to maintenance. Every decision moves budget, schedule, quality and stakeholder trust.
OpenAssemble a structured Claude prompt with roles, context, examples and constraints, scored live against best practices.
OpenWatch retrieval-augmented generation work step by step: chunks, similarity scores, the augmented prompt and a grounded answer.
OpenScore features by Reach, Impact, Confidence and Effort, tag them MoSCoW, and get a ranked roadmap you can export.
Open